The search for time-series predictability-based anomalies
Abstract
This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.
First published online 29 November 2021
Keyword : stock market, investment algorithm, trading rules, alpha maximization, market timing, artificial intelligence, machine learning, reinforcement learning, evolutionary computation, perceptron
How to Cite
Ospina-Holguín, J. H., & Padilla-Ospina, A. M. (2022). The search for time-series predictability-based anomalies. Journal of Business Economics and Management, 23(1), 1–19. https://doi.org/10.3846/jbem.2021.15650
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Kolm, P. N., & Ritter, G. (2021). Modern perspectives on reinforcement learning in finance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3449401
Liu, Y., Zhou, G., & Zhu, Y. (2020). Maximizing the Sharpe ratio: A genetic programming approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3726609
Lo, A. W., & Hasanhodzic, J. (2010). The evolution of technical analysis: Financial prediction from Babylonian tablets to Bloomberg terminals. John Wiley & Sons.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705–1765. https://doi.org/10.1111/0022-1082.00265
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Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence. Journal of Banking & Finance, 34(11), 2573–2586. https://doi.org/10.1016/j.jbankfin.2010.04.014
Mosavi, A., Faghan, Y., Ghamisi, P., Duan, P., Ardabili, S. F., Salwana, E., & Band, S. S. (2020). Comprehensive review of deep reinforcement learning methods and applications in economics. Mathematics, 8(10), 1640. https://doi.org/10.3390/math8101640
Murphy, J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Park, C.-H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), 786–826. https://doi.org/10.1111/j.1467-6419.2007.00519.x
Pendharkar, P. C., & Cusatis, P. (2018). Trading financial indices with reinforcement learning agents. Expert Systems with Applications, 103, 1–13. https://doi.org/10.1016/j.eswa.2018.02.032
Plummer, T. (2010). Forecasting financial markets the psychology of successful investing. Kogan Page.
Pring, M. (2002). Technical analysis explained: The successful investor’s guide to spotting investment trends and turning points (4th ed.). McGraw-Hill.
Rocca, P., Oliveri, G., & Massa, A. (2011). Differential evolution as applied to electromagnetics. IEEE Antennas and Propagation Magazine, 53(1), 38–49. https://doi.org/10.1109/MAP.2011.5773566
Salimans, T., Ho, J., Chen, X., Sidor, S., & Sutskever, I. (2017). Evolution strategies as a scalable alternative to reinforcement learning. http://arxiv.org/abs/1703.03864
Storn, R., & Price, K. (1997). Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT press. http://incompleteideas.net/book/the-book.html
Xufre Casqueiro, P., & Rodrigues, A. J. L. (2006). Neuro-Dynamic trading methods. European Journal of Operational Research, 175(3), 1400–1412. https://doi.org/10.1016/j.ejor.2005.02.015
Zakamulin, V. (2016). Market timing with moving averages: Anatomy and performance of trading rules. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2585056
Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep reinforcement learning for trading. The Journal of Financial Data Science, 2(2), 25–40. https://doi.org/10.3905/jfds.2020.1.030
Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941. https://doi.org/10.1016/j.eswa.2008.07.006
Atsalakis, G. S., & Valavanis, K. P. (2013). Surveying stock market forecasting techniques – Part I: Conventional method. In C. Zopounidis (Ed.), Computation optimization in economics and finance research compendium (pp. 49–104). Nova Science Publishers.
Balduzzi, P., & Lynch, A. W. (1999). Transaction costs and predictability: Some utility cost calculations. Journal of Financial Economics, 52(1), 47–78. https://doi.org/10.1016/S0304-405X(99)00004-5
Bertoluzzo, F., & Corazza, M. (2014). Reinforcement learning for automated financial trading: Basics and applications. In S. Bassis, A. Esposito, & F. Morabito (Eds.), Recent advances of neural network models and applications (pp. 197–213). Springer. https://doi.org/10.1007/978-3-319-04129-2_20
Brogaard, J., & Zareei, A. (2018). Machine learning and the stock market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3233119
Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82. https://doi.org/10.1111/j.1540-6261.1997.tb03808.x
Chan, E. P. (2017). Machine trading: Deploying computer algorithms to conquer the markets. John Wiley & Sons, Inc. https://doi.org/10.1002/9781119244066
Charpentier, A., Élie, R., & Remlinger, C. (2021) Reinforcement learning in economics and finance. Computational Economics, 1–38. https://doi.org/10.1007/s10614-021-10119-4
Cong, L., Tang, K., Wang, J., & Zhang, Y. (2021). AlphaPortfolio: Direct construction through deep reinforcement learning and interpretable AI. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3554486
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2007). Technical analysis of stock trends (9th ed.). CRC Press.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010
Fischer, T. G. (2018, December). Reinforcement learning in financial markets – a survey (FAU Discussion Papers in Economics No. 12/2018). Erlangen. http://hdl.handle.net/10419/183139
Gold, C. (2003). FX trading via recurrent reinforcement learning. In 2003 IEEE International Conference on Computational Intelligence for Financial Engineering (pp. 363–370). Hong Kong, China. https://doi.org/10.1109/CIFER.2003.1196283
Gričar, S., & Bojnec, Š. (2019). Prices of short-stay accommodation: Time series of a eurozone country. International Journal of Contemporary Hospitality Management, 31(12), 4500–4519. https://doi.org/10.1108/IJCHM-01-2019-0091
Han, Y., Yang, K., & Zhou, G. (2013). A new anomaly: The cross-sectional profitability of technical analysis. Journal of Financial and Quantitative Analysis, 48(5), 1433–1461. https://doi.org/10.1017/S0022109013000586
Han, Y., Zhou, G., & Zhu, Y. (2016). A trend factor: Any economic gains from using information over investment horizons? Journal of Financial Economics, 122(2), 352–375. https://doi.org/10.1016/j.jfineco.2016.01.029
Hassouneh, I., Serra, T., & Bojnec, Š. (2015). Nonlinearities in the Slovenian apple price transmission. British Food Journal, 117(1), 461–478. https://doi.org/10.1108/BFJ-03-2014-0109
Hassouneh, I., Serra, T., Bojnec, Š., & Gil, J. M. (2017). Modelling price transmission and volatility spillover in the Slovenian wheat market. Applied Economics, 49(41), 4116–4126. https://doi.org/10.1080/00036846.2016.1276273
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226–251. https://doi.org/10.1016/j.eswa.2019.01.012
Keim, D. B. (2008). Financial market anomalies. In S. N. Durlauf & L. E. Blume (Eds.), The new Palgrave dictionary of economics (2nd ed.). Palgrave Macmillan.
Kidger, P., & Lyons, T. (2020). Universal approximation with deep narrow networks. http://arxiv.org/abs/1905.08539v2
Kirkpatrick, C., & Dahlquist, J. (2011). Technical analysis: The complete resource for financial market technicians. FT Press.
Kolm, P. N., & Ritter, G. (2021). Modern perspectives on reinforcement learning in finance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3449401
Liu, Y., Zhou, G., & Zhu, Y. (2020). Maximizing the Sharpe ratio: A genetic programming approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3726609
Lo, A. W., & Hasanhodzic, J. (2010). The evolution of technical analysis: Financial prediction from Babylonian tablets to Bloomberg terminals. John Wiley & Sons.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705–1765. https://doi.org/10.1111/0022-1082.00265
Lynch, A. W., & Balduzzi, P. (2000). Predictability and transaction costs: The impact on rebalancing rules and behavior. The Journal of Finance, 55(5), 2285–2309. https://doi.org/10.1111/0022-1082.00287
Malkiel, B. G. (2007). A random walk down Wall Street: The time-tested strategy for successful investing (revised and updated). W. W. Norton & Company.
Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence. Journal of Banking & Finance, 34(11), 2573–2586. https://doi.org/10.1016/j.jbankfin.2010.04.014
Mosavi, A., Faghan, Y., Ghamisi, P., Duan, P., Ardabili, S. F., Salwana, E., & Band, S. S. (2020). Comprehensive review of deep reinforcement learning methods and applications in economics. Mathematics, 8(10), 1640. https://doi.org/10.3390/math8101640
Murphy, J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Park, C.-H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), 786–826. https://doi.org/10.1111/j.1467-6419.2007.00519.x
Pendharkar, P. C., & Cusatis, P. (2018). Trading financial indices with reinforcement learning agents. Expert Systems with Applications, 103, 1–13. https://doi.org/10.1016/j.eswa.2018.02.032
Plummer, T. (2010). Forecasting financial markets the psychology of successful investing. Kogan Page.
Pring, M. (2002). Technical analysis explained: The successful investor’s guide to spotting investment trends and turning points (4th ed.). McGraw-Hill.
Rocca, P., Oliveri, G., & Massa, A. (2011). Differential evolution as applied to electromagnetics. IEEE Antennas and Propagation Magazine, 53(1), 38–49. https://doi.org/10.1109/MAP.2011.5773566
Salimans, T., Ho, J., Chen, X., Sidor, S., & Sutskever, I. (2017). Evolution strategies as a scalable alternative to reinforcement learning. http://arxiv.org/abs/1703.03864
Storn, R., & Price, K. (1997). Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT press. http://incompleteideas.net/book/the-book.html
Xufre Casqueiro, P., & Rodrigues, A. J. L. (2006). Neuro-Dynamic trading methods. European Journal of Operational Research, 175(3), 1400–1412. https://doi.org/10.1016/j.ejor.2005.02.015
Zakamulin, V. (2016). Market timing with moving averages: Anatomy and performance of trading rules. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2585056
Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep reinforcement learning for trading. The Journal of Financial Data Science, 2(2), 25–40. https://doi.org/10.3905/jfds.2020.1.030