Chapter 2 Global Earnings Forecasting Efficiency

Abstract

Stock selection models often use momentum and analysts’ expectation data. We find that earnings forecast revisions and direction of forecast revisions are more important than analysts’ forecasts in identifying mispriced securities. Investing with expectations data and momentum variables is consistent with maximizing the geometric mean and Sharpe ratio over the long run. Additional evidence is revealed that supports the use of multifactor models for portfolio construction and risk control. The anomalies literature can be applied in real-world portfolio construction in the U.S., international, and global equity markets during the 1998–2009 time period. Support exists for the use of tracking error at risk estimation procedures.

While perfection cannot be achieved in portfolio creation and modeling, the estimated model returns pass the Markowitz and Xu data mining corrections test and are statistically different from an average financial model that could have been used to select stocks and form portfolios. We found additional evidence to support the use of Arbitrage Pricing Theory (APT) and statistically-based and fundamentally-based multifactor models for portfolio construction and risk control. Markets are neither efficient nor grossly inefficient; statistically significant excess returns can be earned.

Citation

Guerard, J.B. (2012), "Chapter 2 Global Earnings Forecasting Efficiency", Kensinger, J.W. (Ed.) Research in Finance (Research in Finance, Vol. 28), Emerald Group Publishing Limited, Leeds, pp. 19-47. https://doi.org/10.1108/S0196-3821(2012)0000028005