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研究生:謝承佑
研究生(外文):chenyo sie
論文名稱:運用多目標基因演算法於投資組合選擇與交易
論文名稱(外文):Portfolio selection and trading by using multi-objective Genetic Algorithm
指導教授:黃憲彰黃憲彰引用關係
指導教授(外文):Shian-chang Huang
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:企業管理學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:120
中文關鍵詞:平均數變異數模型多目標基因演算法投資組合選擇
外文關鍵詞:mean variance modelmulti-objective genetic algorithmsportfolio selection
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知名的平均數變異數模型不能滿足投資者的要求在不同的投資偏好和風險歧異度。除此之外,我們也考慮運用基因演算法於投資組合的選擇,包含報酬的風險偏好、流動性、報酬分配和交易成本。再者,我們試著改善Markowitz的模型經由多目標基因演算法。為什麼我們會使用多目標基因演算法?因為多目標基因演算法可以在同一時間內考慮所有的目標且解決二次方程式規畫問題和整體性的最適化結果。此外,多目標方程式比單一目標更能解決衝突在複雜的目標中。多目標基因演算法可以解釋風險和報酬之間的取捨。本篇研究提出一個模式整合不同的風險衡量方式、偏態、亂度、流動性和交易成本。交易的例子也可以和過去的方式做比較。在數值的結果下,我們的結果提供一個較高的報酬和好的風險趨避。
The well-known mean-variance model cannot satisfy investors’ request for different investment preference and risk diversification. Consequently, we consider genetic algorithms for portfolio selections which consider risk preference including return, risk, liquidity, return distribution and transaction cost. Further, we try to improve the Markowitz model by multi-objective genetic algorithms (MOGAs). Why we used MOGAs? Because of MOGAs have considered all the objectives in the same time with solving quadric programming problem and optimized the solution in globally pareto optimal. Moreover, Multiobjective functions are prior than single objective because of solving the conflicts exquisitely in complex objections. Multiobjective genetic algorithms (MOGAs) can explain the trade-off between return and risk which behavior finance investigates. This paper proposed method which incorporate different risk measures, skewness, entropy, liquidity and transaction cost. A trading example is also illustrated to compare with the proposed method. On the basis of the numerical results, the method we proposed can provide a higher return on asset and having better risk diversifications.
LIST OF TABLES IV
CHAPTER 1 INTRODUCTION 1
1.1 General Background 1
CHAPTER 2 LITERATURE REVIEW 3
2.1 Portfolio theory and transaction cost 3
2.2 Genetic algorithm (GA) in portfolio 4
2.3 Multi-objective Genetic algorithm (MOGA) 5
CHAPTER 3 METHOD 8
3.1 Research Design 8
3.2 Subjects/ Materials 8
3.2.1 Genetic algorithm 8
3.2.2 Entropy 12
3.2.3 Mean-variance model 13
3.2.4 Liquidity 14
3.2.5 Skewness 14
3.2.6 Local pareto optimal vs. Globle pareto optimal 15
3.2.7 Risk attitude 16
3.3 Data Collection Procedure 17
CHAPTER 4 EMPIRICAL RESULTS 18
4.1 Research Findings 18
4.1.1 The difference between ten, fifteen and twenty firms 18
4.1.2 Observed the difference between two, three objectives 20
4.13 Trading comparability 21
CHAPTER 5 CONCLUSIONS 25
5.1 Conclusions 25
REFERENCE 27
APPENDIX Ⅰ 31

LIST OF FIGURES
Fig. 1. Steps for research process 8
Fig. 2. Genetic algorithm process 10
Fig. 3. Selection method in genetic algorithm 11
Fig. 4. Two point crossover method 12
Fig. 5. Genetic mutation operation 12
Fig. 6. Skewness patterns 14
Fig. 7. The ideal solution sets 15
Fig. 8. Globally optimal vs. Local optimal 16
Fig. 9. MOGAs internal process 17
Fig. 10a. Return and variance(10 firms under alfa0.1 18
Fig.10b.Return and variance(10 firms under alfa0.3) 18
Fig. 11a. Return and variance(15 firms under alfa0.1) 18
Fig. 11b. Return and variance (15 firms under alfa0.3) 18
Fig. 12a. Return and variance (20 firms under alfa0.1) 19
Fig. 12b. Return and variance (20 firms under alfa0.3) 19
Fig. 13 Type of risk preferences 20
Fig. 14a. Return and variance (two objectives) 20
Fig. 14b. Return and variance (three objectives) 20

LIST OF TABLES
Table 1 General trading comparability in different fitness value 22
Table 2 trading comparability in different objectives and firms 23
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