Forecasting earnings with combination of analyst forecasts

Hai Lin, Xinyuan Tao and Chunchi Wu

Journal of Empirical Finance, 2022, vol. 68, issue C, 133-159

Abstract: We propose a regression-based method for combining analyst forecasts to improve forecasting efficiency. This method significantly reduces the bias in earnings forecasts, and generates forecasts that consistently outperform consensus forecasts over time and across firms of different characteristics. Incorporating firm-level and macroeconomic information in the model further improves earnings forecasting performance. Forecasting gains increase with the dispersion and bias of analyst forecasts, and the degree of under/overreactions to earnings news. Moreover, the combination forecast produces larger earnings response coefficients, weakens the anomaly of post-earnings-announcement drift, and provides a better expected profitability measure that has higher power to predict stock returns.

Keywords: Forecast combination; Consensus forecast; Forecast bias and dispersion; Earnings response coefficients; Post-earnings-announcement drift; Profitability factor (search for similar items in EconPapers)
JEL-codes: G12 G13 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:68:y:2022:i:c:p:133-159

DOI: 10.1016/j.jempfin.2022.07.003

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