Generating Realistic Stock Market Order Streams

Sep 27, 2018 ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • 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
  • TL;DR: We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks.
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