Applying investor sentiment to a prediction model of the stock market

Abstract: Using data-driven methods to predict the movements of the stock market is a growing field of research. Recently, large amounts of data sourced from online news and social media have been utilized to predict movements in financial markets. With the emergence of social media platforms, data can be gathered and used to quantify the sentiment of the market. This study investigates whether investor sentiment can be used to improve the precision of a prediction model of the stock market, specifically to explore whether the precision of a model which predicts intraday price change in direction of certain equities can be enhanced by the addition of investor sentiment. By collecting sentiment data derived from the classification of large amounts of messages from a social media platform aimed at investors and traders, a model was trained using technical data and subsequently retrained combined with sentiment data, to compare their performance. The results show that the predictive performance of the model is enhanced slightly by using sentiment data which indicates that there are potential benefits in using sentiment data to predict intraday price change in direction. However, as neither of the models shows significant classification performance, the results of this study should not be viewed as conclusive.