Deep-learning models for forecasting financial risk premia and their interpretations
针对资产收益的噪声和非平稳性，将风险溢价预测拆分为时间序列和横截面两个独立任务，并用带跳跃连接的神经网络训练，模型优于现有标准ML模型，还通过局部近似技术解释了预测结果。
The measurement of financial risk premia, the amount that a risky asset will outperform a risk-free one, is an important problem in asset pricing. The noisiness and non-stationarity of asset returns makes the estimation of risk premia using machine learning (ML) techniques challenging. In this work, we develop ML models that solve the problems associated with risk premia forecasting by separating risk premia prediction into two independent tasks, a time series model and a cross-sectional model, and using neural networks with skip connections to enable their deep neural network training. These models are tested robustly with different metrics, and we observe that our models outperform several existing standard ML models. A known issue with ML models is their ‘black box’ nature, i.e. their opaqueness to interpretability. We interpret these deep neural networks using local approximation-based techniques that provide explanations for our model's predictions.