论文标题
股票回报预测的时变神经网络
Time-varying neural network for stock return prediction
论文作者
论文摘要
我们考虑在随着时间的变化环境中的神经网络培训的问题。机器学习算法在不会随时间变化的问题上表现出色。但是，金融市场遇到的问题通常是时间变化的。我们建议在线早期停止算法，并表明使用该算法训练的神经网络可以跟踪使用未知动力学的功能更改的功能。我们将提议的算法与当前预测美国股票收益的当前方法进行了比较。我们还表明，突出的因素（例如规模和动量影响）和行业指标表现出时间变化的股票回报预测。我们发现，在市场困扰期间，行业指标以牺牲公司水平的特征为代价而增加了重要性。这表明行业在风险增加期间在解释股票回报方面发挥了作用。
We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time-varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly U.S. stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators, exhibit time varying stock return predictiveness. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.
