Abstract:
In the realm of finance, portfolio management plays an important role in linking financial
goals to market uncertainties. This study examines how optimizing asset allocation through
Monte Carlo Simulation (MCS) can strengthen and maintain investment portfolios.
Traditional securities portfolio management models, such as the modern Securities Portfolio
Theory (MPT) and the capital Asset Pricing Model (CAPM), often struggle to take into
account the complexities of modern markets characterized by increased volatility and global
interconnectedness.
The study examines how MCS can transform portfolio management by offering a detailed
view of investment results through statistical probability analysis. By modelling market
scenarios, MCS allows portfolio managers to assess the risks and returns associated with
asset allocation strategies, thereby improving the investment decision-making process.
From the point of view of methodology, this study uses an approach based on the analysis of
investment data from the Unified Accumulative Pension Fund of Kazakhstan (UAPF) for the
past ten years. This research uses historical data on returns by asset class to build a
correlation matrix and simulate 10,000 market scenarios, predict possible outcomes and
evaluate risk indicators such as cost at risk (VaR) and conditional cost at risk (CVaR). The
results obtained indicate that portfolios optimized using MCS demonstrate risk-adjusted
returns compared to portfolios managed using other approaches.
The study shows that the use of Monte Carlo simulation is useful for improving investment
portfolios, as it allows you to get an idea of the relationship between risk and profitability.
This method helps to navigate the uncertainty in the market, which leads to the creation of
effective investment portfolios. The results show how MCS can improve planning and
management by offering suggestions for integrating this advanced analytical method into
real-world portfolio management practices
