Knowledge that Transforms

This article examines some aspects of the portfolio selection problem when the “no-easy-money-condition” holds and the investor is constrained to stay solvent. The possible presence of a non-capital income is also taken into consideration.

The concept of profit sharing—whereby a company provides employees with a contribution above and beyond their regular wages based upon business profits—is very old indeed. Implementation of this concept is witnessed by the many profit sharing plans of all kinds in existence today. However, even though the concept is old and the use widespread, there is a complete void in published literature on the nature of optimal profit sharing plans. It is the purpose of this paper to investigate this subject.

This article has two goals. The first is to contrast two widely used definitions of risk aversion. The second is to establish the feasibility of plunging behavior, in the sense that a possibly large number of risk averse investors will not be diversifiers.

Risk continues to be a widely discussed topic within the field of finance. Academicians add risk variables to their quantitative models, while financial practitioners include risk considerations in their qualitative deliberations. In both contexts, risk — together with some measure of profit or return — generally comprise a dual or composite criteria for investment decision-making purposes. Whereas the decisionmaking situation can be described as ex ante, this article deals with risk in an ex post context. In particular, it reports an investigation of alternative risk-return measures which are designed to rank and evaluate the ex post performance of investment portfolios. Section I reviews three composite measures of performance and examines their interrelationships. A fourth alternative measure is also suggested. In Section II, the measures are used to rank the portfolio performance of a sample of mutual funds. Some difficulties in making performance comparisons of these funds against the market are discussed in Section III. The final section briefly explores the implications of the study and suggests areas for subsequent research.

The Sharpe model of capital asset pricing under conditions of risk has received wide theoretical acclaim and empirical support. This paper presents an econometric study of the model with the following objectives: (a) to show the effect of measuring the model's independent variables incorrectly; (b) to derive and use a new procedure for empirically testing the adequacy of the model as it is currently formulated.

Until very recently, in most work on normative models for capital investment planning, it has been assumed that availability of capital is unconstrained; i.e., that money may be freely borrowed or lent at a single market rate of interest, and that no other constraints affect the proper choice of available productive investment projects to be undertaken. Since practical situations almost universally do involve such constraints, the traditional theories have, for the most part, been an unsatisfactory guide to achievement of optimal capital investment behavior in the real world.

The objectives of this paper are to derive the relationship of the geometric mean of a distribution of positive values to the conventional first four moments — arithmetic mean, variance, absolute skewness, and absolute kurtosis — and to empirically evaluate certain approximations involving these four moments for estimating the geometric means of monthly and annual holding period returns for individual stocks and for portfolios. The geometric mean is shown to be positively related to the arithmetic mean and absolute skewness and negatively related to variance and absolute kurtosis. In the case of a normal distribution a very good approximation to the geometric mean is revealed to be a function of just the arithmetic mean and variance. Additionally, empirical evidence indicates that even though a number of the monthly and annual distributions deviate significantly from normality, the approximation involving only the mean and variance produces quite accurate estimates of the geometric means of these distributions.

The question “What's in a bond rating?” has been asked at least since 1909 when such ratings were started in the United States. Informed persons who answer this question typically admit that ratings depend in part on readily available statistics on a firm's operations and financial condition. Examples of such statistics are earnings coverage, leverage ratios, and profit rates.

John Lintner, The Aggregation of Investor's Diverse Judgments and Preferences in Purely Competitive Security Markets, The Journal of Financial and Quantitative Analysis, Vol. 4, No. 4 (Dec., 1969), pp. 347-400