IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v40y2021ics1544612320304864.html
   My bibliography  Save this article

Forecasting the price of Bitcoin using deep learning

Author

Listed:
  • Liu, Mingxi
  • Li, Guowen
  • Li, Jianping
  • Zhu, Xiaoqian
  • Yao, Yinhong

Abstract

After constructing a feature system with 40 determinants that affect the price of Bitcoin considering aspects of the cryptocurrency market, public attention, and the macroeconomic environment, a deep learning method named stacked denoising autoencoders (SDAE) is utilized to predict the price of Bitcoin. The results show that compared with the most popular machine learning methods, such as back propagation neural network (BPNN) and support vector regression (SVR) methods, the SDAE model performs better in both directional and level prediction, measured using commonly used indicators, i.e., mean absolute percentage error (MAPE), root mean squared error (RMSE), and directional accuracy (DA).

Suggested Citation

  • Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    • Handle: RePEc:eee:finlet:v:40:y:2021:i:c:s1544612320304864
    DOI: 10.1016/j.frl.2020.101755
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612320304864
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2020.101755?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Jue & Zhou, Hao & Hong, Tao & Li, Xiang & Wang, Shouyang, 2020. "A multi-granularity heterogeneous combination approach to crude oil price forecasting," Energy Economics, Elsevier, vol. 91(C).
    2. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    3. Jamal Bouoiyour & Refk Selmi & Aviral Kumar Tiwari & Olaolu Richard Olayeni, 2016. "What drives Bitcoin price?," Economics Bulletin, AccessEcon, vol. 36(2), pages 843-850.
    4. Wei, Lu & Li, Guowen & Zhu, Xiaoqian & Sun, Xiaolei & Li, Jianping, 2019. "Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures," Energy Economics, Elsevier, vol. 80(C), pages 452-460.
    5. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    6. Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco & Vigne, Samuel A., 2018. "Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation," Finance Research Letters, Elsevier, vol. 26(C), pages 145-149.
    7. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    8. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    9. Li, Jianping & Li, Jingyu & Zhu, Xiaoqian & Yao, Yinhong & Casu, Barbara, 2020. "Risk spillovers between FinTech and traditional financial institutions: Evidence from the U.S," International Review of Financial Analysis, Elsevier, vol. 71(C).
    10. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    11. Marianna Succurro & Giuseppe Arcuri & Giuseppina Damiana Costanzo, 2019. "A combined approach based on robust PCA to improve bankruptcy forecasting," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 18(2), pages 296-320, May.
    12. Lu Wei & Guowen Li & Xiaoqian Zhu & Jianping Li, 2019. "Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(3), pages 1519-1552, September.
    13. Godarzi, Ali Abbasi & Amiri, Rohollah Madadi & Talaei, Alireza & Jamasb, Tooraj, 2014. "Predicting oil price movements: A dynamic Artificial Neural Network approach," Energy Policy, Elsevier, vol. 68(C), pages 371-382.
    14. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    15. Matkovskyy, Roman & Jalan, Akanksha, 2019. "From financial markets to Bitcoin markets: A fresh look at the contagion effect," Finance Research Letters, Elsevier, vol. 31(C), pages 93-97.
    16. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    17. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    18. Li, Jingyu & Li, Jianping & Zhu, Xiaoqian, 2020. "Risk dependence between energy corporations: A text-based measurement approach," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 33-46.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lili Pan & Lin Wang & Qianqian Feng, 2022. "A Bibliometric Analysis of Risk Management in Foreign Direct Investment: Insights and Implications," Sustainability, MDPI, vol. 14(12), pages 1-18, June.
    2. Chenlu Dang & Fan Wang & Zimo Yang & Hongxia Zhang & Yufeng Qian, 2022. "RETRACTED ARTICLE: Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model," Operations Management Research, Springer, vol. 15(3), pages 662-675, December.
    3. Samuka Mohanty & Rajashree Dash, 2022. "Neural Network-Based Bitcoin Pricing Using a New Mutated Climb Monkey Algorithm with TOPSIS Analysis for Sustainable Development," Mathematics, MDPI, vol. 10(22), pages 1-23, November.
    4. Naseh Majidi & Mahdi Shamsi & Farokh Marvasti, 2022. "Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning," Papers 2210.03469, arXiv.org.
    5. Kui Wang & Jie Wan & Gang Li & Hao Sun, 2022. "A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry," Energies, MDPI, vol. 15(14), pages 1-18, July.
    6. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    7. Liu, Qingfu & Tao, Zhenyi & Tse, Yiuman & Wang, Chuanjie, 2022. "Stock market prediction with deep learning: The case of China," Finance Research Letters, Elsevier, vol. 46(PA).
    8. Jinghua Wang & Geoffrey M. Ngene & Yan Shi & Ann Nduati Mungai, 2023. "An Investigation of the Predictability of Uncertainty Indices on Bitcoin Returns," JRFM, MDPI, vol. 16(10), pages 1-12, October.
    9. 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).
    10. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    11. Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023. "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
    12. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
    13. Samuka Mohanty & Rajashree Dash, 2023. "A New Dual Normalization for Enhancing the Bitcoin Pricing Capability of an Optimized Low Complexity Neural Net with TOPSIS Evaluation," Mathematics, MDPI, vol. 11(5), pages 1-28, February.

    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. Haffar, Adlane & Le Fur, Eric, 2021. "Structural vector error correction modelling of Bitcoin price," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 170-178.
    2. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    3. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    4. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    5. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    6. Yin, Libo & Nie, Jing & Han, Liyan, 2021. "Understanding cryptocurrency volatility: The role of oil market shocks," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 233-253.
    7. Tiwari, Aviral Kumar & Adewuyi, Adeolu O. & Albulescu, Claudiu T. & Wohar, Mark E., 2020. "Empirical evidence of extreme dependence and contagion risk between main cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    8. Achraf Ghorbel & Wajdi Frikha & Yasmine Snene Manzli, 2022. "Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 387-425, September.
    9. Zhu, Xiaoqian & Wei, Lu & Li, Jianping, 2021. "A two-stage general approach to aggregate multiple bank risks," Finance Research Letters, Elsevier, vol. 40(C).
    10. Khanh Hoang & Cuong C. Nguyen & Kongchheng Poch & Thang X. Nguyen, 2020. "Does Bitcoin Hedge Commodity Uncertainty?," JRFM, MDPI, vol. 13(6), pages 1-14, June.
    11. Lennart Ante, 2020. "A place next to Satoshi: foundations of blockchain and cryptocurrency research in business and economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1305-1333, August.
    12. Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu, 2022. "A novel text-based framework for forecasting agricultural futures using massive online news headlines," International Journal of Forecasting, Elsevier, vol. 38(1), pages 35-50.
    13. Anoop S Kumar & Taufeeq Ajaz, 2019. "Co-movement in crypto-currency markets: evidences from wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-17, December.
    14. Zargar, Faisal Nazir & Kumar, Dilip, 2019. "Long range dependence in the Bitcoin market: A study based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 625-640.
    15. Cagli, Efe Caglar, 2019. "Explosive behavior in the prices of Bitcoin and altcoins," Finance Research Letters, Elsevier, vol. 29(C), pages 398-403.
    16. Zura Kakushadze & Jim Kyung-Soo Liew, 2018. "CryptoRuble: From Russia with Love," Papers 1801.05760, arXiv.org.
    17. Zargar, Faisal Nazir & Kumar, Dilip, 2019. "Informational inefficiency of Bitcoin: A study based on high-frequency data," Research in International Business and Finance, Elsevier, vol. 47(C), pages 344-353.
    18. Bedi, Prateek & Nashier, Tripti, 2020. "On the investment credentials of Bitcoin: A cross-currency perspective," Research in International Business and Finance, Elsevier, vol. 51(C).
    19. Elie Bouri & Rangan Gupta & Chi keung marco Lau & David Roubaud, 2021. "Risk aversion and Bitcoin returns in extreme quantiles," Economics Bulletin, AccessEcon, vol. 41(3), pages 1374-1386.
    20. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.

    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:eee:finlet:v:40:y:2021:i:c:s1544612320304864. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.