Beyond sentiment analysis for FOMC stock return prediction
Authors: Surendra Telidevara, Sudip Gupta


Reviewer paper summaries: This work analyzes the Federal Open Market Committee (FOMC) documents from various viewpoints to predict the stock market returns on the next day of release of the meeting minutes. 
The work constructs a new dataset of FOMC statements and minutes from 2010 to 2023.
It fine-tunes the Mistral model for sentiment classification which is found to outperform other models. 
It also relies on features of FOMC data to predict stock market returns, where uncertainty, complexity, and consensus scores are obtained by zero shot inference of LLM, and sentiment scores are from Mistral.
Summary of strengths: S1. The efforts to curate the sentiment annotated (hawkish or dovish) FOMC dataset is commendable.
S2. The experiments are comprehensive
Summary of weaknesses: W1. There is not much technical novelty. The contributions are mainly curating a dataset and running off-the-shelf models such as LLM/Mistral (to predict sentiments) and sklearn's linear regression (to predict stock market returns).
W2. Sentiments are only annotated by a team of two annotators pursuing graduate degree in Finance.
W3. There are confusing descriptions. For example, what are the "above listed open source models" in Line 161?
Comments, suggestions and typos: Some writing issues:
Line 79, Wonseong Kim et al, (Luo and Gong, 2024))
Line 84, studies it’s impact on
Line 169, perform a rule based post processing step, should be post-processing
Overall assessment scores: 2.5