A machine learning approach in stress testing US bank holding companies

Ahmadou Mustapha Fonton Moffo

International Review of Financial Analysis, 2024, vol. 95, issue PC

Abstract: This paper assesses the utility of machine learning (ML) techniques combined with comprehensive macroeconomic and microeconomic datasets in enhancing risk analysis during stress tests. The analysis unfolds in two stages. I initially benchmark ML’s efficacy in forecasting two pivotal banking variables, net charge-off (NCO) and pre-provision net revenue (PPNR), against traditional linear models. Results underscore the superiority of Random Forest and Adaptive Lasso models in this context. Subsequently, I use these models to project PPNR and NCO for selected bank holding companies under adverse stress scenarios. This exercise feeds into the Tier 1 common equity capital (T1CR) densities simulation. T1CR is the equity capital ratio corrected by some regulatory adjustments to risk-weighted assets. Crucially, findings reveal a pronounced left skew in the T1CR distribution for globally systemically important banks vis-à-vis linear models. By mirroring distress akin to the Great Recession, ML models elucidate intricate macro-financial linkages and enhance risk assessment in downturns.

Keywords: Machine learning; Big data; Forecasting; Scenarios; Stress-test (search for similar items in EconPapers)
JEL-codes: C53 C55 E44 G17 G18 G32 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:95:y:2024:i:pc:s1057521924004083

DOI: 10.1016/j.irfa.2024.103476

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