From Modeling to Consistent Idea Generation
Forecasting & Researching Core Drivers to Look for Variance: Amazon (AMZN) edition
How do you consistently generate a high velocity of ideas? Build the model with the right level of granularity, forecast the core drivers and compare to consensus.
We are starting to put it all together for Amazon (most of the frameworks from this example apply across sectors):
The main plan for today is to build on how to forecast and research the core drivers to consistently find variant views.
We start with retail and then go over common signals analysts look at to inform their cloud (AWS) revenue estimate such as aggregated cloud bills and usage. Remember that last time, we covered a modeling framework to gauge where AWS revenue is headed even before doing any deeper research work.
We also discuss the importance of telling the story behind your forecast, how small deltas vs consensus can compound and how different analysts often end up with different-looking models.
Finally, “The Excel Monkey” idea - an analyst can get all the numbers, including the 1-year projections, 100% right and still get the stock totally wrong. Exactly why the PM matters a great deal, especially given everything around how stocks like AMZN trade…
Note key drivers are redacted in the screenshots above. Phoenix members can download the full excel file here.
Read here: Modeling a Variant View: Forecasts & Change
Read here: L/S Modeling Framework for Hyperscalers/Cloud
Let’s get straight into it.