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Insensitive Investors

Author

Listed:
  • Charles, Constantin
  • Frydman, Cary
  • Kilic, Mete

Abstract

We experimentally study the transmission of subjective expectations into actions. Subjects in our experiment report valuations that are far too insensitive to their expectations, relative to the prediction from a frictionless model. We propose that the insensitivity is driven by a noisy cognitive process that prevents subjects from precisely computing asset valuations. The empirical link between subjective expectations and actions becomes stronger as subjective expectations approach rational expectations. Our results highlight the importance of incorporating weak transmission into belief-based asset pricing models. Finally, we discuss how cognitive noise can provide a microfoundation for inelastic demand in the stock market.

Suggested Citation

  • Charles, Constantin & Frydman, Cary & Kilic, Mete, 2023. "Insensitive Investors," LSE Research Online Documents on Economics 120788, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:120788
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    File URL: http://eprints.lse.ac.uk/120788/
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    References listed on IDEAS

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    1. John Ameriks & Gábor Kézdi & Minjoon Lee & Matthew D. Shapiro, 2020. "Heterogeneity in Expectations, Risk Tolerance, and Household Stock Shares: The Attenuation Puzzle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 633-646, July.
    2. Hirshleifer, David & Li, Jun & Yu, Jianfeng, 2015. "Asset pricing in production economies with extrapolative expectations," Journal of Monetary Economics, Elsevier, vol. 76(C), pages 87-106.
    3. Khaw, Mel Win & Stevens, Luminita & Woodford, Michael, 2017. "Discrete adjustment to a changing environment: Experimental evidence," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 88-103.
    4. Camelia M. Kuhnen, 2015. "Asymmetric Learning from Financial Information," Journal of Finance, American Finance Association, vol. 70(5), pages 2029-2062, October.
    5. Samuel M Hartzmark & Samuel D Hirshman & Alex Imas, 2021. "Ownership, Learning, and Beliefs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(3), pages 1665-1717.
    6. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2020. "Memory, Attention, and Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(3), pages 1399-1442.
    7. Stefan Nagel & Zhengyang Xu, 2022. "Asset Pricing with Fading Memory," The Review of Financial Studies, Society for Financial Studies, vol. 35(5), pages 2190-2245.
    8. Barberis, Nicholas & Greenwood, Robin & Jin, Lawrence & Shleifer, Andrei, 2015. "X-CAPM: An extrapolative capital asset pricing model," Journal of Financial Economics, Elsevier, vol. 115(1), pages 1-24.
    9. Ralph S. J. Koijen & Motohiro Yogo, 2019. "A Demand System Approach to Asset Pricing," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1475-1515.
    10. Nagel, Stefan & Xu, Zhengyang, 2023. "Dynamics of subjective risk premia," Journal of Financial Economics, Elsevier, vol. 150(2).
    11. Pedro Bordalo & Nicola Gennaioli & Rafael La Porta & Andrei Shleifer, 2019. "Diagnostic Expectations and Stock Returns," Journal of Finance, American Finance Association, vol. 74(6), pages 2839-2874, December.
    12. Barberis, Nicholas & Greenwood, Robin & Jin, Lawrence & Shleifer, Andrei, 2018. "Extrapolation and bubbles," Journal of Financial Economics, Elsevier, vol. 129(2), pages 203-227.
    13. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    14. Haoyang Liu & Christopher Palmer, 2021. "Are Stated Expectations Actual Beliefs? New Evidence for the Beliefs Channel of Investment Demand," NBER Working Papers 28926, National Bureau of Economic Research, Inc.
    15. Veronesi, Pietro, 1999. "Stock Market Overreaction to Bad News in Good Times: A Rational Expectations Equilibrium Model," The Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 975-1007.
    16. Stefano Giglio & Bryan Kelly, 2018. "Excess Volatility: Beyond Discount Rates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 71-127.
    17. Hassan Afrouzi & Spencer Y Kwon & Augustin Landier & Yueran Ma & David Thesmar, 2023. "Overreaction in Expectations: Evidence and Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(3), pages 1713-1764.
    18. Drerup, Tilman & Enke, Benjamin & von Gaudecker, Hans-Martin, 2017. "The precision of subjective data and the explanatory power of economic models," Journal of Econometrics, Elsevier, vol. 200(2), pages 378-389.
    19. Michael Johannes & Lars A. Lochstoer & Yiqun Mou, 2016. "Learning about Consumption Dynamics," Journal of Finance, American Finance Association, vol. 71(2), pages 551-600, April.
    20. Robin Greenwood & Andrei Shleifer, 2014. "Expectations of Returns and Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 714-746.
    21. Cary Frydman & Nicholas Barberis & Colin Camerer & Peter Bossaerts & Antonio Rangel, 2014. "Using Neural Data to Test a Theory of Investor Behavior: An Application to Realization Utility," Journal of Finance, American Finance Association, vol. 69(2), pages 907-946, April.
    22. Benjamin Enke & Thomas Graeber, 2023. "Cognitive Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(4), pages 2021-2067.
    23. Gene Amromin & Steven A. Sharpe, 2014. "From the Horse's Mouth: Economic Conditions and Investor Expectations of Risk and Return," Management Science, INFORMS, vol. 60(4), pages 845-866, April.
    24. Da, Zhi & Huang, Xing & Jin, Lawrence J., 2021. "Extrapolative beliefs in the cross-section: What can we learn from the crowds?," Journal of Financial Economics, Elsevier, vol. 140(1), pages 175-196.
    25. Cary Frydman & Lawrence J Jin, 2022. "Efficient Coding and Risky Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 161-213.
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    Keywords

    1749824;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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