Anchor-Based Explainable and Causal Artificial Intelligence for Enhancing Financial Predictions of Future Earnings

Abstract

Accurate prediction of future earnings is crucial for stakeholders. However, existing machine learning models often operate as “black boxes,” offering high accuracy but minimal interpretability. Prior approaches focus on correlational patterns without establishing genuine causal relationships or providing straightforward rule-based explanations. This lack of transparency and causal insight limits the actionable value of current financial prediction models. We propose an anchor-based explainable and causal AI framework for earnings prediction. It integrates an optimized XGBoost classifier (with RENN undersampling to address class imbalance) for high-performance prediction, the Anchor XAI method to generate human-readable “if-then” rules explaining model decisions, and the DoWhy causal inference tool to validate genuine cause-and-effect factors in the financial data. The optimized XGBoost with the RENN model achieved an overall accuracy of ~93.3%, with precision, recall, and F1-scores ranging from 93% to 94%, outperforming other classifiers. Key features such as Inventory/Total Assets, % Δ Net Profit Margin, and Cash Dividends/Cash Flows emerged as the most influential factors. Coordinated adjustments in these variables yielded significantly better predictive outcomes than isolated changes. Furthermore, DoWhy-based analysis confirms that improvements in these factors causally drive earnings growth, as verified by robustness checks like placebo tests. The proposed framework effectively bridges the gap between predictive accuracy and interpretability. It provides financial decision-makers with reliable earnings predicting and transparent, actionable insights for strategic planning and management, making the predictive model trustworthy and informative.