Words matter: News Sentiment Analysis for Financial Markets – Part II

By Svetlana Borovkova, Head of Quantitative Modelling

In the previous column, we argued that news is an important driver of financial markets, but also noted that humans cannot quickly read and process all relevant news for a large diversified portfolio. Fortunately, modern AI techniques such as Natural Language Processing, powered by fast computers, can help us to quantify the sentiment, or “tone” of thousands of news items in real time.

However, even when all this clever news interpretation by NLP is done, the work of quantitative analysts is just beginning. News sentiment data is very noisy (due to diversity of opinions and/or interpretation error), there is still a huge volume of it – it is truly “big data”, due to high frequency of news occurrence, multiple news/media sources and large number of companies, commodities and other assets about which news appears. So the analysis task is to extract a clean, interpretable signal from all these noisy data and to aggregate sentiment data over many individual assets into sentiment signals that reflect the overall “mood” of the market participants about e.g., sectors of the economy, regional or worldwide stock and commodity markets.