Stock market forecasting using DRAGAN and feature matching

Applying machine learning methods to forecast stock prices has been a topic of interest in recent years. However, a few studies have been reported based on generative adversarial networks (GANs) in this area, but their results are promising. While GANs are powerful generative models successfully applied in different areas, they suffer from inherent challenges such as training instability and mode collapse. Another primary concern is capturing correlations in stock prices. Therefore, the main challenges fall into two categories: capturing correlations and addressing the inherent problems of GANs. In this paper, we introduce a novel framework based on DRAGAN1 and feature matching for stock price forecasting, which improves training stability and alleviates mode collapse. We employ windowing to acquire temporal correlations by the generator and exploit conditioning on discriminator inputs to capture temporal correlations and correlations between prices and features. Experimental results on data from several stocks indicate that proposed method outperforms long short-term memory (LSTM) as a baseline method, as well as basic GANs and WGAN-GP2 as two different variants of GANs.