Today, I want to continue with the Principal Components theme and show how the Principal Component Analysis can be used to build portfolios that are not correlated to the market. Most of the content for this post is based on the excellent article, “Using PCA for spread trading” by Jev Kuznetsov. Let’s start by loading 
In the Visualizing Principal Components post, I looked at the Principal Components of the companies in the Dow Jones Industrial Average index over 2012. Today, I want to show how we can use Principal Components to create Clusters (i.e. form groups of similar companies based on their distance from each other) Let’s start by loading 
Principal Component Analysis (PCA) is a procedure that converts observations into linearly uncorrelated variables called principal components (Wikipedia). The PCA is a useful descriptive tool to examine your data. Today I will show how to find and visualize Principal Components. Let’s look at the components of the Dow Jones Industrial Average index over 2012. First, 
Open CPU is a great project. Few months back, I wrote a function for plotting a moving window of the market average correlation. Jeroen C.L. Ooms was nice enough to upload it to their server. Something is now changed. Quotes … Continue reading →
Today, I want to highlight the Financial Turbulence Index idea introduced by Mark Kritzman and Yuanzhen Li in the Skulls, Financial Turbulence, and Risk Management paper. Timely Portfolio did a great series of posts about Financial Turbulence: Part 1, Part 2, Part 3. As example, I will compute Financial Turbulence for the equal weight index 
Finally all the stars have aligned and I can confidently devote some time for back-testing of new trading systems, and Support Vector Machines (SVM) are the new “toy” which is going to keep me busy for a while. SVMs are a well-known tool from the area of supervised Machine Learning, and they are used both
I want to follow up with Extending Gold time series post by showing how we can extend Commodity time series. Most Commodity ETFs began trading in 2006, please see the List of Commodity ETFs page. I will use DBC – PowerShares DB Commodity Fund, one on the most liquid Commodity ETFs as my proxy for 
Today, I want to address some questions that I was getting regarding the Regime Detection post. In the Regime Detection post I showed an example based on the simulated data, and some of you tried to apply this example to actual stocks. There is one big problem that you have to be aware in designing 
In finance and investing the term portfolio refers to the collection of assets one owns. Compared to just holding a single asset at a time a portfolio has a number of potential benefits. A universe of asset holdings within the … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
