Deep Factor Model

TL;DR

This paper introduces a deep learning-based multifactor model for stock returns that combines predictive accuracy with interpretability using layer-wise relevance propagation, applied to the Japanese stock market.

Contribution

It presents a novel deep factor model that integrates deep learning with interpretability techniques to predict stock returns and identify contributing factors.

Findings

Deep factor model outperforms traditional linear models.

Layer-wise relevance propagation effectively explains factor contributions.

Model demonstrates strong predictive capability in empirical tests.

Abstract

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.