<![CDATA[This study presents an engineering-based approach to investment modeling in the context of emerging markets, with a focus on portfolio management strategies in the Indonesian equity market. By integrating computational finance techniques and decision analytics, the research develops a structured framework for assessing risk-return dynamics and optimizing asset allocation decisions. Using daily stock return data from two representative companies across different economic sectors—PT Telekomunikasi Indonesia Tbk (TLKM.JK) from telecommunications and PT Petrosea Tbk (PTRO.JK) from energy/mining—the study applies Monte Carlo simulation, Value at Risk (VaR), and statistical decision modeling to evaluate both individual and combined investment performance. Results indicate distinct risk-return profiles, where PTRO.JK offers higher average returns but with significantly greater volatility compared to TLKM.JK. A cross-sector portfolio consisting of 60% TLKM.JK and 40% PTRO.JK demonstrates notable diversification benefits, reducing overall portfolio risk as evidenced by improved VaR estimates. For a hypothetical IDR 100 million investment, the diversified portfolio reduces maximum expected loss by more than 50% compared to investing solely in TLKM.JK. These findings highlight the effectiveness of quantitative, model-driven approaches in supporting strategic investment decisions. By bridging financial engineering methodologies with practical portfolio management needs in Indonesia, this study contributes a replicable framework that enhances decision-making under uncertainty, particularly for investors seeking to balance growth potential with downside risk control in multi-sector allocations.]]>
