Prior research reports seemingly conflicting evidence and interpretations concerning the relation between dispersion in analysts' earnings forecasts and stock returns. Diether et al. (2002) and Johnson (2004) find a negative relation between levels of dispersion in analysts' forecasts and future stock returns. Yet, changes in forecast dispersion are negatively associated with contemporaneous stock returns (L'Her and Suret 1996). We demonstrate that levels and changes in dispersion reflect different theoretical constructs. Changes in dispersion primarily reflect changes in information asymmetry whereas levels of dispersion primarily reflect levels of uncertainty. Further, the uncertainty component of dispersion levels reflects idiosyncratic risk that is negatively associated with future stock returns. These findings provide support for Johnson's (2004) explanation that dispersion levels reflect idiosyncratic uncertainty that increases the option value of the firm and generally refute Diether et al.'s (2002) explanation that dispersion levels reflect information asymmetry. In addition, we reconcile L'Her and Suret's (1996) findings with the findings of Johnson (2004). We find that the negative association between changes in dispersion and contemporaneous stock returns is not due to increased uncertainty but rather increased information asymmetry.

A new methodology is proposed to estimate theoretical prices of financial contingent claims whose values are dependent on some other underlying financial assets. In the literature, the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. This paper proposes a simulation-based method. When it is used in connection with ML, it can improve the finite-sample performance of the ML estimator while maintaining its good asymptotic properties. The method is implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond and bond option pricing model. It is especially favored when the bias in ML is large due to strong persistence in the data or strong nonlinearity in pricing functions. Monte Carlo studies show that the proposed procedures achieve bias reductions over ML estimation in pricing contingent claims when ML is biased. The bias reductions are sometimes accompanied by reductions in variance. Empirical applications to U.S. Treasury bills highlight the differences between the bond prices implied by the simulation-based approach and those delivered by ML. Some consequences for the statistical testing of contingent-claim pricing models are discussed.

We hypothesize that managers use stock splits to attract more uninformed trading so that market makers can provide liquidity services at lower costs, thereby increasing investors’ trading propensity and improving liquidity. We examine a large sample of stock splits and find that, consistent with our hypothesis, the incidence of no trading decreases and liquidity risk is lower following splits, implying a decline in latent trading costs and a reduced cost of equity capital. Further, split announcement returns are correlated with the improvements in both liquidity levels and liquidity risk. Our analysis suggests nontrivial economic benefits from liquidity improvements, with less liquid firms benefiting more from stock splits.