Introduction

For years now, authors and analysts have worked on financial data using ad-hoc tools or programming languages other than R. So, the package FinNet was born to provide all R users with the ability to study financial networks with a set of tool especially designed to this purpose. Specifically, FinNet offers both brand new tools and an interface to the almost limitless capabilities of igraph and network.

This vignette illustrates how to:

1. Retrieve the desired data using yahoofinancer;
2. Create the firm-firm (FF) matrix;
3. Create the corresponding graph;
4. Plot it with smart, nice-looking defaults.

1. Data retrieval

After having identified the firms of interest, the package can fetch all information on them as long as yahoofinancer is available. Otherwise, built-in data can be used:

# Check if `yahoofinancer` is installed
isTRUE(requireNamespace('yahoofinancer', quietly = TRUE))
#> [1] TRUE

# Create a list of the desired firms
data('firms_US')

2. Matrix construction

There are many function in the FF function family to rapidly build an adjacency matrix. In this step, FF.norm.ownership() will construct a normalised-valued matrix of common ownership

# Identify common-ownership relations in a firm-firm matrix
FF <- FF.norm.ownership(firms)

3. Graphing

A graph can be obtained easily using FF.net(), which include two preset aesthetics: ‘simple’ and ‘nice’

# Create a simple-looking graph
net <- FF.net(FF, aesthetic = 'simple')

Some checks using the S3 methods implemented for financial_matrix objects and the extension of some igraph functions allow to verify the correctness of the graph:

# The order of the graph equals the number of rows in the FF matrix
network.size(net) == nrow(FF)
#> [1] TRUE

# The names of its vertex match the row names of the FF matrix
vertex.names(net) == rownames(FF)
#> [1] TRUE TRUE TRUE

# Plot it
plot_network(net, displaylabels = TRUE, vertex.cex = 3, edge.lwd = 6)

4. Plotting using default nice aesthetics

The ‘nice’ defaults are more indicated for a visual inspection of the network. net

# Load dataset
data('firms_BKB')

# Identify common-ownership relations in a firm-firm matrix
FF <- FF(firms_BKB, who = 'own',
         ties = 'naive', Matrix = TRUE)

# Create a nice-looking graph
net <- FF.net(FF, aesthetic = 'nice')

# Plot it
plot_network(net, displaylabels = FALSE, edge.col = 'darkgray',
             vertex.cex = 3, edge.lwd = 1)