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A discussion of “Inter-industry network structure and the cross-predictability of earnings and stock returns”

Abstract

Aobdia et al. (Rev Account Stud, 2014) view the economy as a network of customers and suppliers. Using the 1997 input–output trade flow data from the Bureau of Economic Analysis to model the inter-industry network, they examine whether an industry’s position in the network, in particular, its “network centrality,” affects the transmission of information and economic shocks. They find that, compared to the accounting performance and stock returns of noncentral industries, those of central industries are explained by aggregate risks to a greater extent and are more highly associated with the contemporaneous and future performance of their linked industries. These findings suggest that network centrality matters—it plays an important role in how economic shocks are transmitted within the economy. The question of why network centrality matters, however, remains unanswered. A fruitful avenue for future research is to explore the origin of shocks to shed light on the fundamental question of whether sectoral shocks can aggregate into macro shocks.

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Fig. 1

Notes

  1. 1.

    For instance, Menzly and Ozbas (2010) find that firms related through customer and supplier relationships have predictable returns. Ahern and Harford (2013) find that stronger customer–supplier links are associated with a greater incidence of cross-industry mergers and that economy-wide merger waves are driven by merger activity in central industries.

  2. 2.

    Ahern (2013) further argues that, by the definition of centrality, more shocks will pass through central industries than non-central (peripheral) ones, and hence central industries will have greater exposure to market risk.

  3. 3.

    For non-central industries, however, shocks from other sectors might not get cancelled out, and hence they can be affected by both idiosyncratic and aggregate shocks. Note that this explanation (unlike the second explanation) does not rely on the assumption that sectoral shocks can aggregate into macro shocks.

  4. 4.

    A simple diversification argument based on the law of large numbers implies that in an economy with N firms or industries with independent shocks, aggregate fluctuations have a magnitude proportional to 1/√N.

  5. 5.

    Since then, two sets of theories (on two alternative propagation mechanisms) have been proposed for why the diversification argument might not hold. The first focuses on customer–supplier intersectoral linkages (Horvath 1998, 2000; Carvalho and Gabaix 2010; Acemoglu et al. 2012). The second is the “granular hypothesis” put forth by Gabaix (2011), who argues that, when the distribution of firm size is fat-tailed, shocks to large firms can generate significant aggregate fluctuations.

  6. 6.

    An important assumption of the second explanation—that sectoral shocks do not always cancel out and can aggregate into macro shocks—is subject to much debate. Unlike the second explanation, the first explanation does not rely on this assumption.

  7. 7.

    ACO focus on examining the relative risk differences across central and non-central industries, that is, whether the proportion of total risk that is explained by aggregate risk is higher, holding total risk constant, for central industries. Ahern (2013), on the other hand, performs standard asset pricing tests to examine whether central industries face higher market risks and hence have higher stock returns.

  8. 8.

    They also perform a similar test using changes in ROA.

  9. 9.

    The R2 test is a joint test of the asset pricing model. To the extent that the pricing model is misspecified, interpretation of R2 would be affected. Further, the latter finding—central and non-central industries have similar exposure to systematic risk—is not consistent with the findings of Ahern (2013), who suggests that central industries contribute to market risk and hence have higher systematic risk and stock returns than non-central industries.

  10. 10.

    ACO do not find that cross-predictability of returns is stronger for central firms. Even if they did, a return test would be difficult to interpret because there would always be two possible explanations: risk and market inefficiency or frictions. The results would have been interesting either way, but it is difficult to interpret the results without at least controlling for known risk factors (e.g., betas for market, SMB, HML, and momentum) in the test.

  11. 11.

    A simple test would be to include accounting beta and other macro factors in the regression and see if the incremental association is explained away. This test can shed light on whether the results are due at least in part to the second explanation.

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Acknowledgments

I would like to thank Judson Caskey, Paul Fischer (the editor), Hanna Lee, Congcong Li, Maria Ogneva, and Wenfeng Wang for their helpful comments and suggestions.

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Correspondence to Rebecca N. Hann.

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Hann, R.N. A discussion of “Inter-industry network structure and the cross-predictability of earnings and stock returns”. Rev Account Stud 19, 1225–1233 (2014). https://doi.org/10.1007/s11142-014-9289-4

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Keywords

  • Industry network
  • Network centrality
  • Economic shocks
  • Aggregate fluctuations
  • Earnings predictability

JEL Classification

  • E32
  • L16
  • M41