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Earnings Prediction Using Recurrent Neural Networks

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  • Moritz Scherrmann
  • Ralf Elsas

Abstract

Firm disclosures about future prospects are crucial for corporate valuation and compliance with global regulations, such as the EU's MAR and the US's SEC Rule 10b-5 and RegFD. To comply with disclosure obligations, issuers must identify nonpublic information with potential material impact on security prices as only new, relevant and unexpected information materially affects prices in efficient markets. Financial analysts, assumed to represent public knowledge on firms' earnings prospects, face limitations in offering comprehensive coverage and unbiased estimates. This study develops a neural network to forecast future firm earnings, using four decades of financial data, addressing analysts' coverage gaps and potentially revealing hidden insights. The model avoids selectivity and survivorship biases as it allows for missing data. Furthermore, the model is able to produce both fiscal-year-end and quarterly earnings predictions. Its performance surpasses benchmark models from the academic literature by a wide margin and outperforms analysts' forecasts for fiscal-year-end earnings predictions.

Suggested Citation

  • Moritz Scherrmann & Ralf Elsas, 2023. "Earnings Prediction Using Recurrent Neural Networks," Papers 2311.10756, arXiv.org.
    • Handle: RePEc:arx:papers:2311.10756
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    References listed on IDEAS

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    1. Fama, Eugene F & French, Kenneth R, 2000. "Forecasting Profitability and Earnings," The Journal of Business, University of Chicago Press, vol. 73(2), pages 161-175, April.
    2. Abarbanell, Jeffery S., 1991. "Do analysts' earnings forecasts incorporate information in prior stock price changes?," Journal of Accounting and Economics, Elsevier, vol. 14(2), pages 147-165, June.
    3. Hou, Kewei & van Dijk, Mathijs A. & Zhang, Yinglei, 2012. "The implied cost of capital: A new approach," Journal of Accounting and Economics, Elsevier, vol. 53(3), pages 504-526.
    4. Vitor Azevedo & Patrick Bielstein & Manuel Gerhart, 2021. "Earnings forecasts: the case for combining analysts’ estimates with a cross-sectional model," Review of Quantitative Finance and Accounting, Springer, vol. 56(2), pages 545-579, February.
    5. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    6. Lewellen, Jonathan & Resutek, Robert J., 2019. "Why do accruals predict earnings?," Journal of Accounting and Economics, Elsevier, vol. 67(2), pages 336-356.
    7. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    8. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
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