Beginning programmer interested in solving problems / creating solutions in day job of finance.
Interested in using data science, predictive analytics to analyze private companies and determine if they are potential merger & acquisition targets, or looking to raise capital.
Private company data is always difficult to get, and so solving two big problems: collecting, scraping, cleaning, all sorts of different data, and most importantly matching that data to a specific company. The idea being to conintue to enrich the dataset of private companies, past, and current, to work on the analysis side.
Have learned basic python, mysql, working with api's, my goals are:
- build as comprehensive dataset as possible of 300,000 private
companies in the U.S., both past and present, with as many columns
as possible on the company, its investors, and management team.
- Analyse that data, and test different hypothesis of what indicators
matter if trying to determine if a company is going to sell or
raise capital.
- I have my learning set of 70,000 companies who have done a transaction over the last 4
years to test against the rest of the population
- Analyse and understand how, why, when those companies hired an
advisor to represent them in those transactions
- Develop a marketing strategy to target companies that fit the
predictive model of likely to do a transaction, to do that, these
fundamental questions need to be answered:
Which companies are likely to sell or raise capital
- How far in advance to they hire an advisor
- How do they find / hire an advisor, and what are their key decision criterion
- How do you market to these CEO's, company owners with the right: message, medium, follow-up., process
