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Complicated firms

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  • Cohen, Lauren
  • Lou, Dong

Abstract

We exploit a novel setting in which the same piece of information affects two sets of firms: one set of firms requires straightforward processing to update prices, while the other set requires more complicated analyses to incorporate the same piece of information into prices. We document substantial return predictability from the set of easy-to-analyze firms to their more complicated peers. Specifically, a simple portfolio strategy that takes advantage of this straightforward vs. complicated information processing classification yields returns of 118 basis points per month before transaction costs. Consistent with processing complexity driving the return relation, we further show that the more complicated the firm, the more pronounced the return predictability. In addition, we find that sell-side analysts are subject to these same information processing constraints, as their forecast revisions of easy-to-analyze firms predict their future revisions of more complicated firms.

Suggested Citation

  • Cohen, Lauren & Lou, Dong, 2012. "Complicated firms," Journal of Financial Economics, Elsevier, vol. 104(2), pages 383-400.
  • Handle: RePEc:eee:jfinec:v:104:y:2012:i:2:p:383-400 DOI: 10.1016/j.jfineco.2011.08.006
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Lauren Cohen & Umit G. Gurun & Christopher J. Malloy, 2012. "Resident Networks and Firm Trade," NBER Working Papers 18312, National Bureau of Economic Research, Inc.
    2. Laopodis, Nikiforos T., 2016. "Industry returns, market returns and economic fundamentals: Evidence for the United States," Economic Modelling, Elsevier, vol. 53(C), pages 89-106.
    3. Akbas, Ferhat & Meschke, Felix & Wintoki, M. Babajide, 2016. "Director networks and informed traders," Journal of Accounting and Economics, Elsevier, vol. 62(1), pages 1-23.
    4. Romain Boulland & François Degeorge & Edith Ginglinger, 2017. "News Dissemination and Investor Attention," Review of Finance, European Finance Association, pages 761-791.
    5. Thomas Gilbert & Shimon Kogan & Lars Lochstoer & Ataman Ozyildirim, 2012. "Investor Inattention and the Market Impact of Summary Statistics," Management Science, INFORMS, pages 336-350.
    6. David Hirshleifer & Po-Hsuan Hsu & Dongmei Li, 2017. "Innovative Originality, Profitability, and Stock Returns," NBER Working Papers 23432, National Bureau of Economic Research, Inc.
    7. Lee, Charles M.C. & Ma, Paul & Wang, Charles C.Y., 2015. "Search-based peer firms: Aggregating investor perceptions through internet co-searches," Journal of Financial Economics, Elsevier, vol. 116(2), pages 410-431.
    8. Lauren Cohen & Umit Gurun & Scott Duke Kominers, 2014. "Patent Trolls: Evidence from Targeted Firms," NBER Working Papers 20322, National Bureau of Economic Research, Inc.
    9. repec:eee:jfinec:v:124:y:2017:i:2:p:331-348 is not listed on IDEAS
    10. Kin-Wai Lee & Gillian Hian-Heng Yeo, 2016. "The association between integrated reporting and firm valuation," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1221-1250, November.
    11. Kewei Hou & Chen Xue & Lu Zhang, 2017. "Replicating Anomalies," NBER Working Papers 23394, National Bureau of Economic Research, Inc.
    12. Filzen, Joshua J. & Schutte, Maria Gabriela, 2017. "Comovement, financial reporting complexity, and information markets: Evidence from the effect of changes in 10-Q lengths on internet search volumes and peer correlations," The North American Journal of Economics and Finance, Elsevier, vol. 39(C), pages 19-37.
    13. Lauren Cohen & Karl B. Diether & Christopher Malloy, 2012. "Legislating Stock Prices," NBER Working Papers 18291, National Bureau of Economic Research, Inc.
    14. Chen, Long & Zhang, Gaiyan & Zhang, Weina, 2016. "Return predictability in the corporate bond market along the supply chain," Journal of Financial Markets, Elsevier, vol. 29(C), pages 66-86.
    15. Rossen Valkanov & Andra Ghent, 2014. "Complexity in Structured Finance: Financial Wizardry or Smoke and Mirrors," 2014 Meeting Papers 104, Society for Economic Dynamics.
    16. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    17. repec:eee:jfinin:v:31:y:2017:i:c:p:45-63 is not listed on IDEAS
    18. Chunmei Lin & Massimo Massa & Hong Zhang, 2014. "Mutual Funds and Information Diffusion: The Role of Country-Level Governance," Tinbergen Institute Discussion Papers 14-079/IV/DSF76, Tinbergen Institute.
    19. repec:eee:jbfina:v:82:y:2017:i:c:p:59-79 is not listed on IDEAS
    20. Cohen, Lauren & Diether, Karl & Malloy, Christopher, 2013. "Legislating stock prices," Journal of Financial Economics, Elsevier, vol. 110(3), pages 574-595.
    21. David Hirshleife, 2015. "Behavioral Finance," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 133-159, December.
    22. Jaspersen, Stefan & Limbach, Peter, 2017. "Knowing Me, Knowing You? Similarity to the CEO and Fund Managers’ Investment Decisions," CFR Working Papers 17-02, University of Cologne, Centre for Financial Research (CFR).
    23. Florian Esterer & David Schröder, 2014. "Implied cost of capital investment strategies: evidence from international stock markets," Annals of Finance, Springer, vol. 10(2), pages 171-195, May.
    24. Li, Ningzhong & Richardson, Scott & Tuna, İrem, 2014. "Macro to micro: Country exposures, firm fundamentals and stock returns," Journal of Accounting and Economics, Elsevier, vol. 58(1), pages 1-20.

    More about this item

    Keywords

    Complicated processing; Return predictability; Standalone; Conglomerate; Market frictions;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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