Topic modelling for mining financial sentiment – extracting topics from the FED

The monetary policy implemented by the Federal Reserve has a significant effect on the economy. Though these policies are usually intended long term, information released by the FED can also have an immediate impact on asset prices, as the market reacts to their statements instantaneously. In this presentation, we are going to take a look at how unsupervised Natural Language Processing techniques can help us quickly gain insights into the contents of the speeches and statements released by the FED. In particular, we will look at Topic Modelling to extract the relative importance of topics discussed, study their trends over time, as well as look at how separating the corpus per topic can provide us with enhanced sentiment signals. Through this use case, I hope to present several techniques that can be applied when looking to extract trends and insights from a large set of unlabelled documents – a common real-world scenario.

Tóth Máté
Data Scientist, BlackRock

Máté is a Data Scientist at BlackRock working on enhancing investment models with NLP features and alternative data sources. Prior to his role at BlackRock, Máté completed a PhD in computational phonetics at the University of the Basque Country. He also holds an MSc in Electrical Engineering from INSA de Lyon.