Machine learning models in financial econometrics: A critical assessment
Keywords:
Machine Learning, Financial Econometrics, Predictive Modelling, Asset Pricing, Risk Management, Volatility Forecasting, Model Interpretability, Explainable AI, Hybrid ModelsAbstract
The present paper is a critical assessment of the application of machine learning (ML) models in financial econometrics. Even though machine learning models, including random forests, gradient boosting, support vectors machines, and deep learning, have demonstrated to exhibit high predictive power in various domains, such as asset pricing, risk management, credit scoring and volatility forecasting, their application results in serious methodological and practical problems. We compare the benefits of ML methods with old econometric methods, which primarily include the flexibility of the method, nonlinear modelling, and high dimension data processing. Meanwhile, we also present issues of interpretability, overfitting, data-snooping bias, and the conflict between prediction accuracy and economic theory. In developing the argument based on the available empirical evidence and theoretical standpoints, we contend that ML is not a replacement to econometrics, but rather a supplementary tool to econometrics in combination with structural modelling and economic intuition. At the end of the paper, the future directions are discussed, which are explainable AI, hybrid modelling frameworks, and incorporation of domain knowledge to make financial applications more reliable and policy relevant.
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