Generating realistic stock market order streams

• Main Authors: LI, Junyi, WANG, Xintong, LIN, Yaoyang, SINHA, Arunesh, WELLMAN, Michael P.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: application in finance; stock markets; generative models; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/5076; https://ink.library.smu.edu.sg/context/sis_research/article/6079/viewcontent/AAAI_LiJ.6697.pdf
• Institution: Singapore Management University

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 mar...