An adjustment procedure for predicting systematic risk
提出一种多重根线性模型来调整贝塔系数的回归倾向，实证表明平方根变换后贝塔分布趋于正态，该调整程序比现有方法误差更小。
Abstract This paper looks at the currently available beta adjustment techniques and suggests a multiple root‐linear model to adjust for the regression tendency of betas. Our empirical investigate on indicates that cross‐sectional betas are not normally distributed, but their distribution tends to normal after a square‐root transformation. The evidence from the Box‐Cox regression model and the multivariate normality observed among betas after the transformation, make the functional form of our model correct. Also, we observe that the disturbance term of the multiple root‐linear model is well behaved. These findings make the ordinary least squares estimates unbiased and efficient. Finally, the mean square and extreme errors are found to be lower when our adjustment procedure is used vis‐à‐vis the existing procedures.