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The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation

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  • Rybinski, Krzysztof

Abstract

This is probably the first ever analysis of sell-side daily economic research to use Natural Language Processing, and it shows that the narrative of such reports can be used to predict economic time series. The NLP indexes are based on Polish and English language reports released at the same time and exhibit predictive power for different sets of economic variables. VAR models with the NLP indexes generate smaller forecast errors than ARIMA. The wordscores scaling model uses Monetary Policy Council statements to generate scores and allows NLP indexes to be created with better forecasting power than the sentiment-based ones.

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  • Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    • Handle: RePEc:eee:finlet:v:34:y:2020:i:c:s1544612319304064
    DOI: 10.1016/j.frl.2019.08.009
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    3. Yuan Luo & William K. Thompson & Timothy M. Herr & Zexian Zeng & Mark A. Berendsen & Siddhartha R. Jonnalagadda & Matthew B. Carson & Justin Starren, 2017. "Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review," Drug Safety, Springer, vol. 40(11), pages 1075-1089, November.
    4. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    5. David-Jan Jansen & Jakob de Haan, 2010. "An Assessment of the Consistency of ECB Communication using Wordscores," DNB Working Papers 259, Netherlands Central Bank, Research Department.
    6. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    7. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    8. Mirjana Pejić Bach & Živko Krstić & Sanja Seljan & Lejla Turulja, 2019. "Text Mining for Big Data Analysis in Financial Sector: A Literature Review," Sustainability, MDPI, vol. 11(5), pages 1-27, February.
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    Cited by:

    1. Rybinski, Krzysztof, 2021. "Ranking professional forecasters by the predictive power of their narratives," International Journal of Forecasting, Elsevier, vol. 37(1), pages 186-204.

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    More about this item

    Keywords

    Economic research; Forecasting; Text mining; NLP; Sentiment analysis; Wordscores;
    All these keywords.

    JEL classification:

    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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