CEO DUALITY AND FIRM PERFORMANCE: WHAT'S THE FUSS?

Rising shareholder activism following poor corporate performance and a subsequent drop in shareholder value at many major U.S. corporations had rekindled interest in duality and corporate governance. Despite limited empirical evidence, duality (chairman of the board and CEO are the same individual) has been blamed, in many cases, for the poor performance, and failure of firms to adapt to a changing environment. In examining the relationship between duality and firm performance, this study considers the announcement effects of changes in duality status, accounting measures of operating performance for firms that have changed their duality structure, and long-term measures of performance for firms that have had a consistent history of a duality structure. Our results suggest that: (1) the market is indifferent to changes in a firm's duality status; (2) there is little evidence of operating performance changes around changes in duality status; and (3) there is only weak evidence that duality status affects long-term performance, after controlling for other factors that might impact that performance.     

GENDER-BIAS IN INDIA: THE IMPORTANCE OF HOUSEHOLD FIXED-EFFECTS

This paper presents an analysis of gender patterns in intra-householdallocation of resources based on household-level consumptiondata. Invoking the assumption that households seek to equalizethe marginal utility of wealth when they allocate resourcesover the life-cycle, the paper provides a rationale for parentalbehaviour pertaining to the intertemporal allocation of goodsamong children. Estimation results based on panel data fromIndia show that controlling for the unobserved marginal utility(household-fixed) effect is crucial. Once allowance is madefor fixed effects, the results indicate that there is no longerany gender-differential in the allocation of resources.     

More Powerful Portfolio Approaches to Regressing Abnormal Returns on Firm-Specific Variables for Cross-Sectional Studies

OLS regression ignores both heteroscedasticity and cross-correlations of abnormal returns; therefore, tests of regression coefficients are weak and biased. A Portfolio OLS (POLS) regression accounts for correlations and ensures unbiasedness of tests, but does not improve their power. We propose Portfolio Weighted Least Squares (PWLS) and Portfolio Constant Correlation Model (PCCM) regressions to improve the power. Both utilize the heteroscedasticity of abnormal returns in estimating the coefficients; PWLS ignores the correlations, while PCCM uses intra-and inter-industry correlations. Simulation results show that both lead to more powerful tests of regression coefficients than POLS.     

A Portfolio Approach to Estimating the Average Correlation Coefficient for the Constant Correlation Model

This paper presents a portfolio approach to estimating the average correlation coefficient of a group of stocks which are considered for portfolio analysis. The average correlation coefficient has been shown to produce a better estimate of the future correlation matrix than individual pairwise correlations. The advantage of the approach described here is that it does not require the estimation of pairwise correlations for estimating their average.     

The Resiliency of the High-Yield Bond Market: The LTV Default

This paper investigates the resiliency of the new-issue high-yield bond market by examining the changes in implied default rates of such bonds before and after the largest high-yield bond default, i.e., the LTV bankruptcy. Specifically, the paper compares implied default probabilities of high-yield bonds during the post-LTV period calculated from actual new-issue yields with instrumental default probabilities calculated on the assumption that the default had not occurred. A comparison of these probabilities reveals that the market's perception of default on the high risk segment of the bond market increased significantly after the LTV bankruptcy. However, the effect was transitory, lasting only six months. Thus, the market was resilient to a major default.     

A methodological note on detecting a location shift in the distribution of abnormal returns: A nonparametric approach

Abstract. The distribution of market model residuals or other measures of prediction errors is skewed and leptokurtic. To detect a location shift in leptokurtic residuals, a nonparametric rank test may be more efficient than a parametric t-test. Event studies that use a nonparametric test generally use the sign test, the Wilcoxon test, or some variation of the Wilcoxon test. The sign and Wilcoxon test statistics are calculated from the event-period residuals, so skewness and potential cross-sectional dependencies are not accounted for, causing a concern about the reliability of these tests for event studies. In this paper we propose a Mann-Whitney (MW) rank statistic in which the rank of the event-period residual is calculated relative to the estimation-period residuals. The MW statistic is a nonparametric analogue of the standardized residual (the residual scaled by its estimated standard deviation) used in the parametric (Patell-type) t-test. The MW statistics enable us to account correctly for skewness as well as potential cross-sectional correlation, and they are more powerful than the sign, Wilcoxon, and standardized residual statistics in detecting a location shift in leptokurtic residuals. Our results are based on a Monte Carlo study of simulated residuals. We simulate cross-sectionally independent residuals (for noncontemporaneous events) and cross-sectionally correlated residuals (for contemporaneous events). Résumé. La distribution des résidus du modèle du marché ou d'autres mesures des erreurs prévisionnelles est asymétrique et leptocurtique. Un test de rang non paramétrique peut être plus efficace qu'un test t paramétrique dans la détection d'un déplacement dans les résidus leptocurtiques. Les études événementielles basées sur un test non paramétrique ont généralement recours au test des signes, au test de Wilcoxon ou à une variante du test de Wilcoxon. Les statistiques du test des signes et du test de Wilcoxon sont calculées à partir des résidus événement-période, de sorte que l'asymétrie et les dépendances transversales possibles ne sont pas prises en compte, ce qui occasionne un problème de fiabilité des tests en ce qui a trait à l'étude événementielle. Dans le présent document, les auteurs proposent une statistique de rang Mann-Whitney (MW) dans laquelle le rang des résidus événement-période est calculé par rapport aux résidus estimation-période. La statistique MW est l'équivalent non paramétrique du résidu normé (soit du résidu pondéré en fonction de son écart-type estimé) utilisé dans le test paramétrique du t (de type Patell). Les statistiques MW nous permettent de tenir compte de façon exacte de l'asymétrie ainsi que de la corrélation transversale possible, tout en étant plus puissantes que les statistiques des signes, de Wilcoxon et des résidus normés dans la détermination d'un déplacement dans les résidus leptocurtiques. Les résultats obtenus par les auteurs sont basés sur une étude de Monte Carlo des résidus? simulés. Ils simulent des résidus transversalement indépendants (pour des événements non simultanés) et des résidus transversalement corrélés (pour des événements simultanés).     

Longitudinal rank tests for detecting location shift in the distribution of abnormal returns: An extension*

Abstract. We extend Chandra and Rohrbach (1990) to explain how to develop a longitudinal rank test (r-test) analogous to any t-test used in the event study literature. We compare all analogous pairs using market model residuals. The r-test is more powerful than the t-test in each pair. This suggests that if the researcher intends to use any t-test then, for more power, the comparable test should be preferred. These results should be useful to the researcher in selecting an r-test for event study because now the same flexibility of choosing an r-test as a t-test is available. Résumé. Les auteurs poussent plus loin les travaux de Chandra et Rohrbach (1990) pour expliquer comment mettre au point un test de rangs logitudinaux (test r) analogue aux différents tests t utilisés dans les ouvrages portant sur l'étude d'événements. Ils comparent toutes les paires analogues en utilisant les résiduels des modèles de marché. Le test r est plus puissant que le test t dans chacune des paires, de sorte qu'on peut penser que si le chercheur prévoit utiliser un test t pour sa puissance, il aurait avantage à recourir au test r comparable. Ces résultats devraient être utiles aux chercheurs dans la sélection d'un test r pour l'étude d'événements puisque, dorénavant, le choix d'un test r peut offrir la même souplesse que celui d'un test t     

A synthesis of alternative testing procedures for event studies*

Abstract. The alternative versions of the t-test found in event studies result from different weighting schemes for abnormal returns, different abnormal return models, and different correlational structures among abnormal returns. In the presence of dependencies among abnormal returns, the generalized least squares t-tests are much more sensitive to the mis-specifications in the abnormal return model than are the nongeneralized t-tests. Therefore, when analyzing contemporaneous returns, particularly with samples exhibiting a large industry concentration, a nongeneralized t-test should be preferred to a generalized least squares t-test because of the dependencies that may exist. Because the generalized least squares t-tests are highly sensitive to errors in specifying an appropriate abnormal return model, a portfolio time-series ordinary least squares regression should be preferred to a generalized least squares regression even when the variance or covariance matrix of abnormal returns can be estimated with a high degree of reliability. In testing for the mean effects, the concern for event period variance increases seems to be unwarranted, and the variance estimators using event period data are inefficient and biased. The issue is not whether variance increases in an event period, but which variance is to be used: that of the event period or of the nonevent period? Answers to such questions are presented in this paper. Résumé. Les différentes versions possibles du test t que l'on trouve dans les études d'événements résultent de différents systèmes de pondération des rendements anormaux, de différents modèles de rendements anormaux et de différentes structures de corrélation des rendements anormaux. Lorsqu'il existe une dépendance entre les rendements anormaux, les tests t généralisés des moindres carrés sont beaucoup plus sensibles aux défauts de construction du modèle des rendements anormaux que ne le sont les tests t non généralisés. C'est pourquoi lorsqu'on analyse des rendements simultanés, en particulier si l'échantillon présente une forte concentration industrielle, le test t non généralisé est préférable au test t généralisé des moindres carrés, compte tenu des dépendances qui peuvent exister. Les tests t généralisés des moindres carrés étant très sensibles aux défauts de construction du modèle approprié de rendements anormaux, l'application de la méthode classique des moindres carrés à une série chronologique relative à un portefeuille est préférable à la régression généralisée des moindres carrés, même s'il est possible d'estimer avec un degré élevé de fiabilité la matrice de variance ou de covariance des rendements anormaux. Dans le test des effets moyens, la préoccupation relative aux augmentations de la variance de la période d'événements semble être injustifiée, et les estimateurs de la variance fondés sur les données de la période d'événements sont inefficients et biaisés. Il ne s'agit pas de déterminer si la variance augmente pendant la période d'événements, mais quelle variance doit être utilisée: celle de la période d'événements ou une autre. Les auteurs répondent à ces questions.     

Do Analyst Stock Recommendations Piggyback on Recent Corporate News? An Analysis of Regular‐Hour and After‐Hours Revisions

Analysts often update their recommendations following corporate news. Questions have been raised regarding analysts’ ability to generate new information beyond recent corporate events. Employing a comprehensive database on corporate news, we show that only a small minority, or 27.9%, of all recommendation revisions directionally confirm the information in the preceding corporate events and even these “confirming revisions” facilitate the information discovery of corporate events and thus cannot simply be dismissed as “piggybacking.” Our analysis further shows that analysts not only facilitate price discovery to corporate news through issuing trending revisions but also help reverse prevailing market sentiments following corporate news by issuing contrarian revisions. Our study is the first to investigate short‐window intraday market reactions to revisions issued after hours, which account for 70% of all recommendation revisions in our sample period. Analysts’ incentives to issue revisions after hours appear to reflect demands from large institutional clients, who dominate after‐hours trading. More importantly, we show that the after‐hours revisions are associated with significantly greater price reactions and different price reaction patterns than revisions issued during regular trading hours. Collectively, our evidence indicates that analysts are a significant source of new information beyond recent corporate news and they also help shape the market's assessment of corporate disclosures.