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研究生:劉慧敏
研究生(外文):Hui-Min Liu
論文名稱:多目標遺傳演算法於基本面選股策略之應用
論文名稱(外文):An Application of Multi-Objective Genetic Algorithms on Fundamental Selection Strategy
指導教授:陳稼興陳稼興引用關係
指導教授(外文):Jiah-Shing Chen
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:70
中文關鍵詞:遺傳演算法多目標遺傳演算法基本分析財務報表選股策略
外文關鍵詞:Financial StatementsFundamental AnalysisMOGAMulti-objective Genetic AlgorithmsGAGenetic AlgorithmsSelection Strategy
相關次數:
  • 被引用被引用:43
  • 點閱點閱:450
  • 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
  多目標最佳化最困難的地方,莫過於如何在多個目標之間取捨,以取得最佳的平衡點。投資人在選股時,也面臨這樣的兩難。因為在選擇投資組合的過程中,需要滿足報酬率和風險等績效目標,由於報酬率和風險兩者間相互衝突的特性,使得投資人往往不清楚其偏好或各目標之相對重要性為何,因此在評估最佳投資組合的過程中,會同時產生多組效率投資組合,此相當於多目標最佳化過程中,所產生的柏拉圖最佳解。故投資組合選擇問題是一複雜的多目標最佳化問題。
  為達到最佳化多項評估指標的目的,過去諸多研究,常常將這些投資組合評估指標,以加權方式結合成單一目標函數,這樣的方法並不能解決目標之間互相衝突的情況,雖然有釵h其他傳統最佳化方法可解決此問題,然而,這些傳統方法的最大限制在於,一次只能求得一個最佳解,且求解的過程相當繁瑣。
  本研究提出此多目標最佳化選股策略架構的目的,為幫助投資人解決運用公司基本面資料選擇最佳投資組合時,必須在多個衝突的評估函數間取捨的困境;同時使用多目標遺傳演算法技術實作此多目標最佳化選股系統,以解決傳統最佳化方法所遇到的瓶頸與限制。經由台灣股市實證結果顯示,在實驗期間,多目標最佳選股策略的所有目標績效大致上比類股、加權指數佳;相較於單目標最佳選股策略,多目標最佳選股策略的目標績效則大致上不比前者差,或甚至更好,代表此多目標最佳選股策略是能滿足投資人的所有目標績效之非超越解。由此驗證本研究架構的確能運用於解決多目標最佳化的選股問題上。
Real-world problems involve multiple objectives that need to be optimized simulta-neously. So does investment problem. In this paper, we propose a framework of stock portfo-lio selection strategies based on MOGA and using fundamental data of company’s financial statements. MOGA is well suited to solve these multi-objective optimization problems since a family of “acceptable” solutions — a Pareto set — can be identified by different individuals through the evaluation process. We implement the framework with VEGA, a kind of MOGA method. Our preliminary experiments show that the results of these MOGA-based strategies are promising.
第1章 緒論1
1.1 研究背景1
1.2 研究動機與目的1
1.3 研究範圍3
1.4 研究限制3
1.5 論文架構3
第2章 文獻探討4
2.1 基本分析與股價報酬之關係4
2.2 投資組合理論6
2.3 多目標最佳化9
2.4 遺傳演算法(GA)11
2.5 多目標遺傳演算法(MOGA)18
第3章 系統架構25
3.1 研究架構26
3.2 選股條件與VEGA編碼26
3.3 選股頻率與投資方式28
3.4 選股目標與適應函數28
第4章 實驗設計與分析31
4.1 實驗設計31
4.2 實驗假設31
4.3 資料來源與處理32
4.4 實驗環境32
4.5 VEGA演化參數33
4.6 投資對象與選股條件34
4.7 移動視窗35
4.8 實驗一數據35
4.9 實驗二數據38
4.10 實驗分析41
第5章 結論與建議46
5.1 研究結論46
5.2 研究貢獻46
5.3 後續研究建議46
參考文獻48
附錄51
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