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Earnings Prediction with Deep Leaning

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KI 2020: Advances in Artificial Intelligence (KI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12325))

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Abstract

In the financial sector, a reliable forecast the future financial performance of a company is of great importance for investors’ investment decisions. In this paper we compare long-term short-term memory (LSTM) networks to temporal convolution network (TCNs) in the prediction of future earnings per share (EPS). The experimental analysis is based on quarterly financial reporting data and daily stock market returns. For a broad sample of US firms, we find that both LSTMs outperform the naive persistent model with up to 30.0% more accurate predictions, while TCNs achieve and an improvement of 30.8%. Both types of networks are at least as accurate as analysts and exceed them by up to 12.2% (LSTM) and 13.2% (TCN).

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Notes

  1. 1.

    For the comparisons only data points are used for which analyst forecasts exist.

  2. 2.

    The following parameters of the data records are used. The parameters in brackets are only used for the assignment and selection of the samples. Computstat Quarterly: (cusip, fpedats, ffi5, ffi10, ffi12, ffi48, financialfirm, EPS_Mean_Analyst), rdq, epsfiq, atq, revtq, nopiq, xoprq, apq, gdwlq, rectq, xrdq, cogsq, rcpq, ceqq, niq, oiadpq, oibdpq, dpq, ppentq, piq, txtq, gdwlq, xrdq, rcpq Daily Shares: (cusip, date), ret, prc, vol, shrout, vwretd.

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Authors and Affiliations

  1. Computational Intelligence Group, Department of Computer Science, Carl von Ossietzky University of Oldenburg, 26111, Oldenburg, Germany

    Lars Elend & Oliver Kramer

  2. Chair of Accounting, Auditing and Sustainability, University of Hamburg, 20146, Hamburg, Germany

    Sebastian A. Tideman & Kerstin Lopatta

Authors
  1. Lars Elend
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  2. Sebastian A. Tideman
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  3. Kerstin Lopatta
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  4. Oliver Kramer
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Corresponding author

Correspondence to Lars Elend .

Editor information

Editors and Affiliations

  1. Universität Bamberg, Bamberg, Germany

    Ute Schmid

  2. Örebro University, Örebro, Sweden

    Franziska Klügl

  3. Universität Bamberg, Bamberg, Germany

    Diedrich Wolter

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Elend, L., Tideman, S.A., Lopatta, K., Kramer, O. (2020). Earnings Prediction with Deep Leaning. In: Schmid, U., Klügl, F., Wolter, D. (eds) KI 2020: Advances in Artificial Intelligence. KI 2020. Lecture Notes in Computer Science(), vol 12325. Springer, Cham. https://doi.org/10.1007/978-3-030-58285-2_22

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  • DOI: https://doi.org/10.1007/978-3-030-58285-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58284-5

  • Online ISBN: 978-3-030-58285-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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