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Information Quality, Learning, and Stock Market Returns

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  • Li, George

Abstract

This paper studies how the precision of noisy public information that investors receive about the expected aggregate dividend growth rate affects stock market returns. I show that less precise information can increase the risk premium and stock return volatility. The numerical results from my calibrated model also show that noisy information can significantly increase the risk premium and stock return volatility. My finding implies that the presence of noisy information may help explain the large average risk premium and return volatility in the U.S. financial market. In addition, my finding suggests it is optimal for firms to disclose to investors more precise information to reduce the cost of equity capital.

Suggested Citation

  • Li, George, 2005. "Information Quality, Learning, and Stock Market Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(3), pages 595-620, September.
    • Handle: RePEc:cup:jfinqa:v:40:y:2005:i:03:p:595-620_00
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    Citations

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    Cited by:

    1. Frederik Neugebauer, 2020. "ECB Announcements and Stock Market Volatility," WHU Working Paper Series - Economics Group 20-02, WHU - Otto Beisheim School of Management.
    2. Frederik Lundtofte, 2013. "The quality of public information and the term structure of interest rates," Review of Quantitative Finance and Accounting, Springer, vol. 40(4), pages 715-740, May.
    3. Wang, Hailong & Hu, Duni & Ma, Chaoqun & Cheng, Fengchao, 2020. "Disagreements with noisy signals and asset pricing," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    4. Bansal, Naresh & Seetharaman, Ananth & Wang, Xu (Frank), 2013. "Managerial risk-taking incentives and non-GAAP earnings disclosures," Journal of Contemporary Accounting and Economics, Elsevier, vol. 9(1), pages 100-121.
    5. Anisha Ghosh & George M Constantinides, 2021. "What Information Drives Asset Prices? [Information quality and long-run risk: Asset pricing implications]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 11(4), pages 837-885.
    6. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    7. Lubos Pastor & Pietro Veronesi, 2009. "Learning in Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 1(1), pages 361-381, November.
    8. Füss, Roland & Grabellus, Markus & Mager, Ferdinand & Stein, Michael, 2018. "Something in the air: Information density, news surprises, and price jumps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 53(C), pages 50-75.
    9. Max Schreder & Pawel Bilinski, 2022. "Information Quality and the Expected Rate of Return: A Structural Equation Modelling Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(2), pages 139-170, June.
    10. David Feldman, 2007. "Incomplete information equilibria: Separation theorems and other myths," Annals of Operations Research, Springer, vol. 151(1), pages 119-149, April.
    11. Fu, Qi & So, Jacky Yuk-Chow & Li, Xiaotong, 2024. "Stable paretian distribution, return generating processes and habit formation—The implication for equity premium puzzle," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    12. Geoffrey J. Warren, 2008. "Implications for Asset Pricing Puzzles of a Roll‐over Assumption for the Risk‐Free Asset," International Review of Finance, International Review of Finance Ltd., vol. 8(3‐4), pages 125-157, September.

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