This paper introduces an event-study method that incorporates the possibility of a random event date. Consistent with empirical evidence, we assume an event may affect not only the conditional mean of a security's return, but also its conditional variance. We compare the statistical power and efficiency of our maximum-likelihood method with the standard application of traditional event-study methods to multiday security returns. Assuming a two-day event period, our empirical results provide evidence that the multiday approach is robust. We use our maximum-likelihood method to investigate the valuation effects of stock splits and stock dividends.

Stocks and other financial assets are traded at prices that lie on a fixed grid determined by the minimum tick size. Observed prices and quoted spreads do not correspond to the equilibrium prices and true spreads that would exist in a market with no minimum tick size. Using Monte Carlo Markov Chain methods, this paper estimates the equilibrium prices and true spreads. For large stocks, most of the quoted spread is attributable to the rounding of prices and the adverse selection component is small. The true spread and the adverse selection component are greater for mid‐sized stocks.