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A New Modus Operandi for Determining Post - IPO Pricing : Analysis of Indian IPOs using Artificial Neural Networks


Affiliations
1 Associate Professor, Department of Commerce, Delhi School of Economics, University of Delhi, Delhi - 110 007, India
2 Associate Professor, Department of Commerce, Zakir Husain Delhi College, University of Delhi, Delhi - 110 007, India
3 Student, Bachelor of Management Studies, Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi - 110 007, India

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The objective of this study was to identify different factors useful in determining post-IPO pricing and test their relative significance by comparing stock performance across 3-, 6-, and 12-months post listing. To do so, the study analyzed data from 299 non-financial companies that had their IPOs listed on the Bombay Stock Exchange from 2005–2018 in India. The data collected were used to train a neural network, called the multilayer perceptron model. The study grouped all factors into four categories viz-a-viz macroeconomic, issue-specific, technical, and fundamental. Analysis of the results generated from 20 iterative constructions of the neural network revealed that the highest relative relevance in prediction was attributed to technical factors. It was also observed that the importance of fundamental factors increased with the investment horizon. The results are country-specific and found that the importance of “underpricing” and “listing gains” as factors reduced within a year post-listing and thus, provide a helpful addition to the present knowledge of financial gains resulting to investors from IPOs.

Keywords

Initial Public Offering, Artificial Neural Networks, Multi-Layer Perceptron, Post-IPO Performance.

JEL Code : C45, G11, G12, G14.

Paper Submission Date : March 10, 2020; Paper Sent Back for Revision : October 26, 2020; Paper Acceptance Date : November 30, 2020.

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  • A New Modus Operandi for Determining Post - IPO Pricing : Analysis of Indian IPOs using Artificial Neural Networks

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Authors

Amit Kumar Singh
Associate Professor, Department of Commerce, Delhi School of Economics, University of Delhi, Delhi - 110 007, India
Mukesh Kumar Jain
Associate Professor, Department of Commerce, Zakir Husain Delhi College, University of Delhi, Delhi - 110 007, India
Saksham Jain
Student, Bachelor of Management Studies, Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi - 110 007, India
Bhavesh Gupta
Student, Bachelor of Management Studies, Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi - 110 007, India

Abstract


The objective of this study was to identify different factors useful in determining post-IPO pricing and test their relative significance by comparing stock performance across 3-, 6-, and 12-months post listing. To do so, the study analyzed data from 299 non-financial companies that had their IPOs listed on the Bombay Stock Exchange from 2005–2018 in India. The data collected were used to train a neural network, called the multilayer perceptron model. The study grouped all factors into four categories viz-a-viz macroeconomic, issue-specific, technical, and fundamental. Analysis of the results generated from 20 iterative constructions of the neural network revealed that the highest relative relevance in prediction was attributed to technical factors. It was also observed that the importance of fundamental factors increased with the investment horizon. The results are country-specific and found that the importance of “underpricing” and “listing gains” as factors reduced within a year post-listing and thus, provide a helpful addition to the present knowledge of financial gains resulting to investors from IPOs.

Keywords


Initial Public Offering, Artificial Neural Networks, Multi-Layer Perceptron, Post-IPO Performance.

JEL Code : C45, G11, G12, G14.

Paper Submission Date : March 10, 2020; Paper Sent Back for Revision : October 26, 2020; Paper Acceptance Date : November 30, 2020.


References





DOI: https://doi.org/10.17010/ijf%2F2021%2Fv15i1%2F157011