Explainable AI in Financial Forecasting: Balancing Predictive Accuracy and Economic Interpretability

Abstract

Background: Financial forecasting has increasingly turned to artificial intelligence (AI) techniques, which can help boost the accuracy of forecasts in complex and volatile markets. However, many advanced AI models are "black boxes," lacking transparency and invoking concerns regarding interpretability, trust, and regulatory compliance of AI-powered solutions. In the world of financial decision-making systems, the challenge lies in balancing high predictive capabilities with economic interpretability. The issue of balancing high predictive capabilities with economic interpretability has emerged in the world of financial decision-making systems as a solution, namely, Explainable AI (XAI).

Objective: This study seeks to test the performance of XAI methods to strike a balance between predictive performance and economic interpretability in financial forecasting models.

Methods: The method used in this study was a quantitative comparative research design where the financial time series data were multivariate data consisting of stock prices, inflation rates, interest rates, trading volumes, and GDP growth. Various models such as ARIMA, linear regression, random forest, gradient boosting, and LSTM networks were implemented. The performance of the models was tested using the following metrics: MSE, RMSE, MAE, and R². Model interpretation was performed with XAI techniques (SHAP, LIME) and feature importance, fidelity, and stability measures were used to evaluate interpretability.

Results: Deep learning models, specifically LSTM models, had the best predictive accuracy with R² = 0.94, with linear models having the best interpretability score. The most important predictors were identified by SHAP analysis, which showed interest rates and inflation. An ensemble model was found to be a good compromise between the trade-off of prediction accuracy and interpretability.

Conclusion: None of the models in the study achieve both the highest accuracy and interpretability. In financial applications with the demands of trust and compliance in mind, complex financial models can be significantly improved in terms of transparency using XAI techniques.

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