隨著投資風氣的普及，金融商品的多元化發展，民眾透過投資來獲取額外財富已是相當常見的管道之一，其中又以股票的高流動性、進入門檻低等好處，而備受一般投資人所喜愛。加上金融科技的快速發展、智能選股平台的問世，使得投資更為便利與效率。因此，本研究結合了基本、技術、籌碼面之指標，並以大立光、精測與台積電三檔股票為例，利用本研究方法-MCDM法進行投資考量因素與決策。MCDM法能找出各項因素之相對關係，並計算出各項因素之權重，找出關鍵的評估準則，依據各項因素權重排列出投資標的之優劣順序，進一步做出最佳決策。
    首先，透過相關文獻回顧，找出「基本面」、「技術面」、「籌碼面」為本研究之三項構面，再進一步深入探討，找出了「本益比」、「每股盈餘」、「股東權益報酬率」、「移動平均線」、「隨機指標」、「券資比」與「三大法人買超」七項次因子。並於研究期間，於相關領域之專家學者回收16份有效問卷，之後透過DEMATEL法計算出各項準則之關聯性，發現「每股盈餘」的影響度最高，顯示此準則最容易影響其他評估準則；而「三大法人買超」的關聯度最高，代表此準則佔整體評估準則之重要性最大。接著利用DANP法算出各準則之權重，其中「三大法人買超」有最高的權重，而「隨機指標」相對不受專家學者重視，權重最低。最後採用VIKOR法針對投資標的進行評估，由分析結果得知，台積電擁有最高的總績效值，同時也擁有最低的平均差距(gaps)，為本研究得出之最佳決策。

With the popularity of investment and the diversified development of financial instruments, so it has become a common way to obtain additional wealth through investments. Among them, the high liquidity of stocks and the low barriers to entry have made them subject to general investment. With the flourishing development of Fintech, the invention of intelligent stock-selection platform. Therefore, lead to more convenient and efficient investment. This study combined with fundamental, technical and chip factors, and based on the MCDM method to explore the considerations and decisions of selecting stock among LARGAN, CHPT and TSMC. MCDM method can find out the relative relationship of various factors, and calculate the weights of each factor, find out the key evaluation criteria, order the investment target based on the weights of each factor, and make the best decision.
  First of all, through the review of relevant literatures, the "Fundamental", "Technical", and "Chip"were selected as the facets of this study. And further in-depth discussion, we identified "Price to Earning Ratio", "Earnings Per Share", "Return on Equity", "Moving Average", "Stochastic Index", "short selling/margin ratio", and "Net-Bought By Institutional Investors" seven minor factors. During the research period, 16 valid questionnaires were recovered from the experts and scholars in related fields. And then using DEMATEL method to calculate the relevance of criterions, we found that "Earnings Per Share" is the most influential factor, which means this factor most easily affects other criterions. And "Net-Bought By Institutional Investors" has maximum relevance, which means experts and scholars consider the criterion is the most important selection criteria. Next, applying the DANP method to calculate the weights of criterions, we found that "Net-Bought By Institutional Investors" has the highest weights, while "Stochastic Index" has the lowest weights, that is to say, this factor is less important to experts and scholars. Finally, we use the VIKOR method for investment evaluation, according to the results from data analysis, TSMC has the highest total performance value, and also the lowest total performance gaps, which is the optimal decision relatively.

目錄

目錄	I
表目錄	III
圖目錄	V
第一章  緒論	1
第一節 研究背景與動機	1
第二節 研究目的	6
第三節 研究流程	7
第四節 研究範圍與限制	9
第二章  文獻探討	10
第一節  智能選股概論	10
第二節 智能選股主因子文獻探討	13
第三節  智能選股次因子文獻探討	17
第四節  投資標的之選擇與介紹	23
第三章  研究方法	29
第一節 研究架構圖及操作型定義	29
第二節 研究方法	34
第三節 問卷設計與資料來源	41
第四章 實證分析	42
第一節 一致性檢定	42
第二節 DEMATEL-評估各準則間之關係	43
第三節 DANP-計算各準則之權重	52
第四節 VIKOR-評選最佳決策	56
第五章 結論與建議	60
第一節 研究結論	60
第二節 研究限制與未來研究建議	61
參考文獻	64
中文部分	64
英文部分	67
附錄	72

表目錄

表2-1 學者提出之基本面次因子	19
表2-2 學者提出之技術面次因子	21
表2-3 學者提出之籌碼面次因子	23
表2-4 大立光歷年獲利能力	24
表2-5 精測歷年獲利能力	26
表2-6 台積電歷年獲利能力	27
表4-1 一致性分析結果	43
表4-2 A矩陣 直接影響關係矩陣	44
表4-3 X矩陣 正規化影響關係矩陣	45
表4-4 T矩陣 總影響關係矩陣	46
表4-5 T矩陣 構面關係矩陣	46
表4-6 各準則之影響度及被影響度	47
表4-7 各構面之影響度及被影響度	47
表4-8 基本面評估準則數據	48
表4-9 技術面評估準則數據	49
表4-10籌碼面評估準則數據	50
表4-11 Unweighted矩陣 未加權超級矩陣	52
表4-12 Weighted矩陣 加權超級矩陣	53
表4-13 Limit矩陣 極限化超級矩陣	53
表4-14 七項評估準則權重排序	54
表4-15 基本面評估準則權重排序	55
表4-16 技術面評估準則權重排序	55
表4-17 籌碼面評估準則權重排序	55
表4-18 構面間權重排序	56
表4-19 評選三檔股票各準則評分總和平均表	57
表4-20 總績效評估表	58

圖目錄

圖1-1 WEF金融服務之六大面向(2015)	3
圖1-2 2010-2015年全球金融科技投資額	3
圖1-3 研究流程圖	8
圖 2-1 大立光股價走勢圖	25
圖 2-2 精測股價走勢圖	26
圖2-3 台積電股價走勢圖	28
圖3-1 本研究架構圖	30
圖4-1 構面因果邏輯關係圖	48
圖4-2 基本面準則因果邏輯關係圖	49
圖4-3 技術面準則因果邏輯關係圖	50
圖4-4 籌碼面準則因果邏輯關係圖	51
圖4-5 所有準則因果邏輯關係圖	51

中文部分
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6.李雅璇(2013)。以巴菲特選股模式建構基本面投資組合：在台股之實證  
7.李資理(2011)。券資比於台灣50 ETF之模擬交易實證－門檻共整合模型之應用。輔仁大學金融與國際企業學系金融碩士班論文
8.辛珍廷(2016)。財務面選股策略運用於台灣50指數成分股實證。國立高雄應用科技大學金融系金融資訊碩士在職專班碩士論文
9.周巧晨(2014)。基本面選股策略之投資績效分析－2000年至2013年台灣上櫃公司實證。東吳大學國際經營與貿易學系碩士論文
10.周照偉、鄭榮祿、蔡賢亮、楊崇宏、牟聖遠(2015)。臺灣股市技術分析實證:以隨機指標，相對強弱指標，指數平滑異同平均線指標及趨向指標為例。高雄應用科技大學人文與社會科學學刊，1(2)，119-133
11.林宗緯(2016)。交易者類別是否影響股價？臺灣股票市場實證分析。國立暨南國際大學財務金融學系碩士論文
12.林皇君(2011)。影響股價報酬波動性及相關因素之研究。國立虎尾科技大學
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16.高慶恩(2012)。結合基本面與技術面之複合選股模型。中華大學資訊管理學系碩士班碩士論文 
17.張哲郡(2001)。股市預測模式及交易策略之研究。國立臺北大學企業管理學系碩士論文
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21.陳明煜(2010)。基本分析選股、技術分析擇時買進與設置停損機制之績效評估。輔仁大學金融研究所碩士論文
22.陳南宇(2017)。結合基本面與技術面之投資策略對台灣股市股價報酬之分析。國立中正大學高階主管管理碩士在職專班碩士論文
23.陳昱安(2015)。巨量資料探討及分析-以台灣50 ETF 為例。淡江大學管理科學學系碩士班學位論文
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