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研究生:韓心維
研究生(外文):Han-Hsin Wei
論文名稱:運用資料探勘技術於公告股利後之行情預測模型: 以台灣股市上市公司為例
論文名稱(外文):Stock prediction model after dividend announcement using data mining technology: Take the listed companies in Taiwan stock market as an example
指導教授:胡雅涵胡雅涵引用關係
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊管理學系在職專班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:58
中文關鍵詞:資料探勘股市預測股利行情預測除權息
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本研究運用資料探勘技術建構股票股利行情之預測模型來預測台灣上市公司股利公告後的股票行情,以台灣證卷市場上市公司作為研究對象,研究變數包含股利政策、基本面指標、技術面指標、籌碼面指標,探討個股股東會公告股利政策後,是否有上漲10%之行情,並找出影響股利行情之關鍵因子。研究之目標變數為「公告股利政策後是否有10%上漲行情」,將每一筆資料標註 Y 或是 N 做為識別。本研究以2010年至2021年作為研究區間,使用多種技術指標和財務指標作為關鍵因子,包括股利政策、基本面、技術面和籌碼面等方面的指標,並採用資料探勘技術及特徵選取的方法,識別出影響除權息前股利發佈行情之關鍵因子,以提供投資者在每年度除權息期間作為投資參考的依據。
本研究使用決策樹、隨機森林、樸素貝葉斯、支援向量機與類神經網路五種監督式學習演算法來建構預測模型。並透過十折交叉驗證來訓練預測模型,最後使用混亂矩陣來評估模型預測準確度。實驗設計使用四種不同的預測資料集,以探討各資料集對預測結果的影響。實驗1包含所有上市公司的資料以及所有相關變數(股利政策、基本面、技術面、籌碼面),用以評估五種監督式學習演算法中哪一種具有較高的準確性;實驗2選取殖利率高於5%以上的上市公司及殖利率低於5%的上市公司資料,並採用所有變數作為預測資料集,比較殖利率高低是否影響結果,以評估針對高殖利率股票是否能獲得更佳的預測結果;實驗3則包含四個子實驗:(1)使用所有上市公司資料,並採用基本面搭配股利政策的指標作為研究變數;(2)使用所有上市公司資料,僅採用技術面財務指標作為研究變數;(3)使用所有上市公司資料,僅採用籌碼面指標作為研究變數;(4)使用所有上市公司資料,並結合股利政策與籌碼面指標;(5)使用所有上市公司資料,並結合股利政策、基本面與籌碼面指標。這些子實驗旨在評估不同變數組合下哪一組能獲得較高的預測分數。
研究建立的67個研究變數,經特徵選取結果顯示,股利行情關鍵因子為股東會至除權息的間隔天數、殖利率、差離狀態(DIF) 、毛利成長率、每股盈餘增減、震盪量指標、乖離率、投信持股率、自營持股率,而基本面指標搭配股利政策的預測能力優於技術指標的及籌碼面指標。
This study applies data mining techniques to construct a predictive model for stock dividend market trends, aiming to predict the stock market trends after Taiwanese listed companies announce their dividends. The research targets are listed companies in Taiwan's securities market. The research variables include dividend policy, fundamental indicators, technical indicators, and capital flow indicators. Examine whether there's a 10% increase in stock price after the announcement of dividend policy in the shareholders' meeting of individual stocks, and identify the key factors affecting the dividend market trends. The target variable of the study is "Whether there is a 10% increase in stock price after the announcement of dividend policy", with each piece of data labeled as 'Y' or 'N' for identification. This study covers the period from 2010 to 2021 and uses various technical indicators and financial indicators as key factors, including dividend policy, fundamental, technical, and capital flow indicators. Data mining techniques and feature selection methods are used to identify the key factors affecting the market trends prior to the ex-dividend date, providing investors with a reference for investing during the annual ex-dividend period.
This study uses five supervised learning algorithms to construct a predictive model: decision trees, random forests, naive Bayes, support vector machines, and artificial neural networks. The predictive model is trained through ten-fold cross-validation, and the prediction accuracy of the model is evaluated using a confusion matrix. The experimental design uses four different prediction datasets to investigate the impact of each dataset on the prediction results.
Out of the 67 research variables established in this study, feature selection results show that the key factors for dividend market trends are the number of days between the shareholders' meeting and the ex-dividend date, the dividend yield, DIF, gross profit growth rate, earnings per share changes, volatility index, deviation rate, investment trust holding ratio, and self-operating holding ratio. Furthermore, the predictive ability of fundamental indicators combined with dividend policy surpasses that of technical indicators and capital flow indicators.
摘要 I
ABSTRACT II
目錄 III
圖目錄 V
表目錄 VI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 5
第二章 文獻探討 7
2.1 股利政策對於股價之影響 7
2.1.1 股東會股利公布 7
2.1.2 股利政策殖利率 8
2.2 財務指標組合與股價報酬之相關文獻 12
第三章 研究方法 15
3.1 研究架構 15
3.3 研究變數 18
3.3.1 依變數 18
3.3.2 自變數 19
3.4 資料探勘演算法 27
3.4.1 支持向量機(Support Vector Machine, SVM) 27
3.4.2 單純貝氏分類器(Naive Bayes Classifier, NB) 27
3.4.3 類神經網絡(Neural Network, NN) 28
3.4.4 隨機森林(Random Forest, RF) 28
3.4.5 決策樹(Decision Tree, DT) 28
3.5 實驗設計與評估 29
3.5.1 實驗設計 29
3.5.2 評估指標 32
第四章 實驗結果 34
4.1 實驗結果 34
4.2 綜合討論 38
第五章 結論與建議 40
5.1 研究結論與貢獻 40
5.2 研究限制 41
5.3 未來研究方向與建議 41
英文參考文獻
Ahmad, T. (2022). A Case of Pakistan Investigating the Relationship between Inflation and Economic Growth: A Case of Pakistan. Acta Pedagogia Asiana, 1(1), 1-8.
Ahmed, T., & Mishra, V. K. (2021). Online Kernel Adaptive Filtering-based approach for mid-price prediction.
Althelaya, K. A., El-Alfy, E. S. M., & Mohammed, S. (2018, April). Evaluation of bidirectional LSTM for short-and long-term stock market prediction. In 2018 9th international conference on information and communication systems (ICICS) (pp. 151-156). IEEE.
Akhigbe, A., & Madura, J. (1996). Dividend policy and corporate performance. Journal of Business Finance & Accounting, 23(9‐10), 1267-1287.
Arévalo, R., García, J., Guijarro, F., & Peris, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications, 81, 177-192.
Bharambe, M. M. P., & Dharmadhikari, S. C. (2017). Stock market analysis based on artificial neural network with big data. In Proceedings of 8th post graduate conference for information technology.
Cheng, K. C., Huang, M. J., Fu, C. K., Wang, K. H., Wang, H. M., & Lin, L. H. (2021). Establishing a multiple-criteria decision-making model for stock investment decisions using data mining techniques. Sustainability, 13(6), 3100.
D'Acunto, F., Malmendier, U., & Weber, M. (2023). What do the data tell us about inflation expectations?. In Handbook of economic expectations (pp. 133-161). Academic Press.
Delen, D., Kuzey, C., & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert systems with applications, 40(10), 3970-3983.
De Cesari, A., & Huang-Meier, W. (2015). Dividend changes and stock price informativeness. Journal of Corporate Finance, 35, 1-17.
Elliott, R. N. (1938). The Wave Principle.(1939) Articles in. Financial World.
Fu, X., Du, J., Guo, Y., Liu, M., Dong, T., & Duan, X. (2018). A machine learning framework for stock selection. arXiv preprint arXiv:1806.01743.
Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190.
He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing, 36, 534-551.
Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537.
Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990, June). Stock market prediction system with modular neural networks. In 1990 IJCNN international joint conference on neural networks (pp. 1-6). IEEE.
Lee, M. C., Chang, J. W., Hung, J. C., & Chen, B. L. (2021). Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction. Computer Science and Information Systems, 18(2), 401-418.
Liao, L.-C., Chou, R. Y., & Chiu, B. (2013). Anchoring effect on foreign institutional investors’ momentum trading behavior: Evidence from the Taiwan stock market. The North American Journal of Economics and Finance, 26, 72-91.
Majumder, M., & Hussian, M. A. (2007). Forecasting of Indian stock market index using artificial neural network. Information Science, 98-105.
Masum, A. (2014). Dividend policy and its impact on stock price–A study on commercial banks listed in Dhaka stock exchange. Global disclosure of Economics and Business, 3(1).
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4), 3007-3057.
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259-268.
Park, K., Fang, Z., & Ha, Y. H. (2019). Stock and bond returns correlation in Korea: Local versus global risk during crisis periods. Journal of Asian Economics, 65, 101136.
Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction. Expert Systems with Applications, 135, 60-70.
Shahzad, S. J. H., Mensi, W., Hammoudeh, S., Sohail, A., & Al-Yahyaee, K. H. (2019). Does gold act as a hedge against different nuances of inflation? Evidence from Quantile-on-Quantile and causality-in-quantiles approaches. Resources Policy, 62, 602-615.
Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26.
Shen, J., & Shafiq, M. O. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of big Data, 7(1), 1-33.
Syofyan, R., Putra, D. G., & Aprayuda, R. (2020). Influence Of Company Value Information, Dividend Policy, And Capital Structure On Stock Price. SAR (Soedirman Accounting Review): Journal of Accounting and Business, 5(2), 152-169.
Tan, Z., Yan, Z., & Zhu, G. (2019). Stock selection with random forest: An exploitation of excess return in the Chinese stock market. Heliyon, 5(8), e02310.
Tsai, L. J., Shu, P. G., & Chiang, S. J. (2019). Foreign investors’ trading behavior and market conditions: Evidence from Taiwan. Journal of Multinational Financial Management, 52, 100591.
中文參考文獻
倪衍森, 黃寶玉, & 古曜嘉. (2011). 台灣高額現金股利宣告效果之實證研究--以富時指數公司所編製的成分股為例. 東吳經濟商學學報, (72), 81-107.
李存修. (1991). 股票股利及現金增資之除權與股價行為: 理論與實證. 臺大管理論叢, 1991, 2.1: 001-040.
李家政. (2009). 利用關聯法則探勘個股之間的關聯性. 大同大學資訊工程學系.
https://hdl.handle.net/11296/dm9xqf
林佳頤. (2020). 應用資料探勘技術於股票投資-以廣達為例. 國立交通大學工業工程與管理系所. https://hdl.handle.net/11296/2zzqqj
郭宏達. (2012). 企業經營屬性與Tobin's Q關係之研究--以現金殖利率作為擇股的交易策略. 國立交通大學管理學院財務金融學程碩士論文.
https://hdl.handle.net/11296/wbr35j
黃翔建. (2016). 基本分析面選股之投資組合報酬率. 南臺科技大學企業管理系碩士論文. https://hdl.handle.net/11296/3yy23k
黃德基. (2016). 台灣股票市場填息情形與股利所得稅關係探討. 國立清華大學高階經營管理碩士在職專班碩士論文. https://hdl.handle.net/11296/4va29r
黃蕙文. (2011). 台灣股票市場填權息資訊投資策略之探討. 世新大學財務金融學研究所(含碩專班)碩士論文. https://hdl.handle.net/11296/g3w3j4
周麗娜. (2013). 股利發放率、股利殖利率與股票報酬之探討. 國立成功大學財務金融研究所在職專班. https://hdl.handle.net/11296/25d5zb
鄭琮翰. (2021). 投資人情緒與填息權天數影響. 國立中正大學財務金融學系碩士在職專班碩士論文. https://hdl.handle.net/11296/cuyyyq
陳人豪. (2018). 台股股利完全填權息關鍵影響因素之研究. 國立政治大學資訊科學系碩士在職專班碩士論文. https://hdl.handle.net/11296/gzqj45
陳欣伶. (2003). 台灣股利發放形式之價格效果與其影響因素. 國立中正大學企業管理研究所碩士論文. https://hdl.handle.net/11296/jh3e6f
陳勁輝. (2019). 股東會期間對市場的影響. 國立中央大學財務金融學系.
https://hdl.handle.net/11296/dey595
沈伊曜. (2015). 台灣上市公司填息行情之研究-以電子產業為例. 國立雲林科技大學財務金融系碩士論文. https://hdl.handle.net/11296/vv95cd
曹木原. (2015). 高股息之股票在除息前漲跌幅與營收成長對股價報酬率之研究-以臺灣證券交易所上市股票為例. 銘傳大學資訊管理學系碩士在職專班碩士論文.
https://hdl.handle.net/11296/q4n9rd
蔡元琳. (2007). 現金股利宣告效果與影響因素之實證--以台灣上市上櫃公司為例. 國立中興大學財務金融系所碩士論文. https://hdl.handle.net/11296/3x5y75
游家星. (2006). 金融控股公司之股價訊息效果與因素分析. 國立臺灣大學經濟學研究所碩士論文. https://hdl.handle.net/11296/rt2ej3
味正五. (2017). 台股除息日的類股異常報酬:結合元大高股息成分股報酬率研究. 健行科技大學財務金融系碩士班碩士論文. https://hdl.handle.net/11296/jn59g3
翁崇傑. (2015). 高現金殖利率選股投資策略之研究. 亞洲大學財務金融學系碩士論文. https://hdl.handle.net/11296/8rap73
余俊憲. (2013). 台灣上市公司現金減資宣告與發放現金股利宣告效果差異研究. 國立成功大學國際企業研究所碩博士班. https://hdl.handle.net/11296/6em8pz
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