Unveiling Hedge Funds: Topic Modeling and Sentiment Correlation with Fund Performance

TL;DR

This study applies advanced topic modeling and sentiment analysis to hedge fund documents, revealing effective methods for extracting actionable insights and linking document sentiment to fund performance, thus aiding investment decisions.

Contribution

It introduces the first application of topic modeling to hedge fund documents and establishes a novel framework connecting sentiment analysis with fund performance.

Findings

LDA with 20 topics yields the most interpretable results.

DistilBERT outperforms FinBERT in sentiment scoring.

Sentiment scores from DistilBERT correlate strongly with fund performance.

Abstract

The hedge fund industry presents significant challenges for investors due to its opacity and limited disclosure requirements. This pioneering study introduces two major innovations in financial text analysis. First, we apply topic modeling to hedge fund documents-an unexplored domain for automated text analysis-using a unique dataset of over 35,000 documents from 1,125 hedge fund managers. We compared three state-of-the-art methods: Latent Dirichlet Allocation (LDA), Top2Vec, and BERTopic. Our findings reveal that LDA with 20 topics produces the most interpretable results for human users and demonstrates higher robustness in topic assignments when the number of topics varies, while Top2Vec shows superior classification performance. Second, we establish a novel quantitative framework linking document sentiment to fund performance, transforming qualitative information traditionally requiring expert interpretation into systematic investment signals. In sentiment analysis, contrary to expectations, the general-purpose DistilBERT outperforms the finance-specific FinBERT in generating sentiment scores, demonstrating superior adaptability to diverse linguistic patterns found in hedge fund documents that extend beyond specialized financial news text. Furthermore, sentiment scores derived using DistilBERT in combination with Top2Vec show stronger correlations with subsequent fund performance compared to other model combinations. These results demonstrate that automated topic modeling and sentiment analysis can effectively process hedge fund documents, providing investors with new data-driven decision support tools.