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

2025-09-09

AI Summary

Financial asset clustering faces three key challenges: (1) difficulty in modeling signed-weighted correlation structures, (2) reliance on lossy transformations, and (3) dependence on pre-specified, fixed numbers of clusters. To address these, this paper proposes a prior-free, graph-based clustering method. It introduces the Graph Coalition Structure Generation algorithm (GCS-Q) to finance for the first time, formulating clustering as a Quadratic Unconstrained Binary Optimization (QUBO) problem subject to structural balance constraints—solvable via both quantum annealing and classical solvers—and enabling automatic determination of the optimal number of clusters. Experiments on synthetic and real-world financial datasets demonstrate that our method significantly outperforms benchmarks—including SPONGE and k-Medoids—in adjusted Rand index and overall clustering quality. The approach yields interpretable, robust clusterings, establishing a novel paradigm for portfolio optimization and statistical arbitrage.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Clustering financial assets using signed correlation structures
• Avoiding lossy transformations and heuristic assumptions in clustering
• Leveraging quantum annealing for efficient solution space exploration

Innovation

• Graph-based Coalition Structure Generation algorithm for clustering
• QUBO problem formulation enabling quantum annealing
• Dynamic cluster number determination without heuristic assumptions