Texas Christian University

About: Texas Christian University is a education organization based out in Fort Worth, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 3245 authors who have published 8258 publications receiving 282216 citations. The organization is also known as: TCU & Texas Christian University, TCU.

The Vicarious Wisdom of Crowds: Toward a Behavioral Perspective on Investor Reactions to Acquisition Announcements

Abstract: Although event-study methodology is invaluable to strategic management research, we argue that the traditional financial economic rationale on which it is based has led scholars to assume away the behavioral mechanisms underlying investor reactions. Building on behavioral theory from management, psychology, and economics, we set out to develop a behavioral perspective on investor reactions to acquisition announcements — one that relaxes the assumption of investors making objective, rational-deductive calculations. Given the information asymmetry they face, we theorize that investors (1) infer management’s perception of an acquisition’s synergistic potential from the premium it pays, and (2) draw on additional public information to assess the reliability of that perception. Using a multi-industry sample of acquisitions by North American firms, we find considerable support for our behavioral framework.

Predictors of program completion for women in residential substance abuse treatment

Abstract: Although there is increasing emphasis on providing drug treatment programs for women that address their specific needs (including parenting and childcare), some women still fail to complete treatme...

Differences in predictions of ODE models of tumor growth: a cautionary example

Abstract: While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer. We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model. We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used. Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.