Abstract
In recent years, cryptocurrencies have garnered significant attention due to their volatility and potential for high returns. This paper systematically reviews scholarly content from 2014 to 2024, focusing on advanced quantitative methods for cryptocurrency price prediction. The study spans early statistical analyses to sophisticated machine and deep learning algorithms, emphasizing emerging technologies like Transformers and hybrid models. By evaluating methodologies, influential parameters (e.g., market sentiment, technical indicators, blockchain features), and research gaps, this review aims to bridge theoretical advancements with practical trading applications, offering actionable insights for future research.
1. Introduction
Cryptocurrencies, enabled by blockchain technology, have revolutionized financial systems by eliminating intermediaries. Their decentralized nature, however, introduces volatility driven by technical, sentimental, and legal factors. This review addresses the following objectives:
- Comprehensive Parameter Analysis: Identifying key parameters (price/volume, technical indicators, blockchain features, social media sentiment) and their predictive potential.
- Methodological Innovation: Evaluating the evolution from statistical models to hybrid deep learning architectures.
- Future Directions: Highlighting underexplored areas like profitability analysis and real-time trading adaptability.
2. Methodology
A systematic review of 93 peer-reviewed studies from Scopus, IEEE Xplore, Elsevier, and ACM Digital Library was conducted. Key steps included:
- Paper Selection: Keywords such as "cryptocurrency price prediction" and "machine learning" filtered 2,323 initial results.
- Duplicate Removal: 504 duplicates were excluded, leaving 1,819 unique studies.
- Content Filtering: Studies lacking quantitative rigor or focusing solely on economic theory were omitted.
Datasets: Predominantly Bitcoin (79% of studies), Ethereum (32%), and Litecoin. Timeframes ranged from minute-to-minute to multi-year analyses.
3. Influential Parameters for Price Prediction
| Parameter | Benefits | Challenges |
|-----------------------|---------------------------------------|-----------------------------------------|
| Price/Volume | High relevancy; abundant historical data | Exchange-specific variations |
| Technical Indicators (e.g., MACD, RSI) | Objective metrics; widely used in trading | Noise from irrelevant indicators |
| Blockchain Features (e.g., miners’ revenue) | Unique to crypto markets | Non-standardized metrics across tokens |
| Social Media Sentiment (Twitter/Reddit) | Real-time sentiment capture | Noise from sarcasm/bias; API access limits |
Key Insight: Hybrid models combining these parameters outperform single-feature approaches (Patel et al., 2023).
4. Methodologies in Cryptocurrency Price Prediction
4.1. Machine Learning Models
- Algorithms: SVM, Random Forest, ARIMA.
- Performance: ARIMAX achieved 61.17% accuracy for Ethereum (Chen et al., 2020).
4.2. Deep Learning Models
- LSTMs: Dominant in literature (RMSE: 33.7 for Bitcoin; Tan et al., 2021).
- Transformers: Emerging potential for parallelized time-series analysis (Vaswani et al., 2017).
4.3. Hybrid Models
- ConvLSTM: MAPE of 2.4% (Kilimci et al., 2022).
- MRC-LSTM: Integrates multi-scale residuals for enhanced trend analysis (Guo et al., 2023).
Comparative Advantage: Hybrid models reduce overfitting and improve generalization (Table 5).
5. Research Gaps and Future Directions
5.1. Under-Addressed Areas
- Profitability Analysis: Few studies evaluate models in live trading scenarios.
- Predictability Metrics: Lag in model responses often misrepresents real-world applicability.
- Open-Source Tools: Limited public datasets/code hinder reproducibility (Table 4).
5.2. Future Recommendations
- Integrate Transformers: Enhance temporal analysis via self-attention mechanisms.
- Develop Standardized Benchmarks: For blockchain feature integration.
- Real-World Testing: Deploy models in simulated trading environments to assess profitability.
6. Conclusion
This review underscores the rapid evolution of cryptocurrency price prediction, driven by advancements in machine learning and hybrid models. While significant progress has been made in parameter selection and algorithmic accuracy, future research must prioritize practical profitability and adaptability to market shifts. Embracing open science and cutting-edge technologies like Transformers will be pivotal in advancing this dynamic field.
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FAQs
Q1: Which cryptocurrency is most studied in price prediction research?
A: Bitcoin (79% of studies), followed by Ethereum (32%).
Q2: What are the limitations of social media sentiment analysis?
A: Noise from biased/sarcastic posts and API access restrictions (e.g., Twitter’s recent API changes).
Q3: How do hybrid models improve prediction accuracy?
A: By combining strengths of multiple architectures (e.g., LSTM + CNN) to capture both temporal and spatial patterns.
Q4: Why are blockchain features underutilized?
A: Lack of standardized metrics and dynamic protocol updates complicate analysis.
Q5: What’s the next breakthrough in prediction models?
A: Transformer-based architectures for parallelized, high-frequency data processing.
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