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The real-time information content of macroeconomic news: implications for firm-level earnings expectations

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  • Carabias, Jose M.

Abstract

This paper investigates the usefulness of the real-time macroeconomic news-flow as a leading indicator of firm-level end-of-quarter realized earnings. Using recent advances in macroeconomics, I develop a nowcasting model for quarterly earnings and provide two main findings. First, I show that my model provides out-of-sample expectations that are as accurate as analysts’ forecasts. Second, macroeconomic news embedded in my nowcasts is not fully incorporated into investors’ earnings expectations and predicts future abnormal returns around earnings announcements. These findings have three main implications for capital markets research. First, real-time macroeconomic news can be used to update earnings expectations in real-time. Second, there are economic benefits of doing so, as evidenced by the magnitude of risk-adjusted returns around earnings announcements. Third, after three decades of almost nonexistent research on time-series models for quarterly earnings, the door is open again for fruitful research in this area.

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  • Carabias, Jose M., 2018. "The real-time information content of macroeconomic news: implications for firm-level earnings expectations," LSE Research Online Documents on Economics 86399, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:86399
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    File URL: http://eprints.lse.ac.uk/86399/
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    References listed on IDEAS

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    Cited by:

    1. Abdalla, Ahmed & Carabias, Jose M. & Patatoukas, Panos N., 2021. "The real-time macro content of corporate financial reports: a dynamic factor model approach," LSE Research Online Documents on Economics 108539, London School of Economics and Political Science, LSE Library.
    2. Yingying Xu & Jichang Zhao, 2022. "Can sentiments on macroeconomic news explain stock returns? Evidence form social network data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2073-2088, April.
    3. Antonio Gil de Rubio Cruz & Steven A. Sharpe, 2024. "Predicting Analysts’ S&P 500 Earnings Forecast Errors and Stock Market Returns using Macroeconomic Data and Nowcasts," Finance and Economics Discussion Series 2024-049, Board of Governors of the Federal Reserve System (U.S.).
    4. Binz, Oliver & Mayew, William J. & Nallareddy, Suresh, 2022. "Firms’ response to macroeconomic estimation errors," Journal of Accounting and Economics, Elsevier, vol. 73(2).
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    6. Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
    7. Abdalla, Ahmed M. & Carabias, Jose M. & Patatoukas, Panos N., 2021. "The real-time macro content of corporate financial reports: A dynamic factor model approach," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 260-280.

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    More about this item

    Keywords

    macroeconomic news; earnings expectations; market efficiency; return predictability;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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