Predicting Tesla Stock Return Using Twitter Data - An Intraday View on the Relation between Twitter Dimensions and the Tesla Stock Return
Abstract: In this thesis, Twitter data is used to predict the intraday stock return for Tesla, Inc. We present two different methods to extract the tweets’ sentiment: A dictionary-based approach (VADER) and a machine learning approach (SVM). Additionally, we control for other dimensions as the user and discussion dimension. Then a Granger causality test and a lasso regression are conducted on a one- and five-minute interval. The results suggest that there is no predictive power in the information of the tweets for the dictionary data set and the machine learning data set. Using a subset of the dictionary data set with only the cashtag does not alter the results. The reason for this may be that we employ two linear models on a possible non-linear problem.
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