A prediction model of stock market trading actions using generative adversarial network and piecewise linear representation approaches

Abstract

Supervised learning applied stock prediction tasks and obtained satisfactory performance. The trading strategies are very complex and diverse but supervised learning is only learned and fitted by gold standard trading strategies. Supervised learning approaches often have over-fitting problems. To learn distribution of gold standard answers, the generative adversarial network (GAN) models can generate extra similar samples to improve performance. Therefore, the paper proposes a generative GAN-based frameworks with the piecewise linear representation (PLR) approach to learn three trading actions, namely buying, selling, and holding. The proposed framework consists of two parts: first, PLR approach uses to detect historical prices to form trading sequences with three actions, PLR can provide a guided trading strategy to discriminator of GAN. Second, the generator of GAN is used to generate/predict daily trading actions, and the discriminator is used to detect the real/fake trading actions from the PLR/generator of GAN. Experimental results indicate that the proposed GAN-based frameworks outperform the long short-term memory network.