本文採用自組織映射（Self-Organizing Maps,SOM）對台灣股票市場進行分析，期待能夠發現股票之間的聯動。本文特別關注這些具有12個一年數據跨度動態共移(co-movement)的動態演變。因此本文採用競爭式學習法的自組織映射對資料進行分析，而自組織映射它的基本理念是在未經標示的樣本群（Unlabelled Samples）尋找某些相似的特徵、規則或是關係，然後再將這些有共同特色的樣本分群成同類。本文的資料為台灣股票市場2010年至2021年的所有上市公司，總計為969家公司，利用自組織映射對公司股價進行分群。根據輸出後的二維圖資料顯示，且進行長期的觀察發現，該方法表現出良好的實證分析，發現該方法能夠將大部分相同類股的公司分群在同樣的組別，這表示在相同類股的公司之間具有相似的歷史股價波動。以金融控股為例，在這12年間會有75%的金融控股業企業被分群在相同組別，也就是說這些企業具有相似的歷史股價行為，而在本地銀行也有著類似的情況。

This thesis employs Self-Organizing Maps (SOM) to analyze the Taiwan stock market in the hope of finding co-movement among some stocks. We specifically focus upon dynamic evolution of these co-movement with 12 one-year data spans.
Empirical analysis finds good results as is expected. Stock clustering are consistent with concept stock. Taking financial holdings industry as an example, 75% of financial holding corporation have been grouped in the same group over the past 12 years, Therefore, these corporations have similar historical stock price behavior, and the local banks industry have a similar phenomenon.

摘要 I
ABSTRACT II
第壹張、介紹 - 1 -
第一節、研究動機 - 1 -
第貳章、文獻回顧與探討 - 3 -
第一節、文獻回顧 - 3 -
第叁章、研究方法 - 5 -
第一節、研究步驟 - 5 -
第二節、演算法模型 - 7 -
第肆章、資料來源與研究結果 - 9 -
第一節、資料來源 - 9 -
第二節、研究結果 - 11 -
第伍章、結論 - 29 -
第陸章、參考文獻 - 30 -

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