Knowledge that Transforms

Several people have attempted to evaluate the performance of mutual funds. Treynor [17] and Sharpe [15] have developed performance measures which make it possible to establish relative rankings for such funds. Treynor and Mazuy [18] have devised a statistical test for determining whether mutual funds successfully anticipate major fluctuations in the stock market. Jensen [7] has provided an absolute measure of performance which can be used to determine whether mutual funds earn higher or lower returns than those expected for the level of risk associated with their portfolios. McDonald [11] has employed the measures of performance developed by Sharpe, Treynor, and Jensen to evaluate the objectives, risk, and return of mutual funds in the period 1960–1969. Although these studies have examined mutual fund performance, none has employed an analytical framework for dealing explicitly with the nonstationarity which is likely to exist in the risk-return relationships for such funds [13].

In a world characterized by perfect and complete capital markets, the success (or failure) of a merger is judged by the merger's impact on stockholder wealth. With completeness, the merger's impact on the probability distribution generating stockholder returns is unimportant. The perfect market assumption guarantees that the stockholder not satisfied with the consolidated firm's return distribution can frictionlessly sell his shares and reorder his portfolio; hence his only concern is the merger's impact on wealth. However, if we acknowledge the existence of commissions, taxes, and other frictions, or if markets are not complete, the merger's impact on the stockholder return distribution becomes relevant. In this study we will analyze 149 mergers involving large N.Y.S.E. firms. We will examine four different hypotheses related to the impact of merger on attributes of the stockholder return distribution. We focus our analysis on risk-related attributes including beta, total variance, residual variance, and several other risk-related attributes. In a companion paper, merger's impact on wealth is calculated for the same sample but will not be reported here.

There is a false, but widely-held belief about orthogonal (“zero-beta”) portfolios: for a given market index, all zero-beta portfolios have the same expected return and the minimal-variance, zero-beta portfolio is unique. This is true only when the index is mean/variance efficient. Every nonefficient index possesses zero-beta portfolios at all levels of expected return. For a given index, minimal-variance zero-beta portfolios corresponding to different expected returns lie along an “orthogonal frontier” in the mean/variance space. The frontier has some unusual properties which turn out to be relevant for empirical work on asset pricing. It is functionally related to deviations about the “securities market line.”

Gabriel A. Hawawini, Intertemporal Cross-Dependence in Securities Daily Returns and the Short-Run Intervaling Effect on Systematic Risk, The Journal of Financial and Quantitative Analysis, Vol. 15, No. 1 (Mar., 1980), pp. 139-149

David F. Larcker, Lawrence A. Gordon, George E. Pinches, Testing for Market Efficiency: A Comparison of the Cumulative Average Residual Methodology and Intervention Analysis, The Journal of Financial and Quantitative Analysis, Vol. 15, No. 2 (Jun., 1980), pp. 267-287

In the Arrow-Debreu approach to uncertainty it has been recognized that an inadequate number of markets in contingent claims would be a source of inefficiency. Several researchers have studied the allocative inefficiencies resulting from incomplete markets, i.e., when the number of independent securities is less than the number of states and therefore the state-space cannot be “spanned” by those securities. In a situation where the number of primitive assets is inadequate to span the state-space, the new spanning opportunities opened up by derived assets written on the primitives is an interesting question.

Beta coefficients were initially defined by Sharpe [11] as the slope term in the simple linear regression function where the rate of return on a market index was the independent variable and a security's rate of return was the dependent variable. As indicated by Brenner and Smidt [4], accurate estimation of beta coefficients is important for at least two reasons. First, they are important for understanding risk-return relationships in capital market theory. Second, they are important for use in making investment decisions. Some confusion has appeared, however, in recent research regarding both the optimal estimation interval and the intertemporal stability of beta coefficients. The purpose of this paper is to examine this confusion and present new evidence on the estimation and stability of beta.

Kenneth J. Arrow, Real and Nominal Magnitudes in Economics, The Journal of Financial and Quantitative Analysis, Vol. 15, No. 4, Proceedings of 15th Annual Conference of the Western Finance Association, June 19-21, 1980, San Diego, California (Nov., 1980), pp. 773-783

A currency is not risky because devaluation is highly likely. If the devaluation were certain, there would be no risk at all. A weak currency can be less risky than a strong currency. A strong currency does not become risky because it has been used to denominate a firm's debt.

Edward I. Altman, Commercial Bank Lending: Process, Credit Scoring, and Costs of Errors in Lending, The Journal of Financial and Quantitative Analysis, Vol. 15, No. 4, Proceedings of 15th Annual Conference of the Western Finance Association, June 19-21, 1980, San Diego, California (Nov., 1980), pp. 813-832