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題名 台股股利完全填權息關鍵影響因素之研究
The key influencing factors of Taiwan stock price successfully remaining previous price after dividend payment作者 陳人豪
Chen, Jen Hao貢獻者 徐國偉
Hsu, Kuo Wei
陳人豪
Chen, Jen Hao關鍵詞 股利
資料探勘
特徵選取
Dividend
Data mining
Feature selection日期 2018 上傳時間 2-三月-2018 12:04:48 (UTC+8) 摘要 本研究以台灣50與中型100成分股為對象，運用資料探勘特徵選取技術，分析影響股票完全填權息成功之關鍵因素，並依此關鍵因素建構一個完全填權息預測模型，最後比較研究結果與過去研究之異同。本研究完全填權息預測模型的建構過程分為五階段：(1)定義完全填權息之股票：運用TEJ資料庫抓到的歷史股價資料與股利資訊，計算除權息前與除權息後股價，標註完全填權息和未完全填權息二個類別。(2)影響填權息相關因素：根據過去文獻所發現，影響短期填權息行情超額報酬的因素，以及影響股價的基本面因素，蒐集與股利相關的指標與基本分析中所用的公開財務報表資料。(3)特徵選取分析：利用循序前進搜尋（SFS）結合分類演算法，整合與計算所有影響因素資料，藉此找出關鍵的影響因素。(4)預測模型建立：根據特徵選取之結果資料，使用Weka軟體進行資料探勘支持向量機和決策樹分類模型訓練。(5)模型準確性比較與分析：本研究所建構之模型可協助存股型投資者，判斷可領取高股息且無股價損失之股票，提供投資人選股參考。
In this study, we use the Feature Selection Method for Data Mining to analyze the key factors that may affect the rate of the stock price successfully remaining previous price after dividend payment among stocks of 50 largest companies and 100 medium-sized companies in Taiwan. Based on these key factors, we construct a forecasting model for stocks with the 100% flat stock price. Finally, We try to find out the similarities and differences between the current study and past research. In this study, the construction of a forecasting model for stocks with the 100% flat stock price is divided into five stages: (1) Defining stocks with the 100% flat stock price: Marking stocks with the 100% flat stock price and the non-100% flat stock price on historical stock data and dividend information captured by the TEJ database; (2) Relevant Factors Affecting increase in the stock price after dividend payment: According to the factors found in the past literature that may affect excess returns from short-term increase in the stock price after dividend payment and the fundamental factors affecting the stock price, we are able to collect indexes related to dividends and public financial statements for basic analysis. (3) Feature Selection Analysis: By using the Sequential Forward Selection (SFS) method and the classification algorithm, all influencing factors are integrated and calculated to find out the key influencing factors; (4) The Establishment of the Prediction Model: According to the results of feature selection, we use the Weka software to conduct data mining and train the classification model based on support vector machines and decision trees. (5) Comparison and Analysis on Accuracy of the Model: The model constructed in this study can help stock-holding investors determine stocks with high dividends without loss of the stock price and provide reference for investors in stock selection.參考文獻 [1]陳怡文(1990)，台灣地區上市股票填息現象之研究－租稅效應與顧客效應之實證，國立政治大學企業管理研究所碩士論文。[2]李存修(1994)，股票股利與除權交易日之稅後抄額報酬與比價心理假說之實證，台大管理論叢，第5期，41-60。[3]林世銘、陳明進、李存修(2000)，兩稅合一前後上市公司除權及除息日股價行為之探討，管理學報，第18卷第3期，477-501。[4]陳秀燕(2005)，除權除息日股價行為研究，國立中山大學企業管理學系研究所碩士論文。[5]姚怡欣(2008)，台灣50成分股除權息日異常報酬分析，國立中山大學經濟學研究所在職專班碩士論文。[6]王彥鈞(2013)，投資除權息後股票之報酬研究-以台灣市場為例，國立成功大學經營管理碩士學位學程學位論文。[7]王素娟(2008)，兩種股利理論的比較-追隨者效應理論與迎合理論，科技與管理，第1期，22-24。[8]張文玉(1998)，財務比率對公開發行公司每股盈餘之預測力，國立臺灣大學財務金融學研究所碩士論文。[9]林冠秀(1999)，財務因素對股價報酬率的影響-台灣半導體上市公司為例，國立台北大學企業管理學系碩士論文。[10]周資輔(2001)，臺灣地區高科技產業股票報酬率之特性試探，國立交通大學高階主管管理學程碩士班碩士論文。[11]王正翔(2002)，盈餘宣告日前後未預期盈餘及股價報酬率之關聯性研究，東海大學管理碩士學程在職專班碩士論文。[12]吳政勳(2002)，股價報酬與財務比率之關聯性-貝氏馬可夫蒙地卡羅之分析研究，國立清華大學經濟學系碩士論文。[13]邱俊仁(2004)，台灣半導體產業經濟附加價值之研究-以台積電為例，世新大學經濟學研究所碩士論文。[14]張呈榜(2010)，雜誌推薦股票及相關財務比率分析，國立中正大學企業管理研究所碩士論文。[15]范曉芬(2014)，基本分析選股之實證研究-以台灣上市電子公司為例，世新大學財務金融學研究所碩士論文。[16]吳淑錂(2015)，以八大財務指標選股並建構投資組合之績效分析，南華大學財務金融學系財務管理碩士班碩士論文。[17]歐嘉文(2012)，基因演算法運用於特徵挑選解決財務危機預測問題，國立中央大學軟體工程研究所碩士論文。[18]陳佳郁(2011)，考慮景氣趨勢應用粒子群最佳化與支援向量機於財務危機預測，臺中技術學院資訊工程系碩士論文。[19]林宗勳(2006)，Support Vector Machines簡介，國立台灣大學通訊與多媒體實驗室。[20]謝佩鈞(2015)，Full Bandwidth RBF核函數參數自動挑選法與其在特徵選取之應用，國立臺中教育大學教育測驗統計研究所碩士論文。[21]陳士杰(2005)，決策樹學習，國立聯合大學訊管理學系。[22]施雅月、賴錦慧(譯)(2000)，資料探勘（原作者：Pang-ning Tan, Michael Steinbach and Vipin Kumar），臺北市：培生教育出版股份有限公司。[23]Elton, E. J., & Gruber, M. J. (1970). Marginal stockholder tax rates and the clientele effect. The Review of Economics and Statistics, 68-74.[24]Kalay, A. (1984). The ex‐dividend day behavior of stock prices: a re‐examination of the clientele effect. The Journal of Finance, 39(2), 557-561.[25]Sikora, R., & Piramuthu, S. (2007). Framework for efficient feature selection in genetic algorithm based data mining. European Journal of Operational Research, 180(2), 723-737.[26]Whitney, A. W. (1971). A direct method of nonparametric measurement selection. IEEE Transactions on Computers, 100(9), 1100-1103.[27]Zhu, Z., Ong, Y. S., & Dash, M. (2007). Wrapper–filter feature selection algorithm using a memetic framework. IEEE Transactions on Systems, Man, and Cybernetics, 37(1), 70-76.[28]Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), 273-324.[29]Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.[30]Miller, M. H., & Modigliani, F. (1961). Dividend policy, growth, and the valuation of shares. The Journal of Business, 34(4), 411-433.[31]Graham, B., & Dodd, D. L. (1934). Security analysis: principles and technique. McGraw-Hill.[32]Poterba, J. M., & Summers, L. H. (1984). New evidence that taxes affect the valuation of dividends. The Journal of Finance, 39(5), 1397-1415.[33]Gordon, M. J. (1959). Dividends, earnings, and stock prices. The Review of Economics and Statistics, 99-105.[34]Quinlan, J. R. (1979). Discovering rules by induction from large collections of example. Expert Systems in the Micro Electronics Age. Edinburgh University Press.[35]Quinlan, J. R. (1992). C4.5: programs for machine learning. Elsevier.[36]Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1(1-4), 131-156.[37]Dieterle, F. J. (2003). Multianalyte quantifications by means of integration of artificial neural networks, genetic algorithms and chemometrics for time-resolved analytical data.[38]Jain, A., & Zongker, D. (1997). Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 153-158.[39]Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. John Wiley & Sons.[40]Sun, W., Chen, J., & Li, J. (2007). Decision tree and PCA-based fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 21(3), 1300-1317.[41]Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: practical machine learning tools and techniques. Morgan Kaufmann.[42]Drazin, S., & Montag, M. (2012). Decision tree analysis using weka. Machine Learning-Project II, University of Miami.[43]Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. 描述 碩士
國立政治大學
資訊科學系碩士在職專班
102971019資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102971019 資料類型 thesis dc.contributor.advisor 徐國偉 zh_TW dc.contributor.advisor Hsu, Kuo Wei en_US dc.contributor.author (作者) 陳人豪 zh_TW dc.contributor.author (作者) Chen, Jen Hao en_US dc.creator (作者) 陳人豪 zh_TW dc.creator (作者) Chen, Jen Hao en_US dc.date (日期) 2018 en_US dc.date.accessioned 2-三月-2018 12:04:48 (UTC+8) - dc.date.available 2-三月-2018 12:04:48 (UTC+8) - dc.date.issued (上傳時間) 2-三月-2018 12:04:48 (UTC+8) - dc.identifier (其他 識別碼) G0102971019 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/116163 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系碩士在職專班 zh_TW dc.description (描述) 102971019 zh_TW dc.description.abstract (摘要) 本研究以台灣50與中型100成分股為對象，運用資料探勘特徵選取技術，分析影響股票完全填權息成功之關鍵因素，並依此關鍵因素建構一個完全填權息預測模型，最後比較研究結果與過去研究之異同。本研究完全填權息預測模型的建構過程分為五階段：(1)定義完全填權息之股票：運用TEJ資料庫抓到的歷史股價資料與股利資訊，計算除權息前與除權息後股價，標註完全填權息和未完全填權息二個類別。(2)影響填權息相關因素：根據過去文獻所發現，影響短期填權息行情超額報酬的因素，以及影響股價的基本面因素，蒐集與股利相關的指標與基本分析中所用的公開財務報表資料。(3)特徵選取分析：利用循序前進搜尋（SFS）結合分類演算法，整合與計算所有影響因素資料，藉此找出關鍵的影響因素。(4)預測模型建立：根據特徵選取之結果資料，使用Weka軟體進行資料探勘支持向量機和決策樹分類模型訓練。(5)模型準確性比較與分析：本研究所建構之模型可協助存股型投資者，判斷可領取高股息且無股價損失之股票，提供投資人選股參考。 zh_TW dc.description.abstract (摘要) In this study, we use the Feature Selection Method for Data Mining to analyze the key factors that may affect the rate of the stock price successfully remaining previous price after dividend payment among stocks of 50 largest companies and 100 medium-sized companies in Taiwan. Based on these key factors, we construct a forecasting model for stocks with the 100% flat stock price. Finally, We try to find out the similarities and differences between the current study and past research. In this study, the construction of a forecasting model for stocks with the 100% flat stock price is divided into five stages: (1) Defining stocks with the 100% flat stock price: Marking stocks with the 100% flat stock price and the non-100% flat stock price on historical stock data and dividend information captured by the TEJ database; (2) Relevant Factors Affecting increase in the stock price after dividend payment: According to the factors found in the past literature that may affect excess returns from short-term increase in the stock price after dividend payment and the fundamental factors affecting the stock price, we are able to collect indexes related to dividends and public financial statements for basic analysis. (3) Feature Selection Analysis: By using the Sequential Forward Selection (SFS) method and the classification algorithm, all influencing factors are integrated and calculated to find out the key influencing factors; (4) The Establishment of the Prediction Model: According to the results of feature selection, we use the Weka software to conduct data mining and train the classification model based on support vector machines and decision trees. (5) Comparison and Analysis on Accuracy of the Model: The model constructed in this study can help stock-holding investors determine stocks with high dividends without loss of the stock price and provide reference for investors in stock selection. en_US dc.description.tableofcontents 第一章緒論 11.1.研究背景與動機 11.2.研究問題與目的 61.3.論文架構 8第二章文獻探討 92.1影響除權息日前後股價變動之指標 92.1.1.股利相關指標 92.1.2.財務相關指標 142.2 特徵選取(Feature Selection) 252.2.1.循序前進搜尋(Sequential Forward Selection, SFS) 272.3 分類 292.3.1.支持向量機(Support Vector Machine, SVM) 302.3.2.決策樹(Decision Tree) 32第三章 研究方法 383.1.系統架構 383.2.樣本選取與資料來源 383.2.1.資料樣本 383.2.2.資料週期 403.2.3.資料來源 423.3.資料前處理 433.4.特徵選取(Feature Selection) 513.5.分類器參數之選擇 533.6.評價方法 54第四章 實驗結果與分析 584.1.影響填權息的關鍵特徵 584.1.1.台灣50與中型100成分股之非金融股 584.1.2.台灣50與中型100成分股之金融股 634.1.3.分析與評估 664.2.預測模型建立分析 674.2.1.台灣50與中型100成分股之非金融股 684.2.2.台灣50與中型100成分股之金融股 754.2.3.分析與評估 81第五章 結論與建議 835.1.結論 835.2.建議 85附錄 參考文獻 87 zh_TW dc.format.extent 2638599 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102971019 en_US dc.subject (關鍵詞) 股利 zh_TW dc.subject (關鍵詞) 資料探勘 zh_TW dc.subject (關鍵詞) 特徵選取 zh_TW dc.subject (關鍵詞) Dividend en_US dc.subject (關鍵詞) Data mining en_US dc.subject (關鍵詞) Feature selection en_US dc.title (題名) 台股股利完全填權息關鍵影響因素之研究 zh_TW dc.title (題名) The key influencing factors of Taiwan stock price successfully remaining previous price after dividend payment en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1]陳怡文(1990)，台灣地區上市股票填息現象之研究－租稅效應與顧客效應之實證，國立政治大學企業管理研究所碩士論文。[2]李存修(1994)，股票股利與除權交易日之稅後抄額報酬與比價心理假說之實證，台大管理論叢，第5期，41-60。[3]林世銘、陳明進、李存修(2000)，兩稅合一前後上市公司除權及除息日股價行為之探討，管理學報，第18卷第3期，477-501。[4]陳秀燕(2005)，除權除息日股價行為研究，國立中山大學企業管理學系研究所碩士論文。[5]姚怡欣(2008)，台灣50成分股除權息日異常報酬分析，國立中山大學經濟學研究所在職專班碩士論文。[6]王彥鈞(2013)，投資除權息後股票之報酬研究-以台灣市場為例，國立成功大學經營管理碩士學位學程學位論文。[7]王素娟(2008)，兩種股利理論的比較-追隨者效應理論與迎合理論，科技與管理，第1期，22-24。[8]張文玉(1998)，財務比率對公開發行公司每股盈餘之預測力，國立臺灣大學財務金融學研究所碩士論文。[9]林冠秀(1999)，財務因素對股價報酬率的影響-台灣半導體上市公司為例，國立台北大學企業管理學系碩士論文。[10]周資輔(2001)，臺灣地區高科技產業股票報酬率之特性試探，國立交通大學高階主管管理學程碩士班碩士論文。[11]王正翔(2002)，盈餘宣告日前後未預期盈餘及股價報酬率之關聯性研究，東海大學管理碩士學程在職專班碩士論文。[12]吳政勳(2002)，股價報酬與財務比率之關聯性-貝氏馬可夫蒙地卡羅之分析研究，國立清華大學經濟學系碩士論文。[13]邱俊仁(2004)，台灣半導體產業經濟附加價值之研究-以台積電為例，世新大學經濟學研究所碩士論文。[14]張呈榜(2010)，雜誌推薦股票及相關財務比率分析，國立中正大學企業管理研究所碩士論文。[15]范曉芬(2014)，基本分析選股之實證研究-以台灣上市電子公司為例，世新大學財務金融學研究所碩士論文。[16]吳淑錂(2015)，以八大財務指標選股並建構投資組合之績效分析，南華大學財務金融學系財務管理碩士班碩士論文。[17]歐嘉文(2012)，基因演算法運用於特徵挑選解決財務危機預測問題，國立中央大學軟體工程研究所碩士論文。[18]陳佳郁(2011)，考慮景氣趨勢應用粒子群最佳化與支援向量機於財務危機預測，臺中技術學院資訊工程系碩士論文。[19]林宗勳(2006)，Support Vector Machines簡介，國立台灣大學通訊與多媒體實驗室。[20]謝佩鈞(2015)，Full Bandwidth RBF核函數參數自動挑選法與其在特徵選取之應用，國立臺中教育大學教育測驗統計研究所碩士論文。[21]陳士杰(2005)，決策樹學習，國立聯合大學訊管理學系。[22]施雅月、賴錦慧(譯)(2000)，資料探勘（原作者：Pang-ning Tan, Michael Steinbach and Vipin Kumar），臺北市：培生教育出版股份有限公司。[23]Elton, E. 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