臺灣博碩士論文加值系統

在投資市場上，投資者最常關注到挑選投資標的以及在市場風險下進行資產配置，因此本研究應用柔性運算的方法，發展兩階段的投資組合方法，第一階段透過資料包絡法(Data Envelopment Analysis)之CCR模式，發展出一套挑選基金模式；接著第二階段以提出兩個整合GA與PSO的演算法-GPSO1及GPSO2進行資產配置。
在實證研究中，本研究以台灣國內股票型基金為例。本研究主要提出以資料包絡法(Data Envelopment Analysis)來挑選基金作為本研究之投資組合標的；在資產配置上，以GPSO1、GPSO2、GA及PSO四種演算法建構投資組合並與大盤共五種投資組合分別比較Sharpe值。本研究之資料共計36個月，實證結果顯示所提之GPSO1與GPSO2投資組合之Sharpe值均優於GA、PSO及大盤分別所構成的投資組合，而其中又以GPSO1表現最佳。實證結果證明本研究提出的兩階段投資組合方法的確能幫助投資人穩健獲利。

In the investment market, investors concern greatly about investment target selection and asset allocation under the risk of investment market. Therefore, this research applies soft computing methods to develop a two-stage portfolio method. The first stage utilize a Data Envelopment Analysis (DEA) - CCR model to develop a model of funds selection, with the second stage integrates Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO) to develop two method, GPSO1 and GPSO2, for assets allocation.
The experiments were conducted by using the Taiwan domestic stock fund. In today, there are 36-month data. This study compares the proposed methods, GPSO1 and GPSO2, with GA, PSO, and market index based on Sharpe value.
The evaluation results showed that GPSO-based methods outperform the other method for portfolio allocation. Especially GPSO1, it has the best performance. Thus, the proposed two-stage portfolio method really can help investors have stable profit.

摘要 I
ABSTRACT II
誌謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 3
1.4 研究架構 3
第二章 文獻探討 5
2.1投資組合(Portfolio) 5
2.1.1投資組合簡介 5
2.1.2投資組合的發展 5
2.1.3 投資組合風險 8
2.2 共同基金 9
2.2.1 共同基金簡介 9
2.2.2 共同基金分類方式 9
2.2.3 共同基金費用 10
2.2.4 共同基金的類型 11
2.2.5 共同基金績效之文獻 12
2.2.6 共同基金績效指標之相關研究 13
2.3 資料包絡分析法(Data Envelopment Analysis) 15
2.3.1 資料包絡法的介紹 15
2.3.2 資料包絡分析法基本模式 16
2.3.3 資料包絡法於共同基金績效之研究 18
2.4 遺傳演算法(Genetic Algorithm) 22
2.4.1 遺傳演算法原理 22
2.4.2 遺傳演算法於投資組合上的應用 24
2.5粒子群最佳化演算法(Particle Swarm Optimization) 26
2.5.1 粒子群最佳化演算法的背景 26
2.5.2 粒子群最佳化演算法介紹 27
2.5.3 整合遺傳演算法與粒子群最佳化演算法之研究 30
第三章 研究方法 32
3.1 研究架構 32
3.2 選取基金策略 34
3.2.1 資料的蒐集 34
3.2.2 選取績效指標流程步驟 34
3.3 模型建構 35
3.3.1 實驗期間 35
3.3.2 資金於投資組合的分配 35
3.4 整合GA與PSO求解投資組合最佳化問題 37
3.4.1 結合基因演算法與粒子群演算法一GPSO1 37
3.4.2 結合基因演算法與粒子群演算法一GPSO2 41
第四章 實驗分析與驗證 44
4.1 資料包絡法變數說明 44
4.2 資料來源與前處理 47
4.3 資料包絡法(DEA)執行結果 50
4.4 演算法參數設計 53
4.4.1 田口式實驗 53
4.5 演算法資產配置結果 61
4.6 假設檢定 70
第五章 結論與建議 75
5.1 結論 75
5.2 研究貢獻 76
5.3 未來研究方向 76
參考文獻 77
附錄A 實驗結果 86
附錄B 參數設計結果 122
附錄C 檢定結果 125

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