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研究生:鍾文薰
研究生(外文):Wen-Hsun Chung
論文名稱:ThePerformanceEvaluationforFundofFundsbyComparingAssetAllocationofFundManagerstothatofMean-Variance/GeneticAlgorithm
論文名稱(外文):The Performance Evaluation for Fund of Funds by Comparing Asset Allocation of Fund Managers to that of Mean-Variance/Genetic Algorithm
指導教授:李宏志李宏志引用關係賴秀卿賴秀卿引用關係
指導教授(外文):Hung-Chih LiSyou-Ching Lai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:財務金融研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:93
外文關鍵詞:fund of fundsmutual fund performanceMean-VarianceGenetic Algorithms
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  Markowitz Mean-Variance Model is a well-known investment theory in selecting security portfolio while the normal distribution of the return is one of the mainly assumptions. Therefore we also try to apply Genetic Algorithms (GA), one of artificial intelligence methodology, to the investment portfolio.
  This paper investigates the ability of security selection by comparing the performance of the portfolios of fund of funds (FoF) constructed by Markowitz Mean-Variance Model or GA to that of fund managers. All target mutual funds held by FoF in the U.S. market from January 1, 2000, to December 31, 2003 are chosen in this study.
  The results imply some things. First, only GA model and Mean-Variance model can beat the market index and the performance of GA model is much better than that of fund managers and Mean-Variance model. Second, for the ability of selecting security, both Markowitz Mean-Variance and GA models can outperform the operation of fund managers. Finally, GA model may dominate Markowitz Mean-Variance model in performance measure and performance persistence.
Chapter 1 Introduction 1
Chapter 2 literature review 5
2.1 INTRODUCTION OF FUND of FUNDS 5
2.1.1 Definition 5
2.1.2 The Benefits from fund of funds 6
2.1.3 The cost of fund of funds 7
2.2 INTRODUCTION OF MODELS 8
2.2.1 Markowitz Mean-Variance Portfolio Selection Model 9
2.2.2 Genetic Algorithm model 12
2.3 THE MEASURE OF MUTUAL FUND PERFORMANCE 14
2.3.1 The performance measure techniques 14
2.3.2 Power for Regression-based Performance Measures 16
2.3.2 Power for Using Characteristics or Styles 17
2.4 THE MUTUAL FUND PERFORMANCE PERISITENCE 18
Chapter 3 Model Specification and Methodology 20
3.1 DATA 20
3.2 REAEARCH HYPOTHESES 23
3.3 REASEARCH DESUGN AND PROCESS 25
3.4 DEFINITION OF OPERATOR 26
3.5 MOTHODOLGOGY 27
3.5.1 Normality test 27
3.5.1Markowitz Mean-Variance Methodology 29
3.5.2 Genetic Algorithm 33
3.5.3 Performance Persistence Test 39
Chapter 4 Empirical Result 41
4.1 DESCRIPTIVE STATISTICS 41
4.2 RESULTS 43
Chapter 5 Conclusion 70
5.1 CONCLUSIONS 70
5.2 FURTHER RESEARCH 72
Reference 73
Appendix A 77
Appendix B 80
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