Markowitz Portfolio Optimization in Stock Market Analysis

The following lecture talks about the Markowitz Portfolio Optimization problem in convex optimization. Indeed, many variants of this problem exists, but the classical one looks like this


    \begin{equation*}\begin{aligned}& \underset{x}{\text{minimize}}& & x^T \Sigma x \\ & \text{subject to}& & \bar{p}^T x \geq r_{min} \\& & & x \geq 0 \\ & & & 1^T x = 1 \end{aligned}\end{equation*}


where x is an n-sized vector containing the amount of assets to invest in. The vector \bar{p} is the mean of the relative price asset change and the matrix \Sigma is the matrix of variance-covariance of assets. The parameter r_{min} is minimum accepted returns.

Learn more about the above problem and its application to the stock market by watching the above lecture.


Ahmad Bazzi

Ahmad Bazzi is an Electrical Engineer and YouTuber. Ahmad Bazzi is a signal processing engineer at CEVA DSP. He holds a PhD in Electrical Engineering from EURECOM. Ahmad Bazzi is a EURECOM alumni. Ahmad Bazzi obtained his Master studies (Summa Cum Laude) in Electrical Engineering at SUPELEC, Paris. Ahmad Bazzi has many publications in well-known IEEE conferences, including a nomination award and is a co-inventor in several patents. Ahmad Bazzi dedicates time to publish high level lectures on Mathematics (including convex optimization) and Programming. Ahmad Bazzi also focuses on Machine Learning, Convex Optimization, Linear Algebra, Python, SymPy, NumPy, Pandas, CVXOPT, MATLAB, C++ for firmware systems.

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