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Intraday Market Predictability: A Machine Learning Approach

Author

Listed:
  • Dillon Huddleston
  • Fred Liu
  • Lars Stentoft

Abstract

Conducting, to our knowledge, the largest study ever of 5-min equity market returns using state-of-the-art machine learning models trained on the cross-section of lagged market index constituent returns, we show that regularized linear models and nonlinear tree-based models yield significant market return predictability. Ensemble models perform the best across time and their predictability translates into economically significant Sharpe ratios of 0.98 after transaction costs. These results provide strong evidence that intraday market returns are predictable during short time horizons, beyond what can be explained by transaction costs. Furthermore, we show that constituent returns hold significant predictive information that is not contained in market returns or in price trend and liquidity characteristics. Consistent with the hypothesis that predictability is driven by slow-moving trader capital, predictability decreased post-decimalization, and market returns are more predictable during the middle of the day, on days with high volatility or illiquidity, and in financial crisis periods.

Suggested Citation

  • Dillon Huddleston & Fred Liu & Lars Stentoft, 2023. "Intraday Market Predictability: A Machine Learning Approach," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 485-527.
  • Handle: RePEc:oup:jfinec:v:21:y:2023:i:2:p:485-527.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbab007
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    Citations

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    Cited by:

    1. Barua, Ronil & Sharma, Anil K., 2023. "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, vol. 58(PC).

    More about this item

    Keywords

    big data; deep neural networks; elastic net; equity market; Fintech; gradient boosting; high frequency; lasso; machine learning; random forest; return prediction;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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