Optimising cryptocurrency portfolios through stable clustering of price correlation networks

2025-05-30

AI Summary

Addressing the high volatility and weak regulatory oversight characteristic of cryptocurrency markets, this paper proposes a robust portfolio optimization framework grounded in price correlation networks. Methodologically, it innovatively integrates Louvain community detection with consensus clustering to identify temporally stable, highly correlated cryptocurrency clusters; subsequently, it combines ARIMA-based price forecasting with mean-variance optimization to construct time-invariant profitable portfolios. The key contributions are: (1) leveraging network structural stability—rather than conventional rolling windows—to enhance portfolio robustness; and (2) achieving consistent positive returns across multiple investment horizons. Empirical evaluation over a five-year period demonstrates that the proposed strategy consistently outperforms equal-weighted, market-cap-weighted, and minimum-variance benchmarks under a 14-day holding period, validating the effective identification and exploitation of latent price co-movement patterns.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Security and Privacy: Cryptocurrency and smart contracts
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The emerging cryptocurrency market presents unique challenges for investment due to its unregulated nature and inherent volatility. However, collective price movements can be explored to maximise profits with minimal risk using investment portfolios. In this paper, we develop a technical framework that utilises historical data on daily closing prices and integrates network analysis, price forecasting, and portfolio theory to identify cryptocurrencies for building profitable portfolios under uncertainty. Our method utilises the Louvain network community algorithm and consensus clustering to detect robust and temporally stable clusters of highly correlated cryptocurrencies, from which the chosen cryptocurrencies are selected. A price prediction step using the ARIMA model guarantees that the portfolio performs well for up to 14 days in the investment horizon. Empirical analysis over a 5-year period shows that despite the high volatility in the crypto market, hidden price patterns can be effectively utilised to generate consistently profitable, time-agnostic cryptocurrency portfolios.

Problem

• Optimizing cryptocurrency portfolios using stable correlation clusters
• Predicting crypto prices with ARIMA for 14-day performance
• Identifying hidden price patterns for consistent portfolio profits

Innovation

• Uses Louvain algorithm for stable cryptocurrency clustering
• Integrates ARIMA model for price prediction
• Combines network analysis with portfolio theory