By the same authors

Detecting Causal Links between Financial News and Stocks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Full text download(s)

Author(s)

Department/unit(s)

Publication details

Title of host publicationProceedings of IEEE Conference on Computational Intelligence for Financial Engineering and Economics
DateAccepted/In press - 14 Feb 2019
Number of pages9
Place of PublicationShenzhen, China
Original languageEnglish

Abstract

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.

    Research areas

  • financial forecasting, financial news, stock prices, Granger causality

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations