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Predicting Stock Returns with Batched AROW

Author

Listed:
  • Rachid Guennouni Hassani

    (X - École polytechnique - IP Paris - Institut Polytechnique de Paris)

  • Alexis Gilles

    (Machina Capital)

  • Emmanuel Lassalle

    (Machina Capital)

  • Arthur Dénouveaux

    (Machina Capital)

Abstract

We extend the AROW regression algorithm developed by Vaits and Crammer in [VC11] to handle synchronous mini-batch updates and apply it to stock return prediction. By design, the model should be more robust to noise and adapt better to non-stationarity compared to a simple rolling regression. We empirically show that the new model outperforms more classical approaches by backtesting a strategy on S&P500 stocks.

Suggested Citation

  • Rachid Guennouni Hassani & Alexis Gilles & Emmanuel Lassalle & Arthur Dénouveaux, 2020. "Predicting Stock Returns with Batched AROW," Working Papers hal-02496048, HAL.
    • Handle: RePEc:hal:wpaper:hal-02496048
    Note: View the original document on HAL open archive server: https://hal.science/hal-02496048v2
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    References listed on IDEAS

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    1. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    2. Terence Tai-Leung Chong & Wing-Kam Ng & Venus Khim-Sen Liew, 2014. "Revisiting the Performance of MACD and RSI Oscillators," JRFM, MDPI, vol. 7(1), pages 1-12, February.
    3. Thilo A. Schmitt & Desislava Chetalova & Rudi Schafer & Thomas Guhr, 2013. "Non-Stationarity in Financial Time Series and Generic Features," Papers 1304.5130, arXiv.org, revised May 2013.
    4. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
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