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系統識別號 U0026-1207201021250100
論文名稱(中文) 在控制資料探勘偏誤下探討社會責任共同基金之績效
論文名稱(英文) Examining the performance of SRI mutual funds based on data-snooping biases free approach
校院名稱 成功大學
系所名稱(中) 財務金融研究所
系所名稱(英) Graduate Institute of Finance & Banking
學年度 98
學期 2
出版年 99
研究生(中文) 邱文輝
研究生(英文) Wen-Hui Chiu
學號 r8697117
學位類別 碩士
語文別 英文
論文頁數 48頁
口試委員 指導教授-顏盟峯
口試委員-陳政芳
口試委員-黃炳勳
口試委員-林軒竹
中文關鍵字 社會責任投資  資料探勘偏誤  假性推論  橫斷面拔靴法 
英文關鍵字 SRI  data-snooping biases  spurious inference  the cross-sectional alpha bootstrap technique 
學科別分類
中文摘要 近二十年來社會責任投資(Socially responsible investment, SRI)是個在共同基金市場中快速成長的領域。一般而言,過去的研究認為社會責任投資基金和一般基金之間並沒有任何績效上的差異,然而在過去風險調整基礎下所做的績效衡量,其統計推論在資料探勘偏誤(data-snooping biases)的影響下是不具可靠性的,因此本研究應用了由Kosowski 等人在2006年所發展的橫斷面拔靴法來檢驗社會責任投資基金和一般的共同基金在1990至2009年間是否有績效上的差異。事實上,本研究發現了資料探勘偏誤的確讓我們做出了假性推論(spurious inference),另一方面也發現了社會責任投資基金和一般共同基金間的績效並無差異,兩者也沒有績效的持續性,甚至對兩類別基金而言,績效都顯著低於市場投組。
英文摘要 Socially responsible investment (SRI) is a rapidly growing field in the mutual fund market for last two decades. In general, past research supports that there is no difference in performance between SRI and conventional funds. However, prior findings, on a risk-adjusted basis, are not reliable due to data-snooping biases. In this paper, I implement a bootstrap analysis in the cross section of alphas provided by Kosowski et al. (2006) to examine the performance in SRI and conventional funds and to compare their performance over the 1990 to 2009 period. In fact, my study uncovers that the data-snooping biases indeed lead us to make spurious inference. It is also found that there is no difference in fund performance between SRI and conventional funds and that performance persistence is inexistent for two groups. Furthermore, both SRI and conventional fund managers underperform the benchmark portfolio.
論文目次 I. Introduction 1
II. Literature Review 4
II.1. Performance Measure Approach 4
II.2. Performance Measure Using Bootstrap Method 6
II.3. Financial Performance of SRI Fund 8
III. Methodology 10
III.1. Data Collection Procedure 10
III.2. Research Method 15
III.2.1. The Factor Models 15
III.2.2. The Stationary Bootstrap 16
III.2.3. KTWW’s Cross-Sectional Alpha Bootstrap Technique 18
III.2.4. The Bootstrap Analysis of Performance Persistence 20
IV. Empirical results 23
IV.1. Bootstrap Tests of Cross Section of Fund Alphas 23
IV.2. Bootstrap Performance Persistence test 29
V. Conclusion 36
REFERENCES 37
APPENDIX 39

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