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研究生:施志樹
研究生(外文):Chih-Shu Shih
論文名稱:以遺傳演算法為基礎考慮市場特徵之股票評價與動態資產配置架構
論文名稱(外文):GA-based Stock Valuation and Dynamic Asset Allocation with Market Pattern consideration
指導教授:林萍珍林萍珍引用關係柯博昌柯博昌引用關係
指導教授(外文):Ping-Chen LinPo-Chang Ko
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:金融資訊研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:50
中文關鍵詞:股票評價資產配置交易時機市場特徵遺傳演算法
外文關鍵詞:Stock valuationAsset allocationTrading momentMarket patternGenetic Algorithm
相關次數:
  • 被引用被引用:1
  • 點閱點閱:217
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
台灣金融市場機制發展至今,市場存在著可獲利的空間,也因此許多投資人致力於投資獲利的方法。對投資人而言,股票評價的目的是希望透過財務分析的方式找出股票的真實價值,進而獲得超額報酬。然而進行股票評價之後,一般傳統的財務分析方式並不能提供資產配置方式及交易時機的資訊。不管是資產配置方式或交易時機,皆扮演著改善投資報酬率的重要角色。待市場內投資人進行交易之後,會漸漸造成市場趨勢的改變,形成市場特徵。投資人可以從網際網路或看盤軟體獲得市場特徵的資料,部分投資人利用這些資料進行分析與投資,但卻不知各種市場特徵下較佳的資產配置方式及適合的交易時機為何。因此本研究提出一個整合性的架構,能判斷目前市場是屬於何種特徵,進而提供符合此市場特徵之交易策略,交易策略包含能夠得知股價高低估程度的合理股價區間、配合股價高低估程度進行較佳的資產配置方式以及適合的交易時機。因遺傳演算法擁有非線性搜尋最佳化的良好特性,以及具有自我學習演化之能力,為解決實際股票市場中許多複雜因素影響的問題,故將此演算法應用結合於此架構,企圖利用過去歷史股價資料演化出各市場特徵下的交易策略。實驗結果證明,本論文提出之研究架構雖然無法優於個股的買入持有策略,但能有效地得到優於大盤的報酬率。
The purpose of stock valuation for investors is to find the true value of stock based on financial analysis and to further earn excess return. However, the better asset allocation and suited trading moment are not considered in the traditional valuation models. Both of them play important roles to improve investment return for investors. Market pattern means the tendency of the stock market. We often know the formation of the market pattern, but we often don’t know what to take a position. In this article, we propose a stock valuation model with asset allocation strategy which could provide a suitable trading strategy for each market pattern. In this model, genetic algorithm is used to optimize the fair stock price range, anticipative rate of investment, and trading moment. From our experiments, although this model could not earn better investment returns than buy and hold strategy, it could earn better investment returns than the return of TAIEX.
摘 要 i
ABSTRACT ii
致 謝 辭 iii
目 錄 iv
圖 目 錄 vi
表 目 錄 vii
一、緒論 1
二、文獻探討 4
2.1 傳統股票評價模式概觀 4
2.2 傳統股票評價模式應用之相關文獻 5
2.4 遺傳演算法 6
2.5 人工智慧應用於財金領域之相關文獻 10
三、研究架構 12
3.1 市場特徵 14
3.2 基因編碼方式 15
3.3 適應函數 17
四、系統開發與測詴環境 23
五、實驗設計與結果分析 25
5.1 樣本資料選取與來源 25
5.2 實驗參數設定 26
5.3 實驗環境 27
5.4 實驗設計 27
5.5 實驗結果與分析 28
六、結論與建議 47
6.1 結論 47
6.2 未來研究方向 47
參考文獻 49
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