政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136378
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113656/144643 (79%)
造访人次 : 51698370      在线人数 : 522
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/136378


    题名: 利用集成學習預測台灣加權股價指數漲跌
    Applying Ensemble Learning to Enhance TAIEX Trend Prediction
    作者: 陳羿妘
    Chen, Yi-Yun
    贡献者: 黃泓智
    Huang, Hong-Chih
    陳羿妘
    Chen, Yi-Yun
    关键词: 集成學習
    羅吉斯迴歸
    隨機森林
    支持向量機
    台灣加權股價指數
    股價趨勢預測
    Ensemble learning
    Logistic regression
    Random forest
    Support vector machine
    TAIEX
    Stock trend prediction
    日期: 2021
    上传时间: 2021-08-04 14:55:15 (UTC+8)
    摘要: 本文旨在利用台灣加權股價指數TAIEX衍生之技術指標預測未來市場漲跌趨勢，藉由集成學習方法提升整體機器學習預測效果，結合羅吉斯迴歸、隨機森林、支持向量機三個異質演算法，增加模型間之差異性，並依據個別模型的特性，採用不同變數挑選方式，以提升資料品質，最終以單一模型作為標竿模型比較預測成效。整體而言，集成學習後之預測結果較單一模型具有更高的準確度，特別針對預測漲的部分，集成學習的效果較顯著，此外在長天期的趨勢預測中，集成學習的效果也更加明顯。
    This study aims to enhance prediction of trends on TAIEX with ensemble learning. As the input, several technical indicators are selected to train the model. To increase diversity of ensemble model, we used three heterogeneous models (logistic regression, random forest, support vector machine) instead of homogeneous models as component learners. Besides, depends on characteristic of component learners, different methods of feature selection are applied to increase the quality of data. To evaluate performance of ensemble models, we used single classifier models as benchmark models, and we found that accuracy of ensemble models is higher than single models. Especially in long-term case, the improvement of ensemble learning is more significant.
    參考文獻: 1. Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert systems with Applications, 42(20), 7046-7056.
    2. Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567.
    3. Di, X. (2014). Stock trend prediction with technical indicators using SVM. Independent Work Report, Stanford Univ.
    4. Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of stock performance in the Indian stock market using logistic regression. International Journal of Business and Information, 7(1), 105.
    5. Jiang, M., Liu, J., Zhang, L., & Liu, C. (2020). An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 541, 122272.
    6. Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. J Stat Softw, 36(11), 1-13.
    7. Li, H., Yang, Z., & Li, T. (2014). Algorithmic trading strategy based on massive data mining. Stanford University Stanford.
    8. Larsen, J. I. (2010). Predicting stock prices using technical analysis and machine learning (Master`s thesis, Institutt for datateknikk og informasjonsvitenskap).
    9. Moews, B., Herrmann, J. M., & Ibikunle, G. (2019). Lagged correlation-based deep learning for directional trend change prediction in financial time series. Expert Systems with Applications, 120, 197-206.
    10. Mierswa, I., & Morik, K. (2005). Automatic feature extraction for classifying audio data. Machine learning, 58(2), 127-149.
    11. Naik, N., & Mohan, B. R. (2019, May). Stock price movements classification using machine and deep learning techniques-the case study of indian stock market. In International Conference on Engineering Applications of Neural Networks (pp. 445-452). Springer, Cham.
    12. 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.
    13. Vapnik, V. N. (1995). The nature of statistical learning. Theory.
    14. Żbikowski, K. (2015). Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications, 42(4), 1797-1805.
    描述: 碩士
    國立政治大學
    風險管理與保險學系
    108358008
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0108358008
    数据类型: thesis
    DOI: 10.6814/NCCU202100893
    显示于类别:[風險管理與保險學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    800801.pdf1010KbAdobe PDF20检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈