Securities litigation is a major and costly source of corporate risk that can affect many aspects of companies’ operations. The task of identifying the causes and consequences of this risk is, however, challenging because researchers observe only companies that are sued and not companies that risk being sued. Moreover, surprisingly little is known about how plaintiffs’ lawyers identify which companies to sue. We use scrutiny of companies’ SEC filings by plaintiffs’ lawyers to improve estimates of litigation risk and provide new insights into why certain companies face risk.
We exploit the plaintiff-lawyer need for public information to monitor companies after the passage of the Private Securities Litigation Reform Act (PSLRA). Because plaintiffs’ lawyers can no longer use confidential information obtained through discovery to build their initial cases, SEC filings are used in virtually all cases to identify misleading statements and establish strong fraud inferences. We conjecture that plaintiff-lawyer views of companies’ SEC filings are evidence of monitoring by those lawyers and serve as a proxy for the relatively persistent and often unobservable factors that create corporate securities litigation risk.
We first validate our new measure in several ways. For example, we observe a spike in these views for companies before and after litigation filing dates but only after class periods end, when the bad news that triggers litigation is first publicly revealed. The spike in views before filing dates is driven by the plaintiffs’ lawyers who participate in the initial filing. We also show that plaintiff-lawyer views that occur after the class period but before the filing date help predict whether cases will settle and the settlement amounts.
We then evaluate whether lagged annual views predict future litigation better than existing approaches. We show that the use of plaintiff-lawyer views over the prior year significantly increases the precision and sensitivity of predictions about which companies will be sued in the current year. We also examine whether these views can provide a more complete measure of which companies faced litigation risk by identifying not only the companies that are sued but also those subjected to publicized plaintiff-lawyer investigations. We find that plaintiff-lawyer views are even better at predicting this combined measure of future litigation risk. We conduct numerous tests to ensure that plaintiff-lawyer views over the prior year can serve as a proxy for securities litigation risk, rather than contemporaneous litigation or related bad news. In sum, using scrutiny at the source of litigation better measures the factors that make companies vulnerable to litigation before any misconduct is revealed.
Given that plaintiff-lawyer views measure the factors that make companies more susceptible to future litigation, we also examine whether these views can predict future stock market outcomes. We find that quarterly plaintiff-lawyer views are associated with lower abnormal returns and higher stock volatility in the subsequent quarter. These results remain if we drop companies that are sued in the subsequent quarter or that have class periods that end in the current quarter. Thus, plaintiffs’ lawyers possess material, adverse information about company fundamentals that is not yet recognized by the market.
We then use our measure to provide insights into why companies face litigation risk. For example, consistent with concerns that company disclosures may attract plaintiff-lawyer scrutiny, both voluntary 8-Ks and earnings warnings are associated with greater plaintiff-lawyer views. However, within the subset of companies that are highly scrutinized by plaintiffs’ lawyers, we find that earnings warnings make it less likely that companies are sued, which helps reconcile our findings with prior research that finds these warnings can lower litigation incidence. Further, ceasing dividends is associated with higher plaintiff-lawyer views. We also find that the determinants of plaintiff-lawyer scrutiny differ in some ways between the top and remaining plaintiffs’ law firms, consistent with disparities in law firm resources.
Our study makes five primary contributions. First, we create and share our new measure of corporate securities litigation risk. Because we show that this measure reduces measurement error and the risk of bias relative to alternative approaches, despite imposing fewer data constraints, we hope the measure will spur future research on litigation risk. For example, researchers can now examine the effects of litigation risk on over-the-counter companies, which are generally excluded from prior research because they lack available stock return data, which is required for other measures of litigation risk. While our measure has not been available in recent years, we also create a predicted measure of plaintiff-lawyer views that can similarly improve predictions of litigation risk, relative to alternative approaches. Second, because these views can also predict stock market outcomes, this data may be useful as an independent assessment of company risk-taking and agency costs, similar to how many researchers use directors’ and officers’ (D&O) insurance data.
Third, we are the first to our knowledge to examine plaintiff-lawyer investigations. Securities lawyers work for contingency fees, so the public disclosure of such investigations is a strong signal that companies face high litigation risk. Companies also allege that these investigations increase their own litigation risk. By combining these investigations with securities litigation filings, researchers can more than double the identification of companies with observable realized litigation risk, significantly reducing false negatives in the identification of firms that face high litigation risk.
Fourth, we provide the first empirical evidence on how plaintiffs’ lawyers select cases and obtain new insights into the determinants of corporate securities litigation risk. Fifth, we use our research setting to highlight problems when using the most common metric to evaluate prediction models for imbalanced datasets. We recommend that researchers examining other rare corporate events (e.g., bankruptcy and fraud) in interdisciplinary research use metrics such as precision and sensitivity.
This post comes to us from professors Antonis Kartapanis and Christopher G. Yust at Texas A&M University. It is based on their recent article, “Getting Back to the Source: A New Approach to Measuring Ex Ante Litigation Risk Using Plaintiff-Lawyer Views of SEC Filings,” forthcoming in the Journal of Financial and Quantitative Analysis and available here.