Rights:
Atribución-NoComercial-SinDerivadas 3.0 España
Abstract:
In this project the author will develop a large-scale automatic valuation method for companies
(with a strong focus on startups). The main challenge is the lack of nancial information which is
usually used when valuating companies with traditional methods. In this project the author will develop a large-scale automatic valuation method for companies
(with a strong focus on startups). The main challenge is the lack of nancial information which is
usually used when valuating companies with traditional methods. This problem will be overcome
using a Machine Learning approach. The models will be fitted using collected data from sources from
the Internet using web scrappers & crawlers. After a holistic analysis on the theoretical valuation of
companies, it will be concluded that the valuation formula will lay its basis on the Discounted Free
Cash Flows model. Also, a research of state-of-the-art Machine Learning techniques will take place.
Then, an estimation model for each one of the formula's inputs will be built accordingly with the
company's information that is known beforehand: in order to determine the company's economic
activity from its description an expert rule-based system will be implemented. The company's
revenues will be estimated using a meta-estimator that fits 700 randomized decision trees with data
related to the number of employees, economic activity, social traction and other features. A similar
estimator will also be used, with the help of an expert system, to transform those revenues into free
cash
ows. On the other hand, the risk of failure, growth and discount rate will be determined with
the development of a system that crawls people's interest trends and matches the already known
metrics of companies with the target ones according to its economic activity similarity.[+][-]