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Nonlinear support vector machines can systematically identify stocks with high and low future returns

Author

Listed:
  • Huerta, Ramon

    (Biocircuits Institute)

  • Corbacho, Fernando

    (Computer Science Department, Universidad Autonoma de Madrid)

  • Elkan, Charles

    (Computer Science Department, University of California)

Abstract

This paper investigates the profitability of a trading strategy based on training a model to identify stocks with high or low predicted returns. A tail set is defined to be a group of stocks whose volatility-adjusted price change is in the highest or lowest quantile, for example the highest or lowest 5%. Each stock is represented by a set of technical and fundamental features computed using CRSP and Compustat data. A classifier is trained on historical tail sets and tested on future data. The classifier is chosen to be a nonlinear support vector machine (SVM) due to its simplicity and effectiveness. The SVM is trained once per month, in order to adjust to changing market conditions. Portfolios are formed by ranking stocks using the classifier output. The highest ranked stocks are used for long positions and the lowest ranked ones for short sales. The Global Industry Classification Standard is used to build a model for each sector such that a total of 8 long-short portfolios for Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, and Information Technology are formed. The data range from 1981 to 2010. Without measuring trading costs, but using 91 day holding periods to minimize these, the strategy leads to annual excess returns (Jensen alpha) of 15% with volatilities under 8% using the top 25% of the stocks of the distribution for training long positions and the bottom 25% for the short ones.

Suggested Citation

  • Huerta, Ramon & Corbacho, Fernando & Elkan, Charles, 2013. "Nonlinear support vector machines can systematically identify stocks with high and low future returns," Algorithmic Finance, IOS Press, vol. 2(1), pages 45-58.
    • Handle: RePEc:ris:iosalg:0024
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    Citations

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    Cited by:

    1. Ślepaczuk Robert & Zenkova Maryna, 2018. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Sciendo, vol. 5(52), pages 186-205, January.
    2. Emilio Colombo & Gianfranco Forte & Roberto Rossignoli, 2019. "Carry Trade Returns with Support Vector Machines," International Review of Finance, International Review of Finance Ltd., vol. 19(3), pages 483-504, September.
    3. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    4. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    5. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
    6. Emilio, Colombo & Gianfranco, Forte & Roberto, Rossignoli, 2016. "Still crazy after all these years: the returns on carry trade," Working Papers 327, University of Milano-Bicocca, Department of Economics, revised 07 Feb 2016.
    7. Michael Pinelis & David Ruppert, 2023. "Maximizing Portfolio Predictability with Machine Learning," Papers 2311.01985, arXiv.org.
    8. XingYu Fu & JinHong Du & YiFeng Guo & MingWen Liu & Tao Dong & XiuWen Duan, 2018. "A Machine Learning Framework for Stock Selection," Papers 1806.01743, arXiv.org, revised Aug 2018.

    More about this item

    Keywords

    Support vector machines; sector neutral portfolios; long-short portfolios; technical analysis; fundamental analysis;
    All these keywords.

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets

    Statistics

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