Robustness Vs. Explainability Stability: A Comparative XAI Analysis of Machine Learning Models in Financial Market Anomalies

Key Points

• The analysis demonstrates significant trade-offs between robustness and explainability, impacting model utility.
• Robust models often sacrifice transparency, while explainable models may lack stability under market shifts.
• Comparative analysis utilizes machine learning techniques on various financial anomalies over multiple data sets.
• These findings highlight the importance of achieving a balance between robustness and explainability in financial applications.