Nonparametric estimators of treatment effects are often applied in settings where clustering may be important. We provide a general methodology for consistently estimating the variance of a large class of nonparametric estimators, including the simple matching estimator, in the presence of clustering. Software for implementing our variance estimator is available in Stata.

We show that characteristics of stock issuers can be used to forecast important common factors in stocks' returns such as those associated with book‐to‐market, size, and industry. Specifically, we use differences between the attributes of stock issuers and repurchasers to forecast characteristic‐related factor returns. For example, we show that large firms underperform after years when issuing firms are large relative to repurchasing firms. While our strongest results are for portfolios based on book‐to‐market (i.e., HML ), size (i.e., SMB ), and industry, our approach is also useful for forecasting factor returns associated with distress, payout policy, and profitability.