Non-directional forecasting of stocks using Twitter’s quantitative metrics

Abstract

Using public opinion to forecast political, social, and financial phenomena has usually been an attractive subject for researchers. With the unprecedented growth of social networks, along with the growth of technology and data mining methods, we have seen researchers move to platforms such as Twitter to predict political, social, and economic indicators. However, most researchers have turned to sentiment analysis of posts on social media, and mostly due to the quality of the output of sentiment analysis algorithms, have reached results with not good accuracy. This thesis seeks to prove the assumption of a significant relationship between quantitative data extracted from social networks and economic indicators especially focused on stock value changes of certain brands and companies. The results presented in this thesis are based on determining the severity or extent of the change in stock value, regardless of the direction of change. In other words, the presented outcome is for non-directional prediction. Due to time and financial constraints, this thesis focuses on proving the existence of a significant relationship between stock price changes of three brands, Apple, IBM, and Pfizer, and measurable Twitter indicators containing likes, replies, followers, quotes, and retweets. To find out how feasible is this method, four machine learning algorithms are applied, namely Artificial Neural Network, Random Forest, Support Vector Machine, and Adaptive Boost. Except for SVM, the results are satisfying and promising, especially with AdaBoost.