In other words: when you see someone publish a “forecast” that a particular company’s product isn’t doing well, the analysis is usually flawed. If it weren’t flawed, the market price of the analysis probably wouldn’t be “free.”

I’m speaking from experience - I know a quantitative researcher who successfully forecasted Apple’s iPhone sales to a fraction of a percent a few years ago (very clever method!), and I’ve personally done this with Tesla model sales/production.

For Tesla auto sales couldn't you just look at tesla vin numbers?

For iPhones, sample mac address's?

You’ve definitely intuited some of the method, yes :). The rest of it entails:

1) how to get all VINs both authoritatively and legally,

2) how to distinguish between valid VINs and assigned VINs,

3) how to reverse the actual revenue projection from the set of all assigned VINs.

The first requirement is the hardest. Tracking self-reported VIN delivery from users isn’t rigorous enough. You could use an endpoint and scrape from it, but how would you do it legally and reliably?

The second requirement is also difficult. Assuming you’ve found an authoritative source for valid VINs, how do you distinguish which VINs are assigned?

Once you have those two, the third requirement is mostly straightforward. You can implement your own VIN decoder using public NHTSA documentation, map each VIN field to options and prices across models, and track sequential VINs using the distinguishing method of requirement 2 on the data you’re getting from requirement 1.

Naturally, there are other ways to do this that don’t involve VINs at all.

Sometimes it's just good old illegal insider information.

I say “simply” because it doesn’t require any of the infrastructure you mentioned, which is what the public typically associates with “alternative data.” It was actually quite impressive and novel at the time, and inspired other projects of mine.

I can talk about this now because (to my knowledge) it no longer works, though it did for a while. He was in charge of approximately $100M when he was working on this.

Popular thinking can't really wrap its head around risk management.

Supplier A talks to a friend in the media and all of a sudden the story is that Apple has a big problem because their new phone isn’t selling.

What no one is saying (possibly because as we all know Apple like secrecy and repolish them) is that supplier B had a 30% increase in orders from Apple, or supplier C is now coming online when they weren’t involved before.

Since we don’t have the whole picture, it’s hard to know what’s actually going on outside of effectively one rumor.

Or it could be stock manipulation.

The simple fact that they had no idea how it would sell could mean they asked suppliers to be ready at the high-end of expectations.

Tons of money exchanged hands on the stock market because of it.

Shares are down from a high of $180 and pre-hours this morning flat. So consistent with what analyst were saying.

It was not about Q1 but about Q2 guidance.