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Activity Number: 411 - Copula Model and Maximum Likelihood Estimation
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #329239
Title: Multi-Level Time Series Clustering for Asset Selection in Allocation Problems
Author(s): Michael Kotarinos* and Christos Tsokos and Kin Doo Young
Companies: and University of South Florida and Arkansas State University
Keywords: Artificial Intelligence; Multi Criteria Decision Analysis; Equities; Multi-Level Time Series Clustering; CAPM

Multi-Level Time Series Clustering (MLTC) is a distance based technique which allows for efficient clustering of time series data. While MLTC has previously been used to study the characteristics of different sectors and form a diversified portfolio it has not been used in context of broader problems in asset allocation. In particular, there is no appealing automated procedure for the generation of portfolios based on complex preferences over risk. In this study the authors first reviewed historical literature relating to asset allocation and sector weighting with a focus on optimization under the Sharpe ratio and current practices in the industry. After briefly reviewing the relevant literature in the machine learning field and the properties of MLTC clusters, the authors proceeded to generate an alternative to portfolios generated under the CAPM framework by modifying the clustering criteria of MLTC based on a decision theoretic Multi Criteria Decision Analysis (MCDA) Framework representing preferences over risk.

Authors who are presenting talks have a * after their name.

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