Forecasting earnings with combination of analyst forecasts

Hai Lin, Xinyuan Tao, Chunchi Wu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)133-159
Number of pages27
JournalJournal of Empirical Finance
Volume68
DOIs
StatePublished - Sep 2022

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

Keywords

  • Consensus forecast
  • Earnings response coefficients
  • Forecast bias and dispersion
  • Forecast combination
  • Post-earnings-announcement drift
  • Profitability factor

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