individual financial institutions in East Asia (one study is Laeven, 1999). Using individual institution data, one can investigate, for example, why, despite the fact that all financial intermediaries faced similar macroeconomic shocks, not all experienced distress and/or eventually failed. One can thus identify the specific characteristics of distressed (or failed) institutions compared to non-distressed (or non-failed) institutions; these characteristics can be used in developing systems to monitor the risk of distress of financial institutions in the future. By studying the resolution and closure processes, one can try to identify what type of processes used to resolve distressed financial institutions are most adequate and lead to the least loss of confidence.

Our work also relates to the literature on predicting individual financial institutions' distress and closures. Models trying to predict the failure of individual financial institution ("early warning systems") have been developed since the '70s. Mainly applied to developed countries' banking systems,<sup>3</sup> these studies focus on the early identification of financial institutions, which are developing financial difficulties. From a banking regulator and supervisory agency's viewpoint, early warning systems can help minimize the use of relatively scarce examination resources while at the same time achieving as much failure-prevention as possible. Indeed, failure prediction models and early warning systems have proven important tools for supervisory agencies to schedule individual on-site bank examinations and initiate remedial actions.

The first generation of financial early warning systems aimed to build screening devices to help in scheduling bank examinations by flagging as early as possible those institutions in (or approaching) financial distress. These studies share a similar approach (Meyer and Pifer 1970; Sinkey 1975; Altman 1977; Martin 1977; Pettaway and Sinkey 1980; see Altman 1981 for a comprehensive survey of the early wave of the literature): on the basis of a set of financial ratios, reflecting the different dimensions of a CAMEL rating system,<sup>4</sup> the statistically best subset of variables is chosen to distinguish between potentially financially-troubled and sound financial institutions, within a certain prediction horizon. As their goal is early warning, these models aim to predict the economic insolvency of a bank rather than the narrow notion of _de jure_ failure. In other words, these studies aim to identify situations in which a bank might become unable to meet its contractual liabilities out of its own resources-due to the negative value of its net worth at market-value--even if it is not followed by a formal declaration of insolvency (and subsequent closure) by the chartering authority. In fact, the closure of the financial institution is only one among the various options available to deal with its distress. Financial distress will, for example, often be resolved via supervisoryencouraged and supported mergers.

A separate, though related, strand of literature has focused on what exactly triggers the decision to close a distressed bank. Kane (1988) suggests casting such decisions within the framework of public choice theory. In this context, the closure

> 3 The US banking system has been particularly investigated.

_4_ CAMEL stands for Capital adequacy, Asset and Management quality, Eamnings, Liquidity.

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