Monetizing Currency Pair Sentiments through LLM Explainability
This work addresses the need for better explainability and prediction accuracy in financial markets, but it appears incremental as it adapts existing LLM methods to a specific domain.
The authors tackled the problem of improving currency-pair price predictions by developing a novel technique that uses LLMs for post-hoc, model-independent sentiment analysis explainability, which when fed back into ML models enhances predictions, though no concrete numbers are provided.
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.