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研究生(外文):CHIH-YUNG Chen
論文名稱(外文):Using Discrete Differential Evolution for Portfolio Optimization
指導教授(外文):Yucheng Kao
口試委員(外文):Yucheng Kao
外文關鍵詞:Discrete differential evolutionPortfolio optimizationEfficient frontier
  • 被引用被引用:6
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In the portfolio optimization problem, return and risk factors are considered at the same time, thus the problem is a multi-objective problem. When the number of stocks is large, the complexity of the problem becomes high and it consumes much more CPU time to solve it. Recently some researchers have utilized particle swarm optimization (PSO) and differential evolution (DE) to solve the portfolio optimization problem. Unfortunately, their CPU usage becomes high as the sizes of test problems increase. This study tries to overcome this problem. A discrete differential evolution (DDE) algorithm is proposed. The solution string consists of two sections: an integer -number section and a real-number section. The length of solution sections is equal to the cardinality number, rather than the number of stocks. Experimental results show that the design of solution string allows DDE to solve the portfolio optimization problem in a more efficient way.
第壹章 簡介1
第一節 研究背景與動機1
第二節 研究範圍與限制2
第三節 研究方法與流程3
第四節 論文架構4
第貳章 文獻探討5
第一節 投資組合問題5
第二節 粒子群演算法7
第三節 差分演算法8
第四節 連續型演算法用於離散型題目10
第五節 2*N與2*K的解字串比較15
第參章 方法論16
第一節 數學模型16
第二節 解的表達18
第三節 演算法流程18
第四節 離散型差分演算法20
第五節 持有投資比率的調整22
第六節 DDE演算說明24
第肆章 範例說明27
第伍章 實驗與比較35
第一節 實驗設計與參數設定35
第二節 第一階段實驗37
第三節 第二階段實驗43
第陸章 結論46
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