Predicting stock price based on media monitoring



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Tartu Ülikool


Using automated systems for finding investment ideas becomes more popular every year and the models are getting more complex. In recent years a lot of studies have been conducted that have researched the possibilities of using social media sentiment as input for stock prediction models. However, the results have been contradicting as the problem is complex. In this research, data was collected from Twitter about Standard Poor’s 100 companies over a period of six months. Also, financial data with one minute interval was collected from Alpha Vantage. Five different machine learning algorithms were used to predict maximum profit and maximum loss for the prediction horizon of five trading days. It was investigated whether adding social media based features to financial data based features would improve the results and if so, then tweets from what type of users would give the highest information gain. It was found out that adding social media data as input is beneficial for both, predicting maximum loss and maximum profit. For the explainability part, Shap library was used. As found out, features extracted from financial data were most important. For social media based features, most information was gained from tweets posted by news agencies and by users having relatively few followers.



machine learning, sentiment analysis, social media monitoring