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用于统计套利组合的量子图聚类

文章 arXiv papers · 作者: Dayne Marcus Lopena et al.
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总结

本研究将标普500股票之间的残差相关性映射为高斯玻色采样的邻接矩阵。高斯玻色采样是一种用于采样稠密子图的光子量子方法。研究将两种经典聚类方法——谱聚类和SPONGE——与量子方法GBS Boost及所提出的GBS Roots进行比较。所得聚类用于构建动态、市场中性的统计套利组合，并采用滚动的一年期窗口。

跨宏观经济状态的模拟报告称，在高波动时期，量子聚类在大型股票集合中产生了更高的阿尔法。报告的优势在模拟低损耗条件下仍然存在；在使用相干位移补偿光子损耗时，也扩展到了高损耗条件。这些是模拟结果，文中未提供具体表现估计、实施成本或实盘交易证据。因此，其主张仅涉及受测的投资组合构建设置，并不代表已证实的现实表现。

核心观点

  • 该方法将股票残差相关性转换为适用于高斯玻色采样的图矩阵。
  • 研究将GBS Boost和GBS Roots与谱聚类和SPONGE方法进行比较。
  • 研究使用聚类构建动态、市场中性的统计套利组合。
  • 模拟结果显示，在高波动状态下，量子聚类在大型资产集合中具有阿尔法收益优势。
  • 报告的抗光子损耗稳健性部分依赖模拟的相干位移。

标签

股票机器学习套利投资组合构建
全文
# Gaussian Boson Sampling for Asset Clustering in Statistical Arbitrage Portfolios


# Gaussian Boson Sampling for Asset Clustering in Statistical Arbitrage Portfolios









Gaussian Boson Sampling (GBS) provides a native photonic quantum heuristic for sampling dense subgraphs from adjacency matrices, offering a scalable physical approach to combinatorial graph search problems. Simultaneously, correlation matrix clustering algorithms, such as Spectral and SPONGE, have established robust benchmarks for identifying co-moving assets from correlation matrices in statistical arbitrage (StatArb) strategies. In this work, we map S&P 500 residual correlation data into GBS-compatible adjacency matrices. We benchmark those classical clustering algorithms against two quantum clustering algorithms, GBS Boost and our novel GBS Roots, to construct dynamic, market-neutral portfolios over a rolling one-year window. Simulations across distinct macroeconomic regimes reveal that quantum clustering generates superior alpha within large stock universes during periods of high volatility, effectively isolating structural market idiosyncrasies. Crucially, this economic advantage persists under simulated low-loss conditions and extends into high-loss regimes via the application of coherent displacement to compensate for photon loss. Our findings underscore the efficacy of GBS-derived graph clustering in constructing robust StatArb portfolios, establishing a quantum foundation for broader quantitative finance 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。