An Adjustment Procedure for Predicting Betas When Thin Trading is Present: Canadian Evidence
针对多伦多证券交易所股票，将真实贝塔生成过程与交易稀疏效应纳入调整模型，发现排序偏差解释86%的预测误差，交易稀疏效应解释14%，并证明正确考虑截面相关和排序偏差的回归倾向模型可完全消除预测误差。
We have incorporated effects of the process that generates true betas for TSE stocks, as well as thin trading effects, into the beta adjustment model. We note the Blume and Dimson and Marsh beta adjustment techniques aim at eliminating beta forecast error through regression tendency bias. Effects of other sources of forecast error have been ignored. We show the process generating security betas affects both cross‐sectional correlation coefficient and order bias, while thin trading affects only cross‐sectional correlation coefficient. We demonstrate that when OLS beta estimates are used to forecast their future risk levels, order bias accounts for 86% of forecast error, while thin trading effects account for 14% of forecast error. A beta regression tendency model which properly accounts for effects of cross‐sectional correlation (which is a function of thin trading) and order bias completely abates forecast error. Our results have implications for the use of correlation coefficient to measure stability of betas across time, for beta adjustment models proposed in the literature, and for event study methodologies that rely on prediction errors.