Cross-influence of information and risk effects on the IPO market: exploring risk disclosure with a machine learning approach

Abstract The paper examines whether the structure of the risk factor disclosure in an IPO prospectus helps explain the cross-section of first-day returns in a sample of Chinese initial public offerings. This paper analyzes the semantics and content of risk disclosure based on an unsupervised machine learning algorithm. From both long-term and short-term perspectives this paper explores how the information effect and risk effect of risk disclosure play their respective roles. The results show that risk disclosure has a stronger risk effect at the semantic novelty level and a more substantial information effect at the risk content level. A novel aspect of the paper lies in the use of text analysis (semantic novelty and content richness) to characterize the structure of the risk factor disclosure. The study shows that initial IPO returns negatively correlate with semantic novelty and content richness. We show the interaction between risk effect and information effect on risk disclosure under the nature of the same stock plate. When enterprise information transparency is low the impact of semantic novelty and content richness on the IPO market is respectively enhanced.
Huosong Xia Juan Weng Sabri Boubaker Zuopeng Zhang and Sajjad M. Jasimuddin Huosong Xia: Wuhan Textile University Juan Weng: Wuhan Textile University Sabri Boubaker: EM Normandie Business School Zuopeng Zhang: University of North Florida Sajjad M. Jasimuddin: Kedge Business School
Risk disclosure ; Information effect ; Risk effect ; Enterprise information transparency ; Unsupervised machine learning algorithm (search for similar items in EconPapers)
Machine Learning
Annals of Operations Research 2024 vol. 334 issue 1 No 28 797 pages
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2024/03/18 03:30
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