Depending on their location, outliers in returns can substantially bias ordinary least-squares estimates of beta. We introduce a new beta estimate that is resistant to outliers that cause the most bias in OLS estimates but produces estimates similar to OLS for outlier-free data. The outlier-resistant beta is an intuitively appealing weighted least-squares estimate with data-dependent weights. We show that the resistant beta is a better predictor of future risk and return characteristics than is the OLS beta in the presence of outliers and is, therefore, a valuable complement to the OLS beta. Our analysis reveals that small companies' betas are most susceptible to outliers.