IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v16y2023i1p51-d1034543.html
   My bibliography  Save this article

Analysis of Bitcoin Price Prediction Using Machine Learning

Author

Listed:
  • Junwei Chen

    (Graduate School of Economics, Kobe University, Kobe 657-8501, Japan)

Abstract

The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy.

Suggested Citation

  • Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
  • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:1:p:51-:d:1034543
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/16/1/51/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/16/1/51/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    2. Dirk G. Baur & Thomas Dimpfl, 2021. "The volatility of Bitcoin and its role as a medium of exchange and a store of value," Empirical Economics, Springer, vol. 61(5), pages 2663-2683, November.
    3. Philip, R., 2020. "Estimating permanent price impact via machine learning," Journal of Econometrics, Elsevier, vol. 215(2), pages 414-449.
    4. Mehmet Levent ERDAS & Abdullah Emre CAGLAR, 2018. "Analysis of the relationships between Bitcoin and exchange rate, commodities and global indexes by asymmetric causality test," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 9, pages 27-45, December.
    5. Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.
    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. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    2. Sasan Barak & Navid Parvini, 2023. "Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1695-1726, December.
    3. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
    4. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    5. Bhuiyan, Rubaiyat Ahsan & Husain, Afzol & Zhang, Changyong, 2021. "A wavelet approach for causal relationship between bitcoin and conventional asset classes," Resources Policy, Elsevier, vol. 71(C).
    6. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    7. Yang, Yanlin & Hu, Xuemei & Jiang, Huifeng, 2022. "Group penalized logistic regressions predict up and down trends for stock prices," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    8. Xiao, Yu-jie & Wang, Xiao-kang & Wang, Jian-qiang & Zhang, Hong-yu, 2021. "An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Mercadier, Mathieu & Lardy, Jean-Pierre, 2019. "Credit spread approximation and improvement using random forest regression," European Journal of Operational Research, Elsevier, vol. 277(1), pages 351-365.
    10. Andreas Thiemann, 2021. "Cryptocurrencies: An empirical view from a Tax Perspective," JRC Working Papers on Taxation & Structural Reforms 2021-12, Joint Research Centre.
    11. Giannellis, Nikolaos, 2022. "Cryptocurrency market connectedness in Covid-19 days and the role of Twitter: Evidence from a smooth transition regression model," Research in International Business and Finance, Elsevier, vol. 63(C).
    12. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    13. Yingrui Zhou & Taiyong Li & Jiayi Shi & Zijie Qian, 2019. "A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices," Complexity, Hindawi, vol. 2019, pages 1-15, February.
    14. Chaeshick Chung & Sukjin Park, 2021. "Deep Learning Market Microstructure: Dual-Stage Attention-Based Recurrent Neural Networks," Working Papers 2108, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    15. Zhang, Dingxuan & Sun, Yuying & Duan, Hongbo & Hong, Yongmiao & Wang, Shouyang, 2023. "Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin," International Review of Financial Analysis, Elsevier, vol. 88(C).
    16. Raza, Syed Ali & Ahmed, Maiyra & Aloui, Chaker, 2022. "On the asymmetrical connectedness between cryptocurrencies and foreign exchange markets: Evidence from the nonparametric quantile on quantile approach," Research in International Business and Finance, Elsevier, vol. 61(C).
    17. Graf von Luckner, Clemens & Reinhart, Carmen M. & Rogoff, Kenneth, 2023. "Decrypting new age international capital flows," Journal of Monetary Economics, Elsevier, vol. 138(C), pages 104-122.
    18. F. Campigli & G. Bormetti & F. Lillo, 2022. "Measuring price impact and information content of trades in a time-varying setting," Papers 2212.12687, arXiv.org, revised Dec 2023.
    19. Min-Yuh Day & Paoyu Huang & Yirung Cheng & Yensen Ni, 2023. "Investing Strategies for Trading Stocks as Overreaction Triggered by Technical Trading Rules with Big Data Concerns," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 148-161, October.
    20. Paulo Rupino Cunha & Paulo Melo & Helder Sebastião, 2021. "From Bitcoin to Central Bank Digital Currencies: Making Sense of the Digital Money Revolution," Future Internet, MDPI, vol. 13(7), pages 1-19, June.

    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:gam:jjrfmx:v:16:y:2023:i:1:p:51-:d:1034543. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.