南華大學機構典藏系統:Item 987654321/27622
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    Please use this identifier to cite or link to this item: http://nhuir.nhu.edu.tw/handle/987654321/27622


    Title: 應用FIOWA和ME-OWA於模糊多準則決策股票評選模型之研究
    Other Titles: A Study of Applying Fiowa and Me-Owa to the Fuzzy Multi-Criteria Decision Making Model for Stock Selection
    Authors: 王佳文
    Contributors: 南華大學資訊管理學系
    Keywords: 模糊多準則決策方法;誘導循序加權平均運算子;循序加權平均運算子;二元語言表示模式;股市技術指標
    Fuzzy multi-criteria decision making method;induced ordered weighted average (IOWA) operator;ordered weighted average (OWA) operator;2-tuple linguistic representation model;Technical Indicators
    Date: 2020
    Issue Date: 2021-01-25 15:48:06 (UTC+8)
    Abstract: 多準則決策（Multi-Criteria Decision Making, MCDM）方法常被使用在選擇與評估方案上。然而偏好順序與權重因子對於評估結果會有不同的影響程度。因此本研究應用FIOWA與ME-OWA於2-tuple加權模糊語意表示模式，期望探討權重因子在方案評估上之影響性。在個案驗證方面採用台灣股市為實驗案例。本研究之優點如下：(1)採用2-tuple語意表示模式，簡化計算過程；(2)在屬性選取部份考慮多影響因子，以個股資料與技術指標進行研究；(3)利用特徵選取方法進行準則篩選，如逐步廻歸、資訊獲利與資訊獲利比率；(3) 引入FIOWA與ME-OWA運算子使在整合屬性與權重考量時更為合理；(4) 利用台灣股市為實驗案例。(5)並實際比較不同權重運算子在個選選股之影響。
    Multi-Criteria Decision Making (MCDM) method is often used to select and evaluate alternatives. However, the preferenceaggregation of decision making and the weighted factors have different degrees of influence on the results. Therefore, this study applies FIOWA operator and ME-OWA operator in the 2-tuple linguistic representation model to simplify the calculation process, and expects to explore the impact of weight factors in the evaluation of alternatives. The advantages of this study are shown as follows: (1) use of 2-tuple linguistic representation model to simplify the calculation process. (2) Consider multiple impact factors in the attribute selection section, and research with stock market technical indicators. (3) Apply FIOWA operator and the ME-OWA operator to the model which makes the integration of attributes and weights more reasonable; (4) Use the Taiwan's stock market as experimental cases, (5) and compare the effects of different weighted operators on stock selection.
    Appears in Collections:[Department of Information Management] NSTC Project

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