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Fama French factors and US stock return predictability

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Abstract

This article investigates whether the HML, SMB along with the long-term reversal and the momentum factors exhibit both in-sample and out-of-sample forecasting ability for the US stock returns. Our findings suggest that these factors contain significantly more information for future stock market returns than the typically employed financial variables. We also go one step further and test whether these variables can proxy for the aforementioned factors and find that the default spread and to a lesser extent the term spread contain important information for the evolution of the factors examined. Finally, we show that appropriate decompositions of the factors in their size and value components can enhance predictability.

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Notes

  1. The size anomaly reflects the empirical finding that small stocks (low market capitalization) outperform large stocks (high market capitalization), even after adjusting for market exposure (Banz, 1981; Fama and French, 1992). Similarly, the value anomaly relates to the outperformance of value stocks (stocks with high ratios of fundamental or book value to market value such as book-to-market equity, cash flow-to-price or earnings-to-price ratios) over growth stocks, which have low book-to-price ratios (see among others DeBondt and Thaler, 1985; Fama and French, 1992; and Lakonishok et al, 1994). Moreover, positive momentum exists in stock returns. Stocks that have performed well relative to other stocks over the past (typically last six months to a year) continue to perform well over the future (next six months to a year), and vice versa (Jegadeesh and Titman, 1993, 2001; Fama and French, 1996).

  2. Asness et al (2013) find that stock portfolios created from past 5-year returns display an average correlation of 86 per cent with portfolios created from other value measures such as book-to-market.

  3. Fama and French (2012) consider the size components of international HML and MOM portfolios and find that value and momentum premiums decrease with size (except for Japan).

  4. The maximum lag value is set at 8 and is selected by means of the SIC criterion.

  5. To save space, we do not describe the bootstrap procedure. Please refer to Rapach and Weber (2004).

  6. This data set can be downloaded from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

  7. This set of data can be downloaded from www.bus.emory.edu/AGoyal/Research.html

  8. The authors argue that value stocks are riskier than growth stocks during economic recessions when the price of risk is high, resulting in an extra premium on value stocks which represents compensation for bearing systematic risk.

  9. For brevity, we only report the horizons for which we find significant in sample predictive ability. Detailed tables are available from the authors on request.

  10. Given a total sample of T observations, the researcher must decide on how to divide the sample into the estimation part (R observations) and the out-of-sample part (P:=T−R observations). Obviously, there is a trade-off, since a large R improves the quality of the estimated parameters of the model but, at the same time, leaves fewer observations for the out-of-sample forecast exercise making the evaluation of the predictive ability of the model difficult. In our analysis, we keep about 1/3 of the available sample for out-of-sample forecasting. This choice gives us a sufficient number of forecasts to evaluate the estimated models, while keeping enough observations to obtain reliable estimates for the parameters of our predictive models.

  11. Detailed tables are available from the authors on request.

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Acknowledgements

We have benefited from comments and suggestions by B. Candelon, T. Angelidis, E. Konstantinidi and conference/seminar participants at the 15th International Conference on Macroeconomic Analysis and International Finance in Crete, the 5th International Conference MAF 2012 in Venice, the 5th CSDA International Conference on Computational and Financial Econometrics in London, the 2nd National Conference of the Financial Engineering and Banking Society in Athens, the University of Macedonia in Thessaloniki, the University of Maastricht, Netherlands. The usual disclaimer applies.

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  1. Kent Business School, University of Kent, Canterbury, CT2 7PE, UK

    Ekaterini Panopoulou

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  1. Ekaterini Panopoulou
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  2. Sotiria Plastira
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Correspondence to Ekaterini Panopoulou.

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Panopoulou, E., Plastira, S. Fama French factors and US stock return predictability. J Asset Manag 15, 110–128 (2014). https://doi.org/10.1057/jam.2014.15

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