Finance and Economics Discussion Series (FEDS)
July 2024
Predicting Analysts’ S&P 500 Earnings Forecast Errors and Stock Market Returns using Macroeconomic Data and Nowcasts
Steven A. Sharpe and Antonio Gil de Rubio Cruz
Abstract:
This study scrutinizes the quality of “bottom-up” forecasts of near-term S&P 500 Composite earnings, derived by aggregating analysts’ forecasts for individual firm-level earnings. We examine whether forecasts are broadly consistent with current macroeconomic conditions reflected in economists’ near-term outlook and other available data. To the contrary, we find that a simple macroeconomic model of aggregate S&P 500 earnings, coupled with GDP forecasts from the Blue Chip Survey and recent dollar exchange rate movements, can predict large and statistically significant errors in equity analysts’ bottom-up forecasts for S&P 500 earnings in the current quarter and the quarter ahead. This finding is robust to the requirement that our econometric model is calibrated using only data available at the time of forecast. Moreover, the discrepancy between the macro-model-based earnings forecasts and analysts’ forecasts has predictive power for 3-month-ahead stock returns.
Keywords: Bottom-up Forecast, Earnings Forecasts, Equity Analyst Bias, Forecast Efficiency, Predicting Returns
DOI: https://doi.org/10.17016/FEDS.2024.049
PDF: Full Paper
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