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研究生:陳瑋旭
研究生(外文):Wei-Syu Chen
論文名稱:應用自組織映像之階層式聚集法選股:台灣的實證研究
論文名稱(外文):Stock Portfolio selection by Hierarchical Clustering with Self-Organizing Map :An Empirical Application to Taiwanese Stock Market
指導教授:林金龍林金龍引用關係
指導教授(外文):Jin-Lung Lin
學位類別:碩士
校院名稱:國立東華大學
系所名稱:財務金融學系
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:2017
畢業學年度:105
論文頁數:28
中文關鍵詞:選股策略自組織映像階層式聚集法
外文關鍵詞:stock selection strategyself-organizing maphierarchical clustering method
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當投資者建立投資組合時,股票選擇是最重要的問題。除了使用各種財務指標外,投資者通常會選擇大市值股票或績效較好的股票。然而,對於理性的投資者來說,應該以最高回報和最低風險為目標。因此,本文提出利用流行的自組織映像(SOM)方法獲得最佳的投資組合。然後將 SOM 的性能與使用傳統產業分類的分類方法進行比較,其分類法也是從分組中選擇一支股票。

本文對投資組合選擇做出了貢獻,因為 SOM 的大多數現有文獻是利用股票報酬的組合的單變量統計量作為輸入向量特徵,因此我們提出使用股票報酬之間的相關性作為新的輸入特徵。然而我們的輸入特徵是更直觀合適的,因為相關性是 Markowitz 投資組合理論的核心,股票報酬之間的關係是投資組合選擇的最重要特徵。此外,本論文使用行業類別作為基準聚類方法。台灣股市的實證分析表明,我們提出的方法的表現優於採用傳統產業類別分類方法,也優於以單一股票報酬統計量作為輸入特徵。本文將此方法應用於股票投資組合選擇,實證分析證實了我們提出的方法的有用性,可以幫助提高組合效率。
When investors build portfolios, stock selection is the most important issue. Besides using various financial indicators, investors usually pick large market capitalization stocks or winner stocks in the recent past. However, for a rational investor, he should aim at highest return and lowest risk. Therefore, this thesis proposes to utilize the popular self-organizing map (SOM) to obtain the optimal portfolio. The performance of SOM is then compared with clustering method using traditional industrial categorization which also selects one stock from each group.

This thesis makes contribution to portfolio selection in that most existing literatures of SOM utilize univariate statistic composed from the stock return as the input vector features while we propose to use correlations among stock returns. Our input feature is intuitively more appropriate as correlations are the core of Markowitz portfolio theory. It is the relationship between the stock returns that are the most important feature of portfolio selection. In addition, this thesis uses the industry category as the benchmark clustering method. Empirical analysis of Taiwanese stock market indicates that our proposed method perform better than that of SOM with univariate statistic of stock returns as input features and that using the traditional industrial categories. And then this paper applies this method to the stock portfolio selection. The empirical analysis confirms the usefulness of our proposed method and could help improve portfolio efficiency.
章節目錄
第一章 緒論...........................................................1
第一節 研究背景與研究動機..............................................1
第二節 研究目的.......................................................3
第二章 文獻探討.......................................................5
第一節 投資組合管理...................................................5
第二節 機器學習.......................................................5
第三章 機器學習演算法..................................................9
第一節 自組織映像演算法(Self-Organizing Map,SOM)......................9
第二節 階層式聚集法(Hierarchical clustering)..........................11
第四章 研究方法.......................................................13
第五章 實證結果.......................................................15
第六章 結論與建議.....................................................23
參考文獻...............................................................25
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