Robin J. Brenner, Kenneth F. Kroner, Arbitrage, Cointegration, and Testing the Unbiasedness Hypothesis in Financial Markets, The Journal of Financial and Quantitative Analysis, Vol. 30, No. 1 (Mar., 1995), pp. 23-42

Charles J. Corrado, Terry L. Zivney, The Specification and Power of the Sign Test in Event Study Hypothesis Tests Using Daily Stock Returns, The Journal of Financial and Quantitative Analysis, Vol. 27, No. 3 (Sep., 1992), pp. 465-478

In this paper, we derive simple, directly computable conditions for minimum-variance portfolios to have all positive weights. We show that either there is no minimum-variance portfolio with all positive weights or there is a single segment of the minimum-variance frontier for which all portfolios have positive weights. Then, we examine the likelihood of observing positively weighted minimum-variance portfolios. Analytical and computational results suggest that: i) even if the mean vector and covariance matrix are compatible with a given positively weighted portfolio being mean-variance efficient, the proportion of the minimum-variance frontier containing positively weighted portfolios is small and decreases as the number of assets in the universe increases, and ii) small perturbations in the means will likely lead to no positively weighted minimum-variance portfolios.

We solve for the optimal dynamic trading strategy of an investor who faces a leverage constraint, i.e., a limitation on his ability to borrow for the purpose of investing in a risky asset. We assume that the investor has constant relative risk aversion, and that the value of the risky asset follows a geometric Brownian motion. In the absence of the leverage constraint, the optimal strategy involves investing a fixed proportion of wealth in the risky asset. We prove that, in the presence of the leverage constraint, the optimal investment also involves investing a fixed proportion of wealth in the risky asset when the leverage constraint is not binding. However, the two proportions are different, reflecting the extent to which the investor alters his strategy even when the leverage constraint is not binding because of the possibility that the leverage constraint will become binding in the future.

Despite nonstationarities in the factor betas and factor prices of the Chen, Roll, Ross (1986) multifactor model, investors are rewarded for bearing risks associated with the change in expected inflation and industrial production in non-January months; however, variations in these factors have opposite influences on stock prices. These findings may partially explain why several recent studies fail to detect a significant non-January risk premium in the stock market, but this evidence is only suggestive since theoretical and statistical difficulties prevent precise interpretations of specific pricing relations in the Chen, Roll, Ross model.

February and August peaks in the growth rates of the seasonally unadjusted Industrial Production Index follow the stock market peaks documented by Rozeff and Kinney (1976) by one month. Coefficients on one-month lead growth rates in industrial production for small firms are positive and significant in time-series regressions even in the presence of the market factor. Moreover, whereas returns on large firms' stocks unidirectionally Granger cause (i.e., predict) future growth rates in industrial production at least six months in advance, returns on small firms' stocks reflect one-month lead as well as past growth rates in industrial production. For these reasons, we argue that seasonal real growth provides a partial explanation for the January stock seasonal among small firms.

Paul A. Spindt, J. Ronald Hoffmeister, The Micromechanics of the Federal Funds Market: Implications for Day-of-the-Week Effects in Funds Rate Variability, The Journal of Financial and Quantitative Analysis, Vol. 23, No. 4 (Dec., 1988), pp. 401-416

Victor L. Bernard, Thomas J. Frecka, Commodity Contracts and Common Stocks as Hedges against Relative Consumer Price Risk, The Journal of Financial and Quantitative Analysis, Vol. 22, No. 2 (Jun., 1987), pp. 169-188

This paper examines the effect of risk differences on the oft-documented negative rela? tionship between stock returns and inflation. We find risk-related patterns of coefficients on our estimates of the level and change in expected inflation and on unexpected inflation. These patterns are consistent with the hypothesis developed in Fama [2] and in Geske and Roll [7] that future real output growth simultaneously helps to determine current stock returns and various measures of inflation.

Financial analysts have been intrigued by bond ratings since John Moody first started publishing them in 1909. Bond ratings are assigned by three agencies (Moody's, Standard and Poor's (S&P), and Fitch); these ratings are widely publicized and are, therefore, critically important. A bond's rating affects investors' purchase decisions and, consequently, the issuing firm's cost of debt and, indirectly, its cost of equity.