Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering

• URL: http://arxiv.org/abs/2509.07766v2
• Date: Sat, 13 Sep 2025 21:01:32 GMT
• Title: Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering
• Authors: Shivam Sharma, Supreeth Mysore Venkatesh, Pushkin Kachroo,
• Abstract summary: Clustering financial assets based on return correlations is a fundamental task in portfolio optimization and statistical arbitrage.<n>In this work, we apply the Graph-based Coalition Structure Generation algorithm (GCS-Q) to directly cluster signed, weighted graphs.<n>We validate our approach on both synthetic and real-world financial data, benchmarking against state-of-the-art classical algorithms.
• Score: 5.523385345486361
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clustering financial assets based on return correlations is a fundamental task in portfolio optimization and statistical arbitrage. However, classical clustering methods often fall short when dealing with signed correlation structures, typically requiring lossy transformations and heuristic assumptions such as a fixed number of clusters. In this work, we apply the Graph-based Coalition Structure Generation algorithm (GCS-Q) to directly cluster signed, weighted graphs without relying on such transformations. GCS-Q formulates each partitioning step as a QUBO problem, enabling it to leverage quantum annealing for efficient exploration of exponentially large solution spaces. We validate our approach on both synthetic and real-world financial data, benchmarking against state-of-the-art classical algorithms such as SPONGE and k-Medoids. Our experiments demonstrate that GCS-Q consistently achieves higher clustering quality, as measured by Adjusted Rand Index and structural balance penalties, while dynamically determining the number of clusters. These results highlight the practical utility of near-term quantum computing for graph-based unsupervised learning in financial applications.

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