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The "Fed Model" and the Predictability of Stock Returns

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  • Paulo Maio

Abstract

The focus of this article is on the predictive role of the stock-bond yield gap--the difference between the stock market earnings (dividend) yield and the 10-year Treasury bond yield--also known as the "Fed model". The results show that the yield gap forecasts positive excess market returns, both at short and long forecasting horizons, and for both value- and equal-weighted stock indexes, and it also outperforms competing predictors commonly used in the literature. These findings go in line with the predictions from a present-value decomposition. The absence of predictive power for dividend growth, dividend payout ratios, earnings growth, and future one-period interest rates, actually strengthens the return predictability associated with the yield gap at very long horizons. By performing an out-of-sample analysis, the results show that the yield gap has reasonable out-of-sample predictability for the equity premium when the comparison is made against a simple historical average, especially when one imposes a restriction of positive equity premia. Furthermore, the yield gap proxies generally show greater out-of-sample forecasting power than the alternative state variables. An investment strategy based on the forecasting ability of the yield gap produces significant gains in Sharpe ratios. Copyright 2013, Oxford University Press.

Suggested Citation

  • Paulo Maio, 2013. "The "Fed Model" and the Predictability of Stock Returns," Review of Finance, European Finance Association, vol. 17(4), pages 1489-1533.
  • Handle: RePEc:oup:revfin:v:17:y:2013:i:4:p:1489-1533
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    File URL: http://hdl.handle.net/10.1093/rof/rfs025
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    Cited by:

    1. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
    2. Wilton Bernardino & João B. Amaral & Nelson L. Paes & Raydonal Ospina & José L. Távora, 2022. "A statistical investigation of a stock valuation model," SN Business & Economics, Springer, vol. 2(8), pages 1-25, August.
    3. Maio, Paulo, 2013. "Return decomposition and the Intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4958-4972.
    4. Lleo, Sebastien & Ziemba, William T., 2014. "Does the bond-stock earning yield differential model predict equity market corrections better than high P/E models?," LSE Research Online Documents on Economics 59290, London School of Economics and Political Science, LSE Library.
    5. McMillan, David G., 2019. "Predicting firm level stock returns: Implications for asset pricing and economic links," The British Accounting Review, Elsevier, vol. 51(4), pages 333-351.
    6. Dladla, Pholile & Malikane, Christopher, 2019. "Stock return predictability: Evidence from a structural model," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 412-424.
    7. Maio, Paulo & Xu, Danielle, 2020. "Cash-flow or return predictability at long horizons? The case of earnings yield," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 172-192.
    8. Maio, Paulo & Philip, Dennis, 2015. "Macro variables and the components of stock returns," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 287-308.
    9. Kadilli, Anjeza, 2015. "Predictability of stock returns of financial companies and the role of investor sentiment: A multi-country analysis," Journal of Financial Stability, Elsevier, vol. 21(C), pages 26-45.
    10. David G. McMillan, 2018. "The Behaviour of the Equity Yield and Its Relation with the Bond Yield: The Role of Inflation," IJFS, MDPI, vol. 6(4), pages 1-18, December.
    11. Andreas Humpe & David G. McMillan, 2018. "Equity/bond yield correlation and the FED model: evidence of switching behaviour from the G7 markets," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 413-428, October.
    12. Lleo, Sébastien & Ziemba, William T., 2015. "Some historical perspectives on the Bond-Stock Earnings Yield Model for crash prediction around the world," International Journal of Forecasting, Elsevier, vol. 31(2), pages 399-425.
    13. David G. McMillan, 2021. "Forecasting sector stock market returns," Journal of Asset Management, Palgrave Macmillan, vol. 22(4), pages 291-300, July.
    14. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    15. repec:zbw:bofrdp:2018_007 is not listed on IDEAS
    16. Gozluklu, Arie & Morin, Annaïg, 2019. "Stock vs. Bond yields and demographic fluctuations," Journal of Banking & Finance, Elsevier, vol. 109(C).
    17. Laborda, Ricardo & Laborda, Juan, 2017. "Can tree-structured classifiers add value to the investor?," Finance Research Letters, Elsevier, vol. 22(C), pages 211-226.
    18. Maio, Paulo, 2016. "Cross-sectional return dispersion and the equity premium," Journal of Financial Markets, Elsevier, vol. 29(C), pages 87-109.
    19. Faria, Gonçalo & Verona, Fabio, 2018. "The equity risk premium and the low frequency of the term spread," Research Discussion Papers 7/2018, Bank of Finland.
    20. Zakamulin, Valeriy & Hunnes, John A., 2021. "Stock earnings and bond yields in the US 1871–2017: The story of a changing relationship," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 182-197.
    21. Ji, Hongyun & Zhang, Han, 2024. "Application of the LPPL model in the identification and measurement of structural bubbles in the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    22. McMillan, David G., 2019. "Stock return predictability: Using the cyclical component of the price ratio," Research in International Business and Finance, Elsevier, vol. 48(C), pages 228-242.
    23. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).

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