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使用量子退火对有符号金融相关网络进行聚类

文章 arXiv papers · 作者: Shivam Sharma et al.
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总结

本文介绍如何使用基于图的联盟结构生成算法GCS-Q，对以收益相关性构成的有符号加权图中的资产进行聚类。研究动机在于，常见聚类方法在转换有符号相关性时可能丢失信息，而且可能需要预先指定簇的数量。GCS-Q则将划分步骤建模为二次无约束二元优化问题，可通过量子退火进行求解。

作者使用合成和真实金融数据评估该方法，并将其与SPONGE及k-Medoids进行比较。他们报告称，该方法的聚类质量更高，调整兰德指数更优、结构平衡惩罚更低，同时还能动态确定簇的数量。描述未提供数据集细节、数值结果、运行时间比较，也未说明量子硬件和实现方式。报告的改进支持将此方法作为投资组合优化和统计套利的研究方向，但本身并不能表明它能改善交易结果，也不能证明它适用于各种规模的资产范围。

核心观点

  • GCS-Q直接根据有符号加权相关图对资产进行聚类。
  • 其划分步骤被表述为供量子退火求解的QUBO问题。
  • 该方法动态确定簇的数量。
  • 据报告，在所述聚类指标上，合成数据和真实数据实验均优于SPONGE及k-Medoids。
  • 本文并未证明该方法会改善投资组合收益或交易表现。

标签

多资产投资组合构建统计学机器学习套利
全文
# Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering


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









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。