  We propose to represent a return model and risk model in a unified manner
with deep learning, which is a representative model that can express a
nonlinear relationship. Although deep learning performs quite well, it has
significant disadvantages such as a lack of transparency and limitations to the
interpretability of the prediction. This is prone to practical problems in
terms of accountability. Thus, we construct a multifactor model by using
interpretable deep learning. We implement deep learning as a return model to
predict stock returns with various factors. Then, we present the application of
layer-wise relevance propagation (LRP) to decompose attributes of the predicted
return as a risk model. By applying LRP to an individual stock or a portfolio
basis, we can determine which factor contributes to prediction. We call this
model a deep factor model. We then perform an empirical analysis on the
Japanese stock market and show that our deep factor model has better predictive
capability than the traditional linear model or other machine learning methods.
In addition , we illustrate which factor contributes to prediction.
