q-fin updates on arXiv.org
Mon, 09 Mar 2020 18:06:16 GMT language
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.