![]() I’m not trying to troll or anything, but do people spend a lot of time looking at analyst estimates? Tobias: When you’re doing your DCF, 6.3 whether it’s a coal company or a SaaS company. On the other hand, analyst forecasts tend to overshoot the mark by 20% to 40% on average, when predicting earnings over two to three. ![]() It turns out the naive estimate of GDP growth is much more accurate on average with a median error of being within a few percentage points of the actual outcome. It’s missing by 4.6% over three years for EBIT. It’s still a little bit over optimistic there for that 1997 to this period. As you can imagine, the naive forecasting is by far and away the best– just using 6.3% is the best estimate. Tobias: I thought it was 3% on top of the 9% inflation. No, 6% nominal minus 3% inflation gets you to 3% real. What are at now? 2% or something like that? So, there’s probably a lot of front end loaded. Tobias: Just after World War II coming off a low base. It helps to start when you bomb the entire competition. Did you have any idea why it’s as high as that? That’s stock market returns.īill: Yeah. US GDP growth has averaged 6.3% per year on a nominal basis, including inflation of around 3.5%. So, I guess, that’s a good date to start. ![]() Yeah, replaced the analysts forecasts with a naive estimate that corresponds with long-term GDP growth since 1947. I’m just trying to find how they justified that GDP number. They looked at 1997 to 2021, “Median analyst estimate for growth over two to three years across a range of metrics.” And then, they compared it to– this what I thought was kind of interesting. However, there’s little forecastability in earnings and analyst estimates tend to be overly optimistic.” So, they tested it themselves. “Authors state that it is commonly suggested that one group of informed participants, security analysts may have some ability to predict growth over long time horizons. It’s called by Brian Chingono and Greg Obenshain. In their latest episode of the VALUE: After Hours Podcast, Brewster, Taylor, and Carlisle discuss The Reason Analysts Systematically Overshoot On Earnings. ![]()
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