Thesis by
Laura Panattoni
In Partial Fulfillment of the Requirements
for the Degree of
Doctor of Philosophy
California Institute of Technology
Pasadena, California
2009
Abstract
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 sub-period 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 sub-period in the ‘Market’ regressions, a one standard deviation change in the absolute value of this proxy, increases Beta by a magnitude of and .15 for firms with positive and negative ‘earnings’. Finally, these results are robust to industry fixed effects.
Introduction
Asset pricing models have been developed, somewhat myopically, with little reference to the product market and therefore to industrial organization. The best known asset pricing models base their predictions on variations in investor preferences, behavioral biases in financial markets, or each asset’s covariance with a market portfolio. Remarkably, these models disregard how economic fundamentals in the product market, such as firm specific or industry wide characteristics, may affect financial equilibria. Fama [17] acknowledged this omission when he advocated that researchers should either relate the behavior of expected returns to “the real economy in a rather detailed way” [p1610] or establish that no such relationship exists.
The Capital Asset Pricing Model (CAPM) provides an excellent example of an equilibrium financial model in which asset prices are derived independent of the ‘real economy’. The CAPM’s main insight is that an asset’s expected excess return is determined by its covariance with a market or aggregate portfolio. The covariance is included in an assets’s Beta term, which within the capital budgeting framework, may be interpreted as a risk adjusted discount rate. While the CAPM relates a firm’s Beta term1 to its expected return, the model provides no relationship between a firm’s profits and its Beta term. Therefore, the model cannot predict which type of firm strategies, types of competition, or industry characteristics create more systematic risk. In terms of capital budgeting, there is no way to update the risk adjusted discount rate in response to product market changes.
In this chapter, I conduct a theoretical investigation of how industry concentration2 affects a firm’s market capitalization and systematic risk within the CAPM framework. I first incorporate an equilibrium theory of firm behavior into the CAPM. This extended version of the CAPM can combine models of both perfect and imperfect industry competition with a competitive model of security pricing. Therefore, I contribute to the emerging theoretical literature establishing a micro-foundation for asset pricing models. I then use this extension of the CAPM with two standard models of firm behavior, the Hotelling model and the Cournot model, to test the effects of concentration.4 These examples illustrate how product market factors, such as different types of firm competition or different sources of uncertainty, influence the way in which concentration affects financial outcomes.
Although firm level variables, such as risky cash flows and the capital structure, have been related to the CAPM since the early 1970’s, (Rubinstein), the literature incorporating an equilibrium theory firm behavior into the CAPM has been sparse. Subrahmanyam and Thomadakis used a quantity choosing model of firm behavior and the Lerner Index to coincidentally study the effect of industry concentration. They found that for a given capital labor ratio, decreasing concentration increased systematic risk. Bhattacharyya and Leach applied the CAPM valuation formula to firm profit and found conditions under which Beta is independent of the quantity chosen. Kazumori used the consumption CAPM and the idea of consumption risk, the cost of switching products if the product fails, to find that increasing market share increased systematic risk. As an increasingly asymmetric distribution of market shares also represents increased concentration, Kazumori’s results contradicts the sign of the results found by Subrahmanyam and Thomadakis.
The literature in this field can be compared according to two salient features of the models. The first feature is the structure of uncertainty. In their one period model, Subrahmanyam and Thomadakis use an additive shock to the demand function and to the labor supply. Bhattacharyya and Leach use state probability pricing. Finally, Kazumori uses a continuous time stochastic calculus framework with shocks to consumption at each period. The structure of uncertainty in these papers makes it difficult to study the effect of a different parameter on systematic risk, add uncertainty to a different parameter, or change the character of firm competition within the framework of these models.
On the other hand, I add local product market shocks to a general profit function which provides a more general framework. Product market shocks are simply a shock added to any primitive of a profit function, such as costs or consumer preferences. This method characterizes the effect of a small amount of uncertainty by linearly approximating a random profit function. Many deterministic models from industrial organization can easily be used within this framework. Therefore, unlike the previous literature, my method is not dependent on the exact model of firm behavior or which parameters are shocked.
I chose to study industry concentration because aside from the above two conflicting theoretical results, the previous work on industry concentration has been contradictory and mostly empirical. The empirical work supports the three contradictory conclusions that in- creasing industry concentration decreases, increases, and does not affect a stock’s expected returns. In support of the first conclusion, Hou and Robinson [31] estimate that firms in the quintile of the most competitive industries have returns nearly four percent greater than firms in the most concentrated quintile. These authors argue that competitive industries are riskier because they are more likely to face change from innovation, Schumpeter’s creative destruction, and that they are more sensitive to demand shocks due to lower barriers to entry. However, they do not provide a formal model that links these arguments to their econometric work, and they do not look at share prices.
In contrast, Lustgarten and Thomadakis and Melicher, Rush, and Winn support the other two conclusions. Lustgarten and Thomadakis found that announced changes in a firm’s accounting earnings led to a greater change in the market capitalization in more concentrated industries. If future prices are treated as exogenous, this weakly implies that the stock returns increased with concentration. Finally, Melicher, Rush, and Winn look at 495 manufacturing firms and find that industry concentration has no effect on a stock return.
Using the Hotelling and the Cournot model, this chapter will provide theoretical examples of how the standard deviation of profit, the market capitalization, and the expected return approximately change with N when there are different sources of uncertainty. First, the standard deviation of profit decreases in N except in the case of a Cournot firm is facing a shock to costs. The market capitalization always decreases in N for sufficiently small shocks. Finally, the expected return either increases in or is independent of N, except in the case of a Hotelling firm with a relatively large market share facing a shock to the intensity of product differentiation. The expected return increases in N when firms face shocks to costs, when a Hotelling firm with a relatively small market share is facing a shock to the intensity of product differentiation, and when a Cournot firm is facing a shock to the slope of the demand function. The expected return is independent of N when the shock simply rescales the profit function, when there is a monopoly, and when there are perfectly competitive firms.
This chapter will be divided into three main sections. The first section will incorporate an equilibrium theory of the firm from industrial organization into CAPM. The second section will use the Hotelling model of firm behavior to study how industry concentration affects a firm’s market capitalization and systematic risk. Finally, the third section uses a Cournot and competitive model to study how industry concentration affects these financial outcomes.
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