Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911463597146112 |
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| author | Sharma, Shivam Venkatesh, Supreeth Mysore Kachroo, Pushkin |
| author_facet | Sharma, Shivam Venkatesh, Supreeth Mysore Kachroo, Pushkin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_07766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering Sharma, Shivam Venkatesh, Supreeth Mysore Kachroo, Pushkin Quantum Physics Machine Learning 05-08 F.4.1; F.2.2 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. |
| title | Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering |
| topic | Quantum Physics Machine Learning 05-08 F.4.1; F.2.2 |
| url | https://arxiv.org/abs/2509.07766 |