Cryptocurrency Price Forecasting Using Machine Learning: ‎Building Intelligent Financial Prediction Models

  • Authors

    • Md Zahidul Islam MBA in Business Analytics, Gannon University, Erie, PA
    • Md Shafiqur Rahman MBA in Management Information Systems, International American University
    • Md Sumsuzoha Master of Science in Business Analytics, Trine University
    • Babul Sarker Master of Science in Business Analytics (MSBA)، Trine University, Angola, Indiana, USA
    • Md Rafiqul Islam MBA in Business Analytics, International American University
    • Mahfuz Alam MBA in Business Analytics, International American University, Los Angeles, California
    • Sanjib Kumar Shil MBA in Management Information Systems, International American University
    https://doi.org/10.14419/s0pktr58

    Received date: June 20, 2025

    Accepted date: July 15, 2025

    Published date: July 26, 2025

  • Cryptocurrency Forecasting; LSTM Neural Networks; Market Liquidity; Machine Learning; VWAP; VVR.

  • Abstract

    Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting ‎cryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast ‎the closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume ‎patterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity ‎proxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer ‎understanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine ‎learning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the ‎liquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed ‎their performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These ‎results underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating ‎these liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers ‎seeking to create smarter and more risk-aware strategies in the U.S. digital assets market‎.

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  • How to Cite

    Islam, M. Z., Rahman, M. S., Sumsuzoha, M., Sarker, B., Islam, M. R., Alam, M., & Shil, S. K. (2025). Cryptocurrency Price Forecasting Using Machine Learning: ‎Building Intelligent Financial Prediction Models. International Journal of Accounting and Economics Studies, 12(3), 255-268. https://doi.org/10.14419/s0pktr58