
doi: 10.2139/ssrn.2574603
handle: 10419/107901
markdownabstract__Abstract__ An analysis of about 300000 earnings forecasts, created by 18000 individual forecasters for earnings of over 300 S&P listed firms, shows that these forecasts are predictable to a large extent using a statistical model that includes publicly available information. When we focus on the unpredictable components, which may be viewed as the personal expertise of the earnings forecasters, we see that small adjustments to the model forecasts lead to more forecast accuracy. Based on past track records, it is possible to predict the future track record of individual forecasters.
G17, Earnings Forecasts, ddc:330, G24, Earnings Forecasts, Earnings Announcements, Financial Markets, Financial Analysts, M41, Earnings Announcements, EUR ESE 31, Financial Markets, Financial Analysts, jel: jel:G24, jel: jel:G17, jel: jel:M41
G17, Earnings Forecasts, ddc:330, G24, Earnings Forecasts, Earnings Announcements, Financial Markets, Financial Analysts, M41, Earnings Announcements, EUR ESE 31, Financial Markets, Financial Analysts, jel: jel:G24, jel: jel:G17, jel: jel:M41
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
