IPO Performance Prediction During Covid-19 Pandemic in Indonesia Using Decision Tree Algorithm

Abstract

The purpose of this study was to explain the IPO underpricing phenomenon and to find out whether the decision tree algorithm model was able to predict the IPO performance during the Covid-19 pandemic in the Indonesian capital market. The model developed uses the IPO performance classification target variables, namely overpricing, zero, underpricing level-1 or underpricing level-2. Through the modeling of the decision tree algorithm using 149 IPO action data for 2017-2019 and tested on 45 IPO action data in 2020, the results of the study found that the decision tree algorithm was able to explain IPO performance based on the specified classification range. The use of the decision tree algorithm model to explain the IPO performance can be an alternative to the linear regression econometric model that has been widely used in previous studies to provide input for investors in making investment decisions. DOI: https://doi.org/10.26905/jkdp.v25i1.5137

Keywords

• covid-19
• decision tree
• indonesia, ipo underpricing