> could be more helpful if it included more realistic numbers.
Any ideas on where to find these? While there's a lot of info out there on valuations most datasets have the problem of hindsight bias, especially missing data at the pre-A round.
> only 2 startups are compared in the NPV calculation. What about joining a BigCo / FAANG?
My original model was actually a FAANG vs two startup model (I left Apple) but it gets too complex to try to explain it in a post that's aimed at the stock options 101 crowd.
> the math gets slightly better if you assume you leave after some amount of vesting.
100%, although that's tough to model without just adding an arbitrary cutoff.
Thanks for the reply! Sorry I don't have any great sources off the top of my head. It makes sense that you'd want to restrict the scope of the article to make it more understandable.
I've only been part of one startup, and it went nowhere, but it's very common (ubiquitous?) for prospective employees to be sold the "if we IPO for $1xB" line when that likelihood is laughably small; hence my suggestion to lower the expectations with some lower numbers.
> could be more helpful if it included more realistic numbers.
Any ideas on where to find these? While there's a lot of info out there on valuations most datasets have the problem of hindsight bias, especially missing data at the pre-A round.
> only 2 startups are compared in the NPV calculation. What about joining a BigCo / FAANG?
My original model was actually a FAANG vs two startup model (I left Apple) but it gets too complex to try to explain it in a post that's aimed at the stock options 101 crowd.
> the math gets slightly better if you assume you leave after some amount of vesting.
100%, although that's tough to model without just adding an arbitrary cutoff.
> https://tldroptions.io/
Thanks, I did not know about this tool!