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

21 Pages Posted: 3 May 2022

See all articles by Masanori HIRANO

Masanori HIRANO

The University of Tokyo

Hiroki Sakaji

The University of Tokyo

Kiyoshi Izumi

University of Tokyo - School of Engineering

Date Written: April 28, 2022

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.

Keywords: Generative adversarial networks (GAN), Financial markets, Policy gradient, Order generation

JEL Classification: G1

Suggested Citation

HIRANO, Masanori and Sakaji, Hiroki and Izumi, Kiyoshi, Policy Gradient Stock Gan for Realistic Discrete Order Data Generation in Financial Markets (April 28, 2022). Available at SSRN: https://ssrn.com/abstract=4095304 or http://dx.doi.org/10.2139/ssrn.4095304

Masanori HIRANO (Contact Author)

The University of Tokyo ( email )

7-3-1 Hongo
Bunkyo-ku
Tokyo, 113-0033
Japan

Hiroki Sakaji

The University of Tokyo ( email )

7-3-1 Hongo
Bunkyo-ku
Tokyo, 113-0033
Japan

Kiyoshi Izumi

University of Tokyo - School of Engineering ( email )

Tokyo
Japan

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
2
Abstract Views
23
PlumX Metrics