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

Anshul Agrawal (), Mukta Mani and Sakshi Varshney
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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

Journal of International Commerce, Economics and Policy (JICEP), 2023, vol. 14, issue 02, 1-18

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.

Keywords: Bitcoin; machine learning; artificial intelligence; decision tree; random forest; RNN; LSTM (search for similar items in EconPapers)
JEL-codes: F32 F36 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S1793993323500084

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