Generative adversarial network for sentiment-based stock prediction

Financial markets received more attention due to technological advancements, such as Artificial Intelligence (AI). In addition to the price index, traders and investors constantly monitor stock news on social media. Therefore, predicting the market by analyzing public opinions is an important issue. In this research, we propose three models based on Generative Adversarial Network (GAN), namely Price-GAN, Price-Sentiment-GAN, and Price-Sentiment-WGAN. The first model uses only optimized price features, and the two other models use sentiment features collected from social media as well as optimized price features. All the proposed GAN models include Long Short-Term Memory (LSTM) as generators and Convolution Neural Networks (CNN) as discriminators. To evaluate the proposed models, two different social media datasets in English and Persian are used. Our proposed models predict the close stock price for 15 English and 5 Persian stocks. All of the proposed GAN models outperform the state-of-the-art models by enhancing the performance of the English dataset by 2.44% and the Persian dataset by 12.11%.