I came across a problem and I have been looking on internet how to solve it without finding a solution that fits my need. I am trying to predict investors behavior. To be precise, I would like to predict if an investor will invest and by how much. This is an exploratory project and I'm not sure if it will work but I think it is worth looking. To give you a context, we are a PE firm that offers investment proposition to investors and our goal would be to try to predict investors appetite of our deal. For this purpose, I have retrieve and built two datasets:
- Mainly categorical and numerical data that describes what my investors are: wealth, where they live, age, family situation, what do they do for living etc...
- Their investment habits. Namely: how much they have invested on our prior offers, when, on which deals, the time they spent looking at our website, time they spent filling their KYC procedure - Time-series data
Would it be feasible to combine these two datasets with one or multiple models and try to regress a potential investment in our next offer. Ofc, we can back-test this on past data.
Thank you for any help / ideas you may have!