A Comprehensive Study of the Reasons for Underpriced IPOs

DOI

10.2991/assehr.k.211209.363How to use a DOI?

Keywords

Initial Public Offerings; Underpricing; Information Asymmetry; Machine Learning; Interest Rate

Abstract

The paper reviews mainstream explanations of the underpricing of IPOs and aims to locate the most logical explanation among both existing literature and hypotheses to be corroborated. The author divides current literature into two groups, informational explanations and non-informational explanations. Informational explanations are further differentiated into three types; by characters of both parties in an asymmetric information situation, issuers vs. investors, underwriters vs. investors and investors vs. investors. For non-informational explanations, there is Legal Cost by Ibbotson and Takeover Defence by Booth and Chua. Among mentioned literature, the author identified strong evidence for the Winners Curse Model via the corroboration of a recent paper published in 2018, as well as the Bookbuilding Theory. Furthermore, the author concluded that there are scopes for future research in machine learning. It can be further studied that which features determine the quality of a firm and which model or what combinations of models can well explain the mispricing. Finally, the author hypothesized that overprinting of U.S. dollars can increase informed investments. According to the Winners Curse Model, IPOs have to be more underpriced to keep the uninformed investors stay.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Kelai Wang
PY  - 2021
DA  - 2021/12/15
TI  - A Comprehensive Study of the Reasons for Underpriced IPOs
BT  - Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)
PB  - Atlantis Press
SP  - 2213
EP  - 2217
SN  - 2352-5428
UR  - https://doi.org/10.2991/assehr.k.211209.363
DO  - 10.2991/assehr.k.211209.363
ID  - Wang2021
ER  -