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Charter values, bailouts and moral hazard in banking

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Abstract

In this study, we examine the disciplining effect of charter values for U.S. insured depository institutions during the 1995–2012 period and the impact of government bailouts on bank risk-taking incentives following the 2008 financial crisis. Bank charter value arises from the monopolistic rents in the imperfect competition in the highly regulated banking industry. It may offset moral hazard resulting from government safety net. We analyze the impact of bank charter values, jointly with bank leverage, within the framework suggested by the Marcus (J Bank Financ 28:2259–2281, 1984) model, which originally introduced the disciplining role of bank charter value. We demonstrate that while high charter values reduce risk-taking incentives, low charter values are positively associated with higher risk-taking incentives in the shareholder value maximization problem. We also show that the impact of government bailouts (Troubled Asset Relief Program, or TARP) on bank risk taking is non-monotonic: It increases risk-taking incentives for bailout recipients relative to non-recipients for “weak” institutions with low charter values and high leverage, while the opposite is true for “strong” institutions with high charter values and low leverage.

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Notes

  1. More precisely, charter value can be defined as the net present value of future rents (Stolz 2007). It is also known as “franchise value” in the literature.

  2. The deregulation of the 1980s, particularly the Depository Institutions Deregulation and Monetary Control Act of 1980 and the Garn-St. Germain Depository Institutions Act of 1982, increased the competitiveness of thrift institutions and phased out the interest rate ceilings on deposit accounts. These acts were followed by the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994 (implemented in 1997), which eliminated restrictions on interstate banking, and the Gramm–Leach–Bliley Act of 1999, which repealed parts of the glass-steagall act (Sherman 2009). The deregulation in the banking industry in the early 1980s was in response to a confluence of several factors, mostly a sharp increase in interest rates during the late 1970s and early 1980s, which led to the “disintermediation” process in depository institutions. Although the deregulation may have contributed to the erosion of charter values, it may have averted more severe problems. We thank an anonymous referee for clarifying this point.

  3. With the exception of the 1995–1997 sub-period.

  4. More formally, Stolz (2007) connects Tobin’s q and bank charter value as follows: Tobin’s q \(=\) Charter Value/(Book Value of Assets) \(+\) 1.

  5. The validity of Tobin’s q as a measure of growth opportunities and market power has also been questioned outside of the banking studies. For example, Tobin and Brainard (1990) respond to an earlier “Crotty’s Critique” and acknowledge that q is an endogenous variable. The “shadow” or implicit q is value of future returns of the firm to dollar cost of purchase of the capital goods. Although it is a “q-like” measure, q based on stock market prices and implicit q can differ significantly, according to Tobin and Brainard (1990).

  6. Following the Federal Deposit Insurance Corporation Improvement Act of 1991 (FDICIA, 1991), starting in 1993, insurance premiums are risk based. However, it remains unclear how well the risk-based premiums reflect the true risk taking by the banks given the opaque nature of banks’ assets (Osborne and Lee 2001).

  7. The details of this model are presented in Sect. 3.

  8. In total, more than 700 public and private firms received funds to support the capital ratios in 2008–2009. The CPP capital injections ranged between 1 and 3 % of risk-weighted assets, with the maximum amount capped at $25 billion (excluding the eight largest recipients). The process of TARP/CPP repayments involved 100 % repurchase of preferred stock and the option to repurchase all related warrants. A significant number of participating institutions have repaid the TARP/CPP funds to the U.S. Department of the Treasury. In March of 2009, the Treasury allowed smaller institutions with total assets under $500 million to apply for the program and increased the upper limit of available funds to 5 % of risk-weighted assets (Office of Financial Stability 2010).

  9. The payoff to bank equity holders can be considered a call option. Conversely, some properties of the depository insurance are isomorphic to those of a put option (Merton 1977).

  10. In this model, liabilities (L) are assumed to be equal to total deposits (D).

  11. This is the version of the formula given by Stolz (2007), whereas Marcus (1984) provides an equivalent expression: \(\frac{\partial E}{\partial {\upsigma }_{\mathrm{A}}}=\hbox {L} \sqrt{\hbox {T}}\hbox {n} (\hbox {d}_2)-\hbox {e}^{-\mathrm{rT}}\hbox {d}_1\hbox {n} (\hbox {d}_2)\hbox {CV} {\upsigma }_{\mathrm{A}}\).

  12. Flannery (2010) notes that although the Marcus (1984) model introduces the charter value as a counteracting factor to risk-taking incentives, the model cannot distinguish whether the increase in default risk for the protected institutions comes from the increased leverage or asset size. In this study it is assumed that the liabilities (L) are equal to total deposits (D), which is normalized to 1 (\(L = D = 1\)) as in Marcus (1984). With this assumption, A/D = A/L = A (e.g., Fig. 1).

  13. Hovakimian and Kane (2000) employ the quarterly change in focus variables, while we use an annual change in this study. As we demonstrate in Sect. 5, the values \({\upsigma }_{{A}}\) of calculated using this method are similar to values reported in Hovakimian and Kane (2000).

  14. http://www.nber.org/cycles/cyclesmain.html.

  15. https://research.stlouisfed.org/fred2/series/FEDFUNDS/.

  16. We use the Federal Reserve Economic Data (FRED) series for the effective federal funds rate and the U.S. stock price index volatility (http://research.stlouisfed.org/fred2/) and business cycle dates determined by the National Bureau of Economic Research (NBER) (http://www.nber.org). We select year-end as a cut-off point for our subsamples due to annual data frequency.

  17. On October 28, 2008, Bank of America Corporation, The Bank of New York Mellon Corporation, Citigroup, Goldman Sachs, JP Morgan Chase, Morgan Stanley, State Street Corporation, and Wells Fargo received an injection of $125 billion in the form of preferred stocks with warrants (www.treasury.gov).

  18. The original macros used in the PSM algorithm are developed by Jon Kosanke and Erik Bergstralh at the Mayo Clinic and utilize the variable optimal matching method. The algorithm is obtained from http://sas-and-r.blogspot.com/2010/05/example-735-propensity-score-matchingn.html and is modified to fit the specifics of our analysis.

  19. For example, Hovakimian and Kane (2000) report mean 2.9 % (median 2.5 %) for the standard deviation of the return on assets for the sample of 123 banks from 1985 to 1994.

  20. The total assets of Mellon Financial Corporation exceed $50 billion (more precisely it reaches $50.8 billion) only during one year (1998), and it was therefore retained in the subsample.

  21. We ran a quartile analysis using similar interactions in each subsample. For brevity, the results, which support our findings using the median point analysis, are not included. They are available from the authors upon request.

  22. Results are not included for brevity and are available from the authors upon request.

  23. The results for NIM as a charter value proxy are mixed in a quartile analysis (not shown for brevity). For the upper quartile, an inverse relationship between high charter value (proxied by NIM) and lower equity vega is significant for the 2008–2012 period similar to the median break point tests. However, for the lowest quartile of NIM, the effect is positive and significant in 1995–2000, and negative and significant during the 2008–2009 crisis.

  24. The following t-values for lagged \(\varDelta {\upsigma }_{\mathrm{A}}\) (used as instrument) are obtained in the first stage of the 2SLS model run in each of the six sub-periods: 8.06 (in 1995–1997), 6.47 (in 1998–2000), 5.57 (in 2001–2004), 4.73 (in 2005–2007), 6.25 (in 2008–2009), and 10.03 (in 2010–2012).

  25. While the PSM method provides a more accurate comparison of groups relative to the unmatched sample, it does not provide an absolute match, i.e., two of the variables (asset size and Tier 1 capital) are not statistically equal. While the matching is performed at the end of 2007 (2007.q4), our analysis focuses on wider periods (crisis, pre-crisis, post-crisis), which may explain this discrepancy.

  26. In this case, A / L ratio is higher for TARP banks post-crisis at the 10 % significance level.

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Authors and Affiliations

  1. Financial Economist, Enterprise Risk Analysis Division, The Office of the Comptroller of the Currency, 400 7th Street SW, Washington, DC, 20219, USA

    Natalya A. Schenck

  2. Department of Finance, Kent State University, 475 Terrace Drive, Kent, OH, 44242, USA

    John H. Thornton Jr.

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  1. Natalya A. Schenck
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Correspondence to Natalya A. Schenck.

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The views herein are those of the authors and do not necessarily represent the views of the Office of the Comptroller of the Currency or the Department of the Treasury.

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Schenck, N.A., Thornton, J.H. Charter values, bailouts and moral hazard in banking. J Regul Econ 49, 172–202 (2016). https://doi.org/10.1007/s11149-015-9292-0

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