Predicting Stock Returns with Batched AROW
Computational Finance
2020-03-23 v2 Machine Learning
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.
Keywords
Cite
@article{arxiv.2003.03076,
title = {Predicting Stock Returns with Batched AROW},
author = {Rachid Guennouni Hassani and Alexis Gilles and Emmanuel Lassalle and Arthur Dénouveaux},
journal= {arXiv preprint arXiv:2003.03076},
year = {2020}
}