Abstract
We systematically examine the relationship between a bank’s characteristics and its exposure to systemic risk. We find that tier 1 requirements are negatively associated with a bank’s exposure, while size has a positive association. This association is nonlinear because larger banks contribute more than their smaller competitors. Banks with greater financial constraints are less exposed to systemic risk. We find evidence that geographic distance between banks has a negative relationship with systemic risk and that institutional ownership has a positive one. Finally, we find that the risk-taking attributes of the board and the CEO have a positive association with systemic risk.
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Notes
The impact of the filing of bankruptcy by Lehman Brothers was severe. The Dow Jones index dropped 4.4% on 15 September 2008.
We use two proxies to capture financial constraints: liquid asset ratio (LAR) and KZ index. We explain these two methods in subsequent sections.
The findings are robust if we control for the size of the population in the city.
In some of the tables, we use industry fixed effects instead of firm fixed effects because the variable of interest has little time-series variations, such as board composition or CEO characteristics.
For details, please see http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
Industry is defined as the Fama and French 49 industries.
LAR is liquid assets over total assets. We define liquid assets according to Basel III. Liquid assets are the sum of levels 1, 2A, and 2B assets. Level 1 assets comprise Federal Reserve bank balances, foreign resources that can be withdrawn quickly, securities issued or guaranteed by specific sovereign entities, and US government issued or guaranteed securities. Level 2A assets comprise securities issued or guaranteed by specific multilateral development banks or sovereign entities, and securities issued by US government-sponsored enterprises. Level 2B assets comprise publicly-traded common stock and investment-grade corporate debt securities issued by nonfinancial sector corporations. If COMPUSTAT reports any item missing in that fiscal year, we consider it as zero.
To calculate the number of banks per city per year, we consider the headquarter city for each bank as in COMPUSTAT. We consider the headquarters as the location of the nontraditional activities rather than a branch office. Since noninterest income to revenue ratio is positive, we use the location of the headquarters to calculate the geographic proximity.
We reexamine the relationship between banks’ systemic risk and their level of institutional holdings, both mutual fund holdings and other institutional holdings, and present the results in Table IA.10 of the Internet Appendix. We also reexamine the relationship between banks’ contagion levels and their level of mutual fund holdings only and present the results in Table IA.10.
We take industry and year fixed effect in this table as banks are less likely to frequently change the composition of their boards. For example the correlation between \(\%IndDir\) and lag \(\%IndDir\) is almost 87%. Hence there is not too much variation in the firm-level for the board attributes. Taking the firm fixed effect will lose the significance of the variable of interest.
A recession year is defined as a NBER recession year. We also control for the year after the recession to control for the monitoring effect of independent directors after the recession; we find that the results are robust.
The data points of board characteristics and CEO characteristics become very small when we categorize the sample into three sub-categories. Thus, we did not test the sub-sample analysis for these two sections as performing tests with reduced sample size may end up with bias results due to non-representing smaller sample size.
We create a subsample of investment banks with the SIC code 6211. By nature, some variables in this sub-sample are missing, such as loan ratio, deposit ratio, and noninterest revenue. Thus, we drop these independent variables when we run regressions. Due to lack of data availability of investment banks, panel C and panel D of this subsample are empty.
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Acknowledgements
We thank Haluk Ünal (the editor) and an anonymous referee as well as Leonid Pugachev for valuable comments and suggestions. We also thank Karen Jang (discussant at FMA 2020) for her comments. All errors are the authors’ sole responsibility.
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Appendix A.1
Appendix A.1
Variable Description
Variable | Definition |
---|---|
\(AvgAge\) | The average age of directors. Source- BoardEx |
\(Bank\_Opacity\) | The average scaled percentile rank of (1-Forecast Accuracy), (1-Analyst Following), Forecast Diversity |
\(Bank\_Opacity\_alt\) | The average scaled percentile rank of (1-Raw Accuracy), (1-Analyst Following), and the standard deviation in forecast diversity. Source- I/B/E/S |
\(CEO\_dual\) | Dummy variable equal to one if the CEO serves as both CEO and Chairman. Source- BoardEx |
\(Deposit\ to\ asset\ ratio\) | Total deposit to the total asset. (DPTC + DPTB)/AT. Source: Compustat |
\(Earnings\ Surprise\) | \(({EPS}_{t}-{EPS}_{t-1})/{P}_{t-1}\). Source: Compustat |
\(Excess\_market\) | The yearly average of excess market returns Source: Kenneth. French website |
\(F{C}_{t}^{\left(i\right)}\), CoVaR, MES,\(F{C}_{t}^{\left(i,CAPM\right)}, F{C}_{t}^{\left(i,FF4f\right)}\) | Yearly average contagion or systemic risk measure described in Appendix I.A.I |
\(Female\ CEO\) | A dummy variable equal to one if the CEO of a firm is a female. Source- Execucomp |
\(FinUnc\) | A dummy variable is one if the bank is financially unconstrained. Financially unconstrained is measured by three criteria: Liquid Asset Ratio (LAR), total capital, and KZ index. Source: Compustat |
\(Forecast\ Accuracy\) | “.. the percentile-ranked residual value from a regression of Raw Accuracy on Earnings Surprise and Forecast Bias, where Raw Accuracy is the absolute value of the forecast error multiplied by − 1, scaled by the stock price at the end of the prior fiscal year and where the forecast error is the analysts’ mean annual earnings forecast less the actual earnings…”- Maffett (2012). Source: I/B/E/S summary |
\(Forecast\ Bias\) | \((Mean eps forecast-actual forecast)/ {P}_{t-1}\). Source: I/B/E/S summary |
\(Forecast\ Diversity\) | “…The percentile-ranked residual value from a regression of Raw Diversity on Earnings Surprise and Forecast Bias, where Raw Diversity is the standard deviation of analysts’ forecasts of the firm’s earnings in the following year, normalized by the mean forecast and then divided by the square root of the number of analysts following that firm….” -Maffett (2012). Source: I/B/E/S summary |
\(HTmLT\) | Maximum director tenure minus minimum director tenure. Source: BoardEx |
\(Inverse\_proximity\) | One divided by the number of banks per city. Source: Compustat |
\(\%IndDir\) | The percentage of directors who are independent on a board. Source: BoardEx |
\(\% InstHolding\) | The percentage of the bank’s common stock that is held by mutual funds or institutional investors. Source: Thompson Reuters |
\(KZ Index\) | Kaplan and Zingales (1997) index \(KZ index= -1.001909* cashflow to capital +0.2826389*tobin\_q+ 3.139193*(Longterm debt+ Short term debt)/ {capital}_{ta-1} -39.3678* dividend to capital - 1.314759* cash/{capital}_{t-1}.\) |
\(LAR\) | LAR is a liquid asset over the total assets. We define liquid assets consistent with Basel III. Liquid assets are the sum of level 1, level 2A, and level 2B. Level 1 assets comprise Federal Reserve bank balances, foreign resources that can be withdrawn quickly, securities issued or guaranteed by specific sovereign entities, and US government-issued or guaranteed securities. Level 2A assets comprise securities issued or guaranteed by specific multilateral development banks or sovereign entities, and securities issued by US government-sponsored enterprises. Level 2B assets comprise publicly traded common stock and investment-grade corporate debt securities issued by non-financial sector corporations. If COMPUSTAT reports any item missing in that fiscal year, we consider it as zero. Source: Compustat |
\(Loan\ to\ asset\ ratio\) | Total loans to the total assets. [LCABG + LCUACU + LLOT + IALTI + MTL]/AT. Source: Compustat |
\(Nonint\ to\ Revenue\) | Noninterest income to total revenue. INITB/REVT. Source: Compustat |
\(Liquidity\) | Current assets (item 4) divided by current liabilities (item 5). Source: Compustat |
\(ln\_age\) | Natural log of CEO age. Source: Execucomp |
\(ln\_TC\) | Natural log of total compensation of CEO (tdc2). Source: Execucomp |
\(NI\ to\ asset\) | Net income to the total assets. NI/AT. Source: Compustat |
\(Ln(Asset)\) | Ln(Total Assets). Source: Compustat |
\({N}_{cf}\) | Cash flow news innovations using the Campbell and Shiller (1988) decomposition |
\({N}_{dr}\) | Discount rate innovations using the Campbell and Shiller (1988) decomposition |
\(Post\_IBBEA\) | A dummy variable for the four years after the IBBEA passed for each of the states |
\(Recession\_year\) | A dummy of one if the year is a recession year defined by NBER. Source: NBER |
\(SocialConnectedness\) | Log of total network size of directors of a bank. Source: BoardEx |
\(Shr\_own\) _10000 | The percentage of ownership stake that a CEO has in the firm scaled by 10,000. Source: Execucomp |
\(Std\_stock\_return\) | The firm’s standard deviation of daily stock returns over year \(t\). Source: CRSP |
\(Tier\ 1\ Capital\ ratio\) | Risk-adjusted capital ratio-Tier1. COMPUSTAT variable- CAPR1. Source: Compustat |
\(Tier1\ Capital\_sqr\) | Risk-adjusted capital ratio-Tier1 squared and scaled by 1 million. Source: Compustat |
\(Total\ Asset\_sqr\) | Total asset squared and scaled by 1 million. Source: Compustat |
\(\#BoardMember\) | The number of board members on a board. Source: BoardEx |
\(\#Bank\) | The number of banks per year. Source: Compustat |
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Mazumder, S., Piccotti, L.R. Systemic Risk: Bank Characteristics Matter. J Financ Serv Res 64, 265–301 (2023). https://doi.org/10.1007/s10693-022-00386-z
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DOI: https://doi.org/10.1007/s10693-022-00386-z