Social Media Sentiment Application for Financial Market Arbitrage Strategies

Student: Samarenko Alexander

Faculty: International College of Economics and Finance

Educational Programme: Double degree programme in Economics of the NRU HSE and the University of London (Bachelor)

Final Grade: 7

Year of Graduation: 2019

This study tests the hypothesis of financial market stock price movement prediction possibility with an application of quantified human sentiment indices gathered from Twitter social network via Python text parsing functions. Machine learning algorithms, trained on the span of 2017 - 2018 data of stock prices and sentiment indices, failed to predict capitalization change of Apple Inc., Tesla Inc., and Walmart Inc. with the highest result of logistic regression function yielding 55% accuracy and Granger causality tests proving no correlation between human sentiment indices and price movements of the stocks under consideration.

Full text (added June 13, 2019)

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