Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets

TL;DR

This paper introduces a novel GAN model utilizing policy gradients to generate realistic discrete order data in financial markets, addressing the limitations of continuous data generation in previous models.

Contribution

The study presents a new GAN architecture that effectively generates discrete financial order data using policy gradients, improving realism and learning monitoring.

Findings

Outperforms previous models in order distribution accuracy

Uses entropy of the policy to monitor GAN learning status

Addresses discrete data generation challenges in financial markets

Abstract

This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures' learning limitations. However, in reality, the orders are discrete, such as order prices, which has minimum order price unit, or order types. Thus, we change the generation method to place the generated fake orders into discrete spaces in this study. Because this change disabled the ordinary GAN learning algorithm, this study employed a policy gradient, frequently used in reinforcement learning, for the learning algorithm. Through our experiments, we show that our proposed model outperforms previous models in generated order distribution. As an additional benefit of introducing the policy gradient, the entropy of the generated policy can be used to check GAN's learning status. In the future, higher performance GANs, better evaluation methods, or the applications of our GANs can be addressed.