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Predicting Stock Returns with Batched AROW

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  • Rachid Guennouni Hassani
  • Alexis Gilles
  • Emmanuel Lassalle
  • Arthur D'enouveaux

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 D'enouveaux, 2020. "Predicting Stock Returns with Batched AROW," Papers 2003.03076, arXiv.org, revised Mar 2020.
    • Handle: RePEc:arx:papers:2003.03076
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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. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    4. 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.
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