Purpose: The objective of this study was to identify the non-linear predictors of mispricing in India. Furthermore, the study evaluated the variation in overpriced and underpriced issues using the spline analysis in India. Design/Methodology/Approach : The study is based on 571 issuers that went public between 2017 and 2025, as derived from CMIE Prowess. It employed an advanced form of spline regression, restricted cubic spline (or natural spline), to examine the non-linear predictors of mispricing.

Findings: Usually studied based on linearity, mispricing in the Indian primary market was predicted by issue price and premium percentage as major determinants on the basis of polynomial evaluations of spline regression. These variables are not identified with linear modeling. The study further illustrated that there was a significant variation in overpriced and underpriced issuers, with most of the issuers having an underpriced listing. Originality/

Value: The novelty of this study lies in its focus on non-linear predictors of IPO mispricing using Spline regression analysis. Linearity assumes a constant linear relation among study variables, but splines have substantial potential to predict the information about fluctuating relations among variables, depicting a clearer picture closer to reality.

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