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Which daily equity returns improve output forecasts?

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  • Jahan-Pavar, Mohammad R.
  • Lang, William J.

Abstract

We document the improvements in short term forecasts of real output growth for the United States and the euro area from incorporating daily financial data and using mixed data sampling (MIDAS) regressions. Furthermore, we show that a significant share of forecast improvements are driven by information embedded in stock returns of large, capital-intensive firms. In comparison, labor-intensive firms contribute less to improvements in output forecasts within a MIDAS framework.

Suggested Citation

  • Jahan-Pavar, Mohammad R. & Lang, William J., 2024. "Which daily equity returns improve output forecasts?," Economics Letters, Elsevier, vol. 243(C).
  • Handle: RePEc:eee:ecolet:v:243:y:2024:i:c:s0165176524003811
    DOI: 10.1016/j.econlet.2024.111897
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    References listed on IDEAS

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    1. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    2. Andrew J. Patton & Michela Verardo, 2012. "Does Beta Move with News? Firm-Specific Information Flows and Learning about Profitability," The Review of Financial Studies, Society for Financial Studies, vol. 25(9), pages 2789-2839.
    3. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    4. van Binsbergen, Jules H. & Koijen, Ralph S.J., 2017. "The term structure of returns: Facts and theory," Journal of Financial Economics, Elsevier, vol. 124(1), pages 1-21.
    5. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    6. Raul Ibarra & Luis M. Gomez-Zamudio, 2017. "Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 173-203, April.
    7. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    8. Arteaga-Garavito, Maria Jose & Croce, Mariano M. & Farroni, Paolo & Wolfskeil, Isabella, 2024. "When the markets get CO.V.I.D: COntagion, Viruses, and Information Diffusion," Journal of Financial Economics, Elsevier, vol. 157(C).
    9. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
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    Cited by:

    1. Mohammad R. Jahan-Parvar & Charles Knipp & Pawel J. Szerszen, 2024. "Trend-Cycle Decomposition and Forecasting Using Bayesian Multivariate Unobserved Components," Finance and Economics Discussion Series 2024-100, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    Keywords

    Mixed-frequency data sampling regressions; Forecasting; High-frequency financial data; Capital- and labor-intensive industry equity returns;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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