IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i23p6803-d292597.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/23/6803/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/23/6803/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Rock, Kevin, 1986. "Why new issues are underpriced," Journal of Financial Economics, Elsevier, vol. 15(1-2), pages 187-212.
    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. 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.
    6. Miller, Edward M, 1977. "Risk, Uncertainty, and Divergence of Opinion," Journal of Finance, American Finance Association, vol. 32(4), pages 1151-1168, September.
    7. François Derrien, 2005. "IPO Pricing in 'Hot' Market Conditions: Who Leaves Money on the Table?," Post-Print hal-00480827, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saade, Samer, 2015. "Investor sentiment and the underperformance of technology firms initial public offerings," Research in International Business and Finance, Elsevier, vol. 34(C), pages 205-232.
    2. Alanazi, Ahmed S. & Al-Zoubi, Haitham A., 2015. "Extreme IPO underpricing and the legal environment in wealthy emerging economies," Journal of Multinational Financial Management, Elsevier, vol. 31(C), pages 83-103.
    3. Dorsman, André & Gounopoulos, Dimitrios, 2013. "European Sovereign Debt Crisis and the performance of Dutch IPOs," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 308-319.
    4. Cook, Douglas O. & Kieschnick, Robert & Van Ness, Robert A., 2006. "On the marketing of IPOs," Journal of Financial Economics, Elsevier, vol. 82(1), pages 35-61, October.
    5. Gao, Yan, 2010. "What comprises IPO initial returns: Evidence from the Chinese market," Pacific-Basin Finance Journal, Elsevier, vol. 18(1), pages 77-89, January.
    6. Natalia Matanova & Tanja Steigner & Bingsheng Yi & Qiancheng Zheng, 2019. "Going concern opinions and IPO pricing accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 53(1), pages 195-238, July.
    7. Grammenos, Costas Th. & Papapostolou, Nikos C., 2012. "US shipping initial public offerings: Do prospectus and market information matter?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 276-295.
    8. Jean-Sébastien Michel, 2013. "VC investment timing and IPO pricing," Chapters, in: Mario Levis & Silvio Vismara (ed.), Handbook of Research on IPOs, chapter 11, pages 219-236, Edward Elgar Publishing.
    9. Feng, Xunan & Johansson, Anders C., 2016. "Judging a Book by Its Cover: Analysts and Attention-Driven Price Patterns in China’s IPO Market," Stockholm School of Economics Asia Working Paper Series 2016-39, Stockholm School of Economics, Stockholm China Economic Research Institute.
    10. Banerjee, Shantanu & Güçbilmez, Ufuk & Pawlina, Grzegorz, 2016. "Leaders and followers in hot IPO markets," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 309-334.
    11. Shen, Zhe & Coakley, Jerry & Instefjord, Norvald, 2013. "Investor participation and underpricing in lottery-allocated Chinese IPOs," Pacific-Basin Finance Journal, Elsevier, vol. 25(C), pages 294-314.
    12. Hao, Qing, 2007. "Laddering in initial public offerings," Journal of Financial Economics, Elsevier, vol. 85(1), pages 102-122, July.
    13. Aissia, Dorsaf Ben, 2014. "IPO first-day returns: Skewness preference, investor sentiment and uncertainty underlying factors," Review of Financial Economics, Elsevier, vol. 23(3), pages 148-154.
    14. Edwards, Amy K. & Hanley, Kathleen Weiss, 2010. "Short selling in initial public offerings," Journal of Financial Economics, Elsevier, vol. 98(1), pages 21-39, October.
    15. Jay R. Ritter & Ivo Welch, 2002. "A Review of IPO Activity, Pricing, and Allocations," Journal of Finance, American Finance Association, vol. 57(4), pages 1795-1828, August.
    16. Roosenboom, Peter, 2012. "Valuing and pricing IPOs," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1653-1664.
    17. Zhang, Zhi & Zhang, Di & Jiang, Senyang & Li, Ao & Yu, Wei, 2023. "The effect of language on IPO underpricing: Evidence from a multinational research," International Review of Financial Analysis, Elsevier, vol. 89(C).
    18. Chen, Yangyang & Goyal, Abhinav & Veeraraghavan, Madhu & Zolotoy, Leon, 2020. "Terrorist attacks, investor sentiment, and the pricing of initial public offerings," Journal of Corporate Finance, Elsevier, vol. 65(C).
    19. Gao, Lin & Süss, Stephan, 2015. "Market sentiment in commodity futures returns," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 84-103.
    20. Agoraki, Maria-Eleni K. & Gounopoulos, Dimitrios & Kouretas, Georgios P., 2022. "U.S. banks’ IPOs and political money contributions," Journal of Financial Stability, Elsevier, vol. 63(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6803-:d:292597. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.