Extracting Entities and Relations in Analyst Stock Ratings News

Massive volumes of finance-related data are created on the Internet daily, whether on question-answering forums, news articles, or stocks analysis sites. This data can be critical in the decision-making process for targeting investments in the stock market. Our research paper aims to extract information from such sources in order to utilize the volumes of data, which is impossible to process manually. In particular, analysts’ ratings on the stocks of well-known companies are considered data of interest. Two subdomains of Information Extraction will be performed on the analysts’ ratings, Named Entity Recognition and Relation Extraction. The former is a technique for extracting entities from a raw text, giving us insights into phrases that have a special meaning in the domain of interest. However, apart from the actual positions and labels of those phrases, it lacks the ability to explain the mutual relations between them, bringing up the necessity of the latter model, which explains the semantic relationships between entities and enriches the amount of information we can extract when stacked on top of the Named Entity Recognition model. This study is based on the employment of different models for word embedding and different Deep Learning classification architectures for extracting the entities and predicting relations between them. Furthermore, the multilingual abilities of a joint pipeline are being explored by combining English and German corpora. For both subtasks, we record state-of-the-art performances of 97.69% \(F_1\) score for named entity recognition and 89.70% \(F_1\) score for relation extraction.