Please use this identifier to cite or link to this item:
|Title:||Dynamic index tracking via multi-objective evolutionary algorithm|
Al Mamun, A.
Multi-objective evolutionary algorithm
|Source:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 28, 2018
WEB OF SCIENCETM
checked on Feb 19, 2018
checked on Mar 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.