Why do we write like that? Wouldn’t it make more sense to write “[analysts] misses [...] target”? It feels to me like writing “Weather rains more and beats meteorologists’ expectations” when “Meteorologists underestimates rainfall” would be more true.
Otherwise, the front page of a financial site would look like this:
* Analysts miss target regarding Tesla
* Analysts are optimistic about Q2 returns for Boeing
* Analysts etc.Analysts are trying to predict how many cars Tesla can build, and if they understand the company really well, their prediction will be accurate. With this result, you haven't learned much about Tesla, but you have learned that the analysts did not predict Tesla very well - so they don't understand it very well, and their reports might be wrong in the same way for other companies.
[1]A team of people, trying to create as many points as possible, who don't give a damn what the analysts are saying. When they win, are we learning about the Patriots or the talking heads (who probably haven't changed much in the past week)?
[2]A guy can dream.
[3]Sportsbook digest, if that is a publication, might have that headline.
Do people talk about "targets" rather than "predictions" or "odds" in American Football analysis? "We set a target of X game wins for the Patriots this season". "Patriots beat analyst targets".
No, it would say "Patriots beat Otherteam!", "Patriots win championship!". Analysts wouldn't get a mention. Phrasing it like Tesla is a team playing against Wall Street analysts is a bit weird, now that commentor mentions it.
Quarterly earnings are all about performance relative to expectation, while it is accurate to say that the analysts are wrong in this instance, the market gives so much "trust" to analysts that we expect their targets to be expressed in good faith (I sense an SEC violation otherwise), so when a company beats expectations we view that as a decent proxy for performance.
Analysts predict value of a company based on a lot of public data, and usually the stock price reflects those expectations.
Who cares a hoot if analysts are wrong? Whereas Tesla beating expectations and their sales numbers are more important.
But if you see companies as organizations made up of humans, that are capable of both overperforming and underperforming, then you have multiple variables. An analyst could provide an estimate for future-sales, and this estimate could be the best possible estimate given the information known at the time. And the estimate could still be wrong because the company's leadership/workers succeeded in turning things around.
TLDR: If you believe in free-will, then it's possible for someone to beat highly-accurate 3rd party estimates. If you don't believe in free-will, then any mis-estimates are purely due to analyst error
How can you call them "highly-accurate" if they aren't accurate? Yes they can be "best available, given the information known", but we're talking about predicting the future - it's impossible to have all the information; if your prediction is wrong, you don't get to call it "accurate".
If you don't believe in free-will, then any mis-estimates are purely due to analyst error
That doesn't make it analyst /error/. It tells you the analysts' understanding of Tesla as is not as good as they thought. If they had predicted the result spot-on, they would be excellent analysts working with excellent information. They didn't, so now you know one or both of those is not excellent, or both, or they made errors.
If you see companies as a force-of-nature that can only be marginally influenced by humans
To go down this route, you have to imagine Tesla didn't really want to produce many cars at all - all the incentives and motivation they have about making lots of cars, earning millions, paying debt back, the incentives employees have for bonuses, doing what the boss says, staying employed, all amount to virtually nothing, but the commentary from Jones of SomeBank is enormously influential and can turn the entire company round. That's so disproportionate, I can't just take it as given.
And what we'd learn is that the meteorologist cannot predict the weather and we shouldn't rely on their predictions as much in future.
We don't call the meteorologist "accurate" because they couldn't do better, we call them "inaccurate" because they were inaccurate.
The very definition of free-will means that the above is impossible for predicting human behavior. Even if you had perfect information, models and computing power, you still can't predict human behavior with 100% accuracy. A good analyst may be able to predict human behavior with higher levels of accuracy than others - but even a perfect analyst with perfect information will still make mistakes due to free will, which makes it qualitatively different compared to a meteorologist.