Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective

Citation

Hoepner AGF, McMillan D, Vivian A & Wese Simen C (2021) Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective. European Journal of Finance, 27 (1-2), pp. 1-7. https://doi.org/10.1080/1351847X.2020.1847725

Abstract
Although machine learning is frequently associated with neural networks, it also comprises econometric regression approaches and other statistical techniques whose accuracy enhances with increasing observation. What constitutes high quality machine learning is yet unclear though. Proponents of deep learning (i.e. neural networks) value computational efficiency over human interpretability and tolerate the ‘black box’ appeal of their algorithms, whereas proponents of explainable artificial intelligence (xai) employ traceable ‘white box’ methods (e.g. regressions) to enhance explainability to human decision makers. We extend Brooks et al.’s [2019. ‘Financial Data Science: The Birth of a New Financial Research Paradigm Complementing Econometrics?’ European Journal of Finance 25 (17): 1627–36.] work on significance and relevance as assessment critieria in econometrics and financial data science to contribute to this debate. Specifically, we identify explainability as the Achilles heel of classic machine learning approaches such as neural networks, which are not fully replicable, lack transparency and traceability and therefore do not permit any attempts to establish causal inference. We conclude by suggesting routes for future research to advance the design and efficiency of ‘white box’ algorithms.

Keywords
explainability; explainable artificial intelligence (xai); neural networks; relevance; regressions; significance

Journal
European Journal of Finance: Volume 27, Issue 1-2

People

Professor David McMillan

Professor in Finance, Accounting & Finance