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.

使用 Company2Vec 模型，通过对公司网站数据进行 Word2Vec 嵌入和降维分析，本研究论文提出了一种新的表示学习应用。Company2Vec 能够维护语义语言结构，并在精细粒度的行业中创建高效的公司嵌入。这些语义嵌入可用于银行业的各种应用，包括语义业务分析和行业预测等。矢量化的嵌入结构还可通过余弦距离衡量公司之间的相似性，从而提供比标准行业标签（NACE）更细粒度的公司比较方法。此外，研究提供了基于公司嵌入的 k-means 聚类的替代行业分割方法，最后还提出了三种算法用于对等公司识别，包括以公司为中心、以行业为中心和以投资组合为中心。