Application of WGAN in financial time series generation compared with RNN

Qingyao Liao; Yuan Lu; Yinghao Luo; Shuyu Yang

doi:10.1117/12.2641413

10 November 2022 Application of WGAN in financial time series generation compared with RNN

Qingyao Liao, Yuan Lu, Yinghao Luo, Shuyu Yang

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123483Y (2022) https://doi.org/10.1117/12.2641413
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

This paper discusses WGAN, an important variant of the GAN model, and applies it to the generation of financial asset time series. Both WGAN and RNN can be applied to generating long period time series. It is found that WGAN is better than RNN in the authenticity of the generated data. The RNN model has the advantage of preventing the gradient from disappearing. WGAN model uses Wasserstein distance to measure the distance between a real distribution and a generated distribution, which overcomes the defect of JS distance in the original GAN model. RNN and WGAN are used to generate the daily frequency and S&P500 monthly frequency yield series of the Shanghai Composite Index. The results show that both can reproduce the long-term correlation and other characteristics of the real series, but WGAN generated series is significantly improved than RNN's. WGAN model can be applied to the generation of financial asset time series, and the effect is better than RNN model.

Citation Download Citation

Qingyao Liao, Yuan Lu, Yinghao Luo, and Shuyu Yang "Application of WGAN in financial time series generation compared with RNN", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123483Y (10 November 2022); https://doi.org/10.1117/12.2641413

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