This master thesis investigates the correlative relationship between both sentiments and hype derived from the social media Twitter and the stock returns of Apple Inc. Rooted in behavioral finance elements, it argues that human irrationalities and biases affect decisionmaking; including financial decisions. In the larger picture, this is important to investigate not only for arbitrage seekers, but also for enterprises, in the form of customer satisfaction online, and governments, in the form of potential targeted cyber-attacks through social media on financial markets. Methodologically, the paper utilizes natural language processing in the shape of a lexiconbased approach and includes both negation and sarcasm detection. Applying a rule-based approach in order to error correct the abovementioned, the sentiment features are then scored and normalized during the timeframe the thesis examines. Based on both the daily sentiment scores and change in the number of daily tweets, a vector autoregression analysis carried out on the stock returns and the Twitter features to allow for endogeneity and interdependencies between the variables. Generally, the thesis finds indicative results that structural shocks to both the sentiment score as well as the change in the daily number of tweets affect the stock price the following day, after which the effect dies out. Furthermore, the thesis reports suggestive Granger causal relationships. The above is true for the sentiment score based on Twitter users who are verified (public figures). The results indicate support for a range of behavioral aspects, which shape our decision-making on a daily basis. Finally, the thesis proposes that further research is conducted on the area as natural language processing techniques become more effective and precise and suggest that further research is conducted on larger data sets spanning a greater timeframe, too.
|Educations||MSc in Finance and Investments, (Graduate Programme) Final Thesis|
|Number of pages||91|