深度学习、可预测性与最优投资组合回报
Deep Learning, Predictability, and Optimal Portfolio Returns
AI总结：
本文通过深度学习方法提升投资组合回报，发现LSTM架构在高频再平衡下能显著提高夏普比率和确定等价收益。
摘要：
我们研究了一位投资者使用深度学习方法预测股票市场超额收益的动态投资组合选择问题。在两项资产配置问题中，深度神经网络——包括前馈网络和长短期记忆（LSTM）递归架构——在确定等价收益和夏普比率方面相对于线性预测回归表现出经济上显著的收益。这些收益在替代绩效指标、交易成本的纳入、借贷和做空限制、不同的再平衡时间跨度和子样本划分下都具有鲁棒性，并且在NBER衰退期和大波动时期尤为明显。在考虑的神经网络类别中，经济表现在不同架构之间大致相似，其中递归LSTM规范在更频繁的再平衡时提供额外的收益。总体而言，我们的证据表明，通过深度学习利用标准预测变量的时间序列结构，可以为投资者带来超越线性模型的有意义的投资组合改进。
英文摘要：
We study the dynamic portfolio selection of an investor who uses deep learning methods to forecast stock market excess returns. In a two-asset allocation problem, deep neural networks -- both feedforward and long short-term memory (LSTM) recurrent architectures -- deliver economically significant gains in terms of certainty equivalent returns and Sharpe ratios relative to linear predictive regressions. These gains are robust to alternative performance measures, the inclusion of transaction costs, borrowing and short-selling constraints, different rebalancing horizons, and subsample splits, and are particularly pronounced during NBER recessions and periods with large return swings. Within the class of neural networks we consider, economic performance is broadly similar across architectures, with the recurrent LSTM specification providing incremental benefits with more frequent rebalancing. Overall, our evidence suggests that exploiting the time-series structure of standard predictor variables via deep learning can generate meaningful portfolio improvements for investors beyond those obtained from linear models.
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