I am interested in modeling startup companies failure and success rates to describe what is the representative startup. I have 40 companies in a dataset. Each company is represented as a list of all the investment financing rounds it has gone through. For example:Company 1: Seed Round, Series A RoundCompany 2: Seed Round, Series A Round, Series B RoundCompany 3: Seed Round, Series A Round, Series B RoundCompany 4: Seed Round, Series A Round, Series B Round, Series C RoundCompany 5: Seed RoundCompany 6: Series A Round, Series B RoundCompany 6: Series A RoundAnd so on.I can see that each round can be represented as a state in a Markov Chain, and transitions are only allowed from earlier stages to later stages. I can go from Seed to Series A, and from Series A to Series B, but not from Series B to Series A.So we have N-order markov chains (production data has N <= 4).The output I'm looking for is a binary tree chart showing each stage as a node and each node can either transition to the next node or to a final state meaning the company has failed.This problem can also be seen as a real options model...Any ideas on how to implement this model?I can code in Python or Ruby (but I am no expert).
What are available methods for modeling startup survival rates?
python;survival analysis