Bias, stability, and predictive ability in the measurement of systematic risk

Abstract

Purpose

Estimates of systematic risk or beta are an important determinant of the cost of capital. The standard technique used to compile beta estimates is an ordinary least squares regression of stock returns on market returns using four to five years of monthly data. This convention assumes that a longer time series of data will not adequately capture risks associated with existing assets. This paper seeks to address this issue.

Design/methodology/approach

Each year from 1980 to 2004, equity betas are estimated for 1,717 Australian firms over periods of four to 45 years, and form equal value portfolios of high, medium and low beta stocks. The paper compares expected returns – derived from the capital asset pricing model (CAPM) and subsequent realised market returns – and actual returns over subsequent annual and four‐year periods.

Findings

The paper shows that the ability of beta estimates to predict future stock returns systematically increases with the length of the estimation window and when the Vasicek bias correction is applied. However, estimation error is insignificantly different from that associated with a naïve assumption that beta equals one for all stocks.

Research limitations/implications

The implication is that using all available returns data in beta estimation, along with the Vasicek bias correction, reduces the imprecision of expected returns estimates derived from the CAPM. A limitation of the method is the use of conditional realised returns as a proxy for expected returns, given that it is not possible directly to observe expected returns incorporated into share prices.

Originality/value

The paper contributes to the understanding of corporate finance practitioners and academics, who routinely use beta estimates derived from ordinary least squares regression.

Citation

Gray, S., Hall, J., Klease, D. and McCrystal, A. (2009), "Bias, stability, and predictive ability in the measurement of systematic risk", Accounting Research Journal, Vol. 22 No. 3, pp. 220-236. https://doi.org/10.1108/10309610911005563