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

 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 highdimensional 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 outof-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

 In this study, we examine the impact of big data and corresponding prediction techniques on insurance risk assessment. We demonstrate that both big data and the Least Absolute Shrinkage and Selection Operator (LASSO) approach, a modern predictor selection technique, effectively improve insurance risk assessment accuracy. In an average of proxies, the out-of-sample health risk prediction accuracy improves by 106 %, in which big data, in addition to traditional insurance policy and demographic information, contributes 82.5 %, and the LASSO model contributes 17.5 %. We also demonstrate that big data obtained from smartphone use offers extra predictive power in addition to past medical histories. We employ Adaptive Group LASSO to determine that the most fruitful data collection sources for health insurance underwriting include personal digital devices, recent travel experience, and insureds’ credit records.

 We conduct a comprehensive study on momentum spillovers in the Chinese stock market using various types of economic linkages, with particular attention to momentum spillover via news co-mention linkages. We utilize millions of Chinese business news articles and develop a flexible and innovative algorithm to identify linkages among listed firms.We find that news co-mention momentum spillover is stronger than others, unifying various forms of momentum spillover effects in the Chinese market and replacing the role of analyst co-coverage in the U.S. News co-mention identifies a wide range of economically important linkages, particularly recovering more cross-industry linkages than other link identification methods, which contributes to its strong performance.

We propose an asset pricing factor model constructed with semiparametric characteristics-based mispricing and factor loading functions.We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging.We estimate this model and test the existence of the mispricing function by a power enhanced hypothesis test.The enhanced test solves the low power problem caused by diverging B-splines coefficients,with the strengthened power approaching one asymptotically.We also investigate the structure of mispricing components through Hierarchical K-means Clusterings.We apply our methodology to CRSP (Center for Research in Security Prices)and Compustat data for the U.S.stock market with oneyear rolling windows during 1967–2017.This empirical study shows the presence of mispricing functions in certain time blocks.We also find that distinct clusters of the same characteristics lead to similar arbitrage returns,forming a“peer group”of arbitrage characteristics.

This paper studies a heterogeneous coefficient spatial factor model that separately addresses both common factor risks (strong cross-sectional dependence) and local dependency (weak cross-sectional dependence) in equity returns. From the asset pricing perspective, we derive the theoretical implications of no asymptotic arbitrage for the heterogeneous spatial factor model, generalizing the work of Kou et al. (2018). We also provide the associated Wald tests for the APT restrictions in the general case when there are both traded and non-traded factors. On the empirical side, it is challenging to measure granular firm-to-firm connectivity for a high-dimensional panel of equity returns. We use extensive business news to construct firms’ links through which local shocks transmit, and we use those news-implied linkages as a proxy for the connectivity among firms. Empirically, we document a considerable degree of local dependency among S&P500 stocks, and the spatial component does a great job in capturing the remaining correlations in the de-factored returns.We find that adding spatial interaction terms to factor models reduces mispricing and boosts model fitting. By comparing the performance of the model estimated using different networks, we show that the news-implied linkages provide a comprehensive and integrated proxy for firm-to-firm connectivity.

We carry out some analysis of the daily data on the number of new cases and the number of new deaths by (191) countries as reported to the European Centre for Disease Prevention and Control (ECDC). Our benchmark models are a quadratic model, a quartic model and a gamma model of time trends, which are applied to the log of new cases and deaths for each country. We use our model to predict when the peak of the epidemic will arise in terms of new cases or new deaths in each country and the peak level. We also predict how long the number of new daily cases in each country will fall by an order of magnitude. Finally, we forecast the total number of cases and deaths for each country. We also consider two models that link the joint evolution of new cases and new deaths.

本文利用异质空间因子模型同时刻画A 股市场中由共同因子驱动的全局相关和由空间关联驱动的局部相关, 并通过文本分析挖掘百万条财经新闻中隐含的股票间关联, 用以构造模型中衡量股票间关联强弱的空间矩阵. 我们介绍了模型框架下的统计推断方法及渐进无套利理论, 并将渐进无套利理论推广到包含不可交易因子的情景. 实证方面, 我们利用2012–2021 年的A 股股票和相关因子的周收益率对模型进行分析, 并将新闻关联网络与其他常用关联网络展开全方位比较. 结果表明: A 股市场局部相关性的影响具有明显的异质性, 与其他关联网络相比, 新闻关联在多个维度拥有最佳表现, 这得益于新闻文本涵盖最多的行业外关联信息, 并且信息更新速度快. 此外, 不可交易因子在A 股市场上具有不可忽略的影响.