| 来源类型 | Discussion paper
|
| 规范类型 | 论文
|
| 来源ID | DP12179
|
| DP12179 Automated Earnings Forecasts:- Beat Analysts or Combine and Conquer? |
| Eric Ghysels
|
| 发表日期 | 2017-07-24
|
| 出版年 | 2017
|
| 语种 | 英语
|
| 摘要 | Prior studies attribute analysts' forecast superiority over time-series forecasting models to their access to a large set of rm, 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 rm-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 rm 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 rm-level earnings, or other accounting performance measures, on a high-frequency basis. |
| 主题 | Financial Economics
|
| URL | https://cepr.org/publications/dp12179
|
| 来源智库 | Centre for Economic Policy Research (United Kingdom)
|
| 资源类型 | 智库出版物
|
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/540990
|
推荐引用方式
GB/T 7714 |
Eric Ghysels. DP12179 Automated Earnings Forecasts:- Beat Analysts or Combine and Conquer?. 2017.
|
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