发表机构
Amirkabir University of Technology（伊朗阿米尔卡比尔理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用时间图机器学习为机构股票持有预测任务引入基准，将其作为节点亲和力预测，在含99名经理和标准普尔500指数成分股的数据集上，NAVIS模型表现出色，优于其他动态图和启发式方法，为时间图机器学习在相关领域提供可重复基础。
AI 中文摘要
美国证券交易委员会表格13F文件中披露的机构股票持有情况，为大型投资经理的投资组合决策提供了丰富的时间记录。然而，由于披露滞后、报告噪音和机构行为的强持续性，预测未来配置和建模未来需求仍然具有挑战性。我们使用时间图机器学习为这些任务引入了第一个基准，将持有预测框架为从预处理文件中提取的经理和证券的离散时间二分图上的节点亲和力预测，即预测投资组合权重。在一个包含99名经理和标准普尔500指数（503只证券，209351条时间边，跨越2013年至2025年的48个季度）的采样数据集上，使用虚拟状态的节点亲和力预测模型（NAVIS）在有特征时达到了0.9127的先进测试归一化折损累计增益（NDCG）（无特征时为0.9121），大幅优于所有动态图表示学习竞争对手，也优于所有启发式方法。显著的是，一个简单的指数移动平均线基线达到了0.8882，超过了除NAVIS之外的所有动态图模型和除持续预测（0.8891）之外的所有启发式方法，突出了机构投资组合的强平滑性和持续性。特定领域的节点特征仅提供了边际收益（<1.2%），表明13F所有权图中的时间和结构信号已经捕获了大部分可预测信息。通过在节点亲和力预测设置下对一系列时间图基准（TGB）模型进行基准测试，包括有特征和无特征的情况，在真实世界的13F数据上，这项工作为持有预测和投资组合分配中的时间图机器学习提供了一个可重复的基础。
英文摘要
Institutional equity holdings disclosed in SEC Form 13F filings provide a rich temporal record of portfolio decisions by large investment managers. However, forecasting future allocations and modeling future demand remains challenging due to disclosure lags, reporting noise, and strong persistence in institutional behavior. We introduce the first benchmark for these tasks using temporal graph machine learning, framing holdings prediction as node affinity prediction -- i.e., forecasting portfolio weights -- on a discrete-time temporal bipartite graph of managers and securities extracted from preprocessed filings. On a sampled dataset comprising 99 managers and the S\&P 500 index (503 securities, 209,351 temporal edges across 48 quarters from 2013--2025), Node Affinity prediction model using Virtual State (NAVIS) achieves a state-of-the-art test Normalized Discounted Cumulative Gain (NDCG) of 0.9127 with features (0.9121 without), outperforming all dynamic graph representation learning competitors by a substantial margin, and outperforming all heuristic methods. Remarkably, a simple Exponential Moving Average baseline achieves 0.8882, surpassing all dynamic graph models except NAVIS and all heuristics except Persistent Forecast (0.8891), highlighting the strong smoothness and persistence of institutional portfolios. Domain-specific node features provide only marginal gains (<1.2\%), indicating that temporal and structural signals in the 13F ownership graph already capture most of the predictable information. By benchmarking a suite of Temporal Graph Benchmark (TGB) models under the node affinity prediction setting, both with and without features, on real-world 13F data, this work provides a reproducible foundation for temporal graph machine learning in holdings prediction and portfolio allocation.
