Three important financial stability issues in banking

In his presidential address, Zingales (2015) observes that large parts of society perceive finance as a rent-seeking activity and that complex financial regulation is prone to arbitrage and capture. In my dissertation, I investigate whether there is some truth in these allegations. In Chapter 1, I estimate the extent of regulatory arbitrage by banks in the securitization market. In Chapters 2 and 3, I show how incentive structures in credit rating agencies and banks can explain credit rating bias and excessive risk-taking, respectively. My findings have important implications for the design of financial regulation

Structured debt ratings: Evidence on conflicts of interest

We test if issuers of asset- and mortgage-backed securities receive rating favors from agencies with which they maintain strong business relationships. Controlling for issuer fixed effects and a large set of credit risk determinants, we show that agencies publish better ratings for those issuers that provide them with more bilateral securitization business. Such rating favors are larger for very complex structured debt deals and for deals issued during the credit boom period. Our analysis is based on a new deal-level rating statistic that accounts for the full distribution of tranche ratings below the AAA cut-off point of a structured debt deal

Freeze! Financial Sanctions and Bank Responses

Using regulatory data, we study German bank lending in countries targeted by financial sanctions. We find that domestic banks in Germany reduce lending in sanctioned countries, whereas their foreign bank affiliates outside Germany increase lending. In some cases, this is because the bank affiliates’ host countries have not imposed sanctions themselves. However, even German bank affiliates in host countries that enact sanctions like Germany increase lending if these host countries lack strong institutions and anticrime policies. These findings suggest that even universally adopted sanctions distort bank capital flows and competition if the level of their enforcement varies across bank locations

Incentive pay and bank risk-taking: Evidence from Austrian, German, and Swiss banks

We use payroll data in the Austrian, German, and Swiss banking sector to identify incentive pay in the critical banking segments of treasury/capital market management and investment banking for 67 banks. We document an economically significant correlation of incentive pay with both the level and volatility of bank trading income particularly for the pre-crisis period 2003-2007, in which incentive pay was strongest. This result is robust if we instrument the bonus share in the capital market divisions with the strength of incentive pay in unrelated bank divisions like retail banking. Moreover, pre-crisis incentive pay appears too strong for an optimal trade-off between trading income and risk, which maximizes the net present value of trading income. Further analyses indicate that the bonus moderation during the crisis has removed excessive pre-crisis incentive pay. (C) 2015 Elsevier B.V. All rights reserved

Bank Bonus Pay as a Risk Sharing Contract

https://ssrn.com/abstract=3202916We show that banker bonuses cannot be understood exclusively as incentive contracts, but also incorporate a significant risk sharing dimension between bank shareholders and bank employees. This contrasts with the conventional view whereby diversified shareholders fully insure risk averse employees. However, financial frictions imply that shareholder value is concave in a bank's cash reserves---making shareholders effectively risk averse. The optimal contract between shareholders and employees then involves some degree of risk sharing. Using extensive payroll data on 1.26 million bank employee years in the Austrian, German, and Swiss banking sectors, we show that the structure of bonus pay within and across banks is compatible with an economically significant risk sharing motive, but difficult to rationalize based on incentive theories of bonus pay only

Bank Bonus Pay as a Risk Sharing Contract

We argue that risk sharing motivates the bankwide structure of bonus pay. In the presence of financial frictions that make external financing costly, the optimal contract between shareholders and employees involves some degree of risk sharing whereby bonus pay partially absorbs negative earnings shocks. Using payroll data for 1.26 million employee-years in all functional divisions of Austrian, German, and Swiss banks, we uncover several empirical patterns in bonus pay that are difficult to rationalize exclusively with incentive theories of bonus pay but that support an important risk sharing motive. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

Bank bonus pay as a risk sharing contract

We argue that risk sharing motivates the bankwide structure of bonus pay. In the presence of financial frictions that make external financing costly, the optimal contract between shareholders and employees involves some degree of risk sharing whereby bonus pay partially absorbs negative earnings shocks. Using payroll data for 1.26. million employee-years in all functional divisions of Austrian, German, and Swiss banks, we uncover several empirical patterns in bonus pay that are difficult to rationalize exclusively with incentive theories of bonus pay but that support an important risk sharing motive. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

Using Social Network Activity Data to Identify and Target Job Seekers

An important challenge for many firms is to identify the life transitions of its customers, such as job searching, being pregnant, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firm opportunities to be more relevant to its customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms. Our proposed approach builds on the hidden Markov model (HMM) framework to recover the latent state of job search from noisy signals obtained from social network activity data. Specifically, our modeling approach combines cross-sectional survey responses to a job seeking status question with longitudinal user activity data. Thus, in some time periods, and for some users, we observe the “true” job seeking status. We fuse the observed state information into the HMM likelihood, resulting in a partially HMM. We demonstrate that the proposed model can not only predict which users are likely to be job seeking at any point in time, but also what activities on the platform are associated with job search, and how long the users have been job seeking. Furthermore, we find that targeting job seekers based on our proposed approach can lead to a 42% increase in profits of a targeting campaign relative to the approach that was used at the time of the data collection