Monetizing Currency Pair Sentiments through LLM Explainability

TL;DR

This paper introduces a novel LLM-based explainability technique for sentiment analysis in finance, which enhances currency-pair prediction accuracy and can be generalized to improve machine learning models across domains.

Contribution

The paper presents a new post-hoc, model-independent explainability method using LLMs for sentiment analysis, applied to currency prediction, and demonstrates its potential to improve ML predictions.

Findings

The technique effectively explains sentiment analysis in financial data.

Using explanations as input enrichment improves currency prediction accuracy.

The method is adaptable to other domains beyond finance.

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.