Stock Prediction using Deep Learning and Sentiment Analysis

Abstract

Stock prediction has been a popular research topic and researchers have done a lot of work in this field. Due to its stochastic nature, predicting the future stock market remains a very difficult problem. This paper studies the application of attention-based LSTM deep neural network in future stock market movement prediction. We also build stock aggregate dataset and individual dataset including stock history data, financial tweets sentiment and technical indicators in the US stock market. The experiment studies the time sensitivity of finance tweet sentiment and methods of collective sentiment calculation. This paper also experiments on conventional LSTM and attention-based LSTM for performance comparison. We find the finance tweets that are posted from market closure to market open in the next day has more predictive power on next day stock movement. The weighted sentiment on max follower on StockTwits also outperforms other methods. In our experiment, the result on our individual stock dataset shows a similar pattern like normal distribution.