Presentation Transcript

1. Does Assigning Priority to Deposits Affect Bank Conduct? Evidence from a Quasi-Experiment

   Piotr Danisewicz, Danny McGowan, Enrico Onali and Klaus Schaeck
2. Introduction Historically depositors have been protected during bankruptcy by having priority on claims (in the US national protection from 1993) Following the Cypriot banking crisis the ECB has called for the introduction of depositor preference throughout the EU Under Depositor Preference Legislation (uninsured) depositors have priority over general creditors on the residual assets of a failed bank Proponents argue this will: Prevent bank runs Engender more stable banking through increased market discipline But the banking industry is sceptical arguing DPL will: Raise costs for customers Destabilize the financial system
3. Introduction We test the theoretical predictions made by Hardy (2013), Birchler (2000), and Osterberg (1996) regarding Funding costs, liability structure, profitability, soundness, and valuation Our research is important for three reasons 1. We inform an important policy debate that has far-reaching consequences that are not understood 2. Provide empirical tests relevant to theories on debt structure and monitoring (Fama, 1980; Goldberg and Hudgins, 2002; Rauh and Sufi, 2010; Hackbarth and Mauer, 2012) 3. Advance understanding of how bank capital structure responds to regulation (Le Lesle, 2012; Gropp and Heider, 2010)
4. What do Depositor Preference Laws do? The assets of a failed bank are paid out to creditors according to a claims structure In most countries this looks like DPL alters the claims structure by elevating uninsured depositors Without DPL Receiver Secured creditors Insured depositors Uninsured depositors & general creditors Shareholders With DPL Receiver Secured creditors Insured & uninsured depositors General creditors Shareholders
5. Theoretical Outline Modigliani and Miller (1958) irrelevance theorem: absent taxation, the composition of corporate financing has no effects unless it: Influences the probability of bankruptcy Affects the costs of bankruptcy Hardy (2013) outlines a model of the bankruptcy process and the role of depositor preference within this Creditors have a lobbying technology that is used to assert claims during bankruptcy proceedings Lobbying is increasing in the residual assets of the failed bank
6. Theoretical Outline Because DPL assigns priority to some creditors, it reduces lobbying, and banks’ funding costs Depositors no longer face losing their capital: demand a lower interest rate Reduces the expected costs of bankruptcy (through less lobbying) The lower funding costs translate into higher profits (and increases firm value) Endogenously lower the probability of bankruptcy Because non-deposits are now junior, they demand higher interest rates (Osterberg, 1996) Banks shuffle their liability structure Birchler (2000) shows DPL leads to better/more efficient monitoring of bank conduct and risk taking
7. Theoretical Outline
8. Institutional Background The assets of insolvent US banks are transferred to a receivership The receiver’s task is to maximize the NPV of recoveries The Banking Act of 1935 outlined a priority structure to the residual assets 1. Receiver 2. Secured creditors 3. Insured depositors {account balances < $100,000} (insured by FDIC) 4. Uninsured depositors (account balances > $100,000) & non-depositors 5. Holders of subordinated debt & shareholders
9. Institutional Background 30 US states opted to implement DPL between 1909 and 1993 (our analysis exploits 15 of these reforms)
10. Institutional Background These state depositor preference laws elevated the priority of uninsured deposits 1. Receiver 2. Secured creditors 3. Insured and uninsured depositors 4. Non-depositors 5. Holders of subordinated debt & shareholders Importantly (for our identification strategy) the state DPLs applied: To state-chartered banks But not to nationally-chartered banks
11. Data Description and Representativeness Quarterly Call Report data for commercial and savings banks in the US Sample covers 1983Q1 to 1993Q2 for banks in 15 enacting states
12. Data Description and Representativeness Sample includes 199,698 observations for 5,506 banks Broadly, the sample appears representative of the US population
13. Data Description and Representativeness Measure funding costs as ratio of Total interest expenses, deposit interest expenses, non-deposit interest expenses to total liabilities Liability structure: Total liabilities to total assets, same for deposits and non-deposits Bank soundness: Z-score, non-performing loans, leverage ratio Profitability ROA, total interest income to total loans, ROE
14. Identification Strategy We exploit plausibly exogenous variation in DPL enactment across states and time using a difference-in-difference estimator We estimate The dependent variable measures Cost of funds; Liability structure; Bank soundness; Profitability is a vector of bank-time varying controls; bank fixed effects; are state-quarter fixed effects We therefore compare banks in the same macro environment
15. Identification Strategy: Treatment Exogeneity Why were the laws enacted? Motivation for the reforms is not systematically documented But like national DPL, adoption seems driven by the FDIC lobbying for DPL following the failure of Penn Square in 1982 DPL made bank resolution easier by allowing purchase and assumption transactions that minimize disruption of the local economy Argued DPL would improve market discipline by exposing non-depositors to greater losses in the event of bankruptcy Because of the limited discussion behind states’ enactment of DPL, we run a series of exogeneity tests
16. Identification Strategy: Treatment Exogeneity Estimate a state-level Cox Proportional Hazards model of the form Opt for a Cox PH model as it imposes no assumption on the shape of the hazard function, or the distribution of unobserved heterogeneity Vector of controls includes: total failed banks’ deposits, estimated losses from bank failures, number of bank failures, HHI for bank deposits, total bank assets to GDP, and the unemployment rate
17. Identification Strategy: Treatment Exogeneity No significant differences in any specification – DPL not influenced by our DVs – no simultaneity bias
18. Identification Strategy: Parallel Trends To what extent do national-chartered banks act as a valid counterfactual?
19. Identification Strategy: Parallel Trends Statistical tests confirm the graphical patterns No significant differences in growth rates in the (immediate) pre-treatment period
20. Pricing Effects: Cost of Funds Total interest expenses fall as deposits priority increases Effect is equivalent to a 1.5% reduction in costs ($250,000 p.a.) Heightened exposure to losses in bankruptcy causes an increase in non-deposit costs – evidence of increased market discipline
21. Quantity Effects: Liability Structure Shrinking of state-chartered banks following treatment Declines in deposits But substitute towards non-deposit liabilities
22. Quantity Effects: Liability Structure What explains this behavior? In equilibrium, and assuming risk neutrality, some depositors will move their deposits to CG banks that pay higher deposit interest rates i.e. state-chartered banks lose out to nationally-chartered banks that were unaffected by DPL TG banks make up for this by using more non-deposit funding
23. Quantity Effects: Market Shares If our hypothesis is correct we should be able to document: A decreasing reliance on deposit funding by state-chartered banks Particularly among uninsured deposits
24. Collateralization Do non-depositors collateralize their claims? Important as this could inhibit resolution costs and time No evidence for such phenomena
25. Soundness and Profitability Policymakers emphasize that DPL will incentivize banks to operate safely, reducing the likelihood of failure Non-depositors have stronger incentives to monitor banks’ risk exposure due to their junior claim More skin in the game which makes them 1) withdraw funds; 2) refuse to roll over funds; 3) demand a higher risk premium; 4) demand collateral These actions put constraints on the risk-taking behavior of banks’ asset allocation choices (Goldberg and Hudgins, 2002) Market discipline consists of two dimensions 1. Monitoring: judge risk exposure and incorporate this information into security prices 2. Influencing: claimants exert pressure on the bank to change conduct
26. Soundness and Profitability Evidence suggests this view is correct Improvement in soundness, reduction in NPL, and leverage Increase in profitability, and a reduction in the variance of returns
27. Threats to Identification Conduct a battery of robustness checks to ensure our results are not confounded by OVB Because our regressions include state-quarter FE, these factors must be collinear with treatment to bias our findings 1. Charter is a choice variable A. Switching is infrequent (3.8%) B. No significant effect of treatment on charter type 2. Texas real estate collapse coupled with the Tax Reform Act of 1986 coincided with DPL in Texas (1985) Same findings when we omit Texas from the sample
28. Threats to Identification 3. Enactment of DPL in the northeast coincided with the New England banking crisis Results unchanged when we omit CT, ME, NH, and RI 4. If treatment is exogenous the magnitude of the ATE should be the same regardless of whether control variables are included or not This is indeed the case – very similar magnitudes 5. Placebo test – observed behavior should be specific to state-chartered banks and the actual treatment Randomly assign placebo treatments to national-chartered banks at the time of DPL enactment: no significant effect
29. Threats to Identification 6. Bertrand, Duflo and Mullainathan (2004) Where the DV is serially correlated through time, DD estimators will yield spuriously low standard errors Our regressions cluster at the “block” level (bank) Check robustness to myriad permutations of the error structure Same results when we cluster at the state level (only 15 groups!) We also collapse the data upon a pre- and post- treatment period for each bank – similar results as before
30. Valuation Effects A key prediction made by Hardy (2013) is that the reduction in funding costs raises firm valuations This is consistent with the previous results We now address how shareholders responded to DPL This necessitates an alteration to the empirical methodology Use event-study methodology to inspect what happened to stock returns following implementation of national DPL in 1993
31. Valuation Effects National DPL was implemented following enactment of the Omnibus Reconciliation and Budget Act of 1993 The Act was devoted mainly to fiscal policy issues and balancing the government budget National DPL went under the radar It was also unexpected, even by regulators: the FDIC had to issue an emergency rule on how to interpret the legislation Use stock price data retrieved from Datastream
32. Valuation Effects Run a time-series regression Allow for AR(1) autocorrelation by applying Prais-Winsten adjustment prior to estimation Focus on the event-day abnormal returns
33. Valuation Effects Shareholders seem to welcome DPL Positive valuation effects in line with the theory Mostly similar evidence for recent EU and UK announcements
34. Concluding Remarks The implications of changes in the regulatory environment on bank conduct are difficult to gauge Our natural experiment yields important insights on this issue and theoretical models’ predictions Robust causal evidence that DPL leads to Lower funding costs Improved profitability Sounder banks Increased valuation No change in collateralization While the context may differ, the cleanness of our experiment helps shed light on an important EU-wide issue