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Deliberate premarket underpricing: New evidence on IPO pricing using machine learning

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  • Pirayesh Neghab, Davood
  • Bradrania, Reza
  • Elliott, Robert

Abstract

We propose a nonlinear approach based on stochastic frontier and Deep Neural Networks (DNN) to estimate the pricing efficiency and the level of premarket inefficiencies for IPOs, using information available before the IPO day and without any distributional assumptions. We apply the proposed approach in the US IPO market to estimate deliberate (premarket) underpricing and find that the IPO offer prices are about 12.43% less than the estimated maximum offer prices on average. We further show that only a few determinants of the value of firms impact the pricing and deliberate underpricing of IPOs. Negative net income and EBITDA play the most important roles in determining the IPO maximum offer price, among various pricing variables. Proceeds followed by underwriter reputation negatively impact the premarket underpricing, and the IPO market activity, measured by the number of new issues, is the most important market cycle proxy that influences the premarket underpricing. We show that aftermarket mispricing is attributed more to offer size and underwriter reputation. The proposed DNN-based method is an easy to implement approach and can be used by academics and practitioners to estimate maximum offer prices and disentangle initial returns into deliberate premarket underpricing and aftermarket mispricing.

Suggested Citation

  • Pirayesh Neghab, Davood & Bradrania, Reza & Elliott, Robert, 2023. "Deliberate premarket underpricing: New evidence on IPO pricing using machine learning," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 902-927.
    • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:902-927
    DOI: 10.1016/j.iref.2023.07.008
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    References listed on IDEAS

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    1. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    2. Hunt-McCool, Janet & Koh, Samuel C & Francis, Bill B, 1996. "Testing for Deliberate Underpricing in the IPO Premarket: A Stochastic Frontier Approach," The Review of Financial Studies, Society for Financial Studies, vol. 9(4), pages 1251-1269.
    3. Koop, Gary & Li, Kai, 2001. "The valuation of IPO and SEO firms," Journal of Empirical Finance, Elsevier, vol. 8(4), pages 375-401, September.
    4. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    5. Hailin Liao & Bin Wang & Tom Weyman-Jones, 2007. "Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition," Global Economic Review, Taylor & Francis Journals, vol. 36(4), pages 361-384.
    6. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    7. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    8. Beat Reber & Bob Berry & Steve Toms, 2005. "Predicting mispricing of initial public offerings," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(1), pages 41-59, March.
    9. J. Griffin & M. Steel, 2008. "Flexible mixture modelling of stochastic frontiers," Journal of Productivity Analysis, Springer, vol. 29(1), pages 33-50, February.
    10. Friesen, Geoffrey C. & Swift, Christopher, 2009. "Overreaction in the thrift IPO aftermarket," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1285-1298, July.
    11. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Martin Abrahamson, 2024. "Offer Price and Post-IPO Ownership Structure," JRFM, MDPI, vol. 17(2), pages 1-12, February.

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    More about this item

    Keywords

    IPO; Underpricing; Deliberate underpricing; Machine learning; DNN; Offer price;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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