TRuE-XAI: causal and explainable ai framework for trustworthy corporate earnings growth forecasting

Abstract

Forecasting corporate earnings growth is fundamental to investment, credit, and regulatory decision-making. Existing forecasting approaches either rely on restrictive linear assumptions or provide limited interpretability, making them less suitable for high-stakes financial applications. This study proposes a transparent and causally informed framework for predicting future corporate earnings growth from financial statement data. We present TRuE-XAI (Transparent, Rule-based, and Explainable Artificial Intelligence), an integrated framework combining imbalance-aware ensemble learning, automated hyperparameter optimization, rule-based explainability, visual analytics, and causal inference. Random Forest, XGBoost, and LightGBM classifiers were optimized using Optuna and Hyperopt and evaluated with multiple class-balancing strategies, including SMOTE, ADASYN, TomekLinks, and Repeated Edited Nearest Neighbours (RENN). Experiments were conducted on real-world SEC-derived quarterly financial statement data from U.S. publicly listed firms covering 2014–2024. Model transparency was achieved through Anchor explanations, multi-metric feature-importance analysis, SilVA visual analytics, and causal effect estimation using DoWhy and EconML. The best-performing configuration, TomekLinks–XGBoost, achieved an F1-score of 0.467 and accuracy of 0.849 on the real SEC dataset while maintaining stable generalization under a leakage-free evaluation protocol. Anchor explanations generated concise, high-precision IF–THEN rules that explained individual predictions, whereas complementary feature-importance analyses identified consistent financial drivers across models. Causal inference showed that Net Profit Margin Change, Sales Growth, EBIT, and Asset Turnover exert positive causal effects on the probability of future earnings growth, while Inventory to Total Assets and Cash Flow to Net Income exhibited negative causal effects. Placebo and refutation tests supported the robustness of the estimated treatment effects. TRuE-XAI integrates predictive modelling, explainable AI, visual analytics, and causal inference into a unified framework for transparent earnings-growth forecasting. By combining competitive predictive performance with interpretable decision rules and causally grounded insights, the framework provides a practical and trustworthy approach for financial decision support and demonstrates how explainable and causal machine learning can be applied in regulated financial environments.