Development of a Classification Model for Underpriced Issuers Using Machine Learning Algorithms

Abstract

This study develops a classification model to identify underpriced Initial Public Offering (IPO) issuers so that it can help investor decision-making. Using the CRISP-DM methodology, this research uses a sample of 209 non-banking IPO issuers of the OJK E-IPO platform (since establishment until December 31, 2024). In addressing the problem of class imbalance (161 underpriced and 48 not underpriced), SMOTE was used. The model utilizes nine features: Year-on-Year, IHSG, IPO price, ratio of shares issued, age of the firm, size of the firm, sales growth, Return on Assets (ROA), Debt to Equity Ratio (DER), and Asset Turnover Ratio (ATO). Seven classifier algorithms were compared based on accuracy, precision, sensitivity, recall, F1-score, and AUC. Random Forest had the best performance with 89.2% accuracy, 88.9% Macro Average F1-score, and an AUC of 0.946. The findings suggest that the Random Forest model accurately identifies underpriced IPO issuers as a good investment decision-making tool. This research demonstrates that machine learning concepts can be implemented to classify underpriced issuers in Indonesia, continuing previous studies that contributed to understanding the correlation and significance of certain variables to underpricing.This study develops a classification model to identify underpriced Initial Public Offering (IPO) issuers so that it can help investor decision-making. Using the CRISP-DM methodology, this research uses a sample of 209 non-banking IPO issuers of the OJK E-IPO platform (since establishment until December 31, 2024). In addressing the problem of class imbalance (161 underpriced and 48 not underpriced), SMOTE was used. The model utilizes nine features: Year-on-Year, IHSG, IPO price, ratio of shares issued, age of the firm, size of the firm, sales growth, Return on Assets (ROA), Debt to Equity Ratio (DER), and Asset Turnover Ratio (ATO). Seven classifier algorithms were compared based on accuracy, precision, sensitivity, recall, F1-score, and AUC. Random Forest had the best performance with 89.2% accuracy, 88.9% Macro Average F1-score, and an AUC of 0.946. The findings suggest that the Random Forest model accurately identifies underpriced IPO issuers as a good investment decision-making tool. This research demonstrates that machine learning concepts can be implemented to classify underpriced issuers in Indonesia, continuing previous studies that contributed to understanding the correlation and significance of certain variables to underpricing.