Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets. (arXiv:2204.13338v1 [cs.LG])

This study proposes a new generative adversarial network (GAN) for generating
realistic orders in financial markets. In some previous works, GANs for
financial markets generated fake orders in continuous spaces because of GAN
architectures' learning limitations. However, in reality, the orders are
discrete, such as order prices, which has minimum order price unit, or order
types. Thus, we change the generation method to place the generated fake orders
into discrete spaces in this study. Because this change disabled the ordinary …

arxiv data financial gan generation gradient markets policy stock