Good morning,
I would like some help stress-testing an approach I have been using to validate or invalidate possible investment theses, as this is the part of investing I personally struggle to find enough time for.
My investing style includes both shorter-term and longer-term equity picking, and I have been trying to structure the DD process into several independent layers.
The first is the macro side, which tracks both market activity and the broader global environment (news, announcements, government agency releases, and various other sources) to understand how information propagates through the market.
At the same time, I independently track the equities side, mostly through market activity.
Once an interesting equity emerges that could potentially form the basis of an investment thesis, I then reconstruct the company from its filing data and market activity, creating a timeline of both the company and the security to see whether there is any divergence between what is happening in the business and what is reflected in the share price.
Lastly, I combine the macro world and the equities world to look for potential price divergence, assess whether there is potential beta carry-through into the equity, and whether the equity itself can generate adequate alpha on top of that beta.
To make the approach a bit less abstract, here is a sample of the kind of DD this process produces:
[https://financial-lab.crescita.cc/](https://financial-lab.crescita.cc/)
I would be interested to hear whether people here see any obvious flaws, blind spots, or missing pieces in this approach to DD.