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The Granularity of the Stock Market: Forecasting Aggregate Returns Using Firm-Level Data

Author

Listed:
  • Stefano Schiaffi

    (Milan University “L. Bocconi”)

Abstract

This paper draws on the theoretical concept of granularity to forecast aggregate stock market returns using firm-level data. When applied to stock market returns, granularity suggests that fluctuations in the returns of individual firms can be used to forecast future aggregate returns. This paper finds that a model including firm-level data outperforms a benchmark model based on aggregate variables alone. Furthermore, a real-time investment strategy based on our model beats a buyand-hold strategy on the stock market either in terms of cumulative returns or in terms of risk-adjusted excess returns or in both, depending on the forecast horizon.

Suggested Citation

  • Stefano Schiaffi, 2013. "The Granularity of the Stock Market: Forecasting Aggregate Returns Using Firm-Level Data," Rivista di Politica Economica, SIPI Spa, issue 4, pages 141-169, October-D.
    • Handle: RePEc:rpo:ripoec:y:2013:i:4:p:141-169
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    Citations

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    Cited by:

    1. Saman Banafti & Tae-Hwy Lee, 2022. "Inferential Theory for Granular Instrumental Variables in High Dimensions," Working Papers 202203, University of California at Riverside, Department of Economics.

    More about this item

    Keywords

    granularity; forecasting; stock market returns;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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