Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?
利用混合数据抽样回归方法整合高频数据构建公司盈利预测，发现其短期预测比分析师更准，尤其在预测分歧大或公司规模小时，且与分析师预测结合效果更优。
Prior studies attribute analysts’ forecast superiority over time-series forecasting models to their access to a large set of firm, industry, and macroeconomic information (an information advantage), which they use to update their forecasts on a daily, weekly or monthly basis (a timing advantage). This study leverages recently developed mixed data sampling (MIDAS) regression methods to synthesize a broad spectrum of high frequency data to construct forecasts of firm-level earnings. We compare the accuracy of these forecasts to those of analysts at short horizons of one quarter or less. We find that our MIDAS forecasts are more accurate and have forecast errors that are smaller than analysts’ when forecast dispersion is high and when the firm size is smaller. In addition, we find that combining our MIDAS forecasts with analysts’ forecasts systematically outperforms analysts alone, which indicates that our MIDAS models provide information orthogonal to analysts. Our results provide preliminary support for the potential to automate the process of forecasting firm-level earnings, or other accounting performance measures, on a high-frequency basis. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2864 . This paper was accepted by Mary Barth, accounting.