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shareholders, the reserves become the property of the new shareholders. Since the reserves form part of the funds used to finance risky ventures, investment in relatively risky loans appeared to offer an attractive risk reward combination. Mutuals are more likely to play safe, and know more about their borrowers. Gowland observed that over 60% of the soon to fail thrifts were shareholder owned, compared to just 25% of mutuals.
Second, banking structure can be a contributory factor in the presence of other conditions. In the southwestern USA in the 1980s, the changes in regulations that allowed S&Ls to compete with the banks for retail business put the banks under greater pressure to find new business, which may be one explanation for their eagerness to lend in the property market, especially the commercial area – to keep market share.
In the USA, an unbalanced deposit base was a contributory factor because regulations have limited access to funding. For example, the savings and loans industry, even after the reforms, had restrictions on accessing the wholesale markets; Continental Illinois had to rely on relatively high cost short-term wholesale funding because of branch banking restrictions in Illinois. First Republic Bank in Texas suffered the same problem but to a lesser degree, because 20% of its deposits came from the wholesale markets and 40% from regional companies. Other US banks faced similar branching restrictions, but did not use the CI route to overcome the problem, relying instead on more innovative methods of funding through bank holding companies, Edge Act banks and non-bank banks.
Deposit protection schemes normally cover the small depositor with a view to preventing bank runs. Several of the cases demonstrate the necessity of 100% deposit insurance, if the objective is to eliminate bank runs. However, such a scheme creates moral hazard problems because banks have an incentive to assume greater risks than they would in the absence of deposit insurance, and depositors have less reason to monitor banks. Eliminating or ‘‘privatising’’ deposit insurance would force a bank with a portfolio of assets showing a comparatively high variance in rate of return to pay higher interest on deposits, or to pay higher deposit insurance premia than banks with less risky portfolios. The recent introduction of risk based deposit insurance premia by the FDIC in the USA is an attempt to reduce the moral hazard problems created by deposit insurance. Co-insurance (an option in the EU scheme but not the US one) encourages depositors to scrutinise bank activities more closely.
Finally, lack of experience with relatively new financial products, especially if they have a global dimension, can be a contributory factor. Bankhaus Herstatt and Franklin National Bank collapsed52 in 1974 after huge losses arising from trading in the (relatively new) foreign exchange markets. The US thrifts had little experience with junk bonds or mortgage backed securities but used them to boost short-term profits. Barings management did not seem to understand that arbitrage would yield modest profits and not normally involve the lodging of funds on an immense scale. It appears that Mssrs Iguchi, Hamanaka and Rusnak did not make personal gains from their rogue trading activities (apart from bonuses), indicating they made mistakes because they did not really understand the markets, then covered up their losses to avoid loss of face and dismissal.
52 What was left of Franklin National Bank was taken over by a consortium of European banks. Bankhaus Herstatt was closed.
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To conclude this section, numerous contributory factors to bank failure have been identified in this review. The advantage of a case study approach is the detailed background provided, which is helpful for the practitioner. However, the discussion itself illustrates that most failures are explained by an interaction of the various causes listed above. More precise answers, such as the relative importance of these factors, can be supplied by the quantitative analysis. The drawback is that it is not possible to test all the causes identified in this section. However, having used econometric analysis to identify significant variables, the two approaches can be brought together to form some tentative conclusions as to the causes of bank failures.
7.5. Bank Failure: Quantitative Models
Qualitative reviews of bank failure provide some insight into what causes a bank to fail, but these ideas need to be subjected to more rigorous testing. Any econometric model of bank failure must incorporate the basic point that insolvency is a discrete outcome at a certain point in time. The outcome is binary: either the bank fails or it does not. The discussion in the previous section shows that banks (or, in Japan, almost the entire banking sector) are often bailed out by the state before they are allowed to fail. For this reason, the standard definition of failure, insolvency (negative net worth), is still extended to include all unhealthy banks which are bailed out as a result of state intervention, using any of the methods outlined in earlier sections, such as the creation of a ‘‘bad bank’’ which assumes all the troubled bank’s unhealthy assets and becomes the responsibility of the state, and a merger of the remaining parts with a healthy bank.
Much of the methodology employed here is borrowed from the literature on corporate bankruptcy, where a firm is either solvent (with a positive net worth) or not. In situations where the outcome is binary, two econometric methods commonly used are discriminant or logit/probit analysis. Multiple discriminant analysis is based on the assumption that all quantifiable, pertinent data may be placed in two or more statistical populations. Discriminant analysis estimates a function (the ‘‘rule’’) which can assign an observation to the correct population. Applied to bank failure, a bank is assigned to either an insolvent population (as defined above) or a healthy one. Historical economic data are used to derive the discriminant function that will discriminate against banks by placing them in one of two populations. Early work on corporate bankruptcy made use of this method. However, since Martin (1977) demonstrated that discriminant analysis is just a special case of logit analysis, most of the studies reported in Table 7.1 use the multinomial logit model.
The logit model has a binary outcome. Either the bank fails, p = 1, or it does not, p = 0. The right-hand side of the regression contains the explanatory variables, giving the standard equation:
where
p = 1 if z > 0 p = 0 if z ≤ 0
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z = log[p/(1 − p)] β0 : a constant term
β: the vector of coefficients on the explanatory variables x : the vector of explanatory variables
ε : the error term
It is assumed var(ε) = 1, and the cumulative distribution of the error term is logistic; were it to follow a normal distribution, the model is known as probit.
Readers who are unfamiliar with logit analysis will find it explained in any good textbook on introductory econometrics. However, an intuitive idea can be obtained by referring to Figure 7.1. In a simple application of equation (7.1), if x consists of just one explanatory variable (e.g. capital adequacy), the logit model becomes a two-dimensional sigmoid shaped curve, as shown. The probability of failure is on the vertical axis and the explanatory variable, in this case capital adequacy, is on the horizontal axis. Recall, in the logit model, that a bank either fails (p = 1) or it does not (p = 0). As the bank’s capital adequacy (which could be measured in a number of ways) falls (approaching 0 in Figure 7.1), the probability of bank failure rises. Note the difference between the logistic curve and the straight line of a standard least squares regression.
A potential problem arises from the use of the multinomial logit function in estimating bank failure. These studies rely on a cross-section of failed banks either in a given year, or over a number of years. They are using panel data and, for this reason, an alternative model could be a panel data logit specification first described by Chamberlain (1980). The ‘‘conditional’’ logit model for panel data is:
Figure 7.1 The logit model.
Probability of Failure
1
p = 1: bank has failed
p = 0: bank has not failed
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where
αi : captures individual group effects, and is separate for each group; i = failed or non-failed β x : as in equation (7.1)
Chamberlain shows that if a multinomial logit regression is used on panel data, and the number of observations per group is small (except in the USA, where the number of failed banks was very high in the early 1930s and throughout the 1980s), the result is inconsistent estimates, arising from omitted variable bias. Furthermore, the αi allow a test for group effects, addressing the question of whether there is something unique to the group of bank failures. In equation (7.2), the αi are not considered independent of x. Heffernan (2003), using a Hausman test,53 shows that the multinomial logit model employed by most studies is superior to a conditional logit model where the αi are different for the failed and healthy groups.
Table 7.3 summarises the results of a selection of econometric studies testing for the determinants of bank failure, most of which use a logit model, though some use discriminant analysis.54 The paper by Cebula (1999) employs linear regression analysis, where the dependent variable is the percentage of banks that failed in a given year. Most of the studies are based on US data, mainly because the sample of bank failures is large enough to allow quantitative tests to be conducted. This means caution should be used when drawing inferences from the results from other countries, because, as was noted in Chapter 5, the structure of the American banking system differs from most other countries. An added problem is the difficulty in testing for the effects of macroeconomic variables when only one country is studied. Two of the studies, Heffernan (2003) and Logan (2000), draw on, respectively, international and British data.
These studies test a large number of variables, creating the potential for multicollinearity problems, arising from lack of independence among the variables, making a stepwise (forward and backward) procedure appropriate. A series of regressions is run, and variables are dropped from or added to the model one at a time based on their individual significance (t-ratios) and their contribution to the overall fit of the model, as measured by pseudo or McFadden R2 (MR2).55
As can be seen from column (5) of Table 7.3, numerous measures of profitability, liquidity, capital adequacy (total loans/total equity capital) and loan quality (reserve for possible loan losses/total loans) were found to be significant with the expected sign in several of the studies reported. In 9 of the 11 studies, the coefficient on a profitability measure (e.g. net income/total assets) is significant and negative: as net income rises, the probability of bank failure declines.
53The Hausman test (see Hausman, 1978 and Madalla, 1988) was developed to test for specification error, and is used to compare a given model with a hypothesised alternative. If there is no misspecification, there exists a consistent and asymptotically efficient estimator, but that estimator is biased and inconsistent if the model is misspecified.
54Martin (1977) showed the linear discriminant function to be a special form of the more general logistic function. See Chapter 6 for a discussion of discriminant analysis.
55The McFadden R2 is a pseudo R2 and is defined as {1 − (log -likelihood)/(restricted (slopes = 0) log -likelihood)}. See McFadden (1974).
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Measures of capital adequacy feature in 8 of the 11 studies. Martin’s early study (1977) is one of the few that used a measure of capital to risky assets. The Basel 1 ratio requirement did not take effect until 1993, making it too early for it to be tested in these studies. But even the ratio of capital to assets, unweighted for risk, appears significant: as capital adequacy rises, the probability of failure declines.
Loan growth rates or loan losses also feature (though studies use different measures), suggesting that too rapid a growth rate in loans will raise the probability of failure. Concentration of loans in real estate, or loans to insiders (which one study treats as a proxy for fraud) was also significantly positive. However, Logan found lower loan growth rates made banks more likely to fail. He argues this is consistent with both demand and supply side factors. On the demand side, borrowers from banks that eventually failed may have suffered more under recession than firms at healthy banks, suggesting poorer screening by the failed banks. On the supply side, weaker banks may be writing off past loans, concentrating staff resources on firms in trouble rather than actively seeking loans, or problems with funding. Logan notes that the deposits of the failed group increased by 1.1% compared to 10% for the survivor group over the same period. The median risk assets ratio was about the minimum required – so capital constraints do not appear to be a factor. In Heffernan (2003), the coefficient on loan losses was negative and nearly significant at the 95% confidence level in the first seven equations. At first this result may appear counterintuitive, but it may be that banks which set aside reserves are explicitly acknowledging loan loss problems and take appropriate action, thereby avoiding failure.
In a minority of studies, other variables are found to be significant with the expected sign. In three of them, increased liquidity reduces the probability of bank failure, and a coefficient on bank size is found to be significantly negative, that is, the larger the bank, the lower the likelihood of failure. Hwang et al. (1997) report that 48 variables were tested year on year from 1985 to 1988, but the significant variables changed every year, which raises some question about being able to use precise measures. For example, profitability appears to be important, but which measure should be monitored? This problem may be less serious than it first appears, because regulators/supervisors are tracking such a large number of measures each year.
As has been noted, it is difficult to test for the influence of macroeconomic variables, if just one country is included in the test. Cebula (1999) overcomes this problem by using the percentage of bank failures over several years as the dependent variable. This approach makes it possible to test for the effects of macroeconomic variables over time. Cebula’s least squares regression results find negative coefficients on the real GDP growth rate and real interest rate are negatively signed: as either increases, the percentage of bank failure falls. Heffernan (2003) was able to test several macroeconomic variables in a logit model because the data set was international, with bank failures from eight countries.56 The inflation and real exchange rate coefficients were, in their respective equations, highly
56 A cross-country data set is beneficial because it permits a broader study of bank failure than that which is possible if one focuses on just one country. However, country accounting differences narrow the range of financial ratios that can be tested. There is a need for consistent international measures of the onand off-balance sheet activities of banks, and it is hoped the agreement to recognise international accounting standards by 2005 (see Chapter 4) will go some way to rectify this problem.
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though over a short period it could be due to contagion effects. The cases also showed that problems could be aggravated by regulatory forbearance, the absence of blanket deposit insurance, and the apparent inability of supervisors and auditors to pick up important signals of problem banks.
Section 7.4 reported a number of studies which used a logit model to identify variables that are significant in explaining the probability of bank failure. The econometric results are broadly consistent with the findings from the case studies, and provide the reader with greater precision with respect to which variables contribute to failure, namely:
žFalling profitability, most commonly measured by the ratio of net income to total assets. The case studies indicate that falling profitability resulting in bank failure is most likely caused by the deterioration in the quality of a bank’s loan portfolio, and different measures of loan losses were found to be significant in some of the econometric work.
žFalling capital adequacy (various measures).
žRising loan losses.
žGreater illiquidity, though the support for measures of illiquidity in the econometric studies is weaker.
žMacroeconomic indicators, according to the findings in Cebula (1999) and Heffernan (2003), are important explanatory variables which should be tested in any model of bank failure, in addition to financial ratios. The likelihood of bank failure rises with inflation and falls as the real exchange rate, real interest rate or real GDP growth rate rises.
žHeffernan (2003) found the Fitch bank rating variable to be significant, suggesting there is a role for these agencies in the assessment of bank quality.
žThe econometric studies found that the larger the bank (in terms of assets) the less likely it is to fail. The finding is consistent with the ‘‘too big to fail’’ hypothesis, which often results in these banks being singled out for close scrutiny by regulators. Or it could also indicate that large banks engage in greater diversification, making them less likely to fail.
žFraud – one econometric model tested for fraud (indirectly) and found its coefficient to be significant and correctly signed. However, a number of US studies report a high incidence of fraud when bank failures are reviewed over time.
žMoral hazard – several US studies find the bank failure rate increased when US deposit insurance was raised from $40 000 to $100 000, which is suggestive of moral hazard.
žRegulatory forbearance and looting are variables that are difficult to quantify, but the case study review suggests they were contributory factors.
This chapter has taken a detailed look at bank failures, and attempted to assess what caused them using qualitative and quantitative analysis. Chapter 8 turns to the subject of financial crises, which are very frequently associated with too many bank failures. Some may ask why the thousands of bank failures in the USA appeared in this chapter rather than the next. The reason is that though the bank failures were numerous, at no time was the US system under systemic threat, the way it was following the bank failures in 1930–33. The next chapter deals with bank failures (and other events) which lead to financial crises, and in some instances, to systemic collapse.
F I N A N C I A L C R I S E S |
8 |
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8.1. Introduction
The previous chapter looked at the causes of bank failures; here, the emphasis shifts to financial crises, often caused by the collapse of a bank or banks. The objective is to explore the definitions, causes and consequences of financial systems in crisis. Section 8.2 looks at the controversies surrounding the different definitions of financial crises, and the costs of resolving more recent crises. Section 8.3 conducts extensive reviews of the crises in three South East Asian economies; those of Japan and Scandinavia are explored in sections 8.4 and 8.5. While there are some similarities in the factors leading up to these crises, the approaches taken to resolve them have been quite different. The choice of countries is deliberate. It covers a continuum, from relatively underdeveloped, such as Indonesia, to newly industrialised (Korea), to small open advanced economies, to the second largest economy in the world. It shows that no country is exempt from crises and their resolution can be complex and costly. The case of Long-Term Capital Management is discussed in section 8.6, to provide another contrast – a non-bank financial institution, the sudden collapse of which, some argue, threatened the world financial order, prompting a lifeboat rescue organised by the US central banking authorities. Given the need for intervention by central banks, other government agencies, the IMF (in the cases of the less wealthy economies) and the private banks to resolve financial crises, section 8.7 discusses the evolution and role of the lender of last resort, together with proposals to establish an international lender of last resort. The chapter concludes with a summary of the key points and findings from the various sections.
8.2. Definitions and Controversies
The title of Kindleberger’s (1978) first chapter is: ‘‘Financial Crisis: A Hardy Perennial’’, an apt description of the problem of crises in the financial sector. This section begins with the question of what constitutes a financial crisis. There is little agreement about this. Economists of a monetarist persuasion employ a narrow definition: they argue that a financial crisis is normally associated with a banking crisis and when the stability of the banking system is threatened, the financial infrastructure could collapse in the absence of central bank intervention. The collapse of a key financial firm normally prompts runs on the banks: customers panic and, unable to distinguish between healthy and problem banks,
