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Stock complex networks based on the GA-LightGBM model: The prediction of firm performance

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成果类型:
期刊论文
作者:
Huang, Chuangxia;Cai, Yaqian;Cao, Jinde;Deng, Yanchen
通讯作者:
Cao, JD
作者机构:
[Huang, Chuangxia] Hunan Univ Sci & Engn, Coll Sci, Yongzhou 425199, Hunan, Peoples R China.
[Cai, Yaqian; Deng, Yanchen; Huang, Chuangxia] Changsha Univ Sci & Technol, Sch Math & Stat, Changsha 410114, Hunan, Peoples R China.
[Cai, Yaqian; Deng, Yanchen; Huang, Chuangxia] Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Hunan, Peoples R China.
[Cao, Jinde; Cao, JD] Southeast Univ, Sch Math, Nanjing 211189, Peoples R China.
[Cao, Jinde] Ahlia Univ, Manama 10878, Bahrain.
通讯机构:
[Cao, JD ] S
Southeast Univ, Sch Math, Nanjing 211189, Peoples R China.
语种:
英文
关键词:
Machine learning;LightGBM;Genetic algorithm;Stock network centrality;Firm performance
期刊:
Information Sciences
ISSN:
0020-0255
年:
2025
卷:
700
页码:
121824
基金类别:
CRediT authorship contribution statement Chuangxia Huang: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, acquisition, Formal analysis, Data curation, Conceptualization. Yaqian Cai: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jinde Cao: Writing –
机构署名:
本校为其他机构
院系归属:
数学与统计学院
摘要:
One of the fundamental issues in predicting firm performance from the perspective of complex systems is how to accurately construct stock networks. Most stock network-based research is mainly limited to traditional econometric models, which suffer from being pairwise, linear, or low-dimensionality. Literature dealing with this issue from the perspective of machine learning seems to be scarce; such investigations are, however, particularly relevant for corporate governance. Using a sample of listed firms in the Chinese A-share market from 2006 to 2021, this paper constructs directed-weighted ne...

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