Predicting Analysts’ S and P 500 Earnings Forecast Errors and Stock Market Returns using Macroeconomic Data and Nowcasts

Steven A. Sharpe, Antonio Gil de Rubio Cruz
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Abstract

This study scrutinizes the quality of “bottom-up” forecasts of near-term S and 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 and 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 and 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
利用宏观经济数据和即时预测预测分析师的 S 和 P 500 盈利预测误差和股市回报率
本研究对 S 和 P 500 指数综合指数近期收益的 "自下而上 "预测质量进行了审查,这些预测是通过汇总分析师对单个公司层面收益的预测得出的。我们研究了预测是否与经济学家的近期展望和其他可用数据所反映的当前宏观经济状况基本一致。相反,我们发现,一个简单的 S 和 P 500 指数总收益宏观经济模型,加上蓝筹股调查的 GDP 预测和最近的美元汇率走势,可以预测股票分析师对当前季度和未来季度 S 和 P 500 指数收益自下而上预测的巨大统计误差。我们的计量经济学模型只使用预测时可用的数据进行校准,因此这一发现是可靠的。此外,基于宏观模型的盈利预测与分析师预测之间的差异对 3 个月前的股票回报率具有预测能力。
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