Portfolios under Different Methods and Scenarios: A Case of Fiji’s South Pacific Stock Exchange
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".