Optimisation Techniques in Investment Risk Management :Scipy, SLSQP and Modern Portfolio Theory

Organizations achieve successful investment risk management by achieving optimal risk exposures and return objectives at the same time. The research examines optimization methodologies through its comparison of SciPy’s optimization suite to SLSQP while incorporating Modern Portfolio theory. This research analyzes historical financial data to examine both risk-adjusted return metrics along with computational efficiency metrics. The research study provides detailed mathematical definitions about optimization procedures and performs quantitative assessments on data output and suggests future work that integrates machine learning with economic domains. The research demonstrates that these optimization approaches present critical methods to achieve maximum portfolio diversity.