I have messed around with some numbers, and got far enough to realize that I don't have enough information to develop a realisitic spreadsheet model even if I put in a great deal of effort. It becomes an involved programing problem, and I am not sure it is worth it, since I can't get past the garbage in garbage out problem when, I don't know the right values.
The starting point for me is gross revenues.
I usually end up developing separate possible answers, such as a high, medium, and low value, and the exercise teaches me that I don't need a lot of detail working with indications like AD, or PD, because the number of participants are going to make stock valuations very large when you assume success for the drug. I think maybe participants (that will take the drug) isthe most important variable. That needs a high, medium and low number.
Right behind participants is average drug price, which needs a high medium and low, but also needs a US, Europe, Asia, and Rest of World break out.
Then, for me the next most important issue is how the drug marketing campaign ramps, ie, year 1, year 2, year 3, etc.
Then I need to do it for each important indication I think is important to include.
This quickly gets so high that it does not matter how I do it, the ending stock value is going to be gargantum. Little is achieved by more exact work at this juncture, but you can make quick assumptions and fill in parts of the income statement to get to cost estimates and net profit figures.
So, then you can see the possible upside is quite amazing.
But the real down side is where the drug washes out and ends up a failure with not drug approvals. That gives you a binary projection great or terrible.
For me, it is quite hard to work out useful middle values where reality may be and the final outcome is potentially more likely to end up.
This is where you try to figure out odds that a particular indication will be approved (using your research and other consideration), and what needs to happen to get that approval, plus there is an extremely large number of ways competition affects the equation almost all of which are filled with unknowns, or what happens if Retts is approved, but AD is not, whether AVXL will get a partner, or be bought out, and and how do you think about and model other variables that are unknown.
I tend to stop with the numbers and return to the big questions about will AVXL gain approvals, and the still unknown results of the clinical trials.
What comes out of the entire exercise is that AVXL is one of the few stocks that can really have an enormously wide group of possible outcomes, with lots of great possibilities.
GLTY.
