Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.asoc.2013.01.021
Title: Dynamic index tracking via multi-objective evolutionary algorithm
Authors: Chiam, S.C.
Tan, K.C. 
Al Mamun, A. 
Keywords: Index tracking
Multi-objective evolutionary algorithm
Portfolio rebalancing
Issue Date: Jul-2013
Citation: Chiam, S.C., Tan, K.C., Al Mamun, A. (2013-07). Dynamic index tracking via multi-objective evolutionary algorithm. Applied Soft Computing Journal 13 (7) : 3392-3408. ScholarBank@NUS Repository. https://doi.org/10.1016/j.asoc.2013.01.021
Abstract: Index tracking has been gaining in popularity in recent years, as sustainable and stable yields exceeding market returns proved to be elusive. Leveraging on the search capability of evolutionary algorithm, this paper proposed a multi-objective evolutionary index tracking platform that could simultaneously optimize both tracking performance and transaction costs throughout the investment horizon and address various real-world implementation issues in index tracking. For model evaluation, a realistic instantiation of the index tracking optimization problem that accounted for stochastic capital injections, practical transactional cost structures and other real-world constraints was formulated. Portfolio rebalancing strategies for the alignment of the tracker portfolio to time-varying market conditions were investigated also. Empirical studies based on equity indices from major global markets were conducted and the results validated the tracking capability of the proposed index tracking system in out-of-sample data sets, whilst minimizing transaction costs throughout the investment horizon. © 2013 Elsevier B.V. All rights reserved.
Source Title: Applied Soft Computing Journal
URI: http://scholarbank.nus.edu.sg/handle/10635/82186
ISSN: 15684946
DOI: 10.1016/j.asoc.2013.01.021
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