ROLE OF INVESTMENT BBANKS IN IPOS

Abstract

Investment banks play a critical role in the success of Initial Public Offerings (IPOs) by acting as underwriters, advisors, and intermediaries between companies and the capital market. In recent years, the use of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has significantly transformed how investment banks evaluate IPO candidates, determine pricing, manage risk, and forecast market response. This study aims to explore and analyze the evolving functions of investment banks in the IPO process through the lens of intelligent technologies. The research applies ML algorithms to historical IPO data, underwriter performance metrics, and post-IPO market behavior to identify patterns that predict IPO success. AI models assist in pricing strategies, risk assessment, investor targeting, and optimizing book-building processes. Sentiment analysis using Natural Language Processing (NLP) is used to evaluate media coverage, investor discussions, and analyst opinions surrounding IPO announcements, which influences investor confidence and stock performance. Furthermore, DL models such as LSTM networks are employed to forecast post-IPO stock trends using time-series data, while classification algorithms are used to evaluate the historical effectiveness of underwriters. This intelligent framework reveals that AI and ML technologies not only improve IPO valuation accuracy and timing but also empower investment banks to make data-driven decisions with greater speed and efficiency. The findings of this study highlight the increasing relevance of AI-driven investment banking strategies in today's competitive financial markets