Fine-tuning Language Models for Equity Trading using RavenPack Annotations

October 24, 2023

We fine-tune FinBERT with RavenPack Annotations to predict stock market movements, delivering an average Information Ratio increase of 48%.

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.

Watch the video summary

Portfolio IR AR Portfolio Size
Model Raw Fine-tuned Raw Fine-tuned Raw Fine-tuned
US Mid/Large-Cap 0.80 1.27 4.35% 6.74% 149.5 169.9
US Small-Caps 2.63 3.42 25.55% 29.63% 128.2 160.6
EU Mid/Large-Cap 1.42 2.36 8.82% 14.76% 62.6 65.8
EU Small-Caps 0.99 2.03 9.79% 20.15% 15.8 18.4
APAC Mid/Large-Cap 2.86 3.07 19.05% 23.29% 47.4 49.0
APAC Small-Caps 2.47 2.94 23.91% 28.12% 26.4 30.5

Download the white paper to explore the methodology and review applicable use cases.

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