IPO Forecasting Using Machine Learning Methodologies: A Systematic Review Apropos Financial Markets in the Digital Era
DOI:
https://doi.org/10.17010/aijer/2024/v13i2/173502Keywords:
initial public offerings (IPOs)
, machine learning (ML), neural networks, SLR, deep learning, artificial intelligence (AI).JEL Classification Codes
, G11, G12, G14Paper Submission Date, January 5, 2024, Paper sent back for Revision, March 5, Paper Acceptance Date, March 15, 2024Abstract
Purpose : With the goal of shedding light on the ways in which machine learning (ML) approaches are now being used in initial public offering (IPO) research, this systematic analysis assessed how well IPOs performed.
Design/Methodology/Approach : To evaluate the efficacy of ML approaches in IPO appraisal, 21 papers from the Scopus and Web of Science databases were analyzed using PRISMA.
Findings : The findings revealed that ML algorithms, including rough set theory, text analytics, fuzzy logic, XGBoost, random forest, SVM, gradient descent, and artificial neural networks, outperformed linear methodologies in IPO evaluation.
Practical Implications : The exclusion of other databases may result in the overlooking of pertinent research, even with the thorough insights obtained from studies within the Scopus and Web of Science databases. Moreover, a singular concentration on ML approaches could overlook more comprehensive viewpoints or other approaches that could provide insightful information on initial public offerings. However, by offering more precise and nuanced assessments of IPO performance, the use of ML algorithms in IPO research can improve organizations’ ability to make decisions. Businesses can use innovative and hybrid algorithms to improve their market success rates by gaining deeper insights and making better decisions about IPOs.
Originality/Value : This review, which focused on the investigation of novel algorithms, offered insightful information about the caliber of ML methods in IPO appraisal.
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