IDEAS home Printed from https://ideas.repec.org/a/tei/journl/v15y2022i3p69-92.html
   My bibliography  Save this article

Price Prediction for Bitcoin: Does Periodicity Matter?

Author

Listed:
  • Gbadebo Adedeji Daniel

    (Department of Accounting Science, Walter Sisulu University, Mthatha, Eastern Cape, South Africa)

  • Akande Joseph Olorunfemi

    (Department of Accounting Science, Walter Sisulu University, Mthatha, Eastern Cape, South Africa)

  • Adekunle Ahmed Oluwatobi

    (Department of Accounting Science, Walter Sisulu University, Mthatha, Eastern Cape, South Africa)

Abstract

Purpose: A major challenge traders, speculators and investors are grappling with is how to accurately forecast Bitcoin price in the cryptocurrency market. This study is aimed to uncover the best model for the forecasts of Bitcoin price as well as to verify the price series that offers the best predictions performance under different periodicity of datasets. Design/methodology/approach: The study adopts three different data periods to verify whether frequency matters in forecasting Bitcoin price. The Bitcoin price, from 01/01/15 to 11/01/2021, is trained and validated on selected forecast models, including the Naïve, Linear, Exponential Smoothing Model, ARIMA, Neural Network, STL and Holt-Winters filters. Five forecast accuracy measures (RSME, MAE, MPE, MAPE and MASE) are applied to confirm the best performing model. The Diebold†Mariano test is used to compare the forecasts based on the daily price with those based on the weekly and monthly. Findings: Based on the accuracy measures, the results indicate that the Naïve model provides more accurate performance for the daily series, while the linear model outperforms others for the weekly and monthly series. Using the Diebold†Mariano statistics, there is evidence that forecasting Bitcoin price is not sensitive to the data periodicity. Research limitations/implications: The study has a major limitation, which is the shared sentiment to apply actual Bitcoin price series, and not the returns or log transformation for the forecast models. Notably, actual data may sometimes be loud, hence increasing the possibility of over predictions. Originality/value: In forecasting, different approaches have been used, this paper compares outputs of both statistical and machine learning methods in order to arrive at the best option for the Bitcoin price forecasts. Hence, we investigate whether the machine learning tools offer better forecasts in terms of lower error and higher model’s accuracy relative to the traditional models.

Suggested Citation

  • Gbadebo Adedeji Daniel & Akande Joseph Olorunfemi & Adekunle Ahmed Oluwatobi, 2022. "Price Prediction for Bitcoin: Does Periodicity Matter?," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 15(3), pages 69-92, December.
    • Handle: RePEc:tei:journl:v:15:y:2022:i:3:p:69-92
    as

    Download full text from publisher

    File URL: http://ijbesar.ihu.gr/docs/volume15_issue3/15_03_06.pdf
    Download Restriction: no
    File URL: http://ijbesar.ihu.gr/volume15_issue3.php
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    2. Nicola Uras & Lodovica Marchesi & Michele Marchesi & Roberto Tonelli, 2020. "Forecasting Bitcoin closing price series using linear regression and neural networks models," Papers 2001.01127, arXiv.org.
    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. Qiutong Guo & Shun Lei & Qing Ye & Zhiyang Fang, 2021. "MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price," Papers 2105.00707, arXiv.org.
    2. Christie Smith & Aaron Kumar, 2018. "Crypto‐Currencies – An Introduction To Not‐So‐Funny Moneys," Journal of Economic Surveys, Wiley Blackwell, vol. 32(5), pages 1531-1559, December.
    3. Hanna Halaburda & Guillaume Haeringer & Joshua Gans & Neil Gandal, 2022. "The Microeconomics of Cryptocurrencies," Journal of Economic Literature, American Economic Association, vol. 60(3), pages 971-1013, September.
    4. Eross, Andrea & McGroarty, Frank & Urquhart, Andrew & Wolfe, Simon, 2019. "The intraday dynamics of bitcoin," Research in International Business and Finance, Elsevier, vol. 49(C), pages 71-81.
    5. Corbet, Shaen & Katsiampa, Paraskevi & Lau, Chi Keung Marco, 2020. "Measuring quantile dependence and testing directional predictability between Bitcoin, altcoins and traditional financial assets," International Review of Financial Analysis, Elsevier, vol. 71(C).
    6. Yongqiang Meng & Dehua Shen & Xiong Xiong & Jorgen Vitting Andersen, 2020. "A Socio-Finance Model: The Case of Bitcoin," Documents de travail du Centre d'Economie de la Sorbonne 20031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Chao Liang & Yaojie Zhang & Xiafei Li & Feng Ma, 2022. "Which predictor is more predictive for Bitcoin volatility? And why?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1947-1961, April.
    8. Gronwald, Marc, 2019. "Is Bitcoin a Commodity? On price jumps, demand shocks, and certainty of supply," Journal of International Money and Finance, Elsevier, vol. 97(C), pages 86-92.
    9. Xun Zhang & Fengbin Lu & Rui Tao & Shouyang Wang, 2021. "The time-varying causal relationship between the Bitcoin market and internet attention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    10. 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.
    11. Fathin Faizah Said & Raja Solan Somasuntharam & Mohd Ridzwan Yaakub & Tamat Sarmidi, 2023. "Impact of Google searches and social media on digital assets’ volatility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    12. Panagiotis Anastasiadis & Stephanos Papadamou, 2022. "The dimension of popularity in the cryptocurrency market," SN Business & Economics, Springer, vol. 2(5), pages 1-15, May.
    13. Neto, David, 2021. "Are Google searches making the Bitcoin market run amok? A tail event analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    14. Ajithakumari Vijayappan Nair Biju & Ann Susan Thomas, 2023. "Uncertainties and ambivalence in the crypto market: an urgent need for a regional crypto regulation," SN Business & Economics, Springer, vol. 3(8), pages 1-21, August.
    15. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    16. Bouraoui, Taoufik, 2020. "The drivers of Bitcoin trading volume in selected emerging countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 218-229.
    17. Wang, Qiyu & Chong, Terence Tai-Leung, 2021. "Factor pricing of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    18. Chen, Conghui & Liu, Lanlan, 2022. "How effective is China's cryptocurrency trading ban?," Finance Research Letters, Elsevier, vol. 46(PB).
    19. Böyükaslan, Adem & Ecer, Fatih, 2021. "Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework," Technology in Society, Elsevier, vol. 67(C).
    20. Borgards, Oliver & Czudaj, Robert L., 2020. "The prevalence of price overreactions in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).

    More about this item

    Keywords

    Bitcoin; Diebold†Mariano test; Univariate forecast models; Forecast accuracy;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:tei:journl:v:15:y:2022:i:3:p:69-92. 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: Kostas Stergidis (email available below). General contact details of provider: https://edirc.repec.org/data/dbikagr.html .

    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.