Integrating Time Series with Social Media Data in an Ontology for the Modelling of Extreme Financial Events
Haizhou Qu, Marcelo Sardelich Nascimento, Nunung Nurul Qomariyah, Dimitar Kazakov · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2016
This article describes a novel dataset aiming to provide insight on the relationship between stock market prices and news on social media, such as Twitter.While several financial companies advertise that they use Twitter data in their decision process, it has been hard to demonstrate whether online postings can genuinely affect market prices.By focussing on an extreme financial event that unfolded over several days and had dramatic and lasting consequences we have aimed to provide data for a case study that could address this question.The dataset contains the stock market price of Volkswagen, Ford and the S&P500 index for the period immediately preceding and following the discovery that Volkswagen had found a way to manipulate in its favour the results of pollution tests for their diesel engines.We also include a large number of relevant tweets from this period alongside key phrases extracted from each message with the intention of providing material for subsequent sentiment analysis.All data is represented as a ontology in order to facilitate its handling, and to allow the integration of other relevant information, such as the link between a subsidiary company and its holding or the names of senior management and their links to other companies.