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Evolution Strategies for IPO Underpricing Prediction

In: Financial Decision Making Using Computational Intelligence

Author

Listed:
  • David Quintana

    (Universidad Carlos III de Madrid)

  • Cristobal Luque

    (Universidad Carlos III de Madrid)

  • Jose Maria Valls

    (Universidad Carlos III de Madrid)

  • Pedro Isasi

    (Universidad Carlos III de Madrid)

Abstract

The prediction of first-day returns of initial public offerings is a challenging task due, among other things, to an incomplete theory on the dynamics and the presence of outliers. In this chapter we introduce an evolutionary system based on prototypes adjusted by evolution strategies. The system, set up in two layers, breaks the input space into different regions and fits specialized sets of models. These models are then combined to offer predictions. The structure of the model is such that it is able to handle the extreme values that hinder prediction in this domain. The system is benchmarked against a set of well-known machine learning algorithms, and the results show competitive performance.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:spochp:978-1-4614-3773-4_7
    DOI: 10.1007/978-1-4614-3773-4_7
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    Cited by:

    1. 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.

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