Financial News Classification Model for NLP-based Bond Portfolio Construction

The objective of this paper is to build an accurate classification model for news articles, and to study the effect of different news categories in corporate bond returns. The first step is to show how to classify news articles for traded companies into different defined categories. A classification model using Bag of Words (BoW) and Term Frequency Inverse Document Frequency (TF-IDF) methodology is developed to classify the news into 10 defined categories, and gives an average accuracy of 71%. After web scrapping news articles from 3 different sources to build a dataset of over 550 thousand articles, these are classified into 10 different categories. Natural Language Processing is one of the main techniques used in systematic portfolio construction. Also in the paper, the constructed portfolio based on the sentiment of news articles, by buying the corporate bonds of companies that have positive sentiment in the news, and shorting the bonds of companies with negative sentiment in the news is presented. The effect of each news category in corporate bond returns is studied through these portfolios.