Abstract: 

How do you use open big data from the stock markets to monitor financial system risk over time? This talk presents recent theoretical and empirical research on new metrics for "systemic" risk using network theory. These metrics have useful properties and may be generated using large-scale public data. We will review essential network modeling concepts and financial theory, before examining empirical results of the model. Models also involve text mining to generate data for network construction. Implementations to financial markets in the US and India will be presented. Overall, this is a new class of financial system risk models using networks.

Bio: 

Sanjiv Das is the William and Janice Terry Professor of Finance and Data Science at Santa Clara University's Leavey School of Business. He previously held faculty appointments as Associate Professor at Harvard Business School and UC Berkeley. He holds post-graduate degrees in Finance (M.Phil and Ph.D. from New York University), Computer Science (M.S. from UC Berkeley), an MBA from the Indian Institute of Management, Ahmedabad, B.Com in Accounting and Economics (University of Bombay, Sydenham College), and is also a qualified Cost and Works Accountant (AICWA). He is a senior editor of The Journal of Investment Management, co-editor of The Journal of Derivatives and The Journal of Financial Services Research, and Associate Editor of other academic journals. Prior to being an academic, he worked in the derivatives business in the Asia-Pacific region as a Vice-President at Citibank. His current research interests include: machine learning, social networks, derivatives pricing models, portfolio theory, the modeling of default risk, systemic risk, and venture capital. He has published over ninety articles in academic journals, and has won numerous awards for research and teaching. His recent book "Derivatives: Principles and Practice" was published in May 2010 (second edition 2016). He currently also serves as a Senior Fellow at the FDIC Center for Financial Research. [See: http://srdas.github.io/]