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factor-adjusted tests for generalized linear models with multimodal data:An application to breast cancer data

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Science China Mathematics 2025年第2期68卷 447-484页

作者： Dongyu Li Lei Wang School of Statistics and Data Science KLMDASRLEBPSand LPMCNankai UniversityTianjin 300071China

With the advancement of modern scientific research,multimodal data is increasingly being collected from multiple sources or *** outcomes derived from generalized linear models with high-dimensional and multimodal covariates,we develop two distinct factor-adjusted tests to assess the significance of high-dimensional modality data and specific low-dimensional linear combinations of predictors from one or more modalities,***,we propose a factor-adjusted decorrelated score test to evaluate the significance of a single *** approach simultaneously transforms a high-dimensional test into a fixed low-dimensional one while addressing the impact of high-dimensional nuisance ***,we construct a factor-adjusted Wald test based on partial penalized estimation to assess the significance of certain low-dimensional combinations of variables from one or more *** limiting distributions of these two proposed tests are analyzed under both the null hypothesis and local alternatives to characterize the asymptotic type-I errors and *** finite sample performance of our proposed tests is evaluated through simulations and further demonstrated with a breast cancer dataset.

关键词： decorrelated score factor model high-dimensional data integrative analysis partial penalized Wald test

Research on Prediction and Early Warning of A-Share Market Volatility Based on HAR-Type models

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Journal of Systems Science and Information 2023年第6期11卷 671-690页

作者： Zhaohao WEI Jichang DONG Zhi DONG School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China

Based on the different premium volatility characteristics of various systematic factors in the A-share market, this paper constructs six representative high-frequency volatility prediction models that consider multiple complex risk structures. On this basis, a detailed comparative analysis of the differences in volatility characteristics among various factors is conducted, and the optimal prediction and early warning framework for the A-share market is proposed. Research shows that: 1) The volatility research results only for individual market indexes are not universally representative. 2) The fluctuation characteristics among different systematic factors and their respective optimal prediction model frameworks generally have significant differences, that is, there is no single fixed combination of model parameters. 3) Complex risk characteristics such as long memory, measurement errors, and high-frequency jump fluctuations obviously exist in the A-share market. The optimal forecast and early warning framework for the A-share market can be constructed by a combination of models that consider one or more of the above risk characteristics. The above conclusions have important practical reference value for the risk warning and prevention of the A-share market and the formulation of related policies.

关键词： volatility prediction high-frequency data factor model HAR-type model

Stock return prediction with multiple measures using neural network models

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Financial Innovation 2024年第1期10卷 1073-1106页

作者： Cong Wang Department of Economics and Law Sapienza University of RomeRomeItaly

In the field of empirical asset pricing,the challenges of high dimensionality,non-linear relationships,and interaction effects have led to the increasing popularity of machine learning(ML)*** study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this *** findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic ***,the inclusion of macroeconomic factors from the financial market,real economic activities,and investor sentiment leads to substantial improvements in the model ***,the degree of improvement varies with the specific measures of stock returns under ***,our analysis indicates that,after the inclusion of macroeconomic factors,there is a dissimilarity in model performance,variable importance,and interaction effects among macroeconomic and firm-specific variables,particularly concerning abnormal returns derived from the Fama–French three-and five-factor models compared with excess *** divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic *** findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models,stock returns,and macroeconomic conditions in the domain of empirical asset pricing.

关键词： Neural network model Stock return Macroeconomic conditions factor model

Fama-French五因子模型在中国股票市场的实证检验

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投资与证券 2017年第11期

作者：李志冰杨光艺冯永昌景亮

本文以1994年7月至2015年8月A股上市公司为样本，考察五因子模型在中国股市不同时期的应用。主要结论有：(1)全样本下规模、账面市值比效应显著，经三因子模型调整后盈利能力及投资风格效应仍显著，但不存在显著的动量或反转效应；(2)... 详细信息

本文以1994年7月至2015年8月A股上市公司为样本，考察五因子模型在中国股市不同时期的应用。主要结论有：(1)全样本下规模、账面市值比效应显著，经三因子模型调整后盈利能力及投资风格效应仍显著，但不存在显著的动量或反转效应；(2)五因子模型有非常强的解释能力，比CAPM、三因子及Carhart四因子模型表现更好；(3)股改前市场风险占据主导地位，盈利能力、投资风格及动量因子“冗余”，股改后这三个因子的风险溢价显著；(4)股改后存在经五因子模型调整后仍显著的反转效应；(5)股改后实际收益率与预期收益率的差异更接近于0，市场趋于“有效”。

关键词：因子模型盈利能力投资风格 factor model Operating Profitability Investment Style

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中国城市收缩的识别及其成因

区域与城市经济 2020年第5期

作者：杨孟禹

本文采用全球夜间灯光数据衡量城市规模变动，并识别城市收缩现象，发现中国城市收缩多集中在中西部和东北地区。分析发现，从城市经济属性看，外部引力因子、内部推力因子和收缩机制因子是导致城市收缩的原因；从城市空间属性看，地区... 详细信息

本文采用全球夜间灯光数据衡量城市规模变动，并识别城市收缩现象，发现中国城市收缩多集中在中西部和东北地区。分析发现，从城市经济属性看，外部引力因子、内部推力因子和收缩机制因子是导致城市收缩的原因；从城市空间属性看，地区中心城市和沿海发达城市的虹吸效应也是引起城市收缩的原因，且城市产业分工对城市收缩趋势有缓解作用。最后，本文认为未来城市发展战略应该坚持顶层设计和分门别类的思路，积极构建城市收缩监测和预警机制，加快转变城市发展方式，优化城市分工体系，避免城市收缩现象持续发生，提高城市发展质量。

关键词：城市收缩影响因素灯光数据因子模型 urban shrinking cities influencing factors lighting data factor model

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中国电力产业链周期性波动实证研究

产业经济 2010年第1期

作者：韩金山谭忠富赵耳岽

电力产业链是以煤炭工业为起点，电力工业为纽带，向钢铁、电解铝、电石等下游工业辐射而形成的相对独立的产业链群系统。对产业链主要产品增长率序列进行因子分析、变量聚类分析和动态分析。结果表明：产业链波动存在很强的关联性，其... 详细信息

电力产业链是以煤炭工业为起点，电力工业为纽带，向钢铁、电解铝、电石等下游工业辐射而形成的相对独立的产业链群系统。对产业链主要产品增长率序列进行因子分析、变量聚类分析和动态分析。结果表明：产业链波动存在很强的关联性，其主要因素以电力为核心，主要覆盖煤炭、钢铁、电石、铁合金等基础工业，它们的动态特征是关联性越来越紧密，长期波动起伏趋势一致，风险积累越来越严重。关联度最高的钢铁等4个下游产业以2倍宏观经济的增速持续增长了7年以上，面临比较大的调整压力。在这样的条件下，在制定行业发展战略和推进体制改革中，电力产业应居于产业链发展的主导地位，注意防范相关产业的风险“外溢”，努力理顺与上下游产业的经济关系。

关键词：电力产业链因子分析模型灰色关联度 industry chain of electricity(ICE) factor model grey relation coefficients

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农作物区域产量保险的时空相依模型及其应用

统计与精算 2023年第1期

作者：张译元孟生旺

在农作物区域产量保险研究中，农作物产量模型具有十分重要的基础性地位。农作物单产数据呈现出复杂的时空相依关系，产量的均值、方差、偏度受技术进步、气候变化等因素影响呈现趋势变化，不同地区产量受气候、生产聚集性等因素影响呈... 详细信息

在农作物区域产量保险研究中，农作物产量模型具有十分重要的基础性地位。农作物单产数据呈现出复杂的时空相依关系，产量的均值、方差、偏度受技术进步、气候变化等因素影响呈现趋势变化，不同地区产量受气候、生产聚集性等因素影响呈现非对称偏相依特征，并且随着外部环境的变化而变化。因此，对农作物产量建模，需要准确刻画其趋势性、异方差性和空间相依的时变性特征。本文基于空间因子模型理论，用高斯过程刻画变量中间部分的空间相依关系；用厚尾分布对高斯过程的尾部相关性结构进行调整，得到非对称左尾强相依结构，并通过对因子分布的参数建模，使得空间左尾相依强度随着外部环境而变化；此外，在对边际分布的方差建模时，考虑了时间和灾害的影响，得到了非对称的异方差模型。本文所构造的模型能够反映农作物区域产量的时空相依性、时空异质性及其时变性特征，具有较强的可解释性。基于一组区域产量数据的实证研究结果表明，该模型在拟合农作物空间产量数据的时空相依关系时，显著优于目前广泛使用的建模方法。

关键词：空间相依因子模型异方差时变性 spatial dependence factor model heteroscedasticity time-varying