The Influence of Data Analytics on Portfolio Optimization and Risk Management

This study examines the influence of data analytics on portfolio optimization and risk management in modern financial markets. The purpose of the research is to evaluate how analytics-driven approaches enhance investment decisionmaking compared to traditional, intuition-based methods. The research addresses the problem that, despite the availability of advanced analytical tools, many investors and financial institutions struggle to effectively integrate data analytics into portfolio construction and risk management practices.The study adopts a descriptive and analytical research design using a mixed approach. Secondary data from academic literature, financial reports, and market databases are analyzed using statistical techniques such as descriptive statistics, correlation, regression analysis, risk metrics (beta, Sharpe ratio, Value at Risk), and comparative analysis of analyticsbased versus traditional portfolios.The findings reveal that data analytics significantly improves portfolio performance by enhancing diversification, optimizing asset allocation, and generating higher risk-adjusted returns. Advanced risk management tools enable more accurate identification, measurement, and mitigation of market risks, particularly during volatile conditions. Predictive analytics and machine learning models further support proactive portfolio rebalancing and reduce behavioral biases in investment decisions.The study concludes that data analytics has transformed portfolio management into a dynamic and adaptive process, strengthening both portfolio optimization and risk control. The findings have important implications for investors, portfolio managers, and policymakers, highlighting the need to invest in analytics infrastructure, develop data-driven skills, and promote transparent and ethical use of analytical models in financial decision-making.