Predicting Stock Returns with Batched AROW

Rachid Guennouni Hassani (), Alexis Gilles (), Emmanuel Lassalle () and Arthur Dénouveaux ()
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Rachid Guennouni Hassani: X - École polytechnique - IP Paris - Institut Polytechnique de Paris
Alexis Gilles: Machina Capital
Emmanuel Lassalle: Machina Capital
Arthur Dénouveaux: Machina Capital

Working Papers from HAL

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.

Date: 2020-03-02
Note: View the original document on HAL open archive server: https://hal.science/hal-02496048v2
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Published in [Research Report] Machina Capital. 2020

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