While there is growing evidence that stock prices do not follow pure random walks, the degree of existence of temporary components in stock prices is not well known. Modeling stock prices as the sum of a random walk and a general stationary (predictable) component, the paper proposes an estimable lower bound on the proportion of total stock return variance caused by the predictable component. Contrary to the absolute value of the first-order auto-correlation coefficient estimates of Fama and French (1988a), this lower bound reasonably estimates the true variance proportion in finite samples also when the temporary component does not follow a first-order autoregressive process. The estimated mean values of the lower bound reach a maximum of 10 percent for the equal-weighted market portfolio of NYSE stocks over the post-war period 1947–1986, while the maximum is 25 percent for the pre-war period 1926–1946. The value-weighted market portfolio exhibits generally smaller variance proportion estimates. The pure random walk hypothesis is also reexamined using a standard variance ratio statistic extended to multiple return horizons.

According to the Diamond-Verrecchia hypothesis, if increases in short interest are correlated with information that is not yet public, they should precipitate a price adjustment. Stocks with unexpected increases in short interest are found to generate statistically significant, but small, negative abnormal returns for a short period around the announcement date. When the sample is divided into stocks with and without tradable options, nonoptioned stocks closely mimic these results but the optioned stocks do not. In a cross-sectional analysis of individual firms, the short-term negative abnormal returns are found to be 1) more negative, the higher the degree of unexpected short interest and, 2) less negative if the firm has tradable options.

This paper provides a direct test of the hypothesis that large January returns can be attributed to omitted risk factors. Data from 1926–1991 show that the January return in the smallest decile of NYSE firms dominates the January returns for all other deciles by the first-order stochastic dominance. Similarly, January returns in all deciles (with the exception of ninth and tenth deciles) dominate non-January returns by first-, second-, or third-order stochastic dominance. The presence of stochastic dominance by January returns suggests that the omitted risk factors are not likely to explain the January effect.

Myers and Majluf (1984) showed that in a world of asymmetric information, managers of overvalued firms issue equity, while managers of undervalued firms use cash, if available. This paper shows that, in a multiperiod world, managers of undervalued firms may find it optimal to issue stock, even though cash is available. Consequently, the market does not interpret all announcements of equity issues as signals of firm overvaluation. The paper also generates predictions regarding the effect of information asymmetry and investment opportunities on i) dividend policy, and ii) the cross-sectional distribution of market reactions when an equity issue is announced.

This paper compares different approaches to developing arbitrage-free models of the term structure. It presents a numerical procedure that can be used to construct a wide range of one-factor models of the short rate that are both Markov and consistent with the initial term structure of interest rates.

This paper investigates the warrant pricing abilities of dilution-adjusted versions of the Black-Scholes and Jump-Diffusion option pricing models. Because of the typically long lives of warrants, their pricing is hypothesized to benefit from use of the Jump-Diffusion model, which relaxes the Black-Scholes restriction against stock price jumps. Empirical results indicate that while the Black-Scholes model almost uniformly provides more efficient estimates, the Jump-Diffusion model generally provides less biased estimates of market value. Particularly for the valuation of out-of-the-money warrants and warrants on stocks with a history of large and/or frequent jumps, the Jump-Diffusion model may be preferred.

This study analyzes the effect of second-hand information on the behavior of security prices and volume using analysts' recommendations published in the monthly “Dartboard” column of the Wall Street Journal. For the two days following the publication of the recommendations, average positive abnormal returns of 4 percent—nearly twice the level of abnormal returns documented in previous research on analyst recommendations—and average volume double normal volume levels on the two days following publication of the recommendations are documented. The positive abnormal return on announcement is partially reversed within 25 trading days. The authors conclude that the positive abnormal return on announcement of the recommendations is a result of naive buying pressure as well as the information content of the analysts' recommendations.

The purpose of this paper is to explain cross-sectional variations in trade credit terms across firms and industries. This study shows that there is a separating equilibrium in which the size of the cash discount conveys information about product quality. The driving forces of this equilibrium outcome are the risk-sharing motives of the producer and buyer as well as asymmetric information about product quality. The empirical implications of the model are derived and discussed in relation to industry practices.

A simple moment-ordering condition is shown to be necessary for stochastic dominance. Closely related results on generalizations of the geometric and harmonic means are also provided. An ordering of the moment-generating functions is shown to be necessary and sufficient for stochastic dominance. The results have a straightforward and useful interpretation in terms of constant relative and absolute risk aversion utility functions. These results are used to provide necessary and sufficient conditions for optimality of distributions on an important class of utility functions.