With Company2Vec, the paper proposes a novel application in representation
learning. The model analyzes business activities from unstructured company
website data using Word2Vec and dimensionality reduction. Company2Vec maintains
semantic language structures and thus creates efficient company embeddings in
fine-granular industries. These semantic embeddings can be used for various
applications in banking. Direct relations between companies and words allow
semantic business analytics (e.g. top-n words for a company). Furthermore,
industry prediction is presented as a supervised learning application and
evaluation method. The vectorized structure of the embeddings allows measuring
companies similarities with the cosine distance. Company2Vec hence offers a
more fine-grained comparison of companies than the standard industry labels
(NACE). This property is relevant for unsupervised learning tasks, such as
clustering. An alternative industry segmentation is shown with k-means
clustering on the company embeddings. Finally, this paper proposes three
algorithms for (1) firm-centric, (2) industry-centric and (3) portfolio-centric
peer-firm identification.Comment: Accepted for Publication in: International Journal of Information
Technology & Decision Making (2023