Fine-tuning Language Models for Equity Trading using RavenPack Annotations

The broader availability of large language models has renewed investors’ interest in sentiment-driven trading strategies. While open-source models adapt well to the task, they rely heavily on quality training data.

In this paper, we report on the impact of refining FinBERT, an open-source language model already fine-tuned on a financial dataset, with the RavenPack Annotations dataset to forecast stock market movements.

Improved Sentiment Analysis and Performance

RavenPack Annotations contains sentiment information on the entities detected in each sentence. Fine-tuning the FinBERT model with this dataset generates a substantial performance boost. When applied to backtesting six investment strategies across global markets, the fine-tuned FinBERT model increases the information ratio by an average of 48% and the annualized returns by 47%.

The results prove robust across all our universes over multiple holding periods, from daily to bi-weekly, with the fine-tuned model consistently outperforming the open-source version.

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