Sentiment-Guided Adversarial Learning for Stock Price Prediction - Zhang, Yiwei; Li, Jinyang; Wang, Haoran; Choi, Sou-Cheng T. (Frontiers in Applied Mathematics and Statistics, 2021)
Prediction of stock prices or trends have attracted financial researchers’ attention for many years. Recently, machine learning models such as neural networks have significantly contributed to this research problem. These methods often enable researchers to take stock-related factors such as sentiment information into consideration, improving prediction accuracies. At present, Long Short-Term Memory (LSTM) networks is one of the best techniques known to learn knowledge from time-series data and to predict future tendencies. The inception of generative adversarial networks (GANs) also provides researchers with diversified and powerful methods to explore the stock prediction problem. A GAN network consists of two sub-networks known as generator and discriminator, which work together to minimize maximum loss on both actual and simulated data. In this paper, we developed a sentiment-guided adversarial learning and predictive models of stock prices, adopting a popular variation of GAN called conditional GAN (CGAN). We adopted an LSTM network in the generator and a multilayer perceptron (MLP) network in the discriminator. After extensively pre-processing historical stock price datasets, we analyzed the sentiment information from daily tweets and computed sentiment scores as an additional model feature. Our experiments demonstrated that the forecast accuracies of the CGAN models were improved with sentiment data. Moreover, our GAN and CGAN models outperformed LSTM and other traditional methods on 11 out of 36 processed stock price datasets, potentially playing a part in ensemble methods.