Journal Article Optimal Trade Policies and Non-Economic Objectives in Models Involving Imported Materials, Inter-Industry Flows and Non-Traded Goods Get access Augustine H. H. Tan Augustine H. H. Tan University of Singapore Search for other works by this author on: Oxford Academic Google Scholar The Review of Economic Studies, Volume 38, Issue 1, January 1971, Pages 105–111, https://doi.org/10.2307/2296625 Published: 01 January 1971

Most textbooks call convertible bonds and convertible preferred stocks hybrid securities because they have the characteristics of senior securities together with many of the attributes of common stocks. In most, if not all, balance sheets they are classified as senior securities and their claim against earnings is reported to be the coupon interest or the dividends declared.' Yet, in most cases the nominal rates of return on these securities are substantially below those of equivalent-risk nonconvertible securities. It is apparent that these nominal rates are an inadequate measure of the real cost of convertible securities to the firm.2 What then is the cost of convertibles? To what extent do published reports understate or overstate their cost? What are the important variables affecting that cost? How does the cost of debt affect earnings per share? This paper presents the results of an empirical study intended to answer those questions. It seems clear that a convertible security, either bond or stock, derives its value from the magnitude of its share of the firm's earnings, present and future. That share however is not limited to the cash payout alone; it also includes a pro-rata, per equivalent common share portion of the current period earnings retained by the firm. To the extent that earnings

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.

In a recent paper by Lusztig and Schwab, a sensitivity analysis was performed on a linear programming capital budgeting problem where selection of projects is based on the criterion of present values. Their model is typical of current practice in the literature, and it is the point of this paper to indicate that a better model exists which allows more flexibility of assumptions and will yield the same results as a present value criterion. The model to be presented here uses a terminal value (horizon value) criterion for selection of the optimum set of investment projects.

Three main approaches to the problem of portfolio selection may be discerned in the literature. The first of these is the mean-variance approach, pioneered by Markowitz [21], [22], and Tobin [30]. The second approach is that of chance-constrained programming, apparently initiated by Naslund and Whinston [26]. The third approach, Latané [19] and Breiman [6], [7], has its origin in capital growth considerations. The purpose of this paper is to contrast the mean-variance model, by far the most well-known and most developed model of portfolio selection, with the capital growth model, undoubtedly the least known. In so doing, we shall find the mean-variance model to be severely compromised by the capital growth model in several significant respects.

Most portfolio analysis is based on the use of two parameters, the mean and variance, of the statistical distribution of returns. Exceptions to this practice can be found in an empirical work by Arditti [1] and a theoretical paper by Levy [4], both using the third moment around the mean. It is the purpose of this paper to begin a general extension of the two-parameter analysis to three or more parameters. Accordingly, some problems will be solved, but others will be suggested for further analysis.

As the authors, Donald L. Tuttle and William L. Wilbur, indicate in footnote 1 of their article, “A Multivariate Time-Series Investigation of Annual Returns on Highest Grade Corporate Bonds,” the equations tested in this paper seem to me to be ad hoc. The problem is not that I think the authors should have estimated a general equilibrium model of the economy, but rather that they have not provided a satisfactory explanation of the single equation they have tested. The use of a technique to choose among alternative variables on the basis of their ability to shrink the coefficient of multiple correlation could be taken as further evidence of the absence of a well-articulated theoretical relationship explaining annual returns on corporate bonds.

Murphy and Nelson in their article, “Random and Nonrandom Relationships Among Financial Variables: A Financial Model,” develop three postulates which deal with the temporal behavior of financial variables. They suggest that the three postulates constitute a useful financial model. The basis for the model is the distinction between dollar or ratio variables on the one hand and percentage change or growth variables on the other hand.

Depreciation, Replacement costs, Cost of Capital, Inflation

In an earlier paper on "Interdependence as a Specification Error," Strotz has considered a recursive model with endogenous variables lagged by θ; letting θ go to 0, he argued that the likelihood function of the limit form of the model differed from the limit form of the likelihood function of the model. We show here that the two limits are obtained under different underlying assumptions; these alternative assumptions are brought out explicitly; under fixed assumptions, the two methods are shown to yield identical results.