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结合中国股市弱式有效特征,以成交量与坏消息和好消息之比作为信息变量,构建新的条件业绩评价模型,全面比较非条件模型和条件模型在基金业绩评价时的差异。本文发现,信息变量对基金的日超额收益率存在显著的解释能力,条件模型有助于控制非条件模型的偏差,改善基金业绩评价,特别是GARCH条件模型;条件模型提高了基金的业绩基准,无论是用阿尔法还是总业绩指标测量业绩,GARCH条件模型的结果均表明样本基金的平均业绩为负,平均而言,基金的积极管理没有体现出其应有的价值。
Combining with the weak characteristic of Chinese stock market, a new conditional performance evaluation model is constructed based on the ratio of volume to bad news and good news as information variables. The differences between non-conditional and conditional models in the performance evaluation of the fund are compared in an all-round way. This paper finds that the information variable has significant explanatory power on the daily excess return rate of the fund. The conditional model helps to control the deviation of the non-conditional model and improve the performance evaluation of the fund, especially the GARCH conditional model. The conditional model improves the fund’s performance benchmark, Regardless of whether the performance is measured by alpha or total performance indicators, the results of the GARCH conditional model show that the average performance of the sample funds is negative. On the average, the active management of the funds does not reflect its due value.