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Predicting Stock Returns with Batched AROW

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.
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https://hal.archives-ouvertes.fr/hal-02496048
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Submitted on : Monday, March 9, 2020 - 6:12:17 PM
Last modification on : Saturday, March 21, 2020 - 1:35:27 AM
Long-term archiving on: : Wednesday, June 10, 2020 - 4:33:53 PM

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  • HAL Id : hal-02496048, version 2
  • ARXIV : 2003.03076

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Rachid Guennouni Hassani, Alexis Gilles, Emmanuel Lassalle, Arthur Dénouveaux. Predicting Stock Returns with Batched AROW. [Research Report] Machina Capital. 2020. ⟨hal-02496048v2⟩

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