Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2020
Abstract
We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks. We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.
Keywords
application in finance, stock markets, generative models
Discipline
Artificial Intelligence and Robotics
Research Areas
Data Science and Engineering
Publication
Proceedings of 34rd AAAI Conference on Artificial Intelligence (AAAI), New York, 2020 February 7-12
First Page
1
Last Page
8
City or Country
New York
Citation
LI, Junyi; WANG, Xintong; LIN, Yaoyang; SINHA, Arunesh; and WELLMAN, Michael P..
Generating realistic stock market order streams. (2020). Proceedings of 34rd AAAI Conference on Artificial Intelligence (AAAI), New York, 2020 February 7-12. 1-8.
Available at: https://ink.library.smu.edu.sg/sis_research/5076
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.