O'Reilly logo

R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Separating investment targets

An alternative method to build an investment strategy could be to separate good investment targets and check what is common between them. A good way to find similarities among stocks that performed well could be to create groups based on the TRS values and compare low- and high-performer clusters. The first step to this should be to analyze the following code:

library(stats)
library(matrixStats)
h_clust <- hclust(dist(d[,19]))
plot(h_clust, labels = F, xlab = "")

The following dendogram is the output for the preceding code:

Separating investment targets

Based on the dendrogram, three clusters separate very well, but to cut the biggest of them into ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required