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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), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 15(3), pages 69-92, December.
    • Handle: RePEc:tei:journl:v:15:y:2022:i:3:p:69-92
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    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)

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    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

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