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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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  • Ali, R., & Afzal, M. (2012). Impact of global financial crisis on stock markets : Evidence from Pakistan and India. E3 Journal of Business Management and Economics, 3(7), 275-282.

  • Alim, K., Ramakrishnan, S., & Khan, S. (2016). Initial public offerings (IPO) performance during hot and cold issue market in Pakistan. https://doi.org/10.2139/ssrn.2912413

  • Bhanu Murthy, K. V., & Singh, A. K. (2008). IPO pricing : Informational inefficiency and misallocation in capital market. https://doi.org/10.2139/ssrn.1303409

  • Bhullar, P. S., & Bhatnagar, D. (2014). Analysis of factors affecting short term performance of IPOs in India. Pacific Business Review International, 7(5). Retrieved from: http://www.pbr.co.in/2014/November2014Sixth.aspx

  • Colak, G., Fu, M., & Hasan, I. (2018). Predicting IPO failures using machine learning technique. Retrieved from fmaconferences.org/SanDiego/Papers/PredictingIPOUnderpricingUsingMachineLearningTechniqu e.pdf

  • Deepak, R., & Gowda, S. (2014). Informational asymmetry between informed and retail investors while investing in the Indian IPO market. Indian Journal of Finance, 8(9), 32–46. https://doi.org/10.17010/ijf/2014/v8i9/71850

  • Esfahanipour, A., Goodarzi, M., & Jahanbin, R. (2015). Analysis and forecasting of IPO underpricing. Neural Computing and Applications, 27(3), 651– 658. https://doi.org/10.1007/s00521-015-1884-1 Haykin, S. (1998). Neural networks : A comprehensive foundation (2nd ed.). New York : Macmillan College Publishing.

  • Jain, B. A. & Nag, B. N. (1997). Performance evaluation of neural network decision models. Journal of Management Information Systems, 14(2), 201-216. Retrieved from www.jstor.org/stable/40398272

  • Kar, B., & Jena, M. K. (2019). Performance and age of companies listed on the Bombay Stock Exchange. Indian Journal of Finance, 13(5), 52-67. https://doi.org/10.17010/ijf/2019/v13i5/144185

  • McClelland, J. L., Rumelhart, D. E., & Hinton, G. E. (1986). The appeal of parallel distributed processing. In, Parallel distributed processing. Explorations in the Microstructure of cognition-foundations (Vol. 1). Cambridge, MA : MIT Press.

  • Pandey, A., & Pattanayak, J. K. (2018). Impact of firm specific and macro-economic factors on the level of underpricing of initial public offerings (IPOs) : Evidence from the Indian market. Indian Journal of Finance, 12(2), 7-25. https://doi.org/10.17010/ijf/2018/v12i2/121367

  • Quah, T. S., & Wong, K.- C. (2006). Utilizing generalized growing and pruning algorithm for radial basis function (GGAP-RBF) network in predicting IPOs performance. The 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, 2006, pp. 3007-3012. https://doi.org/10.1109/ijcnn.2006.247258

  • Reilly, F. K., & Hatfield, K. (1969). Investor experience with new stock issues. Financial Analysts Journal, 25(5), 73-80. https://doi.org/10.2469/faj.v25.n5.73

  • Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge : Cambridge University Press.

  • Singh, A. K., & Shrivastav, R. K. (2017). An empirical study on evaluation of IPOs performance on NSE. Asian Journal of Research in Banking and Finance, 7(6), 24 –31. https://doi.org/10.5958/2249 7323.2017.00046.3

  • Singh, A. K., & Maurya, S. (2018). Corporate governance, ownership structure, and IPO underpricing : Evidence from the Indian new issue market. Indian Journal of Research in Capital Markets, 5(1), 7-24. https://doi.org/10.17010/ijrcm/2018/v5/i1/122905

  • Strauss, D. (2019). Goldman Sachs analyzed 4,481 IPOs over 25 years and concluded that these 5 attributes can make or break a newly public company. Business Insider India. Retrieved from https://www.businessinsider.in/miscellaneous/goldman-sachs-analyzed-4481-ipos-over-25-yearsa nd-concluded-that-these-5-attributes-can-make-or-break-a-newly-public-company/slidelist/71035730.cms

  • Trivedi, S., & Sheth, B. (2013). Higher grades, better performance : Debunking myths associated with IPOs. Indian Journal of Finance, 7(5), 24-31. Retrieved from http://www.indianjournaloffinance.co.in/index.php/IJF/article/view/72111


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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