Deep Portfolio Optimization via Distributional Prediction of Residual Factors

TL;DR

This paper introduces a novel deep learning-based portfolio construction method that predicts the distribution of residual factors, leveraging financial inductive biases for improved risk hedging and robustness in non-stationary markets.

Contribution

It presents a new neural network architecture and extraction method for residual information, enhancing portfolio optimization by incorporating financial inductive biases.

Findings

Effective prediction of residual factors on US and Japanese stock markets.

Improved trading performance through ablation of individual techniques.

Potential wide applications in financial problems.

Abstract

Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. The key technical ingredients are twofold. First, we introduce a computationally efficient extraction method for the residual information, which can be easily combined with various prediction algorithms. Second, we propose a novel neural network architecture that allows us to incorporate widely acknowledged financial inductive biases such as amplitude invariance and time-scale invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. We anticipate our techniques may have wide applications in various financial problems.