论文标题
通过批处理预测股票回报
Predicting Stock Returns with Batched AROW
论文作者
论文摘要
我们扩展了[VC11]中Vaits and Crammer开发的AROW回归算法，以处理同步迷你批次更新，并将其应用于股票回报预测。根据设计，与简单的滚动回归相比，该模型应该更适合噪声并更好地适应非平稳性。我们从经验上表明，新模型通过对S \＆P500股票进行策略进行回测优于更古典的方法。
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.
