Extracting Entities and Relations in Analyst Stock Ratings News

Abstract

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 1 We are thankful to CityFALCON for providing us the data and for their collaboration and support. This work was partially funded by the Faculty of Computer Science and Engineering at the Ss. Cyril and Methodius University in Skopje. Information Extraction from Analysts' Ratings 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% F1 score for named entity recognition and 89.70% F1 score for relation extraction.