Consortium for Data Analytics in Risk

CDAR's First Year, Looking Forward
Symposium Opening slides 2016
Contributors:

• Jessica Donohue, State Street
• Lisa Goldberg, CDAR

Incorporation of Text News Analytics in Risk Assessment
Dan's Symposium slides 2016
Dan DiBartolomeo, President, Northfield

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.

Craig Boutilier, Principal Scientist, Google

Overview of several techniques for solving large-scale reinforcement learning problems of the type that might commonly arise in advertising and recommendation contexts. We place special emphasis on techniques that exploit the data and models that are used for traditional myopic prediction of user behavior (e.g., CTR) to readily construct policies that optimize long-term, cumulative versions of these metrics. We outline challenges and potential solutions that arise in model-free RL in such settings, and derive novel new model-based techniques for the solution of large factored Markov decision processes.

What Can Statistical Methods do (or not do) for Finance?

Modern statistical methods hold much promise for researchers and academics concerned with the measurement and management of financial risk. But as in classical statistics, understanding the confines in which a particular method is useful can be crucial. In this interactive session, we will explore some successes and limitations of statistical methods with examples that touch upon bias, inference, forecasting and related themes.

Contributors:

• Alex Papanicolaou, CDAR
• Alex Shkolnik, CRMR

On Computational Thinking, Inferential Thinking and Data Science
Mike Jordan's Symposium Slides 2016

Mike Jordan, Professor, UC Berkeley

The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in Big Data is apparent from their sharply divergent nature at an elementary level---in computer science, the growth of the number of data points is a source of complexity that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of simplicity in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as runtime in core statistical theory and the lack of a role for statistical concepts such as risk in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, and methods for trading off the speed and accuracy of inference.

ESG Discussion
Moderator: Jeff Bohn, State Street

This ESG panel will look at issues related to data, methodologies and performance as related to portfolio risk modeling and portfolio performance. In particular, the panel will drill into the environment, social and governance issues separately to consider which themes will have more or less impact on financial portfolios.
Contributors:

• Liz Michaels, Aperio
• Lloyd Kurtz, Wells Fargo & UC Berkeley
• Ronald Cohen, UC Berkeley

Closing Remarks

Bob Anderson, CDAR