Please join the Institute for Financial Services Analytics for a presentation by Dr. Vahid Tarokh, Rhodes Family Professor of Electrical & Computer Engineering, Computer Science & Mathmatics, Duke University.  His topic will be Kinetic Approach to Financial Predication and Optimization.

Kinetic Prediction is a new method of predicting the outcomes of an observation. This method is created as an amalgam of Expert Learning and Model Matching Theories and is inspired by Kolmogorov-Tikhomirov Geometric \epsilon-Entropy ideas. Kinetic Prediction can be used to address problems in the area of finance. In his talk, Dr. Tarokh will demonstrate the performance of the Kinetic Prediction method on a generalization of the GARCH model and show its superior performance on predicting volatilities in the S&P 500. Additionally, he will demonstrate its application to co-integration of Gold and Silver prices and demonstrate its superior performance to classical approaches. Finally, Dr. Tarokh will discuss his efforts on optimizing unknown time-varying objective functions using online data (with unknown time-varying statistics). He will demonstrate the application of Kinetics to optimizing portfolios in this setting, and demonstrate that it converges to the performance in full knowledge cases. This talk is based on joint work Dr. Tarokh has done with Professor Jie Ding of the University of Minnesota’s School of Statistics and Dr. Robert Ravier of Duke University’s Mathematics department.

Vahid Tarokh received the Ph.D. degree in Electrical Engineering from the University of Waterloo, Ontario, Canada, in 1995. He worked at AT&T Labs-Research and AT&T Wireless Services until August 2000 as Member, Principal Member of Technical Staff and, finally, as the Head of the Department of Wireless Communications and Signal Processing. In September 2000, he joined the Massachusetts Institute of Technology (MIT) as an Associate Professor of Electrical Engineering and Computer Science. In June 2002, he joined Harvard University as a Gordon McKay Professor of Electrical Engineering and Hammond Vinton Hayes Senior Research Fellow. He was named Perkins Professor of Applied Mathematics and Hammond Vinton Hayes Senior Research Fellow of Electrical Engineering in 2005. In Jan 2018, He joined Duke University, as the Rhodes Family Professor of Electrical and Computer Engineering, Computer Science, and Mathematics. From Jan 2018 to May 2018, He was also a Gordon Moore Distinguished Scholar in the California Institute of Technology (CALTECH). His current research area is in representation, modeling, inference and prediction from data. Some of his current projects are determining how different people will respond to exposure to certain viruses, predicting rare events from small amounts of data, formulation and calculation of limits of learning from observations, and prediction of a macaque monkey's future actions from its brain waves.