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

Author

Listed:
  • Jiwoo Kim

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

  • Sanghun Shin

    (Department of Investment Information Engineering, Yonsei University, Seoul 03722, Korea)

  • Hee Soo Lee

    (Department of Business Administration, Sejong University, Seoul 05006, Korea)

  • Kyong Joo Oh

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

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.

Suggested Citation

  • Jiwoo Kim & Sanghun Shin & Hee Soo Lee & Kyong Joo Oh, 2019. "A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6803-:d:292597
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    References listed on IDEAS

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    1. Miller, Edward M, 1977. "Risk, Uncertainty, and Divergence of Opinion," Journal of Finance, American Finance Association, vol. 32(4), pages 1151-1168, September.
    2. Hyounggun Song & Sung Kwon Han & Seung Hwan Jeong & Hee Soo Lee & Kyong Joo Oh, 2019. "Using Genetic Algorithms to Develop a Dynamic Guaranteed Option Hedge System," Sustainability, MDPI, vol. 11(15), pages 1-12, July.
    3. François Derrien, 2005. "IPO Pricing in 'Hot' Market Conditions: Who Leaves Money on the Table?," Post-Print hal-00480827, HAL.
    4. Franҫois Derrien, 2005. "IPO Pricing in “Hot” Market Conditions: Who Leaves Money on the Table?," Journal of Finance, American Finance Association, vol. 60(1), pages 487-521, February.
    5. Jain, Bharat A & Kini, Omesh, 1994. "The Post-Issue Operating Performance of IPO Firms," Journal of Finance, American Finance Association, vol. 49(5), pages 1699-1726, December.
    6. David Quintana & Cristobal Luque & Jose Maria Valls & Pedro Isasi, 2012. "Evolution Strategies for IPO Underpricing Prediction," Springer Optimization and Its Applications, in: Michael Doumpos & Constantin Zopounidis & Panos M. Pardalos (ed.), Financial Decision Making Using Computational Intelligence, edition 127, chapter 0, pages 189-208, Springer.
    7. Rock, Kevin, 1986. "Why new issues are underpriced," Journal of Financial Economics, Elsevier, vol. 15(1-2), pages 187-212.
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    Cited by:

    1. Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Manos & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2020. "Textual Information and IPO Underpricing: A Machine Learning Approach," MPRA Paper 103813, University Library of Munich, Germany.
    2. Hosun Ryou & Han Hee Bae & Hee Soo Lee & Kyong Joo Oh, 2020. "Momentum Investment Strategy Using a Hidden Markov Model," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    3. Seung Hwan Jeong & Hee Soo Lee & Hyun Nam & Kyong Joo Oh, 2021. "Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market," Sustainability, MDPI, vol. 13(3), pages 1-16, January.

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