A machine learning portfolio allocation system for IPOs in Korean markets using GA-rough set theory

Jiwoo Kim, Sanghun Shin, Hee Soo Lee, Kyong Joo Oh

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number6803
JournalSustainability (Switzerland)
Volume11
Issue number23
DOIs
Publication statusPublished - 2019 Dec 1

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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