Prediction of IPO Subscription – A Logistic Regression Model

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  • Assistant Professor, Department of Business Analytics, Jagdish Sheth School of Management, Bengaluru – 560100, Karnataka
  • Associate Professor, Department of Business Analytics, Jagdish Sheth School of Management, Bengaluru – 560100, Karnataka



Financial Analytics, IPO Subscription, Logistic Regression, Predictive Analytics, SMOTE


The main objective of this research paper is to apply logistic regression to estimate IPO subscription status in terms of oversubscription or under subscription. For this purpose, we used SMOTE (Synthetic Minority Oversampling Technique) to generate minority class cases to rectify class imbalance problems and classification model logistic regression function to further classify the cases into majority class and minority class. KNIME (Konstanz Information Miner) and R Studio were used, as Integrated Development Environments (IDE), to develop the model. The results were quite encouraging with more than 90% accuracy levels for both training and testing datasets. The model was tested with different train-to-test ratios. The model and the results of the study can be used by firms and individuals involved in capital markets to predict the subscription status of a public offering. Further, there is ample scope to improvise the model by using different sets of variables and by applying different machine learning algorithms.


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Arora, N., & Singh, B. (2020). Determinants of oversubscription of SME IPOs in India: Evidence from quantile regression. Asia-Pacific Journal of Business Administration, 12(3/4), 349-370. https://doi. org/10.1108/APJBA-05-2020-0160 DOI:

Baba, B., & Sevil, G. (2020). Predicting IPO initial returns using random forest. Borsa Istanbul Review, 20(1), 13-23. DOI:

Bi, J. (2022). Stock market prediction based on financial news text mining and investor sentiment recognition. Mathematical Problems in Engineering, 2022, 1-9. DOI:

Chawla, N. v., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. DOI:

Fathali, Z., Kodia, Z., & ben Said, L. (2022). Stock market prediction of NIFTY 50 index applying machine learning techniques. Applied Artificial Intelligence, 36(1). DOI:

Gupta, V., Singh, S., & Yadav, S. S. (2022). The impact of media sentiments on IPO underpricing. Journal of Asia Business Studies, 16(5), 786-801. https://doi. org/10.1108/JABS-10-2020-0404 DOI:

Krishnamurti, C., & Kumar, P. (2002). The initial listing performance of Indian IPOs. Managerial Finance, 28(2), 39-51. DOI:

Liu, L., Neupane, S., & Zhang, L. (2022). Firm location effect on underwriting, subscription, and underpricing: Evidence from IPOs in China. Economic Modelling, 108, 105778. DOI:

Liu, L., Zhang, Z., & Lyu, K. (2021). A study of IPO underpricing using regression model based on information asymmetry, media, and institution. Advances in Economics, Business and Management Research. DOI:

Mehmood, W., Mohd-Rashid, R., & Ahmad, A. H. (2020). Impact of pricing mechanism on IPO oversubscription: Evidence from Pakistan stock exchange. Pacific Accounting Review, 32(2), 239-254. https://doi. org/10.1108/PAR-04-2019-0051 DOI:

Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), 16. https://doi. org/10.1186/s40854-019-0131-7 DOI:

Singla, H. K. (2021). Do ownership structure and market sentiment affect the performance of IPOs in India in the short run? A dynamic panel data analysis. Journal of Financial Management of Property and Construction, 26(1), 1-22. 0077 DOI:

Wei, F. J., & Marsidi, A. (2019). Determinants of Initial Public Offering (IPO) underpricing in malaysian stock market. International Journal of Academic Research in Business and Social Sciences, 9(11). https://doi. org/10.6007/IJARBSS/v9-i11/6657 DOI:

Xin-Er, C., Sin Huei, N., Tze San, O., & Boon Heng, T. (2020). Underpinning theories of IPO underpricing. Evidence from Malaysia. International Journal of Asian Social Science, 10(10), 560-573. https://doi. org/10.18488/journal.1.2020.1010.560.573 DOI:

Zhao, Y. (2021). A novel stock index intelligent prediction algorithm based on attention-guided deep neural network. Wireless Communications and Mobile Computing, 2021, 1-12. DOI:




How to Cite

Anand, E., & Pandya, G. (2023). Prediction of IPO Subscription – A Logistic Regression Model. SDMIMD Journal of Management, 14(1), 59–66.