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Generating Realistic Stock Market Order Streams

arXiv 2006.04212 ↗First posted 7 Jun 2020q-fin.ST cs.CE cs.LGcited by 30

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Abstract (arXiv)

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 4; 2024: 5; 2025: 9; 2026: 3.

30 papers.

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  1. 2023Non-adversarial training of Neural SDEs with signature kernel scorescited by 36
  2. 2021Towards Realistic Market Simulations: a Generative Adversarial Networks Approachcited by 29
  3. 2022Learning to simulate realistic limit order book markets from data as a World Agentcited by 27
  4. 2024MarS: a Financial Market Simulation Engine Powered by Generative Foundation Modelcited by 27
  5. 2023Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustnesscited by 14
  6. 2021A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensingcited by 13
  7. 2025From Deep Learning to LLMs: A survey of AI in Quantitative Investmentcited by 9
  8. 2024Time-Causal VAE: Robust Financial Time Series Generatorcited by 8
  9. 2023Adversarial Deep Hedging: Learning to Hedge without Price Process Modelingcited by 5
  10. 2024Optimal Execution with Reinforcement Learningcited by 5
  11. 2025TRADES: Generating Realistic Market Simulations with Diffusion Modelscited by 5
  12. 2025New Money: A Systematic Review of Synthetic Data Generation for Financecited by 5
18 more
  1. 2025TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Datacited by 4
  2. 2021Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulatorscited by 3
  3. 2022Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Marketscited by 3
  4. 2021Convergence of GANs Training: A Game and Stochastic Control Methodologycited by 2
  5. 2025Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluationcited by 2
  6. 2025Towards Causal Market Simulatorscited by 2
  7. 2025Reinforcement Learning in Queue-Reactive Models: Application to Optimal Executioncited by 2
  8. 2021The Limit Order Book Recreation Model (LOBRM): An Extended Analysiscited by 1
  9. 2022A Modular Framework for Reinforcement Learning Optimal Executioncited by 1
  10. 2023INTAGS: Interactive Agent-Guided Simulationcited by 1
  11. 2024Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resamplingcited by 1
  12. 2025Financial Wind Tunnel: A Retrieval-Augmented Market Simulatorcited by 1
  13. 2025DiffVolume: Diffusion Models for Volume Generation in Limit Order Bookscited by 1
  14. 2026Diffusion Models for Adaptive Sequential Data Generationcited by 1
  15. 2022Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Modelscited by 0
  16. 2024Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learningcited by 0
  17. 2026Synthetic data in cryptocurrencies using generative modelscited by 0
  18. 2026FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matchingcited by 0