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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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