ETF Correlation Analysis

In portfolio theory, one of the important aspects of effective portfolio building is using uncorrelated, or less correlated in reality, assets. On the other hand, since cost is one of the investment fundamental principles, index fund as well as low cost ETF are then of the main vehicles to create an efficient portfolio. In this project, we are going to look through ETFs available in Hong Kong stock market, and try to find out the correlation coefficients of different combinations of ETFs.

Correlation analysis of ETF using Python

Previously, we have covered why and how to create a correlation matrix of ETFs available in Hong Kong market using Python. Now we should do some actual correlation analyses on these securities, with the matrix just created. There are two kinds of analyses I am going to demonstrate, which are actually quite similar: one is to find out the n most uncorrelated ETFs in the whole market; the other one is to find out n most uncorrelated ETFs corresponding to a given specific ticker.

Finding correlation coefficients between ETFs with Python

Several months ago I finished reading the book The Intelligent Asset Allocator by William Bernstein. It is a really nice book if you want to have a solid idea and examples on portfolio theory, as well as guidance for building your own investment portfolio by allocating your asset into different classes. One of the main points of building effective portfolio is building with uncorrelated, or less correlated in reality, assets. Whether two assets are correlated or not, or more precisely, the level of correlation, is measured by correlation coefficient, which is ranging from -1 to +1.