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Are the Fama–French factors proxying news related to GDP growth? The Australian evidence

Abstract

Inspired by Vassalou (J Financ Econ 68:47–73, 2003), we investigate the contention that the Fama and French (J Financ Econ 33:3–56, 1993) model’s ability to explain the cross sectional variation in equity returns is because the Fama–French factors are proxying for risk associated with future GDP growth in the Australian equities market. To assess the validity of Vassalou’s findings, we augment the CAPM and the Fama–French model with a GDP growth factor and run system regressions of the GDP-enhanced models using the GMM approach. Our results suggest that news about future GDP growth is not priced in equity returns and that any ability that SMB and HML exhibit in explaining equity returns is not because they contain information about future GDP growth.

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Notes

  1. 1.

    Fama and French found that the average annual book to market premium for a portfolio of high book to market firms is 12.32% higher than a portfolio of low book to market firms in the Australian setting.

  2. 2.

    Alternatively, the observed positive premiums on SMB and HML could be explained with a behavioural argument such as the one proposed by Lakonishok et al. (1994).

  3. 3.

    Breeden et al. (1989) formed a maximum correlation portfolio (MCP) that is highly correlated with the growth rate of real consumption. Lamont (2001) extended this argument to the idea of an “economic tracking portfolio”. His intuition was that changes in asset prices reflect changing information about future economic conditions. Therefore, it is possible to come up with an economic tracking portfolio of assets whose returns track economic variables such as inflation, expected output or future GDP growth. The main advantage of such tracking portfolios is that if the tracking portfolio earns a risk premium, then the signs and the degree of significance of such risk premiums can reveal the identities of the state variables that are important determinants of expected returns.

  4. 4.

    The six equity portfolios are the excess returns on the six Fama–French portfolios described below. The two bond portfolios are TERM and DEF.

  5. 5.

    Vassalou also included CAY, the detrended wealth variable which represents deviations from a common trend found in consumption, asset wealth and labour income. This variable is computed following Lettau and Ludvigson (2000). We do not have data for this variable and hence we exclude it from our analysis.

  6. 6.

    Here we assume that the estimate of the parameter ‘c’ is not impacted by the change in data frequency.

  7. 7.

    Book-to-market is book equity divided by market capitalisation. Book equity is proxied by net tangible assets which is equal to shareholders equity less intangibles. Negative book equity firms are removed from the sample.

  8. 8.

    As most Australian firms have a June financial year end, we rank firms in December so that for most firms, there is a six-month lag between their book value and market value.

  9. 9.

    We only present the system specification for the GDP augmented Fama–French model. The other models are nested versions of this one which we do not present to conserve space.

  10. 10.

    For examples of application of the MLRT, see Connor and Korajczyk (1988) and Faff (1992).

  11. 11.

    Equation 12 was also estimated using lagged quarterly returns on the Fama–French factors, as opposed to lagged yearly returns, and the findings were consistent. Results have been suppressed to conserve space.

  12. 12.

    To further highlight this point, Liew and Vassalou’s (2000) sample ranged from 78 to 182 companies per year (Table 1). In contrast, we had on average 900 companies in our sample each year.

  13. 13.

    Vassalou reported an adjusted R 2 of 38.62% for her mimicking portfolio.

  14. 14.

    The four factors employed in this analysis have been constructed such that a positive premium is consistent with a risk-based explanation for the factors. Thus, an observed negative premium would be inconsistent with a risk-based explanation and would therefore indicate that the factor is not priced and is not systematic.

  15. 15.

    In the final stage of the analysis, we examined the sensitivity of our results to the choice of base assets used to construct our GDP growth factor. Consistent with Vassalou (2003), a mimicking portfolio which only includes bond portfolios has a significant positive premium while the premium on the mimicking portfolio which only includes equity portfolios, is negative although insignificant. However, regardless of how the GDP factor is constructed, the premiums on the Fama–French factors remain positive and significant. This suggests that SMB and HML are not proxies for news about future GDP growth. Results are suppressed to conserve space.

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Acknowledgments

We are grateful to two anonymous referees and to conference participants at the 2007 AFAANZ conference in the Gold Coast, the 2007 European Financial Management Association conference in Vienna and the 2007 Multinational Finance Society conference in Thessaloniki for helpful comments. The financial assistance provided by an AFAANZ grant (1779281) and an ARC Linkage grant (LP0453913) are gratefully acknowledged.

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Correspondence to Philip Gharghori.

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Nguyen, A., Faff, R. & Gharghori, P. Are the Fama–French factors proxying news related to GDP growth? The Australian evidence. Rev Quant Finan Acc 33, 141–158 (2009). https://doi.org/10.1007/s11156-009-0137-8

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Keywords

  • GDP growth
  • Fama–French model
  • Asset pricing

JEL Classification

  • G12