This paper studies the nature of capital adjustment at the plant-level. We use an indirect inference procedure to estimate the structural parameters of a rich specification of capital adjustment costs. In effect, the parameters are optimally chosen to reproduce

We investigate the stock returns subsequent to quarterly earnings surprises, where the benchmark for an earnings surprise is the consensus analyst forecast. By defining the surprise relative to an analyst forecast rather than a time‐series model of expected earnings, we document returns subsequent to earnings announcements that are much larger, persist for much longer, and are more heavily concentrated in the long portion of the hedge portfolio than shown in previous studies. We show that our results hold after controlling for risk and previously documented anomalies, and are positive for every quarter between 1988 and 2000. Finally, we explore the financial results and information environment of firms with extreme earnings surprises and find that they tend to be “neglected” stocks with relatively high book‐to‐market ratios, low analyst coverage, and high analyst forecast dispersion. In the three subsequent years, firms with extreme positive earnings surprises tend to have persistent earnings surprises in the same direction, strong growth in cash flows and earnings, and large increases in analyst coverage, relative to firms with extreme negative earnings surprises. We also show that the returns to the earnings surprise strategy are highest in the quartile of firms where transaction costs are highest and institutional investor interest is lowest, consistent with the idea that market inefficiencies are more prevalent when frictions make it difficult for large, sophisticated investors to exploit the inefficiencies.

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.