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Information spillover and cross-predictability of currency returns: An analysis via Machine Learning

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  • Jia, Yuecheng
  • Liu, Yuzheng
  • Wu, Yangru
  • Yan, Shu

Abstract

This paper documents significant cross-return predictability of news variables, derived from textual analysis of news articles, for a broad cross-section of currencies. By employing forecasts based on the Least Absolute Shrinkage and Selection Operator (LASSO) that incorporate both news variables and forward discounts, we develop a notably profitable trading strategy. This strategy proves robust against transaction costs, risk adjustments, and controls for currency characteristics. Further analyses indicate that both risks and market frictions contribute to the profitability of the trading strategy, highlighting the crucial role of news in financial markets.

Suggested Citation

  • Jia, Yuecheng & Liu, Yuzheng & Wu, Yangru & Yan, Shu, 2024. "Information spillover and cross-predictability of currency returns: An analysis via Machine Learning," Journal of Banking & Finance, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:jbfina:v:169:y:2024:i:c:s0378426624002279
    DOI: 10.1016/j.jbankfin.2024.107313
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    More about this item

    Keywords

    Currency return; Cross-predictability; News; Information spillover; LASSO;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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