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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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References

  1. Shleifer, A.: Inefficient Markets: An Introduction to Behavioral Finance. Oxford University Press, New York (2000)

    Book  Google Scholar 

  2. Feuerriegel, S., Neumann, D.: News or noise? How news drives commodity prices. In: Proceedings of the 34th International Conference on Information Systems (ICIS 2013), Milan, Italy, pp. 1–20 (2013)

    Google Scholar 

  3. Feuerriegel, S., Heitzmann, S.F., Neumann, D.: Do investors read too much into news? How news sentiment causes price formation. In: Proceedings of the 48th Hawaii International Conference on System Sciences (HICSS’ 2015), pp. 4803–4812 (2015)

    Google Scholar 

  4. Smales, L.A.: News sentiment in the gold futures market. J. Bank. Finan. 49, 275–286 (2014)

    Article  Google Scholar 

  5. Borovkova, S., Mahakena, D.: News, volatility and jumps: the case of natural gas futures. Quant. Finan. 15(7), 1217–1242 (2015)

    Article  MathSciNet  Google Scholar 

  6. Alfano, S., Feuerriegel, S., Neumann, D.: Is news sentiment more than just noise? In: Proceedings of the 23rd European Conference on Information Systems (ECIS 2015), Münster, Germany, pp. 1–16 (2015)

    Google Scholar 

  7. Clements, A.E., Todorova, N.: Information flow, trading activity and commodity futures volatility. J. Futures Markets 36(1), 88–104 (2016)

    Article  Google Scholar 

  8. Feuerriegel, S., Lampe, M.W., Neumann, D.: News processing during speculative bubbles: evidence from the oil market. In: Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS 2014), pp. 4103–4112 (2014)

    Google Scholar 

  9. Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finan. 66(1), 67–97 (2011)

    Article  Google Scholar 

  10. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)

    Article  Google Scholar 

  11. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, PA, USA, pp. 79–86 (2002)

    Google Scholar 

  12. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)

    Article  Google Scholar 

  13. Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics-Volume 2, Jeju, South Korea, vol. 94305, pp. 90–94 (2012)

    Google Scholar 

  14. Rao, T., Srivastava, S.: Modeling movements in oil, gold, forex and market indices using search volume index and Twitter sentiments. In: Proceedings of the 5th Annual ACM Web Science Conference, Paris, France, pp. 336–345 (2013)

    Google Scholar 

  15. Loughran, T., McDonald, B.: Loughran and McDonald financial sentiment dictionaries (2014). http://www3.nd.edu/~mcdonald/Word_Lists.html

  16. Henry, E.: Are investors influenced by how earnings press releases are written? J. Bus. Commun. 45(4), 363–407 (2008)

    Article  Google Scholar 

  17. Brown, G.W., Cliff, M.T.: Investor sentiment and asset valuation. J. Bus. 78(2), 405–440 (2005)

    Article  Google Scholar 

  18. Baker, M., Wurgler, J.: Investor sentiment and the cross-section of stock returns. J. Finan. 61(4), 1645–1680 (2006)

    Article  Google Scholar 

  19. Pan, J., Poteshman, A.M.: The information in option volume for future stock prices. Rev. Finan. Stud. 19(3), 871–908 (2006)

    Article  Google Scholar 

  20. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683

    Chapter  Google Scholar 

  21. Klein, A., Altuntas, O., Riekert, M., Dinev, V.: Combined approach for extracting object-specific investor sentiment from weblogs. In: Proceedings of the 11th International Conference on Wirtschaftsinformatik (WI 2013), Leipzig, Germany, pp. 691–705 (2013)

    Google Scholar 

  22. Shleifer, A., Summers, L.H.: The noise trader approach to finance. J. Econ. Perspect. 4(2), 19–33 (1990)

    Article  Google Scholar 

  23. Shleifer, A., Vishny, R.W.: The limits of arbitrage. J. Finan. 52(1), 35–55 (1997)

    Article  Google Scholar 

  24. Barberis, N., Thaler, R.: A survey of behavioral finance. In: Handbook of the Economics of Finance, vol. 1, pp. 1053–1128 (2003)

    Google Scholar 

  25. Lewis, D.D., Rose, T.G., Li, F., Yang, Y., Rose, T.G., Li, F.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)

    Google Scholar 

  26. Dadvar, M., Hauff, C., de Jong, F.: Scope of negation detection in sentiment analysis. In: Proceedings of the Dutch-Belgian Information Retrieval Workshop, pp. 16–20 (2011)

    Google Scholar 

  27. Riekert, M., Leukel, J., Klein, A.: Online media sentiment: understanding machine learning-based classifiers. In: Proceedings of the 24th European Conference on Information Systems (ECIS 2016), Istanbul, Turkey (2016)

    Google Scholar 

  28. Fan, R., Chang, K., Hsieh, C.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    MATH  Google Scholar 

  29. Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. Expert Syst. Appl. 36(7), 10760–10773 (2009)

    Article  Google Scholar 

  30. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Series in Statistics, 2nd edn. Springer, Heidelberg (2009). https://doi.org/10.1007/978-0-387-84858-7

    Book  MATH  Google Scholar 

  31. Leopold, E., Kindermann, J.: Text categorization with support vector machines. How to represent texts in input space? Mach. Learn. 46(1–3), 423–444 (2002)

    Article  Google Scholar 

  32. Lombardi, P., Aprile, G., Gsell, M., Winter, A., Reinhardt, M., Queck, S.: D1.1 definition of market surveillance, risk management and retail brokerage usecases (2011)

    Google Scholar 

  33. Antweiler, W., Frank, M.Z.: Is all that talk just noise? The information content of Internet stock message boards. J. Finan. 59(3), 1259–1294 (2004)

    Article  Google Scholar 

  34. Steiger, J.H.: Tests for comparing elements of a correlation matrix. Psychol. Bull. 87(2), 245–251 (1980)

    Article  MathSciNet  Google Scholar 

  35. Pröllochs, N., Feuerriegel, S., Neumann, D.: Generating domain-specific dictionaries using Bayesian learning. In: Proceedings of the 23rd European Conference on Information Systems (ECIS 2015), pp. 1–14 (2015)

    Google Scholar 

  36. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

    Article  Google Scholar 

  37. Das, S.R., Chen, M.Y.: Yahoo! For Amazon: sentiment extraction from small talk on the Web. Manage. Sci. 53(9), 1375–1388 (2007)

    Article  Google Scholar 

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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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Correspondence to Achim Klein .

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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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