On the interaction between firm level variables, the CAPM beta, and stock returns

In Chapter 1, I conduct a theoretical study of how horizontal industry concentration affects a firm’s market capitalization and systematic risk. I first develop a method for incorporating an equilibrium theory of the firm, drawn from industrial organization, into a single period version of the Capital Asset Pricing Model (CAPM). This extension establishes the microeconomic determinants of systematic risk by relating firm specific variables to Beta. Unlike the previous literature, I add local product market shocks to a general, deterministic profit function and use an orthogonal decomposition of the market return to endogenize the Cov[Ri,RM]. I also use this method with standard Hotelling and Cournot models of firm behavior and with different sources of uncertainty to provide examples of how increasing concentration can increase, decrease, and be independent of Beta. In Chapter 2, I exploit a natural experiment afforded by the announcement of ‘Paragraph IV’ patent infringement decisions. These judgments have two unique features. They create an exogenous change in industry concentration, since they determine whether the corporate owner of a brand name prescription drug will maintain or lose monopoly marketing rights. They also satisfy the methodological requirements to use a short window event study. Against a backdrop of contradictory empirical evidence, this experiment provides a clean test to empirically determine the sign of how a change in horizontal industry concentration affects stock returns. For a sample of 38 District Court decisions between 1992 and 2006, I find that the announcement return is between [1.24%, 2.83%] if the brand firm ‘wins’ the case and between [-5.24%, -5.82%] if the brand ‘loses’. Finally, I use these returns to construct the first market valuation of the monopoly rents for brand name pharmaceutical firms. I find that the value to a brand firm of maintaining marketing exclusivity for 1 ‘average’ drug for 92 months is between [6.48%, 8.65%]. In Chapter 3, I explore the cross-sectional determinants of Beta. The two main goals of this exercise is to understand the explanatory power of popular asset pricing variables and firm level variables, such as the coefficient of variation of profit. The estimation relies on a minimum distance approach that reduces to the familiar least squares estimators. This approach permits the estimation of a dataset where the number of cross sectional observations is larger than the number of time period and accounts for the measurement error in Beta. I use two different sets of variables where one is weighted by assets, referred to as ‘Book’ variables and the other is weighted by market capitalization, referred to as ‘Market’ variables. I include two robust checks, one of which includes adding industry fixed effects. I find some striking results with respect to both the two asset pricing variables and the coefficient of variation of profit proxy. Since my statistics are pooled over different time periods, I cite the statistics from the 2001 subperiod because it has three times as many observations as the rest of the periods combined. Turnover has the largest magnitude and t-statistics in both sets of regressions. In 2001, the means of Beta A and Beta were . 94 and 1.2 respectively. I found that a one standard deviation change in turnover increased the magnitude of Beta A by . 22 and Beta by . 25. The bid ask spread percentage had a larger magnitude coefficient in the ‘Market Regressions’, which indicated that a one standard deviation change in this variable increased Beta by . 08. On the other hand, I found that ln(assets), ln(size), and book-to-market had the smallest magnitudes and t-statistics. Finally, both regressions indicate that as the proxy for the coefficient of variation of profit variable increases (decreases) for firms with a positive (negative) expected profit, Beta increases. For the 2001 subperiod in the ‘Market’ regressions, a one standard deviation change in the absolute value of this proxy, increases Beta by a magnitude of . 1 and . 15 for firms with positive and negative ‘earnings’. Finally, these results are robust to industry fixed effects.