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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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    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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