An Empirical Study On The Performance Evaluation Of Passive Stock Index Funds In China

Chinese passive index funds have been growing rapidly since 2019.Given its increasing number and varieties,it is important to screen them according to their performances.To this end,this thesis conducts an empirical study on the performance evaluation of Chinese passive index funds.The thesis can help not only investors better understand the nature of passive index funds with performance-screening approaches,but also fund managers enhance their management levels and in-depth understanding the fund performance evaluation.Inspired by the existing literature about fund performance evaluation,the thesis selects all Chinese passive stock index funds which are listed for,at least,three years.Firstly,this thesis evaluates the performance of the sample funds from four dimensions:income,risk,risk adjusted income,and the personal ability of fund managers,including five measures in total.Then,the traditional PCA is improved to addresses its weakness in practical application.More specifically,the improvement includes(i)equalization instead of standardization to deal with the data dimensionless;(ii)linearizing the data;(iii)performing Box-Cox transformation on the data to improve the skewness of the original data and make it closer to a normal distribution;(iv)using the correlation coefficient method to differently weight the indicators according to the level of importance;(v)adopting the entropy weight method for the principal components.Among them,the improvement methods of points 1,2 and 5 have been proposed by other scholars and have been widely used,and points 3 and 4 are the innovations of this paper.Finally,the comprehensive performance evaluation of sample funds is carried out based on the improved PCA method.The results show that,firstly in terms of the fund performance over the recent three years,the overall excess return of Chinese passive stock index funds and the tracking error of the benchmark index are high.In addition,there is a reverse relationship between the stock selecting and timing ability of fund managers,and most fund managers have poor timing ability.Secondly,the improved PCA shows good applicability when applied to financial data with strong skewness and nonlinearity due to the Box-Cox transformation and linearization of the data.The calculated comprehensive score of the sample funds is more reasonable and more credible.