American Journal of Mathematical and Computer Modelling

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Portfolio Optimization for Stock Market in Ghana Using Value-at-Risk (VaR)

Received: Jun. 10, 2020    Accepted: Jun. 23, 2020    Published: Jul. 06, 2020
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Abstract

The study was conducted to identify the performing stocks as well as examine the portfolio optimization with associated Value at Risk (VaR) for some selected stocks on the Ghana Stock Exchange (GSE). A historical data of 15 companies categorized into Financial Stock Index (FSI) and Composite Index (CI) from 2000 to 2017 were obtained from Bank of Ghana (BoG), Ghana Stock Exchange (GSE) and Gold Coast Security (GCS). From the study, ETI, HFC, SIC, TOTAL, FML, UNIL and GOIL stocks were identified to be over performing on the Ghana Stock Exchange. Also, CAL, EBG, ALW, AYRTN, GOIL were identified as aggressive stocks; GCB, SCB, TOTAL, GGBL as defensive stocks; and ETI, HFC, SIC, FML, PZC, UNIL as inversely moving towards the market return. The optimal portfolio asset allocation, for the minimum VaR portfolio showed a marginal diversification in other stocks in the cases of FSI, but greater portion was invested in HFC. However, in the case of CI displayed no indication of diversification in the portfolio as 67.30% of investors invested in AYRTN and only 32.70% in the remaining securities. The study then proceeded to find the optimal portfolio with risk-free asset for both indexes. It was recommended that further study should extend the approaches used by considering Conditional Value at Risk (CVaR) as the VaR measure does not give any information about potential losses in the worst cases.

DOI 10.11648/j.ajmcm.20200503.11
Published in American Journal of Mathematical and Computer Modelling ( Volume 5, Issue 3, September 2020 )
Page(s) 61-69
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

Stock, Financial Stock Index, Composite Index, VaR, Portfolio, Optimization

References
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[3] Degutis, A., & Novickytė, L. (2014), “The efficient market hypothesis: a critical review of literature and methodology”, Ekonomika, Vol. 93, No. 2, pp. 7-23.
[4] Engels, M. (2004), Portfolio Optimisation: Beyond Markowitz, Conceptual and Practical Insights Leiden University, Netherlands, pp. 45-53.
[5] Fabozzi, F. J. and Francis, J. C. (1979), “Mutual fund systematic risk for bull and bear markets: an empirical examination”, The Journal of Finance, Vol. 34, No. 5, pp. 1243-1250.
[6] Frimpong, J. M. and Oteng-Abayie E. F. (2007), “Market Returns and weak-form efficiency: The case of the Ghana Stock Exchange”, Journal of Economics and Finance, Vol. 4, No. 3, pp. 88-96.
[7] Jarque, C. M. and Bera, A. K. (1987), “A test for normality of observations and regression residuals”, International Statistical Review, Vol. 55, pp. 163-172.
[8] Karadag, D. T. (2008), “Portfolio Risk Calculation and Stochastic Portfolio Optimisation by a Copula Based Approach”, Working Paper, Economics WPA, Bagazici University, 102 pp.
[9] Konno, H. and Yamazaki, H. (1991), “Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market”, Management science, Vol. 37, No. 5, pp. 519-531.
[10] Magnus, F. J., Fosu, E. and Oteng-Abayie, J. (2006), “Modelling and Forecasting Volatility of Returns on Ghana Stock Exchange Using Garch Models”, Am. Journal of Applied Science, Vol. 3, No. 10, pp. 2042-2048.
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[15] Sharpe, W. (1966), “Mutual Fund Performance”, The Journal of Business, Vol. 39, No. 1, pp. 119-138.
[16] Wiah, E. N., Odoi, B. and Antwi, K. O. (2018), “Asset Portfolio Optimisation of Some Selected Equities Using Geometric Mean and Semi Variance”, Ghana Journal of Technology, Vol. 2, No. 2, pp. 24–33.
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    Eric Kwame Austro Gozah, Eric Neebo Wiah, Albert Buabeng, Paul Yaw Addai Yeboah. (2020). Portfolio Optimization for Stock Market in Ghana Using Value-at-Risk (VaR). American Journal of Mathematical and Computer Modelling, 5(3), 61-69. https://doi.org/10.11648/j.ajmcm.20200503.11

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    ACS Style

    Eric Kwame Austro Gozah; Eric Neebo Wiah; Albert Buabeng; Paul Yaw Addai Yeboah. Portfolio Optimization for Stock Market in Ghana Using Value-at-Risk (VaR). Am. J. Math. Comput. Model. 2020, 5(3), 61-69. doi: 10.11648/j.ajmcm.20200503.11

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    AMA Style

    Eric Kwame Austro Gozah, Eric Neebo Wiah, Albert Buabeng, Paul Yaw Addai Yeboah. Portfolio Optimization for Stock Market in Ghana Using Value-at-Risk (VaR). Am J Math Comput Model. 2020;5(3):61-69. doi: 10.11648/j.ajmcm.20200503.11

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  • @article{10.11648/j.ajmcm.20200503.11,
      author = {Eric Kwame Austro Gozah and Eric Neebo Wiah and Albert Buabeng and Paul Yaw Addai Yeboah},
      title = {Portfolio Optimization for Stock Market in Ghana Using Value-at-Risk (VaR)},
      journal = {American Journal of Mathematical and Computer Modelling},
      volume = {5},
      number = {3},
      pages = {61-69},
      doi = {10.11648/j.ajmcm.20200503.11},
      url = {https://doi.org/10.11648/j.ajmcm.20200503.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajmcm.20200503.11},
      abstract = {The study was conducted to identify the performing stocks as well as examine the portfolio optimization with associated Value at Risk (VaR) for some selected stocks on the Ghana Stock Exchange (GSE). A historical data of 15 companies categorized into Financial Stock Index (FSI) and Composite Index (CI) from 2000 to 2017 were obtained from Bank of Ghana (BoG), Ghana Stock Exchange (GSE) and Gold Coast Security (GCS). From the study, ETI, HFC, SIC, TOTAL, FML, UNIL and GOIL stocks were identified to be over performing on the Ghana Stock Exchange. Also, CAL, EBG, ALW, AYRTN, GOIL were identified as aggressive stocks; GCB, SCB, TOTAL, GGBL as defensive stocks; and ETI, HFC, SIC, FML, PZC, UNIL as inversely moving towards the market return. The optimal portfolio asset allocation, for the minimum VaR portfolio showed a marginal diversification in other stocks in the cases of FSI, but greater portion was invested in HFC. However, in the case of CI displayed no indication of diversification in the portfolio as 67.30% of investors invested in AYRTN and only 32.70% in the remaining securities. The study then proceeded to find the optimal portfolio with risk-free asset for both indexes. It was recommended that further study should extend the approaches used by considering Conditional Value at Risk (CVaR) as the VaR measure does not give any information about potential losses in the worst cases.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Portfolio Optimization for Stock Market in Ghana Using Value-at-Risk (VaR)
    AU  - Eric Kwame Austro Gozah
    AU  - Eric Neebo Wiah
    AU  - Albert Buabeng
    AU  - Paul Yaw Addai Yeboah
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    DO  - 10.11648/j.ajmcm.20200503.11
    T2  - American Journal of Mathematical and Computer Modelling
    JF  - American Journal of Mathematical and Computer Modelling
    JO  - American Journal of Mathematical and Computer Modelling
    SP  - 61
    EP  - 69
    PB  - Science Publishing Group
    SN  - 2578-8280
    UR  - https://doi.org/10.11648/j.ajmcm.20200503.11
    AB  - The study was conducted to identify the performing stocks as well as examine the portfolio optimization with associated Value at Risk (VaR) for some selected stocks on the Ghana Stock Exchange (GSE). A historical data of 15 companies categorized into Financial Stock Index (FSI) and Composite Index (CI) from 2000 to 2017 were obtained from Bank of Ghana (BoG), Ghana Stock Exchange (GSE) and Gold Coast Security (GCS). From the study, ETI, HFC, SIC, TOTAL, FML, UNIL and GOIL stocks were identified to be over performing on the Ghana Stock Exchange. Also, CAL, EBG, ALW, AYRTN, GOIL were identified as aggressive stocks; GCB, SCB, TOTAL, GGBL as defensive stocks; and ETI, HFC, SIC, FML, PZC, UNIL as inversely moving towards the market return. The optimal portfolio asset allocation, for the minimum VaR portfolio showed a marginal diversification in other stocks in the cases of FSI, but greater portion was invested in HFC. However, in the case of CI displayed no indication of diversification in the portfolio as 67.30% of investors invested in AYRTN and only 32.70% in the remaining securities. The study then proceeded to find the optimal portfolio with risk-free asset for both indexes. It was recommended that further study should extend the approaches used by considering Conditional Value at Risk (CVaR) as the VaR measure does not give any information about potential losses in the worst cases.
    VL  - 5
    IS  - 3
    ER  - 

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Author Information
  • Mathematics Department, Dambai College of Education, Dambai, Ghana

  • Department of Mathematical Sciences, Faculty of Engineering, University of Mines and Technology, Tarkwa, Ghana

  • Department of Mathematical Sciences, Faculty of Engineering, University of Mines and Technology, Tarkwa, Ghana

  • Head, University Relations Office, University of Mines and Technology, Tarkwa, Ghana

  • Section