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Increasing the Explanatory Power of Investor Sentiment Analysis for Commodities in Online Media

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Business Information Systems (BIS 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 320))

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Abstract

Online media are an important source for investor sentiment on commodities. Although there is empirical evidence for a relationship between investor sentiment from news and commodity returns, the impact of classifier design on the explanatory power of sentiment for returns has received little attention. We evaluate the explanatory power of nine classifier designs and find that (1) a positive relationship holds between more opinionated online media sentiment and commodity returns, (2) weighting dictionary terms by machine learning increases explanatory power by up to 25%, and (3) the commonly used dictionary of Loughran and McDonald is detrimental for commodity sentiment analysis.

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Author information

Authors and Affiliations

  1. Information Systems 2, University of Hohenheim, Schwerzstrasse 35, 70599, Stuttgart, Germany

    Achim Klein, Martin Riekert, Lyubomir Kirilov & Joerg Leukel

Authors
  1. Achim Klein
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  2. Martin Riekert
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  3. Lyubomir Kirilov
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  4. Joerg Leukel
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Corresponding author

Correspondence to Achim Klein .

Editor information

Editors and Affiliations

  1. Poznan University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. Fraunhofer FOKUS, Berlin, Germany

    Adrian Paschke

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Klein, A., Riekert, M., Kirilov, L., Leukel, J. (2018). Increasing the Explanatory Power of Investor Sentiment Analysis for Commodities in Online Media. In: Abramowicz, W., Paschke, A. (eds) Business Information Systems. BIS 2018. Lecture Notes in Business Information Processing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-93931-5_23

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  • DOI: https://doi.org/10.1007/978-3-319-93931-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93930-8

  • Online ISBN: 978-3-319-93931-5

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