A Machine learning approach in stress testing US bank holding companies”, (Revision requested), International Review of Financial Analysis, (Job Market Paper)

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 NCO and PPNR 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 fortify risk assessment in downturns.

On bank credit and business cycle from a machine learning perspective,” joint with Dalibor Stevanovic

This paper proposes identifying the bank’s credit supply shock using a machine learning approach in a data-rich setup. The analysis consists of two steps. First, we do a pseudo-out-of-sample forecasting exercise to approximate the bankers’ predictive model of the capital-to-asset ratio. Then, we define the aggregate credit shock as the weighted average of bank-specific out-of-sample forecast errors. In the second step, we include the constructed shock in a VAR to assess its impact on the business cycle. We find that a one standard deviation negative aggregate credit shock moves credit volume and prices in the opposite direction, corroborating that it identifies a bank credit supply shock. Furthermore, this shock has significant macroeconomic effects. It triggers a slowdown in GDP growth, inflation, and a substantial commercial and industrial credit volume drop. However, the decline in commercial and industrial credit is more pronounced and persistent than the slowdown in GDP growth.

Bank-level uncertainty and the business cycle: Evidence from large US Bank holding companies

This paper introduces a novel method to quantify bank-level uncertainty from forecast errors of a bank’s return on asset (ROA) derived from an ensemble of machine learning models combined with granular bank data and an extensive macroeconomic dataset. From various ROA forecasts, I compute the optimal forecast errors from the average prediction across models. Then, I define the bank-level uncertainty measure as the standard deviation of these forecast errors. Using Vector Autoregression (VAR) analysis, the paper shows that this measure significantly influences both business cycles and credit markets. Unanticipated spikes in bank-level uncertainty lead to notable economic downturns and worsened credit conditions, exceeding the predictive power of conventional macroeconomic and financial uncertainty metrics. These findings advocate for targeted regulatory action in the banking sector to enhance financial stability.

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