I had participated in trail running races for 2 years. All of those races are exciting and unforgettable, with stunning scenic views and different kinds of challenges. During each of the event, there are many enthusiastic photographers, amateur or professional, taking numerous pictures of racers and putting them online for download either freely or with fees. In order to find the photos for a particular racer, one needs to either look for those photos from countless albums each containing more that hundreds of photos one by one and by naked eyes, or some websites can let you input the bib number to and get the photos with the number for you.
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.
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.