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Information Environment and The Cost of Capital


  • Orie Barron

    () (The Pennsylvania State University)

  • Xuguang Sheng

    () (American University)

  • Maya Thevenot

    () (Florida Atlantic University)


In empirical tests guided by recent theory (e.g., Hughes, Liu and Liu 2007; and Lambert, Leuz and Verrecchia 2012), we examine the joint effects of information asymmetry and information precision on the cost of capital and how these effects vary based on the amount and quality of available information and the level of market competition. Consistent with theory, we find that average information precision is an important factor that may alter the relation between information asymmetry and the cost of capital, leading to erroneous inferences, if not considered. We also show that, while information asymmetry increases the cost of capital in most settings, it decreases the cost of capital when the amount of public information is low, while it has no effect when the total information is of high quality and when there is a high level of market competition. Our final results indicate that the precision of private information decreases the cost of capital when the amount of public information is low, while it increases it when the quality of total information is low. Besides examining various aspects of the environment jointly, our study is also unique in that we use better measures of information asymmetry and precision, which allows us to tease out the economic significance of each factor on cost of capital. We find that cost of equity capital varies greatly with our measures of information asymmetry and average information precision. For example, our regression estimates suggest that information asymmetry and average information precision are comparable in importance to equity beta and firm size in determining firms’ cost of capital.

Suggested Citation

  • Orie Barron & Xuguang Sheng & Maya Thevenot, 2013. "Information Environment and The Cost of Capital," Working Papers 2013-003, The George Washington University, Department of Economics, Research Program on Forecasting.
  • Handle: RePEc:gwc:wpaper:2013-003

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    References listed on IDEAS

    1. Thompson, Samuel B., 2011. "Simple formulas for standard errors that cluster by both firm and time," Journal of Financial Economics, Elsevier, vol. 99(1), pages 1-10, January.
    2. Ng, Jeffrey, 2011. "The effect of information quality on liquidity risk," Journal of Accounting and Economics, Elsevier, vol. 52(2), pages 126-143.
    3. Leland, Hayne E, 1992. "Insider Trading: Should It Be Prohibited?," Journal of Political Economy, University of Chicago Press, vol. 100(4), pages 859-887, August.
    4. Sheng, Xuguang & Thevenot, Maya, 2012. "A new measure of earnings forecast uncertainty," Journal of Accounting and Economics, Elsevier, vol. 53(1), pages 21-33.
    5. Barth, Mary E. & Konchitchki, Yaniv & Landsman, Wayne R., 2013. "Cost of capital and earnings transparency," Journal of Accounting and Economics, Elsevier, vol. 55(2), pages 206-224.
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    Cited by:

    1. Xuguang Sheng & Maya Thevenot, 2013. "Differential Interpretation of Public Information: Estimation and Inference," Working Papers 2013-03, American University, Department of Economics.

    More about this item


    cost of capital; information quality; information asymmetry; ST uncertainty; BKLS;

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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