Template-type: ReDIF-Paper 1.0
Author-Name: Daniele Ballinari
Author-Name-First: Daniele
Author-Name-Last: Ballinari
Author-Name: Jessica Maly
Author-Name-First: Jessica
Author-Name-Last: Maly
Title: FX sentiment analysis with large language models
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.

Length: 39 pages
Creation-Date: 2025
Contact-Email: forschung@snb.ch
File-URL: https://www.snb.ch/en/publications/research/working-papers/2025/working_paper_2025_11
File-Format: text/html
Number: 2025-11
Classification-JEL: F31, G12, G15
Keywords: Large language models, Sentiment analysis, Fine-tuning, Text classification, Natural language processing, Foreign exchange, Financial markets
Handle: RePEc:snb:snbwpa:2025-11