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Using generative adversarial networks to synthesize artificial financial datasets

Author

Listed:
  • Dmitry Efimov
  • Di Xu
  • Luyang Kong
  • Alexey Nefedov
  • Archana Anandakrishnan

Abstract

Generative Adversarial Networks (GANs) became very popular for generation of realistically looking images. In this paper, we propose to use GANs to synthesize artificial financial data for research and benchmarking purposes. We test this approach on three American Express datasets, and show that properly trained GANs can replicate these datasets with high fidelity. For our experiments, we define a novel type of GAN, and suggest methods for data preprocessing that allow good training and testing performance of GANs. We also discuss methods for evaluating the quality of generated data, and their comparison with the original real data.

Suggested Citation

  • Dmitry Efimov & Di Xu & Luyang Kong & Alexey Nefedov & Archana Anandakrishnan, 2020. "Using generative adversarial networks to synthesize artificial financial datasets," Papers 2002.02271, arXiv.org.
    • Handle: RePEc:arx:papers:2002.02271
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    Cited by:

    1. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.
    2. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.
    3. Andrea Coletta & Matteo Prata & Michele Conti & Emanuele Mercanti & Novella Bartolini & Aymeric Moulin & Svitlana Vyetrenko & Tucker Balch, 2021. "Towards Realistic Market Simulations: a Generative Adversarial Networks Approach," Papers 2110.13287, arXiv.org.

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