This paper examines the empirical validity of two exchange ratio determination models for merger, the Larson and Gonedes (LG) PE model and the Yagil dividend growth model. These two models formulate exchange ratios as a function of a different factor: expected post-merger price-earnings multiple and expected post-merger dividend growth, respectively. While the LG model has been tested in previous studies, the Yagil model has yet been subject to empirical testing. This paper finds empirical support for the LG model but finds weak support for the Yagil model. In particular, the results show that the number of stock mergers that result in wealth gains for both acquiring and target firms and hence conform to the rationality assumption of each model is substantially greater for the LG model than for the Yagil model. Regression analysis provides confirmatory evidence on the empirical validity of the LG model that PE-related variables play a more significant role in explaining the actual exchange ratios than growth-related variables. Copyright Blackwell Publishers Ltd 2000.

Statistical inference on a common quantity of interest arising from multiple related studies is quite pervasive in medical, social, statistical genetics, clinical trials, and epidemiological research. However, a scope is identified to study the issues related to pooling proportion across many Bernoulli type of events. The Main objective of the study is to exploit the asymptotic approximation of proportion and logit transformation in random effects model. Seven procedures for estimating between-variance in frequentist inferential methods have been used. Number of Bernoulli trials, number of studies, and events occurred at boundaries of the parameter are prime data characteristics considered in the study. From the results it is possible to device a methodology to choose more appropriate methods under different problem situations. All the procedures are implemented in R and major functions are presented in Appendix.