  Building predictive models for companies often relies on inference using
historical data of companies in the same industry sector. However, companies
are similar across a variety of dimensions that should be leveraged in relevant
prediction problems. This is particularly true for large, complex organizations
which may not be well defined by a single industry and have no clear peers. To
enable prediction using company information across a variety of dimensions, we
create an embedding of company stocks, Stock2Vec, which can be easily added to
any prediction model that applies to companies with associated stock prices. We
describe the process of creating this rich vector representation from stock
price fluctuations, and characterize what the dimensions represent. We then
conduct comprehensive experiments to evaluate this embedding in applied machine
learning problems in various business contexts. Our experiment results
demonstrate that the four features in the Stock2Vec embedding can readily
augment existing cross-company models and enhance cross-company predictions.
