论文标题
深度学习，可预测性和最佳投资组合回报
Deep Learning, Predictability, and Optimal Portfolio Returns
论文作者
论文摘要
我们研究了一个长马投资者的动态投资组合选择，该投资者使用深度学习方法来预测最佳投资组合时的股本回报。我们的结果表明，使用深度学习通过确定性等效回报和夏普比率来形成最佳投资组合，从统计学和经济上具有显着的好处。我们证明，在学习复杂的时间序列依赖性方面出色的长期记忆复发性神经网络在所考虑的各种网络之间产生了卓越的性能。通过深度学习的返回可预测性可在不同子样本中产生大幅改善的投资组合性能，尤其是在衰退期间。这些收益在包括交易成本，短销售和借贷限制方面具有稳健性。
We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty equivalent returns and Sharpe ratios. We demonstrate that a long-short-term-memory recurrent neural network, which excels in learning complex time-series dependencies, generates a superior performance among a variety of networks considered. Return predictability via deep learning generates substantially improved portfolio performance across different subsamples, particularly during recessionary periods. These gains are robust to including transaction costs, short-selling and borrowing constraints.
