Do weakly capitalized banks impair the bank lending channel of monetary policy? In this paper, I provide comprehensive evidence affirming this question for monetary policy easing. First, I show through a theoretical model that weakly capitalized banks do not expand lending in reaction to monetary policy easing due to their binding capital constraints, contrary to strongly capitalized banks. Since these constraints only impose an upper bound on lending, both types of banks similarly reduce lending in reaction to monetary policy tightening. Second, I empirically confirm these predictions using bank-level lending data, loan exposure data, and confidential capital ratio information for German banks. Third, to address the potential endogeneity of bank capital ratios to bank lending, I build a new dataset of exogenous surprises around macroprudential policy announcements. I use these surprises as a proxy for the sensitivity of each bank to each announcement. In line with my previous results, I find that, following a constraining surprise, the least sensitive banks increase their lending more than the most sensitive ones in reaction to monetary policy easing, but also reduce their lending less in reaction to monetary policy tightening.
Working papers
Bank Manager Sentiment, Loan Growth and Bank Risk , with Frank Brückbauer
Award: 11th SUERF/UniCredit Foundation Research Prize "AI in Banking and Finance"
We present evidence on how bank managers’ systematic over-optimism or over-pessimism (bank manager sentiment) affects both the amount and the riskiness of credit that banks supply to the real sector. We show that bank manager sentiment is related to past fundamentals, implying that risk-taking behaviour of banks and contemporaneous economic fundamentals might be systematically disconnected. Finally, we show that bank manager sentiment spills over to their equity investors, who seem to perceive banks with high bank manager sentiment as having a lower systemic risk, and conversely. To estimate bank manager sentiment, we proceed in two steps. We first use textual analysis methods (using both dictionary and machine learning approaches) to build a textual score measuring the tone of bank earnings press release documents. We then use this measure to define bank manager sentiment as the variation in the textual tone score which is orthogonal to bank-specific and macroeconomic fundamentals.
Work in progress
The Green Bond Market Elasticity, with Maurice Bun
The Market Channel of Macroprudential Policies