Casual observers of the New York Stock Exchange are often dumbfounded by the frenetic behavior of its participants. If asked how such chaos generates accurate prices many academicians would reply that the ability to transact frequently is a virtue since it promotes prompt information dissemination and therefore market efficiency. However, in contrast to the NYSE where, during trading hours, trades may be consumated almost continuously, the Paris Stock Exchange trades each security only a handful of times a day. This continental contrast in market structure led us to reexamine the role of speed in markets. We have discovered that if sufficient uncertainty surrounds the dissemination of information, frequent transacting may be deleterious to market efficiency. In fact, in our paradigm we are able to show that our measure of market efficiency may be maximized when there is a unique, non-zero time interval between consecutive trades. The measure of efficiency used throughout the paper is minus the mean squared error. This measure was chosen to focus upon the information content of prices at times when they are posted (i.e., at times of tâtonnements). For this purpose we ignore costs of illiquidity and costs associated with obsolete information that would occur between tâtonnements. In this restricted sphere, maximazation of our efficiency measure is consistent with maximizing Social Pareto Optimality.

Auditing, Substantive test, Sampling, Accounting population

Experiments, Private value of information, Abnormal performance index, Residual analysis

Walter Perrin Heller, Ross M. Starr; Unemployment Equilibrium with Myopic Complete Information, The Review of Economic Studies, Volume 46, Issue 2, 1 April 1979

James M. Malcomson, Michael J. Prior; The Estimation of a Vintage Model of Production for UK Manufacturing, The Review of Economic Studies, Volume 46, Issue 4,

I. Price behavior, 451.—II. Profit, price behavior, and cost efficiency, 452.—III. Mobility and long-run equilibrium, 453.—Conclusion, 454.

Of the many conglomerate studies to date, some have dealt with the risk-return performance of conglomerates in the context of the capital asset pricing model [2, 7, 10, 14], others have considered the motives for the formation of conglomerates [4, 5, 6, 13], and still others have examined the operating characteristics of conglomerates [9, 12, 15]. Within the last group, Weston and Mansinghka [15, p. 928] argued that the primary motivation for conglomerate formation is defensive diversification, “…defined as diversification to avoid adverse effects on profitability from developments taking place in the firm's traditional product market areas.” Another motivation is provided by Levy and Sarnat [4] and Lewellen [5] who demonstrated that the only economic gain from a purely conglomerate merger may be the increased debt capacity resulting from the combination of entities having imperfectly correlated earnings streams.

In this study, alternative real and simulated market indexes are examined as proxies for the “common factor” required by the Sharpe portfolio selection model [13]. The ex post performance of efficient and well-diversified portfolios generated by the model based on the different indexes is compared. The results indicate no significant difference in performance between real and simulated indexes, although the degree of diversification is much lower for portfolios based on indexes which relate well to the universe of securities. It is also shown that portfolios which are selected according to the Sharpe model (regardless of the index) outperform strategies which call for investing in the market portfolio.

In a recent article Lloyd and Shick [3] examined a two-index model of bank stock returns with interest rates as the extra-market source of covariance. Based on their findings, the authors were optimistic that the inclusion of an interest rate index would prove to be worthwhile in market model regressions. The purpose of this comment is to question their conclusions by pointing out some specific deficiencies concerning their data, the statistical tests, and their interpretation of the results.