Knowledge that Transforms

A framework is presented for investigating the mean-variance efficiency of an unobservable portfolio based on its correlation with a proxy portfolio. A sensitivity analysis derives the highest correlation between the proxy and a portfolio that reverses the inference of a test of SHarpe-Lintner tangency. For example, the maximum correlation between the value-weighted NYSE-AMEX portfolio and a portfolio inferred tangent ranges from 0.76 to 0.48. We also test whether the correlation between the proxy and the tangent portfolio exceeds a given level. This hypothesis is often rejected for the NYSE-AMEX proxy at a correlation of 0.7.

Upper bounds on the expected payoff of call and put options are derived. These bounds depend only on the mean and variance of the terminal stock price and not on its entire distribution, so they are termed semi-parametric. A corollary of this result is a set of upper bounds for option prices obtained by the risk-neutral valuation approach of Cox and Ross. As an example, these bounds are shown to obtain across both lognormal diffusions and mixed diffusion-jump processes for any given data set. We present an illustrative example that suggests these bounds may be of considerable practical value.

We measure the differential effects on shareholder wealth and tendering behavior of any-or-all, two-tier and partial tender offers and find no evidence that shareholders are disadvantaged by front-end-loaded corporate takeovers. Shareholders fare as well when the terms of an offer for control are negotiated with target-firm management as when they are not. Most cash tender offers executed between 1981 and 1984 were negotiated, and almost all two-tier offers were negotiated.

This paper evaluates the power of multivariate tests of the Capital Asset Pricing Model. The results indicate that when employing an unspecified alternative hypothesis, the ability of the tests to distinguish between the CAPM and other pricing models is poor. An upper bound is derived for the distance the alternative distribution of the test statistic can be from the null distribution when the deviations from the CAPM are due to missing factors. This upper bound explains the low power of the tests.