Stock market forecasting using DRAGAN and feature matching

TL;DR

This paper introduces a novel DRAGAN-based framework with feature matching for stock market forecasting, addressing GAN training challenges and capturing temporal correlations, outperforming baseline methods like LSTM and other GAN variants.

Contribution

The paper presents a new DRAGAN and feature matching approach that improves GAN training stability and effectively captures temporal and price-feature correlations in stock forecasting.

Findings

Outperforms LSTM, GAN, and WGAN-GP in stock prediction accuracy.

Improves training stability and reduces mode collapse in GANs.

Effectively captures temporal correlations in stock data.

Abstract

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