中國共同基金績效之分析－資料包絡分析法之應用

Abstract

本研究旨在檢視中國市場共同基金的績效，我們運用資料包絡分析法衡量中國共同基金績效，並將資料包絡分析法結果與傳統績效指標做一比較。在資料包絡分析法部分，本研究以總費用率及風險作為投入變數，基金報酬率與傳統基金績效指標分別作為產出變數，進行資料包絡分析法。此外，本研究亦以基金經理人經驗作為投入變數，檢視額外考慮基金經理人經驗年數，對基金績效衡量是否有所助益。 實證結果顯示，中國共同基金在傳統指標的排名具有高度的一致性，而不同資料包絡分析法所得之指標的排名，亦具有一致性。在分別使用傳統指標作為產出變數的資料包絡分析模式中，以加入Sharpe指標的資料包絡分析模式平均效率分數最高，而加入Jensen指標的資料包絡分析模式，其效率分數最低。若考慮風險值作為投入變數時，共同基金的平均效率分數比未加入風險值的平均效率分數為高。然而，將基金經理人經驗作為額外投入變數的模式，對於共同基金的績效衡量沒有顯著的效果。

This study examines the performance of mutual funds in China. We use data envelopment analysis (DEA) to evaluate the performance of mutual funds. Among the inputs considered by the DEA model, this paper uses different risk measures of the portfolio, subscription cost, and redemption fees. The set of outputs taken into consideration comprises the portfolio return and the traditional performance indexes. In addition, this study considers the fund manager’s experience as an input variable of the DEA model. The empirical results reveal that the ranks of traditional index are consistent in the mutual fund in China, and ranks of DEA indexes are also consistent. The results also indicate that the generalized DEA performance indicator with Sharpe index has the highest average efficiency scores; in contrast, the performance indicator with Jensen index has the lowest scores. When we consider the value-at-risk (VaR) as an additional input variable, the average efficiency score of the model is higher. However, when the fund managers’ experience is regarded as an additional input variable, the efficiency score of the mdoel does not significantly different from that of other DEA models.