Using a sample of 384 shareholder meetings, we investigate whether shareholder votes on mergers and acquisitions in both target and acquirer firms relate to the announcement day abnormal returns and whether the voting outcome has implications for the short- and long-run merger performance. We find that shareholder voting dissent is negatively related to both abnormal returns upon merger announcement and recommendations by Institutional Shareholder Services (ISS). The former relationship is stronger for target firms and only borderline significant for acquirer firms. Overall, shareholders seem to take both advisor opinions and market beliefs into account when taking their voting decision. We also find that cumulative abnormal returns on the meeting date are strongly positively related to voting dissent. The observed relationship holds only for mergers with a long negotiation period suggesting that in these mergers a higher fraction of residual uncertainty is re-solved upon a "pass" vote. Furthermore, we find that voting dissent is negatively related to long-run abnormal merger performance suggesting a predictive power of merger votes.
Ein breiter empirischer Befund und neue Erklärungen führen auf drei Verhaltensweisen, mit denen Unternehmen in ihrer Kreditnachfrage auf Zinsänderungen reagieren. Dadurch wird ein Beitrag zum Verständnis der Wirkung der Zins- und Geldpolitik geleistet.
Using the von Mises expansion, we study the higher-order infinitesimal robustness of a general M-functional and characterize its second-order properties. We show that second-order robustness is equivalent to the boundedness of both the estimator's estimating function and its derivative with respect to the parameter. It implies, at the same time, (i) variance-robustness and (ii) robustness of higher-order saddlepoint approximations to the estimator's finite sam- ple density. The proposed construction of second-order robust M-estimators is fairly general and potentially useful in a variety of relevant settings. Besides the theoretical contributions, we discuss the main computational issues and provide an algorithm for the implementation of second-order robust M-estimators. Finally, we illustrate our theory by Monte Carlo simulation and in a real-data estimation of the maximal losses of Nikkei 225 index returns. Our findings indicate that second-order robust estimators can improve on other widely-applied robust esti- mators, in terms of efficiency and robustness, for moderate to small sample sizes and in the presence of deviations from ideal parametric models.
Typical heart rate variability (HRV) times series are cluttered with outliers generated by measurement errors, artifacts and ectopic beats. Robust estimation is an important tool in HRV analysis, since it allows clinicians to detect arrhythmia and other anomalous patterns by reducing the impact of outliers. A robust estimator for a flexible class of time series models is proposed and its empirical performance in the context of HRV data analysis is studied. The methodology entails the minimization of a pseudo-likelihood criterion func- tion based on a generalized measure of information. The resulting estimating functions are typically re-descending, which enable reliable detection of anomalous HRV patterns and stable estimates in the presence of outliers. The infinitesimal robustness and the stability properties of the new method are illustrated through numerical simulations and two case studies from the Massachusetts Institute of Technology and Boston's Beth Israel Hospital data, an important benchmark data set in HRV analysis.
We review some first- and higher-order asymptotic techniques for M- estimators and we study their stability in the presence of data contaminations. We show that the estimating function (psi) and its derivative with respect to the parameter (grad psi) play a central role. We discuss in detail the first-order Gaussian density approximation, saddlepoint density approximation, saddlepoint test, tail area approximation via Lugannani-Rice formula, and empirical saddlepoint density approximation (a technique related to the empirical likelihood method). For all these asymptotics, we show that a bounded (in the Euclidean norm) psi and a bounded (e.g., in the Frobenius norm) grad psi yield stable inference in the presence of data contamination. We motivate and illustrate our findings by theoretical and numerical examples about the benchmark case of one-dimensional location model.
