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Firm Networks and Asset Returns

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Abstract

This paper argues that changes in the propagation of idiosyncratic shocks along firm networks are important to understanding variations in asset returns. When calibrated to match key features of supplier-customer networks in the United States, an equilibrium model in which investors have recursive preferences and firms are interlinked via enduring relationships generates long-run consumption risks. Additionally, the model matches cross-sectional patterns of portfolio returns sorted by network centrality, a feature unaccounted for by standard asset pricing models.

Suggested Citation

  • Carlos Ramírez, 2017. "Firm Networks and Asset Returns," Finance and Economics Discussion Series 2017-014, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2017-14
    DOI: 10.17016/FEDS.2017.014r1
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    References listed on IDEAS

    as
    1. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    2. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    3. Shea, John S, 2002. "Complementarities and Comovements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 412-433, May.
    4. Long, John B, Jr & Plosser, Charles I, 1983. "Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 91(1), pages 39-69, February.
    5. Ravi Bansal & Amir Yaron, 2004. "Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles," Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, August.
    6. Kenneth R. Ahern & Jarrad Harford, 2014. "The Importance of Industry Links in Merger Waves," Journal of Finance, American Finance Association, vol. 69(2), pages 527-576, April.
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    Cited by:

    1. Zhang, Si Ying, 2021. "Using equity market reactions and network analysis to infer global supply chain interdependencies in the context of COVID-19," Journal of Economics and Business, Elsevier, vol. 115(C).
    2. Sally Chen & Eric Tsang & Leanne Si Ying Zhang, 2023. "Global supply chain interdependence and shock amplification – evidence from Covid lockdowns," BIS Working Papers 1123, Bank for International Settlements.

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    More about this item

    Keywords

    Asset returns; Firm networks; Shock propagation;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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