Data Science for Portfolio Optimization: Markowitz Mean-Variance Theory

This story was originally published on HackerNoon at: https://hackernoon.com/data-science-for-portfolio-optimization-markowitz-mean-variance-theory. The theory formulates a mathematical model to optimize the asset allocations to gain the maximum return for a given risk-level. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #asset-management, #modern-portfolio-theory, #portfolio-optimization, #markowtiz-mean-variance, #what-is-the-markowitz-theory, #portfolio-theory, #investment-portfolio-tips, and more. This story was written by: @kustarev. Learn more about this writer by checking @kustarev's about page, and for more stories, please visit hackernoon.com. An investment portfolio comprises various assets such as stocks and bonds. Every investor starts with a fixed investment capital and decides how much to invest in each asset. Data science techniques such as the Markowitz mean-variance theory help determine the optimal share allocation to build the optimal portfolio. The theory formulates a mathematical model to optimize the asset allocations to gain the maximum return for a given risk-level. It analyzes different financial assets and considers their rate of return and risk factors, given their historical trends. The rate of return is an approximation of how much profit the asset will generate over a given time period. The risk factor is quantified using the standard deviation of the asset value. A higher deviation represents a volatile asset and, hence, higher risk. The return and risk values are calculated for various portfolio combinations and are represented on the efficient frontier curve. The curve helps investors determine the highest returns against their selected risk.