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Bitcoin Forecasting Performance Measurement: A Comparative Study of Econometric, Machine Learning and Artificial Intelligence-Based Models

Author

Listed:
  • Anshul Agrawal

    (Department of Humanities, and Social Sciences, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India)

  • Mukta Mani

    (Department of Humanities, and Social Sciences, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India)

  • Sakshi Varshney

    (Department of Humanities, and Social Sciences, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India)

Abstract

Bitcoin is a type of Cryptocurrency that relies on Blockchain technology and its growing popularity is leading to its acceptance as an alternative investment. However, the future value of Bitcoin is difficult to predict due to its significant volatility and speculative behavior. Considering this, the key objective of this research is to assess Bitcoins’ explosive behavior during 2013–2022 including the most volatile COVID-19 pandemic and Russia–Ukraine war period and to forecast its price by comparing the predictive abilities offive different econometric, machine learning and artificial Intelligence methods namely, ARIMA, Decision Tree, Random Forest, SVM, and Artificial Intelligence Long Short-Term Memory Network (AI-LSTM). The precision of such methodologies has been assessed using root mean square error (RMSE) and mean average per cent error (MAPE) values. The findings confirmed that the AI-LSTM model performs better than other forecast models in predicting Bitcoins’ opening price on the following working day. Therefore, Bitcoin traders, policymakers, and financial institutions can use the model effectively to better forecast the next day’s opening price.

Suggested Citation

  • Anshul Agrawal & Mukta Mani & Sakshi Varshney, 2023. "Bitcoin Forecasting Performance Measurement: A Comparative Study of Econometric, Machine Learning and Artificial Intelligence-Based Models," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 14(02), pages 1-18, June.
    • Handle: RePEc:wsi:jicepx:v:14:y:2023:i:02:n:s1793993323500084
    DOI: 10.1142/S1793993323500084
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    Cited by:

    1. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).

    More about this item

    Keywords

    Bitcoin; machine learning; artificial intelligence; decision tree; random forest; RNN; LSTM;
    All these keywords.

    JEL classification:

    • F32 - International Economics - - International Finance - - - Current Account Adjustment; Short-term Capital Movements
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration

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