Cryptocurrency Market Volatility and Forecasting: A Comparative Analysis of Modern Machine Learning Models for Cryptocurrencies Predicting Accuracy

Robina Iqbal (), Madhia Riaz (), Ghulam Sorwar () and Junaid Qadir ()
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Robina Iqbal: Keele Business School, Keele University, Keele, Staffordshire, UK
Madhia Riaz: ��Department of Economics, Islamia University of Bahawalpur, Bahawalpur, Pakistan
Ghulam Sorwar: Keele Business School, Keele University, Keele, Staffordshire, UK
Junaid Qadir: ��Computer Science and Engineering Department, College of Engineering, Qatar University, Qatar

Review of Pacific Basin Financial Markets and Policies (RPBFMP), 2024, vol. 27, issue 04, 1-32

Abstract: Cryptocurrency (CRP) has grown in popularity over the last decade. Since there is no central body to control the Bitcoin (BTC) markets, they are extremely volatile. However, several similar variables that cause price volatility in traditional markets also affect cryptocurrencies. Several bubble phases have taken place in BTC prices, mostly during the years 2013 and 2017. Other digital currencies of primary importance, such as Ethereum and Litecoin, also exhibited several bubble phases. Among traditional methods of analysis for this volatile market, only a small number of studies focused on Machine Learning (ML) techniques. The present study objective is to get an in-depth knowledge of the time series properties of CRP data and combine volatility models with ML models. In the hybrid method, we first apply the Nonlinear Generalized Autoregressive Conditional Heteroskedasticity (NGARCH) model with asymmetric distribution to calculate standardized returns, then forecast the UP and DOWN movement of standardized returns through ML models such as Logistic Regression (LR), Linear Discrimination Analysis (LDA), Quadratic Discrimination Analysis (QDA), Artificial Neural Networks (ANNs), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The findings show that the proposed hybrid approach of time series models and ML accurately predicts prices; specifically, the KNN model reveals that the scheme can be applicable to CRP market prediction. It is deduced that ML methods combined with volatility models have the tendency to better forecast this volatile market.

Keywords: Cryptocurrency; machine learning; forecasting; neural networks; classification; volatility modeling (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219091524500280

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