Generating Realistic Stock Market Order Streams

@inproceedings{Li2020GeneratingRS,
  title={Generating Realistic Stock Market Order Streams},
  author={Junyi Li and Xintong Wang and Yaoyang Lin and Arunesh Sinha and Michael P. Wellman},
  booktitle={AAAI},
  year={2020}
}
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… Expand
Conditional Sig-Wasserstein GANs for Time Series Generation
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Studies on the Computational Modeling and Design of Financial Markets
Decision-Aware Conditional GANs for Time Series Data
...
1
2
...

References

SHOWING 1-10 OF 43 REFERENCES
Adversarial Feature Matching for Text Generation
Wasserstein Learning of Deep Generative Point Process Models
Generative Adversarial Nets
...
1
2
3
4
5
...