Social networks & price forecasting: The case of Bitcoins

Abstract

(eng) The main conceptual element this thesis orbits around is the idea of using social networks as a data source. First, classical trading theory and current usage of data obtained from social networks is reviewed. Taking all this information into account, a forecasting of the Bitcoin price is performed using both classical methods and machine learning Neural Networks. In order to obtain data from social networks, another complexity layer needs to be added by accessing the sources through APIs and directly web-scrapping the net. The results of all of this complex implementation are given with a strong focus on visualisation using several different techniques. Finally, after a critical discussion a Future Work chapter is introduced, where many possible follow-ups are drawn up.