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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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

id	sg-smu-ink.sis_research-6079
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spelling	sg-smu-ink.sis_research-60792020-03-19T08:55:38Z Generating realistic stock market order streams LI, Junyi WANG, Xintong LIN, Yaoyang SINHA, Arunesh WELLMAN, Michael P. 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. 2020-02-01T08:00:00Z text application/pdf 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 http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University application in finance stock markets generative models Artificial Intelligence and Robotics
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	application in finance stock markets generative models Artificial Intelligence and Robotics
spellingShingle	application in finance stock markets generative models Artificial Intelligence and Robotics LI, Junyi WANG, Xintong LIN, Yaoyang SINHA, Arunesh WELLMAN, Michael P. Generating realistic stock market order streams
description	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.
format	text
author	LI, Junyi WANG, Xintong LIN, Yaoyang SINHA, Arunesh WELLMAN, Michael P.
author_facet	LI, Junyi WANG, Xintong LIN, Yaoyang SINHA, Arunesh WELLMAN, Michael P.
author_sort	LI, Junyi
title	Generating realistic stock market order streams
title_short	Generating realistic stock market order streams
title_full	Generating realistic stock market order streams
title_fullStr	Generating realistic stock market order streams
title_full_unstemmed	Generating realistic stock market order streams
title_sort	generating realistic stock market order streams
publisher	Institutional Knowledge at Singapore Management University
publishDate	2020
url	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
_version_	1770575210570842112

Generating realistic stock market order streams

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