Do Affiliated Bankers on Board Enhance Corporate Social Responsibility? US Evidence
Iftekhar Hasan, Hui Li, Haizhi Wang, Yun Zhu
Sustainability,
No. 6,
2021
Abstract
In this study, we examine whether and to what extent affiliated bankers on board may affect firms’ corporate social performance. Using a propensity score-matched sample from 2002 to 2016, we find that board directors from affiliated banks exert significantly positive influence on firms’ corporate social performance. Furthermore, board of directors from affiliated banks are negatively associated with firm investments in corporate social responsibility (CSR) activities when firms experience financial distress. Finally, we find that the effect of affiliated bankers on board on firms’ CSR performance depends on the affiliated banks’ CSR orientation, as affiliated banker directors from banks with higher CSR orientation have a stronger influence on firms’ investments in CSR activities. The results suggest that improving firm’s CSR performance is consistent with the affiliated banks’ interests.
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Lender-specific Mortgage Supply Shocks and Macroeconomic Performance in the United States
Franziska Bremus, Thomas Krause, Felix Noth
IWH Discussion Papers,
No. 3,
2021
Abstract
This paper provides evidence for the propagation of idiosyncratic mortgage supply shocks to the macroeconomy. Based on micro-level data from the Home Mortgage Disclosure Act for the 1990-2016 period, our results suggest that lender-specific mortgage supply shocks affect aggregate mortgage, house price, and employment dynamics at the regional level. The larger the idiosyncratic shocks to newly issued mortgages, the stronger are mortgage, house price, and employment growth. While shocks at the level of shadow banks significantly affect mortgage and house price dynamics, too, they do not matter much for employment.
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Benign Neglect of Covenant Violations: Blissful Banking or Ignorant Monitoring
Stefano Colonnello, Michael Koetter, Moritz Stieglitz
Economic Inquiry,
No. 1,
2021
Abstract
Theoretically, bank's loan monitoring activity hinges critically on its capitalization. To proxy for monitoring intensity, we use changes in borrowers' investment following loan covenant violations, when creditors can intervene in the governance of the firm. Exploiting granular bank‐firm relationships observed in the syndicated loan market, we document substantial heterogeneity in monitoring across banks and through time. Better capitalized banks are more lenient monitors that intervene less with covenant violators. Importantly, this hands‐off approach is associated with improved borrowers' performance. Beyond enhancing financial resilience, regulation that requires banks to hold more capital may thus also mitigate the tightening of credit terms when firms experience shocks.
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Financial Incentives and Loan Officer Behavior: Multitasking and Allocation of Effort under an Incomplete Contract
Patrick Behr, Alejandro H. Drexler, Reint E. Gropp, Andre Guettler
Journal of Financial and Quantitative Analysis,
No. 4,
2020
Abstract
We investigate the implications of providing loan officers with a nonlinear compensation structure that rewards loan volume and penalizes poor performance. Using a unique data set provided by a large international commercial bank, we examine the main activities that loan officers perform: loan prospecting, screening, and monitoring. We find that when loan officers are at risk of losing their bonuses, they increase prospecting and monitoring. We further show that loan officers adjust their behavior more toward the end of the month when bonus payments are approaching. These effects are more pronounced for loan officers with longer tenures at the bank.
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Transmitting Fiscal Covid-19 Counterstrikes Effectively: Mind the Banks!
Reint E. Gropp, Michael Koetter, William McShane
IWH Online,
No. 2,
2020
Abstract
The German government launched an unprecedented range of support programmes to mitigate the economic fallout from the Covid-19 pandemic for employees, self-employed, and firms. Fiscal transfers and guarantees amount to approximately €1.2 billion by now and are supplemented by similarly impressive measures taken at the European level. We argue in this note that the pandemic poses, however, also important challenges to financial stability in general and bank resilience in particular. A stable banking system is, in turn, crucial to ensure that support measures are transmitted to the real economy and that credit markets function seamlessly. Our analysis shows that banks are exposed rather differently to deteriorated business outlooks due to marked differences in their lending specialisation to different economic sectors. Moreover, a number of the banks that were hit hardest by bleak growth prospects of their borrowers were already relatively thinly capitalised at the outset of the pandemic. This coincidence can impair the ability and willingness of selected banks to continue lending to their mostly small and medium sized entrepreneurial customers. Therefore, ensuring financial stability is an important pre-requisite to also ensure the effectiveness of fiscal support measures. We estimate that contracting business prospects during the first quarter of 2020 could lead to an additional volume of non-performing loans (NPL) among the 40 most stressed banks ‒ mostly small, regional relationship lenders ‒ on the order of around €200 million. Given an initial stock of NPL of €650 million, this estimate thus suggests a potential level of NPL at year-end of €1.45 billion for this fairly small group of banks already. We further show that 17 regional banking markets are particularly exposed to an undesirable coincidence of starkly deteriorating borrower prospects and weakly capitalised local banks. Since these regions are home to around 6.8% of total employment in Germany, we argue that ensuring financial stability in the form of healthy bank balance sheets should be an important element of the policy strategy to contain the adverse real economic effects of the pandemic.
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Banks’ Equity Performance and the Term Structure of Interest Rates
Elyas Elyasiani, Iftekhar Hasan, Elena Kalotychou, Panos K. Pouliasis, Sotiris Staikouras
Financial Markets, Institutions and Instruments,
No. 2,
2020
Abstract
Using an extensive global sample, this paper investigates the impact of the term structure of interest rates on bank equity returns. Decomposing the yield curve to its three constituents (level, slope and curvature), the paper evaluates the time-varying sensitivity of the bank’s equity returns to these constituents by using a diagonal dynamic conditional correlation multivariate GARCH framework. Evidence reveals that the empirical proxies for the three factors explain the variations in equity returns above and beyond the market-wide effect. More specifically, shocks to the long-term (level) and short-term (slope) factors have a statistically significant impact on equity returns, while those on the medium-term (curvature) factor are less clear-cut. Bank size plays an important role in the sense that exposures are higher for SIFIs and large banks compared to medium and small banks. Moreover, banks exhibit greater sensitivities to all risk factors during the crisis and postcrisis periods compared to the pre-crisis period; though these sensitivities do not differ for market-oriented and bank-oriented financial systems.
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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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Benign Neglect of Covenant Violations: Blissful Banking or Ignorant Monitoring?
Stefano Colonnello, Michael Koetter, Moritz Stieglitz
Abstract
Theoretically, bank‘s loan monitoring activity hinges critically on its capitalisation. To proxy for monitoring intensity, we use changes in borrowers‘ investment following loan covenant violations, when creditors can intervene in the governance of the firm. Exploiting granular bank-firm relationships observed in the syndicated loan market, we document substantial heterogeneity in monitoring across banks and through time. Better capitalised banks are more lenient monitors that intervene less with covenant violators. Importantly, this hands-off approach is associated with improved borrowers‘ performance. Beyond enhancing financial resilience, regulation that requires banks to hold more capital may thus also mitigate the tightening of credit terms when firms experience shocks.
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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Avoiding the Fall into the Loop: Isolating the Transmission of Bank-to-Sovereign Distress in the Euro Area and its Drivers
Hannes Böhm, Stefan Eichler
Abstract
We isolate the direct bank-to-sovereign distress channel within the eurozone’s sovereign-bank-loop by exploiting the global, non-eurozone related variation in stock prices. We instrument banking sector stock returns in the eurozone with exposure-weighted stock market returns from non-eurozone countries and take further precautions to remove any eurozone crisis-related variation. We find that the transmission of instrumented bank distress, while economically relevant, is significantly smaller than the corresponding coefficient in the unadjusted OLS framework, confirming concerns on reverse causality and omitted variables in previous studies. Furthermore, we show that the spillover of bank distress is significantly stronger for countries with poorer macroeconomic performances, weaker financial sectors and financial regulation and during times of elevated political uncertainty.
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