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Building predictive models for companies often relies on inference usinghistorical data of companies in the same industry sector. However, companiesare similar across a variety of dimensions that should be leveraged in relevantprediction problems. This is particularly true for large, complex organizationswhich may not be well defined by a single industry and have no clear peers. Toenable prediction using company information across a variety of dimensions, wecreate an embedding of company stocks, Stock2Vec, which can be easily added toany prediction model that applies to companies with associated stock prices. Wedescribe the process of creating this rich vector representation from stockprice fluctuations, and characterize what the dimensions represent. We thenconduct comprehensive experiments to evaluate this embedding in applied machinelearning problems in various business contexts. Our experiment resultsdemonstrate that the four features in the Stock2Vec embedding can readilyaugment existing cross-company models and enhance cross-company predictions.

Stock2Vec: An Embedding to Improve Predictive Models for Companies