Mean-variance and Safety-first Portfolio Selection Utilizing Historical Returns of Forbes Asia's Fab50 Companies

Portfolio selection maximizes investment returns with acceptable risk. Mean-variance (MV) and Safety-first (SF) are two methods to achieve this goal. MV explains that an investor will choose an investment with a high return over another if it has the same risk. In contrast, SF focuses on minimizing the investment loss by establishing a loss threshold for the portfolio. This study presents a framework for selecting the portfolio that could outperform the benchmark using MV and SF methods and utilizing the historical returns of Forbes Asia's Fab50 companies. Back-test shows that portfolios have the potential to earn twice as much as the benchmark using MV, but these have high standard deviation or risk. Compared to MV models, SF models are observed with lower risk. Both MV and SF models were found to have exceeded the p-value criterion, indicating that these were unable to outperform the benchmark. Nevertheless, this study found an acceptable portfolio with a marginal p-value but high investment return using one of the MV models. This study serves as a reference for Operation Research application in Finance.