Researches Advanced in Finance based on Generative Adversarial Networks

Abstract

Topics about how to apply Generative Adversarial Networks (GANs) to finance industry and research to promote financial market prosperity and progress with deep learning technology never fail to arouse public attention. For instance, financial academic realm such as portfolio optimization, trade execution strategies and financial information progressing have made use of machine learning. After a large number of literature retrieval and attempts to classify from different aspects, we ultimately find out that GAN is one of the most famous and appropriate technologies widely adopted in academic research and commercial applications. Aiming at assisting reader with having insight into how GAN improved financial work’s efficiency, in this paper, we propose three research perspectives of GAN in financial work: Stock Market Prediction, Fault Detection, and Time Series. By organizing from an innovative and macro perspective, we present and classify the application methods of GAN and the improvement of the basic model of GAN in order to adapt to the corresponding fields under the corresponding research directions. In general, the following improved models based on the basic GAN model for the corresponding financial perspective are involved: GAN of three-layer dense network, GAN with the MLP and LSTM separately as the discriminator and the generator, GAN-FD stock projection model, BERT and GAN in Stock Prediction; SSGANs, DAEGAN in Fault Detection; SeqGAN, Quant GAN, RNN-GAN, PSA-GAN in Time Series.