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Investor Demand Through Oversubscription: A Quantile Regression and Machine Learning Evidence from Emerging Market

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  • Sparsha Mandreker

    (Goa Business School, Goa University, Taleigao Plateau, Taleigao 403206, Goa, India
    Post Graduate Department of Commerce, Government College of Arts, Science and Commerce, Khandola, Marcela 403107, Goa, India)

  • Guntur Anjana Raju

    (Goa Business School, Goa University, Taleigao Plateau, Taleigao 403206, Goa, India)

Abstract

This study investigates the oversubscription in public issues of Small and Medium Enterprises in India, across different quantiles. This study further explores the non-linearity in factors through machine learning. Utilising data from 1014 IPOs during the period 2012 to 2024 listed on SME exchanges of NSE and BSE, this study applies OLS regression, quantile regression and a Random Forest model. SHapley Additive exPlanations values are computed to validate and extend econometric findings. Underpricing, listing delay and hot markets are the prominent factors affecting oversubscription. The Random Forest model outperforms OLS, revealing non-linear effects of predictors. This study offers crucial insights enabling policymakers and regulators to refine disclosure regulations and strengthening investor protection measures, while firms can efficiently structure their offerings and attract significant investor interest.

Suggested Citation

  • Sparsha Mandreker & Guntur Anjana Raju, 2026. "Investor Demand Through Oversubscription: A Quantile Regression and Machine Learning Evidence from Emerging Market," JRFM, MDPI, vol. 19(8), pages 1-18, August.
    • Handle: RePEc:gam:jjrfmx:v:19:y:2026:i:8:p:569-:d:2005193
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