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Thesis
Your name: Wout van Wieringen
Your student number: 10785345
Track (within Economics and Business): Economics and Finance
Field: Finance
Number of credits thesis: 12
Title of your research proposal: ‘Too big to fail’ The effect of bank systematically importance
on risk and return
Assigned supervisor: Robin Döttling
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Statement of Originality
This document is written by Student Wout van Wieringen who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
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TOO BIG TO FAIL
The effect of bank systematically importance on risk and return Wout van Wieringen
Abstract
This research questions if being on the Financial Stability Board’s list for systematically important banks impacts a bank’s performance with respect to risk and return. It tests the effect of such a status on stock returns and shows that it has a small negative direct effect on returns. Listed banks are however more sensitive to market return, as the research shows that these banks are significantly more volatile.
After the financial crisis of 2007-2008 a common heard phrase regarding banks was ‘too big to fail’. A status which in this context means that a bank is of sufficient size and economic importance that if the bank would come under financial duress,
government intervention would be necessary as to not cause a spill over effect on the national economy. This implicit guarantee given to the banks that they will be
supported during times of financial stress might cause them to take on more risk as can be explained through moral hazard theory. Excess risk results in higher
profitability and stock returns, in return for higher measures of risk and volatility. Which leads to the question if a ‘too big to fail’ (TBTF) status impacts a bank’s performance with respect to risk and return. This research confirms that there is a positive relationship between a TBTF status and risk taking behaviour, resulting in more volatile returns.
Starting off a review of the literature is given, then what will follow is the framework of the research as well as the regression. What follows is a short explanation of the meaning of each variable and its proposed relationship on the dependant variable and, if relevant, interaction with another independent variable. Following this, the setup of the research will be explained. The next part will show the results of the regression, the conclusions that can be drawn, and an analysis of the proposed relationships of the variables. Finally the research ends with a short summary, the
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strengths and weaknesses of this research and some recommendations for further research.
LITERATURE REVIEW
RISK TRANSFER
When a Bank transfers risk, it means the bank decreases some risk which is taken over by another party, in this research, the government. However banks have an incentive to directly offset a decrease in risk, by taking on additional risk to return to a position of optimal probability of default (Wolf, 2007). Wolf (2007) further argues that “stability (…) falls if the losses from selling assets in a crisis are reduced” because bank’s incentives to limit risk taking are undermined, and excess risk taking is increased due to limited liability(p.123), This the case for banks which are too big to fail, since government intervention pays for some of the possible losses in case of default, reducing the costs of selling assets during crisis.
MORAL HAZARD
Gorton and Huang (2004) as well as Cordella and Yeyati (2003) show that safety nets, and bank baillouts can result in a moral hazard issue resulting in banks taking on excessive risk. One of the important factors they name in creating moral hazard is the degree of ambiguity. They “(…) show that the ‘constructive ambiguity’ approach often recommended to attenuate moral hazard (…) is always dominated by a policy that commits to rescuing failed banks with certainty, conditional upon the realization of an adverse aggregate shock”( Cordella and Yeyati, 2003, p. 302). Since the FSB list is a form of explicit affirmation of bank institutional importance, it could enforce this moral hazard effect, increasing risk taking. Finally Dam and Koetter (2012) looked at the relationship between bailout expectations and moral hazard in
Germany, and found that an increase in bailout expectations directly effects the risk taking behaviour of banks. Because being listed as TBTF would increase bailout expectation directly, it can be assumed that those banks are more likely to act in excessive risk taking behaviour.
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REGRESSION
The Financial Stability Board (FSB), an international body that monitors and makes recommendations about the global financial system, holds an official list of banks which can be regarded as ‘too big to fail’ (TBTF), called the ‘list of systemically important banks’. Comparing banks that are on this list, with banks that do not qualify for the TBTF status, it is possible to measure the effect of the status on performance measures. A basis for asset pricing is provided by Fama and French in ‘The cross section of expected stock returns’(Fama and French,1992).Using the theory established by the Fama French three factor model, and expanding on it by adding dummy variables for TBTF, and a dummy for crisis periods (CRISIS), it is possible to determine if a TBTF status has an effect on performance, while isolating for factors which could cause a TBTF status, like size alone. This results in the following proposed regression:
𝑅 − 𝑅𝑓 = 𝐶 + 𝛽1(𝑅𝑚 − 𝑅𝑓) + 𝛽2𝐿𝑛(𝑆𝐼𝑍𝐸) + 𝛽3(𝑇𝐵𝑇𝐹) + 𝛽4(𝐶𝑅𝐼𝑆𝐼𝑆) + 𝛽5((𝑅𝑚 − 𝑅𝑓) ∗ 𝑇𝐵𝑇𝐹) + 𝛽6(𝐶𝑅𝐼𝑆𝐼𝑆 ∗ 𝑇𝐵𝑇𝐹)
R-Rf The return of each bank’s stock, minus the risk-free rate. Also known as excess return
C The regression constant
Rm-Rf The return of the market, minus the risk-free rate. Also known as the excess market return
SIZE The size of each bank at a given point in time
TBTF Dummy variable measuring inclusion on the financial stability board global list of systematically important banks
CRISIS Dummy variable measuring if the data was measured during the financial crisis of 2007-2008
(Rm-Rf)
The variable (Rm-Rf) is also known as the market premium. It measures the return of the whole market, minus the risk free return. The related Beta term therefore is a measure of how much the bank returns follow the market return.
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The interaction variable (Rm-Rf)*TBTF tests if the listed banks’ returns are more, less, or
similarly related to the market return. Because this research hypothesises that listed banks take more risk, resulting in gaining more return, it follows that these banks might be more sensitive to market changes. This would mean that the variable (Rm-Rf)*TBTF is significant and positive, if the banks are indeed more sensitive to changes in market return due to being on the FSB list.
SIZE
Economic theory suggest bigger bank size, defined as its assets, might be positively correlated with profitability due to economies of scale and scope. Research by
Regehr, Sengupta and Rajdeep (2016) supports this relationship for the banking sector, as well as research by Shehzad, De Haan, and Scholtens (2010) which looked at more than 15 000 banks from 148 countries from 1988 to 2010. Size however, is also a factor that determines a listing by the financial stability board, since they define TBTF banks as “(…) financial institutions whose distress or
disorderly failure, because of their size, complexity and systemic interconnectedness, would cause significant disruption to the wider financial system and economic
activity”(Addressing SIFIs, n.d.). By including a dummy variable, the size effect can be isolated from the variable of interest, namely the TBTF status. This means the correlation between profit and size will be tested by a variable, while the possible correlation between TBTF status and profitability can be tested by its own variable.
TBTF
Through explicit insurance like deposit insurance, but also through inexplicit safety nets governments provide direct support to banks which in return create hazard incentives for banks through greater risk (Mishkin, 2006), (Cordella and Yeyati
(2003). In other words, the safety nets provided by the government cause the banks to have limited liability which has been shown to lead to higher risk taking in
comparison to full liability( Füllbrunn and Neugebauer, 2013). Financial theory
suggest a trade-off between risk and return, which would imply that TBTF banks see higher return. Furthermore there is empirical evidence that specifically downside risk
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has a significant influence on return (Sévi, 2013). Which is exactly the risk that the safety nets provide. The Variable TBTF is the main variable of interest in this research, and will show if there is a significant positive effect on returns if a bank is listed on the FSB list of systematically important banks.
CRISIS
During the financial crisis of 2008, banking institutions were one of the first to show signs of financial duress. The variable CRISIS will test how big the effect of the crisis was on average returns in the banking sector
TBTF*CRISIS
As mentioned earlier, this research theorises that banks listed by the FSB are more likely to take risk than none listed banks, which would result in increased returns. This excess risk would however mean that during times of financial duress, additional negative returns could be made in comparison to banks that are not listed. The
variable TBTF*CRISIS, tests if this assumption is true. If the main variable TBTF is significant this interaction variable will most likely be significant as well, but it will have a negative effect due to the proposed negative correlation of a crisis on returns.
METHOD
In this research data from US banks is collected through Wharton Research Data Services: CRSP, according to the standardised industrial classification code. The used SIC codes are: 6021 and 6022, for national commercial banks, and state commercial banks respectively. The following data was collected from a total of 341 US commercial banks on a monthly basis: share price, holding period return and shares outstanding. The time range this research focuses on is from January 2007 till December 2017. This range includes the financial crisis of 2007-2008, and there is sufficient information on the included banks to carry out the research. The dummy variable SIZE is defined as the bank’s market capitalization, and can be calculated by multiplying the share price times the number of shares outstanding. The S&P 500 composite index is used as a proxy for the market return. To calculate both the excess market return, as well as the excess return for each stock, the risk free rate is needed. The risk free rate will be defined as the rate of a three month united states treasury bill. The dummy variable CRISIS, is defined by the time range: September
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2008, till February 2009. These dates are based on the Lehman Brothers bank
collapse as a start and the American Recovery and Investment act of 2009 as an end date. The dummy variable TBTF is constructed by following the Financial Stability Board’s yearly report on systematically important financial banks. The FSB started reporting the list yearly starting November 2011, with their latest report being published on November 2017. Banks qualify for TBTF following the year of list inclusion till they are no longer listed. Banks are assumed to be TBTF for every year before 2011, if they were listed in 2011. This leads to the following results.
RESULTS
What follows next is the descriptive data on all collected data points: Table 1: Summary Statistics
Table 2: Regression Results
Regression 1 Regression 2 Regression 3
Rm-Rf .8667739** (.0282448) .8425057** (.0290815) .8439511** (.0289232) Ln(SIZE) .0048277** (.0005637) .0048208** (.000564) .0048214** (.0005641) TBTF -.0244106** (.0043779) -.0276463** (.0041067) -.0267418** (.004379) CRISIS -.0179499* (.0066297) -.0184809** (.0067915) -.0179144** (.0066224) Rm-Rf*TBTF .6197694** (.1053944) .5828234** (.1127271) CRISIS*TBTF .0142679 (.0297534) constant -.0601908** (.0073384) -.0599823** (.0073417) -.0600256** (.0073422) R-squared .0915 0.0928 0.0928 **Significant at 1%, *Significant at 5%
Variable Observations Mean Standard Deviation Minimum value Maximum value R-Rf 22,498 .0009021 .1375416 -.9259 3.6622 Rm-Rf 22,498 .0039672 .0438978 -.1706 .1077
SIZE 22,498 5559082 2.64e+07 594.2626 3.71e+08
TBTF 22,498 .0401813 .1963887 0 1
CRISIS 22,498 .535603 .225153 0 1
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The first regression was done on a simplified model, without interaction variables:
Regression 1: 𝑅 − 𝑅𝑓 = 𝐶 + 𝛽1(𝑅𝑚 − 𝑅𝑓) + 𝛽2(𝑆𝐼𝑍𝐸) + 𝛽3(𝑇𝐵𝑇𝐹) + 𝛽4(𝐶𝑅𝐼𝑆𝐼𝑆)
From regression 1 it can be seen that all the variables significantly different from zero. Rm-Rf has a coefficient of approximately 0.87. This can be interpreted as the analysed banks’ stock returns follow the market quite closely, but they are not as susceptible to high markets, as well as low markets. For every additional percent of market return, banks on average earn 0.87 percent increase on their stock return. The next significant variable is SIZE. This means that the market capitalization of a bank directly has a positive effect on the return of that bank.
The variable TBTF has a significant negative effect on returns. This means that the hypothesis this research proposes is supported by this. The fact that a bank is listed by the FSB as being TBTF directly implies a negative effect on returns. The possible reasoning behind the effect is further explained under regression 2.
The final variable CRISIS also has a significant negative effect on returns, as was hypothesised. The regression shows that on average banks saw about 18% less returns, during the 2007-2008 crisis, as compared to the period before and after.
What follows is the regression expanded by adding the proposed interaction variables: (Rm-Rf)*TBTF and TBTF*CRISIS,which results in the full regression:
Regression 2: 𝑅 − 𝑅𝑓 = 𝐶 + 𝛽1(𝑅𝑚 − 𝑅𝑓) + 𝛽2(𝑆𝐼𝑍𝐸) + 𝛽3(𝑇𝐵𝑇𝐹) + 𝛽4(𝐶𝑅𝐼𝑆𝐼𝑆) + 𝛽5((𝑅𝑚 − 𝑅𝑓) ∗ 𝑇𝐵𝑇𝐹) + 𝛽6(𝐶𝑅𝐼𝑆𝐼𝑆 ∗ 𝑇𝐵𝑇𝐹)
In regression 2 all variables have an influence significantly different from zero, except for CRISIS*TBTF. The coefficient Rm-Rf is somewhat lower than in the first
regression, meaning banks follow the market return less. Every 1 percent increase in market return now results in approximately 0.84 percent return in bank stock on average.
The effect of SIZE is virtually unchanged from the previous regression and still shows the same positive correlation between market capitalization and return.
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In regression 2 it can be seen that TBTF has a significant correlation with returns. It was hypothesised that TBTF would have a positive effect on return, since some risk would be deferred to the government, leading to more return. However the regression shows that banks that are listed, on average see about 0.28 percent less returns than non-listed banks. Apossible explanation for the negative influence of TBTF on return is the fact that the banks listed as TBTF are mostly, if not fully, compromised of value stock. The returns of banks not listed are comprised of both value as well as growth stock. The difference might be due to value premium, which declines with size (Fama and French, 2012). So while chance of TBTF status likely increases with size, return declines.
The effect of CRISIS has slightly increased, and is still significantly negatively related to returns, as was expected.
The variable Rm-Rf*TBTF has a relatively large coefficient, compared to Rm-Rf. This means that listed banks are more sensitive to changes in market return. A one
percent change in market return resulted 0.84 percent change for non-listed banks. For listed banks, an additional 0.61 percent effect is shown. Meaning a one percent change in market return results in approximately 1.45 percent increase on average for listed banks. It can be said that therefore listed banks are also significantly more volatile than non-listed banks. On average TBTF banks can be assumed to
outperform the market during times of high return, but are also more susceptible to decreases or negative returns during times when market return is low. This supports the main hypothesis that a bank listed as TBTF take on more risk which earns them more return.
The variable CRISIS*TBTF was hypothesised to have a significant negative effect on returns, due to the risk-return trade-off. However the regression shows that there is no significant effect of CRISIS*TBTF on R-Rf. This means that listed banks on
average did not earn more or less return during the Crisis. One of the reasons for this insignificance might be that the first list for TBTF banks was published after the crisis, meaning banks were not aware of possible government support during the crisis and we’re therefore unable to defer risk, in order to earn higher returns.
Regression 3 shows the regression without the insignificant variable CRISIS*TBTF. All variables are significant, and largely indifferent from regression 2.
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Regression 3: 𝑅 − 𝑅𝑓 = 𝐶 + 𝛽1(𝑅𝑚 − 𝑅𝑓) + 𝛽2(𝑆𝐼𝑍𝐸) + 𝛽3(𝑇𝐵𝑇𝐹) + 𝛽4(𝐶𝑅𝐼𝑆𝐼𝑆) + 𝛽5((𝑅𝑚 − 𝑅𝑓) ∗ 𝑇𝐵𝑇𝐹)
CONCLUSION
This research looked at the question if a ‘too big to fail’ status impacts a bank’s performance with respect to risk and return. More specifically it looked at the effect a TBTF status, as listed by the FSB, on stock returns. Analysis of the data shows that TBTF has a small negative direct effect on returns. However Listed banks are more sensitive to market return, meaning they outperform other banks during good times, but are also more susceptible to decreasing returns when the market is low.
This research looked at data from around 341 US banks, meaning the tested relationships are likely to hold in different setting. The method can be expanded to research other inputs, and other interaction variables with regard to TBTF. Such variables could include a measure for complexity and interconnectedness, both
measures the FSB uses to determine bank status. Further research could be done, to see how these relations hold in more specific settings. Furthermore this research looks directly at returns, meaning the effects on returns are clear. The measures for risk are assumed relations based on earlier research. Looking at TBTF effect on a direct risk measure could result in interesting result, further clearing these
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Works Cited
Addressing SIFIs. (n.d.). Retrieved from http://www.fsb.org/what-we-do/policy-development/systematically-important-financial-institutions-sifis/
Cordella, & Yeyati. (2003). Bank bailouts: Moral hazard vs. value effect. Journal of Financial Intermediation, 12(4), 300-330.
Dam, L., & Koetter, M. (2012). Bank Bailouts and Moral Hazard: Evidence from Germany. The Review of Financial Studies, 25(8), 2343-2380.
Fama, Eugene F., & French, Kenneth R. (2012). Size, value, and momentum in international stock
returns. Journal of Financial Economics, 105(3), 457-472.
Fama, Eugene F., & French, Kenneth R. (1992). The cross-section of expected stock returns. (includes appendix). Journal of Finance, 47(2), 42
Füllbrunn, S.C., & Neugebauer, T. (2013). Limited Liability, Moral Hazard, and Risk Taking: A Safety Net Game Experiment. Economic Inquiry, 51, 1389-1403.
Gorton, G., & Huang, L. (2004). Liquidity, Efficiency, and Bank Bailouts. American Economic Review, 94(3), 455-483.
Mishkin, F. (2006). How Big a Problem is Too Big to Fail? A Review of Gary Stern and Ron Feldman's Too Big to Fail: The Hazards of Bank Bailouts. Journal of Economic Literature, 44(4), 988-1004. Regehr, K., & Sengupta, Rajdeep. (2016). Has the relationship between bank size and profitability
changed? Economic Review, 101(2), 49-72.
Rosenberg, Reid, & Lanstein, (1985), Persuasive evidence of market inefficiency, Journal of Portfolio Management 11, 9-17.
Sévi, B. (2013). An empirical analysis of the downside risk-return trade-off at daily frequency. Economic Modelling, 31, 189-197.
Shehzad, De Haan, & Scholtens. (2010). The impact of bank ownership concentration on impaired loans and capital adequacy. Journal of Banking and Finance, 34(2), 399-408.
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