This study utilizes the discriminant analysis technique in the development of a model able to predict acquisition targets. The model is tested in a portfolio selection setting to determine its ability to identify portfolios capable of performance superior to that of the market. The sample in the model building phase is composed of seventy-one firms acquired during the years of 1976 and 1977. Another seventy-one firms were drawn randomly from the general corporate population of firms identified for the study. A total of forty-seven variables were considered, including sixteen industry adjusted variables. The variables themselves are financial ratios available in company annual reports. A five variable model is developed which includes the adjusted debt ratio, net working capital, the return on assets ratio, the adjusted net profit margin and the adjusted times interest earned ratio. There is evidence to indicate that acquired firms use less debt, are smaller, and obtain a higher return on assets than firms in the general population. The model itself achieved an overall classification accuracy of 73.9 percent. The model then was subjected to an intertemporal test of validity during the subsequent two year period. A total of 1967 firms were classified, of which 171 represented actual acquired firms. These firms represent an appropriate investment population for a small investor confronted with portfolio investment choices. The model's performance in selecting acquired firms among those that are identified as acquired is significantly superior to that provided by a random chance model. In selecting portfolios, the model is able to identify securities that provide risk-adjusted returns superior to those obtained by the market. Increasing the portfolio size indicated that the model is able to consistently provide superior portfolio performance. One interesting finding is that the performance of the non-acquired segment of the portfolio is superior to the market as well. It is hypothesized that this group represents firms that are attractive to acquisition. / Ph. D.