台灣共同基金績效評估-控制錯誤發現率方法之應用

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：31

、訪客IP：3.142.131.51

姓名

陳冠綸(Kuan-Lun Chen) 查詢紙本館藏

畢業系所

經濟學系

論文名稱

台灣共同基金績效評估-控制錯誤發現率方法之應用

相關論文

★ 美國共同基金優異績效檢定

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 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

關鍵字(英)

★ Mutual Fund Performance
★ Multiple Testing Problem
★ Multifactor Model
★ False Discovery Rate
★ Principal Component Analysis

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章、緒論 1
1-1 研究動機 1
1-2 研究目的 3
第二章、文獻回顧 4
2-1 基金績效衡量指標 4
2-2 資料窺探偏誤 6
2-3 多重檢定問題 8
第三章、研究方法 10
3-1 模型設定 10
3-2 矩陣補全法 12
3-3 估計 Alpha 13
3-3-1 潛在因子模型 13
3-3-2 可觀察因子和潛在因子模型 15
3-4 建構檢定統計量 17
3-5 Wild-Bootstrap 建構 p-value 18
3-6 控制錯誤發現率 20
3-6-1 Benjamini-Hochberg(B-H)方法 21
3-6-2 Alpha 篩選 22
第四章、實證結果 23
4-1 實證資料 23
4-2 控制 FDR 實證結果 25
4-3 穩健性測試 36
第五章、結論 38
參考文獻 40

參考文獻

1.莊惠菁、管中閔. (2010). 無資料窺探偏誤的檢定評估共同基金績效. 證券市場發展季刊, 22(3), 181-206.
2.莊惠菁、管中閔. (2020). 共同基金卓越績效的認定與評估: 新逐步檢定法的應用. 證券市場發展季刊, 32(1), 1-31.
3.Barras, L., Scaillet, O., & Wermers, R. (2010). False discoveries in mutual fund performance: Measuring luck in estimated alphas. The Journal of Finance, 65(1), 179-216.
4.Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300.
5.Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82.
6.Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
7.Giglio, S., Liao, Y., & Xiu, D. (2021). Thousands of alpha tests. The Review of Financial Studies, 34(7), 3456-3496.
8.Giglio, S., & Xiu, D. (2021). Asset pricing with omitted factors. Journal of Political Economy, 129(7), 1947-1990.
9.Hansen, P. R. (2005). A test for superior predictive ability. Journal of Business & Economic Statistics, 23(4), 365-380.
10.Harvey, C. R., Liu, Y., & Saretto, A. (2020). An evaluation of alternative multiple testing methods for finance applications. The Review of Asset Pricing Studies, 10(2), 199-248.
11.Hsu, P.-H., Hsu, Y.-C., & Kuan, C.-M. (2010). Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias. Journal of Empirical Finance, 17(3), 471-484.
12.Jensen, M. C. (1968). The performance of mutual funds in the period 1945-1964. The Journal of Finance, 23(2), 389-416.
13.Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587-615.
14.Lo, A. W., & MacKinlay, A. C. (1990). Data-snooping biases in tests of financial asset pricing models. The Review of Financial Studies, 3(3), 431-467.
15.Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica: Journal of the Econometric Society, 768-783.
16.Romano, J. P., & Wolf, M. (2005). Stepwise multiple testing as formalized data snooping. Econometrica, 73(4), 1237-1282.
17.Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
18.Sharpe, W. F. (1966). Mutual fund performance. The Journal of Business, 39(1), 119-138.
19.White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097-1126.

指導教授

徐之強廖志興(Chih-Chiang Hsu Chih-Hsing Liao)

審核日期

2023-8-14

推文