Abstract Event studies are a popular research paradigm in several fields of business research. They are now beginning to appear in insurance literature. This article enumerates the steps common to all event studies and discusses the guidance that has been provided by others for the problems encountered at each of those steps. Introduction The event study is one of the most popular statistical designs in finance. In 1987 and 1988, 14 event studies were published in the Journal of Finance and another 26 in the Journal of Financial Economics. During that period the Journal of Risk and Insurance published five event studies. The event study will be used more frequently by insurance researchers as they become more familiar with its methodology and better recognize its applicability to insurance issues. The technique is particularly well suited to assessing the impact of insured or uninsured events on individual firms, or measuring the impact of market-wide events such as regulation or legislation on the market as a whole or on individual market (industry) segments. Types of event studies vary. Market efficiency studies assess how quickly and correctly the market reacts to a particular type of new information. Information usefulness studies assess the degree to which company returns react to the release of a particular bit of news. Accounting scholars have used the information usefulness concept to assess the value of accounting information (see, e.g., Foster, 1973 and 1975, or Watts, 1973 and 1978). Such studies have also been used to assess the extent to which market participants were watching the accounting profession's policy making process (see, e.g., Basu, 1981, or Collins, Rozeff and Salatka, 1982). Analogies to each of these topics can be found in recent Journal of Risk and Insurance articles. Sprecher and Pertl (1983) assess the impact that large losses had on shareholders' returns in a number of industries. Davidson, Chandy and Cross (1987) look at the same issue in the airline industry, where insurance is mandatory. Cross, Davidson and Thornton (1988) use the event study to examine the effect of director and officer lawsuits on firm value. Lacey (1988) examines the relationship between excess returns and property-liability firms' income to evaluate a collusion theory of the liability crisis. VanDerhei (1987) assesses the impact of voluntary termination of overfunded pension plans. All these insurance studies follow a recent trend in event studies. They involve what Bowman (1983) calls metric explanation. In a metric explanation study, the event study is only the first step. Early studies explained the metrics (extra returns) by splitting the sample into different subsamples and examining whether the unusual element of returns differed among the subsamples. [1] More recent studies use excess returns as dependent variables in cross-sectional regressions to explain the source of the extra returns. [2] These then are the basic types of event studies: market efficiency, information value, and metric explanation. These classifications are not mutually exclusive. It is quite common for event studies to combine a little of each. There are also methodology studies of the event study design, research that considers how best to run event studies. Event study methodology papers are unusual for business research where econometricians and statisticians typically use statistical theory to define how a test should be run. In event studies, the issues have been tested empirically, not theoretically, to find out what will work given the nature of financial data. Such investigations normally involve simulations. The researcher selects, or creates, [3] a hypothetical sample of securities, injects abnormal returns on arbitrarily defined event dates, and tests competing methodologies to ascertain which is better at detecting the event. This article draws heavily upon the event study methodology literature to identify the problems that a researcher faces and to suggest the solutions that current knowledge provides for these problems. …
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