The approach of selecting a portfolio of stocks on the basis of expected return and variance was introduced by Markowitz [18] in 1952 and subsequently was more fully developed by him [19] in 1959. Since this time, there has been considerable research either directly concerned with, or related to, the Markowitz model. The utility implications of his assumption that an investor chooses a portfolio solely on the basis of expected return and variance (where variance is identified with risk) have been studied, [1], [A], [22], and [31]. A simplified method of solving for the efficient set of portfolios under the assumption of a regression structure has been developed by Sharpe [26], and approximation methods have been suggested [25] and [29]. Empirical tests (with partially contradictory conclusions) of portfolio selection theory are described in [5], [7], [8], [20], and [27]. Studies of economic questions (such as liquidity preference, equilibrium stock prices, substitutability of risky assets, etc.), as formulated within the portfolio model, can be found in [10], [11], [13], [14], [16], [23], and [28]. A related portfolio selection approach, based on the assumption of a Pareto underlying distribution, has been suggested by Fama [6]. A modification by Baumol [2] introduced a confidence limit criterion. Also, some initial attempts have been made at deriving related adaptive models of portfolio selection, [21], [30].

We have discussed some of the effects of rate regulation in the property and casualty insurance industry. One consequence of the regulatory environment is that an optimal capital structure may clearly exist in this industry. If the rate of return to the insureds is generally deficient, we would expect that property and casualty stock companies would have an incentive to lever themselves to the maximum extent permissible by selling insurance. The classic monopoly of the economic literature finances its lucrative investment opportunities in a competitive capital market. The stock insurance company invests in that market, but the relative distribution of the return earned there may be less than equitable due to the process and standards of rate regulation.

Some recent empirical studies have concluded that the common stock investor can virtually eliminate diversifiable risk with a portfolio that contains a “small” number of separate common stock issues [5, 6, 10, 11, 13]. The conclusion has several important implications. One of the inherent limitations of a portfolio manager is his inability to evaluate an infinite number of securities. The seriousness of this problem is directly related to the risks associated with a “small” portfolio. The economic function of a mutual fund industry is to provide diversification and professional management. If it is assured that a “small” portfolio can virtually eliminate diversifiable risk, the necessity of these functions may be questioned. In addition, the strategy of concentration may be less “risky” than is commonly supposed. Finally, the modern portfolio models generally assume that portfolio additions are costless.

The relationship between capital market equilibrium and firm financial policy has received extensive attention in recent years. Until recently, accepted theory was generally consistent in its view that the diversification effect of new investment on firm earnings is a necessary consideration in project selection. In arguing this position, no distinction was made between the perfect market situation exemplified by the models of Modigliani and Miller (M-M) [9, 10, 11] and those of Sharpe [19], Lintner [6, 7] and Mossin [12] (LSM model) and the traditional case in which firm value is not independent of debt policy, e.g., as might be the case if individual investors cannot lever on terms comparable to those available to firms. In a recent article, Mossin [13] examines the implications of the former case of perfect markets. Using a single period model with riskless rate borrowing and lending by individuals and firms, homogeneous expectations, mean-variance portfolio selection, and no taxes, Mossin shows that the effect on the investing firm's value of a new project is independent of the stochastic properties of the other income earned by the firm. This conclusion and the M-M [10] Proposition I follow from the statistical property of Mossin's model that any income stream has the same value regardless of how that stream is divided into the equity or debt streams of one or more firms; or, equivalently, firm value and financial structure are independent. Schall [18] presents a general proof that firm value and financial structure are independent and that firm investment diversification effects are irrelevant in perfect capital markets.

A portfolio frontier superior to the Markowitz one-period buy-and hold efficient frontier does exist. Such a superior frontier can be generated by pursuing a rebalancing policy, even under the conditions of random walk. By rebalancing we mean that an investor maintains a fixed but optimal set of weights among the securities in a portfolio throughout an investment period by buying and selling securities at the end of some predetermined intervals.

It appears to a widely held view that corporations with similar operational characteristics ought to have similar financial characteristics. For example, one might expect that the financial characteristics of two drug companies would be similar. This seems entirely reasonable. Unfortunately however, there does not appear to be any quantitative analysis of this point in the literature. Furthermore, discussions with our financial colleagues lead to the conclusion that, if such financial differentiation of corporations were possible, it is by no means obvious what the variables of differentiation would be. Consequently, such an analysis was undertaken and is described in this paper. The basic question asked is whether the statistical grouping of corporations by their financial characteristics is similar to their predetermined, external, industrial classification

Recently, Cohen and Gujarati [2] have suggested that when multicollinearity is present there is “ …danger involved in mechanically dropping variables from multiple regression equations by t tests because t values of the regression coefficients may not be significantly different from zero when the true (population) values of these coefficients are in fact not zero…” The problem they discuss is not a new one and has been extensively treated in the existing literature. However, their approach is straightforward and will certainly aid the practitioner in his understanding of the problems associated with multicollinearity.

Donald L. Tuttle, William L. Wilbur, A Multivariate Time-Series Investigation of Annual Returns on Highest Grade Corporate Bonds, The Journal of Financial and Quantitative Analysis, Vol. 6, No. 2 (Mar., 1971), pp. 707-721

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