A STUDY ON INITIAL PUBLIC OFFERING

Abstract

Initial Public Offerings (IPOs) represent a pivotal milestone in a company’s lifecycle, marking its transition from private to public ownership. IPOs enable companies to raise substantial capital, expand their operations, and enhance market credibility. However, the IPO process is also fraught with uncertainties such as market volatility, investor sentiment, regulatory risks, and pricing challenges. In this context, the integration of software-driven solutions—especially Machine Learning (ML) and Deep Learning (DL)—is emerging as a transformative force. These technologies allow analysts to model IPO success, predict underpricing, optimize timing, assess risk, and decode investor behavior using real-time data. This study explores the economic, strategic, and technological aspects of IPOs, particularly in the Indian and global financial context. It further evaluates how softwarebased predictive models using ML/DL can assist stakeholders in making informed decisions during the IPO process. Drawing from real IPO case studies, financial data analysis, investor surveys, and algorithmic experiments, this paper highlights the critical factors influencing IPO success, such as promoter reputation, sector performance, financial health, market timing, and institutional participation. Additionally, it proposes a software model that uses ML/DL for IPO return forecasting, sentiment analysis, and investor segmentation.