ENDNOTES for Quantitative asset management by michael robbins

under construction

Part 1: Planning Your worK

1

chapter 1: introduction

opportunity

what you'll learn

key concepts

Risk Is Easier to Estimate than Return V. K. Chopra, W. T. Ziemba, "The effect of errors in means, variances, and covariances on optimal portfolio choice," Journal of Portfolio Management, 19, 6–11., 1993. R.C. Merton, "On estimating the expected return on the market: An exploratory investigation,"Journal of Financial Economics, 8, 323–361., 1980. Active Management Theodore Roosevelt, “Citizenship in a Republic,” April 23, 1910.

book structure

2

Chapter 2: Choosing your product

the value proposition

firm type

proprietary trading firms

hedge funds

Wealth Management Advisories

Advisor Conflicts Daren Fonda,”Independent Advisors Can Still Have Conflicts of Interest. How to Protect Your Finances,” Barrons, January 3rd, 2020. “Frequently Asked Questions Regarding Disclosure of Certain Financial Conflicts Related to Investment Adviser Compensation,” Division of Investment Management, U.S. Securities and Exchange Commission, retrieved February 9th, 2021.

Family Offices

Investment banks, private banks, and insurance companies

Pensions, governments, endowments, charities, sovereign wealth funds, and corporate plans

investment skill

Russ Wermers, "Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent, Style, Transactions Costs, and Expenses," Journal of Finance , August 2000. https://www.jstor.org/stable/222375 Laurent Barras, Oliver Scaillet, and Russ Wermers, "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, February 2021. https://www.jstor.org/stable/25656289 Ronald Kahn, “Seven Quantitative Insights into Active Management Part 3: The Fundamental Law of Active Management,” BARRA Newsletter, Winter 1997.

purpose

client segmentation

Fee Scrutiny for Institutions Martin Z. Braun, “South Carolina Pension’s Hedge-Fund Bet Trails as Fees Soar,” Bloomberg News, February 28th, 2013. Jan Alexander, “Showdown at South Carolina’s Pension Fund, CIO Hershel Harper will defend the state retirement system’s investment strategy and performance results in upcoming meeting,” Institutional Investor, February 26, 2013. Retrocession Baptiste Aboulian, "Court tells UBS it must pay back fees," Financial Times, November 11th, 2012.

fund structure

“FCA Handbook,” Financial Conduct Authority, Accessed January 19th, 2020. Lemke, Lins, Hoenig and Rube, “Hedge Funds and Other Private Funds: Regulation and Compliance,” Thomson West, 2017-2018. ETF Redemptions vs. Mutual Fund Redemptions Karamfil Todorov, "The anatomy of bond ETF arbitrage," BIS Quarterly Review, March 1st, 2021. https://www.bis.org/publ/qtrpdf/r_qt2103d.pdf Matt Levine, "SPACSs Aren't a Guarantee CF Finance III and AEye Are Proof," Bloomberg Opinion, May 3rd, 2021. Shim, J and K Todorov, "Runs on ETFs", working paper, University of Notre Dame and BIS, 2021.

client facing skill

incentives

Waving Fees Susan E. K. Christoffersen, “Why Do Money Fund Managers Voluntarily Waive Their Fees?” The Journal of Finance, June, 2001, Vol. 56, No. 3. Option-Like Payouts J. Carpenter, “Does option compensation increase managerial appetite?” Journal of Finance, 2000. Value-at-Risk (VaR) S. Basak, A. and Shapiro, “Value‐at‐Risk based risk management: Optimal policies and asset prices,” Review of Financial Studies, 2001. Hedge Funds --,Hedge Fund Fees, Hedge Fund Marketing Association. Harry M. Kat and Chris Brooks “The Statistical Properties of Hedge Fund Index Returns and Their Implications for Investors,” Journal of Alternative Investments, 2002. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=289299 E.J. Elton, M.J. Gruber, and C.R. Blake, “Incentive fees and mutual funds,” Journal of Finance, 2003.J.E. Hodder and J.C. Jackwerth, “Incentive Contracts and Hedge Fund Management: A Numerical Evaluation Procedure,” 2003 C. Y. Li and H. Kazemi, “Conditional properties of hedge funds: evidence from daily returns,” 2007. David Kirkman, "Fee Adjustments in StyleADVISOR," December 5, 2014.L.R. McFall, “Asymmetric returns and optimal hedge fund portfolios,” Journal of Alternative Investments 6, Fall 2003.

case study: fee engineering

case study: gates, lock-ups, Settlement periods, etc.

George O. Aragon, and J Spencer Martin, 2012, “A unique view of hedge fund derivatives usage: Safeguard or speculation?,” Journal of Financial Economics 105, 436–456. Ben-David, I., Franzoni, F., Moussawi, R., 2012, Hedge Fund Stock Trading in the Financial Crisis of 2007-2009, Review of Financial Studies 25, 1-54., May 2010. Melvyn Teo, “Liquidity and Hedge Funds. Hedge Fund Insights: Newsletter of the BNP Paribus Hedge Fund Centre at SMU,” April 2011. (Shleifer and Vishny, 1997), (Brunnermeier and Pedersen, 2009)

3

chapter 3: the investment process

The Skorina Letter, 8/5/2020.

Strategic asset allocation

Farshid M. Asl and Erkko Etula, “Advancing Strategic Asset Allocation in a Multi-Factor World,” The Journal of Portfolio Management, Vol. 39 Number 1, Fall 2012. Asset Allocation is Important Gary Brinson, Randolph Hood, and Gilbert Beebower, “Determinants of Portfolio Performance,” The Financial Analysts Journal, July-August, 1986. Gary Brinson, Brian Singer, and Gilbert Beebower, “Determinants of Portfolio Performance II: An Update,” The Financial Analysts Journal, 1991. Roger Ibbotson, Paul Kaplan, “Does Asset Allocation Policy Explain 40, 90 or 100 Percent of Performance?,” The Financial Analysts Journal, January-February, 2000. William Sharpe, “The Arithmetic of Active Management,” The Financial Analysts Journal, 1991. Yesim Tokat, Nelson Wicas, Francis Kinniry, “The Asset allocation Debate: A Review and Reconciliation,” FPA Journal, 2006). James Xiong, Roger Ibbotson, Thomas Idzorek, Peng Chen, “The Equal Importance of Asset Allocation and Active Management,” The Financial Analysts Journal, 2010. Yesim Tokat, Nelson Wicas, Francis Kinniry, “The Asset allocation Debate: A Review and Reconciliation,” FPA Journal, 2006). James Xiong, Roger Ibbotson, Thomas Idzorek, Peng Chen, “The Equal Importance of Asset Allocation and Active Management,” The Financial Analysts Journal, 2010. Mark Kritzman, Sébastien Page, and David Turkington, “In Defense of Optimization: The Fallacy of 1/ N, Financial Analysts Journal, Vol. 66, No. 2, March/April, 2010. Mark Kritzman, L. Randolph Hood, and Gary P. Brinson, "Determinants of Portfolio Performance: 20 Years Later," Financial Analysts Journal, Vol. 62, No. 1, Jan/Feb, 2006. Mark Kritzman and Sébastien Page, “The Hierarchy of Investment Choice,” Journal of Portfolio Management, 29, no. 4, 2003. Kodjovi Assoe, Jean-François L’Her, and Jean-François Plante, “The Relative Performance of Asset Allocation and Security Selection,” Journal of Portfolio Management, 33, no. 1, 2006. Paul Samuleson, ‘‘Summing up on Business Cycles: Opening Address,’’ in Beyond Shocks: What Causes Business Cycles, edited by Jeffrey C. Fuhrer and Scott Schuh, Federal Reserve Bank of Boston, 1998. Robert J. Shiller and Jeeman Jung, “Samuelson’s Dictum and the Stock Market,” Cowles Foundation Paper, 2006. Dorsey D. Farr, “Dynamic Factor Approaches to Equity Portfolio Management,” edited by Frank J. Fabozzi and Harry. M. Markowitz, John Wiley & Sons, 2011. Some Still Resist Rob Arnott, “The Biggest Urban Legend in Finance,” 2011/ No Cushion “The State Pension Funding Gap: 2016 Investment shortfalls, insufficient contributions reduced funded levels for public worker retirement plans.” Pew Charitable Trust, April 12, 2018

Tactical Asset Allocation

Factor investing

Pablo Fernández, Javier Aguirreamalloa, and Luis Corres, “Market Risk Premium Used in 56 Countries in 2011: A Survey with 6,014 Answers.” Working Paper WP-920, IESE Business School, May 2011. Roger G. Ibbotson and Laurence B. Siegel. 1988. “How to Forecast Long-Run Asset Returns.” Investment Management Review (September/October). Antti Ilmanen, “Expected Returns on Major Asset Classes,” June 5, 2012. Ross M. Miller, “Stansky's Monster: A Critical Examination of Fidelity’ Magellan’s ‘Frankenfund’,” February, 2007. “Evaluation of the Riksbank’s forecasts,” Riksbank Studies, April 2020.

the role of theory

John B. Taylor, “Discretion versus Policy Rules in Practice, Carnegie-Rochester Conference Series on Public Policy 39, 1993. Robert Shiller, “Is economics a science,” The Guardian, November 6th, 2013. Adrian Wong, “Is The S&P 500 Guided By Fundamentals Or Behavioral Tendencies?” Mar. 22, 2015.

security selection

Charles M. Cottle, Options Trading: The Hidden Reality, RiskDoctor, 1996.

portfolio construction

Revers Optimization and Implied Parameters Fischer Black, Robert Litterman, “Global Asset Allocation with Equities, Bonds, and Currencies,” Fixed Income Research, Goldman Sachs & Company, 1991. William Sharpe, “Imputing Expected Security Returns from Portfolio Composition,” Journal of Financial and Quantitative Analysis, 1974. William Sharpe, “Adaptive Asset Allocation Policies,” The Financial Analysts Journal, 2010.

rebalancing

Clifford S. Asness, Antti Ilmanen and T. Maloney, “Market Timing: Sin a Little,” AQR Whitepaper, 2016.Clifford S. Asness, Swati Chandra, Antti Ilmanen, and Israel Ronen, Contrarian Factor Timing is Deceptively Difficult, Working Paper, March 7, 2017. Andrea Frazzini, R. Israel, and T. J. Moskowitz, “Trading Costs of Asset Pricing Anomalies,” 2012. Winfried G. Hallerbach, “Disentangling Rebalancing Return,” Journal of Asset Management, 15, 2014. Campbell R. Harvey, N. Granger, D. Greenig, S. Rattray and D. Zou, “Rebalancing Risk,” 2014. Pierre Hillion, “The Ex-Ante Rebalancing Premium,” 2016. Edward Qian, “To Rebalance or Not to Rebalance: A Statistical Comparison of Terminal Wealth of Fixed- Weight and Buy-and-Hold Portfolios,” 2014. William Sharpe, “Adaptive Asset Allocation Policies,” The Financial Analysts Journal, May-June, 2010. John J. Huss, Thomas Maloney, Portfolio Rebalancing: Common Misconceptions, February 1, 2017. John J. Huss, Thomas Maloney, Portfolio Rebalancing: Common Misconceptions, February 1, 2017. Andersen, Robert M., S. W. Bianchi and L. R. Goldberg, “Determinants of Levered Portfolio Performance,” Financial Analysts Journal, 70(5), 2014. Perchet, Romain, Raul Leote de Carvalho, Thomas Heckel and Pierre Moulin, “Inter-temporal Risk Parity: A constant volatility framework for equities and other asset classes,” working paper, 2014. Moreira, Alan, and T. Muir, “Volatility Managed Portfolios,” working paper, 2016.

ongoing management

case study: Large Wealth Management Division

case study: Mid-Sized RIA

4

chapter 4

Jim Mate, ‘Kicking the Tires’ Is Loaded with Meaning, Chicago Tribune, August 14th, 1997. Carl von Clausewitz, Vom Kriege, 1832.

Culture

Good Governance K. Ambachtsheer, “How Much is Good Governance Worth?” June 2006. [Fees eat 40% of alpha] Callan Associates, “Cost of Doing Business Survey: Executive Summary,” 2013. G.L. Clark and A.H.B. Monk, “Principals and policies for in‐house asset management.” December 2012. G.L. Clark and R. Urwin, “Best‐practice investment management: lessons for asset owners.” Oxford‐Watson Wyatt project on governance. September 2007. Richard Feynman, “Surely You're Joking, Mr. Feynman! (Adventures of a Curious Character),” W. W. Norton & Company, April 17, 1997. Responsibility Julian B. Rotter, “Social Learning and Clinical Psychology,” Prentice-Hall, 1954. Scientific Method Derrick Farnell, “How Belief Works,” 2012. You're Better Madison Marriage, “86% of Active Equity Funds Underperform" Financial Times, March, 2016. A. H. Welsh, A. Townsend Peterson and Stuart A. Altmann, “The Fallacy of Averages,” The American Naturalist, The University of Chicago Press, Vol. 132, No. 2, August, 1988. I was told an uncorroborated story about a sales meeting where Michael Bloomberg was asked to lower the price of his Professional terminal for a client to which he responded “if your trader can’t make $68 extra per day, you should get another trader.” Lisa Beilfuss and Alexander Osipovich, “The Race to Zero Commissions, After decades of discounts, the price of trading is suddenly zero with online brokers finding new ways to make money from clients,” The Wall Street Journal, October 5th, 2019. Richard Feynman, “Surely You're Joking, Mr. Feynman! (Adventures of a Curious Character),” W. W. Norton & Company, April 17, 1997. Fund Features and Fees Callan Associates, “Cost of Doing Business Survey: Executive Summary,” 2013. William W. Jennings and Brian C. Payne, “Fees Eat Diversification’s Lunch,” Financial Analysts Journal, Volume 72 · Number 2, 2016. Stephen J. Brown, William Goetzmann, and B. Liang, “Fees on Fees in Funds of Funds.” Journal of Investment Management, vol. 2, no. 4 July-August, 2004. Roger G. Ibbotson, Peng Chen and Kevin X. Zhu, “The ABCs of Hedge Funds: Alphas, Betas, and Costs.” Financial Analysts Journal, vol. 67, no. 1, January-February, 2011. David Shairp, Anthony Werley, and Michael Feser, “Long-Term Capital Market Return Assumptions: 2013 Estimates and the Thinking behind the Numbers,”, J.P. Morgan Asset Management, October, 2012. William W. Jennings and Brian C. Payne, “Fees Eat Diversification’s Lunch,” Financial Analysts Journal, Volume 72 · Number 2, 2016 Stephen J. Brown, William Goetzmann, and B. Liang, “Fees on Fees in Funds of Funds.” Journal of Investment Management, vol. 2, no. 4 July-August, 2004. Callan Associates, “Cost of Doing Business Survey: Executive Summary,” 2013 David Shairp, Anthony Werley, and Michael Feser, “Long-Term Capital Market Return Assumptions: 2013 Estimates and the Thinking behind the Numbers,”, J.P. Morgan Asset Management, October, 2012. William W. Jennings and Brian C. Payne, “Fees Eat Diversification’s Lunch,” Financial Analysts Journal, Volume 72 · Number 2, 2016. Robert H. Jeffrey,and Robert D. Arnott, “Is Your Alpha Big Enough to Cover Its Taxes?” Journal of Portfolio Management, vol. 19, no. 3, Spring, 1993.

Business Case

Methods "PRojects IN Controlled Environments", "OGC brings its shining quartet back into the limelight" (Press release). Office of Government Commerce. 14 December 2005. Jamie Arnold, Agile delivery, Outcomes, not deliverables, Posted by: GDS team, Posted on:30 June 2015. Heuristics Tversky, Amos; Kahneman, Daniel (1974). "Judgment under Uncertainty: Heuristics and Biases" Science. 185 (4157): 1124. Bibcode:1974Sci...185.1124T. doi:10.1126/science.185.4157.1124. Archived from the original on March 18, 2012. The Difference Between a Case and Plan Harvard Business Press, Developing a Business Case, 2011, Harvard Business School Publishing, ISBN: 978-1-4221-2976-0 Business Case Structure U.S. Department of Defense Business Case Analysis, October 22, 2014, https://dodcio.defense.gov/Portals/0/Documents/BPSR/TEMPLATE%20-BCA.pdf?ver=2018-07-26-113256-063 U.S. Government Accountability Office, GAO Cost Estimating and Assessment Guide, March 2009, https://www.gao.gov/new.items/d093sp.pdf U.S. Naval Center for Cost Analysis, NCCA Reference Information, https://www.ncca.navy.mil/references.cfm Umble, E.J., Haft, R.R., Umble, M.M., (2003) Enterprise resource planning: Implementation procedures and critical success factors, European Journal of Operational Research, Vol. 146, pp. 241–257

Executive Summary and Introduction

for the investment committee

for the operating committee

Financial Aanalysis Bent Flyvbjerg, “Public Planning of Mega-Projects: Overestimation of Demand and Underestimation of Costs” in Priemus, Hugo; Flyvbjerg, Bent; van Wee, Bert (eds.). Decision-making on Mega-Projects, 2008. Bent Flyvbjerg, “Over Budget, Over Time, Over and Over Again: Managing Major Projects” in Morris, Peter W. G; Pinto, Jeffrey K; Söderlund, Jonas (eds.). The Oxford Handbook of Project Management, 2011. A “roller coaster” swap is one way of managing currency mismatches based on a project’s funding profile. The currency flows are estimated and written into the agreement to allow the project to fund uneven cash flows. For instance, equipment and services may need to be purchased from foreign suppliers on various dates during the build-out. The resulting cumulative cash flow may be a complex shape.Capability-Based Planning (CBP) was developed but the United States, United Kingdom, Australia, and Canada for defense and military planning but is now used by businesses for strategic planning. Capability-Based Planning (CBP) was developed but the United States, United Kingdom, Australia, and Canada for defense and military planning but is now used by businesses for strategic planning..

investment Policy Statement

Appendicies "New Investment Restrictions Coming for CalPERS CIO,” Asset Management Culture, September 17th, 2020.

Firm-specific consideratins

Institutions "Pension Protection Act (PPA),” Department of Labor website, January, 2021. Pension Protection Act, 2006. Insurance “Asset Valuation Reserves and Interest Maintenance Reserves,” National Association for Insurance Commissioners. 2002. Susan Randall, “Insurance Regulation in the United States: Regulatory Federalism and the National Association of Insurance Commissioners.” Florida State University Law Review 26:3, 1999. “Asset Valuation Reserve (Continued) Basic Contribution, Reserve Objective and Maximum Reserve Calculations Equity and Other Invested Asset Component,” in Annual Statement for the Year 2019, National Asssociation of Insurance Commissioners, October 2019. Doron Nissim, “Analysis and Valuation of Insurance Compnies,” Center for Excellence in Accounting and Security Analysis, November, 2010. Rolf Tolle, “The Cycle Challenge,” Lloyd’s of London, September 28th, 2007. “Annual Underwriter Survey,” Lloyd’s of London, 2006. “Managing the Cycle—How the Market Can Take Control,” 2007. “Basel II: international regulatory framework for banks,” Bank fo International Settlements, January 2021. Private Clients Klakow Akepanidtaworn, Rick Di Masoio, Alex Imas, Lawrence Schmidt, "Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors," December 2018. https://ssrn.com/abstract=3301277 Jean L. P. Brunel “Revisiting the Asset Allocation Challenge through a Behavioral Finance Lens.” Journal of Wealth Management 6:2, 2003. Jean L. P. Brunel, “Integrated Wealth Management: The New Direction for Portfolio Managers,” 2nd ed., Euromoney, 2006. Brian Lawler, Brett Mossman, Patrick Nolan, Andrew Ang, "Factors and Advisor Portfolios," Journal of Wealth Management, Spring 2020. Harry M. Markowitz, Portfolio Selection.” Journal of Finance 7:1, 77–91, 1952.

Investment Due Diligence

Special COnsiderations

Steven Heston and Geert Rouwenhorst, “Does Industrial Structure Explain the benefits of International Diversification?” Journal of Financial Economics, 1994.

ESG Mandates

Camilleri, Mark Anthony, “The Market for Socially Responsible Investing: A Review of the Developments.” Social Responsibility Journal, 2020. Mark A. Holtzblatt, Craig Foltin, and Norbert Tschakert Learning from Ethical Violations in Public Accounting: A South African Audit Scandal and a Firm's Transformation,” Issues in Accounting Education, 35 (2): 37–63, January 17th, 2020. Bernstein, Asaf, Matthew T Gustafson, and Ryan Lewis “Disaster on the Horizon: The Price Effect of Sea Level Rise,” Journal of Financial Economics 2019. Apoorva Mandavilli, “The World’s Worst Industrial Disaster Is Still Unfolding,” The Atlantic, July 10th, 2018. Matt Levine, “Robinhood Ends Its Popularity Contest,” Aug 10, 2020. Eugene Scalia, “Retirees’ Security Trumps Other Social Goals,” Wall Street Journal, June 23, 2020. Virginia Zhelyazkova, SRI Strategies in Asset Management: Typology And Application Trends VUZF University, 1, Gusla St., Sofia, Bulgaria. Blitz, David, and Laurens Swinkels, “Is Exclusion Effective?” Journal of Portfolio Management, 2020. Bruder, Benjamin, Yazid Cheikh, Florent Deixonne, and Ban Zheng, “Integration of ESG in Asset Allocation.” SSRN Working Paper 3473874, 2019. Linda-Gail Bekker, George Alleyne, Stefan Baral, Javier Cepeda, Demetre Daskalakis, David Dowdy, Mark Dybul, Serge Eholie, Kene Esom, Geoff Garnett, Anna Grimsrud, James Hakim, Diane Havlir, Michael T Isbell, Leigh Johnson, Adeeba Kamarulzaman, Parastu Kasaie, Michel Kazatchkine, Nduku Kilonzo, Michael Klag, Marina Klein, Sharon R Lewin, Chewe Luo, Keletso Makofane, Natasha K Martin, Kenneth Mayer, Gregorio Millett, Ntobeko Ntusi, Loyce Pace, Carey Pike, Peter Piot, Anton Pozniak, Thomas C Quinn, Jurgen Rockstroh, Jirair Ratevosian, Owen Ryan, Serra Sippel, Bruno Spire, Agnes So, “Advancing global health and strengthening the HIV response in the era of the Sustainable Development Goals: the International AIDS Society,” Lancet Commission, Vol 392 July 28, 2018. John Hill, Environmental, Social, and Governance (ESG) Investing, Elsevier, 2020. John Hill, Environmental, Social, and Governance (ESG) Investing, Elsevier, 2020. James Mackintosh, “Why Your Good Governance Fund Is Full of Saudi Bonds,” Wall Street Journal, November 26th, 2019. Sebastian Utz and Maximilian Wimmer, "Are they any good at all? A financial and ethical analysis of socially responsible mutual funds," .Greg M. Richey, "Is It Good to Sin When Times Are Bad? An Investigation of the Defensive Nature of Sin Stocks?" The Journal of Investing October 2020, 29 (6) 43-50; DOI: https://doi.org/10.3905/joi.2020.1.144.Robert N. Killins, Thanh Ngo, and Hongxia Wang, "The Underpricing of Sin Stocks," The Journal of Investing June 2020, 29 (4) 67-76; DOI: https://doi.org/10.3905/joi.2020.1.126.David Blitz and Frank J. Fabozzi, "Sin Stocks Revisited: Resolving the Sin Stock Anomaly," The Journal of Portfolio Management Fall 2017, 44 (1) 105-111; DOI: https://doi.org/10.3905/jpm.2017.44.1.105.Greg M. Richey, "Fewer Reasons to Sin: A Five-Factor Investigation of Vice Stocks," January 18, 2017, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2901795.Harrison Hong and Marcin Kacperczyk, "The price of sin: The effects of social norms on markets," Journal of Financial Economics, Volume 93, Issue 1, 2009, Pages 15-36, ISSN 0304-405X, https://doi.org/10.1016/j.jfineco.2008.09.001. https://www.sciencedirect.com/science/article/pii/S0304405X09000634..

Firm Type

Roger Clarke, Hariandra de Silva, and Steven Thorley, "Portfolio constraints and the fundamental law of active management," Financial Analysts Journal, Vol. 58, No. 5, September-October 2002.

Case Study: Business Case for a Systematic Fund

Charles Cao, Peter Iliev, and Raisa Velthuis Style Drift: Evidence from Small-Cap Mutual Funds, August 2017, Journal of Banking and Finance, Volume 47, Issue 8. Susanna Rust, Net-zero asset owner club gains UNJSPF, nears targets framework, July 10, 2020 Nick Reeve, Top European asset owners back UN’s Tobacco-Free Finance pledge, September 2018.Fang Block, Wealthy Millennials are Driving a Rise in Sustainable Investing, December 4, 2019. Cinthia Murphy, Thematic ETFs The New Sectors? June 8, 2020. Martin Arnold and Brendan Greeley, Central bank stimulus is distorting financial markets, BIS finds, Financial Times, October 7, 2019. Jon A. Christoperson, David R. Carino, Wayne E. Ferson, Performance Measurement and Benchmarking, McGraw Hill, 2009. Carl R. Bacon, Practical Portfolio Performance Measurement and Attribution, 2nd Edition, John Wiley & Sons, 2008. Laurent Ferrar and Thanh-Long Huynh, “Macroeconomic nowcasting in times of Covid-19 crisis: On the usefulness of alternative data,” QuantCube Technology. Claude Schwab, Hedge Me, Lynx Media; 4th edition,August 24, 2006. Claude Schwab, Pay Me, Schwab Enterprises, 2007. Keith L. Miller, Hong Li, Tiffany G. Zhou and Daniel Giamouridis, A Risk-Oriented Model for Factor Timing Decisions, The Journal of Portfolio Management Spring 2015, 41 (3) 46-58; DOI: https://doi.org/10.3905/jpm.2015.41.3.046 Mark Kritzman, Simon Myrgren, and Sebastien Page, Edited by Bernd Scherer and Kenneth Winston, Adding the Time Dimension, Optimal Rebalancing, The Oxford Handbook of Quantitative Asset Management, Oxford University Press, 2012. Michael Robbins, “Expected risk and return attribution for a novel machine learning-based global macro investing process,” Columbia University, 2020. Michael Robbins, “Effects of regime change on a novel Markov switching global macro investing process,” Columbia University, 2020. Prequin, Prequin Global Hedge Fund Report Claude Schwab, Hedge Me, Lynx Media; 4th edition,August 24, 2006. Claude Schwab, Pay Me, Schwab Enterprises, 2007.

Part 2: Building Your Process

5

chapter 5: Financial Data

Organizing Financial data

“API Library Support,” Bloomberg Professional, WAPI, February 23rd, 2021. “Datafeed Toolbox Documentation,” Mathworks, 2020B, February 23rd, 2021. “What Is Structured Data,” U.S. Securiities and Exchange Commission, March 26th, 2016. “US Business Cycle Expansions and Contractions,” National Bureau of Economic Research, June 8th, 2020. US GDP and S&P 500, Differenced and Windsorized 2 standard deviations, 1968-2018, Bloomberg LP.

Economic Data

David Adalfatto and Spewak, Andrew, "Does the Yield Curve Really Forecast Recession?," Federal Reserve Bank of St. Louis Economic Synopses, No. 30, 2018. Pierre Collin-Dufresne, Robert S. Goldstein, and J. Spencer Martin, “The Determinants of Credit Spread Changes.” Journal of Finance 56(6), December, 2001. JoostDriessen, “Is Default Event Risk Priced in Corporate Bonds?” Review of Financial Studies 18(1), January, 2005. Kevin Kliesen, Michael McCracken, Kathryn Bokun, and Aaron Amburgey, “The St. Louis Fed’s Financial Stress Index, Version 2.0,” The FRED Blog, March 26th, 2020.

Archival (Point-In-Time) Data

The Importance of Archival Data ALFRED® help page, St. Louis Federal Reserve. How Archival Data Works Eliezer S. Yudkowsky, “An Intuitive Explanation of Bayesian Reasoning,” 2003.; Daniel Kahneman, “Thinking, fast and slow,” Farrar, Straus and Giroux, 2011. Berger, James O. (1985). Statistical Decision Theory and Bayesian Analysis. Springer Series in Statistics (Second ed.). Springer-Verlag. ISBN 978-0-387-96098-2. http://www.bea.gov/newsreleases/regional/rpp/rpp_newsrelease.htm; http://www.bea.gov/regional/methods.cfm; https://www.bea.gov/data/personal-consumption-expenditures-price-index ALFRED help page, St. Louis Federal Reserve; ALFRED help page, St. Louis Federal Reserve; ALFRED help page, St. Louis Federal Reserve. Katrina Stierholz, “ALFRED: Capturing data as it happens,” Federal Reserve Bank of St. Louis. Challenges of Archival Data Domenico Giannone, Michele Lenza, and Giorgio E. Primiceri, “Economic Predictions with Big Data: The Illusion of Sparsity,” Federal Reserve Bank of New York Liberty Street Economics (blog), May 21, 2018. The ALFRED Database. Jesse Edgerton, Dan Weitzenfeld, “Machine learning for macro: What you need to know,” J.P. Morgan Economic Research, October 30th, 2018. Storing Archival Data https://alfred.stlouisfed.org/docs/alfred\_capturing\_data.pdf Replicability, Real-Time Data, and the Science of Economic Research Richard G. Anderson, VDC, the Federal Reserve Bank of St. Louis Review, Jan-Feb 2006. Richard T. Snodgrass, Developing Time-Oriented Database Applications in SQL https://alfred.stlouisfed.org/docs/populating\_alfred.pdf. Nowcasts Maximo Camacho and Gabriel Perez-Quiros, Gabriel, "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, 2010. Troy D. Matheson, "An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys," Economic Modelling, January 2010. Martin D. D. Evans, "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, September 2005. G. Rünstler, K. Barhoumi, S. Benk, R. Cristadoro, A. Den Reijer, A. Jakaitiene, P. Jelonek, A. Rua, K. Ruth, and C. Van Nieuwenhuyze, " Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, 2009. Elena Angelini, Marta Banbura, and Gerhard Rünstler, "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model, OECD Journal, 2010. Giannone Domenico, Lucrezia Reichlin, and Saverio Simonelli, "Is the UK still in recession? We don't think so," November 23rd 2009. Lahiri Kajal and George Monokroussos, "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, 2013. Antolin-Diaz, Juan; Drechsel, Thomas; Petrella, Ivan, "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, 2014. Federal Reserve Bank of Atlanta Center for Quantitative Economic Research https://www.frbatlanta.org/cqer/research/gdpnow.

Bulk and Descriptive Data

Market Data

Iman van Lelyveld, “The use of derivatives trade repository data: possibilities and challenges,” Irving Fisher Committtee on Central Bank Statistics, Bank for International Settlements, May 18-19, 2017.

Fundamental Data

“Filer Manual – Volume II EDGAR Filing,” Draft, September 202.; (XBRL) xbrl.org. W. Prem, A. Rao, and C. Martin, “Channel-stuff or sales with a right to return?” Journal of the International Academy for Case Studies, 25 (1): 1–11, 2019. G. Lai, L. Debo, and L. Nan, “Channel stuffing with short-term interest in market value,” Management Science, 57 (2), 2011. C.W. Mulford and E.E. Comiskey, “The Financial Numbers Game: Detecting Creative Accounting Practices.” John Wiley & Sons, 2002. H. Schilit, J. Perler, and Y Engelhart, “Financial Shenanigans: How to Detect Accounting Gimmicks and Fraud in Financial Reports,” 4th Edition. McGraw-Hill, 2018. O-K. Hope and J. Wang, “Management deception, big-bath accounting, and information asymmetry: Evidence from linguistic analysis,” Accounting, Organizations and Society, 70: 33–51, 2018. H. David Sherman and S. David Young, "Tread lightly through these accounting minefields," Harvard Business Review 79(7):129-35, 146, July 2001. https://www.researchgate.net/publication/11893169_Tread_lightly_through_these_accounting_minefields.

Survey Data

Yuliya Chernova, “Crowdsourced Stock Opinions Beat Analysts News,” March 19, 2014. Georgette Jasen, “Journal's Dartboard Retires After 14 Years of Stock Picks,” April 18, 2002. Johan Bollen, Huina Mao, and Xiao-Jun Zeng, “Twitter mood predicts the stock market,” October 14th, 2010.

Sampling and Synthetic Data

Preprocessing Data, MATLAB Documentation, 2019. Digital Industries Software, Aliasing documentation; Marcellino, Mario Porqueddu, Fabrizio Venditti, Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility, 2012. Will Kenton, SEC Form 13F Definition, June 25, 2019, U.S. Securities & Exchange Commission; Charles R. Nelson and Heejoon Kang, “Spurious Periodicity in Inappropriately Detrended Time Series,” Econometrica 49, no. 3, 1981. “The Bloomberg Commodity Index Methodology,”Blomberg, January 2018.

Roll Examples

Corporate Actions

Abraham Thomas, The Comprehensive Guide to Stock Price Calculation, March 24 2016.

Other PRactical Matters

Matt Levine, Matt Levine's Money Stuff, Sneaky Pegs Found Hidden Orders, Bloomberg Opinion, July 2020. United States of America Before the Securities and Exchange Commission in the Matter of New York Stock Exchange LLC, NYSE American LLC, and NYSE Arca, Inc., Administrative Proceeding File No. 3-18388.

Case Study: RIA Data

Create Simple Text Model for Classification, Mathworks, R2020b Documentation

Case Study: Gibbs Sampler

6

CHAPTER 6: asset types

Company Exposure

Financing

Equity R. Clarke, H. de Silva, and R. Murdock, "A factor approach to asset allocation," Journal of Portfolio Management, 32, 10–18., 2005. E. Dimson, P. Marsh, and M. Staunton, "Credit Suisse global investment returns yearbook 2018," Credit Suisse AG Research Institute, Zurich, Switzerland, 2018.T. M. Idzorek, and M. Kowara, "Factor based asset allocation vs. asset class based asset allocation," Financial Analysts Journal, 69, 1.11, 2013. A. Ilmanen and J. Kizer, "The death of diversification has been greatly exaggerated," Journal of Portfolio Management, 38, 15–27., 2012. Squeeze “CFTC Order Imposes $600,000 Civil Penalty Against Fenchurch Capital Management Inc. of Chicago, In Connection with Market Manipulation and Cornering,” United States Commodities Futures Trading Commission, July 10, 1996. Equity Lending and Borrowing Viktoria Baklanova, Cecilia Caglio, Marco Cipriani, and Adam Copeland, “The Use of Collateral in Bilateral Repurchase and Securities Lending Agreements,” Federal Reserve Bank of New York Staff Reports No. 758, October 2017. Matthew Dive, Ronan Hodge, Catrin Jones, and James Purchase “Developments in the global securities lending market,” Bank of England Quarterly Bullitin 2011 Q3, September 19th, 2011 Darrell Duffiea , Nicolae Garleanu, and Lasse Heje Pedersen,“Securities lending, shorting, and pricing,” Journal of Financial Economics 66, January, 2002. David M. Weiss , “After the Trade Is Made: Processing Securities Transactions,” Portfolios, August 17th, 2006. Michael Simmons, “Securities Operations: A Guide to Trade and Position Management,” Wiley, April 15th, 2002. Keith Dickinson, “Financial Markets Operations Management,” Wiley, March 23rd, 2015. Claude Brown, "How to spot the difference between repos and stock loans," Linklaters & Paines, 1996. TBA, WI Peter Nash, “Effective Product Control Controlling for Trading Desks,” Wiley, 2017. Bank of International Settlements (BIS). “Principles for the Sound Management of Operational Risk, Bank of International Settlements.” June 30th, 2011. Rick Beal, Alex Weisgerber, Claudia Poster, and Esther Becker, “Incentive Compensation/Risk Management – Integrating Incentive Alignment and Risk Mitigation,” Towers Watson, Canadian Institute of Actuaries, Casualty Actuarial Society, Society of Actuaries, 2013. Board of Banking Supervision, “Report into the collapse of Barings Bank.” July 18, 1995. Joshua Corrigan and Paola Luraschi, “Operational risk modelling framework.” Milliman Research Report, February 2013. Satyajit Das, “Traders, Guns and Money: Knowns and unknowns in the dazzling world of derivatives,” Financial Times, Prentice Hall, July 17th, 2010. Robert S. Dunnett, Cindy B. Levy, and Antonio P. Simoes,”The hidden cost of operational risk,” McKinsey & Co., December 2005. Kurt Eichenwald, “Learning the Hard Way How to Monitor Traders.” The New York Times, March 9th, 1995. Terzah Ewing, “Sumitomo Suit to Be Settled For $99 Million,” The Wall Street Journal, August 13th, 1998. Edward Kane, “Breakdown of accounting controls at Barings and Daiwa: Benefits of using opportunity-cost measures for trading activity,” Pacific-Basin Finance Journal, vol. 7, issue 3-4, 1999. Erik Ipsen, “Shell Gains Despite Currency Fiasco., The New York Times, February 26th, 1993. “Investigation and Oversight of Daiwa Bank and Daiwa Trust Company: Hearing before the Committee on Banking, Housing, and Urban Affairs,” United States Senate, November 27th, 1995. D. Mailliard and A. Zaim, “Rogue Traders and the Evolution of Trading Controls,” KPMG, March 7th, 2014. Hugh Son, “JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours,” Bloomberg, February 27, 2017. United States Senate Permanent Subcommittee on Investigations. “JPMorgan Chase Whale Trades: A Case History of Derivatives Risks and Abuses,” March, 15th, 2013. Buy-Sellback Market Practice & Regulatory Policy,” International Capital Market Association, Retrieved January 2021.

Synthetic and Commingled Exposure

Total Return Swaps (TRS) Matt Levine, "Bill Hwang's Archegos Swap Aftermath Is Awkward for Banks," Bloomberg Opinion, April 5th, 2021.

Categories and Geography

International Indexes “Emerging Markets: A 20-year Perspective,” MSCI/Barra, 2008.; “S&P U.S. Indices Methodology,” S&P Dow Jones Indices January, 2021. Bob Pisani, “S&P decision to add Tesla in one shot shows the power of passive investing,” cnbc, December 1st, 2020. George Calhoun, “Will Tesla Break The S&P 500? The Mechanics Of Market Turmoil,” Forbes, Jan 4th , 2021.

fixed income, currencies, and Commodities (FICC)

Cash, Governments, and Munis

Cash, Govies Gerry Mehrling, “The New Lombard Street,” Princeton University Press, 2011. Marcia Stigum and Anthony Crescenzi, “Stigum’s Money Market,” Fourth Edition, McGraw Hill, 2007. Annette Thau, “The Bond Book,” Third Edition, McGraw Hill, 2011. Walter Bagehot, “Lombard Street A Description of the Money Market,” April 30, 2011. David M. Weiss , “After the Trade Is Made: Processing Securities Transactions,” Portfolios, August 17th, 2006. Michael Simmons, “Securities Operations: A Guide to Trade and Position Management,” Wiley, April 15th, 2002. Keith Dickinson, “Financial Markets Operations Management,” Wiley, March 23rd, 2015. “Morningstar Global Fixed Income Classification,” Morningstar Research, October 13th, 2017. Taxes American Recovery and Reinvestment Act (2009).; U.S. Internal Revenue Code, Section 141; Joe Mysak, "Muni Bond Issuers See Tax-Exemption at Eternal Risk," Bloomberg, March 16th, 2021.

Corporates

Distressed Stephen G. Moyer, “Distressed Debt Analysis: Strategies for Speculative Investors,” J. Ross Publishing, 2005. HY Howard Qi, Sheen Liu, and Chunchi Wu, “Personal Taxes, Endogenous Default, and Corporate Bond Yield Spreads,” 2006. Convertibles Harold Bierman Jr., “Convertible Bonds as Investments,” Financial Analysts Journal 36, 1980. Nick Calamos, “Convertible Arbitrage: Insights and Techniques for Successful Hedging,” John Wiley & Sons, 2003. Jonathan Ingersoll, “An Examination of Corporate Call Policies on Corporate Securities,” Journal of Finance, 1977.

Securitized Producs and Derivatives

Leveraged Loans

Structured Notes and Linkers

John Brynjolfsson (Editor), Frank J. Fabozzi (Editor)Handbook of Inflation Indexed Bonds,” Wiley, February 1st, 1999.

Swaps

Marketable Real Estate

RE Is Good Over The Long Term Martin Hoesli, and Elias Oikarinen, “Are Reits Real Estate? Evidence from Sector Level Data,” ERES Conference 2011. RE Indices Andy Naranjo, and David Ling, “Economic Risk Factors and Commercial Real Estate Returns,” The Journal of Real Estate Finance and Economics 14, 1997. Daniel Daniel and Sheridan Titman, “Do Real Estate Prices and Stock Prices Move Together? An International Analysis,” Real Estate Economics 27, 1999. Jianping Mei and Ahyee Lee, “Is There a Real Estate Factor Premium?,” Journal of Real Estate Finance and Economics 9 1994. RE Diversification Piet Eichholtz, “Does International Diversification Work Better for Real Estate than for Stocks and Bonds?,” Financial Analysts Journal 52, 1996. Daniel Quan, Daniel and Sheridan Titman, “‘Commercial Real Estate Prices and Stock Market Returns: An International Analysis,” Financial Analysts Journal 53, 1997. Graeme Newell and James Webb, “Assessing Risk for International Real Estate Investments,” Journal of Real Estate Research 11, 1996. Piet Eichholtz, Martin Hoesli, Bryan MacGregor, and Nanda Nanthakumaran, “Real Estate Portfolio Diversification by Property Type and Region,” Journal of Property Finance 6, 1995. Martin Hoesli, Colin Lizieri, and Bryan MacGregor, “The Spatial Dimensions of the Investment Performance of UK Commercial Property,” Urban Studies 34, 1997. Michael Young, Stephen Lee, and Steven Devaney, “Non-normal Real Estate Return Distributions by Property Type in the UK,” Journal of Property Research 23, 2006 Unsmoothing RE Returns Cornish and Fisher, “Moments and Cumulants in the Specification of Distributions,” Review of the International Statistical Institute, 1937 David Geltner, “Smoothing in Appraisal-Based Returns,” Journal of Real Estate Finance and Economics 4, 1991. David Geltner, “Estimating Market Values from Appraised Values without Assuming an Efficient Market,” Journal of Real Estate Research 8, 1993.

Currencies

Commodities

Robert Greer, “Commodity Indexes for Real Return and Efficient Diversification,” The Handbook of Inflation Hedging Investments, McGraw-Hill, 2005. Claude Erb and Harvey Campbell, ‘The Tactical and Strategic Value of Commodity Futures,” Financial Analysts Journal, 2006. David Booth and Eugene Fama, “Diversification Returns and Asset Contributions,” Financial Analysts Journal, 1992. Claude Erb and Harvey Campbell “The Tactical and Strategic Value of Commodity Futures,” Financial Analysts Journal, 2006. David Booth and Eugene Fama, “Diversification Returns and Asset Contributions,” Financial Analysts Journal, 1992. Jon Caulfield, ‘Timberland Return Drivers and Investing Styles for an Asset that has Come of Age,” Real Estate Finance, 1998. Courtland Washburn Clark and Binkley, “Do Forest Assets Hedge Inflation?,” Land Economics, 1993.

alternatives and illiquids

Hedge FUnds and Managed Futures

William Fund and David Hsieh, “Hedge-Fund Benchmarks: Information Content and Biases,” Financial Analysts Journal, 2002. Nicole Boyson, “Hedge Fund Performance Persistence: A New Approach,” Financial Analysts Journal. 2008. William Fung and David Hsieh, “Hedge Fund Benchmarks: A Risk Based Approach,” Financial Analysts Journal, 2004.

Smart Beta

Sébastien Maillard, Thierry Roncalli, and Jérôme Teïltche, “The Properties of Equally Weighted Risk Contribution Portfolios,” Journal of Portfolio Management, 2010 Robert Fernholz, Garvy Robert, and John Hannon, “Diversity-Weighted Indexing,” Journal of Portfolio Management, 1998. Malcolm Baker, Brendan Bradley, and Jeffrey Wurgler, ”Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly,” Financial Analysts Journal, 2011. Robert Arnott, Jason Hsu, and Philip Moor, “Fundamental Indexation,” Financial Analysts Journal, 2005. Eugene Fama and Kenneth French, “The Cross-Section of Expected Stock Returns,” Journal of Finance, 1992. Eugene Fama, “Common Risk Factors in the returns on stocks and bonds,”. Narasimhan Jagadeesh and Sheridan Titman, “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency,” Journal of Finance, 1993. Mark Carhart, “On Persistence in Mutual Fund Performance,” Journal of Finance, 1997. Robert Haugen and Nardin Baker, “The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios,” Journal of Portfolio Management, 1991. Roger Clarke, Harindra de Silva, and Steven Thorley, “Minimum-Variance Portfolios in the U.S. Equity Market,” Journal of Portfolio Management, 2006. Roger Clarke, Harindra de Silva, and Steven Thorley, “Know Your VMS Exposure,” Journal of Portfolio Management, 2010. Tzee-man Chow, Jason Hsu, Jason, Vitali Kalesnik, and Bryce Little, “A Survey of Alternative Equity Index Strategies,” Financial Analysts Journal, 2011. Clifford Asness, “The Value of Fundamental Indexing,” Institutional Investors, 2006.

PRIVATE EQUITY

Albertus and Denes, Private Equity Fund Debt: Capital Flows, Performance, and Agency Costs, Carnegie Mellon, May 2020Aleksandar Andonov, and Joshua D. Rauh, 2020, The Return Expectations of Institutional Investors. Stanford University Graduate School of Business Research Paper No. 18-5. Andrew Ang, Bingxu Chen, William N Goetzmann, and Ludovic Phalippou, 2017, Estimating Private Equity Returns from Limited Partner Cash Flows, Journal of Finance, Forthcoming. Bain, Global Private Equity Report, 2021. Brown, et al. Evolution of Private Equity Fund Value, June 2020. Brown, et al., Can investors time their private equity?, October 2019. Brown, Harris, Hu, Jenkinson, Kaplan, and Robinson, Private equity portfolio companies: A first look at Burgiss holdings data, January 2020. Cambridge Associates, Should you avoid commitment (facilities)?, June 2018. Capital Dynamics, Private equity co-investment funds--a comparison of risks and returns, February 2020.Cembalest, Michael, "Food Fight: An update on private equity performance vs public equity markets," JP Morgan Asset and Wealth Management, June 2021.Clayton, William W, 2020, The Private Equity Negotiation Myth., Yale Journal on Regulation 37, 67–115. Ennis, Richard M, 2020, Institutional Investment Strategy and Manager Choice: A Critique, The Journal of Portfolio Management. Franzoni, Francesco, Eric Nowak, and Ludovic Phalippou, 2012, Private equity performance and liquidity risk, Journal of Finance 67, 2341–2373. Gredil et al, Benchmarking Private Equity: The Direct Alpha Method, 2014. Hamilton Lane _ Market Overview_ Performance Part I, 2021.Robert S. Harris, Tim Jenkinson, and Steven N. Kaplan, 2014, Private equity performance: What do we know?, Journal of Finance 69, 1851–1882. Robert S. Harris, Tim Jenkinson, and Steven N Kaplan, 2016, How Do Private Equity Investments Perform Compared to Public Equity?, Journal of Investment Management 14, 1–24. Robert S. Harris, et al., Evidence from Buyout and Venture Funds, November 2020.Haynes and Boone LLP, Fund Finance: Utilizing Credit Lines/Subscription Lines of Credit at the Fund Level, February 2018.Ilmanen, Antti, Swati Chandra, and Nicholas McQuinn, 2020, Demystifying Illiquid Assets: Expected Returns for Private Equity, The Journal of Alternative Investments 22, 8–22. Institutional Limited Partners Association, Enhancing Transparency Around Subscription Lines of Credit, June 2020Ivashina, Victoria, and Josh Lerner, 2019, Pay now or pay later? The economics within the private equity partnership., Journal of Financial Economics 131, 61–87. Ivashina, Victoria, and Joshua Lerner, 2019, Patient Capital : The Challenges and Promises of Long-Term Investing. Ed. Princeton University Press. Tim Jenkinson, Wayne R. Landsman, Brian Rountree, and Soonawalla Kazbi, 2020, Private Equity Net Asset Values and Future Cash Flows, Accounting Review. Tim Jenkinson, et al., Long Goodbyes: Why do Private Equity Funds hold onto Public Equity?, February 2021.Johnson, Eric, 2017, Approaches to Selecting a Risk Premium. ILPA White Paper Series. Steven N. Kaplan and Antoinette Schoar, Private equity performance: Returns, persistence, and capital flows, Journal of Finance 60, 1791–1823, 2005. Steven N. Kaplan, Private Equity, Buyouts and Venture Capital: Past, Present and Future, June 2018.Larocque (Notre Dame) et al, Private equity returns, cash flow timing, and investor choices, August 2019L’Her, Jean-Francois, Rossitsa Stoyanova, Kathryn Shaw, William Scott, and Charissa Lai, 2016, A Bottom-Up Approach to the Risk-Adjusted Performance of the Buyout Fund Market, Financial Analysts Journal 72, 36–48. Lerner, Josh, Jason Mao, Antoinette Schoar, and Nan R Zhang, 2020, Investing Outside the Box: Evidence from Alternative Vehicles in Private Equity. NBER working papers. Lopez-de-Silanes, Florencio, Ludovic Phalippou, and Oliver Gottschalg, 2015, Giants at the gate: On the cross section of private equity investment returns, Journal of Financial & Quantitative Analysis 50, 377–411. Mercer, Dry powder meets low interest rates, March 2021. Morris, Peter, and Ludovic Phalippou, 2019, Thirty Years After Jensen’s Prediction – Is Private Equity a Superior Form of Ownership?, Oxford Review of Economic Policy, forthcoming. Ludovic Phalippou, The hazards of using IRR to measure performance : The case of private equity, Journal of Performance Measurement, 1–23, 2008. Ludovic Phalippou, “An Inconvenient Fact: Private Equity Returns & The Billionaire Factory,” University of Oxford, Said Business School.Ludovic Phalippou, Beware of venturing into private equity, Journal of Economic Perspectives 23, 2009. Ludovic Phalippou, et al, Big 5 revenue sources: What do we learn?," June 2021.Ludovic Phalippou, Yale’s Endowment Returns: Case Study in GIPS Interpretation Difficulties, Journal of Alternative Investments 15, 97–103, 2013,. Ludovic Phalippou, Performance of buyout funds revisited?, Review of Finance 18, 189–218, 2014. Ludovic Phalippou, Has the US Finance Industry Become Less Efficient? On the Theory and Measurement of Financial Intermediation, American Economic Review 105, 1408–1438, 2015. Private Equity Internatinal, Subscription credit's shifting landscape, October 2020.David T. Robinson, and Berk A. Sensoy, Do Private Equity Fund Managers Earn Their Fees? Compensation, Ownership, and Cash Flow Performance, Review of Financial Studies 26, 2760–2797, 2013. Rossi, Decreasing Returns or Reversion to the Mean?, December 20219. Rudiger Stucke, Updating History, 2013.Sorensen, Morten, Neng Wang, and Jinqiang Yang, 2013, Valuing Private Equity, Review of Financial Studies, forthcoming. Swensen, D F, 2000, Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment (Simon & Schuster).

internal Fund-of-Funds

Brian Lawler, Brett Mossman, Patrick Nolan, Andrew Ang, "Factors and Advisor Portfolios," Journal of Wealth Management, Spring 2020.

ReaL Estate

The Rate of Return on Real Estate: Long-Run Micro-Level Evidence* David Chambers† University of Cambridge Christophe Spaenjers‡ HEC Paris Eva Steiner§ Penn State University This version: January 2021. Preferences of Institutional Investors in Commercial Real Estate Dragana Cvijanovica, Stanimira Milchevab, Alex van de Minne. Slow-moving capital and resales in real estate markets Jung-Eun Kimy John Krainerz Joseph Nichols. December 20, 2019. Non-risk Determinants of Investment Returns: Quality, Deal Size, and Returns of Commercial Real Estate Liang Peng. Smeal College of Business The Pennsylvania State University University Park, PA 16802 Phone: (814) 863 1046 Fax: (814) 865 6284 Email: liang.peng@psu.edu April 2019. The Cap Rate Premium Puzzle Liang Peng* Smeal College of Business The Pennsylvania State University University Park, PA 16802 Phone: (814) 863 1046 Fax: (814) 865 6284 Email: liang.peng@psu.edu August 2021. Asset-level risk and return in real estate investments Jacob S. Sagiy This version: April 19, 2015. Liquidity in Residential Real Estate Markets* Nadia Kotova†and Anthony Lee Zhang‡ December 2020.

Infrastructure

art and other collectibles

John Stein, ”The Monetary Appreciation of Paintings,” Journal of Political Economy, 1977. Bruno Frey and Reiner Eichenberger, “On the Return of Art Investment Return Analysis,” Journal of Cultural Economics, 1995. John Stein, ‘The Monetary Appreciation of Paintings’. Kenneth Wieand,Jeff Donaldson, and Socorro Quintero, “Are Real Assets Priced Internationally? Evidence from the Art Market.” 1998 Guido Candela and Scorcu Antonello, “A Price Index for Art Market Auctions – An Application to the Italian Market of Modern and Contemporary Oil Paintings,” Journal of Cultural Economics, 1997. James Pesando, “Art as an Investment: The Market for Modern Prints,” The American Economic Review, 1993. Wolfgang Wilke, “Investing in Art – the Art of Investing (Dresdner Bank, 2000. David Sokolin, and Bruce Alexandra, “Investing in Liquid Assets: Uncorking Profits in Today’s Global Wine Market,” Simon & Schuster, 2008.

Cryptocurrencies

Claire Ballentine and Katherine Greifeld, How Wall Street's Quest for a Bitcoin ETF Is Going: QuickTake, Bloomberg News, May 12th, 2021. Aswath Damodaran, “The Bitcoin Boom: Asset, Currency, Commodity or Collectible?” October 24th, 2017. Katie Greifeld, Grayscale CEO Sees ETF as Solution to Biggest Crypto Fund’s Woes, Bloomberg News, May 13th 2021. Vildana Hajric and Katie Greifeld, Largest Bitcoin Fund Drives to Record Discount Amid Crypto Swook, Bloomberg News, April 23rd, 2021. Adam Ludwin, “A Letter to Jamie Dimon And anyone else still struggling to understand cryptocurrencies,” October 16th, 2017. Avi Salzman, Bitcoin Investors Have a New Issue to Worry About, Dow Jones Intstitutional News Feed, June 24th, 2021

Volatility

Risk Transfer

environmental, social, and governance (ESG)

Khuram Chaudhry, Viquar Shaikh, Ayub Hanif, Dubravko Lakos-Bujas, Robert Smith, Berowne Hlavaty, Marko Kolanovic, “ESG - Environmental, Social & Governance Investing A Quantitative Perspective of how ESG can Enhance your Portfolio,” J.P. Morngan Global Quantitative & Derivatives Strategy, December 14th, 2016. A.J. Lindeman, ESG Investment Insights: Benefits and Challenges in Extracting Signals from ESG Data, February 2020.Jean-Xavier Hecker, Europe ESG Research, ESG & Sustainability Research, ESG Integration—Introducing Double Materiality, September 24th, 2020. Jean-Xavier Hecker, Hugo Dubourg, “What happened to ESG? Deciphering the complexity of a booming market ESG & Sustainability research,” J.P. Morgan Global Equity Research, March 6th, 2020.Global Quantitative & Derivatives Strategy, ESG—Environmental, Social & Governance Investing, December 14th, 2016. Berg, F., J. Kölbel and R. Rigobon. "Aggregate Confusion: The Divergence of ESG Ratings." MIT Sloan Research Paper (2019). Bloomberg LP. "2020 Bloomberg Gender-Equality Index Methodology ." 2020. Ellis, G. and A. Fisher. "Valuing the Environment as Input." Division of Agricultural Sciences, University of California Working Paper (1986). Fullerton, D. and Chi. Ta. "Environmental Policy on the back of an envelope: A Cobb- Douglas model is not just a teaching tool." Energy Economics (2019). Henriksson, R., et al. "Integrating ESG in Portfolio Construction." Journal of Portfolio Management (2019): 67-81. Huang, S. and G. Hilary. "Zombie Board: Board Tenure and Firm Performance." Journal of Accounting Research (2018). Khan, M., G. Serafeim and A. Yoon. "Corporate Sustainability: First Evidence on Materiality." Accounting Review (2016). Kotsantonis, S. and G. Serafeim. "Four Things No One Will Tell You About ESG Data." Journal of Applied Corporate Finance (2019): 50-58. Kotsantonis, S., C. Pinney and G. Serafeim. "ESG Integration in Investment Management: Myths and Realities." Journal of Applied Corporate Finance (2016): 10-16. Meyers. "Predictive Modeling with the Tweedie Distribution." CAS Annual Meeting. 2009. OECD. "Handbook on Constructing Composite Indicators: Methodology and User Guide." 2008. Rahdari, A. and A. Rostamy. "Designing a general set of sustainability indicators at the corporate leve." Journal of Cleaner Production (2015). Yang, Y. "Insurance Premium Prediction." arxiv.org (2016). York, R., E. Rosa and T. Dietz. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts." Ecological Economics. (2003)..

7

chapter 7: Features

Andrew Ng, “Machine Learning and AI via Brain Simulations,” 2013. SciKit-Learn “6.4. Imputation of missing values”, Scikit-Learn Documentation, 2021. Tradeoff Between Accuracy and Tractability Mike Masnick, “Why Netflix Never Implemented The Algorithm That Won The Netflix $1 Million Challenge,” Apr 13th 2012. High Precision and Low Accuracy Alon Halevy, Peter Norvig, and Fernando Pereira, “The Unreasonable Effectiveness of Data,” IEEE, 2009. Argument Against Domain Expertise Nicholas Kridler, “Data Agnosticism: Feature Engineering Without Domain Expertise,” SciPy 2013 Presentation Bioinformatics T. Speed, “Statistical Analysis of Gene Expression Microarray Data,” Chapman & Hall/CRC, 2003.; T. Hastie, R. Tibshirani, G. Sherlock, M. Eisen, P. Brown, and D. Botstein, “Imputing missing data for gene expression arrays”, Technical Report, Division of Biostatistics, Stanford University, 1999.; O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R. Altman, ,”Missing value estimation methods for DNA microarrays,” Bioinformatics 17(6) , 2001. Intuitive Examples Richard Feynman, “A Different Box of Tools” in “Surely You’re Joking, Mr. Feynman,” Bantam, 1985. Causality Judea Pearl, "Robustness of Causal Claims" In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, July 2004. J. Tian and Judea Pearl, "A General Identification Condition for Causal Effects" In Proceedings of the Eighteenth Conference on Artificial Intelligence, AAAI/The MIT Press, August 2002. Judea Pearl, "Direct and Indirect Effects" In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, 2001. J.Y. Halpern and JudeaPearl, "Causes and explanations: A structural-model approach--Part I: Causes" In British Journal of Philosophy of Science, 2005. J.Y. Halpern and Judea Pearl, "Causes and explanations: A structural-model approach-- Part II: Explanations" In British Journal of Philosophy of Science, 2005.; Judea Pearl, "The logic of counterfactuals in causal inference," In Journal of American Statistical Association, Vol. 95, No. 450, June 2000. J. Tian and Judea Pearl, "Probabilities of causation: Bounds and identification" In Annals of Mathematics and Artificial Intelligence, Vol. 28, 2000.

Data Exploration

Codebooks ”Guide to Codebooks,” Inter-University Consortium for Political and Social Research, 2011.

Sampling

Visualiztion F.J Anscombe, “Graphs in Statistical Analysis,” American Statistician. 27 (1), 1973.Justin Matejka and George Fitzmaurice, “Download the Datasaurus: Never trust summary statistics alone; always visualize your data,” ACM SIGCHI Conference on Human Factors in Computing Systems, 2017. SMOTE ] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal Of Artificial Intelligence Research, June 9th 2011. Gustavo E. A. P. A. Batista, Ronaldo C. Prati, and Maria Carolina Monard, “A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data,” ACM SIGKDD Explorations Newsletter, June 2004. Borderline SMOTE Hien M. Nguyen, Eric W. Cooper, and Katsuari Kamei, “Borderline Over-sampling for Imbalanced Data Classification,” IEEE SMC Hiroshima Chapter, October 11th, 2009. Hui Han, Wen-Yuan Wang, and Bing-Huan Mao, “Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning,” Advances in Intelligent Computing, 2005. ADASYN Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” IEEE, June 1st, 2008. Condensed NN P. Hart, “The condensed nearest neighbor rule,” IEEE Transactions on Information Theory, vol. 14, no. 3, May 1968. Tomek Jianping Zhang and Inderjeet Mani, “KNN Approach to Unbalanced Data Distributions: A Case Study in involving Information Extraction,” Proceeding of International Conference on Machine Learning, 2003. Marcelo Beckmann, Nelson F. F. Ebecken, and Beatriz S. L. Pires de Lima, “A KNN Undersampling Approach for Data Balancing,” Journal of Intelligent Learning Systems and Applications, Vol.7 No.4, November 11th, 2015. “Two Modifications of CNN,” in IEEE Transactions on Systems,” Man, and Cybernetics, vol. SMC-6, no. 11, November 1976. Edited Nearest Neighbors D. L. Wilson, “Asymptotic Properties of Nearest Neighbor Rules Using Edited Data,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-2, no. 3, July 1972. "An Experiment with the Edited Nearest-Neighbor Rule,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-6, no. 6, June 1976. One-Sided Selection Miroslav Kubat and Stan Matwin, “Addressing the Curse of Imbalanced Training Sets: One-Sided Selection.”; Neighborhood Cleaning Rule Jorma Laurikkala, “Improving Identification of Difficult Small Classes by Balancing Class Distribution,” Artificial Intelligence in Medicine, 2001. Gustavo E. A. P. A. Batista, Ronaldo C. Prati, and Maria Carolina Monard, “A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data,” ACM SIGKDD Explorations Newsletter, June 2004. Yield Curve Charles R. Nelson and Andrew F.Siegel, “Parsimonious Modeling of Yield Curves,” The Journal of Business, 1987. F. Ametrano and M. Bianchetti, “Everything You Always Wanted to Know About Multiple Interest Rate Curve Bootstrapping but Were Afraid to Ask,” April 2nd, 2013. M. Fujii, Y. Shimada, and A. Takahashi, “A Note on Construction of Multiple Swap Curves with and without Collateral,” CARF Working Paper Series No. CARF-F-154, January 2nd, 2010. Cost Matrix Nathalie Japkowicz and Stephen Shaju, “The class imbalance problem: A systematic study,“Intelligent Data Analysis, Volume 6, Number 5, October 2002. Peter D. Turney, “Types of Cost in Inductive Concept Learning,” December 11th, 2002. Charles Elkan, “The foundations of cost-sensitive learning, "Proceedings of the 17th international joint conference on Artificial intelligence, Volume 2, August 2001. Bianca Zadrozny, John Langford, and Naoki Abe, “Cost-Sensitive Learning by Cost-Proportionate Example Weighting,” Proceedings of the Third IEEE International Conference on Data Mining, November 2003. N. Thai-Nghe, Z. Gantner, and L. Schmidt-Thieme, "Cost-sensitive learning methods for imbalanced data," The 2010 International Joint Conference on Neural Networks (IJCNN), 2010. Claude Sammut and Geoffrey I. Webb, Editors, “Encyclopedia of Machine Learning 2010th Edition,” Springer, March 28th, 2011. Balaji Krishnapuram, Shipeng Yu, R. Bharat Rao, Editors, “Cost-Sensitive Machine Learning, Chapman & Hall/CRC, December 19, 2011. “Cost-sensitive Learning Methods For Imbalanced Data,” 2010. “Misclassification Costs in Classification Learner App,” MATLAB Documentation, 2021a. “Handle Imbalanced Data or Unequal Misclassification Costs in Classification Ensembles,” MATLAB Documentation, 2021a Sklearn.utils.class_weight,” Scikit-Learn Documentation, retrieved 2021. Resampling Cost-Sensitive Learning by Cost-Proportionate Example Weighting, 2003. In the Methods Wei Fan, Salvatore J. Stolfo, Junxin Zhang, and Philip K. Chan, “AdaCost: Misclassification Cost-Sensitive Boosting,” Proceedings of the Sixteenth International Conference on Machine Learning, June 1999. Gary King and Langche Zeng, “Logistic regression in rare events data,” Political Analysis 9(2), 2001. Charles F. Manski and Steven R. Lerman, “The Estimation of Choice Probabilities from Choice Based Samples,” Econometrica, Volume 45, Number 8, November 1977. K. Veropoulos, C. Campbell, N. Cristianini, “Controlling the Sensitivity of Support Vector Machines,” SVM Approach to Medical Applications, June 1999. Xulei Yang, Qing Song, and A. Cao, “Weighted support vector machine for data classification,” IEEE International Joint Conference on Neural Networks, 2005. Hamed Masnadi-Shirazi, Nuno Vasconcelos, and Arya Iranmehr, “Cost-Sensitive Support Vector Machines,” February 15th, 2015. Matjaz Kukar and Igor Kononenko, “Cost-Sensitive Learning with Neural Networks,” Proceedings of the 13th European Conference on Artificial Intelligence, 1998. Zhi-Hua Zhou and Xu-Ying Liu, “Training cost-sensitive neural networks with methods addressing the class imbalance problem,” IEEE Transactions on Knowledge and Data Engineering, Volume 18, Number 1, January 2006. S. Wang, W. Liu, J. Wu, L. Cao, Q. Meng, and P. J. Kennedy, “Training deep neural networks on imbalanced data sets,” 2016 International Joint Conference on Neural Networks (IJCNN), 2016. Tianqi Chen, Carlos Guestrin, “XGBoost: A Scalable Tree Boosting System,” June 10th, 2016.; X. Liu, J. Wu, and Z. Zhou, “Exploratory Undersampling for Class-Imbalance Learning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Volume 39, Number 2, April 2009. Chao Chen, Andy Liaw, and Leo Breiman, “Using Random Forest to Learn Imbalanced Data,” July 1, 2004. Bootstrap A. C. Davison and D. V. Hinkley, “Bootstrap Methods and their Application,” Cambridge University Press, 1997.

Visualizations

F.J Anscombe, “Graphs in Statistical Analysis,” American Statistician. 27 (1), 1973. Justin Matejka and George Fitzmaurice, “Download the Datasaurus: Never trust summary statistics alone; always visualize your data,” ACM SIGCHI Conference on Human Factors in Computing Systems, 2017. D.G. Altman, J.M. Bland, “Measurement in medicine: the analysis of method comparison studies,” The Statistician. 32 (3), 1983.; William S. Cleveland, “Visualizing data,” AT&T Bell Laboratories, 1993. Market ProfileJ. Peter Steidlmayer and Steven B. Hawkins, “Steidlmayer on Markets: Trading with Market Profile,” Second Edition, Wiley, 2007.Contingency TablesKarl Pearson in "On the Theory of Contingency and Its Relation to Association and Normal Correlation", Drapers' Company Research Memoirs Biometric Series I, 1904.Correspondence AnalysisMichael Greenacre, “Correspondence Analysis in Practice,” Third Edition, Chapman & Hall/CRC, 2017.Partial Regression PlotsM. Kutner, C. Nachtschiem, W. Wasserman, and J. Neter, “Applied Linear Statistical Models, Fourth Edition, McGraw-Hill, 1996.; N.R. Draper and H. Smith, “Applied Regression Analysis,” Third Edition, John Wiley, 1998.

Feature Selection

D.G. Altman, J.M. Bland, “Measurement in medicine: the analysis of method comparison studies,” The Statistician. 32 (3), 1983.; William S. Cleveland, “Visualizing data,” AT&T Bell Laboratories, 1993.

Interactions and Conditioning

Tobias Bollenbach, "Antimicrobial interactions: mechanisms and implications for drug discovery and resistance evolution," Current Opinion in Microbiology, Volume 27, 2015, Pages 1-9, ISSN 1369-5274, https://doi.org/10.1016/j.mib.2015.05.008. (https://www.sciencedirect.com/science/article/pii/S1369527415000594) Rodea-Palomares, Ismael, Miguel González-Pleiter, Keila Martín-Betancor, Roberto Rosal, and Francisca Fernández-Piñas. 2015. "Additivity and Interactions in Ecotoxicity of Pollutant Mixtures: Some Patterns, Conclusions, and Open Questions" Toxics 3, no. 4: 342-369. https://doi.org/10.3390/toxics3040342 Design of Experiments P. Guttorp and G. Lindgren, "Karl Pearson and the Scandinavian school of statistics". International Statistical Review, 2009. J.W. Creswell, "Educational research: Planning, conducting, and evaluating quantitative and qualitative research, 3rd Edition," Prentice-Hall, 2008. Leonard E. Burman, Robert W. Reed, and James Alm, "A call for replication studies", Public Finance Review, 38 (6): 787–793, 2010. Jack Sifri, "How to Use Design of Experiments to Create Robust Designs With High Yield," 2014. Joseph Simmons, Leif Nelson, and Uri Simonsohn, "False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant". Psychological Science, 2011. Chris Chambers, "Physics envy: Do 'hard' sciences hold the solution to the replication crisis in psychology?" The Guardian, 2014.S. Bisgaard, "Must a Process be in Statistical Control before Conducting Designed Experiments?", Quality Engineering, 2008.Douglas Montgomery, "Design and analysis of experiments, 8th Ed.," John Wiley & Sons, Inc., 2013. J.C. Spall, "Factorial Design for Efficient Experimentation: Generating Informative Data for System Identification". IEEE Control Systems Magazine, 2010. C. F. Jeff Wu and Michael S. Hamada, "Experiments: Planning, Analysis, and Optimization 3rd Edition," 2021. Interactions Thorsten Dickhaus, "Simultaneous statistical inference," Springer, 2014. https://www.springer.com/gp/book/9783642451812 J. Friedman, "Greedy function approximation: A gradient boosting machine," Annals of Statistics, 2001. https://www.jstor.org/stable/2699986?seq=1#metadata_info_tab_contents False Discovery and the Multiple Comparisons Problem Keegan Korthauer, Patrick K. Kimes, Claire Duvallet, Alejandro Reyes, Ayshwarya Subramanian, Mingxiang Ten6, Chinmay Shukl, Eric J. Alm, and Stephanie C. Hicks, "A practical guide to methods controlling false discoveries in computational biology," Genome Biology,2019. 20:118 https://doi.org/10.1186/s13059-019-1716-1 Mohieddin Jafari and Naser Ansari-Pour, "Why, When and How to Adjust Your P Values?." Cell J. 2019. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099145/ Shi-Yi Chen, Zhe Feng, and Xiaolian Yi, “A general introduction to adjustment for multiple comparisons.” Journal of thoracic disease vol. 9,6, 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506159/ Jerome H. Friedman and Bogdan E Popescu. "Predictive learning via rule ensembles." The Annals of Applied Statistics, 2008.Brandon M. Greenwell, Bradley C. Boehmke, and Andrew J. McCarthy. "A simple and effective model-based variable importance measure," 2018.

FEature Selection

Robust Models Can Be Difficult to Interpret Galit Shmueli, “To Explain or to Predict,” Statistical Science, Volume 25, Number 3, 2010.

PreproCessing, Missing Data, and Outliers

Missing and Minority Data Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, Francisco Herrera “Learning From Imbalanced Data Sets,” 2016. Haibo He and Yunqian Ma, “Imbalanced Learning Foundations, Algorithms, and Applications,” Wiley/IEEE Press, 2018. Bartosz Krawczyk “Learning From Imbalanced Data: Open Challenges And Future Directions,” Progress in Artificial Intelligence, Volume 5, 2016. Mikel Galar, Alberto Fernandez, Edurne Barrenechea, Humberto Bustince, and Francisco Herrera, “A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches,” IEEE 2011. Surrogate Splits Achilleas Tsoumanis, “Help, my data has gone missing! An introduction to incomplete data,” Institute of Tropical Medicine Antwerp. Ad Feelders, “Handling Missing Data in Trees: Surrogate SPlits or Statistical Imputation,” Third European Conference on Principles of Data Mining and Knowledge, Volume 1704, March 2000. Novelties “Novelty and Outlier Detection,” Scikit Learn Documentation, 2021. Charu C. Aggarwal, “Outlier Analysis,” second edition, Springer, 2017. Questionnaires Robie Whelan, “Robo-signer debate: Was it fraud?” The Wall Street Journal, October 10th, 2010. King Efffect J. Laherrère and D. Sornette, “Stretched exponential distributions in nature and economy: “fat tails” with characteristic scales,” The European Physical Journal B - Condensed Matter and Complex Systems volume 2, April, 1998. SMOTE N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer “SMOTE: Synthetic Minority Over-sampling Technique,” Journal Of Artificial Intelligence Research, Volume 16, 2002. Detecting Outliers Robert C. Holte, Liane E. Acker, and Bruce W. Porter, “ Concept Learning And The Problem Of Small Disjuncts,” 1989 Kernel Density Longin Jan Latecki, Aleksandar Lazarevic, and Dragoljub Pokrajac, “Outlier Detection with Kernel Density Functions,” in “Machine Learning and Data Mining in Pattern Recognition,” Springer, 2007. Y. Xu, N. Xu, and X. Feng, “A New Outlier Detection Algorithm Based on Kernel Density Estimation for ITS,” 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China, 2016. Extreme Value Theory Sandra G. Dykes, “Poster: An Extreme Value Theory Approach to Anomaly Detection (EVT-AD),” 2012. Autoencoders Liat Antwarg, Ronnie Mindlin Miller1, Bracha Shapira, and Lior Rokach, “Explaining Anomalies Detected by Autoencoders Using SHAP,” Journal of Artificial Intelligence, July 2nd, 2020. Andrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, and Luca Benini, “Anomaly Detection Using Autoencoders in High Performance Computing Systems,” The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence, 2019. Decision Trees David Cortes, “Explainable outlier detection through decision tree conditioning,” January 2020. Time Series Outliers Manish Gupta, Jing Gao, Charu C. Aggarwal, and Jiawei Han, “Outlier Detection for Temporal Data: A Survey,” Ieee Transactions On Knowledge And Data Engineering, Vol. 25, No. 1, January 2014. D. Birant and A. Kut, “Spatio-temporal outlier detection in large databases,” 28th International Conference on Information Technology Interfaces, 2006. Metrics for Imbalanced Data Yanmin Sun, Andrew Wong, Mohamed S. Kamel, “Classification Of Imbalanced Data: A Review,” International Journal of Pattern Recognition and Artificial Intelligence 23(04), November 2009. Paula Branco, Luis Torgo, Rita P. Ribeiro, “A Survey of Predictive Modelling under Imbalanced Distributions,” May 8th, 2015. C. Ferri, J. Hernandez-Orallo, and R. Modroiu, “An Experimental Comparison Of Performance Measures For Classification,” Pattern Recognition Letters, Volume 30, Issue 1, January 1st 2009. Marina Sokolova and Guy Lapalme, “A Systematic Analysis Of Performance Measures For Classication Tasks,” Information Processing & Management, Volume 45, Issue 4, July 2009. Machine Learning for Imbalanced Data Alexandru Niculescu and Rich Caruana, “Predicting Good Probabilities With Supervised Learning,” Machine Learning, Proceedings of the Twenty-Second International Conference, August 7-11, 2005 Byron C. Wallace and Issa J. Dahabreh, “Class Probability Estimates are Unreliable for Imbalanced Data (and How to Fix Them),” 2012 IEEE 12th International Conference on Data Mining, January 17th, 2013. Platt Scaling John Platt, “Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods,” Advances in Large Margin Classifiers. 10 (3), 1999. Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, and Sayan Mukherjee, “Choosing multiple parameters for support vector machines,” Machine Learning, 46, 2002. Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng, “A note on Platt's probabilistic outputs for support vector machines,” Machine Learning. 68 (3), 2007. Logistic Regression for Extreme Values “CalibratedClassifierCV Class,” Scikit-Learn Documentation, 2021. Unsupervised Outlier, Novelty, and Anomaly Detection P. Oliveri, “Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial,” Analytica Chimica Acta, August 2017. Kolby Crammer, “A needle in a haystack: local one-class optimization,” ICML Proceedings of the Twenty-first International Conference on Machine Learning, 26, 2004. M. Moya and D. Hush,”Network constraints and multi- objective optimization for one-class classification,” Neural Networks, 9 (3), 1996. Oxana Ye. Rodionova, Paolo Oliveri, and Alexey L.Pomerantsev, “Rigorous and compliant approaches to one-class classification,” Chemometrics and Intelligent Laboratory Systems, Volume 159, December 15th, 2016. W. Liu, G. Hua, and J. R. Smith, “Unsupervised One-Class Learning for Automatic Outlier Removal,” IEEE Conference on Computer Vision and Pattern Recognition, 2014. Hirofumi Fujita, Tetsu Matsukawa, and Einoshin Suzuki, “Detecting outliers with one-class selective transfer machine,” Knowledge and Information Systems, Volume 62, 2020. Bernhard Schölkopf, John C. Platt, John C. Shawe-Taylor, Alex J. Smola, and Robert C. Williamson, “Estimating the Support of a High-Dimensional Distribution.” Neural Comput. 13, 7, July 2001. F. T. Liu, K. M. Ting, and Z. Zhou, “Isolation Forest,” Eighth IEEE International Conference on Data Mining, 2008.; Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. “Isolation-Based Anomaly Detection,” ACM Trans. Knowl. Discov. Data 6, 1, Article 3,March 2012. Peter J. Rousseeuw and Katrien Van Driessen “A Fast Algorithm for the Minimum Covariance Determinant Estimator,” Technometrics, 1999. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander, “LOF: identifying density-based local outliers,” SIGMOD Rec. 29, 2, June 2000.

feature extraction

TSFRESH Ben D. Fulcher, “Feature-Based Time-Series Analysis,” 2017 Maximilian Christ, “Time Series Feature Extraction on basis of Scalable Hypothesis tests (tsfresh—A Python package),” Neurocomputing, 2018. Daycount Marcia Stigum and Franklin Robinson, “Money Market and Bond Calculations,” Richard D. Irwin, 1996. Jan Mayle, “Standard Securities Calculation Methods: Fixed Income Securities Formulas for Analytic Measures,” Vol. 2, Securities Industry Association, 1995. Featuretools “Featuretools | An open source framework for automated feature engineering."

Ordinal Transformation

Transformations “Econometrics Toolbox,” MATLAB documentation, 2021. James D. Hamilton, “Time Series Analysis,” Princeton University Press, 1994. Technical Analysis “Technical Indicators,” MATLAB documentation, 2021. "Chartered Market Technician Level I, II, and III 2017: Theory and Analysis 2nd Edition,” by Market Technician's Association. 20-17. John J. Murphy, “Technical Analysis of the Financial Markets, A Comprehensive Guide to Trading Methods and Applications,” New York Institute of Finance, 1999. Martin J. Pring, “Technical Analysis Explained, The Successful Investor’s Guide to Spotting Investment Trends and Turning Points,” fifth edition, McGraw-Hill Education, 2014. Fundamental Analysis Benjamin Graham and David Dodd, “Security Analysis,” Sixth Edition, July 1st, 2017. Benjamin Graham, “The Intelligent Investor,” sixth edition, McGraw Hill Education, July 1st, 2017. Seth A. Klarman, Margin of Safety, Risk-Averse Value Investing Strategies for the Thoughtful Investor,” Harper Collins, 1991. Peter Lynch and John Rothchild, “Learn to Earn,” Simon & Schuster; 2011. Box-Cox G. E. P. Box and D. R. Cox, “An analysis of transformations, Journal of the Royal Statistical Society,” Series B, 1964. Yeo-Johnson Sanford Weisberg, “Yeo-Johnson Power Transformations,” October 26th, 2001. ReLU Vinod Nair and Geoffrey E. Hinton, “Rectifid Linear Units Improve Restricted Boltzmann machines,” 2010.

Categorical Encoding

Aliasing M. O. Rabin, “Fingerprinting by random polynomials,” Center for Research in Computing Technology, Harvard University,1981 A. Z. Broder. “Some applications of Rabin's fingerprinting method. In Sequences II: Methods in Communications,” Security, and Computer Science, Springer-Verlag, 1993. LSH/LPH Kang Zhao, Hongtao Lu; Jincheng Mei, “Locality Preserving Hashing,” 2014/ Yi-Hsuan Tsa and Ming-Hsuan Yang, “Locality preserving hashing,” IEEE International Conference on Image Processing (ICIP), 2014. Hisashi Koga, Tetsuo Ishibashi and Toshinori Watanabe, “Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing,” Knowledge and Information Systems, 12 (1), 2007. Jonathan Oliver, Chun Cheng, Yanggui Chen, “TLSH - a locality sensitive hash,” 4th Cybercrime and Trustworthy Computing Workshop, 2013. Other Graham Cormode and S. Muthukrishnan, “An improved data stream summary: the count-min sketch and its applications,” December 16th, 2003. Target Rate Daniele Micci-Barreca, :A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems,” ACM SIGKDD Explorations, July 2001. Odds Ratio D. Taeger, Y. Sun, and K. Straif, “On the use, misuse and interpretation of odds ratios,” August 10th, 1998. Effect Coding ”What is Effect Coding?,” UCLA Institute for Digital Research & Education Statistical Consulting, retrieved 2021.

temporal transformations

Window Dressing D. Givoly and A. Ovadia, “Year-end tax-induced sales and stock market seasonality, Journal of Finance, March 1983. M.R. Reinganum, “The anomalous stock market behavior of small firms in January: Empirical tests for tax-loss selling effects,” Journal of Financial Economics, Vol. 12, 1983. H. Chen and V. Singal, “A December effect with tax-gain selling?” Financial Analysts Journal, Vol. 59, No. 4, July-August 2003. J. Bildersee and N. Kahn, “A preliminary test of the presence of window dressing,” Journal of Accounting, Auditing and Finance, Summer 1987. A.R. Haugen and J. Lakonishok, “The Incredible January Effect,” Dow Jones lrwin, 1998. F. Ackert and G. Athanassakos, “Institutional investors, analyst following, and the January anomaly,” Federal Reserve Bank of Atlanta, 1998. G. Athanassakos, “The scrutinized-firm effect, portfolio rebalancing, stock return seasonality, and the pervasiveness of the January effect in Canada,” Multinational Finance Journal, Vol. 6, No. 1, March 2002. B. Branch and K. Chang, “Low price stocks and the January effect,” Quarterly Journal of Business and Economics, Vol. 29, No. 3, Summer 1990. M. Blume, and R. Stambaugh, “Biases in computed returns: An application to the size effect,” Journal of Financial Economics, Vol. 12, 1983. H. Seyhun, “Can omitted risk factors explain the January effect: A stochastic dominance approach,” Journal of Financial and Quantitative Analysis, Vol. 28, 1993. R.D. Arnott, C.M. Kelso, Jr., S. Kiscadden, and R. Macedo, “Forecasting factor returns: An intriguing possibility,” Journal of Portfolio Management, Vol. 16, Fall 1989. D.L. Kao and R.D. Shumaker, “Equity style timing,” Financial Analyst Journal, 37-48, January-February 1999. S. Benartzi and R.H. Thaler, “Myopic loss aversion and the equity premium puzzle,” Quarterly Journal of Economics, Vol. 110, No. 1, 1995. D. Givoly and J. Lakonishok, “The information content of financial analysts' forecasts of earnings,” Journal of Accounting and Economics, 1979. E.H. Hawkins, S.C. Chamberlin, and W.E. Daniel, “Earnings expectations and security prices,” Financial Analysts Journal, Vol. 40, September-October 1984. R.D. Arnott, “The use and misuse of consensus earnings,” Journal of Portfolio Management, Vol. 11, 18-28, Spring 1985.; T.J. Kerrigan, “When forecasting earnings, it pays to watch forecasts,” Journal of Portfolio Management, Vol. 10, 19-27, Summer 1984.; R.M. Richards and J.D. Martin, “Revisions in earnings forecasts: How much response?,” Journal of Portfolio Management, Vol. 5, Summer 1979.; [Markov] A. Markov, translated by Jacques J. Schorr-Kon, “Theory of Algorithms,”. Academy of Sciences of the USSR, 1954.; Stewart N. Ethier and Thomas G. Kurtz, “Markov Processes: Characterization and Convergence,” Willey, 1986. .[Market Profile] J. Peter Steidlmayer, “Steidlmayer on Markets: A New Approach to Trading,” Wiley, 1989.; Chicago Board of Trade, “A Six-Part Study Guide to Market Profile,” Chicago Board of Trade 1991;. [Daycount] Marcia Stigum and Franklin Robinson, “Money Market and Bond Calculations,” Richard D. Irwin, 1996.; Jan Mayle, “Standard Securities Calculation Methods: Fixed Income Securities Formulas for Analytic Measures,” Vol. 2, Securities Industry Association, 1995. Dynamic Time Warping D. Berndt and James Clifford, “Using dynamic time warping to find patterns in time series,” 10, 1994. Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, and Eamonn Keogh., “Experimental comparison of representation methods and distance measures for time series data,” Data Mining Knowledge Discovery, 26, 2012. Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh, “The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances,” Data Mining Knowledge Discovery, 31, 2017. C. A. Ratanamahatana and E. Keogh, “Making time-series classification more accurate using learned constraints,” SIAM International Conference on Data Mining, 2004. Slutsky-Yule Effect Eugen Slutsky, “The Summation of Random Causes as a Source of Cyclic Processes,” Problems of Economic Conditions 3, 1927 (Russian) and Econometrica 5, April 1937 (Engilsh).

conditional factors

“Business Cycle Dating,” National Bureau of Economic Research, Retrieved April, 2020. Howard Marks, "Mastering the Market Cycle: Getting the Odds on Your Side," Houghton Mifflin, Harcourt, 2018.

statistics and machine learning

The Bias/Variance Tradeoff

Bias/Variance Stuart German, Elie Bienenstock, and rene Doursat, “Neural Networks and the Bias/Variance Dilemma,” Neural Computation, 4, Massachusetts Institute of Technology, 1992. Perturbation Vinodkumar Prabhakaran, Ben Hutchinson, and Margaret Mitchell. “Perturbation Sensitivity Analysis to Detect Unintended Model Biases,” 2019. Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein, “Sensitivity and Generalization in Neural Networks: An Empirical Study,” June 18th, 2018.

Linear and Kernel Transformations

Konstantinos Koutroumbas and Sergios Theodoridis, “Pattern Recognition,” 4th Edition, Academic Press, October 20th, 2008.

Dimension Reduction and Feature Importance

Supervised Discretization James Dougherty, Ron Kohavi, and Mehran Sahami, “Supervised and Unsupervised Discretization of Continuous Features,” in Armand Prieditis and Stuart Russell, eds., Machine Learning: Proceedings of the Twelfth International Conference, Morgan Kaufmann, 1995. Salvador García, Julián Luengo, José A. Sáez, and Victoria López, “A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning ,” in IEEE Transactions on Knowledge and Data Engineering 25(4), February 2013. Huan Liu, Farhad Hussain, Chew Lim Tan, and Manoranjan Dash, “Discretization: An Enabling Technique,” in Data Mining and Knowledge Discovery 6(4), October 2002. Adaptive Quantization S.D. Teddy, E.M. Lai, and C. Quek, “Hierarchically clustered adaptive quantization CMAC and its learning convergence,” IEEE Transactions Neural Networks, 18(6), Nov 2007. Maxime Bichon, Julien Le Tanou, Michael Ropert, Wassim Hamidouche, and Luce Morin, “Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC,” IEEE Transactions on Image Processing, 28(11), November 2019. Polynomial Kernel PCA Christopher Bishop, “Pattern Recognition and Machine Learning,” Springer, 2011. Minimum Torsion Attilio Meucci, Alberto Santangelo, and Romain Deguest, “Risk Budgeting and Diversification Based on Optimized Uncorrelated Factors,” November 10th, 2015. Attilio Meucci, Alberto Santangelo, and Romain Deguest, “Measuring Portfolio Diversification Based on Optimized Uncorrelated Factors,” January 2013. PCA/SVD Gene H. Golub, and Charles F. Van Loan, “Matrix Computations,” 4th Edition, Johns Hopkins University Press, 2012. Distributed PCA Maria-Florina Balcan, Vandana Kanchanapally, Yingyu Liang, David Woodruff, “Improved Distributed Principal Component Analysis,” December 23rd, 2014. Xi Chen, Jason D. Lee, He Li, Yun Yang, “Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis,” February 3rd, 2021. Map-Reduce Qiang Liu,Dezhi Kong, S. Joe Qin, Quan Xu, “Map-Reduce Decentralized PCA for Big Data Monitoring and Diagnosis of Faults in High-Speed Train Bearings,” IFAC Papers On Line, Volume 51, Issue 18, 2018. “Using MapReduce to Compute Covariance and Related Quantities,” Matlab Documentation, 2021a, 2021.; [ICA] Adelbert W. Bronkhorst, “The Cocktail Party Phenomenon: A Review on Speech Intelligibility in Multiple-Talker Conditions,” Acta Acustica United, 2000. ZCA Anthonly J. Bell and Terrence J. Sejnowski, “Edges are the ‘Independent Components’ of Natural Scenes, 1996. Sammon Hujun Yin, “Learning Nonlinear Principal Manifolds by Self-Organising Maps,” in Principal Manifolds for Data Visualization and Dimension Reduction, Lecture Notes in Computer Science and Engineering, vol. 58, Springer, 2007. Kernel PCA B. Schölkopf, A. Smola, and K.-R. Müller, “Nonlinear Component Analysis as a Kernel Eigenvalue Problem,” Neural Computation 10(5), MIT Press, 1998. Laplacian Eigenmaps Mikhail Belkin, Partha Niyogi, “Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering,” Advances in Neural Information Processing Systems,” MIT Press, 2001. Mikhail Belkin, “Problems of Learning on Manifolds,” PhD. Thesis, August 2003. Yoshua Bengio, Jean-Franc¸ois Paiement, Pascal Vincent Olivier Delalleau, Nicolas Le Roux, and Marie Ouimet, “Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering,” Advances in Neural Information Processing Systems, 2004. Isomaps J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, 2000. LLE S. T. Roweis and L. K. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, Volume 290, December 22nd, 2000. D. Donoho, C. Grimes, “Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data,” 2003.; Z. Zhang and J. Wang, “MLLE: Modified Locally Linear Embedding Using Multiple Weights,”. Gagan Sidhu, “Locally Linear Embedding and fMRI feature selection in psychiatric classification,” IEEE Journal of Translational Engineering in Health and Medicine, 2019. [LTSA]Zhenyue Zhang, Zha Hongyuan, “Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment,” SIAM Journal on Scientific Computing, 26 (1), 2005. Maximum Variance Unfolding Yoshua Bengio, Martin Monperrus, Hugo Larochelle, “Nonlocal Estimation of Manifold Structure,”. Neural Computation. 18, October 2006. François Chollet and J. J. Allaire, “Deep Learning with R,” Manning Publications, January 2018.; [GPLVM] N. Lawrence, “Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models,” Journal of Machine Learning Research, Volume 6, November 2005. M. Ding and G. Fan, “Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling,” IEEE Transactions on Cybernetics, Volume 45, Issue: 11, November 2015. t-SNE L.J.P. van der Maaten, G.E. Hinton, “Visualizing High-Dimensional Data Using t-SNE,” Journal of Machine Learning Research, November 2008. J. Gower, “A general coefficient of similarity and some of its properties,” Biometrics, 1971. Raini Hassan1, Wan Haslina Hassan, Imad Fakhri Taha Al-Shaikhli, Salmiah Ahmad, and Mojtaba Alizadeh, “Feature Ranking Through Weights Manipulations for Artificial Neural Networks-Based Classifiers,” 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation, 2014.

Reduction Metrics

A. Benavoli, G. Corani, J. Demsar, and M. Zaffalon, M., “Time for a change: A tutorial for comparing multiple classifiers through Bayesian analysis,” 2016.; T. Hothorn, K. Hornik, and A. Zeileis, “Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics,” 2006.; Richard McElreath, “Statistical Rethinking: A Bayesian Course with Examples in R and Stan,” Second Edition, CRC Press. 2020.; Brady T. West, Kathleen B. Welch, and Andrzej T Galecki, “Linear Mixed Models: A Practical Guide using Statistical Software,” Second Edition, Chapman and Hall/CRC, 2015.

Wrappers and Embedded Selection

Imputation

Imputing P. Allison, “Missing Data,” Sage Publications, 2001.;R. Little and D. Rubin, “Statistical Analysis with Missing Data,” John Wiley and Sons, 2014. Imputation with Trees Stekhoven, Daniel J and Peter Bühlmann, “MissForest—Non-Parametric Missing Value Imputation for Mixed-Type Data,” Bioinformatics, 2011. Shah, Anoop D, Jonathan W Bartlett, James Carpenter, Owen Nicholas, and Harry Hemingway, “Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using Mice: A Caliber Study.” American Journal of Epidemiology, 2014. Schafer, Joseph L., “Multiple Imputation: A Primer.” Statistical Methods in Medical Research, 1999. Enders, Craig K., “A Primer on Maximum Likelihood Algorithms Available for Use with Missing Data.” Structural Equation Modeling, 2001. Enders, Craig K., “Applied Missing Data Analysis” Guilford Press, 2010. Garcı́a-Laencina, Pedro J, José-Luis Sancho-Gómez, Anı́bal R Figueiras-Vidal, and Michel Verleysen, “K Nearest Neighbours with Mutual Information for Simultaneous Classification and Missing Data Imputation.” Neurocomputing, 2009.

Synthetic Data Generation

Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar, “Time-series Generative Adversarial Networks,” 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.

8

CHapter 8: financial and economic features

feature complexity and hierarchy

decriptive features

economic features

cross-asset features

style features

Fama and French, 1993, "Common Risk Factors in the Returns on Stocks and Bonds," Journal of Financial Economics, for a complete description of the factor returns. Value Factor Greg Bond and Ben Zhao, “Questioning Quality, Why Investing in Well-Run Companies Doesn’t Always Make For Exceptional Returns,” Man Numeric, November 2020. Max Kozlov and Antti Petajisto,“Global Return Premiums on Earnings Quality, Value, and Size” January 7, 2013. Jean-Philippe Bouchaud, Stefano Ciliberti, Guillaume Simon, and David Thesmar, “The Excess Returns of ‘Quality’ Stocks: A Behavioral Anomaly,” HEC Paris Research Paper No. FIN-2016-1134, November 27th, 2017. Clifford S. Asness, Andrea Frazzini, and Lasse H. Pedersen: 'Quality Minus Junk', 2013. Jason Hsu, PhD, Vitali Kalesnik, PhD, and Engin Kos, “What Is” Quality?” May 19th, 2017. Robert Novy-Marx, “Quality Investing,” May 2014. Timothy Strauts, Michael Manetta, and Patrica Wang, “Morningstar Factor Profile methodology, Version 1.0,” November 20th, 2019. Edward E. Qian, Ronald H. Hua, Eric H. Sorensen, "Quantitative Equity Portfolio Management, Modern Techniques and Applications," Chapman & Hall/CRC, 2007. Analyst Bias Saud Al-Thaqeb, “Survey on forecast bias (analysts and earnings),” Asia-Pacific Journal of Accounting & Economics. 25. 2017. B.M Barber, R. Lehavy, M. McNichols, B. Trueman, “Buys, holds, and sells: the distribution of investment banks’ stock ratings and the implications for the profitability of analysts’ recommendations,” Journal of Accounting and Economics 41, 2006. A. Brav, R. Lehavy, “An empirical analysis of analysts’ target prices: short-term informativeness and long term dynamics,” The Journal of Finance 43, 2003. E. M Cervellati, A.C.F. Della Bina, “Analysts recommendations and conflict of interest,” Working paper, 2005. L.K.C. Chan, J. Karceski, J. Lakonishok, “Analysts’ conflict of interest and biased earning forecasts,” NBER Working Papers n. 9544, h, 2003. A. Ljungqvist F. Marston, L.T. Starks, K. D. Wei, H. Yan, “Conflicts of interest in sell-side research and the moderating role of institutional investors,” Journal of Financial Economics 85, 2007. A.R. Jackson, “Trade generation, reputation and sell-side analysts,” The Journal of Finance 60,2005. N. Jegadeesh, W. Kim, “Value of analyst recommendations: international evidence,” Journal of Financial Markets 2006. P. Sinha, L. Brown, S. Das, “A re-examination of financial analysts’ differential earning forecast accuracy,” Contemporary Accounting Research 14, 1997. S. E. Stickel, “Reputation and performance among security analysts,” The Journal of Finance 47, 1992. K.L. Womack, “Do brokerage analysts’ recommendations have investment value?” The Journal of Finance 51, 1996. Phillip Ballard Kennedy, “Investment Banking Conflicts: Research Analysits and IPO Allocations”, North Carolina Banking Institute, Volume 7, Issue 1, 2003. Benjamin J. Catalano, “The Promise of Unfavorable Research: Ramifications of Regulations Separating Research and Investment Banking for IPO Issuers and Investors,” Business Lawyer, 72, 2016. Erik Sirri, “Investment Banks, Scope, and Unavoidable Conflicts of Interest, Federal Reserve Bank of Atlanta, Fourth Quarter 2004”. Earnings Manipulation D. Burgstahler and I. Dichev, “Earnings management to avoid earnings decreases and losses,” Journal of Accounting and Economics, Vol. 24, No. 1, 99, December 1997. F. Degeorge, J. Patel, and R. Zeckhauser, “Earnings management to exceed thresholds,” Journal of Business, Vol. 72, No. 1, 1, January 1999. C. Hayn, “The information content of losses,” Journal of Accounting and Economics, Vol. 20, No. 2, 125, September 1995. P.M. Healy and R.S. Kaplan, “The effect of bonus schemes on accounting decisions f comment,” Journal of Accounting and Economics, Vol. 7, No. 1-3, 85, April 1985. D.C. Burgstahler and M.J. Eames, “Earnings management to avoid losses and earnings decreases: Are analysts fooled?,” Contemporary Accounting Research, Vol. 20, No. 2, 253, Summer 2003. A. Levitt, Jr., “The numbers game,” The CPA Journal, Vol. 68, No. 12, 14, December 1998. Hal Varian, Hal (1972). "Benford's Law (Letters to the Editor)". The American Statistician. 26 (3): 65. doi:10.1080/00031305.1972.10478934, 1972. Frank Benford, "The law of anomalous numbers," Proceedings of the American Philosophical Society 78 (4): 551–572. JSTOR 984802, March 1938. George K. Zipf, “The Psychobiology of Language,” Houghton-Mifflin, 1935.; George K. Zipf, “Human Behavior and the Principle of Least Effort,” Addison-Wesley, 1949. F. Auerbach, “Das Gesetz der Bevölkerungskonzentration,” Petermann’s Geographische Mitteilungen 59, 74–76, 1913. Big Bath Accounting Petra Nieken and Dirk Sliwka, "Management changes, reputation, and ‘big bath’ earnings management," Journal of Economics & Management Strategy. 24 (3): 501–522. doi:10.1111/jems.12101, September 2015. Utz Schäffer, Utz, Jan-Philipp Lüdtke, Diedrich Bremer, Matthias Häußler, Matthias, "The effect of accounting standards on Big Bath behavior". Zeitschrift für Betriebswirtschaft. 82(1): 47–73. doi:10.1007/s11573-011-0531-2, 2012. Paul Mozur, "Samsung's Galaxy Note 7 Debacle Wipes Out Its Mobile Profit," The New York Times, October 27, 2016. Brian Blackstone, "Some New Bank CEOs Take an 'Earnings Bath' When They Start," Wall Street Journal, March 3rd, 2014. D. Burgstahlerand I. Dichev, “Earnings management to avoid earnings decreases and losses,” Journal of Accounting and Economics, Vol. 24, No. l, December 1997. D.C. Burgstahler and M.J. Eames, “Earnings management to avoid losses and earnings decreases: Are analysts fooled?,” Contemporary Accounting Research, Vol. 20, No. 2, Summer 2003. P.M. Dechow, R.G. Sloan, A.P. Sweeney, “Detecting earnings management,” The Accounting Review, Vol. 70, No. 2, April 1995. R. Kasznik and B. Lev, “To warn or not to warn: Management disclosures in the face of an earnings surprise,” The Accounting Review, Vol. 70, No. 1, January 1995. Accruals Anomaly Richard G. Sloan, “Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings?” The Accounting Review, Vol. 71, No. 3, Jul., 1996. Baruch Itamar Lev and Nissim, Doron, “The Persistence of the Accruals Anomaly,” April 2004.; Patricia Dechow, Natalya V. Khimich, Richard G. Sloan, “The Accrual Anomaly, March 22, 2011. P.M. Dechow, R.G. Sloan, A.P. Sweeney, “Detecting earnings management,” The Accounting Review, Vol. 70, No.2, 193-215, April 1995. J.J. Jones, “Earnings management during import relief investigations,” Journal of Accounting Research, Vol. 29, No. 2, 193-228, Autumn 1991. D.C. Jeter and L. Shivakumar, “Cross-sectional estimation of abnormal accruals using quarterly and annual data: Effectiveness in detecting event-specific earnings management,” Accounting and Business Research, Vol. 29, No. 4, 299, Autumn 1999. Edward E. Qian, Ronald H. Hua, Eric H. Sorensen, "Quantitative Equity Portfolio Management, Modern Techniques and Applications," Chapman & Hall/CRC, 2007. Management Messaging Jesse M. Fried, “Informed Trading and False Signaling with Open Market Repurchases,” California Law Review, Vol. 93, October 2005. Victoria Lemieux, Ed., “Financial Analysis and Risk Management Data Governance, Analytics and Life Cycle Management,” Springer-Verlag Berlin Heidelberg 2013. Size Factor Tyler Shumway, and Vincent A. Warther.m “The Delisting Bias in CRSP’s Nasdaq Data and Its Implications for the Size Effect,” Journal of Finance, vol. 54, no. 6, December 1999.

asset and market features

Apley, D.. “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models.” arXiv: Methodology (2016): n. pag. Turan G. Bali, Amit Goyal, Dashan Huang, Fuwei Jang, and Quan Wen, "Different Strokes: Return PRedictaility Across Stocks and Bonds with Machine Learning and Big Data," February 2021. Oliver Binz, Katherine Schipper,, and Kevin Standridge, "What Can Analysts Learn from Artificial Intelligence about Fundamental Analysis?," November 2020. Cao, Kai and You, Haifeng, Fundamental Analysis Via Machine Learning (November 27, 2020). HKUST Business School Research Paper No. 2020-009 (also appears in Emerging Finance and Financial Practices eJournal), Available at SSRN: https://ssrn.com/abstract=3706532 or http://dx.doi.org/10.2139/ssrn.3706532 Stefano Cassella, Benjamin Golez, Huseyin Gulen, Peter Kelly, "Horizon Bias and the Term Structure of Equity Returns," May 2019. https://krannert.purdue.edu/academics/Finance/Finance_Files/kelly.pdf Dichev, Ilia D., 50 years of Capital Markets Research in Accounting: Achievements So Far and Opportunities Ahead (June 22, 2020). Available at SSRN: https://ssrn.com/abstract=3633489 or http://dx.doi.org/10.2139/ssrn.3633489 Ding, Kexing and Lev, Baruch Itamar and Peng, Xuan and Sun, Ting and Vasarhelyi, Miklos A., Machine Learning Improves Accounting Estimates: Evidence from Insurance Payments (May 1, 2020). Available at SSRN: https://ssrn.com/abstract=3253220 or http://dx.doi.org/10.2139/ssrn.3253220 Easton, Peter D. and Kapons, Martin M. and Kelly, Peter and Neuhierl, Andreas, Attrition bias and inferences regarding earnings properties (August 18, 2020). Available at SSRN: https://ssrn.com/abstract=3040354 or http://dx.doi.org/10.2139/ssrn.3040354 PD Easton, P Kelly, A Neuhierl, "Beating a random walk," Working Paper, 2017. Rapach, David & Strauss, Jack & Tu, Jun & Zhou, Guofu. (2019). Industry Return Predictability: A Machine Learning Approach. The Journal of Financial Data Science. 1. 9-28. 10.3905/jfds.2019.1.3.009. Shihao Gu, Bryan Kelly, Dacheng Xiu, Empirical Asset Pricing via Machine Learning, The Review of Financial Studies, Volume 33, Issue 5, May 2020, Pages 2223–2273, https://doi.org/10.1093/rfs/hhaa009 Earnings Surprise Coefficient W. R. Scott, "Financial accounting theory, 7th ed.," Pearson, 2014. Edith S. Hotchkiss and Deon Strickland, "Does Shareholder Composition Matter? Evidence from the Market Reaction to Corporate Earnings Announcements," Journal of Finance, 58(4), 2003.Dennis J. Chambers, Robert N. Freeman, and Adam S. Koch, "The Effect of Risk on Price Responses to Unexpected Earnings," Journal of Accounting, Auditing & Finance, Vol. 20 Issue 4, 2005.

feature conditioning and timing

Rob Arnott, Noah Beck, Vitali Kalesnik , Timing “Smart Beta” Strategies? Of Course! Buy Low, Sell High! September 2016. Cliff Asness, Swati Chandra, Antti Ilmanen, Ronen Israel, Contrarian Factor Timing is Deceptively Difficult, Journal of Portfolio Management, March 7, 2017. John H. Cochrane, “Asset Pricing,” Revised Ed., Princeton University Press, January 23, 2005.Eugene F. Fama and James D. MacBeth, “Risk Return, and Equilibrium: Empirical Tests,”The Journal of Political Economy, Vol. 81, No.3, May-June, 1973.Eugene .F. Fama and Kenneth R. French, "Permanent and temporary components of stock prices," Journal of Political Economy. 96 (2), 1988R. O. Michaud, “Efficient Asset Management,” Harvard Business School Press, 1998. Tobias J. Moskowitz, Yao Hua Oo1, Lasse Heje Pedersen, Time Series Momentum, Journal of FInancial Economics, v. 104, Issue 2, May 2012, pages 228-250. Mitchell Petersen, "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches". Review of Financial Studies. 22, 2009. Edward E. Qian, Ronald H. Hua, Eric H. Sorensen, "Quantitative Equity Portfolio Management, Modern Techniques and Applications," Chapman & Hall/CRC, 2007.

business cycle

Brandyn Bok, Daniele Caratelli, Domenico Giannone, Argia Sbordone, and Andrea Tambalotti, "Macroeconomic Nowcasting and Forecasting with Big Data," FRBNY Staff Reports, Staff Report No. 830 November 2017. "Modifications to GDPNow Model Effective with the April 30, 2020, Forecast," Federal Reserve Bank of Atlanta. Patrick Higgins, "GDPNow: A Model for GDP 'Nowcasting'," Federal Reserve Bank of Atlanta, Working Paper 2014-7, July 2014. Adam Richardson, Thomas van Florenstein Mulder, Tugrul Vehbi, "Nowcasting New Zealand GDP using machine learning algorithms," Reserve Bank of New Zealand, July 2018.

REGIMES

portfolio sorts

Ken French, Description of Fama/French Factor, Dartmoth, referenced 2021. https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html

Factor Efficacy

Cliff S. Asness, “The siren song of factor timing aka “smart beta aka style timing.”,” Journal of Portfolio Management, 2016. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2763956 Cliff S. Asness, S. Chandra, A. Ilmanen, and R. Israel, “Contrarian timing is deceptively difficult, Journal of Portfolio,” Management, 2017. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2928945 Todd E. Clark Michael W. McCracken, "Advances in Forecast Evaluation," Federal Reserve Bank of Cleveland, June 2012. https://s3.amazonaws.com/real.stlouisfed.org/wp/2011/2011-025.pdf Hubert Dichtl, Wolfgang Drobetz, and Viktoria-Sophie Wendt, “How to build a factor portfolio: Does the allocation strategy matter?,” European Financial Management, John Wiley & Sons Ltd., 2020. https://onlinelibrary.wiley.com/doi/epdf/10.1111/eufm.12264 P. R. Hansen, A test for superior predictive ability. Journal of Business and Economic Statistics, 2005. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.485.5482&rep=rep1&type=pdf J. Hsu, V. Kalesnik, and V. Viswanathan, “A framework for assessing factors and implementing smart beta strategies.,” Journal of Index Investing, 2015. https://www.valuewalk.com/wp-content/uploads/2015/07/JII_Summer_2015_RA-1.pdf K. Pukthuanthong, R. Roll, and A Subrahmanyam, “A protocol for factor identification,” Review of Financial Studies, 2019. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3005477

interactions and conditioning

Dorsey D. Farr, “Dynamic Factor Approaches to Equity Portfolio Management,” edited by Frank J. Fabozzi and Harry. M. Markowitz, John Wiley & Sons, 2011. Jerome H. Friedman and Bogan E. Popescu, "Predictive Learning via Rule Ensambles," The Annals of Applied Statistics 2008, Vol. 2, No. 3Jeremy Grantham, “Everything I Know About the Market in 15 Minutes,” GMO, Quarterly Letter, Special Topic, July 2006, 1991. Giles Hookar, "discovering Additive Structure in Black Box Functions, 2004. Brendon M. Greenwell, Bradley C. Boehmke, and Andrew J. McCarthy, "A Simple and Effective Model-Based Variable Importance Measure," 2018.Yin Luo, Javed Jussa, Sheng Wang, Multi-Dimensional Alpha,” Wolfe Research, February 8th, 2017.

mechanistic transformations

Shaun A. Bond, Soosung Hwang, and Gianluca Marcato, "Evaluating Unsmoothing Procedures for Appraisal Data," March 2005. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.488.3204&rep=rep1&type=pdf Shaun A. Bond, Soosung Hwang, and Gianluca Marcato, "An Analysis of Commercial Real Estate Returns: An Anatomy of Smoothing in Asset and Index Returns," https://core.ac.uk/download/pdf/107953.pdf Gregory W. Brown, Eric Ghysels, and Oleg Gredil, "Nowcasting Net Asset Values: The Cse of Private Equity," December 27th, 2019. Galen Burghardt, The Treasury Bond Basis, McGraw-Hill,. Charles M. Cottle, Options Trading: The Hidden Reality, Risk Doctor, 1996. John B. Corgel and Jan A. deRoos, "Recovery of Real Estate Returns for Portfolio Allocation," https://ecommons.cornell.edu/bitstream/handle/1813/72419/deRoos2_Recovery.pdf?sequence=1 David Michael Geltne, "Smoothing in Appraisal-Based Returns," Journal of Real Estate Finance and Economics, Kluwer Academic Publishers, 4:327-345 (1991) . https://link.springer.com/content/pdf/10.1007/BF00161933.pdf Andreas Marcus Gohs, Correction Procedures for Appraisal-Based Real Estate, 24th Annual Conference of the European Real Estate Society, June 28th-July 1st, 2017. https://eres.architexturez.net/system/files/274_0.pdfArthur Korteweg and Stefan Nagel, "Risk-Adjusting the Returns to Venture Capital," National Bureau of Economic Research, 2013, http://www.nber.org/papers/w19347 Arthur Korteweg, "Risk Adjustment in Private Equity Returns," Annual Review of Financial Economics, 2019. 11:131–52, https://doi.org/10.1146/annurev-financial-110118-123057 Ludovic Phalippou, “Why is the evidence on private equity performance so confusing?” University of Oxford, Said Business School, September, 2011. Ananth Madhavan and Aleksander Sobczyk, "Fine-Tuning Private Equity Replication Using Textual Analysis," The Journal of Financial Data Science Winter 2019, 1 (1) 111-121; DOI: https://doi.org/10.3905/jfds.2019.1.1.111 Munier Salem, Joshua Younger, Zhan Zhao, Jay Barry, Jason Hunter, Devdeep Sarkar, Phoebe A. White, Alix Tepper, Luke Y Chang, “Do androids dream of electric bonds?, Machine learning in interest rate markets,” J.P. Morgan, US Fixed Income Strategy, November 21st, 2017. Sidney C. Porter and Sheridan Porter, "Introducing Objective Benchmark-Based Attribution in Private Equity," The Journal of Financial Data Science Winter 2019, 1 (1) 130-140; DOI: https://doi.org/10.3905/jfds.2019.1.1.130

case study: esg Factors

case study: global Macro tactical asset allocation (GTAA)

case study: par yield curve

9

chapter 9 creating forecasts

Michael Aked, “The Dirty Little Secret of Passive Investing,” Research Affiliates, January 2016.Michael Aked, Michael, and Max Moroz “The Market Impact of Passive Trading.” Research Affiliates, August 2015. Reiner Braun, Tim Jenkinson, and Ingo Stoff, "How Persistent is Private Equity Performance? Evidence from Deal-Level Data", December 21, 2015. https://ssrn.com/abstract=2314400 Mark M. Carhart, "On Persistence in Mutual Fund Performance," Published by Wiley for the American Finance Association, The Journal of Finance, Vol. 52, No. 1, pp. 57-82, March, 1997. https://doi.org/10.2307/2329556. Bill Casselman, "The Legend of Abraham Wald," American Mathematical Socisety, Kirsten Grind, Tom McGinty and Sarah Krouse, "The Morningstar Mirage," The Wall Street Journal, Oct. 25, 2017. https://www.wsj.com/articles/the-morningstar-mirage-1508946687

Capital Markets Assumptions

Diebold-Mariano Test,”Charles Zaiontz, Diebold-Mariano Test | Real Statistics Using ExcelReal Statistics Using Excel, real-statistics.com. Campbell R. Harvey, John C. Liechty, and Merrill W. Liechty, "Parameter Uncertainty in Asset Allocation" in Bernd Morgan D. Jones, "The Thinker's Toolkit: 14 Powerful Techniques for Problem Solving," Currency, 1998. Francois-Serge Lhabitant and Denis Mirlesse, "Alpha Capture Systems: Past, Present, and Future Directions," June 25, 2021. http://dx.doi.org/10.2139/ssrn.3873884. Philip E. Tetlock, Dan Gardner, "Superforecasting: The Art and Science of Prediction," Crown Publishers, 2015.Scherer and Kenneth Winston, "The Oxford Handbook of Quantitative Asset Management," Oxford University Press, 2012.

Strategic Forecasting

Paul Schmelzing, “Eight Centuries of Global Real Interest Rates, R-G, and the ‘Suprasecular’ Decline, 1311–2018,” Staff Working Paper No. 845, Bank of England, January 3rd, 2020.

Tactical Forecasting

“What Is a Business Cycle?” NBER Working Paper # 3863, October 1991. Robert Alan Feldman, "Cobwebs and CRICs, Japan Economics," Morgan Stanley Dean Witter Economic Research, April 4th, 2001. https://www.yumpu.com/en/document/read/4429905/cobwebs-and-crics-japan-economics/1 Milton Friedman and Anna Schwartz, "Monetary History"Alan Greenspan, "The Map and the Territory: Risk, Human Nature and the Future of Forecasting." Robert T. McGee, "Applied Financial Macroeconomics and Investment Strategy, A Practitioner's Guide to Tactical Asset Allocation," Palgrave Macmillan, 2015.

Risk Premia

Sebastien Betermier, Laurent E Calvet, and Evan Jo, “A Supply and Demand Approach to Equity Pricing.” 2019. Farshid M. Asl and Erkko Etula, “Advancing Strategic Asset Allocation in a Multi-Factor World”, The Journal of Portfolio Management, 39(1), 2012. https://jpm.pm-research.com/content/39/1/59 M.W. Brandt and K.Q. Wang, "Time-varying risk aversion and unexpected inflation," Journal of Monetary Economics, v50, 2003. E. Dimson, P Marsh and M Staunton, "Triumph of the Optimists: 101 Years of Global Investment," Princeton University Press, 2002. "Returns, Global Investment Returns Yearbook," ABN AMRO/London Business School, 2009. L.G. Epstein and S.E. Zin, "Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: An Empirical Analysis," Journal of Political Economy, v99, no. 2, April 1991. Antti Ilmanen, "Expected Returns: An Investor’s Guide to Harvesting Market Rewards. Hoboken," John Wiley & Sonsk. 2011. Elroy Dimson, Paul Marsh , Mike Staunton, "Triumph of the Optimists: 101 Years of Global Investment Returns," Princeton University Press, 2002. Pablo Fernández, Javier Aguirreamalloa, and Luis Corres. 2011. “Market Risk Premium Used in 56 Countries in 2011: A Survey with 6,014 Answers,” IESE Business School, May 2011. https://www.iese.edu/research/pdfs/DI-0920-E.pdf K. L. Fisher and M. Statman, "Investor Sentiment and Stock Returns," Financial Analysts Journal, v56, 2000. Franco Modigliani and Richard Cohn, "Inflation, Rational Valuation, and the Market," Financial Analysts Journal, v37(3), 1979. A. Goyal and I. Welch, "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," Review of Financial Studies, v21, 2007. E.R. McGrattan and E.C. Prescott, "Taxes, Regulations, and Asset Prices," Federal Reserve Bank of Minneapolis, 2001.Aviv Nevo, and Michael Whinston, “Taking the Dogma out of Econometrics: Structural Modeling and Credible Inference.” Journal of Economic Perspectives, vol. 24, no. 2, Spring 2010. Challenging Risk Premia Andrew Y. Chen and Tom Zimmermann, “Publication Bias and the Cross-Section of Stock Returns,” 2020. Jeremy J. Siegel, "Stocks for the Long Run : The Definitive Guide to Financial Market Returns and Long-Term Investment Strategies, 5th ed.," McGraw-Hill, 2014. Edward F. McQuarrie, "Stocks for the Long Run? Sometimes Yes. Sometimes No," . https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3805927 Interest Rates Aswath Damodaran, "What is the riskfree rate? A Search for the Basic Building Block," New York University , December 2008. Robert B Litterman and Josè Scheinkman, "Common Factors Affecting Bond Returns,"The Journal of Fixed Income Jun 1991, 1 (1) 54-61; DOI: 10.3905/jfi.1991.692347 https://jfi.pm-research.com/content/1/1/54 Economic Risk Premia Pierluigi Balduzzi and Fabio Moneta, "Economic Risk Premia in the Fixed Income Markets: The Intra-day Evidence," 2017.William J. Bernstein and Robert D. Arnott, "Earnings growth: the two percent dilution," Financial Analyst Journal, September/October 2003.Bradford Cornell, "Economic Growth and Equity Investing," Financial Analyst Journal, 2010.Jeffrey J. Diermeier, Roger G. Ibbotson, and Laurence B. Siegel, "The Supply of Capital Market Returns," Financial Analyst Journal, March/April 1984.Wayne E. Ferson and Campbell R. Harvey, "The Variation of Economic Risk Premiums," Journal of Political Economy, 1991. Peter Hordahl, Eli M. Remolona, and Giorgio Valente, "Expectations and Risk Premia at 8:30 a.m.: Deciphering the Responses of Bond Yields to Macroeconomic Announcements.MSCI Barra, “Is There a Link Between GDP Growth and Equity Returns,” May 2010. MSCI Barra, "What Drives Long Term Equity Returns?," January 2010.Jeremy J. Siegel, "Stocks for the Long Run," 2nd Ed., 1998. Equity Risk Premium G. Bekaert, C.R. Harvey, and C. Lundblad, "Liquidity and Expected Returns: Lessons from Emerging Markets," The Review of Financial Studies, 2006. J.Y. Campbell and T. Vuolteenaho, "Inflation Illusion and Stock Prices," American Economic Review, v94, 2004. J. Cochrane and C. Culp, “Equilibrium Asset Pricing and Discount Factors: Overview and Implications for Derivatives Valuation and Risk Management.” In Modern Risk Management: A History, Risk Books., 2003. Elroy Dimson, Paul Marsh, and Staunton, Mike, "The Worldwide Equity Premium: A Smaller Puzzle." in R Mehra (Ed), Handbook of the Equity Risk Premium. Elsevier, 2008, SSRN: https://ssrn.com/abstract=891620 or http://dx.doi.org/10.2139/ssrn.891620. Elroy Dimson, Paul Marsh, and Mike Staunton, "Equity Premia Around the World," October 7, 2011. http://dx.doi.org/10.2139/ssrn.1940165 Aswath Damodaran, Equity Risk Premiums (ERP): Determinants, Estimation, and Implications -- The 2021 ERition," 2021. Gustavo Grullon and Roni Michaely, "Dividends, Share Repurchases, and the Substitution Hypothesis, Journal of Finance vol. 57, no. 4, August 2002. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=AF2E400600F2FE984CB909455DA81894?doi=10.1.1.538.6732&rep=rep1&type=pdf Ellen R. McGrattan and Edward C. Prescott, “Taxes, Regulations, and Asset Prices,” NBER , December 2001. MSCI Barra, “Is There a Link between GDP Growth and Equity Returns?” MSCI Barra Research Bulletin, May 2010. N. Mougeot and R. Gibson, "The Pricing of Systematic Liquidity Risk: Empirical Evidence from the US Stock Market. Journal of Banking and Finance, v28: 157–78. 8 M. Lettau, S.C. Ludvigson and J.A. Wachter, The Declining Equity Risk Premium: What role does macroeconomic risk play?" Review of Financial Studies, v21, 2008. Lubos Pástor and Robert F. Stambaugh, "Are Stocks Really Less Volatile in the Long Run?, March 27th, 2012. https://doi.org/10.1111/j.1540-6261.2012.01722.x. P. Santa-Clara and S. Yan, "Crashes, Volatility, and the Equity Premium: Lessons from S&P500 Options," Review of Economics and Statistics. 2--6.. Jeremy J. Siegel, "2002. Stocks for the Long Run: The Definitive Guide to Financial Market Returns and Long-Term Investment Strategies. 3rd ed. New York: McGraw-Hill.,2002. Ivo Welch and Amit Goyal, “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction., Review of Financial Studies, vol. 21, no. 4, July 2008. https://www.nber.org/papers/w10483 Bond Risk Premium Pierluigi Balduzzi and Fabio Moneta, "Economic Risk Premia in the FIxed Income Markets: The Intra-day Evidence," 2017. Demir Bekti, Britta Hachenberg and Dirk Schiereck, "Factor.based investing in government bond markets: a survey of the current state of research," Journal of Asset Management, 2020. https://doi.org/10.1057/s41260-020-00156-3 Demir Bektic, Josef-Stefan Wenzler, Michael Wegener, Dirk Schiereck, and Timo Spielmann, "Extending Fama–French Factors to Corporate Bond Markets<" Journal of Portfolio Management, 2019. Francis X. Diebold and Canlin Li. “Forecasting the Term Structure of Government Bond Yields.” Journal of Econometrics, vol. 130, no. 2, February 2006. https://www.sas.upenn.edu/~fdiebold/papers/paper49/Diebold-Li.pdf Peter Hordahl, Eli M. Remolona, and Giorgio Valente, "Expectations and Risk Premia at 8:30 a.m.: Deciphering the Responses of Bond Yields to Macroeconomic Announcements. Antti Ilmanen, "Overview of Foward Rate Analysis, Understanding the Yield Curve: Part 1," Salomon Brothers, 1995. Antti Ilmanen, "Market's Rate Expectations and Forward Rates, Understanding the Yield Curve: Part 2," Salomon Brothers, 1995. Antti Ilmanen, "Does Duration Extension Enhance Long-Term Expected Returns, Understanding the Yield Curve: Part 3," Salomon Brothers, 1995. Antti Ilmanen, "Forecasting U.S. Bond Returns, Understanding the Yield Curve: Part 4," Salomon Brothers, 1995. Antti Ilmanen, "Convexity Bias and the Yield Curve, Understanding the Yield Curve: Part 5," Salomon Brothers, 1995.Antti Ilmanen, "A Framework for Analyzing Yield Curve Trades, Understanding the Yield Curve: Part 6," Salomon Brothers, 1995.Antti Ilmanen, "The Dynamic Shape of The Yield Curve, Understanding the Yield Curve: Part 7," Salomon Brothers, 1995.Martin L. Leibowitz, Sidney Homer, and Stanley Kogelman, "Inside the Yield Curve, 3rd edition," Wiley, 2012 Robert Litterman, and José A. Scheinkman, “Common Factors Affecting Bond Returns.” Journal of Fixed Income, vol. 1, no. 1 (June): 54–61, 1991. "2020 Annual Global Corporate Default And Rating Transition Study April 7, 2021," S&P Global Ratings, https://www.maalot.co.il/Publications/TS20210408160139.PDF. "Default, Transition, and Recovery: 2020 Annual U.S. Corporate Default and Rating Transition Study," S&P Global Ratings, https://www.spglobal.com/ratings/en/research/articles/210506-default-transition-and-recovery-2020-annual-u-s-corporate-default-and-rating-transition-study-11937151. Credit Risk Premium Demir Bektic, Josef-Stefan Wenzler, Michael Wegener, Dirk Schiereck, and Timo Spielmann, “Extending Fama-French Factors to Corporate Bond Markets,” Journal of Portfolio Management 2019. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2715727 Maria Correia, Scott Richardson, and Irem Tuna, “Value Investing in Credit Markets,” Review of Accounting Studies, 2012. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1800212 P. Crosbie and J. Bohn, "Modelling Default Risk," Moody's KMV, 2003. Lennart Dekker, Patrick Houweling, and Frederik Muskens, “Factor Investing in Emerging Market Credits,” 2019. http://ssrn.com/abstract=3457127 Mathieu L’Hoir and Mustafa Boulhabel, “A Bond-Picking Model for Corporate Bond Allocation,” Journal of Portfolio Management, 2010. https://jpm.pm-research.com/content/36/3/131.short J. Lewellen, "Accounting Anomalies and Fundamental Analysis: An Alternative View," Journal of Accounting and Economics, 2010. Gergana Jostova, Stanislava Nikolova, Alexander Philipov, and Christof W. Stahel, “Momentum in Corporate Bond Returns,” Review of Financial Studies 26(7), 2013. https://www.jstor.org/stable/23470056?seq=1#metadata_info_tab_contents Patrick Houweling and Jeroen Van Zundert, “Factor Investing in the Corporate Bond Market,” Financial Analysts Journal, 2017. https://www.tandfonline.com/doi/full/10.2469/faj.v73.n2.1 Ronen Israel, Diogo Palhares, and Scott Richardson, “Common Factors in Corporate Bond Returns,” Journal of Investment Management 16(2), 2018. https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/Common-Factors-in-Corporate-Bond-Returns.pdf James Moore, "Who’s Buying Now? Some Long-Term Investors Are Snapping Up Bonds.” Institutional Investor, July 8th,2013. https://www.institutionalinvestor.com/article/b14zb9jzqgyx89/whos-buying-now-some-long-term-investors-are-snapping-up-bondsKwok-Yuen Ng and B. Phelps, “Capturing Credit Spread Premium.” Financial Analysts Journal, vol. 67, no. 3 (May/June): 63–75, 2011. J. Ohlson, "Financial Ratios and the Probablilitic Prediction of Bankruptcy," Journal of Accounting Research, 1980.Simon Polbennikov, Albert Desclée, and Mathieu Dubois, "Implementing Value and Momentum Strategies in Credit Portfolios," Journal of Portfolio Management, 2021. P. Schultz, "Corporate Bond Trading Costs: A Peek Behind the Curtain," Jurnal of FInance, 2001. Alternative Assets Risk Premia Dimson, E. 1979. “Risk Measurement When Shares Are Subject to Infrequent Trading.” Journal of Financial Economics, vol. 7, no. 2 (June): 197–226 Scholes, M., and J. Williams. 1977. “Estimating Betas from Nonsynchronous Data.” Journal of Financial Economics, vol. 5, no. 3 (December): 309–327. F. Franzoni, E. Nowak, and L. Phalippou, “Private Equity Performance and Liquidity Risk.” Journal of Finance, 2012. G. Gallais-Hamonno and H. Nguyen-Thi-Thanh., “The Necessity to Correct Hedge Fund Returns: Empirical Evidence and Correction Method.” Universite Libre de Bruxelles, 2007. D. Geltner,“Estimating Market Values from Appraised Values without Assuming an Efficient Market.” Journal of Real Estate Research, 1993. M. Getmansky, A. Lo, and I. Makarov, “An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns.” Journal of Financial Economics, 2004. Correlation and Right/Wrong Way Risk Premia Andrew W. Lo and A. Craig MacKinlay, “Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, vol. 1, no. 1, Spring 1988. Brian W. McCulloch, “Geometric Return and Portfolio Analysis,” New Zealand Treasury, 2003. Robert F. Stambaugh, “Analyzing Investments Whose Histories Differ in Length,” Journal of Financial Economics, vol. 45, no. 3 September 1997.

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Case Study: Economic Surprise

Pierluigi Balduzzi and Fabio Moneta, "Economic Risk Premia in the FIxed Income Markets: The Intra-day Evidence," 2017.Peter Hordahl, Eli M. Remolona, and Giorgio Valente, "Expectations and Risk Premia at 8:30 a.m.: Deciphering the Responses of Bond Yields to Macroeconomic Announcements.

10

chapter 10: strategy, objective, and conditions

signals

sizing

market impact and transaction costs

conditional likelihood

transition matricies

special orders

valuation models

case study: triple barrier vs. single barrier

case study: treasury basis valuation

11

chapter 11: Time Series and Cross-Sectional Analysis for Financial Markets

N. Ahmed, , A. Atiya, N. Gayar, and H. El-Shishiny, “An empirical comparison of machine learning models for time series forecasting.” Econometric Reviews, Vol. 29, No. 5, 2010. A. Akansu, S. Kulkarni, and D. Malioutov, “Financial Signal Processing and Machine Learning, John Wiley & Sons, 2016.D. Aronson and T. Masters, “Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB,” CreateSpace, 2013.ckt Publishing.M. Dixon, D. Klabjan, and J. Bang, “Classification-based financial markets prediction using deep neural networks.”, 2016. J. Patel, S. Sha, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,”Expert Systems with Applicatiosn, 2014. https://www.sciencedirect.com/science/article/pii/S0957417414004473. A. Zhang, Z.C. Lipton, M. Li, A.J. Smola, Dive into Deep Learning, Online: http:// d2l.ai.

THE PROBLEM WITH MODELS

Structural Economic ModelsJohn B. Taylor, “Discretion versus Policy Rules in Practice, Carnegie-Rochester Conerence Series on Public Policy 39, 1993.John B. Taylor, “A Historical Analysis of Monetary Policy Rules,” in “Monetary Policy Rules,” University of Chicago Press, January 1999.Lars E. O. Svensson, What Is Wrong with Taylor Rules? Using Judgment in Monetary Policy through Targeting Rules (June 2003), Journal of Economic Literature.Boris Hofmann, Taylor rules and monetary policy: a global “Great Deviation”? (September 2012).Emiliano Brancaccio and Giuseppe Fontana, ‘Solvency rule’ versus ‘Taylor rule’: an alternative interpretation of the relation between monetary policy and the economic crisis (August 2012 ), Cambridge Journal of Economics.https://www.frbatlanta.org/cqer/research/taylor-rule?“Federal Open Market Committee Meeting calendars, statements, and minutes” Summary of Economic Projections (SEP)Thomas Laubach and John C. Williams , "Measuring the Natural Rate of Interest," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1063-1070, November 2003.Kathryn Holston, Thomas Laubach, and John C. Williams, “Measuring the natural rate of interest: International trends and determinants, Journal of International Economics,Volume 108, Supplement 1, May 2017, Pages S59-S75.Thomas Lubik and Christian Matthes, “Calculating the Natural Rate of Interest: A Comparison of Two Alternative Approaches,” Federal Reserve Bank of Richmond, October 2015Charles A. Fleischman and John M. Roberts, “From Many Series, One Cycle: Improved Estimates of the Business Cycle from a Multivariate Unobserved Components Model,” 2011.Congressional Budget Office's (CBO) estimate of potential real GDP.Arthur M. Okun, "Potential GNP: Its Measurement and Significance," American Statistical Association, Proceedings of the Business and Economics Statistics Section 1962.David Brauer, “ The Natural Rate of Unemployment,” Congressional Budget Office Washington, D.C. April 2007.U.S. Bureau of Labor Statistics, Economic News Release, Table A-15. Alternative measures of labor underutilization, Household Data.“The ZPOP Ratio: A Simple Take on a Complicated Labor Market,” The Federal Reserve Bank of Atlanta, September 22nd, 2015.“Constructing Gaps for U6 and ZPOP,” Federal Reserve Bank of Atlanta.Michael Dotsey and Keith Sill, “Using Rules for Benchmarking,” Federal Reserve Bank of Philadelphia, 2015.Flint Brayton, Thomas Laubach, and David Reifschneider, “The FRB/US Model: A Tool for Macroeconomic Policy Analysis,” April 2014. GARCH Christian Francq and Jean-Michel Zakoian, “GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition,” Wiley, 2019. ARIMA Vasileios Lampos, “Advances in Nowcasting Influenza-Like Illness Rates Using Search Query Logs,” Scientific Reports, 2015. David Farrow, “Modeling the Past, Present, and Future of Influenza,” Carnegie Mellon University, 2016. . DUMP Peter Bühlmann, Jonas Peters, Jan Ernest, et al. “CAM: Causal additive models, high-dimensional order search and penalized regression. The Annals of Statistics,” 42(6):2526–2556, 2014.Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman, “Causation, prediction, and search,” MIT press, 2000.Clark Glymour, Kun Zhang, and Peter Spirtes, “Review of causal discovery methods based on graphical models,” Frontiers in genetics, 10:524, 2019.“Constructing Gaps for U6 and ZPOP,” Federal Reserve Bank of Atlanta“Empower Experts with Causal AI,” CausaLens, October, 2020.“Federal Open Market Committee Meeting calendars, statements, and minutes” Summary of Economic Projections (SEP)“From Predicting to Shaping the Future,” CausaLens, November, 2020.“The ZPOP Ratio: A Simple Take on a Complicated Labor Market,” The Federal Reserve Bank of Atlanta, September 22nd, 2015“Towards Artificial Imagination,” CausaLens, 2021.“Truly Explainable AI: Putting the ’Cause’ in ‘Because’,” CausaLens, 2021“Why Correlation-Based Machine Learning Leads to Bad Predictions,” CausaLens, 2021.Aileen Nielsen, “Practical Time Series Anaysis, Prediction with Statistics & Machine Learning, O’Reilly, 2020Aiusha Sangadiev, Rodrigo Rivera-Castro, Kirill Stepanov, and Evgeny Burnaev, “DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data,” August 2020Alexi Jourovski, Vladyslav Dubikovsky, Pere Adell, Ravi Ramakrishnan, and Robert Kosowski, “Forecasting Beta Using Machine Learning,” in Machine Learning for Asset Management, New Developments and Financial Applications, edited by Emmanuel Juczenko, Wiley, 2020Andrew R. Lawrence, Marcus Kaiser, Rui Sampaio, and Maksim Sipos, “Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data,” Causal Discovery and Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems, 2020.Arthur M. Okun, "Potential GNP: Its Measurement and Significance," American Statistical Association, Proceedings of the Business and Economics Statistics Section 1962Ben-David A. (2008). About the relationship between ROC curves and Cohen’s kappa. Engineering Applications of Artificial Intelligence, 21(6), 874–882Boris Hofmann, Taylor rules and monetary policy: a global “Great Deviation”? (September 2012)Charles A. Fleischman and John M. Roberts, “From Many Series, One Cycle: Improved Estimates of the Business Cycle from a Multivariate Unobserved Components Model,” 2011Charles Geyer, “Burn-In is Unnecessary”Christian Francq and Jean-Michel Zakoian, “GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition,” Wiley, 2019.Christina Heinze-Deml, Jonas Peters, and Nicolai Meinshausen, “Invariant Causal Prediction for Nonlinear Models.” Journal of Causal Inference, 2018Christoph Molinar, “Interpretable Machine Learning, A Guide for Making Black Box Models Explainable,” October 26th, 2020Christopher Watkins and Peter Dayan, “Q-Learning,” Machine Learning, 1992Claude Shannon and Warren Weaver, “The Mathematical Theory of Communication,” University of Illinois Press, 1949Cohen, Jacob (1960). "A coefficient of agreement for nominal scales". Educational and Psychological Measurement. 20 (1): 37–46. doi:10.1177/001316446002000104. hdl:1942/28116. S2CID 15926286Congressional Budget Office's (CBO) estimate of potential real GDPD. Bacham and J. Zhao, :Machine Learning: Challenges, Lessons, and Opportunities in Credit Risk Modeling,” Moody’s Analytics, 2017.David Baudera , Taras Bodnarb,1 , Nestor Parolyac , and Wolfgang Schmidd, “Bayesian Inference of the Multi-Period Optimal Portfolio for an Exponential Utility,” May 18th, 2017David Brauer, “ The Natural Rate of Unemployment,” Congressional Budget Office Washington, D.C. April 2007David Farrow, “Modeling the Past, Present, and Future of Influenza,” Carnegie Mellon University, 2016Dorsey D. Farr, “Dynamic Factor Approaches to Equity Portfolio Management,” edited by Frank J. Fabozzi and Harry. M. Markowitz, John Wiley & Sons, 2011.E Hoseinzade, S Haratizadeh, and A Khoeini, “U-CNNpred: A Universal CNN-based Predictor for Stock Markets,” 2019Emiliano Brancaccio and Giuseppe Fontana, ‘Solvency rule’ versus ‘Taylor rule’: an alternative interpretation of the relation between monetary policy and the economic crisis (August 2012 ), Cambridge Journal of EconomicsFlint Brayton, Thomas Laubach, and David Reifschneider, “The FRB/US Model: A Tool for Macroeconomic Policy Analysis,” April 2014Geman, S.; Geman,”Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu, “Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks,” Carnegie Mellon, 2018Hastings, W.K., “Monte Carlo Sampling Methods Using Markov Chains and Their Applications,” Biometrika. 1970Hema Parmar, Katherine Burton, Nishant Kumar, “Human-Run Hedge Funds Beat Quants in Year Ruled by PandemicIan J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, “Generative Adversarial Networks,” June 2014IEEE Computer Society Magazine Honors Artificial Intelligence Leaders". DigitalJournal.com. August 24, 2011. Retrieved September 18, 2011Jason Hunter, Veronica Mejia Bustamante, Alix Tepper, “Quantifying Technical Analysis with AI and Machine Learning: Identifying potential turning points with price and momentum based pattern recognition,” J.P. Morgan Global Fixed Income and US Equity Index Technical Strategy, June 12th, 2018John B. Taylor, “A Historical Analysis of Monetary Policy Rules,” in “Monetary Policy Rules,” University of Chicago Press, January 1999John B. Taylor, “Discretion versus Policy Rules in Practice, Carnegie-Rochester Conerence Series on Public Policy 39, 1993.Jonas Peters, Dominik Janzing, and Bernhard Schölkopf, “Elements of Causal Inference: Foundations and Learning Algorithms,” MIT Press, 2017Josh Younger, Jason Hunter, Munier Salem, Tucker Balch, “J.P. Morgan Quant Fixed Income Online Conference,” J.P. Morgan Securities, J.P. Morgan Securities, J.P. Morgan, Fixed Income Strategy, May, 2019Joshua Younger, Jason Hunter, Munier Salem, Manuela Veloso, "J.P. Morgan Quant Fixed Income Online Conference," J.P. Morgan Securities, J.P. Morgan, Fixed Income Strategy, September, 2018Judea Pearl and Dana Mackenzie, “The Book of Why: The New Science of Cause and Effect”, Basic Books, 2018Judea Pearl, “Causality: Models, Reasoning, and Inference,” Cambridge University Press, 2000K. V. Rashmi and Ran Gilad-Bachrach, “DART: Dropouts meet Multiple Additive Regression Trees,” May 7th, 2015Kathryn Holston, Thomas Laubach, and John C. Williams, “Measuring the natural rate of interest: International trends and determinants, Journal of International Economics,Volume 108, Supplement 1, May 2017, Pages S59-S75Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L Scott, “Inferring Causal Impact Using Bayesian Structural Time-Series Models,” Annals of Applied Statistics, 2015Kris Boudt, Muzafer Cela, and Majeed Simaan, “In Search of Return Predictability: Application of Machine Learning Algorithms in Tactical Allocation,” in “Machine Learning for Asset Management, New Developments and Financial Applications,” edited by Emmanuel Jurczenko, Wiley, 2020Lars E. O. Svensson, What Is Wrong with Taylor Rules? Using Judgment in Monetary Policy through Targeting Rules (June 2003), Journal of Economic LiteratureLeo Breiman, “Random Forests,” January 2001Luo Y., “Natural Language Proceess,” in “Machine Learning for Asset Management, New Developments and Financial Applications,” edited by Emmanuel Jurczenko, Wiley, 2020M.A. Tanner, "Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions," Springer Series in Statistics, New York, 1998.Marco Tulio Ribeiro, Sameer Singh, amd Carlos Guestrin, "Why Should I Trust You?: Explaining the Predictions of Any” Classifier,” August 9th, 2016Matthews, B. W. (1975). "Comparison of the predicted and observed secondary structure of T4 phage lysozyme". Biochimica et Biophysica Acta (BBA) - Protein Structure. 405(2): 442–451. doi:10.1016/0005-2795(75)90109-9. PMID 1180967Max Kuhn, “Feature Engineering,” .Metropolis, N. and Ulam, S. “The Monte Carlo method,” J. Amer. Statist. Assoc., 1949Michael Dotsey and Keith Sill, “Using Rules for Benchmarking,” Federal Reserve Bank of Philadelphia, 2015Michal Balcerak and Thomas Schmelzer, “Constructing trading strategy ensembles by classifying market states,” arXiv:2012.03078v1 [q-fin.TR] 5 Dec 2020Munier Salem, Joshua Younger, and Jimmy (Guanjie) Huang, “TradeRunner: Ensemble learning-driven systematic trading in interest rate markets,” J.P. Morgan North America Fixed Income Strategy August 16, 2018Peter M. Aronow and Fredrik SAVJEm “Why: The New Science of Cause and Effect,” Journal of the American Statistical Association, 2019Philip E. Tetlock and Dan Gardner, “Superforecasting: The Art and Science of Prediction,” Crown, September 13th, 2016.Richard S. Sutton and Angrew G. Barto, “Reinforcement Learning: An Introduction (2ndRobert Dochow, “Online Algorithms for the Portfolio Selection Problem,” Springer Gabler,2016Rosario Delgado and Xavier-Andoni Tibau, “Why Cohen’s Kappa should be avoided as performance measure in classification,” September 26, 2019, https://doi.org/10.1371/journal.pone.0222916Saravan Erp, Daniel L. Oberski, JorisMulder, “Shrinkage priors for Bayesian penalized regression,” April 2019Scott Lundberg, Su-In Lee (v1), “A Unified Approach to Interpreting Model Predictions,” May 22nd, 2017 revised Nov 25th 2017Steven Hoi, Doyen Sahoo, Jing Lu, and Peilin Zhao, “Online Learning: A Comprehensive Survey,” 2018Thomas Laubach and John C. Williams , "Measuring the Natural Rate of Interest," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1063-1070, November 2003Thomas Lubik and Christian Matthes, “Calculating the Natural Rate of Interest: A Comparison of Two Alternative Approaches,” Federal Reserve Bank of Richmond, October 2015U.S. Bureau of Labor Statistics, Economic News Release, Table A-15. Alternative measures of labor underutilization, Household DataValliappa Lakshmanan, Sara Robinson, and Michael Munn, Machine Learning Design Patterns: Solutions to Common Challenges in Data, Preparation, Model Building, and MLOps,” O’Reilly, 2020.Virginia Gold, "Judea Pearl Wins ACM A.M. Turing Award for Contributions that Transformed Artificial Intelligence". The Association for Computing Machinery, March 17, 2012.

DATA GATHERING AND MANIPULATION

Heading

Privacy Erika McCallister, Tim Grance, and Karen Scarfone, "Guide to Protecting the Confidentiality of Personally Identifiable Information (PII) Recommendations of the National Institute of Standards and Technology," NIST Special Publication 800-122, April 2010. "Regulation FD, Section 101, Rule 100: General Rule Regarding Selective Disclosure, Question 101.03," Securities and Exchange Commission Corporate Guidance. https://www.sec.gov/divisions/corpfin/guidance/regfd-interp.htm

Availability, sparseness, and imbalances

Abbasi, A. and H. Chen (2009). A comparison of fraud cues and classification methods for fake escrow website detection. Information Technology and Management 10(2-3): 83-101. Abeysinghe, C., J. Li, et al. (2016). A Classifier Hub for Imbalanced Financial Data. Australasian Database Conference, Springer.Al-Ghraibah, A., L. E. Boucheron, et al. (2015). A Study of Feature Selection of Magnetogram Complexity Features in an Imbalanced Solar Flare Prediction Data-set. 2015 IEEE International Conference on Data Mining Workshop (ICDMW), IEEE.Alfaro, E., N. García, et al. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems 45(1): 110-122.Ali, S., A. Majid, et al. (2016). Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data. Computers in biology and medicine 73: 38-46.Alibeigi, M., S. Hashemi, et al. (2012). DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets. Data & Knowledge Engineering 81: 67-103.Alshomrani, S., A. Bawakid, et al. (2015). A proposal for evolutionary fuzzy systems using feature weighting: Dealing with overlapping in imbalanced datasets. Knowledge-Based Systems73: 1-17.Alsulaiman, F. A., J. J. Valdes, et al. (2012). Identity verification based on haptic handwritten signatures: Genetic programming with unbalanced data. Computational Intelligence for Security and Defence Applications (CISDA), 2012 IEEE Symposium on, IEEE.Anand, A., G. Pugalenthi, et al. (2010). An approach for classification of highly imbalanced data using weighting and undersampling. Amino acids 39(5): 1385-1391.Anderson, B. and P. Adey (2012). Governing events and life:‗Emergency‘in UK Civil Contingencies. Political Geography 31(1): 24-33.Ando, S. (2015). Classifying imbalanced data in distance-based feature space. Knowledge and Information Systems: 1-24.Ashkezari, A. D., H. Ma, et al. (2013). Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers. Dielectrics and Electrical Insulation, IEEE Transactions on 20(3): 965-973.Azaria, A., A. Richardson, et al. (2014). Behavioral Analysis of Insider Threat: A Survey and Bootstrapped Prediction in Imbalanced Data. Computational Social Systems, IEEE Transactions on 1(2): 135-155.Bae, S.-H. and K.-J. Yoon (2015). Polyp Detection via Imbalanced Learning and Discriminative Feature Learning. Medical Imaging, IEEE Transactions on 34(11): 2379-2393.Bagherpour, S., À. Nebot, et al. (2016). FIR as Classifier in the Presence of Imbalanced Data. International Symposium on Neural Networks, Springer.Bahnsen, A. C., A. Stojanovic, et al. (2013). Cost sensitive credit card fraud detection using Bayes minimum risk. Machine Learning and Applications (ICMLA), 2013 12th International Conference on, IEEE.Bao, F., Y. Deng, et al. (2016a). ACID: association correction for imbalanced data in GWAS. IEEE/ACM Transactions on Computational Biology and Bioinformatics.Bao, L., C. Juan, et al. (2016b). Boosted Near-miss Under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets. Neurocomputing 172: 198-206.Beyan, C. and R. Fisher (2015). Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recognition 48(5): 1653-1672.Blagus, R. and L. Lusa (2013). SMOTE for high-dimensional class-imbalanced data. BMC bioinformatics 14(1): 1-16.Błaszczyński, J. and M. Lango (2016). Diversity Analysis on Imbalanced Data Using Neighbourhood and Roughly Balanced Bagging Ensembles. International Conference on Artificial Intelligence and Soft Computing, Springer.Bogina, V., T. Kuflik, et al. (2016). Learning Item Temporal Dynamics for Predicting Buying Sessions. Proceedings of the 21st International Conference on Intelligent User Interfaces, ACM. Boyu Wang, J. P. (2016). Online Bagging and Boosting for Imbalanced Data Streams. IEEE Transactions on Knowledge and Data Engineering in press.Branco, P., L. Torgo, et al. (2016). A Survey of Predictive Modeling on Imbalanced Domains. ACM Computing Surveys (CSUR) 49(2): 31.Braytee, A., W. Liu, et al. (2016). A Cost-Sensitive Learning Strategy for Feature Extraction from Imbalanced Data. International Conference on Neural Information Processing, Springer.Brekke, C. and A. H. Solberg (2008). Classifiers and confidence estimation for oil spill detection in ENVISAT ASAR images. Geoscience and Remote Sensing Letters, IEEE 5(1): 65-69.Bria, A., C. Marrocco, et al. (2012). A ranking-based cascade approach for unbalanced data. Pattern Recognition (ICPR), 2012 21st International Conference on, IEEE.Brown, I. and C. Mues (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications 39(3): 3446-3453.Cao, H., X.-L. Li, et al. (2013). Integrated oversampling for imbalanced time series classification. Knowledge and Data Engineering, IEEE Transactions on 25(12): 2809-2822.Cao, H., V. Y. Tan, et al. (2014). Aparsimonious mixture of Gaussian trees model for oversampling in imbalanced and multimodal time-series classification. Neural Networks and Learning Systems, IEEE Transactions on 25(12): 2226-2239.Cao, Y. and J. Wu (2002). Projective ART for clustering data sets in high dimensional spaces. Neural Networks 15(1): 105-120.Casañola-Martin, G., T. Garrigues, et al. (2016). Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling. Molecular diversity 20(1): 93-109.Castro, C. L. and A. P. Braga (2013). Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data. Neural Networks and Learning Systems, IEEE Transactions on 24(6): 888-899.Cateni, S., V. Colla, et al. (2014). A method for resampling imbalanced datasets in binary classification tasks for real-world problems. Neurocomputing 135: 32-41.Cerf, L., D. Gay, et al. (2013). Parameter-free classification in multi-class imbalanced data sets. Data & Knowledge Engineering 87: 109-129.Chang, J.-S. and W.-H. Chang (2012). A cost-effective method for early fraud detection in online auctions. ICT and Knowledge Engineering (ICT & Knowledge Engineering), 2012 10th International Conference on, IEEE. Chawla, N. V., K. W. Bowyer, et al. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research: 321-357.Chen, K., B.-L. Lu, et al. (2006). Efficient classification of multi-label and imbalanced data using min-max modular classifiers. Neural Networks, 2006. IJCNN'06. International Joint Conference on, IEEE.Chen, S., H. He, et al. (2010). RAMOBoost: ranked minority oversampling in boosting. Neural Networks, IEEE Transactions on 21(10): 1624-1642.Chen, X.-w. and M. Wasikowski (2008). Fast: a roc-based feature selection metric for small samples and imbalanced data classification problems. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM. Chen, Y.-S. (2016). An empirical study of a hybrid imbalanced-class DT-RST classification procedure to elucidate therapeutic effects in uremia patients. Medical & biological engineering & computing 54(6): 983-1001.Chen, Z.-Y., Z.-P. Fan, et al. (2012). Ahierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of Operational Research 223(2): 461-472.Cheng, F., J. Zhang, et al. (2016). Cost-Sensitive Large margin Distribution Machine for classification of imbalanced data. Pattern Recognition Letters 80: 107-112.Cheng, J. and G.-Y. Liu (2015). Affective detection based on an imbalanced fuzzy support vector machine. Biomedical Signal Processing and Control 18: 118-126.Cheng, T.-H. and P.-H. Hu (2009). Adata-driven approach to manage the length of stay for appendectomy patients. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 39(6): 1339-1347.Chetchotsak, D., S. Pattanapairoj, et al. (2015). Integrating new data balancing technique with committee networks for imbalanced data: GRSOM approach. Cognitive neurodynamics 9(6): 627-638.D'Este, C., G. Timms, et al. (2014). Ensemble aggregation methods for relocating models of rare events. Engineering Applications of Artificial Intelligence 34: 58-65.Díez-Pastor, J. F., J. J. Rodríguez, et al. (2015a). Random balance: ensembles of variable priors classifiers for imbalanced data. Knowledge-Based Systems 85: 96-111.Díez-Pastor, J. F., J. J. Rodríguez, et al. (2015b). Diversity techniques improve the performance of the best imbalance learning ensembles. Information Sciences 325: 98-117.D‘Addabbo, A. and R. Maglietta (2015). Parallel selective sampling method for imbalanced and large data classification. Pattern Recognition Letters 62: 61-67.da Silva, I. B. V. and P. J. Adeodato (2011). PCA and Gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data sets. Neural networks (IJCNN), the 2011 international joint conference on, IEEE.Dai, H.-L. (2015). Imbalanced Protein Data Classification Using Ensemble FTM-SVM. NanoBioscience, IEEE Transactions on 14(4): 350-359.Dal Pozzolo, A., G. Boracchi, et al. (2015). Credit card fraud detection and concept-drift adaptation with delayed supervised information. Neural Networks (IJCNN), 2015 International Joint Conference on, IEEE.Das, B., N. C. Krishnan, et al. (2015). RACOG and wRACOG: Two Probabilistic Oversampling Techniques. Knowledge and Data Engineering, IEEE Transactions on 27(1): 222-234.Datta, S. and S. Das (2015). Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs. Neural Networks 70: 39-52.de Souza, J. C. S., S. G. Claudino, et al. (2016). Recent advances for handling imbalancement and uncertainty in labelling in medicinal chemistry data analysis. SAI Computing Conference (SAI), 2016, IEEE.del Río, S., V. López, et al. (2014). On the use of MapReduce for imbalanced big data using random forest. Information Sciences 285: 112-137.Denil, M. and T. Trappenberg (2010). Overlap versus Imbalance. Canadian Conference on Advances in Artificial Intelligence.Ditzler, G. and R. Polikar (2013). Incremental learning of concept drift from streaming imbalanced data. Knowledge and Data Engineering, IEEE Transactions on 25(10): 2283-2301.Dong, A., F.-l. Chung, et al. (2016). Semi-supervised classification method through oversampling and common hidden space. Information Sciences 349: 216-228.Drown, D. J., T. M. Khoshgoftaar, et al. (2009). Evolutionary sampling and software quality modeling of high-assurance systems. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 39(5): 1097-1107.Duan, L., M. Xie, et al. (2016a). A new support vector data description method for machinery fault diagnosis with unbalanced datasets. Expert Systems with Applications 64: 239-246.Duan, L., M. Xie, et al. (2016b). Support vector data description for machinery multi-fault classification with unbalanced datasets. Prognostics and Health Management (ICPHM), 2016 IEEE International Conference on, IEEE. Dubey, R., J. Zhou, et al. (2014). Analysis of sampling techniques for imbalanced data: An n= 648 ADNI study. NeuroImage 87: 220-241.Engen, V., J. Vincent, et al. (2008). Enhancing network based intrusion detection for imbalanced data. International Journal of Knowledge-Based and Intelligent Engineering Systems 12(5, 6): 357-367.Escudeiro, N. F. and A. M. Jorge (2012). D-Confidence: an active learning strategy to reduce label disclosure complexity in the presence of imbalanced class distributions. Journal of the Brazilian Computer Society 18(4): 311-330.Fabris, F., L. R. Margoto, et al. (2009). Novel approaches for detecting frauds in energy consumption. Network and System Security, 2009. NSS'09. Third International Conference on, IEEE.Fahimnia, B., C. S. Tang, et al. (2015). Quantitative models for managing supply chain risks: A review. European Journal of Operational Research 247(1): 1-15.Fan, J., Z. Niu, et al. (2016). Probability Model Selection and Parameter Evolutionary Estimation for Clustering Imbalanced Data without Sampling. Neurocomputing.Farvaresh, H. and M. M. Sepehri (2011). A data mining framework for detecting subscription fraud in telecommunication. Engineering Applications of Artificial Intelligence 24(1): 182-194.Fernández, A., M. J. Del Jesus, et al. (2010a). Multi-class imbalanced data-sets with linguistic fuzzy rule based classification systems based on pairwise learning. Computational Intelligence for Knowledge-Based Systems Design, Springer: 89-98.Fernández, A., M. J. del Jesus, et al. (2010b). On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets. Information Sciences 180(8): 1268-1291.Fernández, A., V. López, et al. (2013). Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-Based Systems 42: 97-110.Ferri, C., J. Hernández-Orallo, et al. (2011). A coherent interpretation of AUC as a measure of aggregated classification performance. Proceedings of the 28th International Conference on Machine Learning (ICML-11).Folino, G., F. S. Pisani, et al. (2016). An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, ACM.Frasca, M., A. Bertoni, et al. (2013). A neural network algorithm for semi-supervised node label learning from unbalanced data. Neural Networks 43: 84-98.Freund, Y. and R. E. Schapire (1996). Experiments with a new boosting algorithm. ICML.Freund, Y. and R. E. Schapire (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences 55(1): 119-139.Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics: 1189-1232.Fu, J. and S. Lee (2013). Certainty-based active learning for sampling imbalanced datasets. Neurocomputing 119: 350-358.Galar, M., A. Fernández, et al. (2013). EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognition 46(12): 3460-3471.Galar, M., A. Fernandez, et al. (2012). A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 42(4): 463-484.Gao, M., X. Hong, et al. (2014). Construction of neurofuzzy models for imbalanced data classification. Fuzzy Systems, IEEE Transactions on 22(6): 1472-1488.Gao, X., Z. Chen, et al. (2016). Adaptive weighted imbalance learning with application to abnormal activity recognition. Neurocomputing 173: 1927-1935.García-Pedrajas, N. and C. García-Osorio (2013). Boosting for class-imbalanced datasets using genetically evolved supervised non-linear projections. Progress in Artificial Intelligence 2(1): 29-44.García, V., J. S. Sánchez, et al. (2012). Surrounding neighborhood-based SMOTE for learning from imbalanced data sets. Progress in Artificial Intelligence 1(4): 347-362.Garcia-Pedrajas, N., J. A. R. del Castillo, et al. (2015). A Proposal for Local k Values for k-Nearest Neighbor Rule.Ghazikhani, A., R. Monsefi, et al. (2013a). Ensemble of online neural networks for non-stationary and imbalanced data streams. Neurocomputing 122: 535-544.Ghazikhani, A., R. Monsefi, et al. (2013b). Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams. Neural Computing and Applications 23(5): 1283-1295.Ghazikhani, A., R. Monsefi, et al. (2014). Online neural network model for non-stationary and imbalanced data stream classification. International Journal of Machine Learning and Cybernetics 5(1): 51-62.Gong, R. and S. H. Huang (2012). A Kolmogorov–Smirnov statistic based segmentation approach to learning from imbalanced datasets: With application in property refinance prediction. Expert Systems with Applications 39(6): 6192-6200.Govindan, K. and M. B. Jepsen (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research 250(1): 1-29.Gu, Q., L. Zhu, et al. (2009). Evaluation measures of the classification performance of imbalanced data sets. International Symposium on Intelligence Computation and Applications, Springer.Guo, H., Y. Li, et al. (2016).BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification. Engineering Applications of Artificial Intelligence 49: 176–193.Guyon, I. and A. Elisseeff (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research 3: 1157-1182.Ha, J. and J.-S. Lee (2016). A NewUnder-Sampling Method Using Genetic Algorithm for Imbalanced Data Classification. Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, ACM. Hajian, S., J. Domingo-Ferrer, et al. (2011). Discrimination prevention in data mining for intrusion and crime detection. Computational Intelligence in Cyber Security (CICS), 2011 IEEE Symposium on, IEEE.Hand, D. J. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine learning 77(1): 103-123.Hand, D. J. and R. J. Till (2001). A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine learning 45(2): 171-186.Hao, M., Y. Wang, et al. (2014). An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data. Analytica chimica acta 806: 117-127.Hartmann, W. M. (2004). Dimension reduction vs. variable selection. Applied Parallel Computing. State of the Art in Scientific Computing, Springer: 931-938.Hassan, A. K. I. and A. Abraham (2016). Modeling insurance fraud detection using imbalanced data classification. Advances in Nature and Biologically Inspired Computing, Springer: 117-127.He, H. and E. A. Garcia (2009). Learning from imbalanced data. Knowledge and Data Engineering, IEEE Transactions on 21(9): 1263-1284.He, H. and Y. Ma (2013). Imbalanced learning. Foundations, algorithms, and applications.Herndon, N. and D. Caragea (2016). A Study of Domain Adaptation Classifiers Derived From Logistic Regression for the Task of Splice Site Prediction. IEEE transactions on nanobioscience 15(2): 75-83.Hilas, C. S. and P. A. Mastorocostas (2008). An application of supervised and unsupervised learning approaches to telecommunications fraud detection. Knowledge-Based Systems 21(7): 721-726.Hoens, T. R., R. Polikar, et al. (2012). Learning from streaming data with concept drift and imbalance: an overview. Progress in Artificial Intelligence 1(1): 89-101.Hong, X., S. Chen, et al. (2007). A kernel-based two-class classifier for imbalanced data sets. Neural Networks, IEEE Transactions on 18(1): 28-41.Hu, S., Y. Liang, et al. (2009). MSMOTE:improving classification performance when training data is imbalanced. 2009 Second International Workshop on Computer Science and Engineering, IEEE.Huang, G.-B., Q.-Y. Zhu, et al. (2006a). Extreme learning machine: theory and applications. Neurocomputing 70(1): 489-501.Huang, K., H. Yang, et al. (2006b). Imbalanced learning with a biased minimax probability machine. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 36(4): 913-923.Huang, X., Y. Zou, et al. (2016). Cost-sensitive sparse linear regression for crowd counting with imbalanced training data. Multimedia and Expo (ICME), 2016 IEEE International Conference on, IEEE.Jacques, J., J. Taillard, et al. (2015). Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets. Applied Soft Computing 34: 705-720.Jeni, L. A., J. F. Cohn, et al. (2013). Facing Imbalanced Data--Recommendations for the Use of Performance Metrics. Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on, IEEE. Jian, C., J. Gao, et al. (2016). A new sampling method for classifying imbalanced data based on support vector machine ensemble. Neurocomputing 193: 115-122.Jin, X., F. Yuan, et al. (2014). Weighted local and global regressive mapping: A new manifold learning method for machine fault classification. Engineering Applications of Artificial Intelligence 30: 118-128.Jo, T. and N. Japkowicz (2004). Class imbalances versus small disjuncts. ACM SIGKDD Explorations Newsletter 6(1): 40-49. Kim, J., K. Choi, et al. (2012). Classification cost: An empirical comparison among traditional classifier, Cost-Sensitive Classifier, and MetaCost. Expert Systems with Applications 39(4): 4013-4019.Kim, S., H. Kim, et al. (2016). Ordinal Classification of Imbalanced Data with Application in Emergency and Disaster Information Services. IEEE Intelligent Systems 31(5): 50 - 56.King, G. and L. Zeng (2001). Logistic regression in rare events data. Political analysis 9(2): 137-163.Kirlidog, M. and C. Asuk (2012). A fraud detection approach with data mining in health insurance. Procedia-Social and Behavioral Sciences 62: 989-994.Krawczyk, B., M. Galar, et al. (2016). Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Applied Soft Computing 38: 714-726.Krawczyk, B. and G. Schaefer (2013). An improved ensemble approach for imbalanced classification problems. Applied Computational Intelligence and Informatics (SACI), 2013 IEEE 8th International Symposium on, IEEE.Krawczyk, B., M. Woźniak, et al. (2014). Cost-sensitive decision tree ensembles for effective imbalanced classification. Applied Soft Computing 14: 554-562.Krivko, M. (2010). A hybrid model for plastic card fraud detection systems. Expert Systems with Applications 37(8): 6070-6076.Kumar, N. S., K. N. Rao, et al. (2014). Undersampled K-means approach for handling imbalanced distributed data. Progress in Artificial Intelligence 3(1): 29-38.Kwak, J., T. Lee, et al. (2015). An Incremental Clustering-Based Fault Detection Algorithm for Class-Imbalanced Process Data. Semiconductor Manufacturing, IEEE Transactions on 28(3): 318-328.López, V., S. del Río, et al. (2015). Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data. Fuzzy Sets and Systems 258: 5-38.López, V., A. Fernández, et al. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences 250: 113-141.López, V., A. Fernández, et al. (2012). Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics. Expert Systems with Applications 39(7): 6585-6608.Lan, J.-s., V. L. Berardi, et al. (2009). A joint investigation of misclassification treatments and imbalanced datasets on neural network performance. Neural Computing and Applications 18(7): 689-706.Lane, P. C., D. Clarke, et al. (2012). On developing robust models for favourability analysis: Model choice, feature sets and imbalanced data. Decision Support Systems 53(4): 712-718.Lerner, B., J. Yeshaya, et al. (2007). On the classification of a small imbalanced cytogenetic image database. Computational Biology and Bioinformatics, IEEE/ACM Transactions on4(2): 204-215.Lessmann, S. and S. Voß (2009). A reference model for customer-centric data mining with support vector machines. European Journal of Operational Research 199(2): 520-530.Li, H. and M.-L. Wong (2015). Financial fraud detection by using Grammar-based multi-objective genetic programming with ensemble learning. Evolutionary Computation (CEC), 2015 IEEE Congress on, IEEE.Li, J., S. Fong, et al. (2015). Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms. The Journal of Supercomputing: 1-21.Li, J., L.-s. Liu, et al. (2016a). Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data. Computerized Medical Imaging and Graphics.Li, K., X. Kong, et al. (2014). Boosting weighted ELM for imbalanced learning. Neurocomputing 128: 15-21.Li, L., L. Jing, et al. (2009). Protein-protein interaction extraction from biomedical literatures based on modified SVM-KNN. Natural Language Processing and Knowledge Engineering, 2009. NLP-KE 2009. International Conference on, IEEE.Li, Q., B. Yang, et al. (2013a). Constructing support vector machine ensemble with segmentation for imbalanced datasets. Neural Computing and Applications 22(1): 249-256.Li, S., B. Tang, et al. (2016b). An Imbalanced Learning based MDR-TB Early Warning System. Journal of medical systems 40(7): 1-9.Li, X., Q. Shao, et al. (2013b). Classification of tongue coating using Gabor and Tamura features on unbalanced data set. Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on, IEEE.Li, Y., H. Guo, et al. (2016c). Adapted ensemble classification algorithm based on multiple classifier system and feature selection forclassifying multi-class imbalanced data. Knowledge-Based Systems 94: 88-104.Liang, J., L. Bai, et al. (2012). The-Means-Type Algorithms Versus Imbalanced Data Distributions. Fuzzy Systems, IEEE Transactions on 20(4): 728-745.Liao, T. W. (2008). Classification of weld flaws with imbalanced class data. Expert Systems with Applications 35(3): 1041-1052.Lima, R. F. and A. C. Pereira (2015). A Fraud Detection Model Based on Feature Selection and Undersampling Applied to Web Payment Systems. 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE.Lin, M., K. Tang, et al. (2013a). Dynamic sampling approach to training neural networks for multiclass imbalance classification. Neural Networks and Learning Systems, IEEE Transactions on 24(4): 647-660.Lin, S.-J., C. Chang, et al. (2013b). Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction. Knowledge-Based Systems 39: 214-223.Liu, N., Z. X. Koh, et al. (2014). Risk scoring for prediction of acute cardiac complications from imbalanced clinical data. Biomedical and Health Informatics, IEEE Journal of 18(6): 1894-1902.Liu, X.-Y., J. Wu, et al. (2009). Exploratory undersampling for class-imbalance learning. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 39(2): 539-550.Loyola-González, O., J. F. Martínez-Trinidad, et al. (2016). Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases. Neurocomputing 175: 935-947.Lu, J., C. Zhang, et al. (2016). A Classification Method of Imbalanced Data Base on PSO Algorithm. International Conference of Young Computer Scientists, Engineers and Educators, Springer.Lu, W. Z. and D. Wang (2008). Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme. Science of the Total Environment 395(2-3): 109-116.Lusa, L. (2010). Class prediction for high-dimensional class-imbalanced data. BMC bioinformatics 11(1): 523.Lusa, L. (2016). Gradient boosting for high-dimensional prediction of rare events. Computational Statistics & Data Analysis.Márquez-Vera, C., A. Cano, et al. (2013). Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied intelligence 38(3): 315-330.Maalouf, M. and M. Siddiqi (2014). Weighted logistic regression for large-scale imbalanced and rare events data. Knowledge-Based Systems 59: 142-148.Maalouf, M. and T. B. Trafalis (2011). Robust weighted kernel logistic regression in imbalanced and rare events data. Computational Statistics & Data Analysis 55(1): 168-183.Maldonado, S. and J. López (2014). Imbalanced data classification using second-order cone programming support vector machines. Pattern Recognition 47(5): 2070-2079.Maldonado, S., R. Weber, et al. (2014). Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines. Information Sciences 286: 228-246.Mandadi, B. and A. Sethi (2013). Unusual event detection using sparse spatio-temporal features and bag of words model. Image Information Processing (ICIIP), 2013 IEEE Second International Conference on, IEEE.Mao, W., L. He, et al. (2017). Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine. Mechanical Systems and Signal Processing 83: 450-473.Mao, W., J. Wang, et al. (2016). Two-Stage Hybrid Extreme Learning Machine for Sequential Imbalanced Data. Proceedings of ELM-2015 Volume 1, Springer: 423-433.Maratea, A., A. Petrosino, et al. (2014). Adjusted F-measure and kernel scaling for imbalanced data learning. Information Sciences 257: 331-341.Mardani, S. and H. R. Shahriari (2013). A new method for occupational fraud detection in process aware information systems. Information Security and Cryptology (ISCISC), 2013 10th International ISC Conference on, IEEE.Maurya, C. K., D. Toshniwal, et al. (2015). Online anomaly detection via class-imbalance learning. Contemporary Computing (IC3), 2015 Eighth International Conference on, IEEE. Maurya, C. K., D. Toshniwal, et al. (2016). Online sparse class imbalance learning on big data. Neurocomputing.Menardi, G. and N. Torelli (2014). Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery 28(1): 92-122.Mikolov, T., K. Chen, et al. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.Mirza, B., Z. Lin, et al. (2015a). Voting based weighted online sequential extreme learning machine for imbalance multi-class classification. 2015 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE.Mirza, B., Z. Lin, et al. (2015b). Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing 149(PA): 316-329.Mirza, B., Z. Lin, et al. (2013). Weighted online sequential extreme learning machine for class imbalance learning. Neural processing letters 38(3): 465-486.Moepya, S. O., S. S. Akhoury, et al. (2014). Applying Cost-Sensitive Classification for Financial Fraud Detection under High Class-Imbalance. Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, IEEE.Moreo, A., A. Esuli, et al. (2016). Distributional Random Oversampling for Imbalanced Text Classification. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, ACM.Motoda, H. and H. Liu (2002). Feature selection, extraction and construction. Communication of IICM (Institute of Information and Computing Machinery, Taiwan) Vol 5: 67-72.Nagi, J., K. Yap, et al. (2008). Detection of abnormalities and electricity theft using genetic support vector machines. TENCON 2008-2008 IEEE Region 10 Conference, IEEE.Napierala, K. and J. Stefanowski (2015). Types of minority class examples and their influence on learning classifiers from imbalanced data. Journal of Intelligent Information Systems: 1-35. Napierała, K. and J. Stefanowski (2015). Addressing imbalanced data with argument based rule learning. Expert Systems with Applications 42(24): 9468-9481.Natwichai, J., X. Li, et al. (2005). Hiding classification rules for data sharing with privacy preservation. Data Warehousing and Knowledge Discovery, Springer: 468-477.Nekooeimehr, I. and S. K. Lai-Yuen (2016). Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets. Expert Systems with Applications 46: 405-416.Ng, W. W., G. Zeng, et al. (2016). Dual autoencoders features for imbalance classification problem. Pattern Recognition 60: 875-889.Niehaus, K. E., I. A. Clark, et al. (2014). MVPA to enhance the study of rare cognitive events: An investigation of experimental PTSD. Pattern Recognition in Neuroimaging, 2014International Workshop on, IEEE.Oh, S.-H. (2011). Error back-propagation algorithm for classification of imbalanced data. Neurocomputing 74(6): 1058-1061.Oh, S., M. S. Lee, et al. (2011). Ensemble learning with active example selection for imbalanced biomedical data classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 8(2): 316-325.Olszewski, D. (2012). A probabilistic approach to fraud detection in telecommunications. Knowledge-Based Systems 26: 246-258.Pérez-Godoy, M. D., A. Fernández, et al. (2010). Analysis of an evolutionary RBFN design algorithm, CO 2 RBFN, for imbalanced data sets. Pattern Recognition Letters 31(15): 2375-2388. Pai, P.-F., M.-F. Hsu, et al. (2011). A support vector machine-based model for detecting top management fraud. Knowledge-Based Systems 24(2): 314-321.Pan, J., Q. Fan, et al. (2011). Soft margin keyframe comparison: Enhancing precision of fraud detection in retail surveillance. Applications of Computer Vision (WACV), 2011 IEEE Workshop on, IEEE.Panigrahi, S., A. Kundu, et al. (2009). Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. Information Fusion 10(4): 354-363.Park, Y. and J. Ghosh (2014). Ensembles of$({alpha}) $-Trees for Imbalanced Classification Problems. Knowledge and Data Engineering, IEEE Transactions on 26(1): 131-143.Peng, J. Yang, et al. (2014). Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD. Computerized Medical Imaging and Graphics 38(3): 137-150.Phoungphol, P., Y. Zhang, et al. (2012). Robust multiclass classification for learning from imbalanced biomedical data. Tsinghua Science and technology 17(6): 619-628.Prusa, J. D., T. M. Khoshgoftaar, et al. (2016). Enhancing Ensemble Learners with Data Sampling on High-Dimensional Imbalanced Tweet Sentiment Data. The Twenty-Ninth International Flairs Conference.Raj, V., S. Magg, et al. (2016). Towards effective classification of imbalanced data with convolutional neural networks. IAPR Workshop on Artificial Neural Networks in Pattern Recognition, Springer.Ramentol, E., S. Vluymans, et al. (2015). IFROWANN: imbalanced fuzzy-rough ordered weighted average nearest neighbor classification. Fuzzy Systems, IEEE Transactions on 23(5): 1622-1637.Raposo, L. M., M. B. Arruda, et al. (2016). Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks. Journal of medical systems 40(3): 1-7.Razavian, A., H. Azizpour, et al. (2014). CNN features off-the-shelf: an astounding baseline for recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.Ren, F., P. Cao, et al. (2016a). Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm. Computerized Medical Imaging and Graphics.Ren, Y., Y. Wang, et al. (2016b). Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model. Accident Analysis & Prevention 95: 266-273.Richardson, A. M. and B. A. Lidbury (2013). Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data. BMC bioinformatics 14(1): 1.Rodriguez, D., I. Herraiz, et al. (2014). Preliminary comparison of techniques for dealing with imbalance in software defect prediction. Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, ACM.Sáez, J. A., J. Luengo, et al. (2015). SMOTE– IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Information Sciences 291: 184-203.Saeys, Y., I. Inza, et al. (2007). A review of feature selection techniques in bioinformatics. bioinformatics 23(19): 2507-2517.Sahin, Y., S. Bulkan, et al. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications 40(15): 5916-5923.Sanz, J. A., D. Bernardo, et al. (2015). A compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data. Fuzzy Systems, IEEE Transactions on 23(4): 973-990. Schapire, R. E. and Y. Singer (1999). Improved boosting algorithms using confidence-rated predictions. Machine learning 37(3): 297-336.Seiffert, C., T. M. Khoshgoftaar, et al. (2010). RUSBoost: A hybrid approach to alleviating class imbalance. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 40(1): 185-197.Shao, Y.-H., W.-J. Chen, et al. (2014). An efficient weighted Lagrangian twin support vector machine for imbalanced data classification. Pattern Recognition 47(9): 3158-3167.Song, J., X. Huang, et al. (2016). A bi-directional sampling based on K-means method for imbalance text classification. Computer and Information Science (ICIS), 2016 IEEE/ACIS 15th International Conference on, IEEE.Song, L., D. Li, et al. (2014). nDNA-prot: identification of DNA-binding proteins based on unbalanced classification. BMC bioinformatics 15(1): 1. Su, C.-T. and Y.-H. Hsiao (2007). An evaluation of the robustness of MTS for imbalanced data. IEEE Transactions on Knowledge and Data Engineering 19(10): 1321.Subudhi, S. and S. Panigrahi (2015). Quarter-Sphere Support Vector Machine for Fraud Detection in Mobile Telecommunication Networks. Procedia Computer Science 48: 353-359.Sultana, M. A. H. F. N. S. R. A. J. (2012).Enhancing the performance of decision tree: A research study of dealing with unbalanced data. Digital Information Management (ICDIM), 2012 Seventh International Conference on. Macau.Sun, L., J. Mathew, et al. (2010). Algorithms for rare event analysis in nano-CMOS circuits using statistical blockade. SoC Design Conference (ISOCC), 2010 International, IEEE.Sun, Y., M. S. Kamel, et al. (2006). Boosting for learning multiple classes with imbalanced class distribution. Data Mining, 2006. ICDM'06. Sixth International Conference on, IEEE.Sun, Y., M. S. Kamel, et al. (2007). Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 40(12): 3358-3378.Sun, Y., A. K. Wong, et al. (2009). Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence 23(04): 687-719.Sun, Z., Q. Song, et al. (2015). A novel ensemble method for classifying imbalanced data. Pattern Recognition 48(5): 1623-1637.Tahir, M. A., J. Kittler, et al. (2009). A multiple expert approach to the class imbalance problem using inverse random under sampling. Multiple Classifier Systems, Springer: 82-91.Tajik, M., S. Movasagh, et al. (2015). Gas turbine shaft unbalance fault detection by using vibration data and neural networks. Robotics and Mechatronics (ICROM), 2015 3rd RSI International Conference on, IEEE.Tan, M., L. Tan, et al. (2015a). Online defect prediction for imbalanced data. Proceedings of the 37th International Conference on Software Engineering-Volume 2, IEEE Press.Tan, S. C., J. Watada, et al. (2015b). Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects. Neural Networks and Learning Systems, IEEE Transactions on 26(5): 933-950.Taneja, M., K. Garg, et al. (2015). Prediction of click frauds in mobile advertising. Contemporary Computing (IC3), 2015 Eighth International Conference on, IEEE.Tian, J., H. Gu, et al. (2011). Imbalanced classification using support vector machine ensemble. Neural Computing and Applications 20(2): 203-209.Tomek, I. (1976). A generalization of the k-NN rule. Systems, Man and Cybernetics, IEEE Transactions on(2): 121-126.Topouzelis, K. N. (2008). Oil spill detection by SAR images: dark formation detection, feature extraction and classification algorithms. Sensors 8(10): 6642-6659.Trafalis, T. B., I. Adrianto, et al. (2014). Machine-learning classifiers for imbalanced tornado data. Computational Management Science 11(4): 403-418.Tsai, C. H., L. C. Chang, et al. (2009). Forecasting of ozone episode days by cost-sensitive neural network methods. Science of the Total Environment 407(6): 2124-2135.Vajda, S. and G. A. Fink (2010). Strategies for training robust neural network based digit recognizers on unbalanced data sets. Frontiers in Handwriting Recognition (ICFHR), 2010 International Conference on, IEEE.Vani, K. S. and T. Sravani (2014). Multiclass unbalanced protein data classification using sequence features. Computational Intelligence in Bioinformatics and Computational Biology, 2014 IEEE Conference on, IEEE.Verbeke, W., K. Dejaeger, et al. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research 218(1): 211-229.Vigneron, V. and H. Chen (2015). A multi-scale seriation algorithm for clustering sparse imbalanced data: application to spike sorting. Pattern Analysis and Applications: 1-19.Vluymans, S., D. S. Tarragó, et al. (2015). Fuzzy rough classifiers for class imbalanced multi-instance data. Pattern Recognition.Vo, N. H. and Y. Won (2007). Classification of unbalanced medical data with weighted regularized least squares. Frontiers in the Convergence of Bioscience and Information Technologies, 2007. FBIT 2007, IEEE.Voigt, T., R. Fried, et al. (2014). Threshold optimization for classification in imbalanced data in a problem of gamma-ray astronomy. Advances in Data Analysis and Classification 8(2): 195-216.Vong, C.-M., W.-F. Ip, et al. (2015). Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine. Cognitive Computation 7(3): 381-391.Vorobeva, A. A. (2016). Examining the performance of classification algorithms for imbalanced data sets in web author identification. Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT), 2016 18th Conference of, FRUCT.Wan, X., J. Liu, et al. (2014). Learning to improve medical decision making from imbalanced data without a priori cost. BMC medical informatics and decision making 14(1): 1.Wang, B. X. and N. Japkowicz (2010). Boosting support vector machines for imbalanced data sets. Knowledge and Information Systems 25(1): 1-20.Wang, J., P. Zhao, et al. (2014a). Cost-sensitive online classification. IEEE Transactions on Knowledge and Data Engineering 26(10): 2425-2438.Wang, S., H. Chen, et al. (2010). Negative correlation learning for classification ensembles. Neural Networks (IJCNN), The 2010 International Joint Conference on, IEEE.Wang, S., L. L. Minku, et al. (2013). A learning framework for online class imbalance learning. Computational Intelligence and Ensemble Learning (CIEL), 2013 IEEE Symposium on, IEEE.Wang, S., L. L. Minku, et al. (2014b). A multi-objective ensemble method for online class imbalance learning. Neural Networks (IJCNN), 2014 International Joint Conference on, IEEE.Wang, S., L. L. Minku, et al. (2015a). Resampling-based ensemble methods for online class imbalance learning. Knowledge and Data Engineering, IEEE Transactions on 27(5): 1356-1368.Wang, S. and X. Yao (2009). Diversity analysis on imbalanced data sets by using ensemble models. Computational Intelligence and Data Mining, 2009. CIDM'09. IEEE Symposium on, IEEE.Wang, S. and X. Yao (2012). Multiclass imbalance problems: Analysis and potential solutions. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 42(4): 1119-1130.Wang, S. and X. Yao (2013). Using class imbalance learning for software defect prediction. Reliability, IEEE Transactions on 62(2): 434-443.Wang, Y., X. Li, et al. (2016a). Probabilistic framework of visual anomaly detection for unbalanced data. Neurocomputing.Wang, Y., Y. Tian, et al. (2015b). Detecting Rare Actions and Events from Surveillance Big Data with Bag of Dynamic Trajectories. Multimedia Big Data (BigMM), 2015 IEEE International Conference on, IEEE.Wang, Z., J. Xin, et al. (2016b). Distributed Weighted Extreme Learning Machine for Big Imbalanced Data Learning. Proceedings of ELM-2015 Volume 1, Springer: 319-332.Wasikowski, M. and X.-w. Chen (2010). Combating the small sample class imbalance problem using feature selection. Knowledge and Data Engineering, IEEE Transactions on 22(10): 1388-1400.Wei, M.-H., C.-H. Cheng, et al. (2013a). Discovering medical quality of total hip arthroplasty by rough set classifier with imbalanced class. Quality & Quantity 47(3): 1761-1779.Wei, W., J. Li, et al. (2013b). Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16(4): 449-475.Weiss, G. M. (2004). Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter 6(1): 7-19.Weiss, G. M. and H. Hirsh (2000). Learning to predict extremely rare events. AAAI workshop on learning from imbalanced data sets.Wen, H., S. Ge, et al. (2015). Abnormal event detection via adaptive cascade dictionary learning. Image Processing (ICIP), 2015 IEEE International Conference on, IEEE.Wilk, S., J. Stefanowski, et al. (2016). Application of Preprocessing Methods to Imbalanced Clinical Data: An Experimental Study. Information Technologies in Medicine, Springer: 503-515.Wu, D., Z. Wang, et al. (2016). Mixed-kernel based weighted extreme learning machine for inertial sensor based human activity recognition with imbalanced dataset. Neurocomputing 190: 35-49.Wu, X. and S. Meng (2016). E-commerce customer churn prediction based on improved SMOTE and AdaBoost. Service Systems and Service Management (ICSSSM), 2016 13th International Conference on, IEEE.Xiao, W., J. Zhang, et al. (2016). Imbalanced Extreme Learning Machine for Classification with Imbalanced Data Distributions. Proceedings of ELM-2015 Volume 2, Springer: 503-514.Xin, W., L. Yi-ping, et al. (2011). A new classification method for LIDAR data based on unbalanced support vector machine. Image and Data Fusion (ISIDF), 2011 International Symposium on, IEEE.Xiong, W., B. Li, et al. (2014). Collaborative web service QoS prediction on unbalanced data distribution. Web Services (ICWS), 2014 IEEE International Conference on, IEEE.Xu, J., S. Denman, et al. (2016). Detecting rare events using Kullback–Leibler divergence: A weakly supervised approach. Expert Systems with Applications 54: 13-28.Xu, J., S. Denman, et al. (2014). Real-time video event detection in crowded scenes using MPEG derived features: A multiple instance learning approach. Pattern Recognition Letters 44: 113-125.Xu, L., M.-Y. Chow, et al. (2007a). Power distribution fault cause identification with imbalanced data using the data mining-based fuzzy classification e-algorithm. Power Systems, IEEE Transactions on 22(1): 164-171.Xu, L., M.-Y. Chow, et al. (2007b). Power distribution outage cause identification with imbalanced data using artificial immune recognition system (AIRS) algorithm. Power Systems, IEEE Transactions on 22(1): 198-204. Xu, Y., Z. Yang, et al. (2015). A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification. Knowledge-Based Systems.Y. Qing, W. B., Z. Peilan, C. Xiang, Z. Meng and W. Yang (2015). The prediction method of material consumption for electric power production based on PCBoost and SVM. 2015 8th International Congress on Image and Signal Processing (CISP): 1256-1260.Yang, J., J. Zhou, et al. (2016a). Iterative ensemble feature selection for multiclass classification of imbalanced microarray data.Journal of Biological Research-Thessaloniki 23(1): 13.Yang, P., L. Xu, et al. (2009). A particle swarm based hybrid system for imbalanced medical data sampling. BMC genomics 10(3): 1.Yang, X., D. Lo, et al. (2016b). Automated Identification of High Impact Bug Reports Leveraging Imbalanced Learning Strategies. Computer Software and Applications Conference (COMPSAC), 2016 IEEE 40th Annual, IEEE.Yeh, C.-W., D.-C. Li, et al. (2016). A Learning Approach with Under-and Over-Sampling for Imbalanced Data Sets. Advanced Applied Informatics (IIAI-AAI), 2016 5th IIAI International Congress on, IEEE.Yi, W. (2010). The Cascade Decision-Tree Improvement Algorithm Based on Unbalanced Data Set. 2010 International Conference on Communications and Mobile Computing, IEEE. Yu, H., C. Mu, et al. (2015). Support vector machine-based optimized decision threshold adjustment strategy for classifying imbalanced data. Knowledge-Based Systems 76: 67-78.Yu, H., J. Ni, et al. (2012). Mining and integrating reliable decision rules for imbalanced cancer gene expression data sets. Tsinghua Science and technology 17(6): 666-673.Yu, H., C. Sun, et al. (2016). ODOC-ELM:Optimal decision outputs compensation-based extreme learning machine for classifying imbalanced data. Knowledge-Based Systems 92: 55-70.Yun, J., J. Ha, et al. (2016). Automatic Determination of Neighborhood Size in SMOTE. Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, ACM. Zakaryazad, A. and E. Duman (2016). A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing. Neurocomputing 175: 121-131.Zhai, J., S. Zhang, et al. (2015). The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers. International Journal of Machine Learning and Cybernetics: 1-9.Zhang, B., Y. Zhou, et al. (2008). Toward a comprehensive model in internet auction fraud detection. Hawaii International Conference on System Sciences, Proceedings of the 41st Annual, IEEE.Zhang, C., W. Gao, et al. (2016a). An imbalanced data classification algorithm of improved autoencoder neural network. 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), IEEE. Zhang, D., J. Ma, et al. (2015a). An ensemble method for unbalanced sentiment classification. Natural Computation (ICNC), 2015 11th International Conference on, IEEE.Zhang, K., A. Li, et al. (2009). Fraud Detection in Tax Declaration Using Ensemble ISGNN. Computer Science and Information Engineering, 2009 WRI World Congress on, IEEE.Zhang, N. (2016). Cost-sensitive spectral clustering for photo-thermal infrared imaging data. International Conference on Information Science & Technology.Zhang, X., B. Wang, et al. (2015b). Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowledge-Based Systems 89: 56-85.Zhang, X., Z. Yang, et al. (2015c). Boosting mobile Apps under imbalanced sensing data. Mobile Computing, IEEE Transactions on 14(6): 1151-1161.Zhang, X., Y. Zhuang, et al. (2015d). 3-D Laser-Based Multiclass and Multiview Object Detection in Cluttered Indoor Scenes.Zhang, Y., P. Fu, et al. (2014). Imbalanced data classification based on scaling kernel-based support vector machine. Neural Computing and Applications 25(3-4): 927-935.Zhang, Y., D. Zhang, et al. (2012). Using ensemble methods to deal with imbalanced data in predicting protein–protein interactions.Computational Biology and Chemistry 36: 36-41.Zhang, Z., B. Krawczyk, et al. (2016b). Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data. Knowledge-Based Systems.Zhao, X. M., X. Li, et al. (2008). Protein classification with imbalanced data. Proteins: Structure, function, and bioinformatics 70(4): 1125-1132.Zhao, Z., P. Zhong, et al. (2011). Learning SVM with weighted maximum margin criterion for classification of imbalanced data. Mathematical and Computer Modelling 54(3): 1093-1099.Zhong, W., B. Raahemi, et al. (2013). Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream. Peer-to-Peer Networking and Applications 6(3): 233-246.Zhou, L. (2013). Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods. Knowledge-Based Systems 41: 16-25.Zhou, Z.-H., Ed. (2016). Machine Learning, Tsinghua University press.Zhou, Z.-H. and X.-Y. Liu (2006). Training cost-sensitive neural networks with methods addressing the class imbalance problem. Knowledge and Data Engineering, IEEE Transactions on 18(1): 63-77.Zhu, X. and A. B. Goldberg (2009). Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning 3(1): 1-130.Zięba, M. and J. M. Tomczak (2015). Boosted SVM with active learning strategy for imbalanced data. Soft Computing 19(12): 3357-3368.Zięba, M., J. M. Tomczak, et al. (2014). Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients. Applied Soft Computing 14: 99-108.Zou, Q., S. Xie, et al. (2016). Finding the Best Classification Threshold in Imbalanced Classification. Big Data Research.

Asset-Specific Data Considerations

x

Features, Generally

x

Financial-specific features

Predictions

Creating Factor Forecasts

x

Strategy, Objective, and Conditions

x

Alpha Models

Regime Identification Sandra Johnson, "Integrated Bayesian network frameworks for modelling complex ecological issues," 2009. https://www.researchgate.net/publication/43376231_Integrated_Bayesian_network_frameworks_for_modelling_complex_ecological_issues/download Credit Ratings D. Bacham and J. Zhao, :Machine Learning: Challenges, Lessons, and Opportunities in Credit Risk Modeling,” Moody’s Analytics, 2017. PROCESS. Abe, M., and Nakayama H. 2018. "Deep Learning for Forecasting Stock Returns in the Cross- Section." Advances in Knowledge Discovery and Data Mining, edited by D. Phung, V. S. Tseng, G.I. Webb, B. Ho, M. Ganji, and L. Rashidi. New York: Springer, 2018.
Almgren, R., and N. Chriss. 2001. "Optimal Execution of Portfolio Transactions." Journal of Risk3: 5-40.
Antweiler, W., and M. Z. Frank. 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards." The Journal of Finance 59: 1259-1294.
Arrieta-Ibarra, I., and N. L. Ignacio. 2015. "Testing for Predictability in Financial Returns Using Statistical Learning Procedures." Journal of Time Series Analysis 36: 672-686.
Avramov, D., S. Cheng, and L. Metzker. "Machine Learning versus Economic Restrictions: Evidence from Stock Return Predictability." SSRN, 2020, ssrn.com/abstract=3450322.
Azimi, M., and A. Agrawal. "Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning." SSRN, 2019, 3258821.
Baldacci, B., and I. Manziuk. "Adaptive Trading Strategies across Liquidity Pools." arXiv 2008.07807, 2020.
Bartram, S. M., J. Branke, and M. Motahari. Artificial Intelligence in Asset Management. Charlot- tesville, VA: CFA Institute Research Foundation, 2020.
Bartram, S. M., G. W. Brown, and J. Conrad. 2011. "The Effects of Derivatives on Firm Risk and Value." Journal of Financial and Quantitative Analysis 46 (4): 967-999.
Bertsimas, D., and A. W. Lo. 1998. "Optimal Control of Execution Costs." Journal of Financial Markets 1 (1): 1-50.
Bew, D., R. H. Campbell, A. Ledford, S. Radnor, and A. Sinclair. 2019. "Modeling Analysts' Rec- ommendations via Bayesian Machine Learning." The Journal of Financial Data Science 1: 75-98.
Black, F., and R. Litterman. 1992. "Global Portfolio Optimization." Financial Analysts Journal48 (5): 28-43.
Booth, A., E. Gerding, and F. McGroarty. 2014. "Automated Trading with Performance Weighted Random Forests and Seasonality." Expert Systems with Applications 41 (8): 3651-3661.
--. 2015. "Performance-Weighted Ensembles of Random Forests for Predicting Price Impact."Quantitative Finance 15: 1823-1835.
Borghi, R., and G. De Rossi. "The Artificial Intelligence Approach to Picking Stocks." In Machine Learning and Asset Management, edited by E. Jurczenko. Hoboken: Wiley, 2020.
Borochin, P. A., J. E. Cicon, R. J. DeLisle, and S. M. Price. 2018. "The Effects of Conference Call Tones on Market Perceptions of Value Uncertainty." Journal of Financial Markets 40: 75-91.
Branke, J., B. Scheckenbach, M. Stein, K. Deb, and H. Schmeck. 2009. "Portfolio Optimization with an Envelope-Based Multi-Objective Evolutionary Algorithm." European Journal of Operational Research 199: 684-693.
Brière, M., C. Lehalle, T. Nefedova, and A. Raboun. "Modelling Transaction Costs When Trades May Be Crowded: A Bayesian Network Using Partially Observable Orders Imbalance." SSRN 3420665, 2019.
Bryzgalova, S., M. Pelger, and J. Zhu. "Forest through the Trees: Building Cross-Sections of Stock Returns." SSRN 3493458, 2019.
Cao, L., and F. Tay. 2003. "Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting." IEEE Transactions on Neural Networks 14 (6): 1506-1518.
Chaouki, A., S. Hardiman, C. Schmidt, E. Sérié, and J. De Lataillade. 2020. "Deep Deterministic Portfolio Optimization." The Journal of Finance and Data Science 6: 16-30.
Chapados, N., and Y. Bengio. 2001. "Cost Functions and Model Combination for VaR-Based Asset Allocation Using Neural Networks." IEEE Transactions on Neural Networks 12: 890-906.
Charpentier, A., R. Elie, and C. Remlinger. "Reinforcement Learning in Economics and Finance."arXiv 2003.10014, 2020.
Checkley, M., D. A. Higón, and H. Alles. 2017. "The Hasty Wisdom of the Mob: How Market Senti- ment Predicts Stock Market Behaviour." Expert Systems with Applications 77: 256-263.
Chen, H., P. De, Y. J. Hu, and B. H. Hwang. 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted through Social Media." The Review of Financial Studies 27 (5): 1367-1403.
Chen, W. H., J. Y. Shih, and S. Wu. 2006. "Comparison of Support-Vector Machines and Back Propagation Neural Networks in Forecasting the Six Major Asian Stock Markets." International Journal of Electronic Finance 1: 49-67.
Chinco, A., A. D. Clark-Joseph, and M. Ye. 2019. "Sparse Signals in the Cross-Section of Returns."The Journal of Finance 74: 449-492.
Cong, L. W., K. Tang, J. Wang, and Y. Zhang. "Alpha Portfolio for Investment and Economically Interpretable AI." SSRN 3554486, 2020.
Connor, G., L. Goldberg, and R. A. Korajczyk. Portfolio Risk Analysis. Princeton, NJ: Princeton University Press, 2010.
Coqueret, G., and T. Guida. "Stock Returns and the Cross Section of Characteristics: A Tree-Based Approach." 2018, papers.ssrn.com/sol3/papers.cfm?abstract_id=3169773.
--. 2020. "Training Trees on Tails with Applications to Portfolio Choice." Annals of Operations Research 288: 181-221.
Creamer, G., and Y. Freund. 2010. "Automated Trading with Boosting and Expert Weighting."Quantitative Finance 10: 401-420.
Dabérius, K., E. Granat, and P. Karlsson. "Deep Execution-Value and Policy Based Reinforcement Learning for Trading and Beating Market Benchmarks." SSRN 3374766, 2019.
Das, S. R. 2014. "Text and Context: Language Analytics in Finance." Foundations and Trends in Finance 8: 145-261.
Das, S. R., and M. Y. Chen. 2007. "Yahoo! For Amazon: Sentiment Extraction from Small Talk on the Web." Management Science 53 (9): 1375-1388.
Demers, E., and C. Vega. "Soft Information in Earnings Announcements: News or Noise?" Discus- sion papers 951, Board of Governors of the Federal Reserve System International Finance, 2008.
DeMiguel, V., L. Garlappi, and R. Uppal. 2007. "Optimal versus Naive Diversification: How Inefficient Is the 1/n Portfolio Strategy?" The Review of Financial Studies 22: 1915-1953.
Dixon, M., and N. G. Polson. "Deep Fundamental Factor Models. arXiv 1711.04837, 2019.
Egger, D. J., C. Gambella, J. Marecek, S. McFaddin, M. Mevissen, R. Raymond, A. Simonetto, S. Woerner, and E. Yndurain. 2020. "Quantum Computing for Finance: State of the Art and Future Prospects." IEEE Transactions on Quantum Engineering 1: 1-24.
European Commission. "White Paper on Artificial Intelligence-A European Approach to Excellence and Trust." 2020, ec.europa.eu/info/sites/default/files/commission-white-paper-artificial-intelli- gence-feb2020_en.pdf.
Fama, E. F., and K. R. French. 1993. "Common Risk Factors in the Returns on Stocks and Bonds."Journal of Financial Economics 33 (1): 3-56.
--. 2015. "A Five-Factor Asset Pricing Model." Journal of Financial Economics 116 (1): 1-22.
Fan, J., J. Lv, and L. Qi. 2011. "Sparse High-Dimensional Models in Economics." Annual Review of Economics 3 (1): 291-317.
Fan, J., J. Zhang, and K. Yu. 2012. "Vast Portfolio Selection with Gross-Exposure Constraints."Journal of the American Statistical Association 107 (498): 592-606.
Feng, G., S. Giglio, and D. Xiu. 2020. "Taming the Factor Zoo." The Journal of Finance 75: 1327- 1370.
Ferreira, T. A. 2020. Reinforced Deep Markov Models With Applications in Automatic Trading. arXiv preprint arXiv:2011.04391, 2020.
Fischer, T. G. "Reinforcement Learning in Financial Markets-A Survey." FAU Discussion Papers in Economics 12/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, 2018.
Fischer, T. G., and C. Krauss. 2018. "Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions." European Journal of Operational Research 270 (2): 654-669.
Fisher, I. E., M. R. Garnsey, and M. E. Hughes. 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research." Intelligent Systems in Accounting, Finance and Management 23: 157-214.
Freyberger, J., A. Neuhierl, and M. Weber. 2020. "Dissecting Characteristics Nonparametrically."Review of Financial Studies 33 (5): 2326-2377.
Geva, T., and J. Zahavi. 2014. "Empirical Evaluation of an Automated Intraday Stock Recommen- dation System Incorporating Both Market Data and Textual News." Decision Support Systems 57: 212-223.
Goldman Sachs. "Global Market Microstructure Stock Clustering." Technical report, Goldman Sachs, 2019.
Grinold, R. C., and R. N. Kahn. 2000. Active Portfolio Management: A Quantitative Approach for Providing Superior Returns and Controlling Risk. New York: McGraw-Hill, 2000.
Groß-Klußmann, A., S. König, and M. Ebner. 2019. "Buzzwords Build Momentum: Global Financial Twitter Sentiment and the Aggregate Stock Market." Expert Systems with Applications 136: 171-186.
Gu, S., B. Kelly, and D. Xiu. 2020. "Empirical Asset Pricing via Machine Learning." Review of Finan- cial Studies 33 (5): 2223-2273.
Harvey, C. R., Y. Liu, and H. Zhu. 2016. "… and the Cross-Section of Expected Returns."The Review of Financial Studies 29 (1): 5-68.
Heaton, J. B., N. G. Polson, and J. H. Witte. 2017. "Deep Learning for Finance: Deep Portfolios."Applied Stochastic Models in Business and Industry 33: 3-12.
Hendricks, D., and D. Wilcox. 2014. "A Reinforcement Learning Extension to the Almgren-Chriss Framework for Optimal Trade Execution." IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr): 457-464.
Huang, A. H., A. Y. Zang, and R. Zheng. 2014. "Evidence on the Information Content of Text in Analyst Reports." Accounting Review 89: 2151-2180.
Huang, W., Y. Nakamori, and S. Wang. 2005. "Forecasting Stock Market Movement Direction with Support Vector Machine." Computers and Operations Research 32: 2513-2522.
Israel, R., B. T. Kelly, and T. J. Moskowitz. "Can Machines 'Learn' Finance?" SSRN 3449401, 2020.
Jiang, Z., D. Xu, and J. Liang. A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv preprint arXiv:1706.10059, 2017.
Jin, O., and H. El-Saawy. Portfolio Management Using Reinforcement Learning. Stanford: Stanford University, 2016.
Ke, Z., B. T. Kelly, and D. Xiu. 2019. "Predicting Returns with Text Data." 2019, papers.ssrn.com/ sol3/papers.cfm?abstract_id=3389884.
Kearney, C., and S. Liu. 2014. "Textual Sentiment in Finance: A Survey of Methods and Models."International Review of Financial Analysis 33: 171-185.
Kearns, M., and Y. Nevmyvaka. Machine Learning for Market Microstructure and High Frequency Trading, High Frequency Trading: New Realities for Traders, Markets, and Regulators. New York: Risk Books, 2013.
Kim, K. 2003. "Financial Time Series Forecasting Using Support Vector Machines." Neurocom- puting 55 (1): 307-319.
Kolm, P. N., and G. Ritter. 2019. "Dynamic Replication and Hedging: A Reinforcement Learning Approach." The Journal of Financial Data Science 1 (1): 159-171.
--. "Modern Perspectives on Reinforcement Learning in Finance." SSRN 3449401, 2020.
Kolm, P. N., R. Tütüncü, and F. J. Fabozzi. 2014. "60 Years of Portfolio Optimization: Practical Challenges and Current Trends." European Journal of Operational Research 234: 356-371.
Kozak, S., S. Nagel, and S. Santosh. 2020. "Shrinking the Cross-Section." Journal of Financial Economics 135 (2): 271-292.
Krauss, C., X. A. Do, and N. Huck. 2017. "Deep Neural Networks, Gradient-Boosted Trees, Ran- dom Forests: Statistical Arbitrage on the S&P 500." European Journal of Operational Research 259 (2): 689-702.
Larcker, D. F., and A. A. Zakolyukina. 2012. "Detecting Deceptive Discussions in Conference Calls."Journal of Accounting Research 50: 495-540.
Leal, L., M. Laurière, and C. Lehalle. "Learning a Functional Control for High-Frequency Finance."arXiv 2006.09611, 2020.
Lee, J., R. Kim, S. W. Yi, and J. Kang. "MAPS: Multi-Agent Reinforcement Learning-Based Portfolio Management System." arXiv 2007.05402, 2020.
Leung, E., H. Lohre, D. Mischlich, Y. Shea, and M. Stroh. "The Promises and Pitfalls of Machine Learning for Predicting Stock Returns." 2020, ssrn.com/abstract=3546725.
Li, F. 2010. "The Information Content of Forward-Looking Statements in Corporate Filings-A Naïve Bayesian Machine Learning Approach." Journal of Accounting Research 50: 495-540.
Lim, B., S. Zohren, and S. Roberts. 2019. "Enhancing Time-Series Momentum Strategies Using Deep Neural Networks." The Journal of Financial Data Science 1: 19-38.
Lopez de Prado, M. 2016. "Building Diversified Portfolios That Outperform Out of Sample."The Journal of Portfolio Management 42: 56-69.
Loughran, T., and B. McDonald. 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks." The Journal of Finance 66: 35-65.
--. 2016. "Textual Analysis in Accounting and Finance." Journal of Accounting Research54: 1187-1230.
Lowe, D. 1994. "Novel Exploitation of Neural Network Methods in Financial Markets." IEEE Inter- national Conference on Neural Networks 6: 3623-3628.
Markowitz, H. 1952. "Portfolio Selection." The Journal of Finance 7 (1): 77-91.
Messmer, M., and F. Audrino. "The (Adaptive) LASSO in the Zoo-Firm Characteristic Selection in the Cross-Section of Expected Returns." 2017, http://dx.doi.org/10.2139/ssrn.2930436.
Michaud, R. O., and R. O. Michaud. Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation, Oxford, UK: Oxford University Press, 2008.
Mitchell, T. M. Machine Learning. New York: McGraw-Hill International Editions, 1997.
Moody, J., L. Wu, Y. Liao, and M. Saffell. 1998. "Performance Functions and Reinforcement Learn- ing for Trading Systems and Portfolios." Journal of Forecasting 17: 441-470.
Moritz, B., and T. Zimmermann. "Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns." 2016, papers.ssrn.com/abstract=2740751.
Mounjid, O., and C. A. Lehalle. "Improving Reinforcement Learning Algorithms: Towards Optimal Learning Rate Policies. arXiv 1911.02319, 2020.
Nevmyvaka, Y., F. Yi, and M. Kearns. "Reinforcement Learning for Optimized Trade Execution." In Proceedings of the 23rd International Conference on Machine Learning, pp. 673-680. New York: ACM, 2006.
Nuij, W., V. Milea, F. Hogenboom, F. Frasincar, and U. Kaymak. 2014. "An Automated Framework for Incorporating News into Stock Trading Strategies." IEEE Transactions on Knowledge and Data Engineering 26: 823-835.
Oliveira, N., P. Cortez, and N. Areal. 2017. "The Impact of Microblogging Data for Stock Mar- ket Prediction: Using Twitter to Predict Returns, Volatility, Trading Volume and Survey Sentiment Indices." Expert Systems With Applications 73: 125-144.
Papaioannou, G. V., and D. Giamouridis. "Enhancing Alpha Signals from Trade Ideas Data Using Supervised Learning." In Machine Learning and Asset Management. New York: Springer, 2020.
Park, S., J. Lee, and Y. Son. 2016. "Predicting Market Impact Costs Using Nonparametric Machine Learning Models." Plos One 11: 1-13.Previde Massara, G., and T. Aste. 2019. "Learning Clique Forests." arXiv 1905.02266, 2019. Philip, R. 2020. "Estimating Permanent Price Impact via Machine Learning." Journal of Econometrics215 (2): 414-449.
Qian, E. E., R. H. Hua, and E. H. Sorensen. Quantitative Equity Portfolio Management: Modern Techniques and Applications." Financial Mathematics Series. Boca Raton, FL: Chapman & Hall/ CRC, 2007.
Rapach, D. E., J. K. Strauss, J. Tu, and G. Zhou. 2013. "International Stock Return Predictability: What Is the Role of the United States?" The Journal of Finance 68: 1633-1662.
--. 2019. "Industry Return Predictability: A Machine Learning Approach." The Journal of Financial Data Science 1: 9-28.
Rasekhschaffe, K. C., and R. C. Jones. 2019. "Machine Learning for Stock Selection." Financial Analysts Journal 75: 70-88.
Ryans, J. "Textual Classification of SEC Comment Letters. Review of Accounting Studies." 2019, ssrn.com/abstract=2474666.
Schumaker, R., and H. Chen. 2006. "Textual Analysis of Stock Market Prediction Using Financial News Articles." AMCIS Proceedings 185: 1-29.
Skolpadungket, P., D. Keshav, and H. Napat. "Handling Model Risk in Portfolio Selection Using Multi-Objective Genetic Algorithm." In Artificial Intelligence in Financial Markets, pp. 285-310. New York: Springer, 2016.
Snow, D. 2020a. "Machine Learning in Asset Management-Part 1: Portfolio Construction-Trading Strategies." The Journal of Financial Data Science 2 (1): 10-23.
--. 2020b. "Machine Learning in Asset Management-Part 2: Portfolio Construction-Weight Optimization." The Journal of Financial Data Science 2 (2): 17-24.
Sprenger, T. O., A. Tumasjan, P. G. Sandner, and I. M. Welpe. 2014. "Tweets and Trades: The Information Content of Stock Microblogs." European Financial Management 20: 926-957.
Sutton, R. S., and A. G. Barto. Reinforcement Learning: An Introduction. Boston: MIT Press, 2018.
Takeuchi, L., and Y. A. Lee. "Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks." Working paper, Stanford, Stanford University, 2013.
Tan, Z., C. Quek, and P. Y. K. Cheng. 2011. "Stock Trading with Cycles: A Financial Application of Anfis and Reinforcement Learning." Expert Systems With Applications 38: 4741-4755.
Tashiro, D., H. Matsushima, K. Izumo, and S. Sakaji. 2019. "Encoding of High-Frequency Order Information and Prediction of Short-Term Stock Price by Deep Learning." Quantitative Finance 19: 1499-1506.
Tetlock, P. C., M. Saar-Tsechansky, and S. Macskassy. 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals." The Journal of Finance 63: 1437-1467.
Trippi, R., and T. Efraim. Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance. New York: McGraw-Hill, 1992.
Tsai, C., Y. Lin, D. C. Yen, and Y. Chen. 2011. "Predicting Stock Returns by Classifier Ensembles."Applied Soft Computing 11: 2452-2459.
Varaku, K. "Stock Price Forecasting and Hypothesis Testing Using Neural Networks." 2020, ssrn.com/abstract=3597684.
Vui, C. S., G. K. Soon, C. K. On, R. Alfred, and P. Anthony. "A Review of Stock Market Prediction with Artificial Neural Network." In International Conference on Control System, Computing and Engineering (ICCSCE), pp. 477-482. IEEE, 2013.
Wang, H., and X. Y. Zhou. 2020. "Continuous-Time Mean-Variance Portfolio Selection: A Reinforce- ment Learning Framework." Mathematical Finance 30 (4): 1273-1308.
Wolff, D., and F. Echterling. "Stock Picking with Machine Learning." 2020, ssrn.com/ abstract=3607845.
Xiong, Z., X. Y. Liu, S. Zhong, H. Yang, and A. Walid. "Practical Deep Reinforcement Learning Approach for Stock Trading." arXiv 1811.07522, 2018.
Yu, L., S.. Wang, and K. K. Lai. 2008. "Neural Network-Based Mean-Variance-Skewness Model for Portfolio Selection." Computers and Operations Research 35: 34-46.
Zhang, Z., S. Zohren, and S. Roberts. "Deep Reinforcement Learning for Trading." arXiv 1911.10107, 2019.
--. "Deep Learning for Portfolio Optimisation." 2020, ssrn.com/abstract=3613600.
Zheng, B., E. Moulines, and F. Abergel. 2013. "Price Jump Prediction in a Limit Order Book."Journal of Mathematical Finance 3: 242-255.
Zimmermann, H., R. Neuneier, and R. Grothmann. 2002. "Active Portfolio Management Based on Error Correction Neural Networks." Advances in Neural Information Processing Systems, pp. 1465-1472.

Risk Models

Transaction cost models

.[1] F. Abergel et al. Limit Order Books. PHYSICS OF SOCIETY: ECONOPHYSICS. Cambridge University Press, 2016. ISBN: 9781107163980. URL: https://books.google.co.uk/books?id=5JIrDAAAQBAJ. [2] Ethem Alpaydm. “Combined 5 2 cv F test for comparing supervised classification learning algorithms”. In:Neural computation 11.8 (1999), pp. 1885–1892.[3] Ron Artstein and Massimo Poesio. “Inter-coder agreement for computational linguistics”. In: Computational Linguistics 34.4 (2008), pp. 555–596.[4] Alessio Benavoli et al. “Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis”. In: The Journal of Machine Learning Research 18.1 (2017), pp. 2653–2688.[5] James O Berger and Thomas Sellke. “Testing a point null hypothesis: The irreconcilability of p values and evidence”. In: Journal of the American statistical Association 82.397 (1987), pp. 112–122.[6] Jean-Philippe Bouchaud, J Doyne Farmer, and Fabrizio Lillo. “How markets slowly digest changes in supply and demand”. In: Handbook of financial markets: dynamics and evolution. Elsevier, 2009, pp. 57–160.[7] Antonio Briola and Jeremy David Turiel. CNN-LSTM_Limit_Order_Book: Third Release. Version v1.3. Oct. 2020. DOI: 10.5281/zenodo.4104275. URL: https://doi.org/10.5281/zenodo.4104275.[8] Arno Candel et al. “Deep learning with H2O”. In: H2O. ai Inc (2016).[9] Francois Chollet. Deep learning with Python. 2017.[10] François Chollet et al. Keras. https://keras.io. 2015.[11] Jacob Cohen. “A coefficient of agreement for nominal scales”. In: Educational and psychological measurement20.1 (1960), pp. 37–46.[12] Giorgio Corani and Alessio Benavoli. “A Bayesian approach for comparing cross-validated algorithms on mul- tiple data sets”. In: Machine Learning 100.2-3 (2015), pp. 285–304.[13] Janez Demšar. “On the appropriateness of statistical tests in machine learning”. In: Workshop on Evaluation Methods for Machine Learning in conjunction with ICML. 2008, p. 65.[14] James Dickey. “Scientific reporting and personal probabilities: Student’s hypothesis”. In: Journal of the Royal Statistical Society: Series B (Methodological) 35.2 (1973), pp. 285–305.[15] Thomas G Dietterich. “Approximate statistical tests for comparing supervised classification learning algo- rithms”. In: Neural computation 10.7 (1998), pp. 1895–1923.[16] Ward Edwards, Harold Lindman, and Leonard J Savage. “Bayesian statistical inference for psychological re- search.” In: Psychological review 70.3 (1963), p. 193.[17] Sumitra Ganesh et al. “Reinforcement Learning for Market Making in a Multi-agent Dealer Market”. In: arXiv preprint arXiv:1911.05892 (2019).[18] Jan Gorodkin. “Comparing two K-category assignments by a K-category correlation coefficient”. In: Computa- tional biology and chemistry 28.5-6 (2004), pp. 367–374.[19] William H Greene. Econometric analysis. Pearson Education India, 2003.[20] Isabelle Guyon et al. “Design of the 2015 chalearn automl challenge”. In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE. 2015, pp. 1–8.[21] Isabelle Guyon et al. “Analysis of the AutoML Challenge series 2015-2018”. In: AutoML. Springer series on Challenges in Machine Learning. 2019.[22] Simon Haykin and Neural Network. “A comprehensive foundation”. In: Neural networks 2.2004 (2004), p. 41.[23] Sepp Hochreiter and Jürgen Schmidhuber. “Long short-term memory”. In: Neural computation 9.8 (1997), pp. 1735–1780.[24] Ruihong Huang and Tomas Polak. “LOBSTER: Limit order book reconstruction system”. In: Available at SSRN 1977207 (2011).[25] Nathalie Japkowicz and Mohak Shah. Evaluating learning algorithms: a classification perspective. Cambridge University Press, 2011.[26] Michael Kearns and Yuriy Nevmyvaka. “Machine learning for market microstructure and high frequency trad- ing”. In: High Frequency Trading: New Realities for Traders, Markets, and Regulators (2013).[27] John D Kelleher, Brian Mac Namee, and Aoife D’arcy. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press, 2015.[28] Diederik P Kingma and Jimmy Ba. “Adam: A method for stochastic optimization”. In: arXiv preprint arXiv:1412.6980 (2014).[29] Yann LeCun, Yoshua Bengio, et al. “Convolutional networks for images, speech, and time series”. In: The handbook of brain theory and neural networks 3361.10 (1995), p. 1995.[30] Xiaoxiao Li et al. “Classifying and Understanding Financial Data Using Graph Neural Network”. In: (2020).[31] Zachary C Lipton, John Berkowitz, and Charles Elkan. “A critical review of recurrent neural networks for sequence learning”. In: arXiv preprint arXiv:1506.00019 (2015). [32] Lawrence Mosley. “A balanced approach to the multi-class imbalance problem”. In: (2013).

[33] Claude Nadeau and Yoshua Bengio. “Inference for the generalization error”. In: Advances in neural information processing systems. 2000, pp. 307–313.

[34] F. Pedregosa et al. “Scikit-learn: Machine Learning in Python”. In: Journal of Machine Learning Research 12 (2011), pp. 2825–2830.

[35] Steven L Salzberg. “On comparing classifiers: Pitfalls to avoid and a recommended approach”. In: Data mining and knowledge discovery 1.3 (1997), pp. 317–328.

[36] Justin A Sirignano. “Deep learning for limit order books”. In: Quantitative Finance 19.4 (2019), pp. 549–570.

[37] Justin Sirignano and Rama Cont. “Universal features of price formation in financial markets: perspectives from deep learning”. In: Quantitative Finance 19.9 (2019), pp. 1449–1459.

[38] Avraam Tsantekidis et al. “Forecasting stock prices from the limit order book using convolutional neural net- works”. In: 2017 IEEE 19th Conference on Business Informatics (CBI). Vol. 1. IEEE. 2017, pp. 7–12.

[39] Ashish Vaswani et al. “Attention is all you need”. In: Advances in neural information processing systems. 2017, pp. 5998–6008.

[40] Pauli Virtanen et al. “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python”. In: Nature Methods 17 (2020), pp. 261–272. DOI: https://doi.org/10.1038/s41592-019-0686-2.

[41] Ian H Witten, Eibe Frank, and Mark A. Hall. “Data Mining: Practical Machine Learning Tools and Techniques”. In: (2016).

[42] Z. Zhang, S. Zohren, and S. Roberts. “DeepLOB: Deep Convolutional Neural Networks for Limit Order Books”. In: IEEE Transactions on Signal Processing 67.11 (June 2019), pp. 3001–3012. ISSN: 1941-0476. DOI: 10. 1109/TSP.2019.2907260.

[43] Zihao Zhang, Stefan Zohren, and Stephen Roberts. “Bdlob: Bayesian deep convolutional neural networks for limit order books”. In: arXiv preprint arXiv:1811.10041 (2018).

[44] Zihao Zhang, Stefan Zohren, and Stephen Roberts. “Extending deep learning models for limit order books to quantile regression”. In: arXiv preprint arXiv:1906.04404 (2019)..

Reinforcement Learning

execution Models

Integrated Models

Noah Beck, Jason Hsu, Vitali Kalesnik, and Helge Kostka. “Will Your Factor Deliver? An Examination of Factor Robustness and Implementation Costs.” Financial Analysts Journal, September/October 2016. Tzee Chow, Feifei Li, PhD, and Joseph Shim. “Smart-Beta Multifactor Construction Methodology: Mixing versus Integrating.” Journal of Index Investing, Spring, 2018. Shaun Fitzgibbons, Jacques Friedman, Lukasz Pomorski, and Laura Serban “Long-Only Style Investing: Don’t Just Mix, Integrate.” AQR, June, 2016. Markus Leippold and Roger Ruegg. “The Mixed vs. the Integrated Approach to Style Investing: Much Ado About Nothing?” European Financial Management, 2018.

Ordering, Selecting, and Weighting Groups

Ordering, Selecting, and Weighting Holdings

Simulation

Attribution, monitoring, Feedback, and Adjustment

PARSE ME

.[1] R. C. Cavalcante, R. C. Brasileiro, V. L. F. Souza, J. P. Nobrega, and A. L. I. Oliveira, ‘‘Computational intelligence and .nancial markets: A survey and future directions,’’ Expert Syst. Appl., vol. 55, pp. 194–211, Aug. 2016. [2] J. De Spiegeleer, D. B. Madan, S. Reyners, and W. Schoutens, ‘‘Machine learning for quantitative .nance: Fast derivative pricing, hedging and .tting,’’ Quant. Finance, vol. 18, no. 10, pp. 1635–1643, Oct. 2018. [3] T.-M. Choi, H. K. Chan, and X. Yue, ‘‘Recent development in big data analytics for business operations and risk management,’’ IEEE Trans. Cybern., vol. 47, no. 1, pp. 81–92, Jan. 2017. [4] A. Bahrammirzaee, ‘‘A comparative survey of arti.cial intelligence applications in .nance: Arti.cial neural networks, expert system and hybrid intelligent systems,’’ Neural Comput. Appl., vol. 19, no. 8, pp. 1165–1195, Nov. 2010. [5] G. Kou, X. Chao, Y. Peng, F. E. Alsaadi, and E. Herrera-Viedma, ‘‘Machine learning methods for systemic risk analysis in .nancial sec­tors,’’ Technol. Econ. Develop. Economy, vol. 25, no. 5, pp. 716–742, May 2019. [6] D. Bisias, M. Flood, A. W. Lo, and S. Valavanis, ‘‘A survey of systemic risk analytics,’’ Annu. Rev. Financial Econ., vol. 4, no. 1, pp. 255–296, Oct. 2012. [7] P. O. J. Kelliher, D. Wilmot, J. Vij, and P. J. M. Klumpes, ‘‘A common risk classi.cation system for the actuarial profession,’’ Brit. Actuarial J., vol. 18, no. 1, p. 91–121, 2013. [8] S. Emerson, R. Kennedy, L. O’Shea, and J. O’Brien, ‘‘Trends and applications of machine learning in quantitative .nance,’’ in Proc. 8th Int. Conf. Econ. Finance Res. (ICEFR), 2019. [Online]. Available: https://ssrn.com/abstract=3397005 [9] K. A. Horcher, Essentials of Financial Risk Management, vol. 32. Hoboken, NJ, USA: Wiley, 2011. [10] P. Christoffersen, Elements of Financial Risk Management. New York, NY, USA: Academic, 2011. [11] J.-P. Bouchaud and M. Potters, Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management. Cambridge, U.K.: Cambridge Univ. Press, 2003. [12] T. Van Gestel and B. Baesens, Credit Risk Management: Basic Concepts: Financial Risk Components, Rating Analysis, Models, Economic and Regulatory Capital. London, U.K.: Oxford Univ. Press, 2008. [13] M. Beyhaghi and J. P. Hawley, ‘‘Modern portfolio theory and risk management: Assumptions and unintended consequences,’’ J. Sustain. Finance Investment, vol. 3, no. 1, pp. 17–37, Jan. 2013. [14] W.-Y. Lin, Y.-H. Hu, and C.-F. Tsai, ‘‘Machine learning in .nancial crisis prediction: A survey,’’ IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 42, no. 4, pp. 421–436, Jul. 2012. [15] A. Kuritzkes et al., ‘‘Risk measurement, risk management and capital adequacy in .nancial conglomerates,’’ Wharton Financial Inst. Center, Philadelphia, PA, USA, Tech. Rep., 2002, pp. 2–3. [Online]. Available: https://muse.jhu.edu/article/45247/summary [16] J. Hull, Risk Management and Financial Institutions,+ Web Site, vol. 733. Hoboken, NJ, USA: Wiley, 2012. [17] T. M. Mitchell, Machine Learning, vol. 45, no. 37. Burr Ridge, IL, USA: McGraw-Hill, 1997, pp. 870–877. [18] M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning. Cambridge, MA, USA: MIT Press, 2018. [19] J. O. Awoyemi, A. O. Adetunmbi, and S. A. Oluwadare, ‘‘Credit card fraud detection using machine learning techniques: A comparative anal­ysis,’’ in Proc. Int. Conf. Comput. Netw. Informat. (ICCNI), Oct. 2017, pp. 1–9. [20] T. K. Lee, J. H. Cho, D. S. Kwon, and S. Y. Sohn, ‘‘Global stock market investment strategies based on .nancial network indicators using machine learning techniques,’’ Expert Syst. Appl., vol. 117, pp. 228–242, Mar. 2019. [21] A. Ghodselahi, ‘‘A hybrid support vector machine ensemble model for credit scoring,’’ Int. J. Comput. Appl., vol. 17, no. 5, pp. 1–5, Mar. 2011. [22] G. Gan and E. A. Valdez, ‘‘Regression modeling for the valuation of large variable annuity portfolios,’’ North Amer. Actuarial J., vol. 22, no. 1, pp. 40–54, Jan. 2018. [23] M. V. Wuthrich and C. Buser, ‘‘Data analytics for non-life insurance pric­ing,’’ Swiss Finance Inst. Res., Geneva, Switzerland, Paper no. 16–68, 2019. [24] M. V. Whrich, ‘‘Neural networks applied to chain–ladder reserving,’’ Eur. Actuarial J., vol. 8, no. 2, pp. 407–436, 2018. [25] R. Richman and M. V. Wuthrich. (2019). Lee and Carter Go Machine Learning: Recurrent Neural Networks. [Online]. Available: https://ssrn.com/abstract=344103 [26] A. O’Hagan and C. Ferrari, ‘‘Model-based and nonparametric approaches to clustering for data compression in actuarial applications,’’ North Amer. Actuarial J., vol. 21, no. 1, pp. 107–146, Jan. 2017. [27] G. Kou, Y. Peng, and G. Wang, ‘‘Evaluation of clustering algorithms for .nancial risk analysis using MCDM methods,’’ Inf. Sci., vol. 275, pp. 1–12, Aug. 2014. [28] K. Nian, H. Zhang, A. Tayal, T. Coleman, and Y. Li, ‘‘Auto insur­ance fraud detection using unsupervised spectral ranking for anomaly,’’ J. Finance Data Sci., vol. 2, no. 1, pp. 58–75, Mar. 2016. [29] N. Boodhun and M. Jayabalan, ‘‘Risk prediction in life insurance industry using supervised learning algorithms,’’ Complex Intell. Syst., vol. 4, no. 2, pp. 145–154, Jun. 2018. [30] D. Hainaut, ‘‘A neural-network analyzer for mortality forecast,’’ ASTIN Bull., vol. 48, no. 2, pp. 481–508, May 2018. [31] H. Buehler, L. Gonon, J. Teichmann, and B. Wood, ‘‘Deep hedging,’’ Quant. Finance, vol. 19, no. 8, pp. 1271–1291, Aug. 2019. [32] G. Gan, ‘‘Application of data clustering and machine learning in variable annuity valuation,’’ Insurance Math. Econ., vol. 53, no. 3, pp. 795–801, Nov. 2013. [33] C.-F. Tsai, ‘‘Financial decision support using neural networks and support vector machines,’’ Expert Syst., vol. 25, no. 4, pp. 380–393, Sep. 2008. [34] J. Garrido, C. Genest, and J. Schulz, ‘‘Generalized linear models for dependent frequency and severity of insurance claims,’’ Insurance Math. Econ., vol. 70, pp. 205–215, Sep. 2016. [35] E. Ohlsson and B. Johansson, Non-Life Insurance Pricing With General­ized Linear Models, vol. 174. Berlin, Germany: Springer, 2010. [36] T. Fischer and C. Krauss, ‘‘Deep learning with long short-term memory networks for .nancial market predictions,’’ Eur. J. Oper. Res., vol. 270, no. 2, pp. 654–669, Oct. 2018. [37] H. Y. Kim and C. H. Won, ‘‘Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models,’’ Expert Syst. Appl., vol. 103, pp. 25–37, Aug. 2018. [38] M. Ahmed, A. N. Mahmood, and M. R. Islam, ‘‘A survey of anomaly detection techniques in .nancial domain,’’ Future Gener. Comput. Syst., vol. 55, pp. 278–288, Feb. 2016. [39] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA, USA: MIT Press, 2018. [40] Z. Jiang and J. Liang, ‘‘Cryptocurrency portfolio management with deep reinforcement learning,’’ in Proc. Intell. Syst. Conf. (IntelliSys), Sep. 2017, pp. 905–913. [41] X. Zhu and A. B. Goldberg, ‘‘Introduction to semi-supervised learning,’’ Synth. Lectures Artif. Intell. Mach. Learn., vol. 3, no. 1, pp. 1–130, 2009. [42] X. J. Zhu, ‘‘Semi-supervised learning literature survey,’’ Dept. Comput. Sci., Univ. Wisconsin-Madison, Madison, WI, USA, Tech. Rep. 1530, 2005. [43] S. A. Hejazi and K. R. Jackson, ‘‘A neural network approach to ef.cient valuation of large portfolios of variable annuities,’’ Insurance Math. Econ., vol. 70, pp. 169–181, Sep. 2016. [44] J. B. Heaton, N. G. Polson, and J. H. Witte, ‘‘Deep learning for .nance: Deep portfolios,’’ Appl. Stochastic Models Bus. Ind., vol. 33, no. 1, pp. 3–12, Jan. 2017. [45] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org [46] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, ‘‘Backpropagation applied to handwrit­ten zip code recognition,’’ Neural Comput., vol. 1, no. 4, pp. 541–551, Dec. 1989. [47] Y. LeCun, K. Kavukcuoglu, and C. Farabet, ‘‘Convolutional networks and applications in vision,’’ in Proc. IEEE Int. Symp. Circuits Syst., May 2010, pp. 253–256. [48] A. Borovykh, S. Bohte, and C. W. Oosterlee, ‘‘Conditional time series forecasting with convolutional neural networks,’’ 2017, arXiv:1703.04691. [Online]. Available: http://arxiv.org/abs/1703.04691 [49] K. Fu, D. Cheng, Y. Tu, and L. Zhang, ‘‘Credit card fraud detection using convolutional neural networks,’’ in Proc. Int. Conf. Neural Inf. Process. Cham, Switzerland: Springer, 2016, pp. 483–490. [50] H. Kvamme, N. Sellereite, K. Aas, and S. Sjursen, ‘‘Predicting mortgage default using convolutional neural networks,’’ Expert Syst. Appl., vol. 102, pp. 207–217, Jul. 2018. [51] F. J. Pineda, ‘‘Generalization of back-propagation to recurrent neural networks,’’ Phys. Rev. Lett., vol. 59, no. 19, p. 2229, 1987. [52] S. Hochreiter and J. Schmidhuber, ‘‘Lstm can solve hard long time lag problems,’’ in Proc. Adv. Neural Inf. Process. Syst., 1997, pp. 473–479. [53] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, ‘‘Empirical evalua­tion of gated recurrent neural networks on sequence modeling,’’ 2014, arXiv:1412.3555. [Online]. Available: http://arxiv.org/abs/1412.3555 [54] K. Kuo, ‘‘DeepTriangle: A deep learning approach to loss reserving,’’ Risks, vol. 7, no. 3, p. 97, Sep. 2019. [55] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, ‘‘Playing Atari with deep rein­forcement learning,’’ 2013, arXiv:1312.5602. [Online]. Available: http://arxiv.org/abs/1312.5602 [56] Y. Li, ‘‘Deep reinforcement learning: An overview,’’ 2017, arXiv:1701.07274. [Online]. Available: http://arxiv.org/abs/1701.07274 [57] W. L. Hamilton, Z. Ying, and J. Leskovec, ‘‘Inductive representation learning on large graphs,’’ in Proc. NIPS, 2017, pp. 1024–1034. [58] T. N. Kipf and M. Welling, ‘‘Semi-supervised classi.cation with graph convolutional networks,’’ 2016, arXiv:1609.02907. [Online]. Available: http://arxiv.org/abs/1609.02907 [59] P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Li, and Y. Bengio, ‘‘Graph attention networks,’’ in Proc. Int. Conf. Learn. Represent., 2018. [Online]. Available: https://openreview.net/forum?id= rJXMpikCZ [60] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling, ‘‘Modeling relational data with graph convolutional networks,’’ 2017, arXiv:1703.06103. [Online]. Available: http://arxiv.org/abs/1703.06103 [61] M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, ‘‘Geometric deep learning: Going beyond Euclidean data,’’ IEEE Signal Process. Mag., vol. 34, no. 4, pp. 18–42, Jul. 2017. [62] Z. Chen, X. Li, and J. Bruna, ‘‘Supervised community detection with line graph neural networks,’’ 2017, arXiv:1705.08415. [Online]. Available: http://arxiv.org/abs/1705.08415 [63] M. Bennett, ‘‘The .nancial industry business ontology: Best practice for big data,’’ J. Banking Regulation, vol. 14, nos. 3–4, pp. 255–268, Jul. 2013, doi: 10.1057/jbr.2013.13.
[64] N. Yerashenia and A. Bolotov, ‘‘Computational modelling for bankruptcy prediction: Semantic data analysis integrating graph database and .nan­cial ontology,’’ in Proc. IEEE 21st Conf. Bus. Informat. (CBI), vol. 1, Jul. 2019, pp. 84–93. [65] Q. Zhan and H. Yin, ‘‘A loan application fraud detection method based on knowledge graph and neural network,’’ in Proc. 2nd Int. Conf. Innov. Artif. Intell., 2018, pp. 111–115. [66] R. J. Shiller, Market Volatility. Cambridge, MA, USA: MIT Press, 1992. [67] S. Mayhew, ‘‘Implied volatility,’’ Financial Analysts J., vol. 51, no. 4, pp. 8–20, 1995. [68] P. Ursone, How to Calculate Options Prices and Their Greeks: Exploring the Black Scholes Model From Delta to Vega. Hoboken, NJ, USA: Wiley, 2015. [69] H. M. Markovitz, Portfolio Selection: Ef.cient Diversi.cation Invest­ments. Hoboken, NJ, USA: Wiley, 1959. [70] R. F. Engle, ‘‘Autoregressive conditional heteroscedasticity with esti­mates of the variance of United Kingdom in.ation,’’ Econometrica J. Econ. Soc., vol. 50, no. 4, pp. 987–1007, 1982. [71] T. Lux and M. Marchesi, ‘‘Volatility clustering in .nancial markets: A microsimulation of interacting agents,’’ Int. J. Theor. Appl. Finance, vol. 03, no. 04, pp. 675–702, Oct. 2000. [72] T. Bollerslev, ‘‘Generalized autoregressive conditional heteroskedastic­ity,’’ J. Econometrics, vol. 31, no. 3, pp. 307–327, Apr. 1986. [73] D. B. Nelson, ‘‘Conditional heteroskedasticity in asset returns: A new approach,’’ Econometrica, vol. 59, no. 2, pp. 347–370, 1991. [Online]. Available: http://www.jstor.org/stable/2938260 [74] L. R. Glosten, R. Jagannathan, and D. E. Runkle, ‘‘On the relation between the expected value and the volatility of the nominal excess return on stocks,’’ J. Finance, vol. 48, no. 5, pp. 1779–1801, Dec. 1993. [75] D. Li, X. Zhang, K. Zhu, and S. Ling, ‘‘The ZD-GARCH model: A new way to study heteroscedasticity,’’ J. Econometrics, vol. 202, no. 1, pp. 1–17, Jan. 2018. [76] S. J. Taylor, ‘‘Financial returns modelled by the product of two stochas­tic processes—A study of the daily sugar prices 1961–75,’’ Time Ser. Anal., Theory Pract., vol. 1, pp. 203–226, 1982. [Online]. Available: https://ci.nii.ac.jp/naid/10018822959/en/ [77] S. L. Heston, ‘‘A closed-form solution for options with stochastic volatil­ity with applications to bond and currency options,’’ Rev. Financial Stud., vol. 6, no. 2, pp. 327–343, Apr. 1993. [78] A. A. Dr.agulescu and V. M. Yakovenko, ‘‘Probability distribution of returns in the Heston model with stochastic volatility,’’ Quant. Finance, vol. 2, no. 6, pp. 443–453, 2002. [79] R. Luo, W. Zhang, X. Xu, and J. Wang, ‘‘A neural stochastic volatility model,’’ in Proc. 32nd AAAI Conf. Artif. Intell., 2018, pp. 6401–6408. [80] R. G. Donaldson and M. Kamstra, ‘‘An arti.cial neural network-GARCH model for international stock return volatility,’’ J. Empirical Finance, vol. 4, no. 1, pp. 17–46, Jan. 1997. [81] A. Manela and A. Moreira, ‘‘News implied volatility and disaster con­cerns,’’ J. Financial Econ., vol. 123, no. 1, pp. 137–162, Jan. 2017. [82] W. Kristjanpoller, A. Fadic, and M. C. Minutolo, ‘‘Volatility forecast using hybrid neural network models,’’ Expert Syst. Appl., vol. 41, no. 5, pp. 2437–2442, Apr. 2014. [83] C. Luong and N. Dokuchaev, ‘‘Forecasting of realised volatility with the random forests algorithm,’’ J. Risk Financial Manage., vol. 11, no. 4, Oct. 2018, Art. no. 61. [84] H. Park, N. Kim, and J. Lee, ‘‘Parametric models and non-parametric machine learning models for predicting option prices: Empirical compar­ison study over KOSPI 200 index options,’’ Expert Syst. Appl., vol. 41, no. 11, pp. 5227–5237, Sep. 2014. [85] S. Hochreiter and J. Schmidhuber, ‘‘Long short-term memory,’’ Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997. [86] Y. Liu, ‘‘Novel volatility forecasting using deep learning–long short term memory recurrent neural networks,’’ Expert Syst. Appl., vol. 132, pp. 99–109, Oct. 2019. [87] S. S. Groth and J. Muntermann, ‘‘An intraday market risk management approach based on textual analysis,’’ Decis. Support Syst., vol. 50, no. 4, pp. 680–691, 2011. [88] W. Antweiler and M. Z. Frank, ‘‘Is all that talk just noise? The information content of Internet stock message boards,’’ J. Finance, vol. 59, no. 3, pp. 1259–1294, Jun. 2004. [89] P. S. M. Nizer and J. C. Nievola, ‘‘Predicting published news effect in the Brazilian stock market,’’ Expert Syst. Appl., vol. 39, no. 12, pp. 10674–10680, Sep. 2012. [90] N. Oliveira, P. Cortez, and N. Areal, ‘‘The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices,’’ Expert Syst. Appl., vol. 73, pp. 125–144, May 2017. [91] Q. Xu, Z. Bo, C. Jiang, and Y. Liu, ‘‘Does Google search index really help predicting stock market volatility? Evidence from a modi.ed mixed data sampling model on volatility,’’ Knowl.-Based Syst., vol. 166, pp. 170–185, Feb. 2019. [92] Z. Hu, W. Liu, J. Bian, X. Liu, and T. Liu, ‘‘Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction,’’ in Proc. 11th ACM Int. Conf. Web Search Data Mining, 2018, pp. 261–269. [93] Y. Xu and S. B. Cohen, ‘‘Stock movement prediction from tweets and historical prices,’’ in Proc. ACL, 2018. [94] F. Z. Xing, E. Cambria, and R. E. Welsch, ‘‘Natural language based .nan­cial forecasting: A survey,’’ Artif. Intell. Rev., vol. 50, no. 1, pp. 49–73, Jun. 2018. [95] P. Glasserman, Monte Carlo Methods in Financial Engineering, vol. 53. New York, NY, USA: Springer, 2013. [96] S. Mohamed, M. Rosca, M. Figurnov, and A. Mnih, ‘‘Monte Carlo gradi­ent estimation in machine learning,’’ 2019, arXiv:1906.10652. [Online]. Available: http://arxiv.org/abs/1906.10652 [97] G. Gan and X. S. Lin, ‘‘Ef.cient greek calculation of variable annuity portfolios for dynamic hedging: A two-level metamodeling approach,’’ North Amer. Actuarial J., vol. 21, no. 2, pp. 161–177, Apr. 2017. [98] M. Hardy, Investment Guarantees: Modeling and Risk Management for Equity-Linked Life Insurance, vol. 215. Hoboken, NJ, USA: Wiley, 2003. [99] Y. C. M. Chueh, ‘‘Ef.cient stochastic modeling for large and consolidated insurance business: Interest rate sampling algorithms,’’ North Amer. Actu­arial J., vol. 6, no. 3, pp. 88–103, Jul. 2002. [100] S. A. Klugman, H. H. Panjer, and G. E. Willmot, Loss Models: From Data to Decisions. Hoboken, NJ, USA: Wiley, 2012, vol. 715. [101] G. Gan and E. A. Valdez, Metamodeling for Variable Annuities. Boca Raton, FL, USA: CRC Press, 2019. [102] J. Vadiveloo, ‘‘Replicated strati.ed sampling—A new .nancial mod­elling option,’’ Tower Watson Emphasis Mag., pp. 1–4, 2011. [103] J. C. Helton and F. J. Davis, ‘‘Latin hypercube sampling and the propaga­tion of uncertainty in analyses of complex systems,’’ Rel. Eng. Syst. Saf., vol. 81, no. 1, pp. 23–69, Jul. 2003. [104] C. Ai and E. C. Norton, ‘‘Interaction terms in logit and probit models,’’ Econ. Lett., vol. 80, no. 1, pp. 123–129, Jul. 2003. [105] L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classi.cation and Regression Trees. Boca Raton, FL, USA: CRC Press, 1984. [106] X. Cheng, W. Luo, G. Gan, and G. Li, ‘‘Deep neighbor embedding for evaluation of large portfolios of variable annuities,’’ in Proc. Int. Conf. Knowl. Sci., Eng. Manage. Cham, Switzerland: Springer, 2019, pp. 472–480. [107] X. Cheng, W. Luo, G. Gan, and G. Li, ‘‘Fast valuation of large portfolios of variable annuities via transfer learning,’’ in Proc. Paci.c Rim Int. Conf. Artif. Intell. Cham, Switzerland: Springer, 2019, pp. 716–728. [108] D. Doyle and C. Groendyke, ‘‘Using neural networks to price and hedge variable annuity guarantees,’’ Risks, vol. 7, no. 1, p. 1, Dec. 2018. [109] G. Gan and E. A. Valdez, ‘‘Valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets,’’ Dependence Model., vol. 5, no. 1, pp. 354–374, Dec. 2017. [110] G. Gan and E. A. Valdez, ‘‘Nested stochastic valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets,’’ Data, vol. 3, no. 3, p. 31, 2018. [111] W. Shen, J. Wang, and S. Ma, ‘‘Doubly regularized portfolio with risk minimization,’’ in Proc. AAAI, 2014, pp. 1286–1292. [112] A. Ponsich, A. L. Jaimes, and C. A. C. Coello, ‘‘A survey on multiobjective evolutionary algorithms for the solution of the port­folio optimization problem and other .nance and economics appli­cations,’’ IEEE Trans. Evol. Comput., vol. 17, no. 3, pp. 321–344, Jun. 2013. [113] G.-Y. Ban, N. El Karoui, and A. E. Lim, ‘‘Machine learning and portfolio optimization,’’ Manage. Sci., vol. 64, no. 3, pp. 1136–1154, 2018. [114] A. M. Rather, V. N. Sastry, and A. Agarwal, ‘‘Stock market prediction and portfolio selection models: A survey,’’ OPSEARCH, vol. 54, no. 3, pp. 558–579, Sep. 2017. [115] B. Li, P. Zhao, S. C. H. Hoi, and V. Gopalkrishnan, ‘‘PAMR: Passive aggressive mean reversion strategy for portfolio selection,’’ Mach. Learn., vol. 87, no. 2, pp. 221–258, May 2012. [116] B. Li and S. C. Hoi, ‘‘Online portfolio selection: A survey,’’ ACM Comput. Surv., vol. 46, no. 3, pp. 1–36, 2014. [117] Z. Jiang, D. Xu, and J. Liang, ‘‘A deep reinforcement learning framework for the .nancial portfolio management problem,’’ 2017, arXiv:1706.10059. [Online]. Available: http://arxiv.org/abs/1706.10059 [118] S. Almahdi and S. Y. Yang, ‘‘An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown,’’ Expert Syst. Appl., vol. 87, pp. 267–279, Nov. 2017. [119] M.-Y. Chen, ‘‘Bankruptcy prediction in .rms with statistical and intelligent techniques and a comparison of evolutionary computation approaches,’’ Comput. Math. with Appl., vol. 62, no. 12, pp. 4514–4524, Dec. 2011. [120] D. West, ‘‘Neural network credit scoring models,’’ Comput. Oper. Res., vol. 27, nos. 11–12, pp. 1131–1152, Sep. 2000. [121] H. A. Abdou and J. Pointon, ‘‘Credit scoring, statistical techniques and evaluation criteria: A review of the literature,’’ Intell. Syst. Accounting, Finance Manage., vol. 18, nos. 2–3, pp. 59–88, Apr. 2011. [122] L. Yu, W. Yue, S. Wang, and K. K. Lai, ‘‘Support vector machine based multiagent ensemble learning for credit risk evaluation,’’ Expert Syst. Appl., vol. 37, no. 2, pp. 1351–1360, Mar. 2010. [123] H. S. Kim and S. Y. Sohn, ‘‘Support vector machines for default prediction of SMEs based on technology credit,’’ Eur. J. Oper. Res., vol. 201, no. 3, pp. 838–846, Mar. 2010. [124] A. Chaudhuri and K. De, ‘‘Fuzzy support vector machine for bankruptcy prediction,’’ Appl. Soft Comput., vol. 11, no. 2, pp. 2472–2486, Mar. 2011. [125] J. Efrim Boritz and D. B. Kennedy, ‘‘Effectiveness of neural network types for prediction of business failure,’’ Expert Syst. Appl., vol. 9, no. 4, pp. 503–512, Jan. 1995. [126] M. Leshno and Y. Spector, ‘‘Neural network prediction analysis: The bankruptcy case,’’ Neurocomputing, vol. 10, no. 2, pp. 125–147, Mar. 1996. [127] Y.-C. Lee, ‘‘Application of support vector machines to corporate credit rating prediction,’’ Expert Syst. Appl., vol. 33, no. 1, pp. 67–74, Jul. 2007. [128] K.-J. Kim and H. Ahn, ‘‘A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach,’’ Comput. Oper. Res., vol. 39, no. 8, pp. 1800–1811, Aug. 2012. [129] A. E. Khandani, A. J. Kim, and A. W. Lo, ‘‘Consumer credit-risk models via machine-learning algorithms,’’ J. Banking Finance, vol. 34, no. 11, pp. 2767–2787, Nov. 2010. [130] S.-C. Huang, ‘‘Using Gaussian process based kernel classi.ers for credit rating forecasting,’’ Expert Syst. Appl., vol. 38, no. 7, pp. 8607–8611, Jul. 2011. [131] C. Luo, D. Wu, and D. Wu, ‘‘A deep learning approach for credit scoring using credit default swaps,’’ Eng. Appl. Artif. Intell., vol. 65, pp. 465–470, Oct. 2017. [132] P. Hajek and K. Michalak, ‘‘Feature selection in corporate credit rating prediction,’’ Knowl.-Based Syst., vol. 51, pp. 72–84, Oct. 2013. [133] S. Y. Kim and A. Upneja, ‘‘Predicting restaurant .nancial distress using decision tree and AdaBoosted decision tree models,’’ Econ. Model., vol. 36, pp. 354–362, Jan. 2014. [134] A. Verikas, Z. Kalsyte, M. Bacauskiene, and A. Gelzinis, ‘‘Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: A survey,’’ Soft Comput., vol. 14, no. 9, pp. 995–1010, Jul. 2010. [135] F. Barboza, H. Kimura, and E. Altman, ‘‘Machine learning models and bankruptcy prediction,’’ Expert Syst. Appl., vol. 83, pp. 405–417, Oct. 2017. [136] G. Wang, J. Ma, L. Huang, and K. Xu, ‘‘Two credit scoring models based on dual strategy ensemble trees,’’ Knowl.-Based Syst., vol. 26, pp. 61–68, Feb. 2012. [137] G. Wang, J. Hao, J. Ma, and H. Jiang, ‘‘A comparative assessment of ensemble learning for credit scoring,’’ Expert Syst. Appl., vol. 38, no. 1, pp. 223–230, Jan. 2011. [138] G. Wang and J. Ma, ‘‘A hybrid ensemble approach for enterprise credit risk assessment based on support vector machine,’’ Expert Syst. Appl., vol. 39, no. 5, pp. 5325–5331, Apr. 2012. [139] J. Kruppa, A. Schwarz, G. Arminger, and A. Ziegler, ‘‘Consumer credit risk: Individual probability estimates using machine learning,’’ Expert Syst. Appl., vol. 40, no. 13, pp. 5125–5131, Oct. 2013. [140] M. Malekipirbazari and V. Aksakalli, ‘‘Risk assessment in social lending via random forests,’’ Expert Syst. Appl., vol. 42, no. 10, pp. 4621–4631, Jun. 2015. [141] M. K. Lim and S. Y. Sohn, ‘‘Cluster-based dynamic scoring model,’’ Expert Syst. Appl., vol. 32, no. 2, pp. 427–431, Feb. 2007. [142] M. Eling and M. Lehmann, ‘‘The impact of digitalization on the insurance value chain and the insurability of risks,’’ Geneva Papers Risk Insurance-Issues Pract., vol. 43, no. 3, pp. 359–396, Jul. 2018. [143] E. W. Frees, R. A. Derrig, and G. Meyers, Predictive Modeling Appli­cations in Actuarial Science, vol. 1. Cambridge, U.K.: Cambridge Univ. Press, 2014. [144] S. Gschll and C. Czado, ‘‘Spatial modelling of claim frequency and claim size in non-life insurance,’’ Scandin. Actuarial J., vol. 2007, no. 3, pp. 202–225, Sep. 2007. [145] C. Czado, R. Kastenmeier, E. C. Brechmann, and A. Min, ‘‘A mixed copula model for insurance claims and claim sizes,’’ Scandin. Actuarial J., vol. 2012, no. 4, pp. 278–305, Dec. 2012. [146] C. J. Lancaster, ‘‘Method of risk modeling by estimating frequencies of loss and loss distributions for individual risks in a portfolio,’’ U.S. Patent 7 698 213, Apr. 13, 2010. [147] L. Guelman, ‘‘Gradient boosting trees for auto insurance loss cost mod­eling and prediction,’’ Expert Syst. Appl., vol. 39, no. 3, pp. 3659–3667, Feb. 2012. [148] T. Binion, J. Harr, B. Fields, S. Cielocha, and S. J. Balbach, ‘‘Risk assess­ment for an automated vehicle,’’ U.S. Patent 14 057 467, Apr. 23, 2015. [149] V. Roel, K. Antonio, and G. Claeskens, ‘‘Unravelling the predictive power of telematics data in car insurance pricing,’’ J. Roy. Stat. Soc., Ser. C, Appl. Statist., vol. 67, no. 5, pp. 1275–1304, 2018. [Online]. Avail­able: https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12283, doi: 10.1111/rssc.12283.
[150] P. Baecke and L. Bocca, ‘‘The value of vehicle telematics data in insur­ance risk selection processes,’’ Decis. Support Syst., vol. 98, pp. 69–79, Jun. 2017. [151] T. Mack, ‘‘Distribution-free calculation of the standard error of chain ladder reserve estimates,’’ ASTIN Bull., vol. 23, no. 2, pp. 213–225, Nov. 1993. [152] P. D. England and R. J. Verrall, ‘‘Stochastic claims reserving in general insurance,’’ Brit. Actuarial J., vol. 8, no. 3, pp. 443–544, 2002. [153] A. Gabrielli, R. Richman, and M. V. Whrich, ‘‘Neural network embed­ding of the over-dispersed Poisson reserving model,’’ Scandin. Actuarial J., vol. 2020, no. 1, pp. 1–29, Jan. 2020. [154] R. D. Lee and L. R. Carter, ‘‘Modeling and forecasting us mortality,’’ J. Amer. Stat. Assoc., vol. 87, no. 419, pp. 659–671, 1992. [155] A. J. G. Cairns, D. Blake, and K. Dowd, ‘‘A two-factor model for stochastic mortality with parameter uncertainty: Theory and calibration,’’ J. Risk Insurance, vol. 73, no. 4, pp. 687–718, Dec. 2006. [156] P. Boyle and M. Hardy, ‘‘Mortality derivatives and the option to annu­itize,’’ Insurance Math. Econ., vol. 29, no. 3, pp. 299–318, 2001. [157] M. Dahl, ‘‘Stochastic mortality in life insurance: Market reserves and mortality-linked insurance contracts,’’ Insurance Math. Econ., vol. 35, no. 1, pp. 113–136, Aug. 2004. [158] V. Russo, R. Giacometti, S. Rachev, and F. J. Fabozzi, ‘‘A three-factor model for mortality modeling,’’ North Amer. Actuarial J., vol. 19, no. 2, pp. 129–141, Apr. 2015. [159] G. H. Golub and C. Reinsch, ‘‘Singular value decomposition and least squares solutions,’’ in Linear Algebra. Berlin, Germany: Springer, 1971, pp. 134–151. [160] I. D. Currie, ‘‘On .tting generalized linear and non-linear models of mor­tality,’’ Scandin. Actuarial J., vol. 2016, no. 4, pp. 356–383, Apr. 2016. [161] R. Richman and M. V. Whrich, ‘‘A neural network extension of the Lee– Carter model to multiple populations,’’ Ann. Actuarial Sci., pp. 1–21, 2019, doi: 10.1017/S1748499519000071.
[162] G. Petneházi and J. Gáll, ‘‘Mortality rate forecasting: Can recurrent neural networks beat the lee-carter model?’’ 2019, arXiv:1909.05501. [Online]. Available: http://arxiv.org/abs/1909.05501 [163] R. Biddle, S. Liu, P. Tilocca, and G. Xu, ‘‘Automated underwriting in life insurance: Predictions and optimisation,’’ in Proc. Australas. Database Conf. Cham, Switzerland: Springer, 2018, pp. 135–146. [164] Y. Tan and G.-J. Zhang, ‘‘The application of machine learning algorithm in underwriting process,’’ in Proc. Int. Conf. Mach. Learn. Cybern., vol. 6, 2005, pp. 3523–3527. [165] R. Biddle, S. Liu, and G. Xu, ‘‘Semi-supervised soft K-means clustering of life insurance questionnaire responses,’’ in Proc. 5th Int. Conf. Behav., Econ., Socio-Cultural Comput. (BESC), Nov. 2018, pp. 30–31. [166] H. Zhou, H.-F. Chai, and M.-L. Qiu, ‘‘Fraud detection within bankcard enrollment on mobile device based payment using machine learning,’’ Frontiers Inf. Technol. Electron. Eng., vol. 19, no. 12, pp. 1537–1545, Dec. 2018. [167] Z. Chen, L. D. Van Khoa, E. N. Teoh, A. Nazir, E. K. Karuppiah, and K. S. Lam, ‘‘Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: A review,’’ Knowl. Inf. Syst., vol. 57, no. 2, pp. 245–285, Nov. 2018. [168] A. Sharma and P. K. Panigrahi, ‘‘A review of .nancial accounting fraud detection based on data mining techniques,’’ 2013, arXiv:1309.3944. [Online]. Available: http://arxiv.org/abs/1309.3944 [169] UK Insurance Key Facts. (2014). Association of British Insurers. [Online]. Available: https://www.abi.org.uk/globalassets/sitecore/.les/ documents/publications/public/2014/key-facts/abi-key-facts-2014.pdf [170] C. Phua, V. Lee, K. Smith, and R. Gayler, ‘‘A comprehensive survey of data mining-based fraud detection research,’’ 2010, arXiv:1009.6119. [Online]. Available: http://arxiv.org/abs/1009.6119 [171] E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen, and X. Sun, ‘‘The application of data mining techniques in .nancial fraud detection: A classi.cation framework and an academic review of literature,’’ Decis. Support Syst., vol. 50, no. 3, pp. 559–569, Feb. 2011. [172] Y. Sahin, S. Bulkan, and E. Duman, ‘‘A cost-sensitive decision tree approach for fraud detection,’’ Expert Syst. Appl., vol. 40, no. 15, pp. 5916–5923, Nov. 2013. [173] A. Correa Bahnsen, D. Aouada, A. Stojanovic, and B. Ottersten, ‘‘Feature engineering strategies for credit card fraud detection,’’ Expert Syst. Appl., vol. 51, pp. 134–142, Jun. 2016. [174] A. Abbasi, C. Albrecht, A. Vance, and J. Hansen, ‘‘MetaFraud: A meta-learning framework for detecting .nancial fraud,’’ Mis Quart., vol. 36, no. 4, pp. 1293–1327, 2012. [175] M. Zareapoor and P. Shamsolmoali, ‘‘Application of credit card fraud detection: Based on bagging ensemble classi.er,’’ Procedia Comput. Sci., vol. 48, pp. 679–685, 2015. [176] T. Loughran and B. Mcdonald, ‘‘Textual analysis in accounting and .nance: A survey,’’ J. Accounting Res., vol. 54, no. 4, pp. 1187–1230, Sep. 2016. [177] S. L. Humpherys, K. C. Mof.tt, M. B. Burns, J. K. Burgoon, and W. F. Felix, ‘‘Identi.cation of fraudulent .nancial statements using linguistic credibility analysis,’’ Decis. Support Syst., vol. 50, no. 3, pp. 585–594, Feb. 2011. [178] Y. Wang and W. Xu, ‘‘Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud,’’ Decis. Support Syst., vol. 105, pp. 87–95, Jan. 2018. [179] N. Mahmoudi and E. Duman, ‘‘Detecting credit card fraud by modi­.ed Fisher discriminant analysis,’’ Expert Syst. Appl., vol. 42, no. 5, pp. 2510–2516, Apr. 2015. [180] N. Papernot, P. McDaniel, and I. Goodfellow, ‘‘Transferability in machine learning: From phenomena to black-box attacks using adversarial samples,’’ 2016, arXiv:1605.07277. [Online]. Available: http://arxiv.org/abs/1605.07277 [181] R. Chalapathy and S. Chawla, ‘‘Deep learning for anomaly detection: A survey,’’ 2019, arXiv:1901.03407. [Online]. Available: http://arxiv.org/abs/1901.03407 [182] U. Fiore, A. De Santis, F. Perla, P. Zanetti, and F. Palmieri, ‘‘Using generative adversarial networks for improving classi.cation effective­ness in credit card fraud detection,’’ Inf. Sci., vol. 479, pp. 448–455, Apr. 2019. [183] D. Wang, Y. Qi, J. Lin, P. Cui, Q. Jia, Z. Wang, Y. Fang, Q. Yu, J. Zhou, and S. Yang, ‘‘A semi-supervised graph attentive network for .nancial fraud detection,’’ in Proc. IEEE Int. Conf. Data Mining (ICDM), Nov. 2019, pp. 598–607. [184] L. Purda and D. Skillicorn, ‘‘Accounting variables, deception, and a bag of words: Assessing the tools of fraud detection,’’ Contemp. Accounting Res., vol. 32, no. 3, pp. 1193–1223, Sep. 2015. [185] E. Eckhaus and Z. Sheaffer, ‘‘Managerial hubris detection: The case of enron,’’ Risk Manage., vol. 20, no. 4, pp. 304–325, Nov. 2018. [186] K. Peterson, M. Hohensee, and F. Xia, ‘‘Email formality in the workplace: A case study on the Enron corpus,’’ in Proc. Workshop Lang. Social Media (LSM), 2011, pp. 86–95. [187] B. Klimt and Y. Yang, ‘‘The Enron corpus: A new dataset for email clas­si.cation research,’’ in Proc. Eur. Conf. Mach. Learn. Berlin, Germany: Springer, 2004, pp. 217–226. [188] I. E. Fisher, M. R. Garnsey, and M. E. Hughes, ‘‘Natural language pro­cessing in accounting, auditing and .nance: A synthesis of the literature with a roadmap for future research,’’ Intell. Syst. Accounting, Finance Manage., vol. 23, no. 3, pp. 157–214, Jul. 2016. [189] P. Hajek and R. Henriques, ‘‘Mining corporate annual reports for intel­ligent detection of .nancial statement fraud—A comparative study of machine learning methods,’’ Knowl.-Based Syst., vol. 128, pp. 139–152, Jul. 2017. [190] B. Goodman and S. Flaxman, ‘‘European union regulations on algorith­mic decision-making and a ‘right to explanation,’’’ AI Mag., vol. 38, no. 3, pp. 50–57, 2017. [191] S. Corbett-Davies and S. Goel, ‘‘The measure and mismeasure of fairness: A critical review of fair machine learning,’’ 2018, arXiv:1808.00023. [Online]. Available: http://arxiv.org/abs/1808.00023 [192] Yahoo! Finance. Accessed: Nov. 9, 2020. [Online]. Available: https:// au..nance.yahoo.com/ [193] Bloomberg Markets. Accessed: Nov. 9, 2020. [Online]. Available: https://www.bloomberg.com/markets [194] Wall Street Journal Markets. Accessed: Nov. 9, 2020. [Online]. Available: https://www.wsj.com/news/markets [195] Two Sigma: Using News to Predict Stock Movements. Accessed: Nov. 9, 2020. [Online]. Available: https://www.kaggle.com/c/two-sigma­.nancial-news/data [196] Fred Economic Data. Accessed: Nov. 9, 2020. [Online]. Available: https://fred.stlouisfed.org/ [197] The Economist Economic and Financial Indicators. Accessed: Nov. 9, 2020. [Online]. Available: https://www.economist.com/economic-and­.nancial-indicators/2018/03/20/economic-and-.nancial-indicators [198] Synthetic Variable Annuity Datasets. Accessed: Nov. 9, 2020. [Online]. Available: https://www.dshunks.com/home/vadataset [199] Home Credit Default Risk. Accessed: Nov. 9, 2020. [Online]. Available: https://www.kaggle.com/c/home-credit-default-risk/data [200] Statlog (Australian Credit Approval) Data Set. Accessed: Nov. 9, 2020. [Online]. Available: http://archive.ics.uci.edu/ml/datasets/statlog+ (australian+credit+approval) [201] Statlog (German Credit Data) Data Set. Accessed: Nov. 9, 2020. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/ statlog+(german+credit+data) [202] C. Dutang and A. Charpentier. (2019). CASdatasets: Insurance Datasets. R Package Version 1.0-10. Accessed: Nov. 9, 2020. [Online]. Available: http://dutangc.free.fr/pub/RRepos/web/CASdatasets-index.html [203] Loss Reserving Data Pulled From NAIC Schedule P. Accessed: Nov. 9, 2020. [Online]. Available: https://www.casact.org/research/index. cfm?fa=loss_reserves_data [204] Human Mortality Database. Accessed: Nov. 9, 2020. [Online]. Available: https://www.mortality.org/ [205] Prudential Life Insurance Assessment. Accessed: Nov. 9, 2020. [Online]. Available: https://www.kaggle.com/c/prudential-life-insurance­assessment/data/ [206] Credit Card Fraud Detection. Accessed: Nov. 9, 2020. [Online]. Avail­able: https://www.kaggle.com/mlg-ulb/creditcardfraud [207] IEEE-CIS Fraud Detection. Accessed: Nov. 9, 2020. [Online]. Available: https://www.kaggle.com/c/ieee-fraud-detection/data [208] Audit Dataset. Accessed: Nov. 9, 2020. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Audit+Data) [209] G. Gan and E. A. Valdez, Metamodeling for Variable Annuities. Boca Raton, FL, USA: CRC Press, 2019. [210] G. Gan, ‘‘Application of metamodeling to the valuation of large variable annuity portfolios,’’ in Proc. Winter Simul. Conf. (WSC), Dec. 2015, pp. 1103–1114. [211] G. Livan, J.-I. Inoue, and E. Scalas, ‘‘On the non-stationarity of .nancial time series: Impact on optimal portfolio selection,’’ J. Stat. Mech. Theory Exp., vol. 2012, no. 7, Jul. 2012, Art. no. P07025. [212] A. D. Pozzolo, O. Caelen, Y.-A. Le Borgne, S. Waterschoot, and G. Bontempi, ‘‘Learned lessons in credit card fraud detection from a prac­titioner perspective,’’ Expert Syst. Appl., vol. 41, no. 10, pp. 4915–4928, Aug. 2014. [213] R. Ghazali, A. J. Hussain, N. M. Nawi, and B. Mohamad, ‘‘Non­stationary and stationary prediction of .nancial time series using dynamic ridge polynomial neural network,’’ Neurocomputing, vol. 72, nos. 10–12, pp. 2359–2367, Jun. 2009. [214] T. A. Schmitt, D. Chetalova, R. Schäfer, and T. Guhr, ‘‘Non-stationarity in .nancial time series: Generic features and tail behavior,’’ Europhys. Lett., vol. 103, no. 5, p. 58003, Sep. 2013. [215] S. K. Chandrinos, G. Sakkas, and N. D. Lagaros, ‘‘AIRMS: A risk management tool using machine learning,’’ Expert Syst. Appl., vol. 105, pp. 34–48, Sep. 2018. [216] W. Zhou and G. Kapoor, ‘‘Detecting evolutionary .nancial statement fraud,’’ Decis. Support Syst., vol. 50, no. 3, pp. 570–575, Feb. 2011. [217] H. Hassani, A. Dionisio, and M. Ghodsi, ‘‘The effect of noise reduction in measuring the linear and nonlinear dependency of .nancial markets,’’ Nonlinear Anal. Real World Appl., vol. 11, no. 1, pp. 492–502, Feb. 2010. [218] G. G. Sundarkumar and V. Ravi, ‘‘A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance,’’ Eng. Appl. Artif. Intell., vol. 37, pp. 368–377, Jan. 2015. [219] M. V. Whrich, ‘‘Bias regularization in neural network models for gen­eral insurance pricing,’’ Eur. Actuarial J., pp. 1–24, 2019. [220] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, ‘‘Attention is all you need,’’ in Proc. Adv. Neural Inf. Process. Syst., 2017, pp. 5998–6008. [221] S. Bai, J. Z. Kolter, and V. Koltun, ‘‘An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,’’ 2018, arXiv:1803.01271. [Online]. Available: http://arxiv.org/abs/1803.01271 [222] J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, ‘‘Attention-based models for speech recognition,’’ in Proc. Adv. Neural Inf. Process. Syst., 2015, pp. 577–585. [223] L. Bottou, ‘‘Large-scale machine learning with stochastic gradient descent,’’ in Proc. COMPSTAT. Berlin, Germany: Physica-Verlag HD, 2010, pp. 177–186. [224] R. Richman, N. von Rummell, and M. V. Wuthrich. (2019). Believing the Bot-Model Risk in the Era of Deep Learning. [Online]. Available: https://ssrn.com/abstract=3444833 [225] M. Ayuso, M. Guillen, and A. M. Pérez-Marín, ‘‘Telematics and gender discrimination: Some usage-based evidence on whether men’s risk of accidents differs from women’s,’’ Risks, vol. 4, no. 2, p. 10, 2016. [226] R. K. E. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilovic, S. Nagar, K. N. Ramamurthy, J. Richards, D. Saha, P. Sattigeri, M. Singh, K. R. Varshney, and Y. Zhang, ‘‘AI fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias,’’ 2018, arXiv:1810.01943. [Online]. Available: http://arxiv.org/abs/1810.01943 [227] R. Binns, ‘‘Fairness in machine learning: Lessons from political phi­losophy,’’ in Proc. Conf. Fairness, Accountability Transparency, 2018, pp. 149–159. [228] J. Konecny,` H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, ‘‘Federated learning: Strategies for improving com­munication ef.ciency,’’ 2016, arXiv:1610.05492. [Online]. Available: http://arxiv.org/abs/1610.05492 [229] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, ‘‘Communication-ef.cient learning of deep networks from decentralized data,’’ in Proc. Artif. Intell. Statist., 2017, pp. 1273–1282. [230] Q. Yang, Y. Liu, T. Chen, and Y. Tong, ‘‘Federated machine learning: Concept and applications,’’ ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, pp. 1–19, 2019. [231] C. Dwork, ‘‘Differential privacy: A survey of results,’’ in Proc. Int. Conf. Theory Appl. Models Comput. Berlin, Germany: Springer, 2008, pp. 1–19. [232] K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. S. Quek, and H. V. Poor, ‘‘Federated learning with differential pri­vacy: Algorithms and performance analysis,’’ IEEE Trans. Inf. Forensics Security, vol. 15, pp. 3454–3469, 2020. [233] M. Abadi, A. Chu, I. Goodfellow, H. B. Mcmahan, I. Mironov, K. Talwar, and L. Zhang, ‘‘Deep learning with differential privacy,’’ in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., Oct. 2016, pp. 308–318. [234] C. Dwork and A. Roth, ‘‘The algorithmic foundations of differential pri­vacy,’’ Found. Trends Theor. Comput. Sci., vol. 9, nos. 3–4, pp. 211–407, 2014. [235] S. Asoodeh and F. Calmon, ‘‘Differentially private federated learning: An information-theoretic perspective,’’ in Proc. ICML-FL, 2020. [Online]. Available: http://federated-learning.org/.-icml-2020/ [236] P. Bracke, A. Datta, C. Jung, and S. Sen, ‘‘Machine learning explainabil­ity in .nance: An application to default risk analysis,’’ Bank England, London, U.K., Working Paper 816, 2019. [237] Google. Arti.cial Intelligence at Google: Our Principles. Accessed: Nov. 9, 2020. [Online]. Available: https://ai.google/principles/ [238] Microsoft. Responsible Ai. Accessed: Nov. 9, 2020. [Online]. Available: https://www.microsoft.com/en-us/ai/responsible-ai [239] R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi, ‘‘A survey of methods for explaining black box models,’’ ACM Comput. Surv., vol. 51, no. 5, pp. 1–42, Jan. 2019. [240] A. Adadi and M. Berrada, ‘‘Peeking inside the black-box: A sur­vey on explainable arti.cial intelligence (XAI),’’ IEEE Access, vol. 6, pp. 52138–52160, 2018. [241] C. Rudin, ‘‘Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,’’ Nature Mach. Intell., vol. 1, no. 5, pp. 206–215, May 2019. [242] W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K.-R. Mler, Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, vol. 11700. Cham, Switzerland: Springer, 2019. [243] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, ‘‘A survey on bias and fairness in machine learning,’’ 2019, arXiv:1908.09635. [Online]. Available: http://arxiv.org/abs/1908.09635 [244] A. Khosravi, S. Nahavandi, D. Creighton, and A. F. Atiya, ‘‘Lower upper bound estimation method for construction of neural network-based pre­diction intervals,’’ IEEE Trans. Neural Netw., vol. 22, no. 3, pp. 337–346, Mar. 2011. [245] L. Kong, J. Sun, and C. Zhang, ‘‘SDE-net: Equipping deep neural net­works with uncertainty estimates,’’ 2020, arXiv:2008.10546. [Online]. Available: http://arxiv.org/abs/2008.10546 [246] Y. Gal and Z. Ghahramani, ‘‘Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,’’ in Proc. Int. Conf. Mach. Learn., 2016, pp. 1050–1059. [247] B. Lakshminarayanan, A. Pritzel, and C. Blundell, ‘‘Simple and scalable predictive uncertainty estimation using deep ensembles,’’ in Proc. Adv. Neural Inf. Process. Syst., 2017, pp. 6402–6413. [248] Y. Gal, ‘‘Uncertainty in deep learning,’’ Ph.D. dissertation, Univ. Cam­bridge, Cambridge, U.K., 2016. [249] K. Kuo, ‘‘Generative synthesis of insurance datasets,’’ 2019, arXiv:1912.02423. [Online]. Available: http://arxiv.org/abs/1912.02423 [250] Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, ‘‘Generalizing from a few examples: A survey on few-shot learning,’’ ACM Comput. Surv., vol. 53, no. 3, pp. 1–34, 2020. [251] B. Frénay and M. Verleysen, ‘‘Classi.cation in the presence of label noise: A survey,’’ IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 5, pp. 845–869, May 2014. [252] N. K. Ahmed, A. F. Atiya, N. E. Gayar, and H. El-Shishiny, ‘‘An empirical comparison of machine learning models for time series forecasting,’’ Econ. Rev., vol. 29, nos. 5–6, pp. 594–621, Aug. 2010. [253] G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing, ‘‘Learning from class-imbalanced data: Review of methods and applica­tions,’’ Expert Syst. Appl., vol. 73, pp. 220–239, May 2017. .

Abe, Masaya, and Hideki Nakayama. 2018. “Deep Learning for Forecasting Stock Returns in the Cross-Section.” In Advances in Knowledge Discovery and Data Mining, Part 1, edited by Dinh Phung, Vincent S. Tseng, GeoffreyI. Webb, Bao Ho, Mohadeseh Ganji, and Lida Rashidi, 273–84. Cham, Switzerland: Springer International Publishing.Aggarwal, Charu C. 2018. Neural Networks and Deep Learning. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-94463-0.Aggarwal, Charu C., and Chandan K. Reddy, eds. 2014. Data Clustering: Algorithms and Applications. Boca Raton, FL: CRC Press. Ahmed, Nesreen K., Amir F. Atiya, Neamat El Gayar, and Hisham El-Shishiny. 2010. “An Empirical Comparison of Machine Learning Models for Time Series Forecasting.” Econometric Reviews 29 (5–6): 594–621. https:// doi.org/10.1080/07474938.2010.481556.Ahn, Jae Joon, Kyong Joo Oh, Tae Yoon Kim, and Dong Ha Kim. 2011. “Usefulness of Support Vector Machine to Develop an Early Warning System for Financial Crisis.” Expert Systems with Applications 38 (4): 2966–73. https:// doi.org/10.1016/j.eswa.2010.08.085.Alberg, John, and Zachary C. Lipton. 2017. “Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals,” version 2. arXiv.org/abs/1711.04837v2.
Allen, Franklin, and Risto Karjalainen. 1999. “Using Genetic Algorithms to Find Technical Trading Rules.” Journal of Financial Economics 51 (2): 245–71. https://doi.org/:10.1016/S0304-405X(98)00052-X.Alpaydin, Ethem. 2010. Introduction to Machine Learning, 2nd ed. Cambridge, MA: MIT Press.Arrieta-ibarra, Imanol, and Ignacio N. Lobato. 2015. “Testing for Predictability in Financial Returns Using Statistical Learning Procedures.” Journal of Time Series Analysis 36 (5): 672–86. https://doi.org/10.1111/ jtsa.12120. Atsalakis, George S., and Kimon P. Valavanis. 2009. “Surveying Stock Market Forecasting Techniques – Part II: Soft Computing Methods.” Expert Systems with Applications 36 (3): 5932–41. http://doi.org/10.1016/j. eswa.2008.07.006.Auria, Laura, and Rouslan A. Moro. 2008. “Support Vector Machines (SVM) as a Technique for Solvency Analysis.” DIW Berlin Discussion Paper No. 811 (August). https://ssrn.com/abstract=1424949.Avramov, Doron, Si Cheng, and Lior Metzker. 2019. “Machine Learning versus Economic Restrictions: Evidence from Stock Return Predictability.” https://ssrn.com/abstract=3450322.Azimi, Mehran, and Anup Agrawal. 2019. “Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning.” https://ssrn.com/abstract=3258821. Aziz, Saqib, and Michael Dowling. 2019. “Machine Learning and AI for Risk Management.” In Disrupting Finance: FinTech and Strategy in the 21st Century, edited by Theo Lynn, John G. Mooney, Pierangelo Rosati, and Mark Cummins, 33–50. Cham, Switzerland: Palgrave Pivot. https://doi. org/10.1007/978-3-030-02330-0_3.Ballings, Michel, Dirk Van den Poel, Nathalie Hespeels, and Ruben Gryp. 2015. “Evaluating Multiple Classifiers for Stock Price Direction Prediction.” Expert Systems with Applications 42 (20): 7046–56. https://doi.org/10.1016/j. eswa.2015.05.013.Bao, Yang, and Anindya Datta. 2014. “Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures.” Management Science 60 (6): 1371–91. https://doi.org/10.1287/mnsc.2014.1930.