Purpose : With the goal of shedding light on the ways in which machine learning (ML) approaches are now being used in initial public offering (IPO) research, this systematic analysis assessed how well IPOs performed.

Design/Methodology/Approach : To evaluate the efficacy of ML approaches in IPO appraisal, 21 papers from the Scopus and Web of Science databases were analyzed using PRISMA.

Findings : The findings revealed that ML algorithms, including rough set theory, text analytics, fuzzy logic, XGBoost, random forest, SVM, gradient descent, and artificial neural networks, outperformed linear methodologies in IPO evaluation.

Practical Implications : The exclusion of other databases may result in the overlooking of pertinent research, even with the thorough insights obtained from studies within the Scopus and Web of Science databases. Moreover, a singular concentration on ML approaches could overlook more comprehensive viewpoints or other approaches that could provide insightful information on initial public offerings. However, by offering more precise and nuanced assessments of IPO performance, the use of ML algorithms in IPO research can improve organizations’ ability to make decisions. Businesses can use innovative and hybrid algorithms to improve their market success rates by gaining deeper insights and making better decisions about IPOs.

Originality/Value : This review, which focused on the investigation of novel algorithms, offered insightful information about the caliber of ML methods in IPO appraisal.

1. Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
2. Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18(3), 505–529. https://doi.org/10.1016/0378-4266(94)90007-8
3. Baba, B., & Sevil, G. (2020). Predicting IPO initial returns using random forest. Borsa Istanbul Review, 20(1), 13–23. https://doi.org/10.1016/j.bir.2019.08.001
4. Basti, E., Kuzey, C., & Delen, D. (2015). Analyzing initial public offerings' short-term performance using decision trees and SVMs. Decision Support Systems, 73, 15–27. https://doi.org/10.1016/j.dss.2015.02.011
5. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
6. Colak, G., Fu, M., & Hasan, I. (2022). On modeling IPO failure risk. Economic Modelling, 109, 105790. https://doi.org/10.1016/j.econmod.2022.105790
7. Colak, G., Fu, M., & Hasan, I. (2020). Why are some Chinese firms failing in the US capital markets? A machine learning approach. Pacific-Basin Finance Journal, 61, 101331. https://doi.org/10.1016/j.pacfin.2020.101331
8. Esfahanipour, A., Goodarzi, M., & Jahanbin, R. (2016). Analysis and forecasting of IPO underpricing. Neural Computing and Applications, 27(3), 651–658. https://doi.org/10.1007/s00521-015-1884-1
9. Füllbrunn, S., Neugebauer, T., & Nicklisch, A. (2020). Underpricing of initial public offerings in experimental asset markets. Experimental Economics, 23, 1002–1029. https://doi.org/10.1007/s10683-019-09638-7
10. Haefke, C., & Helmenstein, C. (1996). Forecasting Austrian IPOs: An application of linear and neural network error-correction models. Journal of Forecasting, 15(3), 237–251. https://doi.org/10.1002/(SICI)1099-131X(199604)15:3<237::AID-FOR621>3.0.CO;2-5
11. Han, J. J., & Kim, H.-J. (2021). Stock price prediction using multiple valuation methods based on artificial neural networks for KOSDAQ IPO companies. Investment Analysts Journal, 50(1), 17–31. https://doi.org/10.1080/10293523.2020.1870860
12. Jain, B. A., & Nag, B. N. (1995). Artificial neural network models for pricing initial public offerings. Decision Sciences, 26(3), 283–302. https://doi.org/10.1111/j.15405915.1995.tb01430.x
13. Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215–236. https://doi.org/10.1016/09252312(95)00039-9
14. Kanas, A. (2001). Neural network linear forecasts for stock returns. International Journal of Finance & Economics, 6(3), 245–254. https://doi.org/10.1002/ijfe.156
15. Khosla, R., & Tara, D. (2019). Artificial intelligence and robotics - Transforming the industrial economies. Arthashastra Indian Journal of Economics & Research, 8(5), 16–21. https://doi.org/10.17010/aijer/2019/v8i5/149679
16. Kim, J., Shin, S., Lee, H. S., & Oh, K. J. (2019). A machine learning portfolio allocation system for IPOs in Korean markets using GA-Rough set theory. Sustainability, 11(23), 1–15. https://ideas.repec.org/a/gam/jsusta/v11y2019i23p6803-d292597.html
17. Kitchenham, B. (2004). Procedures for performing systematic reviews (Keele University Technical Report TR/SE-0401). 1–28. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=29890a936639862f45cb 9a987dd599dce9759bf5
18. Luque, C. E., Quintana, D., & Isasi, P. (2012). Predicting IPO underpricing with genetic algorithms. International Journal of Artificial Intelligence, 8(S12), 133–146. http://www.ceser.in/ceserp/index.php/ijai/article/view/2360
19. Nayak, D., & Barodawala, R. (2021). The impact of macroeconomic factors on the Indian stock market : An assessment. Arthashastra Indian Journal of Economics & Research, 10(2–3), 27–40. https://doi.org/10.17010/aijer/2021/v10i2-3/167172
20. Patanjali, S., & Subramaniam, D. (2019). India, the fourth industrial revolution, and government policy. Arthashastra Indian Journal of Economics & Research, 8(2), 32–44. https://doi.org/10.17010/aijer/2019/v8i2/145224
21. Quintana, D., Sáez, Y., & Isasi, P. (2017). Random forest prediction of IPO underpricing. Applied Sciences, 7(6), 636. https://doi.org/10.3390/app7060636
22. Reber, B. (2014). Estimating the risk–return profile of new venture investments using a riskneutral framework and 'thick' models. The European Journal of Finance, 20(4), 341–360. https://doi.org/10.1080/1351847X.2012.708471
23. Reber, B., Berry, B., & Toms, S. (2005). Predicting mispricing of initial public offerings. Intelligent Systems in Accounting, Finance, and Management, 13(1), 41–59. https://doi.org/10.1002/isaf.253
24. Robertson, S. J., Golden, B. L., Runger, G. C., & Wasil, E. A. (1998). Neural network models for initial public offerings. Neurocomputing, 18(1–3), 165–182. https://doi.org/10.1016/S0925-2312(97)00077-5
25. Ross, G., Das, S., Sciro, D., & Raza, H. (2021). CapitalVX: A machine learning model for startup selection and exit prediction. The Journal of Finance and Data Science, 7, 94–114. https://doi.org/10.1016/j.jfds.2021.04.001
26. Singh, A. K., Jain, M. K., Jain, S., & Gupta, B. (2021). A new modus operandi for determining post-IPO pricing: Analysis of Indian IPOs using artificial neural networks. Indian Journal of Finance, 15(1), 8–22. https://doi.org/10.17010/ijf/2021/v15i1/157011
27. Tao, J., Deokar, A. V., & Deshmukh, A. (2018). Analysing forward-looking statements in initial public offering prospectuses: A text analytics approach. Journal of Business Analytics, 1(1), 54–70. https://doi.org/10.1080/2573234X.2018.1507604
28. Tealab, A. (2018). Time series forecasting using artificial neural networks methodologies: A systematic review. Future Computing and Informatics Journal, 3(2), 334–340. https://doi.org/10.1016/j.fcij.2018.10.003
29. Tkáč, M., & Verner, R. (2016). Artificial neural networks in business: Two decades of research. Applied Soft Computing, 38, 788–804. https://doi.org/10.1016/j.asoc.2015.09.040
30. Verner, R., & Rosocha, L. (2015). Yield spread prediction using a genetic neural network. Investment Management and Financial Innovations, 12(4-si), 192–199.
31. Yu, T., & Huarng, K.-H. (2013). Entrepreneurial firms' wealth creation via forecasting. The Service Industries Journal, 33(9–10), 833–845. https://doi.org/10.1080/02642069.2013.719893