اندازه گیری ریسک شکایت های قانونی اوراق بهادار
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|17861||2012||21 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Accounting and Economics, Volume 53, Issues 1–2, February–April 2012, Pages 290–310
Extant research commonly uses indicator variables for industry membership to proxy for securities litigation risk. We provide evidence on the construct validity of this measure by reporting on the predictive ability of alternative models of litigation risk. While the industry measure alone does a relatively poor job of predicting litigation, supplementing this variable with measures of firm characteristics (such as size, growth, and stock volatility) considerably improves predictive ability. Additional variables such as those that proxy for corporate governance quality and managerial opportunism do not add much to predictive ability and so do not meet the cost–benefit test for inclusion.
A large body of research in accounting and finance investigates whether litigation risk (the risk of securities class action lawsuits) affects corporate decisions. While much research investigates the effect of litigation risk on managers' disclosure choices, authors also investigate how litigation affects a large array of managerial decisions.1 Much of this research measures litigation risk using an industry-based proxy, either alone or in conjunction with other variables. A common proxy is based on membership in the biotechnology, computers, electronics, and retail industries. This proxy originates from Francis et al., 1994a and Francis et al., 1994b, who sample firms drawn from these industries to study the relation between litigation and disclosure because those industries were subject to “a high incidence of litigation during 1988–1992” (1994a, p. 144). These authors do not advocate the use of industry membership generally, or these industries in particular, as a universal proxy for litigation risk. However, the use of this industry proxy (hereafter, the FPS measure) has become pervasive in the literature. It is reasonable to expect that litigation is associated with industry membership. Stock volatility and stock turnover directly affect litigation risk because both are directly related to measures of stockholder damages that drive plaintiff lawyers' decisions to file lawsuits (e.g., Alexander, 1991 and Jones and Weingram, 1996a). Both of these variables are likely to be associated with industry; for example, high technology stocks are by their nature inherently more uncertain with more variable earnings, and hence are more volatile. The use of industry to proxy for litigation risk results from a cost–benefit tradeoff by researchers. While this proxy is simple and readily available, it likely captures industry characteristics that are unrelated to litigation risk but that affect managers' decisions, creating a potential correlated omitted variables problem. The fact that this proxy is ubiquitous in the literature seems to indicate that it passes the cost–benefit test. However, there is little evidence (of which we are aware) on the construct validity of this proxy or whether other proxies are available that might represent a better cost–benefit tradeoff. Further, beyond reporting pseudo-R squareds, there is little systematic evidence on the ability of extant measures to actually predict litigation. We report on some relatively simple and low cost models that significantly outperform the industry-based proxies in terms of predictive and discriminatory ability. The use of industry membership to capture litigation risk makes it difficult to ensure that industry captures litigation risk as opposed to different underlying factors that affect managers' disclosure decisions. Consider a study that investigates whether litigation risk affects managers' disclosure choices and uses industry to proxy for litigation risk. If managers' disclosure decisions depend on their firms' information environments (Einhorn and Ziv, 2008) and information environment varies systematically across industry, disclosure will be associated with industry for reasons that have little to do with litigation risk.2 A similar problem arises if firms in high technology industries have higher proprietary costs than firms in more mature industries and proprietary costs systematically affect disclosure. The existence of a well-developed theory of litigation would allow us to identify all of the economic determinants of litigation, in which case the FPS measure would presumably no longer be useful in explaining litigation risk. Although we do not have such a theory (we discuss previous literature in Section 2), one goal of our research is to investigate systematically whether the inclusion of an extensive set of firm-specific characteristics reduces the usefulness of the FPS variable in predicting litigation, as would be expected if these characteristics directly capture litigation risk. We provide two sets of empirical analyses to evaluate how well industry membership proxies for securities litigation risk. We first provide evidence on how litigation rates vary across industries and through time. This evidence shows that while litigation tends to cluster in certain industries, the set of industries varies over time. Nevertheless, the FPS industries generally have consistently higher litigation rates than other industries, although this result is weaker when we focus the analysis on large firms generally subject to higher rates of litigation. Second, we provide evidence on the predictive ability of alternative models of litigation risk. We show that while the relationship between the FPS industry measure and litigation is robust in a statistical sense, using industry membership alone does a relatively poor job of predicting litigation. However, when we supplement this variable with measures of firm characteristics that include size, growth, and stock performance and volatility, predictive ability improves considerably. These variables are readily available to researchers in a broad variety of settings. Further, including additional variables, such as proxies for corporate governance quality, issuance of securities, insider trading, and so forth, adds relatively little to predictive ability. Given the cost of obtaining these variables (which includes possible sample selection biases), more sophisticated models that include these variables are unlikely to be cost beneficial. Conventional measures of goodness of fit (such as pseudo-R-squareds) do not perform well in assessing the fit and predictive ability of these models. We use a number of alternative approaches suggested in the statistics literature (e.g., Hosmer and Lemeshow, 2000 and Long and Freese, 2006) to evaluate model fit and predictive ability, most notably the area under the receiver operating characteristic (ROC) curve, or AUC. 3 These techniques confirm that models that supplement the FPS measure with a small set of variables that are readily available from CRSP/Compustat provide significant improvements in predictive ability relative to a model that includes the FPS measure alone. By securities litigation risk, we are referring specifically to the risk of securities class action lawsuits, as opposed to the risk of legal action brought by government agencies such as the U.S. Securities and Exchange Commission (SEC), the U.S. Department of Justice, or state attorney generals, which we view as a related but distinct form of litigation risk. SEC Accounting and Auditing Enforcement Releases (AAERs) have been extensively studied in the accounting literature (see Feroz et al., 1991, Beneish, 1999, Dechow et al., 1996, Dechow et al., 2011 and Schrand and Zechman, 2011, among others).4 As noted in those studies, SEC enforcement actions typically result from cases of serious accounting irregularities, including fraud. While such cases are likely to lead to securities class actions, many securities class actions involve less serious allegations, such as failure to disclose in a timely manner, and so do not result in SEC enforcement actions. Consistent with this, we provide evidence that less than 10% of securities class actions are associated with SEC enforcement actions. Our goal is to measure ex ante litigation risk. It is well known that certain factors, most notably large and sudden declines in stock price at the time of an information release, increase the risk of litigation considerably ex post ( Alexander, 1991 and Jones and Weingram, 1996a). Our goal is not to examine whether outcomes such as stock price declines or accounting frauds result in litigation—the evidence confirms that this is the case (e.g., Hennes et al., 2008). Instead, our goal is to capture factors that make firms more vulnerable to litigation before such “triggering events” occur, which is the construct likely to be of most interest to researchers investigating, say, how litigation risk affects firms' ongoing disclosure practices. Our paper contributes to the literature in several ways. First, we provide comprehensive evidence on the usefulness of the FPS industry variable as a measure and predictor of litigation risk, an important task given the ubiquity of this measure in the literature. Second, we provide evidence that allows us to better understand what makes particular firms and industries vulnerable to litigation. Third, we provide more precise measures of the predictive ability and goodness of fit of models of litigation risk than those typically used in prior literature. Section 2 reviews previous research. Section 3 details the sample and provides evidence on how litigation rates vary over time and across industries and sectors. Section 4 provides evidence on the determinants of litigation risk, comparing the predictive ability of the conventional FPS industry proxy to models that supplement this proxy with additional drivers of litigation risk. Section 5 concludes.
نتیجه گیری انگلیسی
We provide evidence on the validity of the industry-based litigation risk proxy commonly used in previous research. We define litigation risk as the risk of private securities class action lawsuits, as opposed to more serious legal actions such as SEC enforcement actions. We provide two principal empirical findings. First, we show that although litigation rates vary significantly across sectors and industries over time, litigation rates in the four FPS industries (biotechnology, computers, electronics, and retail) are generally consistently higher than those in other industries. While the overall litigation rate across all firm/years in our sample is 1.6%, the rate for firms in the FPS industries is 2.7%, a difference that is statistically significant. Differences in litigation rates between the FPS industries as a group and other industries are statistically significant in 8 of 13 sample years. For the largest firms in the economy (those in the top 5% of the size distribution), the litigation rate is 5.1% across all firm/years, with the rate for firms in the FPS industries at 7.8% (this rate is not significantly higher than that for non-FPS industries). Second, we estimate and compare a number of models of litigation risk. While the FPS industry measure is simple, readily available, and associated with higher litigation rates, it is nevertheless unclear how well this variable performs as a predictor of litigation risk. We evaluate predictive ability using a number of measures in addition to the pseudo-R squareds usually reported in extant research. While the FPS variable is clearly associated with litigation risk—the coefficients on this variable are both economically and statistically significant—the ability of this variable to predict litigation is modest. Pseudo-R squareds from models that only include the FPS variable are around 1%. This conclusion is supported by alternative measures of predictive ability, such as AUC and the Hosmer-Lemeshow chi-squared, which the statistical literature suggests as better measures of predictive ability. When the FPS variable is augmented with measures of firm size, sales growth, and return characteristics, predictive ability increases markedly, suggesting that the inclusion of a few widely available variables can result in significant improvements in model performance. AUC is around 0.55 for models that include the FPS variable alone, only marginally higher than 0.5, the benchmark for no predictive ability. When we augment the FPS variable with size, growth, and return volatility, AUC increases substantially, to 0.76, which indicates good predictive ability. This improvement in predictive ability is achieved at relatively low cost because these additional variables are readily available to researchers. Interestingly, the FPS variable complements these other variables, which have larger effects on estimated litigation risk for FPS firms than for non-FPS firms. Augmenting the model with additional covariates, such as those that measure the quality of firms' corporate governance, insider trades, the extent to which firms are raising capital, etc., does little to further increase predictive ability, and so fails the cost–benefit tradeoff given the costs usually associated with obtaining these variables. Our suggested model of litigation risk generates predicted probabilities that have desirable properties. This model generates a relatively continuous distribution of predicted probabilities, ranging from close to zero to over 70%. While most observations have predicted probabilities of less than 10%, some firm/years have probabilities well in excess of this level. We show that firms in FPS industries that are relatively large, with high volatility and sales growth, have litigation rates that can be substantially higher than 10%, and that the joint distribution of these variables is important in determining litigation risk (firms that are both relatively large and volatile face substantially higher litigation rates than firms that are relatively large or volatile).