Empirically identified industry classification

Dissertation supervisor: Dr. Dan French.Includes vita.This study examines return based correlations between industry returns and firm returns to create more objective and comparable industry classifications. In my first essay I model a market with firms that invest in one or more categories of assets. Firms that invest in assets with similar return correlations are grouped into categories that are comparable to industry groups in the standard scheme of classifying firms into industries based on offering a common product or service. Because these categories are based on objective rather than subjective criteria, use of these categories by investors might have advantages when using industry information to make investment decisions and construct portfolios. I also derive estimable equations to measure firms' exposures to category risk thereby identifying in which category or categories the firm belongs, and we use simulation to explore the efficacy of three different estimation methods. In my second essay I evaluate the question does the number of industry exposures (i.e. diversification level) affect corporate value. I find an unconditional diversification premium. However, there is substantial time series variation in the relation between diversification and valuation. This variation is able to reconcile many of the conflicting conclusions in the prior literature. In my third essay I perform empirical tests to determine whether industry returns can be refined by applying an iterative regression of firm returns on industry returns to create returns than are more inter-correlated but also more orthogonal to other industries' returns. I find strong evidence that an iterative process of return generation provides benefits to researchers as well as practitioners.Includes bibliographical references (pages 140-143)