Detecting Causal Links between Financial News and Stocks

This article describes a novel framework for the detection of causal links between financial news and the subsequent movements of the stock market. The approach builds on and substantially improves a previously published in-house design for the detection and measurement of correlation between news and time series in the financial domain, which has been used here to detect a predictive causality relationship from news to prices and volumes of trade. While the original framework makes use of matrices of pairwise distances between companies, one based on news, the other - on financial performance, in order to produce a single measure of correlation between these two types of information for all traded companies, this article shows how the company contributing the most to the news-to-price/volume causal link can be singled out. The potential benefits of such information are made clear through its use in a straight-forward trading strategy, the results of which compare favourably to two strong, real-life alternatives that only make use of the time series.