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QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE by Zhao, Junjie; Zhang, Chengxi; Qin, Min; Yang, Peng is a scholarly article available to read on EtoBox.

What is QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE about?

The goal of alpha factor mining is to discover indicative signals of investment opportunities from the historical financial market data of assets, which can be used to predict asset returns and gain excess profits. Recently, a promising framework is proposed for generating formulaic alpha factors using deep reinforcement learning, and quickly gained research focuses from both academia and industries. This paper first argues that the originally employed policy training method, i.e., Proximal Policy Optimization (PPO), faces several important issues in the context of alpha factors mining, making it ineffective to explore the search space of the formula. Herein, a novel reinforcement learning based on the well-known REINFORCE algorithm is proposed. Given that the underlying state transition function adheres to the Dirac distribution, the Markov Decision Process within this framework exhibit minimal environmental variability, making REINFORCE algorithm more appropriate than PPO. A new dedicated baseline is designed to theoretically reduce the commonly suffered high variance of REINFORCE. Moreover, the information ratio is introduced as a reward shaping mechanism to encourage the genera

Author

Zhao, Junjie; Zhang, Chengxi; Qin, Min; Yang, Peng

Published

2024

Language

EN