政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143773

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143773

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题名:	運用深度強化學習建立虛擬貨幣投資組合 Establish The Portfolio of Crypto Currency by Applying Deep-Reinforcement Learning
作者:	蘇育正 Su, Yu-Cheng
贡献者:	蔡炎龍蕭明福 Thai, YenLung Shaw, MingFu 蘇育正 Su, Yu-Cheng
关键词:	深度學習強化學習深度強化學習虛擬貨幣投資組合 Reinforcement Learning Portfolio Crypto
日期:	2022
上传时间:	2023-03-09 18:23:05 (UTC+8)
摘要:	本研究運用深度強化學習建立虛擬貨幣的投資組合，研究標的主要以 2021 年 12 月 31 日市值排名前 50 大的虛擬貨幣。研究期間從 2017 年 1 月 3 日至2021 年 12 月 31 日，並主要以五個因子(Factor):開盤價(Open)、最高價(High)、最低價(Low)、收盤價(Close)、成交量(Volume)為輸入資料(Input)，並在一開始先以(1)市值、(2)平均振幅抓取 30 檔虛擬幣組建投資組合，輸入給深度強化學習模型進行訓練，最終發現相較於其他種因子建立的投資組合，平均振幅打造的投資組合表現更好，也比單一持續持有比特幣來的更合適。
參考文獻:	[1] Fan Fang, Carmine Ventre, Michail Basios, Leslie Kanthan, DavidMartinez-Rego, Fan Wu and Lingbo Li. Cryptocurrency trading: a comprehensive survey. Finanical Innovation,8(13),2022. [2] Timothy King and Dimitrios Koutmos. Herding and feedback trading in cryptocurrency markets. Annals of Operations Research,300:79-97,2021. [3] WeiSun, Alisher Tohirovich, Dedahanov, Ho YoungShin and Wei PingLi. Factors affecting institutional investors to add cryptocurrency to asset portfolios. The North American Journal of Economics and Finance,volume 58,2021. [4] Paraskevi Katsiampa, Larisa Yarovaya and DamianZięba. Highfrequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis. Journal of International Financial Markets, Institutions and Money,volume 79,2022. [5] Andrés Arévalo, Jaime Niño, G. Hernández and Javier Sandoval. High-Frequency Trading Strategy Based on Deep Neural Networks.International Conference on intelligent Computing, LNAI,volume 9773,2016. [6] Maria Čuljak,BojanTomić and SašaŽiković . Benefits of sectoral cryptocurrency portfolio optimization. Research in International Business and Finance,volume 60,2022. [7] Golnoosh Babaei,Paolo Giudici and EmanuelaRaffinetti. Explainable artificial intelligence for crypto asset allocation. Finance Research Letters,volume 47,Part B,2022. [8] Leonardo Kanashiro Felizardo,Francisco CaioLima Paiva,Catharinede Vita Graves,Elia Yathie Matsumoto,Anna Helena Reali Costa,Emilio DelMoral-Hernandez and Paolo Brandimarte. Outperforming algorithmic trading reinforcement learning systems: A supervised approach to the 30 cryptocurrency market. Expert Systems with Applications,volume 202,2022. [10] Hongfeng Xu,Lei Chai,Zhiming Luo and Shaozi Li. Stock movement prediction via gated recurrent unit network based on reinforcement learning with incorporated attention mechanisms. Neurocomputing,volume 467,Pages 214-228,2022. [11] Fengrui Liu,Yang Li,Baitong Li,Jiaxin Li and Huiyang Xie . Bitcoin transaction strategy construction based on deep reinforcement learning. Applied Soft Computing,volume113,Part B,2021. [12] Thibaut Théate and Damien Ernst . An application of deep reinforcement learning to algorithmic trading. Expert Systems with Applications,volume 173,2021. [13] Liguo Weng,Xudong Sun,Min Xia,Jia Liu and Yiqing Xu. Portfolio Trading System of Digital Currencies: A Deep Reinforcement Learning with Multidimensional Attention Gating Mechanism. Neurocomputing,volume 402,Pages 171-182,2019
描述:	碩士國立政治大學經濟學系 108258034
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0108258034
数据类型:	thesis
显示于类别:	[經濟學系] 學位論文

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