Optimising cryptocurrency portfolios through stable clustering of price correlation networks

TL;DR

This paper presents a novel framework combining network analysis, clustering, and forecasting to identify stable cryptocurrency groups for constructing profitable, risk-aware portfolios over short-term horizons.

Contribution

It introduces a method that uses consensus clustering and ARIMA forecasts to find persistent correlation clusters for improved portfolio performance.

Findings

Predictive consensus-clustering portfolios perform well up to 14-day horizons.

Portfolios exhibit favorable gain-loss asymmetry.

Tighter tail-risk control is achieved with the proposed method.

Abstract

The rapidly evolving cryptocurrency market presents unique challenges for investment due to its inherent volatility and evolving regulatory environment. Collective price movements can be exploited to construct diversified portfolios with improved risk-return profiles. This paper introduces an integrated framework that combines network analysis, price forecasting, and portfolio theory to identify stable groups of highly correlated cryptocurrencies for profitable portfolio construction. We employ the Louvain community detection algorithm together with consensus clustering to extract temporally persistent correlation clusters, and incorporate ARIMA-based price forecasts to enhance forward-looking cluster formation. Using 5 years of daily closing prices, we evaluate portfolio performance across multiple strategies and holding horizons, assessing both profitability and downside risk with return-based and tail-risk metrics. Our empirical results show that predictive consensus-clustering portfolios maintain consistently positive and stable performance up to a 14-day horizon, exhibit favourable gain-loss asymmetry, and achieve tighter tail-risk control. These findings demonstrate that stable interdependencies in cryptocurrency markets can be leveraged to construct profitable and risk-aware portfolios across short-term holding horizons.