Balancing Risk and Return in High-Volume and High-Value Stocks: A Retrospective Analysis of Mean-Variance and Safety-First Portfolio Strategies
Year: 2024
Type: conference-paper
Abstract: This study analyzed the risk and returns of high-volume and high-value traded stocks on the Philippine Stock Exchange (PSE) from 1993 to 2022 using Mean-Variance (MV) and Safety-First (SF) models. Utilizing 30 years of daily trading data, we constructed portfolios based on specified volume and price criteria and optimized them using Python and Gurobi. The MV model aimed to balance risk and return, while the SF model focused on minimizing the probability of negative returns. Our results indicated distinct performance patterns across the portfolios. The MV model generally provided more consistent returns with a balanced risk-reward approach, whereas the SF model exhibited higher risk-reward profiles, often resulting in higher returns but with increased exposure to potential losses. A comparative analysis highlighted significant variability in portfolio performance due to changing market conditions, underscoring the importance of flexibility in portfolio management. The MV model's performance suggested that a balanced approach could yield steady gains, making it suitable for investors with moderate risk tolerance. In contrast, the SF model's higher returns were attractive to more risk-tolerant investors who could withstand potential downturns. The findings offered actionable insights for risk-averse investors by emphasizing the critical role of understanding trading volumes, stock price fluctuations, and market dynamics in crafting effective investment strategies. By comparing the two models, this research demonstrated how different approaches to risk management could significantly impact investment outcomes. It suggested that while both models had merits, investors needed to align their choice of model with their risk tolerance and investment goals. Ultimately, this study contributed to a deeper understanding of portfolio optimization in the PSE, providing valuable guidance for investors navigating this emerging market.
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4409869896