Predicting Stock Returns with Batched AROW
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.
- Pub Date:
- March 2020
- Quantitative Finance - Computational Finance;
- Statistics - Machine Learning