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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 管中閔(Chung-Ming Kuan) | |
| dc.contributor.author | Wen-Yu Lin | en |
| dc.contributor.author | 林汶郁 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:04:06Z | - |
| dc.date.available | 2021-07-08 | |
| dc.date.available | 2022-11-24T03:04:06Z | - |
| dc.date.copyright | 2021-07-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-06-26 | |
| dc.identifier.citation | 一、外文文獻 Barras, L., O. Scaillet, R. Wermers (2010). False discoveries in mutual fund performance: Measuring luck in estimated alphas. Journal of Finance, 65(1), 179-216. Benjamini, Y., Y. Hochberg (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. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82. Cuthbertson, K., D. Nitzsche (2013). Performance, stock selection and market timing of the German equity mutual fund industry. Journal of Empirical Finance, 21, 86-101. Cuthbertson, K., D. Nitzsche, N. O'Sullivan (2012). False discoveries in UK mutual fund performance. European Financial Management, 18(3), 444-463. Dudoit, S., J. Fridlyand, T. P. Speed (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97(457), 77-87. Fama E. K. French (1992). The cross-section of expected stock returns. Journal of Finance, 47(2), 427-465. Genovese, C. R., N. A. Lazar, T. Nichols (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage, 15(4), 870- 878. Glickman, M. E., S. R. Rao, M. R. Schultz (2014). False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. Journal of Clinical Epidemiology, 67(8), 850-85j7. Hansen, P. R. (2005). A test for superior predictive ability. Journal of Business Economic Statistics, 23(4), 365-380. Hawinkel, S., F. Mattiello, L. Bijnens, O. Thas (2019). A broken promise: microbiome differential abundance methods do not control the false discovery rate. Briefings in Bioinformatics, 20(1), 210-221. Hommel, G., T. Hoffmann (1988). Controlled uncertainty. In Multiple Hypothesenprüfung/Multiple Hypotheses Testing (pp. 154-161). Springer. Hong, J., Y. Luo, Y. Zhang, J. Ying, W. Xue, T. Xie, L. Tao, F. Zhu (2020). Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning. Briefings in Bioinformatics, 21(4), 1437-1447. Hsu, P. H., Y. C. Hsu, C. M. Kuan (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. Hsu, Y. C., C.M. Kuan, M.F. Yen (2014). A generalized stepwise procedure with improved power for multiple inequalities testing. Journal of Financial Econometrics, 12(4), 730-755. Kamil, N. K., S. O. Alhabshi, O. I. Bacha, M. Masih (2014). Heads we win, tails you lose: Is there equity in Islamic equity funds? Pacific-Basin Finance Journal, 28, 7-28. Kim, S., F. In (2012). False discoveries in volatility timing of mutual funds. Journal of Banking Finance, 36(7), 2083-2094. Kosowski, R., A. Timmermann, R. Wermers, H. White (2006). Can mutual fund “stars” really pick stocks? New evidence from a bootstrap analysis. Journal of Finance, 61(6), 2551-2595. Lehmann, E. L., Romano, J. P. (2005). Generalizations of the familywise error rate. The Annals of Statistics, 33(3), 1138-1154. Li, J., D. M. Witten, I. M. Johnstone, R. Tibshirani (2012). Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics, 13(3), 523-538. Nelson, C. P., A. Goel, A. S. Butterworth, S. Kanoni, T. R. Webb, E. Marouli, L. Zeng, I. Ntalla, F. Y. Lai, J. C. Hopewell (2017). Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nature Genetics, 49(9), 1385. Romano, J. P., M. Wolf (2005). Stepwise multiple testing as formalized data snooping. Econometrica, 73(4), 1237-1282. Romano, J. P., M. Wolf (2007). Control of generalized error rates in multiple testing. The Annals of Statistics, 35(4), 1378-1408. Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3), 479-498. Storey, J. D. (2003). The positive false discovery rate: a Bayesian interpretation and the q-value. The Annals of Statistics, 31(6), 2013-2035. Storey, J. D., R. Tibshirani (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, 100(16), 9440-9445. Tusher, V. G., R. Tibshirani, G. Chu (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences, 98(9), 5116-5121. Walzthoeni, T., M. Claassen, A. Leitner, F. Herzog, S. Bohn, F. Förster, M. Beck, R. Aebersold (2012). False discovery rate estimation for cross-linked peptides identified by mass spectrometry. Nature Methods, 9(9), 901-903. Weisberg, S. P., D. McCann, M. Desai, M. Rosenbaum, R. L. Leibel, A. W. Ferrante (2003). Obesity is associated with macrophage accumulation in adipose tissue. Journal of Clinical Investigation, 112(12), 1796-1808. White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097-1126. Yu, C. Y., X. X. Li, H. Yang, Y. H. Li, W. W. Xue, Y. Z. Chen, L. Tao, F. Zhu (2018). Assessing the performances of protein function prediction algorithms from the perspectives of identification accuracy and false discovery rate. International Journal of Molecular Sciences, 19(1), 183. 二、中文文獻 莊惠菁、管中閔 (2010)。以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，第 22 卷第 3 期，181-206。 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，第 32 卷第 1 期，1-31。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80302 | - |
| dc.description.abstract | "實務與學術上，檢定共同基金績效是否較大盤更為優異是相當重要的議題。對多檔基金同時進行多重假設檢定，會面臨總體型一錯誤率膨脹的問題，藉由控制錯誤發現率 (False Discovery Rate, FDR) 則可避免此問題。本研究檢定共同基金績效，探究控制錯誤發現率在辨別真實優異基金的表現。研究結果顯示，控制 FDR 選出的優異基金在樣本外區間依然優異的比例，較未控制 FDR 更高，意味該方法有助於篩選出真正優異的基金。其次，以控制 FDR 選出的優異基金形成的資產組合在樣本外區間的績效，優於未控制 FDR 組成的資產組合，顯示該方法能建構績效更優異的基金資產組合。所以採用 Benjamini \ Hocberg (1995) 控制 FDR 的方法，較容易選出實務上具有優異表現的基金。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:04:06Z (GMT). No. of bitstreams: 1 U0001-2606202108490500.pdf: 2453391 bytes, checksum: c16897528319c9cf613473b8db61cc7d (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書........................................................................................................... i 誌謝............................................................................................................................... ii 摘要 .............................................................................................................................. iii Abstract ....................................................................................................................... iv 目錄............................................................................................................................... v 表目錄.............................................................................................................................vi 圖目錄.............................................................................................................................vii 1 緒論............................................................................................................................. 1 2 文獻回顧...................................................................................................................... 5 3 實證方法..................................................................................................................... 11 3.1 基金績效指標......................................................................................................... 11 3.2 研究方法............................................................................................................... 13 4 研究結果分析.............................................................................................................. 18 4.1 資料與基本統計量.................................................................................................. 18 4.2 檢定結果............................................................................................................... 19 4.3 優異基金的持續性................................................................................................. 22 5 結論............................................................................................................................ 27 參考文獻........................................................................................................................ 30 | |
| dc.language.iso | zh-TW | |
| dc.subject | 錯誤拒絕率 | zh_TW |
| dc.subject | 因子異常報酬 | zh_TW |
| dc.subject | 全族錯誤率 | zh_TW |
| dc.subject | 多重假設檢定 | zh_TW |
| dc.subject | 拔靴法 | zh_TW |
| dc.subject | 資產組合 | zh_TW |
| dc.subject | FWER | en |
| dc.subject | Type I error | en |
| dc.subject | multiple hypothesis test | en |
| dc.subject | Bonferroni procedure | en |
| dc.subject | bootstrap | en |
| dc.subject | FDR | en |
| dc.title | 以錯誤發現率控制法評估共同基金績效 | zh_TW |
| dc.title | Evaluating Mutual Fund Performance with False Discovery Rate Control | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 許育進(Hsin-Tsai Liu),莊惠菁(Chih-Yang Tseng),何耕宇 | |
| dc.subject.keyword | 因子異常報酬,全族錯誤率,多重假設檢定,拔靴法,資產組合,錯誤拒絕率, | zh_TW |
| dc.subject.keyword | Bonferroni procedure,bootstrap,FDR,FWER,multiple hypothesis test,Type I error, | en |
| dc.relation.page | 42 | |
| dc.identifier.doi | 10.6342/NTU202101144 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-06-28 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
| 顯示於系所單位： | 財務金融學系 | |
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