How do pods come up with quarterly estimates?
When building models, how do pod analyst comes up with expected revenue or margin beats/misses?
In software, for example, how does one take a view on whether Microsoft Azure was going to be 28% growth (vs 25-26% guide) and expects 26-27%? Or Salesforce margins would be 27.6% vs. Street at 25.8% and buyside expects at 26%?
like, one could say “AI should contribute more” or “salesforce margins can still be another 10pts higher” but that’s a qualitative opinion not based on anything specific for the quarter.
Or one can do spreadsheet math and say the expected net new growth or incremental margins on a 2 year stack or three year stack are hard or easy comps. But that’s the baseline. And I would still be plugging a number slight above or below based on an opinion.
And yipit just changes the expects, but they are also wrong 50% of the time, and its just a better “street estimate.”
any real life examples to explain would be tremendous. Thank you!
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My intuition is that on companies like MSFT and AMZN, it is very hard to actually nail that with a ton of confidence, and you aren't necessarily looking for the $3.50 in EPS vs. the $3.48 factset consensus and $3.52 buyside whisper, and more about, will azure growth be +27% instead of +22%, and is my delta significant? I believe they are leveraging everything you described (seasonality, detailed revenue + cost models, YoY stacks, and primarily alt data). Throw in commentary intra-quarter on your channel checks and you get closer - more importantly its about interpreting the expectations game on these huge stocks, and how these data points feed the bigger story and how you can catch the inflection points on these names. No conviction or delta, then you stay perfectly hedged, or don't compete. Alternatively, you could find an aspect that is going underappreciated, but that seems more common for longer duration bets and not the MM model. Yipit isn't the only data provider, and I'm sure there is someone who had to backtest the significance of an alt data provider that measures AWS instance startups vs. the internally built 3-statement model's AWS revenue build.
On less broad and widely followed mega mega caps, I think you start to find more "edgy" opportunities to apply those tools we discussed (leading indicators for the sector, how is the stock trading intra-quarter on those indicators and vs. peers, what does that say about the "story" and expectations, and then what are the battleground numbers in the print, and can you do enough due diligence intra-quarter on the key drivers of your model for those numbers + where is the risk / reward?
This is just my intuition here, could be completely wrong though. I work at an RIA.
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