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研究生:陳智勇
研究生(外文):CHIH-YUNG Chen
論文名稱:利用離散型差分演算法解決投資組合最佳化問題
論文名稱(外文):Using Discrete Differential Evolution for Portfolio Optimization
指導教授:高有成高有成引用關係
指導教授(外文):Yucheng Kao
口試委員:高有成
口試委員(外文):Yucheng Kao
口試日期:2014-07-03
學位類別:碩士
校院名稱:大同大學
系所名稱:資訊經營學系(所)
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:63
中文關鍵詞:離散型差分演算法投資組合最佳化效率前緣
外文關鍵詞:Discrete differential evolutionPortfolio optimizationEfficient frontier
相關次數:
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在投資組合問題中,除了要考慮收益外,同時須考慮風險因素,所以投資組
合問題是屬於一種多目標的問題,當可供選擇的股票標的數量愈多時,問題的複
雜度就更高,其所需要的求解時間也相對的更多。近年來的學者提出了粒子演算
法、差分演算法進行求解投資組合最佳化問題,然而都有隨著題目增大使得求解
時間花費倍增的問題。本文提出使用離散型差分演算法的方法可直接將整數演化
,無需將解做二進位編碼,並且改良解表達方式,讓演化結果無需進行修補的動
作。本文經由實驗證明,使用離散型差分演算法,提供了一種更省時,且更有效
率的求解方法。
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.
致謝I
摘要II
AbstractIII
目次IV
圖次V
表次VII
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
參考文獻47
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