CS Forum: “Non-parametric Structural Change Detection in Multivariate Systems – an application financial news analytics” Pekka Malo
17.5.2018 @ 14:15 - 15:00
Assistant Professor of Statistics
Aalto University School of Business
Host: Professor Jaakko Lehtinen
Time: 14:15 (coffee at 14:00)
Venue: T3, CS building
Non-parametric Structural Change Detection in Multivariate Systems – an application financial news analytics
Structural change detection problems are often encountered in analytics and econometrics, where the performance of a model can be significantly affected by unforeseen changes in the underlying relationships. Although these problems have a comparatively long history in statistics, the number of studies done in the context of multivariate data under nonparametric settings is still small. In this paper, we propose a consistent method for detecting multiple structural changes in a system of related regressions over a large dimensional variable space. In most applications, practitioners also do not have a priori information on the relevance of different variables, and therefore, both locations of structural changes as well as the corresponding sparse regression coefficients need to be estimated simultaneously. The method combines nonparametric energy distance minimization principle with penalized regression techniques. After showing asymptotic consistency of the model, we compare the proposed approach with competing methods in a simulation study. As an example of a large scale application, we consider structural change point detection in the context of news analytics during the recent financial crisis period.
Pekka Malo is an Assistant Professor of Statistics in the Department of Information and Service Management at Aalto University School of Business, Finland. He has a PhD degree in quantitative methods from Helsinki School of Economics and M.Sc. degree in mathematics from Helsinki University. Before joining the department he worked as a business simulation developer at Cesim and head of research at Fosta Consulting. His research interests include optimization, evolutionary computation, semantic information retrieval, computational statistics and machine learning, and their applications to marketing, finance, and healthcare.