Optimising cryptocurrency portfolios through stable clustering of price correlation networks

ArXiv ID: 2505.24831 “View on arXiv”

Authors: Ruixue Jing, Ryota Kobayashi, Luis Enrique Correa Rocha

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.

Keywords: Network Analysis, Louvain Algorithm, ARIMA, Portfolio Theory, Consensus Clustering, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper integrates advanced network theory (Louvain algorithm), consensus clustering, and ARIMA forecasting, representing moderate-to-high mathematical density; its empirical rigor is supported by a 5-year backtest, specific investment horizon (14 days), and multiple data-driven validation steps.

  flowchart TD
    A["Research Goal<br/>Identify Stable Crypto Clusters for<br/>Profitable, Low-Risk Portfolios"] --> B{"Data Input<br/>5 Years Daily Closing Prices"}
    B --> C["Network Analysis<br/>Louvain Algorithm for Community Detection"]
    C --> D["Consensus Clustering<br/>Identify Robust & Temporally Stable Clusters"]
    D --> E["Selection & Prediction<br/>Select Assets & Forecast with ARIMA<br/>14-Day Horizon"]
    E --> F["Portfolio Theory Application<br/>Construct Optimized Portfolio"]
    F --> G["Key Outcome<br/>Consistently Profitable<br/>Time-Agnostic Portfolios"]