Beating human analysts in nowcasting corporate earnings by using publicly available stock price and correlation features

Michael Kamp, Mario Boley, Thomas Gartner

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


Corporate earnings are a crucial indicator for investment and business valuation. Despite their importance and the fact that classic econometric approaches fail to match analyst forecasts by orders of magnitude, the automatic prediction of corporate earnings from public data is not in the focus of current machine learning research. In this paper, we present for the first time a fully automatized machine learning method for earnings prediction that at the same time a) only relies on publicly available data and b) can outperform human analysts. The latter is shown empirically in an experiment involving all S&P 100 companies in a test period from 2008 to 2012. The approach employs a simple linear regression model based on a novel feature space of stock market prices and their pair wise correlations. With this work we follow the recent trend of now casting, i.e., of creating accurate contemporary forecasts of undisclosed target values based on publicly observable proxy variables.

Original languageEnglish
Title of host publication2013 IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781479931422
ISBN (Print)9781479931439
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Data Mining Workshops 2013 - Dallas, United States of America
Duration: 7 Dec 201310 Dec 2013
Conference number: 13th


ConferenceIEEE International Conference on Data Mining Workshops 2013
Abbreviated titleICDMW 2013
Country/TerritoryUnited States of America


  • Earnings prediction
  • Finance
  • Regression

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