Econometric, Signal Processing and Big Data Perspective in Marketing and Finance Research

Director of Communication and Signal Processing Applications Laboratory (CASPAL), Department of Electrical & Computer Engineering; Professor of Finance, Ted Rogers School of Management, Ryerson University, Canada.

Can short-term (or temporary) marketing efforts generate long-lasting (or persistent) market performance effects, and whether and how should marketing managers budget for short-term and long-term spending? How is the security risk changing over time? Does past data represent future in financial markets? These are important questions continuously challenging marketing managers, quantitative hedge fund PMs and market participants. In this talk, we will give a brief overview and examples on how we use econometric and signal processing models, machine learning, and big data statistics to research these emerging questions.