美联储-使用宏观经济数据和Nowscast预测分析师的标普500收益预测误差和股市回报（英）

Finance and Economics Discussion SeriesFederal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print)ISSN 2767-3898 (Online)Predicting Analysts’ S&P 500 Earnings Forecast Errors and StockMarket Returns using Macroeconomic Data and NowcastsSteven A. Sharpe and Antonio Gil de Rubio Cruz2024-049Please cite this paper as:Sharpe, Steven A., and Antonio Gil de Rubio Cruz (2024).“Predicting Analysts’ S&P500 Earnings Forecast Errors and Stock Market Returns using Macroeconomic Data andNowcasts,” Finance and Economics Discussion Series 2024-049.Washington: Board ofGovernors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2024.049.NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment. The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or theBoard of Governors. References in publications to the Finance and Economics Discussion Series (other thanacknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.1 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** June 21, 2024 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. * The views expressed herein are those of the authors and do not necessarily reflect those of the Board of Governors nor the staff of the Federal Reserve System. Special thanks to Nelson Rayl for extensive research assistance and to Kirstin Hubrich for her leadership on an early version of this project. We thank the participants of the R&S Lunch Workshop and the Board of Governor’s Finance Forum workshop for helpful comments and suggestions. We particularly appreciate thoughtful feedback from colleagues Andrew Chen, Dino Palazzo, and Benjamin Knox. Of course, the authors are responsible for all remaining shortcomings. ** Division of Research and Statistic