Macroeconomic attention and stock market return predictability

Feng Ma, Xingjie Liu*, Jia Liu, Dengshi Huang

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

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    Our investigation evaluates the novel macroeconomic attention indices (MAI) of Fisher et al. (2021) in terms of their ability to predict stock market returns based on dimension reduction methods and shrinkage methods. Our results demonstrate that macroeconomic attention indices can predict stock market returns with a significant degree of accuracy. In addition, the components of MAI indices based on partial least squares (PLS) and the least absolute shrinkage and selection operator (LASSO) methods have a greater capacity to improve the accuracy of the prediction of stock market returns than the components of the traditional macroeconomic variables. Moreover, we find that shrinkage methods can generate performances superior to those of the other models for forecasting stock market returns. We further demonstrate that macroeconomic attention indices embody superior predictive ability during the COVID-19 pandemic and over longer periods of time. Our study sheds new light on stock market returns’ prediction from the perspective of macroeconomic fundamentals.
    Original languageEnglish
    Article number101603
    Number of pages17
    JournalJournal of International Financial Markets, Institutions and Money
    Publication statusPublished - 4 Jul 2022


    • macroeconomic attention indices
    • macroeconomic vaiables
    • stock market return predictability
    • shrinkage methods
    • selection operator (LASSO) methods
    • COVID-19 pandemic


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