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Stock market firm-level information and real economic activity

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  • di Mauro, Filippo
  • Fornari, Fabio
  • Mannucci, Dario

Abstract

We provide evidence that changes in the equity price and volatility of individual firms (measures that approximate the definition of 'granular shock' given in Gabaix, 2010) are key to improve the predictability of aggregate business cycle fluctuations in a number of countries. Specifically, adding the return and the volatility of firm-level equity prices to aggregate financial information leads to a significant improvement in forecasting business cycle developments in four economic areas, at various horizons. Importantly, not only domestic firms but also foreign firms improve business cycle predictability for a given economic area. This is not immediately visible when one takes an unconditional standpoint (i.e. an average across the sample). However, conditioning on the business cycle position of the domestic economy, the relative importance of the two sets of firms - foreign and domestic - exhibits noticeable swings across time. Analogously, the sectoral classification of the firms that in a given month retain the highest predictive power for future IP changes also varies significantly over time as a function of the business cycle position of the domestic economy. Limited to the United States, predictive ability is found to be related to selected balance sheet items, suggesting that structural features differentiate the firms that can anticipate aggregate fluctuations from those that do not help to this aim. Beyond the purely forecasting application, this finding may enhance our understanding of the underlying origins of aggregate fluctuations. We also propose to use the cross sectional stock market information to macro-prudential aims through an economic Value at Risk. JEL Classification: C53, C58, F37, G15

Suggested Citation

  • di Mauro, Filippo & Fornari, Fabio & Mannucci, Dario, 2011. "Stock market firm-level information and real economic activity," Working Paper Series 1366, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20111366
    Note: 437559
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp1366.pdf
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    References listed on IDEAS

    as
    1. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29, January.
    2. Vasco Carvalho, 2007. "Aggregate fluctuations and the network structure of intersectoral trade," Economics Working Papers 1206, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2010.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
    5. Gianni De Nicolò & Marcella Lucchetta, 2011. "Systemic Risks and the Macroeconomy," NBER Chapters, in: Quantifying Systemic Risk, pages 113-148, National Bureau of Economic Research, Inc.
    6. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    7. Boyan Jovanovic, 1987. "Micro Shocks and Aggregate Risk," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(2), pages 395-409.
    8. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    9. Arturo Estrella, 2005. "Why Does the Yield Curve Predict Output and Inflation?," Economic Journal, Royal Economic Society, vol. 115(505), pages 722-744, July.
    10. Fabio Fornari & Antonio Mele, 2013. "Financial Volatility and Economic Activity," Journal of Financial Management, Markets and Institutions, Società editrice il Mulino, issue 2, pages 155-198, December.
    11. Raphael Espinoza & Fabio Fornari & Marco J. Lombardi, 2012. "The Role of Financial Variables in predicting economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(1), pages 15-46, January.
    12. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    13. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    14. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    15. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
    16. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    17. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    18. Timothy G. Conley & Bill Dupor, 2003. "A Spatial Analysis of Sectoral Complementarity," Journal of Political Economy, University of Chicago Press, vol. 111(2), pages 311-352, April.
    19. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 75, European Central Bank.
    20. Xavier Gabaix, 2011. "The Granular Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 79(3), pages 733-772, May.
    21. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    22. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    23. Marcella Lucchetta & Mr. Gianni De Nicolo, 2010. "Systemic Risks and the Macroeconomy," IMF Working Papers 2010/029, International Monetary Fund.
    24. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    25. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    26. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    27. Dr. James Mitchell, 2008. "Combining Forecast Densities from VARs with Uncertain Instabilities," National Institute of Economic and Social Research (NIESR) Discussion Papers 303, National Institute of Economic and Social Research.
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    Cited by:

    1. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    2. Stijn Claessens & M. Ayhan Kose, 2017. "Asset prices and macroeconomic outcomes: A survey," CAMA Working Papers 2017-76, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

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    More about this item

    Keywords

    Business cycle forecasting; granular shock; international linkages;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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