Multi-Factor Timing with Deep Learning
开发了带有经济约束的深度神经网络，通过多任务学习和长短期记忆网络捕捉因子共同结构及宏观经济状态，在预测精度和经济收益上超越所有基准，并识别出失业率、杠杆、盈利能力和货币等关键预测变量。
Abstract We develop deep neural networks with economically motivated restrictions that are designed to overcome the main challenges of factor timing. Our critical innovations include integrating multitask (MT) learning to capture the common structure across factors, with long short-term memory neural networks to extract financial and macroeconomic states. This dynamic MT neural network outperforms all benchmarks in terms of predictive accuracy and economic gains. We pinpoint unemployment, along with variations on leverage, profitability, and money as key predictors, and highlight the importance of capturing their nonlinear interactions. Improved factor timing through neural networks with economic restrictions facilitates more reliable investigation into the economic mechanisms driving factor risk premia, and underscores the value of deep learning for factor investing.