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Maximally Machine-Learnable Portfolios

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  • Philippe Goulet Coulombe
  • Maximilian Goebel

Abstract

When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable. Precisely, we introduce MACE, a multivariate extension of Alternating Conditional Expectations that achieves the aforementioned goal by wielding a Random Forest on one side of the equation, and a constrained Ridge Regression on the other. There are two key improvements with respect to Lo and MacKinlay's original maximally predictable portfolio approach. First, it accommodates for any (nonlinear) forecasting algorithm and predictor set. Second, it handles large portfolios. We conduct exercises at the daily and monthly frequency and report significant increases in predictability and profitability using very little conditioning information. Interestingly, predictability is found in bad as well as good times, and MACE successfully navigates the debacle of 2022.

Suggested Citation

  • Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2306.05568
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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    4. Lo, Andrew W. & Mackinlay, A. Craig, 1997. "Maximizing Predictability In The Stock And Bond Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 1(1), pages 102-134, January.
    5. Campbell, John Y. & Hentschel, Ludger, 1992. "No news is good news *1: An asymmetric model of changing volatility in stock returns," Journal of Financial Economics, Elsevier, vol. 31(3), pages 281-318, June.
    6. Harris, Richard D.F. & Shen, Jian & Yilmaz, Fatih, 2022. "Maximally predictable currency portfolios," Journal of International Money and Finance, Elsevier, vol. 128(C).
    7. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    8. Hui Guo, 2006. "On the Out-of-Sample Predictability of Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 79(2), pages 645-670, March.
    9. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    10. Yoshihiro Takaya & Hiroshi Konno, 2010. "A Maximal Predictability Portfolio Subject To A Turnover Constraint," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 27(01), pages 1-13.
    11. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    12. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    13. Luiz Renato Lima & Fanning Meng, 2017. "Out‐of‐Sample Return Predictability: A Quantile Combination Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 877-895, June.
    14. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.
    15. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
    16. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    17. T. Griveau-Billion & B. Calderhead, 2021. "Efficient computation of mean reverting portfolios using cyclical coordinate descent," Quantitative Finance, Taylor & Francis Journals, vol. 21(4), pages 673-684, April.
    18. Hiroshi Konno & Yuuhei Morita & Rei Yamamoto, 2010. "A maximal predictability portfolio using absolute deviation reformulation," Computational Management Science, Springer, vol. 7(1), pages 47-60, January.
    19. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    20. Hiroshi Konno & Yoshihiro Takaya & Rei Yamamoto, 2010. "A Maximal Predictability Portfolio Using Dynamic Factor Selection Strategy," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 355-366.
    21. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    22. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    23. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    24. Ziping Zhao & Daniel P. Palomar, 2016. "Mean-Reverting Portfolio Design via Majorization-Minimization Method," Papers 1611.08393, arXiv.org.
    25. R. Gopinathan & S. Raja Sethu Durai, 2019. "Stock market and macroeconomic variables: new evidence from India," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-17, December.
    26. Fogarasi, Norbert & Levendovszky, Janos, 2013. "Sparse, mean reverting portfolio selection using simulated annealing," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 197-211.
    27. Balder, Sven & Schweizer, Nikolaus, 2017. "Risk aversion vs. the Omega ratio: Consistency results," Finance Research Letters, Elsevier, vol. 21(C), pages 78-84.
    28. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    29. Christopher Krauss, 2017. "Statistical Arbitrage Pairs Trading Strategies: Review And Outlook," Journal of Economic Surveys, Wiley Blackwell, vol. 31(2), pages 513-545, April.
    30. Chordia, Tarun & Subrahmanyam, Avanidhar & Tong, Qing, 2014. "Have capital market anomalies attenuated in the recent era of high liquidity and trading activity?," Journal of Accounting and Economics, Elsevier, vol. 58(1), pages 41-58.
    31. Rei Yamamoto & Daisuke Ishii & Hiroshi Konno, 2007. "A Maximal Predictability Portfolio Model: Algorithm And Performance Evaluation," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(06), pages 1095-1109.
    32. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    33. Xingyi Li & Valeriy Zakamulin, 2020. "Stock volatility predictability in bull and bear markets," Quantitative Finance, Taylor & Francis Journals, vol. 20(7), pages 1149-1167, July.
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    Cited by:

    1. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    2. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.

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