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.

一、中文部分
李冰（2007）。我國投資基金績效評級方法及實證研究，經濟研究導刊，16。頁85-89。
李雙杰與秦軼翀（2009）。開放式基金績效實證分析—StoNED與DEA方法的比較，經濟論壇，08。頁12-15。
谷偉、余穎與周潔（2007）。中國證券投資基金績效持續效應研究，管理工程學報，03。頁163-167。
尚虹（2008）。證券投資基金業績評價方法比較研究，商業時代，15。頁75-76。
胡珍全與劉凱（2006）。LOF基金的績效評價—傳統方法與DEA方法的比較，時代金融，09。頁42-44。
康衛星、趙承黎與戚莎莎（2007）。我國開放式基金業績評價研究—基于交叉效率單元與DEA方法的實證研究，經濟師，10。頁105-107。
董鐵牛、楊乃定、姜繼嬌與王良（2007）。基於極效率DEA的開放式基金業績評價，管理評論，11。頁13-19。
趙承黎、康衛星與袁鋒軍（2001）。我國開放式基金業績評價研究—基于因子分析和DEA模型的實證考察，新西部(下半月)，07。頁14-16+23。

二、英文部份
Basso, A. and Funari, S. (2001). A data envelopment analysis approach to measure the mutual fund performance. European Journal of Operational Research 135, 17-32.
Basso, A. and Funari, S. (2002). A generalized performance attribution technique for mutual funds. Working Paper.
Banker R.D., Charnes A., and Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30(9), 1078-1092.
Charnes A., Cooper W.W., and Rhodes E. (1978). Measuring efficiency of decision-making units. European Journal of Operational Research 2(6), 429-444.
Chen Z. and Lin R. (2006). Mutual fund performance evaluation using data envelopment analysis with new risk measures. OR Spectrum 28, 375-398.
Ferson, W.E. and Schandt R.W. (1996). Measuring fund strategy and performance in changing economic conditions. Joutnal of Finance 51(2), 425-461.
Golec, J. H. (1996). The effects of mutual fund managers, characteristics on their portfolio performance, risk and fees. Financial Services Review 5(2), 133-148.
McMullen P.R. and Strong R.A. (1998). Selection of mutual fund using data envelopment analysis. Journal of Business and Economics Studies 4, 1-12.
Morey, M.R. and Morey R.C. (1999). Mutual fund performance appraisals: a multi-horizon perspective with endogenous benchmarking. Omega 27, 241-258.
Murthi, B.P.S., Choi, Y.K., and Desai, P. (1997). Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach. European Journal of Operational Research 98, 408-418.