I mainly post this visualization because I think it’s pretty. It reminds a little of the work by the famous Dutch painter Mondrian. The complete matrix can be found here. The plot is a heatmap of an adjacency matrix generated by a weighted dir...

Being professional involved with analyzing source code I get to work with a much larger number of programming languages than most people. There is a huge difference between knowing the intricate details of the semantics of a language and being able to fluently program in a language like a native developer. There are languages whose

In my last post, Factor Attribution to improve performance of the 1-Month Reversal Strategy, I discussed how Factor Attribution can be used to boost performance of the 1-Month Reversal Strategy. Today I want to dig a little dipper and examine this strategy for each sector and also run a sector-neutral back-test. The initial steps to

Locally weighted scatterplot smoothing (LOWESS) or local regression (LOESS) is widely used to highlight “signal” in variables from stratigraphic sequences. It is a user-friendly way of fitting a local model that derives its form from the data themselves rather than having to be specified a priori by the user. There are generally two things that a user has...

Earlier today, a minor update / maintenance release of RcppGSL---our interface package between R and the GNU GSL using our Rcpp package for seamless R and C++ integration---arrived on on CRAN. It contains a number of minor changes to accomodate chan...

One of the shortcomings of regression (both linear and logistic) is that it doesn’t handle categorical variables with a very large number of possible values (for example, postal codes). You can get around this, of course, by going to another modeling technique, such as Naive Bayes; however, you lose some of the advantages of regression Related posts: