English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113311/144292 (79%)
Visitors : 50915888      Online Users : 797
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/147206


    Title: 機器學習方法建構股票市場投資策略及波動度管理
    Constructing Stock Risk Portfolios with Volatility Control Using Machine Learning
    Authors: 許茱媛
    Hsu, Jhu-Yuan
    Contributors: 黃泓智
    許茱媛
    Hsu, Jhu-Yuan
    Keywords: 機器學習
    ETF
    波動度控制
    Machine Learning
    ETF
    Volatility Control
    Date: 2023
    Issue Date: 2023-09-01 16:06:25 (UTC+8)
    Abstract: 本研究以台灣股票市場作為研究標的,納入每季的財報資料和常用技術指標進行集成學習,集成學習模型包含XGBoost、MLP和SVR進行投票法,選用不同類型的模型期望能提升單一模型績效,以更好的預測股票波動,進而建立最適投資組合,觀察合適的投資組合方法與檔數,挑選股票類投資策略和ETF投資策略進行後續的波動度管理。
    本文採用低波動之ETF標的建立不同風險投資人的投資策略,搭配目標波動度方法進行波動控制,並使用不同指標,包含日報酬、最大回落、VIX與LSTM預測隔日報酬做波動度上限指標,同時配合不同門檻值觀察來實證波動度管理之績效。實證結果發現,採用ETF控制波動下的投資策略除了降低波動度外,可以達到更好的夏普比率。
    This study focuses on the Taiwan stock market as the research subject, incorporating quarterly financial data and commonly used technical indicators for ensemble learning. The ensemble learning model includes XGBoost, MLP, and SVR using the voting method, with the expectation of improving the performance of individual models to better predict stock volatility. The ultimate goal is to establish an optimal investment portfolio and observe suitable investment methods and the number of holdings. Stock investment strategies and ETF investment strategies will be selected for subsequent volatility control.
    In this paper, low-volatility ETFs are used to establish investment strategies for different risk-tolerant investors. Target volatility methods are applied for volatility control, and various indicators, including daily returns, maximum drawdown, VIX, and LSTM prediction, are used as volatility upper bound indicators. Different threshold values are used to empirically test the performance of volatility management. Empirical results indicate that employing ETFs for volatility control in investment strategies not only reduces volatility but also leads to better Sharpe ratios.
    Reference: 林晏緯(2021)。利用集成學習建構股市最適投資組合。〔未出版之碩士論文〕。淡政治大學風險管理與保險學系。
    錢慧娟(2022)。訊號分解對於集成學習預測股價準確率之影響—以台灣加權股價指數為例。〔未出版之碩士論文〕。淡政治大學風險管理與保險學系。
    Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
    Choudhury, S., Ghosh, S., Bhattacharya, A., Fernandes, K. J., & Tiwari, M. K. (2014). A real-time clustering and SVM based price-volatility prediction for optimal trading strategy. Neurocomputing, 131, 419-426. ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2013.10.002
    Connell, P., & Hodgson, M. (2016). Managing investment outcomes with volatility control. Schroder Investment Management North America Inc.
    Hochreiter, Sepp & Schmidhuber, Jürgen. (1997). Long Short-term Memory. Neural computation. 9. 1735-80. 10.1162/neco.1997.9.8.1735.
    Huang, Y., Capretz, L. F., & Ho, D. (2021). Machine Learning for Stock Prediction Based on Fundamental Analysis. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01-10). Orlando, FL, USA. https://doi.org/10.1109/SSCI50451.2021.9660134
    Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, 115537. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2021.115537.
    Jidong, L., & Ran, Z. (2018). Dynamic Weighting Multi Factor Stock Selection Strategy Based on XGBoost Machine Learning Algorithm. In 2018 IEEE International Conference of Safety Produce Informatization (IICSPI) (pp. 868-872). Chongqing, China. doi: 10.1109/IICSPI.2018.8690416.
    Li, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analysis, 13, 139–149. https://doi.org/10.1007/s41060-021-00279-9
    Ma, Y., Wang, Y., Wang, W., & Zhang, C. (2023). Portfolios with return and volatility prediction for the energy stock market. Energy, 270, 126958. ISSN 0360-5442. https://doi.org/10.1016/j.energy.2023.126958
    Maqbool, J., Aggarwal, P., Kaur, R., Mittal, A., & Ganaie, I. A. (2023). Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach. Procedia Computer Science, 218, 1067-1078. ISSN 1877-0509. https://doi.org/10.1016/j.procs.2023.01.086.
    Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
    Mehta, S., Rana, P., Singh, S., Sharma, A., & Agarwal, P. (2019). Ensemble Learning Approach for Enhanced Stock Prediction. In 2019 Twelfth International Conference on Contemporary Computing (IC3) (pp. 1-5). Noida, India. doi: 10.1109/IC3.2019.8844891.
    Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172. https://doi.org/10.1016/j.eswa.2014.10.031
    Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
    Vapnik, V., & Chervonenkis, A. (1997). Support Vector Regression Machines. In Advances in Neural Information Processing Systems 9 (pp. 281-287).
    Yun, K. K., Yoon, S. W.&Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716. https://doi.org/10.1016/j.eswa.2021.115716.
    Description: 碩士
    國立政治大學
    風險管理與保險學系
    110358011
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110358011
    Data Type: thesis
    Appears in Collections:[風險管理與保險學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    801101.pdf1709KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    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 ©   - Feedback