Strategic Decision Analysis for Investment Portfolios: Computational Risk Assessment in Transportation Asset Management

Abstract

This study presents a strategic decision-making framework for portfolio investment by applying computational risk assessment methods to the transportation sector in Indonesia. Focusing on two key players—PT. Adi Sarana Tbk (ASSA) and PT. Blue Bird Tbk (BIRD)—the research evaluates their individual and combined risk-return profiles using daily stock return data from December 2023 to November 2024. A hybrid analytical approach is employed, integrating traditional financial metrics with advanced computational techniques such as Monte Carlo simulation and Value at Risk (VaR) modeling to support managerial decision-making. The analysis reveals contrasting performance patterns: BIRD exhibits marginally positive expected returns (0.04%) but higher downside risk exposure, whereas ASSA shows negative average returns (-0.06%) yet demonstrates lower volatility and reduced extreme loss potential. Portfolio optimization results demonstrate that a diversified allocation of 60% ASSA and 40% BIRD generates improved risk-adjusted returns, achieving a positive expected return while maintaining lower overall risk compared to the individual assets. By incorporating monetary VaR estimates, this study enhances the practical relevance of risk analytics for portfolio managers, particularly in navigating volatile emerging markets. The findings underscore the importance of strategic asset allocation, business model differentiation, and quantitative risk modeling in constructing resilient investment portfolios. This research contributes both methodologically and managerially by offering a robust, replicable framework for strategic decision analysis in transportation asset management and beyond.