Banking with an Overreaction (Job Market Paper)
Using data on loan loss provisions, we analyze the expectations of US banks and find evidence of departure from rational expectations. In particular, we find that banks overreact to actual losses incurred in the recent past. In good times, the presence of overreaction leads to neglect of risks, resulting in a rise in credit growth. This subsequently results in higher non-performing loans and lower return on assets for banks when the risks get realized in the future. Additionally, shareholders fail to adequately recognize the risky lending behavior of such banks and earn predictably lower returns in subsequent years.
Banking Sector Expectations and Financial Stability
with Vipul Mathur ( SSRN Link: Revised July,2023)
Featured in World Bank Finance Blog: https://blogs.worldbank.org/allaboutfinance/bias-banking
Winner of Second Best Paper Award at IGIDR Macroeconomics and Finance
Conference: http://www.igidr.ac.in/conf/money/mfc19/index.htm
We analyze banks' expectations embedded in loan loss provisions and document evidence of overreaction for the US banking sector. Using diagnostic-expectations general equilibrium setting, we establish that overreaction in provisions accentuates the procyclicality of lending rate and credit growth. Crucially, our results suggest that for the cyclically-adjusted provisioning rules (IFRS 9/CECL) to have the desired effect on financial stability, the extent of cyclical adjustment may need to be strengthened in proportion to the degree of overreaction in expectations.
Unravelling Credibility in Stress Test Result Disclosures
(SSRN Link: Revised August, 2023 )
We model the credibility problems financial regulators often face in disclosing the stress test result. The regulator has motives to lie about the result. The motives come from the desire to guide agents' actions by influencing their beliefs. We show that the regulator can disclose some information credibly by making an imprecise announcement, revealing only the range (or interval) in which the result lies. In the interest of credibility, the regulator must reveal information less precisely when the stress test result is either too good or too bad. As the result moves away from both extremes, information can be revealed credibly with more precision.
How does bank opacity affect credit growth and return predictability?
with Malvika Chhatwani
Prior research finds that bank credit growth predicts lower bank equity returns in subsequent one to three years. Stocks of banks with high credit growth are initially overvalued because of overoptimism or elevated sentiment of bank shareholders. Eventually, these stocks underperform, generating lower returns. We argue that shareholder sentiment should exhibit its strongest effects on the performance of bank stocks when banks are opaque, or there is uncertainty about the quality of bank loans. Accordingly, we show that a one-standard-deviation increase in bank's financial reporting opacity amplifies the predictive ability of credit growth for equity returns by 1.5 to 2 times relative to when opacity is at its mean.
Who invests in Cryptocurrency? The role of Overconfidence among American investors
with Malvika Chhatwani
Cryptocurrency has received increasing attention due to its uniqueness among financial assets, and the characteristics of crypto investors have recently gained much interest. The present study aims to examine the linkage between financial confidence and cryptocurrency ownership. Using the representative sample of American investors, we find that financially overconfident investors are nearly 8% more likely to be crypto owners and 10% more likely to be probable crypto owners as compared to underconfident investors. Our findings are robust to sample selection bias and help in understanding the characteristics of crypto owners in terms of financial confidence. Based on these findings, regulators and policymakers may raise awareness about the overconfidence bias among existing and potential crypto investors.
Work in Progress
Assessing Bank Default Risk in an Irrational World
with Partha Ray and Vipul Mathur
We measure default probability of banks by improving structural models to account for the presence of bias in expectations. The usual approach to infer the underlying credit risk from observed variables and solve for implied probability of default can be erroneous when the variables are not representative of credit risk, rather distorted by bias. Adapting structural models to allow for provisions, we estimate a default risk measure, more apt for banks, and show that default predictions are well explained by it. Use of provisions allows us to analyze banks' expectations and then refine the estimation of bank default probability after controlling for the presence of bias in expectations.