Comparing machine learning models for predicting stock market volatility using social media sentiment : A comparison of the predictive power of the Artificial Neural Network, Support Vector Machine and Decision Trees models on price volatility using socia

Abstract: We aimed to explore how the machine learning models Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision tree (DT) compared in analyzing the effects of investor sentiment (from the forum www.reddit.com/r/wallstreetbets) in conjunction with other key parameters, to predict asset price volatility of major US corporations. The paper explores the effect on asset price volatility that the addition of sentiment based indicators had on companies listed in the S&P500 index since 2012. None of the models we used could accurately predict the volatility of stocks using our collected sentiment and financial data. While social media sentiment has been shown by previous research to impact parts of financial markets, the market as a whole does not seem to be as susceptible to this interference as some analysts have suggested. Therefore, the training data for the algorithms had too much noise to find strong relationship. Furthermore we believe that more research is required in order to better understand which financial (or other) indicators play a role in shaping online sentiment.