The process of security price adjustment to the release of new information has long held the interest of the finance profession, both in academics and in practice. The efficiency of financial markets in reflecting new information significantly impacts the allocation of capital and income within the markets1 and, consequently, can affect social welfare. Thus, public, business, and investment policies are all related to an understanding of the functioning of security markets and their utilization of information. As a result, a significant body of economic research has considered the impact of information upon security markets under a number of alternative market structures. In this paper, we attempt to contribute to this literature by extending previous research in the two related areas of speculation and information dissemination.

Several studies indicate the presence of large abnormal returns accruing to shareholders of merged firms in the period immediately before the merger. For example, Mandelker [18] reports that stockholders of acquired firms earn abnormal returns of approximately 14 percent in the seven months preceding merger. Franks, Broyles, and Hecht [15] find abnormal returns of 26 percent for British firms during the four months prior to merger; Elgers and Clark [11] report 43 percent abnormal returns accruing over two years before merger to shareholders of acquired firms.

The standard models of consumer behavior under uncertainty are the expected utility model and the mean-variance model. As with any models involving unobservables one might well ask about the empirical content of these hypotheses: what restrictions on observed behavior do these models impose? How can one test observed behavior for consistency with these models? How can one recover the underlying utility function and forecast behavior in new situations?

In a recent study, Levy and Markowitz [15] demonstrate that, at least for some utility functions, expected utility can be approximated by a judiciously chosen function defined over mean and variance. In addition to resurrecting mean-variance analysis from the limbo into which it was placed by the criticisms of Borch [10] and others, the analysis by Levy and Markowitz yields a more direct approach to portfolio analysis than that provided by the current empirical literature. The current portfolio literature is concerned with notions of efficient sets and systematic risk rather than with utility functions and mean-variance. While much has been gained from a utility-free methodology, it is ultimately predicated upon a separation theorem and, hence, an environment with zero transactions costs. But security markets are not costless and the separation theorem may not hold. In that event, a utility-dependent approach to portfolio analysis could potentially lead to more powerful results especially if such an approach could be empirically implemented.

We have proposed two additions to the theory of leverage optimization by firms operating in a competitively structured industry.

First, whether the capital market is or is not subject to leverage-related imperfections, the force of entry will cause the long-run relationship between profits, π, and leverage, γ, to be given by the equation

(17) π(γ) = 0.

Even if it is possible in the short run for firms to add positive increments to profits by increasing γ beyond its current level, competitive pressures will erode these increments completely.

Second, if the capital market is subject to leverage-related imperfections, firms will choose smaller leverage ratios than they would in the absence of these imperfections. If, in addition, the industry is subject to the force of entry, optimal leverage ratios will be even smaller. Put another way, each firm's leverage ratio in the long run will reflect not just the impact of imperfections on capital costs, but also the impact of entry on product price, output, and operating profits.

In their seminal paper, Modigliani and Miller [11], [12] demonstrate that if capital markets are perfect and investment policy is held constant, the market value of the firm is independent of its financial decisions. Furthermore, if capital markets are perfect, stockholders have incentive to choose the investment policy which maximizes the market value of the firm (see [6]). Motivated by this assumption, the firm has been viewed as a “black box;” namely, as one homogeneous unit whose clear objective is to maximize its market value. However, in a growing body of recent literature (see [1], [2], [7], [9], [13], and [14]), researchers recognize that the firm in an “imperfect” capital market is a collection of groups whose interests can, and do, conflict. Jensen and Meckling [9] study the roles of three important groups—the owner-manager, the stockholders, and the bondholders—focusing on the potential costs resulting from divergence of interests among them. They provide a theory of optimal capital structure in terms of reducing the costs of these conflicts.

The effect of dividends on the valuation of securities has been a controversial subject in financial research in recent years. Since Miller and Modigliani [8] demonstrated the irrelevance of dividend policy, researchers have tested and attempted to explain market price reaction to firms’ dividend decisions. Explanations of market reactions to dividend policyhave centered around information issues and tax effects. Information issues have been empirically investigated by examining market reactions to announcements of dividend changes. The effect of differential tax treatments of dividends and capital gains usually has been examined through cross-sectional regression testing the significance of dividend yield in explaining returns. Any market return study of dividends, however, should consider both the potential information effect and the tax effect.

Normatively oriented approaches to decision making under uncertainty occupy a major portion of the literature of decision analysis. Among the proposed approaches, the Von Neumann-Morgenstern expected utility (EU) theory has been the most prominent model of rational choice despite some controversies that surround this model with respect to its descriptive ability as well as the plausibility of utility assessment in actual decision situations [6], [10]. Although most applications of the EU model have been concerned with decisions involving monetary outcomes, its scope is not limited to any particular class of consequences. The basic appeal of the model is attributable partly to its generality in this regard, and, more importantly, to the apparent reasonableness of its preferential axioms.

Corporate investment in an economy without a complete set of contingent claims markets has the characteristic of a public good in the sense that the stockholders’ consumption planscannot be separated from, but depend on, the specific investment plans of the firms. Drèze [4] has shown that a constrained Pareto optimal (CPO) allocation of investment in a stock market economy must satisfy a generalization of the Samuelson [24] condition for efficient production of public goods: the investment plan should maximize a weighted sum of the stockholders’ personal valuations of future output minus current input cost. However, except for those special cases in which CPO investment plans are unanimously supported by stockholders (see [17], [20], and [2]), the theory of the firm in incomplete markets lacks a suitable maximization criterion. Although the Drèze-Samuelson condition is a most appealing candidate, it is not unanimously preferred by stockholders, each of whom prefers that his or her own valuation of future output receives all the weight in the investment decision. Furthermore, the application of the Drèze-Samuelson condition depends on the correct revelation of stockholders’ preferences, which, in the absence of special inducements, cannot be expected from economic agents.

Several studies (see [7], [15], [9], and [16]) have attempted to ascertain the empirical relationships that exist between the financing and investment decisions of firms. The primary impetus for these efforts was provided by Modigliani and Miller (MM) [20], [21] when they demonstrated that under the assumption of perfect capital markets, the optimal investment decisions of a firm are separable from its financing decisions. In the presence of market imperfections, interdependencies between these decisions that violate the basic MM propositions may exist. Therefore, the studies referred to above have essentially focused upon determining whether market imperfections have been of sufficient magnitude to lead to joint determination of investment and financing decisions. Though the original MM propositions were introduced more than two decades ago, the nature of the empirical relationship between investment and financing decisions remains a controversial issue. The fact that this matter is still unresolved is not attributable to a lack of attention, for the original propositions have been examined in the presence of taxes, bankruptcy costs, and agency costs with no clear consensus emerging.

In 1959, Henry Lataná [2] proposed an approximation to the geometric mean that was a simple function of the arithmetic mean and variance, thereby indicating a mathematical relationship between the risky investment choice model of Bernoulli and the Markowitz mean-variance model. In 1969, Young and Trent [4] presented empirical test results of the Latané approximation, as well as a set of other approximations to the geometric mean based on moments, and concluded that the Latane formula yielded a quite accurate approximation to the geometric mean. In Jean's 1980 paper [1] relating the geometric mean model to stochastic dominance models, the infinite series representation of the geometric mean used suggests a more accurate approximation with moments of the geometric mean than that contained in the earlier papers may be possible. Various forms of that series expressed in alternate-origin moments are tested empirically below, and the results confirm that this later series does yield the greatest accuracy of the three approaches.

Based on Markowitz's pioneering study [40], Sharpe [56] and Lintner [38] advanced the first positivist formulations of the capital asset pricing model (CAPM). Their models were subsequently refined by Mossin [45], Fama [15], Black [1], and others. Even though the CAPM has been studied extensively, it has not been empirically validated. According to Roll [48], the CAPM cannot be tested in an unambiguous fashion because of a number of intractable measurement and computational difficulties, and the joint nature of the hypotheses to be tested.

The purpose of this paper is to demonstrate how multiple regression software may be used for computing estimates of efficient set parameters and for performing tests of mean-standard deviation efficiency. Regression software also is shown to be useful for selecting, from a set of assets, a subset that maximizes performance and for comparing the performance of the set to the subset. The underlying multiple regression model fitted by the software has no relation to the analysis; the regression software is employed simply as a computing device. Since the multiple regression procedure is familiar to most finance researchers and since regression software is commonly available, the techniques presented here should be of wide interest.

Numerous studies in recent years have emphasized the importance of accounting properly for abandoment value in capital budgeting (see [1], [4], [7], [10], and [11]). For a variety of reasons, a project need be neither physically exhausted nor have negative cash flows to be abandoned. Robichek and Van Home [10] suggested that a project should be abandoned in any period in which the present value of future cash flows does not exceed its abandonment value. In a modification of this rule, Dyl and Long [4] proposed that the firm give consideration to all possible future abandonment opportunities. They argued that abandonment need not occur at the earliest possible date that the abandonment condition is satisfied, but rather at the date that yields the highest NPV over all future abandonment possibilities. A generalization of these models was offered by Bonini [1], who developed a dynamic programming model to analyze investment projects with abandonment possibilities and uncertain cash flows. More recently, Gaumnitz and Emery [7] compared the abandonment decision to the like-for-like replacement decision and noted that the correct model for a particular case depends on the suitability of the assumptions.

The specification of a statistical distribution which accurately models the behavior of stock returns continues to be a salient issue in financial economics. With the introduction of arithmetic and geometric Brownian motion models, much attention has recently focused on a Poisson mixture of distributions as an appropriate specification of stock returns. For example, see [12], [3], [8], [10], [5], and [1]. Consistent with empirical evidence, these models yield leptokurtic security return distributions and, furthermore, the specification has much economic intuition. In particular, one may always decompose the total change in stock price into “normal” and “abnormal” components. The “normal” change may be due to variation in capitalization rates, a temporary imbalance between supply and demand, or the receipt of any other information which causes marginal price changes. This component is modelled as a lognormal diffusion process. The “abnormal” change is due to the receipt of any information which causes a more than marginal change in the price of the stock and is usually modeled as a Poisson process.

In deciding among these estimators for a particular application, gains in efficiency must be weighed against additional computational effort. On these grounds, there would seem to be good reason in most circumstances for preferring the wrap-around overlap estimator to the simple unbiased one. The gain in efficiency is achieved at a small cost in additional computation, and, in addition, no data need be discarded if the sample size (T) does not happen to be an integral multiple of N. In going beyond the wrap-around estimator to the combinatorial one, however, the decision is less clear. The gain in efficiency is relatively small and the number of return relatives to be computed may become quite large. For example, with T=30 and N=10, the last parameter set for which computations are made in Table 1, evaluation of the combinatorial estimator requires computation of (= approximately 3×108) ten-period return relatives. In many applications, this additional effort is probably not worthwhile. Significantly, the ordinary overlap estimator seems to fare worst, probably due to its asymmetric use of the data, and, therefore, should not be used.

It also should be noted that when relatively large amounts of data are available, the simple unbiased estimator will in most cases perform quite adequately. A common application in financial research involves computing annual stock returns from monthly data. Representative relative efficiency computations using μ = 1.01, σ = .05, and N = 12 are given in panel B of Table 1 for T = 60, 120, and 360 months. The incremental improvement of the wrap-around estimator relative to the simple one is quite small. Figures for the combinatorial estimator are not presented because the number of permutations would be prohibitively large.

Financial analysts have been intrigued by bond ratings since John Moody first started publishing them in 1909. Bond ratings are assigned by three agencies (Moody's, Standard and Poor's (S&P), and Fitch); these ratings are widely publicized and are, therefore, critically important. A bond's rating affects investors' purchase decisions and, consequently, the issuing firm's cost of debt and, indirectly, its cost of equity.

Periods of high mortgage interest rates that characterized the early 1980s have escalated the use of assumption financing in home buying. In these periods, there is an increased demand for alternative sources of financing and a movement away from conventional sources. The purpose of this study is to provide a theoretical and empirical analysis of the effect of the increased use of assumption financing on selling prices of single-family residential housing. Aside from physical characteristics, the financing arranged by the homebuyer is considered a major influence in determining house prices. This is especially true in real estate appraisal practice where differences in types of financing are one of the major categories of adjustment (see [1], [4], and [10]).

Empirical studies in finance generally use data defined over the shortest return period available. Originally, data bases such as CRSP, tended to have data collected over monthly periods and most analyses tended to use monthly data rather than data compounded over periods greater than a month with the implicit argument that the more data the “better”. Since the development of data bases with data collected over shorter differencing intervals, there has been a growing tendency in finance to use returns data defined over increasingly shorter differencing intervals. This development is desirable, but is not without problems. The problem with using data defined over shorter differencing intervals is that, although greater estimating efficiency will be achieved, nontrading effects could be introduced into the analysis. These will lead to biased beta estimators and biases in tests of capital market efficiency. The purpose of this paper is to investigate, analytically, the interrelation of the intervaling and nontrading effects both in estimating beta factors and in testing capital market efficiency.

The search for an economically sound procedure for estimating an appropriate rate of return on equity consistent with the Supreme Court's ruling in the Hope case [13] has led many economists, financial experts, and public service commissions to estimate the rate of return on equity with the capital asset pricing model (CAPM) (see [30], [19], and [21]). The popularity of the CAPM in regulatory proceedings was reported by Harrington [15] who, in a survey of public service commissions, found that 38 states were considering or had seen the CAPM used, two jurisdictions preferred the CAPM, Oregon required the CAPM, and South Carolina would require the CAPM in all future cases. Hence, given the popularity of the CAPM and the tremendous economic impact that outcomes of regulatory proceedings have on the financial well-being of both the regulated firm and the consumer, it is critical that if the CAPM is used in regulatory proceedings that it be applied in the best manner possible and that any limitations associated with the CAPM be recognized fully.