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Identifying Price Informativeness

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  • Eduardo Dávila
  • Cecilia Parlatore

Abstract

We show that outcomes (parameter estimates and R-squareds) of regressions of prices on fundamentals allow us to recover exact measures of the ability of asset prices to aggregate dispersed information. Formally, we show how to recover absolute and relative price informativeness in dynamic environments with rich heterogeneity across investors (regarding signals, private trading needs, or preferences), minimal distributional assumptions, multiple risky assets, and allowing for stationary and non-stationary asset payoffs. We implement our methodology empirically, finding stock-specific measures of price informativeness for U.S. stocks. We find a right-skewed distribution of price informativeness, measured in the form of the Kalman gain used by an external observer that conditions its posterior belief on the asset price. The recovered mean and median are 0.05 and 0.02 respectively. We find that price informativeness is higher for stocks with higher market capitalization and higher trading volume.

Suggested Citation

  • Eduardo Dávila & Cecilia Parlatore, 2018. "Identifying Price Informativeness," NBER Working Papers 25210, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:25210
    Note: AP CF EFG
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    References listed on IDEAS

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    1. Bai, Jennie & Philippon, Thomas & Savov, Alexi, 2016. "Have financial markets become more informative?," Journal of Financial Economics, Elsevier, vol. 122(3), pages 625-654.
    2. Marcin Kacperczyk & Savitar Sundaresan & Tianyu Wang, 2018. "Do Foreign Investors Improve Market Efficiency?," NBER Working Papers 24765, National Bureau of Economic Research, Inc.
    3. Brian M. Weller, 2018. "Does Algorithmic Trading Reduce Information Acquisition?," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2184-2226.
    4. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
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    Cited by:

    1. Maryam Farboodi & Adrien Matray & Laura Veldkamp & Venky Venkateswaran, 2020. "Where Has All the Data Gone?," NBER Working Papers 26927, National Bureau of Economic Research, Inc.
    2. Nicolae Gârleanu & Lasse Heje Pedersen, 2022. "Active and Passive Investing: Understanding Samuelson’s Dictum [A noisy rational expectations equilibrium for multi-asset securities markets]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 12(2), pages 389-446.
    3. Dávila, Eduardo & Parlatore, Cecilia, 2023. "Volatility and informativeness," Journal of Financial Economics, Elsevier, vol. 147(3), pages 550-572.
    4. Eduardo Dávila & Cecilia Parlatore, 2021. "Trading Costs and Informational Efficiency," Journal of Finance, American Finance Association, vol. 76(3), pages 1471-1539, June.
    5. Chen, Deqiu & Ma, Yujing & Martin, Xiumin & Michaely, Roni, 2022. "On the fast track: Information acquisition costs and information production," Journal of Financial Economics, Elsevier, vol. 143(2), pages 794-823.
    6. Gholampour, Vahid, 2022. "Exchange rates and information about future fundamentals," Journal of International Money and Finance, Elsevier, vol. 127(C).
    7. Laura Veldkamp, 2023. "Valuing Data as an Asset," Review of Finance, European Finance Association, vol. 27(5), pages 1545-1562.

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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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