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Estimating and Adjusting for the Intervalling-Effect Bias in Beta

Author

Listed:
  • Kalman J. Cohen

    (Duke University)

  • Gabriel A. Hawawini

    (INSEAD, Fontainebleau, France)

  • Steven F. Maier

    (Duke University)

  • Robert A. Schwartz

    (New York University)

  • David K. Whitcomb

    (Rutgers University)

Abstract

The concept of beta as the measure of systematic risk has been widely accepted in the academic and financial community. Increasingly, betas are being used to estimate the cost of capital for corporations. Despite this, however, biases are generally present in ordinary least squares (OLS) estimates of beta. In particular, empirical estimates of beta are affected by friction in the trading process which delays the adjustment of a security's price to informational change and hence leads to an "intervalling-effect" bias. In this paper, we present and empirically test two procedures for correcting this bias. The first is to estimate the asymptotic value that OLS beta approaches as the differencing interval is lengthened without bound. The second procedure is to infer the value of beta by adjusting OLS beta for cross-sectional differences in the intervalling effect as a function of the depth of the market for a security (as measured by its value of shares outstanding). Our results suggest that a substantial correction is needed to get "true" beta estimates from short differencing interval data.

Suggested Citation

  • Kalman J. Cohen & Gabriel A. Hawawini & Steven F. Maier & Robert A. Schwartz & David K. Whitcomb, 1983. "Estimating and Adjusting for the Intervalling-Effect Bias in Beta," Management Science, INFORMS, vol. 29(1), pages 135-148, January.
  • Handle: RePEc:inm:ormnsc:v:29:y:1983:i:1:p:135-148
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    File URL: http://dx.doi.org/10.1287/mnsc.29.1.135
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