近日，南开大学金融学院准任副教授戈舒怡老师连续在国际顶级期刊发表两项重要研究成果：一篇发表于《Journal of Financial Economics》（JFE），论文题为《Dual peer effects and cross-stock predictability》；另一篇发表于《Journal of Econometrics》，论文题为《Should we augment large covariance matrix estimation with auxiliary network information？》。彰显我院青年学者的科研实力与学术影响力。

Dual peer effects and cross-stock predictability

发表于金融学国际顶刊

《Journal of Financial Economics》Volume 180, June 2026, 104274

作者：Doron Avramov, Shuyi Ge, Shaoran Li, Oliver Linton

文章摘要

This paper introduces a Peer Index (PI) constructed from economically motivated peer networks that summarizes (i) the strength of a firm’s peers and (ii) the firm’s position within its peer group. PI predicts stock returns and earnings surprises over short and long horizons. Machine-learning models based solely on firm-level characteristics do not subsume PI’s predictive power, supporting the interpretation that it captures genuine cross-stock information. Lag-augmented local projections show that positive PI innovations are followed by higher next-month returns that gradually decay without reversal, consistent with slow diffusion of peer information into prices.

内容介绍

研究提出了一种基于经济关联的“同行指数”（Peer Index），通过构建同行网络系统性地捕捉两类同行效应——同行群体整体强度与企业在群体中的相对位置。实证分析表明，该指数能够有效预测股票在短期和长期内的收益率以及盈余公告超预期程度，且其预测能力无法被基于个股特征的机器学习模型所涵盖。进一步通过滞后增强型局部投影方法发现，当同行指数出现正向冲击时，下月收益率随之攀升，后续效应缓慢衰减但并未出现反转，这一动态过程与市场信息缓慢扩散的理论预期一致。在控制主流因子模型、投资者情绪与关注度指标后，该指数的预测能力依然稳健。研究还在供应链客户-供应商关系与分析师共同覆盖两类非对称网络中发现，同行预测能力部分来源于因子中性的方向性信息传递。整体而言，该研究通过赋予同行信息以清晰的经济结构，揭示了跨股票可预测性中因市场对网络信息反应不足而产生的系统性定价偏差。

Should we augment large covariance matrix estimation with auxiliary network information?

发表于经济学国际顶刊

《Journal of Econometrics》Volume 255, May 2026, 106236

作者：Shuyi Ge, Shaoran Li, Oliver Linton, Weiguang Liu, Wen Su

文章摘要

This paper uses the auxiliary network information, observed in addition to the original sample, to infer latent network structures in the population correlation matrix and thus improve high-dimensional covariance matrix estimation. Building on estimated Location Indicator and Relative Importance matrices, we propose two Network-Guided estimators. Network-Guided Thresholding uses auxiliary network data to regularize the large and small elements in the sample covariance matrices differentially, delivering a faster convergence rate over a more general class of sparse covariance matrices when auxiliary information is informative. Network-Guided Banding extends the banding estimators to allow for data without a natural ordering, using the relative importance of elements indicated by the auxiliary datasets to construct a neighbor ordering, which can achieve the optimal convergence rate that would be infeasible without the auxiliary network information. Extensive simulation studies show robust finite-sample gains of the proposed Network-Guided estimators over existing benchmark methods. The proposed methods also deliver superior out-of-sample performance relative to the established baseline models in the empirical application of constructing Global Minimum Variance (GMV) and Mean-Variance Optimal (MVO) portfolios in the Chinese stock market with various sources of auxiliary network information, including analyst co-coverage, news co-mentions, and industry classifications.

内容介绍

本文聚焦于大数据金融背景下高维协方差矩阵估计所面临的关键挑战：当资产数量迅速扩张而历史样本相对有限时，如何有效整合行业分类、分析师共同覆盖、新闻共现等多源异构信息，以提升对资产间风险关联结构的刻画能力。文章的核心贡献在于，不再将这些辅助网络数据仅视为外部描述，而是将其转化为识别协方差矩阵结构特征的重要依据，用以判断哪些元素更可能具有经济相关性、哪些资产之间存在更紧密的联动关系。基于此，作者构建位置指示矩阵与相对重要性矩阵，并提出 Network-Guided Thresholding 与 Network-Guided Banding 两类方法：前者针对不同协方差元素实施差异化收缩，后者则借助网络关系为原本缺乏自然排序的大规模资产数据重建“邻居结构”。理论分析表明，在辅助信息具有较强识别能力时，所提方法能够实现更快的收敛速度，并在高维无序数据环境下逼近最优估计表现。模拟研究与中国股票市场投资组合实证进一步显示，该框架能够显著改善有限样本估计质量和样本外配置绩效。总体而言，本文生动体现了大数据时代多源信息融合与高维金融计量方法深度结合的研究方向，具有鲜明的前沿性与方法论价值。

戈舒怡

南开大学金融学院准任副教授，英国剑桥大学博士。

研究方向：金融计量，资产定价，机器学习与金融大数据分析。

在Journal of Financial Economics, Journal of Econometrics, Journal of Business and Economic Statistics, Journal of Banking and Finance,  Journal of Economic and Dynamic Control等权威期刊发表学术论文。教材《Empirical Finance：Theory and Application》出版于国际知名出版社CRC Press Taylor & Francis Group。主持国家自然科学基金项目一项, 参与国家自然科学基金重大项目一项。

祝贺舒怡老师！

期待更多高水平成果持续涌现！