Mean-Variance Portfolio Optimization with Lot Size Constraints in Energy Stocks: A Monte CarloApproach

Stock investment requires portfolio optimization strategies that maximize returns and consider risks and practical constraints, such as target lot sizes. These constraints are crucial to ensuring the realistic implementation of portfolios in compliance with market regulations, particularly in Indonesia, where 1 lot equals 100 shares. However, existing research on the Mean-Variance model and Monte Carlo simulation has rarely incorporated target lot constraints, limiting the applicability of these models in real-world scenarios. To bridge this gap, this study conducts a systematic literature review (SLR) on portfolio optimization in Indonesia's energy sector stocks, focusing on the Mean-Variance model, risk aversion, Monte Carlo simulation, and target lot constraints. The PRISMA framework guides this SLR, with bibliometric analysis performed using RStudio. A rigorous selection process from Scopus and ScienceDirect databases yielded 13 relevant articles for in-depth analysis creates a more practical and effective approach to portfolio management. This advancement enables investors to achieve balanced portfolios that are both theoretically robust and feasible in practice. The study contributes significantly to optimizing investment strategies for Indonesia’s energy sector and opens avenues for further research into practical portfolio optimization methods.