Predicting Stock Returns with Batched AROW

Rachid Guennouni Hassani; Alexis Gilles; Emmanuel Lassalle; Arthur Dénouveaux

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arXiv · 2003.03076

Predicting Stock Returns with Batched AROW

Rachid Guennouni Hassani·Alexis Gilles·Emmanuel Lassalle·Arthur Dénouveaux

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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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Rachid Guennouni Hassani, Alexis Gilles, Emmanuel Lassalle, Arthur Dénouveaux. 2020-03-06. Predicting Stock Returns with Batched AROW. https://arxiv.org/abs/2003.03076

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