Knowledge that Transforms

Gary C. Sanger, John J. McConnell, Stock Exchange Listings, Firm Value, and Security Market Efficiency: The Impact of NASDAQ, The Journal of Financial and Quantitative Analysis, Vol. 21, No. 1 (Mar., 1986), pp. 1-25

Recent empirical studies have found ex post common stock returns to be consistently positively skewed. The frequency of positive skewness in this study is found to be relatively stable over varying time periods from 1961 to 1980. However, the skewness of individual stocks and portfolios of stocks does not persist across different time periods. Positively-skewed equity portfolios in one period are not likely to be positively skewed in the next time period. Past positively-skewed returns do not predict future positively-skewed returns.

New cross-sectional tests of the Mixture of Distributions Hypothesis are presented. The tests assume that the distribution of the mixing variable (often interpreted as the daily rate of flow of information) is not identical for all securities. Cross-security differences in the mixing distribution cause cross-security differences in the joint distribution of returns and volume. The Hypothesis provides predictions about how these differences appear in the joint distribution. The predictions are confirmed in tests based on cross-security correlations among summary statistics that characterize shape and covariational attributes of the joint distribution of returns and volume. The results are consistent with the Mixture of Distributions Hypothesis.

This paper examines the influence of risk aversion on the pricing policies of a market maker for securities. It is shown that a market maker's bid-ask spread can be decomposed into a portion for the known limit orders, a risk-neutral adjustment for expected market orders, and a risk adjustment for market order and inventory value uncertainty. It is demonstrated that a risk-averse market maker may set a smaller spread than a risk-neutral specialist. Finally, this paper demonstrates the pervasive role of inventory in affecting both the placement and size of the spread.

When portfolio optimization is implemented using the historical characteristics of security returns, estimation error can degrade the desirable properties of the investment portfolio that is selected. Given the problem of estimation risk, it is natural to formulate rules of portfolio selection within a Bayesian framework. In this framework, portfolio selection is based on maximization of expected utility conditioned on the predictive distribution of security returns. Most researchers have addressed the problem of estimation risk by asserting a noninformative diffuse prior that reduces the detrimental effect of estimation risk, but does not directly reduce estimation error. Portfolio performance can be improved by specifying an informative prior that reduces estimation error. An informative prior that all securities have identical expected returns, variances, and pairwise correlation coefficients is asserted. This informative prior reduces estimation error by drawing the posterior estimates of each security's expected return, variance, and pairwise correlation coefficients toward the average return, average variance, and average correlation coefficient, respectively, of all the securities in the population. The amount that each of these parameters is drawn toward its grand mean depends upon the degree to which the sample is consistent with the informative prior. This empirical Bayes method is shown to select portfolios whose performance is superior to that achieved, given the assumption of a noninformative prior or by using classical sample estimates.

The word revolution is entirely appropriate for describing the changes in financial institutions and instruments that have occurred in the past twenty years. The major impulses to successful financial innovations have come from regulations and taxes. The outlook for the future is for a slowing down of the rate of financial innovation, but much growth and improvement are still in prospect.

Wi Saeng Kim, Eric H. Sorensen, Evidence on the Impact of the Agency Costs of Debt on Corporate Debt Policy, The Journal of Financial and Quantitative Analysis, Vol. 21, No. 2 (Jun., 1986), pp. 131-144

In portfolio analysis, uncertainty about parameter values leads to suboptimal portfolio choices. The resulting loss in the investor's utility is a function of the particular estimator chosen for expected returns. So, this is a problem of simultaneous estimation of normal means under a well-specified loss function. In this situation, as Stein has shown, the classical sample mean is inadmissible. This paper presents a simple empirical Bayes estimator that should outperform the sample mean in the context of a portfolio. Simulation analysis shows that these Bayes-Stein estimators provide significant gains in portfolio selection problems.