IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n2611063.html

Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning

Author

Listed:
  • Si Chen

Abstract

Digital currency is considered a form of currency which is used in the digital world such as digital forms or electronic devices. Several terms are synonyms for digital currency like digital money, electronic money, and cyber cash. Accurate prediction of the digital currency is an urgent necessity due to its impacts on the economic community. The electronic economy is very dangerous and must be approached with great caution, so as to avoid or minimize the risks that occur in such cases. Deep neural network (DNN) algorithm was improved to predict the Bitcoin price and then achieve the main goal of reducing financial risks to proceed with electronic business, and good estimation was achieved by using informative data such as transactions and currency return. The proposed method extracted features of related Bitcoin and used the informative ones. Transaction plan considered building nodes in terms of network. Development of deep learning algorithms opens the horizons for the development of electronic businesses that use digital currency. The proposed method achieved worthy results in terms of accuracy (53.4%) and correct prediction (MSE 1.02) and offers the prospect of other research in this area.

Suggested Citation

  • Si Chen, 2022. "Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:2611063
    DOI: 10.1155/2022/2611063
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/2611063
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/2611063?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mr. John Kiff & Jihad Alwazir & Sonja Davidovic & Aquiles Farias & Mr. Ashraf Khan & Mr. Tanai Khiaonarong & Majid Malaika & Mr. Hunter K Monroe & Nobu Sugimoto & Hervé Tourpe & Peter Zhou, 2020. "A Survey of Research on Retail Central Bank Digital Currency," IMF Working Papers 2020/104, International Monetary Fund.
    2. Irene Henriques & Perry Sadorsky, 2018. "Can Bitcoin Replace Gold in an Investment Portfolio?," JRFM, MDPI, vol. 11(3), pages 1-19, August.
    3. John “Jack” Castonguay & Sean Stein Smith, 2020. "Digital Assets and Blockchain: Hackable, Fraudulent, or Just Misunderstood?†," Accounting Perspectives, John Wiley & Sons, vol. 19(4), pages 363-387, December.
    4. Khiaonarong, Tanai & Humphrey, David, 2019. "Cash use across countries and the demand for central bank digital currency," Journal of Payments Strategy & Systems, Henry Stewart Publications, vol. 13(1), pages 32-46, March.
    5. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
    6. Nils Bundi & Marc Wildi, 2019. "Bitcoin and market-(in)efficiency: a systematic time series approach," Digital Finance, Springer, vol. 1(1), pages 47-65, November.
    7. Huynh Kim P. & Molnar Jozsef & Shcherbakov Oleksandr & Yu Qinghui (Jerry), 2024. "Demand for Payment Services and Consumer Welfare: The Introduction of a Central Bank Digital Currency," Review of Network Economics, De Gruyter, vol. 23(4), pages 199-230.
    8. Mr. Tanai Khiaonarong & David Humphrey, 2019. "Cash Use Across Countries and the Demand for Central Bank Digital Currency," IMF Working Papers 2019/046, International Monetary Fund.
    9. Adam S. Hayes, 2019. "Bitcoin price and its marginal cost of production: support for a fundamental value," Applied Economics Letters, Taylor & Francis Journals, vol. 26(7), pages 554-560, April.
    10. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hoang, Yen Hai & Ngo, Vu Minh & Bich Vu, Ngoc, 2023. "Central bank digital currency: A systematic literature review using text mining approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "How well do investor sentiment and ensemble learning predict Bitcoin prices?," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. C, Viviana Alfonso & Kamin, Steven & Zampolli, Fabrizio, 2025. "Central bank digital currencies (CBDCs) in Latin America and the Caribbean," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 6(1).
    4. Morales-Resendiz, Raúl & Ponce, Jorge & Picardo, Pablo & Velasco, Andrés & Chen, Bobby & Sanz, León & Guiborg, Gabriela & Segendorff, Björn & Vasquez, José Luis & Arroyo, John & Aguirre, Illich & Hayn, 2021. "Implementing a retail CBDC: Lessons learned and key insights," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(1).
    5. Peterson K. Ozili, 2022. "Circular Economy and Central Bank Digital Currency," Circular Economy and Sustainability, Springer, vol. 2(4), pages 1501-1516, December.
    6. Sofia Priazhkina & Samuel Palmer & Pablo Martín-Ramiro & Román Orús & Samuel Mugel & Vladimir Skavysh, 2024. "Digital Payments in Firm Networks: Theory of Adoption and Quantum Algorithm," Staff Working Papers 24-17, Bank of Canada.
    7. repec:osf:socarx:9vdwf_v1 is not listed on IDEAS
    8. Michele Manna, 2022. "The bonfire of banknotes," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 25, Bank of Italy, Directorate General for Markets and Payment System.
    9. Hwang, Jen-Te & Wen, Min, 2024. "Electronic payments and money demand in China," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 47-64.
    10. Shy, Oz, 2021. "Cashless stores and cash users," Journal of Policy Modeling, Elsevier, vol. 43(3), pages 622-638.
    11. Ahmed, Walid M.A., 2021. "How do Islamic equity markets respond to good and bad volatility of cryptocurrencies? The case of Bitcoin," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    12. Ozili, Peterson K, 2025. "100 Quotes about central bank digital currencies," MPRA Paper 124263, University Library of Munich, Germany.
    13. repec:osf:edarxi:5dwrt_v1 is not listed on IDEAS
    14. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    15. Gordon Kuo Siong Tan, 2022. "Citizens go digital: A discursive examination of digital payments in Singapore’s Smart Nation project," Urban Studies, Urban Studies Journal Limited, vol. 59(12), pages 2582-2598, September.
    16. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    17. World Bank & Bank for International Settlements, 2022. "What Does Digital Money Mean for Emerging Market and Developing Economies?," World Bank Publications - Reports 37358, The World Bank Group.
    18. Li, Fangmin & Yang, Tianle & Du, Min & Huang, Miao, 2023. "The development fit index of digital currency electronic payment between China and the one belt one road countries," Research in International Business and Finance, Elsevier, vol. 64(C).
    19. Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.
    20. Yue, Yao & Li, Xuerong & Zhang, Dingxuan & Wang, Shouyang, 2021. "How cryptocurrency affects economy? A network analysis using bibliometric methods," International Review of Financial Analysis, Elsevier, vol. 77(C).
    21. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    22. Berg, Tobias & Keil, Jan & Martini, Felix & Puri, Manju, 2024. "CBDCs, Payment Firms, and Geopolitics," CEPR Discussion Papers 19367, Centre for Economic Policy Research.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:2611063. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.