臺灣博碩士論文加值系統

本研究採用二階段實驗方法。第一階段首先引用品質管理製程能力指標對台灣上市公司進行選股，發展出一套製程績效評估方法。針對投資標的，建構一個具望大型報酬特性的投資滿意能力指標,推導出投資績效指標之最佳估計式，以有效地評估投資標的績效與投資滿意度間之關係，推導出真實投資績效指標之信賴下限，用來判斷股票之投資績效是否達到要求水準。因此，投資者不僅透過 指標值可迅速得知個別股票的績效，同時檢視績效是否能達到投資者心中的報酬滿意程度。
第二階段實驗則透過粒子群最佳化演算法，以移動時間視窗法尋求最佳投資資金配置，設計出績效良好的股票投資組合。粒子群最佳化演算法(Particle Swarm Optimization，PSO)在財務領域的應用上，乃是創新的方法之一且相對一些演算法更具效率性。
實證結果顯示，本研究所建構股票投資組合報酬率高於台灣加權股價指數報酬率。並能提供未來投資人，對於創新財務技術使用有綜合性的認知，以期創造更佳的投資績效。

The experiment is divided into two stages. The first stage is to adduce Process Capability Indices ( PCI ) of quality management to develop a new performance appreciation method. This research is to construct the capability of a return-the-better process based on an investment satisified capability index. Therefore, investors can utilize to accurately realize individual stock performance and select stocks, which their investment satisfaction can be achieved. In second stage, the Partice Swarm Optimization algorithm was applied to these stocks for finding the optimal investment allocation of this portfolio by using the moving interval windows. The PSO algorithm is the innovative method in the financial application and more efficient than other algorithm.
The result shows that the Ratio of Return of our research is better than The Weighted Price Index of the Taiwan Stock Exchange(TAIEX). Therefore, investors could use the portfolio to create better performance.

摘 要 I
ABSTRACT II
誌謝 III
第一章 緒論 3
第一節 研究背景和動機 3
第二節 研究目的 6
第三節 論文架構與研究流程 7
第二章 文獻探討 9
第一節 投資組合 9
第二節 投資組合演進相關文獻 17
第三節 投資組合績效評估相關文獻 21
第四節 製程能力指標 26
第五節 粒子群最佳化 30
第三章 研究方法 41
第一節 研究架構 41
第二節 實驗設計 50
第三節 粒子群最佳化演算法 54
第四章 實證結果與分析 57
第一節 股票選取 57
第二節 收斂度分析 60
第三節 研究結果分析 64
第五章 結論與建議 82
第一節 結論 82
第二節 研究貢獻 84
第三節 研究限制 86
第四節 未來研究建議 87
參考文獻 88
附錄一 96

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