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Biases in Arithmetic and Geometric Averages as Estimates of Long-Run Expected Returns and Risk Premia
Daniel C. Indro and Wayne Y. Lee
Vol. 26, No. 4 (Winter, 1997), pp. 81-90
Stable URL: http://www.jstor.org/stable/3666130
Page Count: 10
You can always find the topics here!Topics: Arithmetic, Estimation bias, Estimators, Autocorrelation, Expected returns, Weighted averages, Financial management, Simulations, Investment risk, Mathematical tables
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The empirically documented presence of negative autocorrelation in long-horizon common stock returns magnifies the upward (downward) bias inherent in the use of arithmetic (geometric) averages as estimates of long-run expected returns and risk premia. Failure to account for this autocorrelation can lead to incorrect project accept/reject decisions. Through simulations, we show that a horizon-weighted average of the arithmetic and geometric averages contains a smaller bias and is a more efficient estimator of long-run expected returns.
Financial Management © 1997 Financial Management Association International