The Market and Investors Reactions to Mariana’s and Brumadinho’s Environmental Disasters: Sentimental or Rational Decisions?

This research carried out event studies to analyze the reactions of the market and investors in Vale S.A. to the collapses of the Mariana and Brumadinho dams. It also assessed the extent to which the causes attributed to the market reactions to major disasters in previous research has helped to explain the reactions of the market and investors to the collapses of these dams. The analyses have shown that, in the case of the Fundão dam, there was a relevant reduction in the abnormal cumulative returns of common stocks and ADRs at the end of the eleven days of the collapse, despite the fact that the daily abnormal returns were not statistically significant. However, the abnormal trading volumes of these securities in the eleven days after the dam failure were generally negative and all statistically significant. In contrast, concerning the collapse of the Brumadinho dam, the abnormal returns on common stocks and ADRs were negative, relevant, and statistically significant, and, after the eleven days, the losses were considerable. The abnormal trading volumes of the securities were all positive and statistically significant, but the reactions of ADR investors were more intense than those of investors in common stocks. Examining the causal attributions made previously, there are indications that the market and investor reactions to the failures of the two dams were probably derived from the expectation that Vale and the other companies involved would incur severe losses and high contracting costs in political processes that would follow to the disasters, and from the difficulty the investors have had to assess the magnitude of these losses and costs.

Consequences of Outlier Returns for Event Studies: A Methodological Investigation and Treatment

Stock returns are decomposed into their regular and outlier components using a maximum likelihood outlier-resistant estimation method. Analytical results depicting the impact of outliers on the ordinary least square (OLS) estimated models and cumulative abnormal return (CAR) statistics are derived and validated using Monte Carlo simulations. The implications of outliers for past event studies are investigated using samples drawn randomly from the universe of stocks in the CRSP database. The OLS-CAR statistics fail to forecast about 37% of the negative-impact and 43% of the positive-impact events. These results raise serious concerns about the validity of conclusions of past event studies, especially those that rejected the hypothesis of significant-impact events.

Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data

Background Meta-analysis is a statistical method to synthesize evidence from a number of independent studies, including those from clinical studies with binary outcomes. In practice, when there are zero events in one or both groups, it may cause statistical problems in the subsequent analysis. Methods In this paper, by considering the relative risk as the effect size, we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction, namely the generalized linear mixed models (GLMMs). To further advance the literature, we also introduce a new method of the continuity correction for estimating the relative risk. Results From the simulation studies, the new method performs well in terms of mean squared error when there are few studies. In contrast, the generalized linear mixed model performs the best when the number of studies is large. In addition, by reanalyzing recent coronavirus disease 2019 (COVID-19) data, it is evident that the double-zero-event studies impact the estimate of the mean effect size. Conclusions We recommend the new method to handle the zero-event studies when there are few studies in a meta-analysis, or instead use the GLMM when the number of studies is large. The double-zero-event studies may be informative, and so we suggest not excluding them.

Event studies with daily stock returns in Stata: Which command to use?

In this article, I provide an overview of existing community-contributed commands for executing event studies. I assess which command could have been used to conduct event studies that have appeared in the past 10 years in 3 leading accounting, finance, and management journals. The older command eventstudy provides a comfortable graphical user interface and good functionality for event studies that do not require hypotheses testing. The command estudy, described in Pacicco, Vena, and Venegoni (2018, Stata Journal 18: 461–476; 2021, Stata Journal 21: 141–151), provides a set of commonly applied test statistics and useful exporting routines to spreadsheet software and LATEX for event studies with a limited number of events. The most complete command in terms of available test statistics and benchmark models as well as its ability to handle events with insufficient data, thin trading, and large samples is eventstudy2.