Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets
This addresses the need for accurate discrete order generation in financial markets, though it is incremental as it adapts existing methods to a specific domain limitation.
The study tackled the problem of generating realistic discrete order data in financial markets, which previous GANs failed to do due to continuous-space limitations, by proposing a policy gradient GAN that outperformed previous models in order distribution.
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 GAN learning algorithm, this study employed a policy gradient, frequently used in reinforcement learning, for the learning algorithm. Through our experiments, we show that our proposed model outperforms previous models in generated order distribution. As an additional benefit of introducing the policy gradient, the entropy of the generated policy can be used to check GAN's learning status. In the future, higher performance GANs, better evaluation methods, or the applications of our GANs can be addressed.