台灣共同基金績效評估-控制錯誤發現率方法之應用

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題名:	台灣共同基金績效評估-控制錯誤發現率方法之應用
作者:	陳冠綸;Chen, Kuan-Lun
貢獻者:	經濟學系
關鍵詞:	共同基金績效;多重檢定問題;多因子模型;錯誤發現率;主成分分析;Mutual Fund Performance;Multiple Testing Problem;Multifactor Model;False Discovery Rate;Principal Component Analysis
日期:	2023-08-14
上傳時間:	2023-10-04 16:01:55 (UTC+8)
出版者:	國立中央大學
摘要:	基金績效是衡量基金經理人能力的重要指標。當我們對基金績效進行檢定時，同時進行多個假設檢定會增加型一錯誤的機率，也就是錯誤地認為基金績效是具有顯著超額報酬的。因此，我們使用控制錯誤發現率 ( False Discovery Rate, FDR ) 來解決此問題。同時，我們使用 Giglio et al. (2021) 提出的方法，在資產定價模型中，考慮了遺漏因子和資料缺失的問題。以主成分分析萃取出的主成分當作潛在因子，與 CAPM、Fama-French 三因子模型、Carhart 四因子模型組成多因子模型，來評估台灣國內投資股票型與國內投資平衡型共同基金在 2014 年至 2022 年基金績效，並進一步把基金區分為存續三年以上的基金與存續五年以上的基金進行分析。實證結果發現，對於存續三年以上的基金，Fama-French 三因子結合主成分分析萃取出來的潛在因子組成的多因子模型，在所有模型中表現最佳，在漸進分配下能識別出2 檔具有顯著超額報酬的基金。對於存續五年以上的基金，實證結果顯示，表現的最好的是 CAPM 結合主成分分析萃取出來的潛在因子組成的多因子模型，在漸進分配下可以識別出3 檔具有顯著超額報酬的基金。在我們的實證結果中，有2 檔基金在總共32 個模型中，有28 個模型可以檢定出這2 檔基金在統計上是具有顯著超額報酬的。我們也考慮不同的潛在因子數量，以確認我們的檢定結果是具有穩健性的。;Fund performance is an important indicator for evaluating the abilities of fund managers. When testing fund performance, conducting multiple hypothesis tests simultaneously increases the probability of committing Type I errors, which means incorrectly concluding that the fund performance has significant excess returns. To address this issue, we employ methods that control the False Discovery Rate (FDR). Furthermore, we adopt the method proposed by Giglio et al. (2021) to address the challenges posed by omitted factors and missing data in asset pricing models. We use principal components, extracted through principal component analysis, as latent factors, combined with CAPM, Fama-French three-factor model, and Carhart four-factor model to form multifactor models. These models are used to evaluate the performance of domestic equity and balanced mutual funds in Taiwan from 2014 to 2022.We divide the funds into those with a duration of more than three years and those with more than five years for analysis. Empirical results show that, for funds with a duration of more than three years, the multifactor model combining the Fama-French three-factor model with the latent factors extracted through principal component analysis outperforms all other models. This model identifies two funds with significantly excess returns under an asymptotic distribution. For funds with a duration of more than five years, the empirical results demonstrate that the CAPM combined with the latent factors extracted through principal component analysis performs the best. Under an asymptotic distribution, this model can identify three funds with significantly excess returns. In our empirical results, out of a total of 32 models, 2 funds were found to have statistically significant positive excess returns in 28 of the models. We also considered different numbers of latent factors to confirm the robustness of our testing results.
顯示於類別:	[經濟研究所 ] 博碩士論文

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