政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143837
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 110189/141115 (78%)
造访人次 : 46789441      在线人数 : 350
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
    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/143837


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/143837


    题名: 基於深度強化學習來探索市場與資產因子於資產配置策略優化
    Exploring Portfolio Optimization Strategy with Market and Asset Factors in Deep Reinforcement Learning
    作者: 張柏詠
    Chang, Pai-Yung
    贡献者: 胡毓忠
    Hu, Yuh-Jong
    張柏詠
    Chang, Pai-Yung
    关键词: 機器學習
    深度強化學習
    資產配置
    投資組合
    DDPG
    LSTM
    Machine Learning
    Deep Reinforcement Learning
    Asset Allocation
    Investment portfolio
    DDPG
    LSTM
    日期: 2023
    上传时间: 2023-03-09 18:37:46 (UTC+8)
    摘要: 本文共有三個實驗命題,命題一,研究深度強化學習模型在不同市場情境下如何應變。命題二,比較深度強化學習資產配置模型與加入市場分數之資產配置模型之風險報酬。命題三,探討加入市場分數之資產配置模型與加入無風險資產之資產配置模型差異。以三命題探索不同市場趨勢與不同資產池對於深度強化學習資產配置模型之各類比較,命題一研究顯示深度強化學習資產配置模型在市場趨勢屬於恐慌時期與熊市時皆能有效改善風險報酬率;命題二研究成果顯示將資產配置模型拆成資產分數與市場分數兩部分,能在有效降低風險同時保有一定的獲利能力,風險報酬率更勝單純資產配置模型。命題三研究成果顯示加入市場分數與於資產池中加入無風險資產之資產模型各有優缺點,然長期來看加入市場分數較能在承受相同風險條件下追求更優的獲利率。三命題皆以風險與報酬指標來比較不同資產配置模型優劣,期望能建構出穩定獲利資產配置模型。
    There are three experimental purposes in this paper. First, how will the DRL asset management model respond to different market trends? Second, compare the risk-reward of the DRL asset management model with the DRL asset management model added market score. Third, discuss the difference between the asset management model adding market scores and the asset management model adding risk-free assets. Explore various comparisons of different market trends and different asset pools for the DRL asset management model with three propositions. The results revealed that the DRL model can effectively improve the risk-reward ratio during the great depression and bear market. The research results of proposition 2 show that splitting the asset management model into two parts, the asset score and the market score, can effectively reduce risks while maintaining certain profitability, and the risk-reward ratio is better than the traditional asset management model. The research results of proposition 3 show that adding market scores and adding risk-free assets to the asset pool have its own advantages and disadvantages. However, in the long term, adding market scores can pursue better profitability under the same risk conditions.
    參考文獻: [1] Chen, C., Zhao, L., Bian, J., Xing, C., & Liu, T. Y. (2019). Investment behaviors can tell what inside: Exploring stock intrinsic properties for stock trend prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2376-2384)
    [2] Darapaneni, N., Basu, A., Savla, S., Gururajan, R., Saquib, N., Singhavi, S., ... & Paduri, A. R. (2020). Automated portfolio rebalancing using Q-learning. In 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0596-0602).
    [3] Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2016). Deep direct reinforcement learning for financial signal representation and trading. IEEE transactions on neural networks and learning systems, 28(3) (pp.653-664).
    [4] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8) (pp. 1735-1780).
    [5] Hoseinzade, E., & Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129 (pp. 273-285).
    [6] Jiang, Z., Xu, D., & Liang, J. (2017). A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint arXiv:1706.10059.
    [7] Jin, O., & El-Saawy, H. (2016). Portfolio management using reinforcement learning. Stanford University.
    [8] Lintner, J. (1975). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. In Stochastic optimization models in finance (pp. 131-155).
    [9] Liu, F., Li, Y., Li, B., Li, J., & Xie, H. (2021). Bitcoin transaction strategy construction based on deep reinforcement learning. Applied Soft Computing, 113, 107952.
    [10] Liu, X. Y., Yang, H., Chen, Q., Zhang, R., Yang, L., Xiao, B., & Wang, C. D. (2020). FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance. arXiv preprint arXiv:2011.09607.
    [11] Nelson, D. M., Pereira, A. C., & De Oliveira, R. A. (2017). Stock market`s price movement prediction with LSTM neural networks. In 2017 International joint conference on neural networks (IJCNN) (pp. 1419-1426).
    [12] Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384.
    [13] Roondiwala, M., Patel, H., & Varma, S. (2017). Predicting stock prices using LSTM. International Journal of Science and Research (IJSR), 6(4) (pp. 1754-1756).
    [14] Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2020). A review of unsupervised feature selection methods. Artificial Intelligence Review, 53(2) (pp. 907-948).
    [15] Statman, M. (1987). How many stocks make a diversified portfolio?. Journal of financial and quantitative analysis, 22(3) (pp. 353-363).
    [16] Understanding LSTM Networks. http://colah.github.io/posts/2015-08-Understanding-LSTMs. [Online; accessed 9-NOV-2022]
    [17] Vargas, M. R., De Lima, B. S., & Evsukoff, A. G. (2017). Deep learning for stock market prediction from financial news articles. In 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA) (pp. 60-65)
    [18]Wang, B., & Zhang, X. Deep Learning Applying on Stock Trading. Stanford University.
    [19] Wang, J., Zhang, Y., Tang, K., Wu, J., & Xiong, Z. (2019). Alphastock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1900-1908).
    [20] Wang, Z., Huang, B., Tu, S., Zhang, K., & Xu, L. (2021). DeepTrader: a deep reinforcement learning approach for risk-return balanced portfolio management with market conditions Embedding. In Proceedings of the AAAI Conference on Artificial Intelligence, 35(1) (pp. 643-650).
    [21] What are neural networks? https://www.ibm.com/cloud/learn/neural-networks. [Online; accessed 9-NOV-2022]
    [22] Wu, X., Chen, H., Wang, J., Troiano, L., Loia, V., & Fujita, H. (2020). Adaptive stock trading strategies with deep reinforcement learning methods. Information Sciences, 538 (pp. 142-158).
    描述: 碩士
    國立政治大學
    資訊科學系
    110753114
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110753114
    数据类型: thesis
    显示于类别:[資訊科學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    311401.pdf3097KbAdobe 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