政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/148551

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/148551

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113648/144635 (79%)
造訪人次 : 51613507 線上人數 : 794

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 資訊學院 > 資訊科學系碩士在職專班 > 學位論文 > Item 140.119/148551

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/148551

題名:	應用深度強化學習將加密貨幣納入資產配置組合優化之探索 Applying Deep Reinforcement Learning to Explore Adding Cryptocurrencies in Asset Allocation Optimization
作者:	李昂縣 Lee, Ang-Hsien
貢獻者:	胡毓忠 Hu, Yuh-Jong 李昂縣 Lee, Ang-Hsien
關鍵詞:	深度強化學習 Soft Actor-Critic 加密貨幣資產配置現代投資組合理論均值-變異數最佳化 Deep reinforcement learning Soft Actor-Critic Cryptocurrency Asset portfolio Modern Portfolio Theory Mean-Variance Optimization
日期:	2023
上傳時間:	2023-12-01 14:09:06 (UTC+8)
摘要:	隨著近年來加密貨幣市場的蓬勃發展，越來越多的投資者將加密貨幣納入其投資組合，以期望獲得更高的回報率。相較於傳統交易市場，加密貨幣市場提供更透明的交易資訊，更靈活地反應市場變化，且不受休市時間的限制。然而，加密貨幣市場的高波動及流動性不足等特性，使得投資者通常對納入其投資組合持保守態度，或者將其與其他傳統資產結合以對沖風險。深度強化學習在複雜環境中的自我學習和適應能力、對高維度數據的有效處理，以及良好的泛化性能，有望在加密貨幣投資決策中發揮顯著的優勢。因此本研究的目的在於應用Soft Actor Critic深度強化學習演算法，整合加密貨幣風險評估並探討加密貨幣納入資產配置組合的效益。透過實證分析，我們比較了加密貨幣資產配置組合與傳統金融資產配置組合的成效，以及DRL模型與現代投資組合理論的均值-變異數最佳化模型在加密貨幣資產配置優化的效益表現，為投資者提供具參考價值且系統性的投資建議。 With the rapid growth of the cryptocurrency market, an increasing number of investors are incorporating cryptocurrencies into their portfolios in pursuit of potential high returns. Compared to traditional markets, cryptocurrencies provide heightened transparency and flexibility, operating continuously around the clock. However, due to their unpredictable nature, some investors may approach them cautiously or combine them with traditional assets for risk management. Deep Reinforcement Learning (DRL) excels in navigating complex environments, handling high-dimensional data, and demonstrating robust adaptability. This study aims to integrate cryptocurrency risk assessment using the Soft Actor-Critic algorithm and explore the benefits of incorporating cryptocurrencies into asset allocation while determining suitable allocation ratios. By comparing the performance of cryptocurrency and traditional asset portfolios and evaluating the effectiveness of this research model, we aim to provide valuable and systematic investment guidance.
參考文獻:	[1] Hamid Ali, Hammad Majeed, Imran Usman, and Khaled A Almejalli. Reducing entropy overestimation in soft actor critic using dual policy network. Wireless Communications and Mobile Computing, 2021:1– 13, 2021. [2] Lennart Ante. How elon musk’s twitter activity moves cryptocurrency markets. Technological Forecasting and Social Change, 186:122112, 2023. [3] Amirreza Attarzadeh and Mehmet Balcilar. On the dynamic return and volatility connectedness of cryptocurrency, crude oil, clean energy, and stock markets: a time-varying analysis. Environmental Science and Pollution Research, 29(43):65185–65196, 2022. [4] Golnoosh Babaei, Paolo Giudici, and Emanuela Raffinetti. Explain- able artificial intelligence for crypto asset allocation. Finance Research Letters, 47:102941, 2022. [5] Manjula BC, Shilpa BS, et al. Analysis of cryptocurrency, bitcoin and the future. East Asian Journal of Multidisciplinary Research, 1(7):1293– 1302, 2022. [6] Eric Benhamou, David Saltiel, Jean Jacques Ohana, Jamal Atif, and Rida Laraki. Deep reinforcement learning (drl) for portfolio allocation. In Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V, pages 527–531. Springer, 2021. [7] Eric Benhamou, David Saltiel, Sandrine Ungari, and Abhishek Mukhopadhyay. Bridging the gap between markowitz planning and deep reinforcement learning. arXiv preprint arXiv:2010.09108, 2020. [8] Elie Bouri, Peter Molnár, Georges Azzi, David Roubaud, and Lars Ivar Hagfors. On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier? Finance Research Letters, 20:192–198, 2017. [9] Gerard Cornuejols and Reha Tütüncü. Optimization methods in finance, volume 5. Cambridge University Press, 2006. [10] Kwamie Dunbar and Johnson Owusu-Amoako. Cryptocurrency re- turns under empirical asset pricing. International Review of Financial Analysis, 82:102216, 2022. [11] Ricard Durall. Asset allocation: From markowitz to deep reinforcement learning. arXiv preprint arXiv:2208.07158, 2022. [12] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International conference on machine learning, pages 1861–1870. PMLR, 2018. [13] Miquel Noguer i Alonso, Sonam Srivastava, et al. Deep reinforcement learning for asset allocation in us equities. Technical report, 2020. [14] Damian Kisiel and Denise Gorse. Portfolio transformer for attention- based asset allocation. In International Conference on Artificial Intelligence and Soft Computing, pages 61–71. Springer, 2022. [15] Petter N Kolm, Reha Tütüncü, and Frank J Fabozzi. 60 years of portfolio optimization: Practical challenges and current trends. European Journal of Operational Research, 234(2):356–371, 2014. [16] Edmond Lezmi and Jiali Xu. Time series forecasting with transformer models and application to asset management. Available at SSRN 4375798, 2023. [17] Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Con- tinuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015. [18] Xiao-Yang Liu, Jingyang Rui, Jiechao Gao, Liuqing Yang, Hongyang Yang, Zhaoran Wang, Christina Dan Wang, and Guo Jian. FinRL- Meta: Data-driven deep reinforcementlearning in quantitative finance. Data-Centric AI Workshop, NeurIPS, 2021. [19] Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, and Jian Guo. FinRL- Meta: Market environments and benchmarks for data-driven financial reinforcement learning. NeurIPS, 2022. [20] Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, and Christina Dan Wang. Finrl: Deep reinforcement learning framework to automate trading in quantitative finance. In Proceedings of the second ACM international conference on AI in finance, pages 1–9, 2021. [21] Yukun Liu and Aleh Tsyvinski. Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6):2689–2727, 2021. [22] Luis Lorenzo and Javier Arroyo. Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algo- rithm. Financial Innovation, 9(1):25, 2023. [23] Yue Ma, Ziping Liu, and Chuck McAllister. Deep reinforcement learn- ing for portfolio management. In Yan Shi, Gongzhu Hu, Krishna Kamb- hampaty, and Takaaki Goto, editors, Proceedings of 35th International Conference on Computer Applications in Industry and Engineering, vol- ume 89 of EPiC Series in Computing, pages 41–51. EasyChair, 2022. [24] Leonard C MacLean, Edward O Thorp, and William T Ziemba. The Kelly capital growth investment criterion: Theory and practice, vol- ume 3. world scientific, 2011. [25] Iwao Maeda, David DeGraw, Michiharu Kitano, Hiroyasu Matsushima, Hiroki Sakaji, Kiyoshi Izumi, and Atsuo Kato. Deep reinforcement learning in agent based financial market simulation. Journal of Risk and Financial Management, 13(4):71, 2020. [26] H Markowitz. The utility of wealth, journal of political economy, vol. 60. 1952. [27] Akib Mashrur, Wei Luo, Nayyar A Zaidi, and Antonio Robles-Kelly. Machine learning for financial risk management: A survey. IEEE Access, 8:203203–203223, 2020. [28] Adebayo Oshingbesan, Eniola Ajiboye, Peruth Kamashazi, and Tim- othy Mbaka. Model-free reinforcement learning for asset allocation. arXiv preprint arXiv:2209.10458, 2022. [29] Sebastian Palmquist and Janis Mednis. Portfolio optimization with crypto assets:analyzing the impact of the investors’ subjective views on portfolio risk, 2022. [30] Alla Petukhina and Erin Sprünken. Evaluation of multi-asset investment strategies with digital assets. Digital Finance, 3:45–79, 2021. [31] Emmanouil Platanakis and Andrew Urquhart. Portfolio management with cryptocurrencies: The role of estimation risk. Economics Letters, 177:76–80, 2019. [32] Emmanouil Platanakis and Andrew Urquhart. Should investors include bitcoin in their portfolios? a portfolio theory approach. The British accounting review, 52(4):100837, 2020. [33] Refk Selmi, Walid Mensi, Shawkat Hammoudeh, and Jamal Bouoiyour. Is bitcoin a hedge, a safe haven or a diversifier for oil price movements? a comparison with gold. Energy Economics, 74:787–801, 2018. [34] Efthymia Symitsi and Konstantinos J Chalvatzis. The economic value of bitcoin: A portfolio analysis of currencies, gold, oil and stocks. Research in International Business and Finance, 48:97–110, 2019. [35] United States Department of the Treasury. Crypto-assets: Implications for consumers, investors, and businesses. Publication by the United States Department of the Treasury, September 2022.
描述:	碩士國立政治大學資訊科學系碩士在職專班 110971018
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0110971018
資料類型:	thesis
顯示於類別:	[資訊科學系碩士在職專班] 學位論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
101801.pdf		5725Kb	Adobe PDF	0	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

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 © - 回饋