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Strategic Decision Making in Portfolio Management with Goal Programming Model

Received: 23 October 2016    Accepted: 12 November 2016    Published: 12 December 2016
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Abstract

Enhanced index tracking is a popular type of portfolio management which aims to construct the optimal portfolio in order to generate higher portfolio mean return than the benchmark index mean return. Enhanced index tracking is a dual objective optimization problem which can be represented by a goal programming model to determine the trade-off between maximizing the portfolio mean return and minimizing the tracking error. The objective of this paper is to apply the goal programming model in constructing the optimal portfolio to track the Technology Index in Malaysia. In this study, the data consists of weekly return of the companies from technology sector in Malaysia stock market. The results of this study indicate that the optimal portfolio is able to outperform Technology Index by generating weekly excess mean return 0.3798% at minimum tracking error 2.0980%. The significance of this study is to identify and apply the goal programming model as a strategic decision-making tool for the fund managers to track the benchmark Technology Index effectively in Malaysia stock market.

Published in American Journal of Operations Management and Information Systems (Volume 1, Issue 1)
DOI 10.11648/j.ajomis.20160101.14
Page(s) 34-38
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Goal Programming Model, Enhanced Index Tracking, Optimal Portfolio, Mean Return, Tracking Error

References
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[2] R. Roll, “A mean variance analysis of tracking error,” The Journal of Portfolio Management, vol. 18, pp. 13-22, 1992.
[3] L. C. Wu, S. C. Chou, C. C. Yang, and C. S. Ong, “Enhanced Index Investing Based on Goal Programming,” The Journal of Portfolio Management, vol. 33, pp. 49-56, 2007.
[4] N. A. Canakgoz, and J. E. Beasley, “Mixed integer programming approaches for index tracking and enhanced indexation,” European Journal of Operational Research, vol. 196, pp. 384-399, 2008.
[5] G. Guastaroba, and M. G. Speranza, “Kernel Search: An application to index tracking problem,” European Journal of Operational Research, vol. 217, pp. 54-68, 2012.
[6] W. S. Lam, J. Saiful, and I. Hamizun, “The impact of different economic scenarios towards portfolio selection in enhanced index tracking problem,” Advanced Science Letters, vol. 21, no. 5, pp. 1285-1288, 2015.
[7] W. S. Lam, J. Saiful, and I. Hamizun, “An empirical comparison of different optimization models in enhanced index tracking problem,” Advanced Science Letters, vol. 21, no. 5, pp. 1278-1281, 2015.
[8] W. S. Lam, J. Saiful, and I. Hamizun, “The impact of human behavior towards portfolio selection in Malaysia.” Procedia of Social and Behavioral Sciences, vol. 172, pp. 674-678, 2015.
[9] N. Meade, and J. E. Beasle, “Detection of momentum effects using an index out-performance strategy,” Quantitative Finance, vol. 11, no. 2, 313-326, February 2011.
[10] L. C. Wu, and L. H. Wu, “Tracking a benchmark index – using a spreadsheet-based decision support system as the driver,” Expert Systems, vol. 30, pp. 79-88, 2012.
[11] W. S. Lam, and W. H. Lam, “Portfolio optimization for index tracking problem with mixed integer programming model,” Journal of Scientific Research and Development, vol. 2, no. 10, pp. 5-8, 2015.
[12] Bursa Malaysia, n.d.. Company Announcements | Bursa Malaysia Market. [online] Available at: < http://www.bursamalaysia.com/market/securities/equities/prices/#/?filter=BS08&board=MAIN-MKT&sector=TECHNOLOGY&page=1>
[13] L. J. Gitman, M. D. Joehnk, and L. J. Smart, Fundamentals of Investing, 11th ed, Pearson, 2011.
[14] W. S. Lam, and W. H. Lam, “Selection of mobile telecommunications companies in portfolio optimization with mean-variance model,” American Journal of Mobile Systems, Applications and Services, vol. 1, no. 2, pp. 119-123, 2015.
[15] LINGO, Version 12. Chicago: LINDO Systems Inc, 2010.
[16] H. A. Taha, Operations Research:An Introduction. 9th ed, New Jersey, Prentice Hall, 2011.
[17] W. S. Lam, J. Saiful, and I. Hamizun, “Comparison between Two Stage Regression Model and Variance Model in Portfolio Optimization,” Journal of Applied Science and Agriculture, vol. 9, no. 18, pp. 36-40, 2014.
[18] W. S. Lam, J. Saiful, and I. Hamizun, “Index tracking modelling in portfolio optimization with mixed integer linear programming,” Journal of Applied Science and Agriculture, vol. 9, no. 18, pp. 47-50, 2014.
[19] N. Meade, and G. R. Salkin, “Developing and Maintaining an Equity Index Fund,” Journal of Operation Research Society, vol. 41, no. 7, pp. 599-607, 1990.
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Cite This Article
  • APA Style

    Lam Weng Siew, Lam Weng Hoe. (2016). Strategic Decision Making in Portfolio Management with Goal Programming Model. American Journal of Operations Management and Information Systems, 1(1), 34-38. https://doi.org/10.11648/j.ajomis.20160101.14

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    ACS Style

    Lam Weng Siew; Lam Weng Hoe. Strategic Decision Making in Portfolio Management with Goal Programming Model. Am. J. Oper. Manag. Inf. Syst. 2016, 1(1), 34-38. doi: 10.11648/j.ajomis.20160101.14

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    AMA Style

    Lam Weng Siew, Lam Weng Hoe. Strategic Decision Making in Portfolio Management with Goal Programming Model. Am J Oper Manag Inf Syst. 2016;1(1):34-38. doi: 10.11648/j.ajomis.20160101.14

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  • @article{10.11648/j.ajomis.20160101.14,
      author = {Lam Weng Siew and Lam Weng Hoe},
      title = {Strategic Decision Making in Portfolio Management with Goal Programming Model},
      journal = {American Journal of Operations Management and Information Systems},
      volume = {1},
      number = {1},
      pages = {34-38},
      doi = {10.11648/j.ajomis.20160101.14},
      url = {https://doi.org/10.11648/j.ajomis.20160101.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajomis.20160101.14},
      abstract = {Enhanced index tracking is a popular type of portfolio management which aims to construct the optimal portfolio in order to generate higher portfolio mean return than the benchmark index mean return. Enhanced index tracking is a dual objective optimization problem which can be represented by a goal programming model to determine the trade-off between maximizing the portfolio mean return and minimizing the tracking error. The objective of this paper is to apply the goal programming model in constructing the optimal portfolio to track the Technology Index in Malaysia. In this study, the data consists of weekly return of the companies from technology sector in Malaysia stock market. The results of this study indicate that the optimal portfolio is able to outperform Technology Index by generating weekly excess mean return 0.3798% at minimum tracking error 2.0980%. The significance of this study is to identify and apply the goal programming model as a strategic decision-making tool for the fund managers to track the benchmark Technology Index effectively in Malaysia stock market.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Strategic Decision Making in Portfolio Management with Goal Programming Model
    AU  - Lam Weng Siew
    AU  - Lam Weng Hoe
    Y1  - 2016/12/12
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ajomis.20160101.14
    DO  - 10.11648/j.ajomis.20160101.14
    T2  - American Journal of Operations Management and Information Systems
    JF  - American Journal of Operations Management and Information Systems
    JO  - American Journal of Operations Management and Information Systems
    SP  - 34
    EP  - 38
    PB  - Science Publishing Group
    SN  - 2578-8310
    UR  - https://doi.org/10.11648/j.ajomis.20160101.14
    AB  - Enhanced index tracking is a popular type of portfolio management which aims to construct the optimal portfolio in order to generate higher portfolio mean return than the benchmark index mean return. Enhanced index tracking is a dual objective optimization problem which can be represented by a goal programming model to determine the trade-off between maximizing the portfolio mean return and minimizing the tracking error. The objective of this paper is to apply the goal programming model in constructing the optimal portfolio to track the Technology Index in Malaysia. In this study, the data consists of weekly return of the companies from technology sector in Malaysia stock market. The results of this study indicate that the optimal portfolio is able to outperform Technology Index by generating weekly excess mean return 0.3798% at minimum tracking error 2.0980%. The significance of this study is to identify and apply the goal programming model as a strategic decision-making tool for the fund managers to track the benchmark Technology Index effectively in Malaysia stock market.
    VL  - 1
    IS  - 1
    ER  - 

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Author Information
  • Faculty of Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia; Centre for Mathematical Sciences, Centre for Business and Management, Universiti Tunku Abdul Rahman, Kampar, Malaysia

  • Faculty of Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia; Centre for Mathematical Sciences, Centre for Business and Management, Universiti Tunku Abdul Rahman, Kampar, Malaysia

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