Stock Market Analysis is of interest to many investors, economists, and financial engineers. This lecture is twofold. First, we derive the closed form weight vector solution of the Markowitz Portfolio Optimization problem using Convex Optimization tools, which enables us to highlight potential assets or stocks within our selected portfolio. Second, we use this essential equation to build our Markowitz Portfolio solver on Python. For the sake of demonstration, we compare the outcome of our solver versus the one given by SciPy’s minimize solver (which uses Sequential Least Squares for Quadratic Programs) and discuss the advantages/disadvantages of both solvers (i.e. our solver vs SciPy). This lecture is outlined as follows:

⏲Outline⏲
00:00​ Introduction
00:47​ Markowitz Portfolio Optimization Problem (a recap)
03:08​ Lagrangian Function
05:38​ Optimal Weights
11:11​ Lagrangian Multiplier Solutions
21:35​ Our Portfolio Solver Equation
22:17​ Python Implementation: SciPy approach (method 1)
33:36​ Python Implementation: Our Solver (method 2)
37:28​ Comparisons: SciPy Solver vs Our Solver
41:00​ Summary
41:40​ Outro

Instructor: Dr. Ahmad Bazzi

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