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Portfolios under Different Methods and Scenarios: A Case of Fiji’s South Pacific Stock Exchange

Author

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  • Ronald Ravinesh Kumar

    (Department of Economics and Finance, School of Business and Management, RMIT Vietnam, Saigon South Campus, 702 Nguyen Van Linh, District 7, Ho Chi Minh City 700000, Vietnam)

  • Peter Josef Stauvermann

    (Department of Global Business and Economics, Changwon National University, Changwon 51-140, Republic of Korea)

Abstract

In this study, we analyze portfolio performance under different methods and scenarios for the small island economy of Fiji. In addition to documenting the historical performance and the smallness of the stock market, the study looks at the possibility of opting for an equally weighted (naïve) portfolio against market and minimum variance portfolios. To this end, we extract monthly stock price data of 17/19 listed companies from August 2019 to July 2022 and invoke different approaches to develop portfolios under different scenarios. We consider the mean-variance, minimum variance, semi-variance, utility maximization, and minimum turbulence portfolios, based on beta-adjusted (CAPM-based) returns. The different portfolios presented in the study should provide some insights on asset allocation in Fiji’s stock market. Interestingly, unlike average returns, the beta-adjusted returns indicate that an equally weighted portfolio can yield relatively higher expected returns than market portfolios, although, with a relatively higher standard deviation and lower Sharpe ratio than the optimized results. In a semi-variance analysis (where we account for downside risk only), equally weighted portfolio yields superior returns, albeit with a relatively lower Sortino ratio. Given that Fiji’s stock market is currently a small, with a relatively small number of listed companies, potential and less sophisticated investors and analysts considering portfolios based on beta-adjusted returns, may simply opt for 1/N (naïve) portfolios as a diversification strategy while realizing decent expected returns. The optimized portfolio under mean-variance, semi-variance, and utility are presented as alternative considerations for nuanced investors. Additionally, equally weighted turbulence-adjusted and minimum-turbulence portfolios are constructed to capture periods of unusualness and calmness in the market. The methodologies and the results presented can be adjusted and applied to other small markets and hence can influence investment decisions of investors in creating diversified portfolios under different scenarios.

Suggested Citation

  • Ronald Ravinesh Kumar & Peter Josef Stauvermann, 2022. "Portfolios under Different Methods and Scenarios: A Case of Fiji’s South Pacific Stock Exchange," JRFM, MDPI, vol. 15(12), pages 1-27, November.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:549-:d:983004
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    References listed on IDEAS

    as
    1. Shasnil Avinesh Chand & Ronald Ravinesh Kumar & Peter Josef Stauvermann, 2021. "Determinants of bank stability in a small island economy: a study of Fiji," Accounting Research Journal, Emerald Group Publishing Limited, vol. 34(1), pages 22-42, January.
    2. Safdar Ullah Khan & Satyanarayana Ramella & Habib Ur Rahman & Zulfiqar Hyder, 2022. "Household Portfolio Allocations: Evidence on Risk Preferences from the Household, Income, and Labour Dynamics in Australia (HILDA) Survey Using Tobit Models," JRFM, MDPI, vol. 15(4), pages 1-13, April.
    3. Candauda Arachchige Saliya, 2022. "Stock market development and nexus of market liquidity: The case of Fiji," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4364-4382, October.
    4. Ronald Ravinesh Kumar & Peter Josef Stauvermann & Aristeidis Samitas, 2022. "An Application of Portfolio Mean-Variance and Semi-Variance Optimization Techniques: A Case of Fiji," JRFM, MDPI, vol. 15(5), pages 1-25, April.
    5. Rajni Mala & Michael White, 2009. "The South Pacific Stock Exchange: Is it a Market or Status Symbol?," Australian Accounting Review, CPA Australia, vol. 19(1), pages 54-63, March.
    6. Sebastian Stockl & Michael Hanke, 2014. "Financial Applications of the Mahalanobis Distance," Applied Economics and Finance, Redfame publishing, vol. 1(2), pages 78-84, November.
    7. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    8. Tihana Škrinjarić & Derick Quintino & Paulo Ferreira, 2021. "Transfer Entropy Approach for Portfolio Optimization: An Empirical Approach for CESEE Markets," JRFM, MDPI, vol. 14(8), pages 1-12, August.
    9. Fahrenwaldt, Matthias A. & Sun, Chaofan, 2020. "Expected utility approximation and portfolio optimisation," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 301-314.
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