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News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar

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  • Naderi Semiromi, Hamed
  • Lessmann, Stefan
  • Peters, Wiebke

Abstract

The paper proposes a novel approach to predict intraday directional-movements of currency-pairs in the foreign exchange market based on news story events in the economy calendar. Prior work on using textual data for forecasting foreign exchange market developments does not consider economy calendar events. We consider a rich set of text analytics methods to extract information from news story events and propose a novel sentiment dictionary for the foreign exchange market. The paper shows how news events and corresponding news stories provide valuable information to increase forecast accuracy and inform trading decisions. More specifically, using textual data together with technical indicators as inputs to different machine learning models reveals that the accuracy of market predictions shortly after the release of news is substantially higher than in other periods, which suggests the feasibility of news-based trading. Furthermore, empirical results identify a combination of a gradient boosting algorithm, our new sentiment dictionary, and text-features based-on term frequency weighting to offer the most accurate forecasts. These findings are valuable for traders, risk managers and other consumers of foreign exchange market forecasts and offer guidance how to design accurate prediction systems.

Suggested Citation

  • Naderi Semiromi, Hamed & Lessmann, Stefan & Peters, Wiebke, 2020. "News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    • Handle: RePEc:eee:ecofin:v:52:y:2020:i:c:s1062940820300784
    DOI: 10.1016/j.najef.2020.101181
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    4. Xu, Yingying & Dai, Yifan & Guo, Lingling & Chen, Jingjing, 2024. "Leveraging machine learning to forecast carbon returns: Factors from energy markets," Applied Energy, Elsevier, vol. 357(C).

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