In recent years the expected-utility approach to decision making under risk has gained increasing acceptance among portfolio theorists. On the other hand, the mean-variance (MV) approach of Markowitz [13], which has dominated portfolio theory in the past, continues to enjoy great popularity. In MV theory, the investor is assumed to rank his preferences for risky returns solely in terms of their means and variances, with higher means and lower variances, being preferred. Tobin [20] showed that MV theory is consistent with expected utility theory in the special case of joint-normally distributed asset returns. The MV approach enjoys a ready acceptance among practitioners, and requires only modest informational and computational inputs. Perhaps its most attractive feature is its ability to decompose the overall portfolio problem into a sequence of much simpler problems: first, the “efficient” set of portfolios (which minimize variance for any given mean return) is calculated, and then the investor chooses one of the efficient portfolios in a manner consistent with his personal preferences. This efficient set is the same for all investors having the same mean-variance-covariance estimates of risky-asset returns, and can, in principle, be determined once and for all using parametric quadratic programming [12, 22] Despite these real advantages, the MV theory embodies certain problems of principle in the case of nonnormally distributed asset returns, and this fact has led to increasing emphasis on the presumably more rational expected-utility theory.

Although they did not affect the individual rates of return per inflationary period that were the main concern of our study previously published in the Journal, it has recently come to our attention that there were computational errors in the summary weighted average real returns. Thus, we are taking this opportunity to correct the original errors and also to update the study results for the period from December 31, 1968, through December 31, 1973. In the analysis and discussion which follows it is assumed that the reader is familiar with the previous study and the methodology employed. Therefore, Table 1 is simply a correction of Table 4 in the 1971 article. This is followed by a discussion of the revised results and changes in conclusions prompted by the adjusted results. Table 2 contains nominal and real rates of return for the period from December 31, 1968, to December 31, 1973, nominal and real rates of returns for the full period from December 31, 1965, to December 31, 1973, and finally, a new set of weighted average returns using the figures updated through 1973.

The purpose of this note is to present a simple computational algorithm to approximate the E, S portfolio selection model. The essential feature of the model is the utilization of the familiar linear programming framework by representing risks as a series of linear constraints. Suppose we have m states and n securities, and we assume the investor is able to specify the contingent returns for all securities in each state. Following [7], we define risk as being the downside deviation from the investor's target rate of return.

The dramatic surge of the commodity option market over the past few years may well be illustrated by the growth (before its collapse) of one leading option writer, Goldstein, Samuelson, Inc., whose sales rose from $1 million in 1971 to over $45 million by the end of 1972. The commodity option market is closely related to the commodity futures market, except that options are available only on the so-called “international” commodities.

Listing of common stock on an exchange is believed by many to have a positive value for the firm. One implication of purported advantages has been improvement in the price of the stock when it is listed. Another advantage to an investor could be a reduction in the risk level of a security through trading location changes. The purpose of this research is to investigate the effect upon a security's risk level when its trading location is transferred to the New York Stock Exchange.

In recent years the application of discriminant analysis to two-category (dichotomous) classification problems in empirical financial research has substantially increased. However, these studies have given relatively little attention to design and interpretation difficulties associated with discriminant analysis. Consequently, the conclusions and generalizations that can be drawn from such studies are frequently tenuous and questionable. This paper's purpose is to discuss the methodology of discriminant analysis. While the paper is oriented toward financial applications of discriminant analysis, our discussion is not peculiar to finance. Furthermore, many of the methodological issues we address are relevant to the general problem of developing and testing dichotomous classification models and arise whether model developing is by discriminant analysis or some other method.

More than twenty years ago the portfolio selection problem was stated as a parametric quadratic programming problem [3]. Since that time there has been an ongoing search for methods that would allow reductions in both the data and the computational effort required to implement the Markowitz formulation. Markowitz himself developed a special algorithm for the problem [4] Sharpe followed with his famous diagonal model [6], a linear programming approximation for the special case of mutual funds [7], and a linear programming approximation for the general problem [0]. And during this period there were substantial advances in quadratic programming computer codes. A very fast code is now widely available [1], but the size of the code itself (a listing of the annotated program runs to more than 3,000 lines) makes its everyday use for portfolio selection somewhat unattractive.

Preference orderings of uncertain prospects have progressed from the two-moment EV model first developed by Markowitz [1952] to the more general efficiency analysis that is based on the entire probability function. This general efficiency approach, referred to as the Stochastic Dominance (SD) approach, does not depend on specific assumptions about the investor's utility function and has been shown to be theoretically superior to the “moment methods” [1].

Strides have been made recently in the discovery and refinement of theoretical models which purport to describe the relationship between asset prices and their risk attributes. (See especially Lintner [13,14,15], Sharpe [19], Mosin [17,18] and Fama [7,8.9].) The models have gained widespread acceptance because of their intuitive appeal and because most reported empirical evidence [1,4,5,11,20,21] allegedly supports their predictive value. It is our purpose to analyze critically one aspect of the nature of this evidence, reveal its inherent weakness, and to design an alternative test to examine the risk-return function. After observing the performance of an extremely large number of issues over long periods of time, we find little support for the notion that risk premiums have, in fact, manifested themselves in realized rates of return.

The first major study of industry effects in market returns was performed by King [2]. He used principal components analysis and clustering techniques on a sample of 63 companies chosen from six 2-digit industries based on Security and Exchange Commission codes. SEC codes are similar to the Standard Industrial Classification codes defined by the U.S. Bureau of the Budget [4]. SIC codes are 4-digit codes based on the principal end product of the firm. They are chosen so that, as the lowest order digit is removed, the companies are aggregated into broader but still similar groups.

The financial experiences of the last two years impel a careful and wide-ranging review of the stability of our major types of financial institutions. That review ought to be followed by actions to redress weaknesses or proclivities that, upon analysis, are judged to contribute an undesirable degree of instability within the financial system.

The purpose of this paper is to present a more meaningful interpretation of the empirical finding of a distributed lag relationship between the nominal (market) rate of interest and the rate of inflation than that offered by Irving Fisher [8] and subsequent writers (see e.g., [2], [9], [10], [11], [15], [17]). In Part II we identify a paradox between Fisher's theory and his empirical results and examine previous explanations for the paradox. In III and IV, we offer what appears to be a more satisfactory resolution of the paradox and subject it to empirical test.

Market return and risk should be jointly considered in any investment decision. In an effort to examine some of the underlying components of market excess return and risk, the relationship between these two measures and firm financial characteristics is analyzed. Since both market return and risk are considered simultaneously in an investment decision, they are also simultaneously related to firm financial characteristics using canonical correlation analysis.

It is clear from several previous empirical studies that diversification reduces portfolio return variability and that the reduction of variability appears very quickly. The purpose of this paper is to reexamine the question of how diversification affects the distribution of portfolio returns. There is no doubt that increasing the number of securities in a portfolio will reduce the nonmarket related return variability. However, there are several areas of controversy. For example, it should be recognized that the term “variability” can have different definitions. Furthermore, the ability of diversication to eliminate independent elements of the variability of return is important only if this reduction can be incorporated into predictions of future risk. In addition, the method of portfolio formation may determine the extent to which diversification enables portfolio managers to more accurately assist future risk.

Recent evidence supports the notion that capital markets are substantially integrated in a return/systematic risk sense [1, 12]. Other studies show that investors can reduce the total level of risk borne without reducing expected returns by holding an internationally diversified portfolio [8, 9, 17]. Together these studies imply that the various economies mirrored by financial markets are not perfectly integrated. Some assert, however, that because of controls on capital flows, differential trading costs, different tax structures, and a number of other factors, markets are imperfectly (albeit substantially) integrated. Hence investors may not actually be diversifying internationally and thus forego advantages which would accrue to them if they were willing to hold foreign security issues.

The purpose of this study is to investigate the determinants of savings and loan profitability. Although considerable discussion has been devoted to studying the cyclical behavior of thrift institution profitability, there is a void with respect to the determinants of individual association profitability. This paper investigates whether performance variations are more determined by differences in the individual association's financial characteristics than by the external factors of the market in which they operate, such as state usury laws, market structure, and other economic characteristics of market areas.