Deep Portfolio Optimization via Distributional Prediction of Residual Factors

Authors

  • Kentaro Imajo Preferred Networks, Inc.
  • Kentaro Minami Preferred Networks, Inc.
  • Katsuya Ito Preferred Networks, Inc.
  • Kei Nakagawa Nomura Asset Management Co.,Ltd.

Keywords:

Economic/Financial

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.

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Published

2021-05-18

How to Cite

Imajo, K., Minami, K., Ito, K., & Nakagawa, K. (2021). Deep Portfolio Optimization via Distributional Prediction of Residual Factors. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 213-222. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16095

Issue

Section

AAAI Technical Track on Application Domains