Feature.LM_risk.adj: Feature selection using momentum
In Bjerring/rispackage4: RIS alpha

Description Usage Arguments Details Value Note Author(s) References Examples

hierarchical clustering a momentum as selection criteria for universe reduction

1	Feature.LM_risk.adj(data, Cno)

`data`	return matrix
`Cno`	number of clusters

hierarchical clustering and feature selection for portfolio construction

returns a number of assets according to inputs

RIS

Thomas

none

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (data, Cno)
{
    c <- cor.shrink(data, verbose = FALSE)
    d <- as.dist(1 - c)
    hc <- hclust(d, method = "ward")
    c.tree <- cutree(hc, k = Cno)
    assets <- rep(NA, max(c.tree))
    for (i in 1:(max(c.tree))) {
        i.data <- apply(data[(dim(data)[1] - 52):dim(data)[1],
            which(c.tree == i)], 2, cumprod)
        trends <- rep(NA, length(i.data[1, ]))
        for (j in 1:(length(i.data[1, ]))) {
            trends[j] <- lm(i.data[, j] ~ c(1:length(i.data[,
                j])))$coefficients[2]
        }
        assets[i] <- colnames(i.data)[which(max(trends) == trends)]
    }
    return(assets)
  }