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

arXiv: https://arxiv.org/abs/2508.01419 · HTML: https://mlsanity.com/paper/2508.01419/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [Machine Learning-Based Detection and Analysis of Suspicious Activities in Bitcoin Wallet Transactions in the USA](https://mlsanity.com/paper/2504.03092/) (2025)
- [Designing and Deploying AI Models for Sustainable Logistics Optimization: A Case Study on Eco-Efficient Supply Chains in the USA](https://mlsanity.com/paper/2503.14556/) (2025)

## Built on by (most cited first, 2 of 2)

- [Machine Learning Enabled Early Warning System For Financial Distress Using Real-Time Digital Signals](https://mlsanity.com/paper/2510.22287/) (2025)
- [Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics](https://mlsanity.com/paper/2601.00893/) (2025)

## Abstract (arXiv)

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.