IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v16y2023i12p520-d1302258.html
   My bibliography  Save this article

Predicting the Profitability of Directional Changes Using Machine Learning: Evidence from European Countries

Author

Listed:
  • Nicholas D. Belesis

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

  • Georgios A. Papanastasopoulos

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

  • Antonios M. Vasilatos

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

Abstract

In this paper, we follow the suggestions of past literature to further explore the prediction of the profitability direction by employing different machine learning algorithms, extending the research in the European setting and examining the effect of profits mean reversion for the prediction of profitability. We provide evidence that simple algorithms like LDA can outperform classification trees if the data used are preprocessed correctly. Moreover, we use nested cross-validation and show that sample predictions can be obtained without using the classic train–test split. Overall, our prediction results are in line with previous studies, and we also found that cash flow-based measures like Free Cash Flow and Operating Cash Flow can be predicted more accurately compared to accrual-based measures like return on assets or return on equity.

Suggested Citation

  • Nicholas D. Belesis & Georgios A. Papanastasopoulos & Antonios M. Vasilatos, 2023. "Predicting the Profitability of Directional Changes Using Machine Learning: Evidence from European Countries," JRFM, MDPI, vol. 16(12), pages 1-14, December.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:12:p:520-:d:1302258
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/16/12/520/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/16/12/520/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lev, Baruch, 1983. "Some economic determinants of time-series properties of earnings," Journal of Accounting and Economics, Elsevier, vol. 5(1), pages 31-48, April.
    2. Artikis, Panagiotis G. & Diamantopoulou, Lydia & Papanastasopoulos, Georgios A. & Sorros, John N., 2022. "Asset growth and stock returns in european equity markets: Implications of investment and accounting distortions," Journal of Corporate Finance, Elsevier, vol. 73(C).
    3. Ball, Ray & Shivakumar, Lakshmanan, 2005. "Earnings quality in UK private firms: comparative loss recognition timeliness," Journal of Accounting and Economics, Elsevier, vol. 39(1), pages 83-128, February.
    4. Patricia M. Fairfield & Sundaresh Ramnath & Teri Lombardi Yohn, 2009. "Do Industry‐Level Analyses Improve Forecasts of Financial Performance?," Journal of Accounting Research, Wiley Blackwell, vol. 47(1), pages 147-178, March.
    Full references (including those not matched with items on IDEAS)

    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. Pedro J. García-Teruel & Pedro Martínez-Solano & Juan P. Sánchez-Ballesta, 2014. "Supplier Financing and Earnings Quality," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 41(9-10), pages 1193-1211, November.
    2. Hao, Qian & Shi, Xiangyan & Bu, Danlu, 2018. "The Chinese stimulus program from 2008 to 2010 and accounting information quality," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 33(C), pages 64-78.
    3. Lee, Sangho & Lee, Sejoong & Ryu, Ji Yeon, 2021. "Do Competent Managers Hoard Bad News? Self-regulation Theory and Korean Evidence," Finance Research Letters, Elsevier, vol. 41(C).
    4. Himanshu & Jatinder P. Singh & Ashwani Kumar, 2020. "Prioritizing and Establishing Cause and Effect Relationships Among Financial Reporting Quality Metrics," Vision, , vol. 24(3), pages 330-344, September.
    5. Dmitri Byzalov & Sudipta Basu, 2016. "Conditional conservatism and disaggregated bad news indicators in accrual models," Review of Accounting Studies, Springer, vol. 21(3), pages 859-897, September.
    6. Du, Kai & Huddart, Steven & Xue, Lingzhou & Zhang, Yifan, 2020. "Using a hidden Markov model to measure earnings quality," Journal of Accounting and Economics, Elsevier, vol. 69(2).
    7. Demmer, Matthias, 2015. "Improving profitability forecasts with information on earnings quality," Discussion Papers 2015/16, Free University Berlin, School of Business & Economics.
    8. Maury, Benjamin, 2018. "Sustainable competitive advantage and profitability persistence: Sources versus outcomes for assessing advantage," Journal of Business Research, Elsevier, vol. 84(C), pages 100-113.
    9. Dechow, Patricia & Ge, Weili & Schrand, Catherine, 2010. "Understanding earnings quality: A review of the proxies, their determinants and their consequences," Journal of Accounting and Economics, Elsevier, vol. 50(2-3), pages 344-401, December.
    10. Karel Janda, 2019. "Earnings Stability and Peer Company Selection for Multiple Based Indirect Valuation," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 69(1), pages 37-75, February.
    11. Ludovic Vigneron & Yves Mard, 2016. "Earnings management across publicly traded and privately held French SMEs," International Journal of Entrepreneurship and Small Business, Inderscience Enterprises Ltd, vol. 29(3), pages 416-440.
    12. Ryan Ball & Robert M. Bushman & Florin P. Vasvari, 2008. "The Debt‐Contracting Value of Accounting Information and Loan Syndicate Structure," Journal of Accounting Research, Wiley Blackwell, vol. 46(2), pages 247-287, May.
    13. Loureiro, Gilberto & Silva, Sónia, 2022. "Earnings management and stock price crashes post U.S. cross-delistings," International Review of Financial Analysis, Elsevier, vol. 82(C).
    14. Wang, Weimin & (Frank) Wang, Xu, 2014. "Predicting earnings in a poor information environment," Journal of Contemporary Accounting and Economics, Elsevier, vol. 10(1), pages 46-58.
    15. Mawuena Akosua Kukah & Mohammed Amidu & Joshua Yindenaba Abor, 2016. "Corporate governance mechanisms and accounting information quality of listed firms in Ghana," African Journal of Accounting, Auditing and Finance, Inderscience Enterprises Ltd, vol. 5(1), pages 38-58.
    16. Abd El-Rahman M. Selmy & Khairy A. El-Giziry, 2016. "On the Informational Role of Delayed Good News: A Firm-Level Crash Risk Evidence from Egypt," Accounting and Finance Research, Sciedu Press, vol. 5(3), pages 1-44, August.
    17. Goncharov, Igor & Werner, Joerg R. & Zimmermann, Jochen, 2009. "Legislative demands and economic realities: Company and group accounts compared," The International Journal of Accounting, Elsevier, vol. 44(4), pages 334-362, December.
    18. Kim, Incheol & Miller, Steve & Wan, Hong & Wang, Bin, 2016. "Drivers behind the monitoring effectiveness of global institutional investors: Evidence from earnings management," Journal of Corporate Finance, Elsevier, vol. 40(C), pages 24-46.
    19. Campa, Domenico & Camacho-Miñano, María-del-Mar, 2015. "The impact of SME’s pre-bankruptcy financial distress on earnings management tools," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 222-234.
    20. Jani Saastamoinen & Hanna Savolainen, 2021. "Does a leopard change its spots? Auditors and lawyers as valuation experts for minority shareholders in the judicial appraisal of private firms," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 48(3-4), pages 613-636, March.

    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:jjrfmx:v:16:y:2023:i:12:p:520-:d:1302258. 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.