A Regression Analysis of the Parameters Influencing the Share Price During the First Day After an IPO

Abstract: This study focuses on initial public offerings (IPOs), which are the process of making a company's shares available for public trading on a stock market. Despite global uncertainties in recent years, there has been a high demand for company listings in the market. Many IPOs have experienced a positive trend in share prices on the first day of trading as a publicly traded company.The objective of this study is to develop a multiple linear regression model to analyze the impact of various parameters on the first day return of IPOs. The generated model will be evaluated to create a reduced model with an optimal subset of variables. The study will specifically focus on IPOs listed on some Nordic marketplaces during the period 2017-2022.The results of the study suggest that the created models are not effective in capturing the variance of first day returns. The deficiency of the created models is likely due to both the complexity of the stock market and the difficulties of quantifying and capturing some of the factors impacting the initial performance of IPOs. However, the reduced model performs slightly better and indicates that variables such as NGM SME, First North, PreSubscribed, and AmountNewlyIssued explain most of the variance in the response variable.