AssetsSelection: Selecting Assets from Multivariate Asset Sets

Description

A collection and description of functions which allow for the selection and clustering of individual assets from portfolios using several kinds of clustering approaches. The functions are: ll{ assetsSelect Asset Selection from Portfolios. }

Usage

assetsSelect(x, method = c("hclust", "kmeans"), 
    kmeans.centers = 5, kmeans.maxiter = 10, doplot = TRUE, ...)

Arguments

doplot

[assetsSelect] - a logical, should a plot be displayed?

kmeans.centers

[assetsSelect] - either the number of clusters or a set of initial cluster centers. If the first, a random set of rows in x are chosen as the initial centers.

kmeans.maxiter

[assetsSelect] - the maximum number of iterations allowed.

method

[assetsSelect] - a character string, which clustering method should be applied? Either hclust for hierarchical clustering of dissimilarities, or kmeans for k-means clustering.

any rectangular time series object which can be converted by the function as.matrix() into a matrix object, e.g. like an object of class timeSeries, data.frame, or mts.

...

optional arguments to be passed.

Value

assetsSelect if method="hclust" was selected then the function returns a S3 object of class "hclust", otherwise if method="kmeans" was selected then the function returns an obkject of class list. For details we refer to the help pages of hclust and kmeans.

Details

Assets Selection: The function assetsSelect calls the functions hclust or kmeans from R's "stats" package. hclust performs a hierarchical cluster analysis on the set of dissimilarities hclust(dist(t(x))) and kmeans performs a k-means clustering on the data matrix itself.

Examples

Run this code

## berndtInvest -
   data(berndtInvest)
   # Market and Interest Rate columns from data frame,
   berndtAssets.tS = as.timeSeries(berndtInvest)[, -c(10, 17)]
   head(berndtAssets.tS)
    
## assetsSelect -
   # Hierarchical Clustering:
   clustered = assetsSelect(berndtAssets.tS, doplot = FALSE)
   clusteredAssets.tS = berndtAssets.tS[, c(clustered$order[1:4])]
   colnames(clusteredAssets.tS)
   # Cluster Dendogram:
   par(mfrow = c(1, 1))
   plot(clustered)