The effects of leadership change on team escalation of commitment

Although teams benefit from developing plans and processes that boost efficiency and reduce uncertainty, they may become too attached to these plans and escalate commitment when an alternative response is needed. Drawing on theories of team leadership, team processes and escalation of commitment, we propose that a change in leadership can help the team reduce commitment to outdated plans and avoid further escalation over time. Across two studies, we tested and found support for our hypotheses and provide evidence that leadership change can break the cycle of escalation by enhancing leader-driven team reflection and refocusing the team on error correction instead of additional investment. We discuss how the results of these studies extend existing theory and add to our understanding of the important role leaders play in enhancing team adaptation and preventing team escalation.     

The impact of acquisitions on firm performance: A review of the evidence

This paper presents a review of empirical research on the impact of acquisitions on firm performance. The evidence suggests that, in the short run, acquisitions have at best an insignificant impact on shareholder wealth. Long‐run performance analysis reveals overwhelmingly negative returns, while the evidence using accounting performance measures is mixed. The review also examines the impact of bid characteristics on performance. The acquisition of hostile targets, transactions that are paid for with cash and acquisitions of larger targets are associated with superior (or at least less negative) performance, while there is mixed evidence on the benefits of related acquisitions. A number of recent studies find that acquirers with superior pre‐bid performance tend to experience significant underperformance in the post‐bid period.     

Deciding between GARCH and stochastic volatility via strong decision rules

The GARCH and stochastic volatility (SV) models are two competing, well-known and often used models to explain the volatility of financial series. In this paper, we consider a closed form estimator for a stochastic volatility model and derive its asymptotic properties. We confirm our theoretical results by a simulation study. In addition, we propose a set of simple, strongly consistent decision rules to compare the ability of the GARCH and the SV model to fit the characteristic features observed in high frequency financial data such as high kurtosis and slowly decaying autocorrelation function of the squared observations. These rules are based on a number of moment conditions that is allowed to increase with sample size. We show that our selection procedure leads to choosing the model that fits best, or the simplest model under equivalence, with probability one as the sample size increases. The finite sample size behavior of our procedure is analyzed via simulations. Finally, we provide an application to stocks in the Dow Jones industrial average index.