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Certainly, the concept of skewness of returns and its role in the context of portfolio analysis has gained increasing attention in recent literature. Witness the studies by Alderfer and Bierman, Arditti, Jean, and Simonson. Each of these studies has treated skewness as the third moment of a series expansion—accordingly, skewness has been measured and interpreted as a logical extension of the traditional two-dimensional return-versus-standard deviation analysis of security evaluation. The purpose of this note is to illustrate that the measurement of skewness is more sample sensitive than similar measurements for the mean and standard deviation. In particular, skewness measures will be shown to be highly sensitive to both the size of the differencing interval and the initialization point. [ABSTRACT FROM AUTHOR]

The article focuses on the cost implications of fluctuating demand. It states that in a market characterized by fluctuations in demand, businesses need to minimize total cost functions associated with probability distribution production and produce a probability distribution of outputs. It suggests that using a static total cost function would thus be different from a cost function that resulted from production of a stable level of output and would also overstate the total costs of suboptimum and superoptimum outputs. It claims that pricing policies and profit maximizing decisions should reflect the cost effects from output variability.

Markowitz's [2] portfolio selection model was originally concerned with financial investments, but the model's implications for capital budgeting are now well recognized. Markowitz's basic idea is that the optimal portfolio for an investor is not simply any collection of good securities, but a balanced whole, providing the investor with the best combination of “return” and “risk.” Return and risk are to be measured by the expected value and variance of the probability distribution of portfolio return. Although financial writers have generally accepted Markowitz's measure of return, they have not been completely satisfied with his suggested measure of risk [1]. In fact, Markowitz himself had reservations about choosing variance as a measure of risk.1 Besides variance, he considered five other alternative measures of risk:(1) The expected value of loss;(2) The probability of loss;(3) The expected absolute deviation;(4) The maximum expected loss; and(5) The semivariance. [ABSTRACT FROM PUBLISHER]

The article focuses on using investment portfolios in risk management. The effect on the variance of an investment fund through adding dependent investments is considered. A trend in risk analysis focuses on the mean and variance of the probability distribution of returns. The author notes that a person looking to avoid risk should search for negatively correlated investments. Another method of reducing risk is to find independent investments, limited by information and transaction costs, and bring them into an investment portfolio. Risk can also be reduced by adding investments with less positive correlations.

The article reports on the effect of information on the investment behavior of a decision maker. The information on the probability distribution of returns on a risky asset like a portfolio or mutual fund can bring a decision maker to a risk aversion decision. The utility functions with convex segments imply similar investment behaviors. The opportunity to access information results in the acceptance of a single-period actuarially unfair gamble with respect to future wealth levels.

Ex ante predictions of the riskiness of returns on common stocks — or, in more general terms, predictions of the probability distribution of returns — can be based on fundamental (accounting) data for the firm and also on the previous history of stock prices. In this article, we attempt to combine both sources of information to provide efficient predictions of the probability distribution of returns. We predict two parameters of the distribution of returns for each security in each year: the response to the overall market return (β), and the variance of the part of risk, specific to the security, that is uncorrelated with the market return. A cross section of time series data on returns and accounting variables, taken primarily from the Compustat tape, is used. Several recent developments in statistical methodology are applied. [ABSTRACT FROM AUTHOR]

This article provides the specifications of the market's composite parameters and probability assessments when investors differ in their investment decisions as well as in their risk-aversion, when there is no riskless asset available for holding or borrowing, and when short sales are limited and when most investors are unaware about the prospects of most available securities. Throughout each section of the article formulae for the Pareto-optimal distribution of the holding of any risk asset among investors will be analysed in terms of each investor's parameters and assessments relative to those of the market.