This paper studies the association between the market's expectations of Saddam Hussein's fall from power, as reflected in “Saddam contract” prices, and stock prices, oil prices and exchange rates. During the war, a rise in the probability of Saddam's fall, which also indicated a speedy end to the war, was positively and significantly associated with stock prices, strengthened the dollar against the Euro, and lowered oil prices. Before the war, a rise in the probability of Saddam's fall, which may also have indicated the probability of a costly war breaking out, lowered stock prices, which adjusted gradually to this information.

ABSTRACT We provide an economic basis for permitting freezeouts of nontendering shareholders following successful takeovers. We describe a specific freezeout mechanism based on easily verifiable information that induces desirable efficiency and welfare properties in models of both corporations with widely dispersed shareholdings and corporations with large pivotal shareholders. The mechanism dominates previous proposals along some important dimensions. We also examine takeover premia that arise in the presence of competition among raiders. Our mechanism is closely related to the practice of takeover law in the United States; thus, our analysis may be thought of as analyzing the economic foundations of current regulations.

Standard predictive regressions produce biased coefficient estimates in small samples when the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. See Stambaugh (1999) for the single regressor model. This paper proposes a direct and convenient method to obtain reduced-bias estimators for single and multiple regressor models by employing an augmented regression, adding a proxy for the errors in the autoregressive model. We derive bias expressions for both the ordinary least-squares and our reduced-bias estimated coefficients. For the standard errors of the estimated predictive coefficients, we develop a heuristic estimator that performs well in simulations, for both the single predictor model and an important specification of the multiple predictor model. The effectiveness of our method is demonstrated by simulations and empirical estimates of common predictive models in finance. Our empirical results show that some of the predictive variables that were significant under ordinary least squares become insignificant under our estimation procedure.