Predicting Stock Returns with Batched AROW
This work addresses stock prediction for financial analysts, but it is incremental as it builds on an existing algorithm.
The authors tackled stock return prediction by extending the AROW regression algorithm to support synchronous mini-batch updates, resulting in a model that outperformed classical approaches in backtesting on S&P500 stocks.
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.