Evolution Strategies for IPO Underpricing Prediction

Quintana, David; Luque, C.; Valls, José María; Isasi, Pedro

doi:10.1007/978-1-4614-3773-4_7

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…Joshi (2017) notes that linear regression is not suitable for classification because it relies on linearly interconnected data, while decision tree is a compatible algorithm for classification. Most IPO performance research has been performed on linear regression models, but in this study we find previous research using the decision tree algorithm (Basti, Kuzey, & Delen, 2015;Chen, Chen, & Cheng, 2010;Chen & Cheng, 2012;Han, 2016;Quintana, Luque, Valls, & Isasi, 2012;Quintana, Sáez, & Isasi, 2017). We find that machine learning algorithms (the decision tree is known in this case as supervised learning) contain fewer error predictions than linear regression.…”

Section: Decision Tree Algorithm Model For Ipo Researchmentioning

confidence: 62%

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

Muditomo

Broto

2021

J. Keuang. dan Perbank.

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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.

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Section: Decision Tree Algorithm Model For Ipo Researchmentioning

confidence: 62%

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

Muditomo

Broto

2021

J. Keuang. dan Perbank.

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“…However, various market environments could not be verified due to the limitation of using data for a relatively short period from 1996 to 1999. Quintana et al [15] presented strategies for IPO underpricing prediction and Esfahanipour et al [16] examined probability of withdrawal and underpricing of IPO stock using neural network and fuzzy regression. They found that the probability of IPO withdrawal plays an important role in precise evaluation of underpricing.…”

Section: Related Studiesmentioning

confidence: 99%

A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory

et al. 2019

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An initial public offering (IPO) is a type of public offering in which a company’s shares are sold to institutional and individual investors. While the majority of studies on IPOs have focused on the efficiency of raising capital and price adequacy in IPOs, studies on portfolio allocation strategies for IPO stocks are relatively scarce. This paper develops a machine learning investment strategy for IPO stocks based on rough set theory and a genetic algorithm (GA-rough set theory). To reduce issues of information asymmetry, we use nonfinancial data that are publicly available to individual and institutional investors in the IPO process. Based on the rule sets generated from the training sets, we conduct 120 tests with various conditions involving the target days and the partition of the training and testing sets, and we find excess returns of the constructed portfolios compared to the benchmark portfolios. Investors in IPO stocks can formulate more efficient investment strategies using our system. In this sense, the system developed in this paper contributes to the efficiency of financial markets and helps achieve sustained economic growth.

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Evolution Strategies for IPO Underpricing Prediction

Cited by 2 publications

References 40 publications

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

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

A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory

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