Stock Market Prediction from Sentiment and Financial Stock Data Using Machine Learning

Abstract

Forecasting stock values is challenging due to market volatility and numerous financial variables, such as news, social media, political changes, investor emotions, and the general economy. Predicting stock value using financial data alone may be insufficient. By combining sentiment analysis from social media with financial stock data, more accurate predictions can be achieved. We use an ensemble-based model employing multi-layer perceptron, long short-term memory, and convolutional neural network models to estimate sentiment in social media posts. Our models are trained on AAPL, CSCO, IBM, and MSFT stocks, using financial data and sentiment from Twitter between 2015-2019. Results show that combining financial and sentiment information improves stock market prediction performance, achieving a next-hour prediction performance of 74.3%.