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This study tests the validity of using the CAPM beta as a risk control in cross-sectional accounting and finance research. We recognize that high-risk stocks should experience either very good or very bad returns more frequently compared to low-risk stocks, that is, high-risk stocks should cluster in the tails of the cross-sectional return distribution. Building on this intuition, we test the risk interpretation of the CAPM's beta by examining if high-beta stocks are more likely than low-beta stocks to experience either very high or very low returns. Our empirical results indicate that beta is a strong predictor of large positive and large negative returns, which confirms that beta is a valid empirical risk measure and that researchers should use beta as a risk control in empirical tests. Further, we show that because the relation between beta and returns is U-shaped, that is, high betas predict both very high and very low returns, linear cross-sectional regression models, for example, Fama–MacBeth regressions, will fail on average to reject the null hypothesis that beta does not capture risk. This result explains why previous studies find no significant cross-sectional relation between beta and returns
