Artificial Intelligence and Exploratory-Data-Analysis-Based Initial Public Offering Gain Prediction for Public Investors

Manushi Munshi, Manan Patel, Fayez Alqahtani, Amr Tolba, Rajesh Gupta, Nilesh Kumar Jadav, Sudeep Tanwar, Bogdan-Constantin Neagu and Alin Dragomir
Additional contact information
Manushi Munshi: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Manan Patel: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Fayez Alqahtani: Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
Amr Tolba: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Rajesh Gupta: Department of Computer Engineering, U. V. Patel College of Engineering, Ganpat University, Mehsana 384012, India
Nilesh Kumar Jadav: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Bogdan-Constantin Neagu: Department of Power Engineering, Faculty of Electrical Engineering, “Gheorghe Asachi” Technical University of Iasi, 67 D. Mangeron Blvd., 700050 Iasi, Romania
Alin Dragomir: Department of Power Engineering, Faculty of Electrical Engineering, “Gheorghe Asachi” Technical University of Iasi, 67 D. Mangeron Blvd., 700050 Iasi, Romania

Sustainability, 2022, vol. 14, issue 20, 1-16

Abstract: An initial public offering (IPO) refers to a process by which private corporations offer their shares in a public stock market for investment by public investors. This listing of private corporations in the stock market leads to the easy generation and exchange of capital between private corporations and public investors. Investing in a company’s shares is accompanied by careful consideration and study of the company’s public image, financial policies, and position in the financial market. The stock market is highly volatile and susceptible to changes in the political and socioeconomic environment. Therefore, the prediction of a company’s IPO performance in the stock market is an important study area for researchers. However, there are several challenges in this path, such as the fragile nature of the stock market, the irregularity of data, and the influence of external factors on the IPO performance. Researchers over the years have proposed various artificial intelligence (AI)-based solutions for predicting IPO performance. However, they have some lacunae in terms of the inadequate data size, data irregularity, and lower prediction accuracy. Motivated by the aforementioned issues, we proposed an analytical model for predicting IPO gains or losses by incorporating regression-based AI models. We also performed a detailed exploratory data analysis (EDA) on a standard IPO dataset to identify useful inferences and trends. The XGBoost Regressor showed the maximum prediction accuracy for the current IPO gains, i.e., 91.95%.

Keywords: initial public offering (IPO); stock market; random forest; XGBoost Regressor; exploratory data analysis (EDA) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/13406/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/13406/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:20:p:13406-:d:945617

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().