Predicting Adverse Media Risk using a Heterogeneous Information Network
The media plays a central role in monitoring powerful institutions and identifying any activities harmful to the public interest. In the investing sphere constituted of 46,583 officially listed domestic firms on the stock exchanges worldwide, there is a growing interest “to do the right thing”, i.e., to put pressure on companies to improve their environmental, social and government (ESG) practices. However, how to overcome the sparsity of ESG data from non-reporting firms, and how to identify the relevant information in the annual reports of this large universe? Here, we construct a vast heterogeneous information network that covers the necessary information surrounding each firm, which is assembled using seven professionally curated datasets and two open datasets, resulting in about 50 million nodes and 400 million edges in total. Exploiting this heterogeneous information network, we propose a model that can learn from past adverse media coverage patterns and predict the occurrence of future adverse media coverage events on the whole universe of firms. Our approach is tested using the adverse media coverage data of more than 35,000 firms worldwide from January 2012 to May 2018. Comparing with state-of-the-art methods with and without the network, we show that the predictive accuracy is substantially improved when using the heterogeneous information network. This work suggests new ways to consolidate the diffuse information contained in big data in order to monitor dominant institutions on a global scale for more socially responsible investment, better risk management, and the surveillance of powerful institutions.
Adverse media coverage sometimes leads to fatal results for a company. In the press release sent out by Cambridge Analytica on May 2, 2018, the company wrote that “Cambridge Analytica has been the subject of numerous unfounded accusations, ... media coverage has driven away virtually all of the company’s customers and suppliers” . This is just one recent example highlighting the impact of adverse media coverage on a firm’s fate. In another example, the impact of adverse media coverage on Swiss bank profits was estimated to be 3.35 times the median annual net profit of small banks and 0.73 times that of large banks . These numbers are significant, indicating how adverse media coverage can cause huge damage to a bank. Moreover, a new factor, priced as the “no media coverage premium” , has been identified to help explain financial returns: stocks with no media coverage earn higher returns than stocks with high media coverage. Within the rational-agent framework, this may result from impediments to trade and/or from lower investor recognition leading to lower diversification . Another mechanism could be associated with the fact that most of the coverage of mass media is negative [15, 23].