An adjustment procedure for predicting systematic risk

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 investigation 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.