Earnings prediction with deep leaning

Lars Elend, Sebastian A. Tideman, Kerstin Lopatta, Oliver Kramer

Research output: Chapter in Book/Entry/PoemConference contribution

3 Scopus citations


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).

Original languageEnglish (US)
Title of host publicationKI 2020
Subtitle of host publicationAdvances in Artificial Intelligence - 43rd German Conference on AI, Proceedings
EditorsUte Schmid, Diedrich Wolter, Franziska Klügl
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030582845
StatePublished - 2020
Externally publishedYes
Event43rd German Conference on Artificial Intelligence, KI 2020 - Bamberg, Germany
Duration: Sep 21 2020Sep 25 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12325 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference43rd German Conference on Artificial Intelligence, KI 2020


  • EPS forecasts
  • Earnings prediction
  • Finance
  • Long short term memory
  • Temporal convolutional network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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