Decrypting IPO pricing: an integrated bibliometric and content analysis approach

Abstract

Purpose

Since 2018, there has been a resurgence in initial public offering (IPO) pricing studies. The authors aim to consolidate the knowledge and explore current dynamics, understand knowledge progression, elicit trends, and provide future research directions for IPO pricing research.

Design/methodology/approach

The authors conducted a two-stage hybrid review based on 512 high-quality Scopus articles on IPO pricing published over the last decade. The authors deploy bibliometric analysis, and then, based on 61 curated articles, the authors conduct content analysis and offer future research directions.

Findings

Four key research streams emerged: information asymmetry, agency problems, legal, regulatory, and social environment, and behavioral finance. Future research may focus on behavioral explanations for IPO underpricing, the role of investor sentiment in IPO pricing, text analytics, machine learning, and big data in alleviating information asymmetry and agency problems. The authors summarize and present content analysis using the classic Theory, Context, Characteristics, Methods (TCCM) framework.

Research limitations/implications

Using different databases, bibliometric analysis tools, sample period or article screening criteria for the study might give different results. However, the study's major findings are robust to alternative choices.

Practical implications

This study serves as a ready reckoner for the research scholars, practitioners, regulators, policymakers, and investors interested in understanding the nuances of IPO pricing.

Originality/value

The study sheds light on the most influential documents, authors, and journals, offers an understanding of knowledge structure, identifies and discusses primary research streams and related implications, and provides future research directions.

Citation

Joshipura, M., Mathur, S. and Gwalani, H. (2023), "Decrypting IPO pricing: an integrated bibliometric and content analysis approach", Managerial Finance, Vol. 49 No. 1, pp. 135-162. https://doi.org/10.1108/MF-06-2022-0293