FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis
This provides explainability for AI-driven financial decision-making, though it is incremental as it combines existing statistical techniques in a novel way for a specific domain.
The paper tackled the problem of explainability in financial analysis by developing a method that uses aspect-based sentiment analysis, Pearson correlation, Granger causality, and uncertainty coefficient to derive statistically significant relationships between sentiment scores and stock prices, resulting in a more informative and accurate understanding compared to other methods.
This paper presents a novel approach for explainability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis, Pearson correlation, Granger causality & uncertainty coefficient. The proposed methodology involves constructing an aspect list from financial literature and applying aspect-based sentiment analysis on social media text to compute sentiment scores for each aspect. Pearson correlation is then applied to uncover financially explainable relationships between aspect sentiment scores and stock prices. Findings for derived relationships are made robust by applying Granger causality to determine the forecasting ability of each aspect sentiment score for stock prices. Finally, an added layer of interpretability is added by evaluating uncertainty coefficient scores between aspect sentiment scores and stock prices. This allows us to determine the aspects whose sentiment scores are most statistically significant for stock prices. Relative to other methods, our approach provides a more informative and accurate understanding of the relationship between sentiment analysis and stock prices. Specifically, this methodology enables an interpretation of the statistical relationship between aspect-based sentiment scores and stock prices, which offers explainability to AI-driven financial decision-making.