Generating realistic stock market order streams

Generating realistic stock market order streams

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...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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