Mathematical Finance & Financial Data Science Seminar
Incorporation of Text News Analytics in Risk Assessment
Speaker: Dan diBartolomeo, Northfield Information Services, Inc.
Location: Warren Weaver Hall 1302
Date: Tuesday, October 2, 2018, 5:30 p.m.
Analytical models in finance all share some basic concepts. Financial market participants observe some period of past events they deem relevant, build a statistical model of the observed data, and then make the heroic assumption that events in the future will be like those in the past. While almost every financial institution has extensive risk modeling systems in place (as often mandated by regulators) the Global Financial Crisis has shown that such systems are frequently grossly inadequate. What is missing from nearly all models is an explicit recognition of how the present is different from the past, and therefore how the short-term future is also likely to be different from the past. By defining “news” explicitly as the information set that informs us of the differences between past and present, we can condition our estimates of the distribution of future outcomes more robustly. Building upon the methods in diBartolomeo, Mitra, and Mitra (2009), and Kyle, Obizhaeva, Sinha and Tuzun (2012), we will introduce a new approach to using quantified news flows and related sentiment scores in the prediction of asset portfolio risk. This process can operate in real time, and can address tens of thousands of global companies and financial institutions (for counterparty risk).
Bio: Dan diBartolomeo
Dan diBartolomeo is President and founder of Northfield Information Services, Inc. He sits on the board of numerous industry organizations include LQG, IAQF and CQA. His publication record includes more than thirty books, book chapters and research journal articles. In addition, Dan is a Visiting Professor at Brunel University and has been admitted as an expert witness in litigation matters regarding investment management practices and derivatives in both US federal and state courts.