A Study of IPO Underpricing Using Regression Model Based on Information Asymmetry, Media, and Institution
- DOI
- 10.2991/aebmr.k.210917.051How to use a DOI?
- Keywords
- IPO underpricing, information asymmetry theory, Investor sentiment theory, government control
- Abstract
This paper reviews the empirical studies of IPO underpricing, with an emphasis on Chinese stock market. It surveys the driving factors of IPO underpricing using the fitting regression analysis and disaggregate model. The studies have been sorted into three categories: information asymmetry, media coverage, and institutional factors. From the information asymmetry, this paper reviews five information asymmetry theories and uses the empirical analysis of the Winner’s Curse as the basis. It also lists the implications of some information asymmetry theories on various fields in the economy. In terms of media coverage, we used control variate model to analyse influence of each factor. It has been found that both media attention and media tone will affect investor sentiment and thus affect IPO underpricing. Therefore, to reduce the IPO underpricing rate, enterprises can carry out media information management to increase the proportion of negative tone in media reports. In the last section, we use linear regression model and discovered that IPO underpricing phenomenon was significantly correlated with institutional control and speculation behavior.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Liangda Liu AU - Zixuan Zhang AU - Kexin Lyu PY - 2021 DA - 2021/09/18 TI - A Study of IPO Underpricing Using Regression Model Based on Information Asymmetry, Media, and Institution BT - Proceedings of the 2021 International Conference on Financial Management and Economic Transition (FMET 2021) PB - Atlantis Press SP - 322 EP - 333 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.210917.051 DO - 10.2991/aebmr.k.210917.051 ID - Liu2021 ER -