Generative Adversarial Networks in finance: an overview

Understand

Modelling 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.