PRMIA NY Event Re-cap - Data Science and Global Risk

PRMIA and Columbia University in January offered a joint event that brought practitioners and academics together to discuss how modern data science methods can be applied to quantify macroeconomic and political risk for investment management. The event was hosted bythe Program for Financial Studies at Columbia Business School and the Columbia University Data Science Institute.

Prof. Paul Glasserman gave an introductory talk describing how the Data Science Institute at Columbia University provides a channel for researchers from different disciplines to collaborate and advance modern computational and modeling techniques. 

The keynote speaker Prof. Harry Mamaysky described the use of Natural Language Processing methods in analyzing news articles effects on markets and the way these impacts differ between developed and emerging economies. (Presentation)

Another keynote speaker Tsveta Petrova talked how political risk can be a basis for investment decisions and what type of economic activities policies may affect.
 
After a break Eugene Neduv presented Network Analysis and graph theory for understanding interconnectedness in contemporary economies.

Prof. Sharyn O’Halloran continued describing applications of network analysis for financial system stability measures and regulatory practices.  (Paper)

The panel discussions, chaired by Ron D'Vari, Executive Chairman of NewOak, focused on how various organizations approach systemic risk scenarios.

During the closing panel discussion Michelle Tuveson described Cambridge Centre for Risk Studies work on combining 22 different risk scenarios to build an index of City Risk.  Economist Gideon Magnus and Mansour Haroun talked about challenges of quantifying scenarios such as elections, climate change, tax policies and population growth.  Ron D’ Vari discussed the difficulties of gathering comprehensive relative and absolute data for assessing muni credit risk incorporating economic, financial, demographic and political structure.