Short post: The Establishment tools used to 'model' the value of a company (CAPM, DCF, etc.) use values (variance in returns, revenue, etc.) as a way to quantify and prescribe 'value' to a company. People are comfortable with it (despite Mandelbrot, Taleb et. al.) because the quantified values it uses are 'primary' to the underlying asset (company): revenue, variance of returns, growth rate etc.
I think in applying machine learning, we may look at 'secondary' values, so-called 'alternative data;' satellite images, metadata on companies, investor network graphs, hidden relationships. The private investment system is complex but there must be a mechanical, abstract representation of the system that we can use to improve the likelihood of investment success beyond 50-year-old establishment-level financial theory.
Note: For a great primer on the usefulness of mechanical representations (models) in system representation and the application of data (machine learning) in solving problems read Peter Norvig's classic 'The Unreasonable Effectiveness of Data' or watch here.