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How hard is it to pick the right model? MCS and backtest overfitting

Author

Listed:
  • Aparicio, Diego

    (Department of Economics, Massachusetts Institute of Technology)

  • López de Prado, Marcos

    (True Positive Technologies)

Abstract

Recent advances in machine learning, artificial intelligence, and the availability of billions of high frequency data signals have made model selection a challenging and pressing need. However, most of the model selection methods available in modern finance are subject to backtest overfitting. This is the probability that one will select a financial strategy that outperforms during backtest, but underperforms in practice. We evaluate the performance of the novel model confidence set (MCS) introduced in Hansen et al. (2011a) in a simple machine learning trading strategy problem. We find that MCS is not robust to multiple testing and that it requires a very high signal-to-noise ratio to be utilizable. More generally, we raise awareness on the limitations of model selection in finance.

Suggested Citation

  • Aparicio, Diego & López de Prado, Marcos, 2018. "How hard is it to pick the right model? MCS and backtest overfitting," Algorithmic Finance, IOS Press, vol. 7(1-2), pages 53-61.
    • Handle: RePEc:ris:iosalg:0067
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    Cited by:

    1. Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo, 2024. "Reservoir computing for macroeconomic forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1206-1237.
    2. Axel Groß-Klußmann, 2024. "Learning deep news sentiment representations for macro-finance," Digital Finance, Springer, vol. 6(3), pages 341-377, September.

    More about this item

    Keywords

    Forecasting; model confidence set; machine learning; model selection; multiple testing JEL Codes: G17; C52; C53;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

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