GANs for synthetic financial data make “considerable progress”

Modeling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications.

Generative Adversarial Networks (GANs) are a neural network architecture family that has achieved good results in image generation and is being successfully applied to generate time series and other types of financial data. The purpose of this study is to present an overview of how these GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry.

As a proof of concept, three known GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field.

Among the different applications of GANs being researched in finance, the topic of generation of synthetic datasets deserves awareness. Synthetic data is important because it can be tailor-made for specific uses or conditions where real data may be lacking or unavailable. This can be useful in numerous cases such as:

  • Privacy and compliance rules may severely limit data availability and application
  • Data is often required in product testing environments and is often limited or unavailable to testers
  • Machine learning requires large amounts of training data, such data can be expensive and scarce

Read the full paper

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