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系統識別號 U0002-2108201511411000
中文論文名稱 以共演化式遺傳演算法輔助動態股票投資決策分析
英文論文名稱 Using Co-Evolutionary Genetic Algorithm to Assist Dynamic Stock Investment Analysis
校院名稱 淡江大學
系所名稱(中) 資訊管理學系碩士班
系所名稱(英) Department of Information Management
學年度 103
學期 2
出版年 104
研究生中文姓名 夏承億
研究生英文姓名 Cheng-Yi Hsia
學號 600630999
學位類別 碩士
語文別 中文
口試日期 2015-07-14
論文頁數 40頁
口試委員 指導教授-張應華
委員-梁恩輝
委員-林文修
中文關鍵字 遺傳演算法  共演化模式  馬可夫決策  動態股票投資決策  最佳化 
英文關鍵字 Genetic algorithms  Co-evolutionary mode  Markov decision process  Dynamic stock investment decisions  Optimization 
學科別分類
中文摘要 股票投資為時下眾多理財工具中的一種,其風險與報酬是相對的,想要獲得高額的報酬得承擔較高的風險,一般投資民眾缺乏專業知識與資料分析的能力,其投資決策多依電視股市交易分析節目或一些小道消息來進行決策,但股市的行情變化快速,投資者想要在股票市場裏獲利,必須慎選股票,並在適當的時機進場交易,以及最佳的資金配置。因此,如何制定正確的投資策略,成為眾多投資者關注的議題。
目前有許多決策方法可以輔助股票投資策略的制定,但是當投資者考慮的投資準則過多時,在判斷評估準則的重要性時容易失去客觀與正確的分析,尤其是當投資者臨時考慮到新準則時,傳統決策方法只能重新評量問題,無法累積之前所分析出準則的重要性,導致決策制定的不易,再者,投資人經常錯過股票買賣的時機與不知如何分配投資資金,有鑑於此,本研究利用共演化式遺傳演算法結合馬可夫決策過程來幫助股票投資策略的制定,利用遺傳演算法的平行空間廣度搜尋最佳解特性,加上模擬人類思考模式的共演化理論,使得遺傳演算法的執行過程可以隨著環境的改變而做動態的演化,從而找出最適當的進場時機、選股與資金配置,以搭配出一套完整的股票投資策略。
英文摘要 In the rapid changes stock market, investors want to get profit that they must carefully choose stocks, buy or sell the stocks at the appropriate time and with the best capital allocation strategy. How to make the right investment strategy is the subject of investors. There are many ways to assist the development of decision for stock investment strategy. But when investors consider too many investment criteria, it is easy to lose objectivity and proper analysis to determine the importance of the evaluation criteria. Especially when investors take into account new guidelines when to make investment decision. The traditional decision making methods just can only re-evaluate the decision problem and cannot be accumulated the importance of criteria before adding new guidelines. It is difficult to make decision. Furthermore, investors often miss the good opportunity to do stock transaction and do not know how to allocate investment funds.
For this reason, this study integrated co-evolutionary genetic algorithm with Markov decision process to help investors develop a dynamic stock investment strategies system. The genetic algorithms have the ability of parallel search in breadth space. The co-evolutionary mechanism has simulated human thought patterns. These making the process of the genetic algorithm can implement with adjustment in the dynamic changes environments. And by Markov decision process, the decision system can inform investors when must to adjust the portfolio and to identify the appropriate stock timing, the stock selection and the capital allocation. The complete stock investment strategy allows investors to obtain excess returns when invest in the stock market.
論文目次 目錄
壹、緒論1
貳、文獻探討3
第一節、股票投資組合與股市指標3
第二節、馬可夫決策過程4
第三節、遺傳演算法與共演化模式5
参、研究架構7
肆、實驗分析13
第一節、歷年間共演化與各指數評比實驗13
第二節、共演化遺傳演算法與一般遺傳演算法比較27
伍、結論與未來展望36
参考文獻37

表目錄
表1:移轉次數矩陣(單位:次)9
表2:移轉機率矩陣(單位:%)9
表3:遺傳演算法參數設定表11
表4:準則對應表12
表5:無風險利率表(單位:報酬率%)13
表6:共演化遺傳演算法代碼表14
表7:2004年進場調整當日報酬率表(單位:報酬率%)14
表8:2004年共演化之結果與各指數報酬率比較表(單位:報酬率%)15
表9:2005年進場調整當日報酬率表(單位:報酬率%)15
表10:2005年共演化之結果與各指數報酬率比較表(單位:報酬率%)16
表11:2006年進場調整當日報酬率表(單位:報酬率%)16
表12:2006年共演化之結果與各指數報酬率比較表(單位:報酬率%)17
表13:2007年進場調整當日報酬率表(單位:報酬率%)18
表14:2007年共演化之結果與各指數報酬率比較表(單位:報酬率%)18
表15:2008年進場調整當日報酬率表(單位:報酬率%)19
表16:2008年共演化之結果與各指數報酬率比較表(單位:報酬率%)19
表17:2009年進場調整當日報酬率表(單位:報酬率%)20
表18:2009年共演化之結果與各指數報酬率比較表(單位:報酬率%)20
表19:2010年進場調整當日報酬率表(單位:報酬率%)21
表20:2010年共演化之結果與各指數報酬率比較表(單位:報酬率%)21
表21:2011年進場調整當日報酬率表(單位:報酬率%)22
表22:2011年共演化之結果與各指數報酬率比較表(單位:報酬率%)22
表23:2012年進場調整當日報酬率表(單位:報酬率%)23
表24:2012年共演化之結果與各指數報酬率比較表(單位:報酬率%)24
表25:2013年進場調整當日報酬率表(單位:報酬率%)24
表26:2013年共演化之結果與各指數報酬率比較表(單位:報酬率%)25
表27:2014年進場調整當日報酬率表(單位:報酬率%)25
表28:2014年共演化之結果與各指數報酬率比較表(單位:報酬率%)26
表29:歷年共演化之結果與各指數報酬率比較表(單位:報酬率%)26
表30:歷年表現比較表27
表31:各執行五次之結果與平均表(單位:報酬率%)35
表32:各演算法收斂代數與時間比較表35

圖目錄
圖1:遺傳演算法流程圖5
圖2:研究架構圖7
圖3:馬可夫狀態示意圖8
圖4:染色體編碼示意圖10
圖5:共演化適應值計算示意圖11
圖6:COGA1染色體適應值圖28
圖7:COGA1準則適應值圖28
圖8:COGA2染色體適應值圖29
圖9:COGA2準則適應值圖30
圖10:COGA3染色體適應值圖30
圖11:COGA3準則適應值圖31
圖12:COGA4染色體適應值圖32
圖13:COGA4準則適應值圖33
圖14:COGA5染色體適應值圖33
圖15:COGA5準則適應值圖34
圖16:SGA染色體適應值圖34

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