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A Theory of Asset Prices Based on Heterogeneous Information

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We propose a theory of asset prices that emphasizes heterogeneous information as the main element determining prices of different securities. Our main analytical innovation is in formulating a model of noisy information aggregation through asset prices, which is parsimonious and tractable, yet flexible in the specification of cash flow risks. We show that the noisy aggregation of heterogeneous investor beliefs drives a systematic wedge between the impact of fundamentals on an asset price, and the corresponding impact on cash flow expectations. The key intuition behind the wedge is that the identity of the marginal trader has to shift for different realization of the underlying shocks to satisfy the market-clearing condition. This identity shift amplifies the impact of price on the marginal trader's expectations. We derive tight characterization for both the conditional and the unconditional expected wedges. Our first main theorem shows how the sign of the expected wedge (that is, the difference between the expected price and the dividends) depends on the shape of the dividend payoff function and on the degree of informational frictions. Our second main theorem provides conditions under which the variability of prices exceeds the variability for realized dividends. We conclude with two applications of our theory. First, we highlight how heterogeneous information can lead to systematic departures from the Modigliani-Miller theorem. Second, in a dynamic extension of our model we provide conditions under which bubbles arise.

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  • Elias Albagli & Christian Hellwig & Aleh Tsyvinski, 2011. "A Theory of Asset Prices Based on Heterogeneous Information," Cowles Foundation Discussion Papers 1827, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:1827
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    Cited by:

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    2. Banerjee, Snehal & Green, Brett, 2015. "Signal or noise? Uncertainty and learning about whether other traders are informed," Journal of Financial Economics, Elsevier, vol. 117(2), pages 398-423.
    3. Jessica Roldan Pena & Virginia Olivella, 2010. "Re-examining the role of financial constraints in business cycles: is something wrong with the credit multiplier?," 2010 Meeting Papers 377, Society for Economic Dynamics.
    4. Kenneth Kasa & Todd B. Walker & Charles H. Whiteman, 2014. "Heterogeneous Beliefs and Tests of Present Value Models," Review of Economic Studies, Oxford University Press, vol. 81(3), pages 1137-1163.
    5. Jean-Paul L'Huillier & William R. Zame, 2015. "The Flattening of the Phillips Curve and the Learning Problem of the Central Bank," EIEF Working Papers Series 1503, Einaudi Institute for Economics and Finance (EIEF), revised Oct 2014.
    6. Asriyan, Vladimir & Fuchs, William & Green, Brett, 2021. "Aggregation and design of information in asset markets with adverse selection," Journal of Economic Theory, Elsevier, vol. 191(C).
    7. Szkup, Michal, 2017. "Preventing Self-fulfilling debt crises," MPRA Paper 82754, University Library of Munich, Germany.
    8. Chabakauri, Georgy & Yuan, Kathy & Zachariadis, Konstantinos, 2014. "Multi-asset noisy rational expectations equilibrium with contingent claims," LSE Research Online Documents on Economics 60736, London School of Economics and Political Science, LSE Library.
    9. Eduardo Dávila & Cecilia Parlatore, 2021. "Trading Costs and Informational Efficiency," Journal of Finance, American Finance Association, vol. 76(3), pages 1471-1539, June.
    10. Bianchi, Milo & Jehiel, Philippe, 2015. "Financial reporting and market efficiency with extrapolative investors," Journal of Economic Theory, Elsevier, vol. 157(C), pages 842-878.
    11. Luca Bernardinelli & Paolo Guasoni & Eberhard Mayerhofer, 2022. "Informational efficiency and welfare," Mathematics and Financial Economics, Springer, volume 16, number 2, October.
    12. Xuewen Liu, 2015. "Short-Selling Attacks and Creditor Runs," Management Science, INFORMS, vol. 61(4), pages 814-830, April.

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    More about this item

    Keywords

    Information aggregation; Information wedge; Heterogeneous beliefs; Modigliani-Miller theorem; Bubbles;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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