| dc.description.abstract | Sentiment analysis, at scale, has become an essential tool in the methodological toolbox of
finance. In this thesis, we construct a sentiment lexicon using a supervised machine learning
model by Taddy (2013) and compare it to the traditional finance lexicon by Loughran and
McDonald (2011). Additionally, a state-of-the-art AI natural language processing model from
OpenAI's GPT family is introduced to challenge both of these classical lexical sentiment analysis
approaches. Utilizing unbalanced panel data regressions, we compare the different approaches
in a "horse race". First, we find that textual sentiment significantly explains stock returns.
Secondly, we find that GPT outperforms both lexical approaches in terms of economic and
statistical significance, with an adjusted R2 of 3.9% versus 2.5% and 2.2% for the machine
learned and Loughran and McDonald lexicon, respectively. Thirdly, we find that by fine-tuning
GPT models for detecting sentiment, the performance increases significantly. Lastly, we find
that the current optimal available GPT model for financial sentiment analysis in the GPT model
library is GPT-3.5-Turbo. | en_US |
