Do external finance constraints affect the timing of large investment projects? Simulations of a model with fixed capital-stock adjustment costs establish the hypothesis that external finance constraints lower a firm's investment hazard: the probability of undertaking a large project today as a function of the time since the last project. Hazard model estimation that controls for productivity and adjustment costs supports this hypothesis. Small firms that distribute cash to shareholders have higher hazards than small firms that do not; very small firms have lower hazards than small firms; small stand-alone firms have significantly lower hazards than small segments of conglomerates.

We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts and the realized state. If the market expects forecasters to report their posterior expectations honestly, then forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded. The second theory posits that forecasters compete in a forecasting contest with pre-specified rules. In a winner-take-all contest, equilibrium forecasts are excessively differentiated.

Conventional tests of the predictability of stock returns could be invalid, that is reject the null too frequently, when the predictor variable is persistent and its innovations are highly correlated with returns. We develop a pretest to determine whether the conventional t-test leads to invalid inference and an efficient test of predictability that corrects this problem. Although the conventional t-test is invalid for the dividend–price and smoothed earnings–price ratios, our test finds evidence for predictability. We also find evidence for predictability with the short rate and the long-short yield spread, for which the conventional t-test leads to valid inference.

Streetlore since at least 1973 has touted the market return in January as a predictor of market returns for the remainder of the year. We systematically examine the predictive power of January returns over the period 1940–2003 and find that January returns have predictive power for market returns over the next 11 months of the year. The effect persists after controlling for macroeconomic/business cycle variables that have been shown to predict stock returns, the Presidential Cycle in returns, and investor sentiment, and it persists among both large and small capitalization stocks and among both value and growth stocks. In addition, we find that January has predictive power for two of the three premiums in the Fama–French [1993. Journal of Financial Economics 33, 3–50] three-factor model of asset pricing.

There are two variance components embedded in the returns constructed using high frequency asset prices: the time-varying variance of the unobservable efficient returns that would prevail in a frictionless economy and the variance of the equally unobservable microstructure noise. Using sample moments of high frequency return data recorded at different frequencies, we provide a simple and robust technique to identify both variance components. In the context of a volatility-timing trading strategy, we show that careful (optimal) separation of the two volatility components of the observed stock returns yields substantial utility gains.