Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative dataset. Further, we investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model‐based clustering approach. The statistical challenge in our application comes from the difficulty in extending distance‐based clustering approaches to the problem of identifying groups of similar time series in a panel of discrete‐valued time series. We use Markov chain clustering, which is an approach for clustering discrete‐valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first‐order time‐homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model. Copyright © 2011 John Wiley & Sons, Ltd.     

Human resource practices and performance of mergers and acquisitions in Israel

An extensive body of literature has investigated financial and strategic variables as predictors of mergers and acquisitions (M&A) performance without finding clear relationships. This paper proposes a knowledge-based view of acquisitions and suggests that to enhance M&A performance acquirers must use human resources (HR) practices that develop integration capabilities during post-merger integration. The paper explains the specific effects of HR practices in M&A in Israel, a country that serves as a microcosm for developed Western countries.     

American option valuation: Implied calibration of GARCH pricing models

This study analyzes the issue of American option valuation when the underlying exhibits a GARCH‐type volatility process. We propose the usage of Rubinstein's Edgeworth binomial tree (EBT) in contrast to simulation‐based methods being considered in previous studies. The EBT‐based valuation approach makes an implied calibration of the pricing model feasible. By empirically analyzing the pricing performance of American index and equity options, we illustrate the superiority of the proposed approach. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark     

Regulating the timing of disclosure: Insights from the acceleration of 10-K filing deadlines

We study the effects of regulating the timing of disclosure on the quality of accounting information, using a 2003 US regulatory change that accelerates 10-K filing deadlines as a research setting. Employing a difference-in-differences design, we find that the likelihood of issuing financial statements that are later restated increases for firms that are required to file more quickly, relative to firms whose filing practices are not affected by the regulatory change. This effect is particularly pronounced during the audit busy season, when auditors also face significant time pressure. These results are consistent with a tradeoff between how quickly accounting reports are required to be filed and the reliability of the resulting reports.     

Macrocognition in Collaboration: Analyzing Processes of Team Knowledge Building with CoPrA

Sophisticated collaboration software allows teams that are dispersed in space and time to work together. Nevertheless, to reach their common goals, distributed teams—and the professional facilitators who support them by intervention techniques—are faced with the communication challenges arising from dispersed settings, including task coordination and effective information exchange. When distributed teams use collaboration software, however, traces of their collaboration are left behind. These traces provide an underused source of data which can be analyzed and be used to inform the design of interventions aimed at improving collaboration in distributed teams. This paper investigates the untapped potential for understanding collaboration, and in particular, the macro-cognitive processes of team knowledge building. These processes rely on information shared and knowledge structures developed by team members which are also referred to as team cognition. We performed a qualitative content analysis applying the COllaboration PRocess Analysis technique, CoPrA, and a framework for measuring team knowledge building. Communication data was collected from 18 participants assigned to six distributed teams. While working collaboratively on a problem-solving task teams were supported with synchronous collaboration software. The results show that by using a cognitive perspective on teams, all the hypothesized processes of team knowledge building could be identified in collaboration traces. Moreover, our analysis shows that CoPrA enables us to identify key characteristics of (1) team behavior, e.g., whether teams are rather solution-oriented or problem minded, show consensus-oriented behavior, withhold evaluative arguments, discuss ideas in breadth and/or depth, or spend much effort on coordination as well as (2) behavior of team members, e.g., who show non-participation, are willing to share or predominantly guide coordination. Future research could adopt this approach to improve our understanding of the dynamics of collaboration patterns and its effects on team performance to inform collaboration facilitation in distributed settings.