Monetizing Currency Pair Sentiments through LLM Explainability

• URL: http://arxiv.org/abs/2407.19922v1
• Date: Mon, 29 Jul 2024 11:58:54 GMT
• Title: Monetizing Currency Pair Sentiments through LLM Explainability
• Authors: Lior Limonad, Fabiana Fournier, Juan Manuel Vera Díaz, Inna Skarbovsky, Shlomit Gur, Raquel Lazcano,
• Abstract summary: Large language models (LLMs) play a vital role in almost every domain in today's organizations. We contribute a novel technique to leverage LLMs as a post-hoc model-independent tool for the explainability of sentiment analysis. We apply our technique in the financial domain for currency-pair price predictions using open news feed data merged with market prices.
• Score: 2.572906392867547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) play a vital role in almost every domain in today's organizations. In the context of this work, we highlight the use of LLMs for sentiment analysis (SA) and explainability. Specifically, we contribute a novel technique to leverage LLMs as a post-hoc model-independent tool for the explainability of SA. We applied our technique in the financial domain for currency-pair price predictions using open news feed data merged with market prices. Our application shows that the developed technique is not only a viable alternative to using conventional eXplainable AI but can also be fed back to enrich the input to the machine learning (ML) model to better predict future currency-pair values. We envision our results could be generalized to employing explainability as a conventional enrichment for ML input for better ML predictions in general.

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