Ivailo Dimov is a quant researcher at Bloomberg LP where he provides quantitative, data science and analytics solutions to senior management, external and internal clients in both traditional asset classes and alternative data. Ivailo is also an Adjunct Professor at the NYU Courant Institute where he teaches Data Science in Quantitative Finance.
Mining News Topic Codes With Sentiment
Stories on the Bloomberg newsfeed are tagged with “topic codes” containing information about their origin, subject matter, or other characteristics. One might expect that sentiment analysis of news stories may be enhanced by taking into account these topic codes, but the sheer number of topic codes is an obstacle to doing so systematically.
In this talk, we present evidence that some groups of topic codes are indeed associated with stronger sentiment impact on stock prices than others, and discuss a method to condense the mass of topic codes by identifying and retrieving latent factors which may be interpreted as broad themes shared by groups of topic codes.