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Measuring firm-specific informational efficiency without conditioning on a public announcement

  • Yu Cong
  • Murugappa Krishnan
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    We exploit the availability of active single-stock futures on India's National Stock Exchange (NSE) to provide estimates of overall informational efficiency, without conditioning on a public announcement. The key is the estimation of the primitive parameters of an asset pricing model with private information and noise. The variance--covariance parameters governing futures prices and terminal values can be inverted to obtain the Maximum Likelihood Estimators (MLEs) of the precision of private information and the variance of liquidity motivated trades. The Signal-to Signal-plus-Noise (SSN) ratio -- our measure of overall informational efficiency -- is a function of these primitive parameters. Our primary findings show that there is considerable variation across firms in these parameters despite only large active firms being available for futures trading. We also examine the cross-sectional relationship of this measure of informational efficiency and corporate governance. Overall informational efficiency increases in promoters’ and foreign institutional investors’ shareholding, and if the board of directors has a majority that is independent, and decreases if the chairman of the board is also the CEO, and if overall trading activity is fragmented across domestic and international markets. The NIFTY index shows a higher SSN ratio than for any of the firms. This is consistent with the idea that less manipulability is associated with greater informational efficiency.

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    File URL: http://hdl.handle.net/10.1080/09603107.2012.681023
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    Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

    Volume (Year): 22 (2012)
    Issue (Month): 21 (November)
    Pages: 1799-1809

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    Handle: RePEc:taf:apfiec:v:22:y:2012:i:21:p:1799-1809
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    1. Easley, David, et al, 1996. " Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-36, September.
    2. Foster, F Douglas & Viswanathan, S, 1995. "Can Speculative Trading Explain the Volume-Volatility Relation?," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 379-96, October.
    3. Cho, Jin-Wan & Krishnan, Murugappa, 2000. "Prices as Aggregators of Private Information: Evidence from S&P 500 Futures Data," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(01), pages 111-126, March.
    4. Easley, David & Kiefer, Nicholas M & O'Hara, Maureen, 1997. "One Day in the Life of a Very Common Stock," Review of Financial Studies, Society for Financial Studies, vol. 10(3), pages 805-35.
    5. Landsman, Wayne R. & Maydew, Edward L. & Thornock, Jacob R., 2012. "The information content of annual earnings announcements and mandatory adoption of IFRS," Journal of Accounting and Economics, Elsevier, vol. 53(1), pages 34-54.
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