FX sentiment analysis with large language models

Daniele Ballinari and Jessica Maly

No 2025-11, Working Papers from Swiss National Bank

Abstract: We enhance sentiment analysis in the foreign exchange (FX) market by fine-tuning large language models (LLMs) to better understand and interpret the complex language specific to FX markets. We build on existing methods by using state-of-the-art open source LLMs, fine-tuning them with labelled FX news articles and then comparing their performance against traditional approaches and alternative models. Furthermore, we tested these fine-tuned LLMs by creating investment strategies based on the sentiment they detect in FX analysis articles with the goal of demonstrating how well these strategies perform in real-world trading scenarios. Our findings indicate that the fine-tuned LLMs outperform the existing methods in terms of both the classification accuracy and trading performance, highlighting their potential for improving FX market sentiment analysis and investment decision-making.

Keywords: Large language models; Sentiment analysis; Fine-tuning; Text classification; Natural language processing; Foreign exchange; Financial markets (search for similar items in EconPapers)
JEL-codes: F31 G12 G15 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2025
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-ifn and nep-mon
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