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Can Machine Learning Be Useful in Corporate Finance and Business Valuation? Overview of Current Research
[Může být strojové učení užitečné ve financích podniku a jeho ocenění? Přehled současného výzkumu]

Author

Listed:
  • Veronika Staňková

Abstract

Prediction of financial time series has been at the centre of scientific research for a long time. Recently, there have been a wide range of possibilities to apply machine learning methods. Currently, there are so many scientific papers in the field of application of machine learning in finance that it is very difficult to find the way around. The presented paper aims to provide a fundamental overview of the current state of knowledge in this area, specifically within the area of corporate finance and business valuation, and to assist in orientation in the methods of machine learning those who have not yet encountered machine learning.

Suggested Citation

  • Veronika Staňková, 2021. "Can Machine Learning Be Useful in Corporate Finance and Business Valuation? Overview of Current Research [Může být strojové učení užitečné ve financích podniku a jeho ocenění? Přehled současného vý," Oceňování, Prague University of Economics and Business, vol. 14(4), pages 53-66.
    • Handle: RePEc:prg:jnloce:v:14:y:2021:i:4:id:2021_4_04:p:53-66
    DOI: 10.18267/j.ocenovani.270
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    References listed on IDEAS

    as
    1. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    2. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Machine learning; Deep learning; Finance; Shares; Strojové učení; Hluboké učení; Akcie;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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