发表机构
Gopal K. Saxena Centre for Advanced Research, Bangalore（班加罗尔 Gopal K. Saxena 高级研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将张量网络方法应用于场耦合XY模型，构建仅做多投资组合，经五个股票市场实验，得出从金融时间序列到网络调整分配的可行路径，其预测交易表现不在研究范围内。
AI 中文摘要
我们将张量网络方法应用于场耦合XY模型以构建仅做多投资组合。日收益率统计量设定资产特有的场和关联耦合。利用关联距离、四点格罗莫夫双曲性和沃德聚类构造稀疏相互作用路径。傅里叶-贝塞尔展开将连续角配分函数映射为有限电流张量网络；自底向上和自顶向下收缩得到单点边缘分布和平衡余弦得分。通过softmax映射将这些得分转换为正的、完全投资的投资组合权重。我们研究了五个股票市场在逆温度β和集中度γ的连续范围内的情况，并将选定的参数对与仅做多的马科维茨前沿和标准基准进行比较。同一样本的比较表明，存在一条从金融时间序列到网络调整分配的可行路径；预测交易表现不在其研究范围内。
英文摘要
We apply tensor-network methods to a field-coupled XY model for long-only portfolio construction. Daily return statistics set asset-specific fields and correlation couplings. Correlation distance, four-point Gromov hyperbolicity, and Ward clustering are used to construct a sparse interaction path. A Fourier-Bessel expansion maps the continuous angular partition function to a finite-current tensor network; bottom-up and top-down contractions then give the one-site marginals and equilibrium cosine scores. A softmax map converts these scores into positive, fully invested portfolio weights. We study five equity markets over continuous ranges of inverse temperature beta and concentration gamma, and compare selected parameter pairs with long-only Markowitz frontiers and standard benchmarks. The same-sample comparisons demonstrate a tractable route from financial time series to network-adjusted allocations; predictive trading performance is outside their scope.
Comments52 pages, 10 figures, 5 tables
