Skip to main content

Corporate Network Analysis

The complex interconnections that define modern society are most readily visible from our Facebook, Twitter, or Linkedin networks. What is perhaps less talked about is the extent to which corporations are linked. I just started building a network of cross shareholding among Malaysia's corporations using listed company data. The map below is a preliminary visualization of the country's corporate network as of 2015. I use social network analysis (SNA) tools to partition or color the network into key connected components or communities therein. I have deliberately excluded company names but I will tell you that the biggest nodes represent the most connected shareholders including governments, fund managers, and international asset managers. 
Malaysia's Corporate Network in 2015
Created Using Gephi 0.9.1

As the Malaysia economy has grown so has the complexity of the corporate network from few shareholders to many interlinked players. Below is a visualization using shareholding information as of 2005. It would be interesting to understand corporate Malaysia's resilience to crisis contagion as well as the extent to which financial performance can be explained by a company's location on the map. Preliminary analysis suggests that more densely connected firms tend to have superior financial performance (profitability and return on shareholders' funds) than those companies on the periphery of the network. 

Malaysia's Corporate Network in 2005
Created using Gephi 0.9.1




Labels:

Comments

  1. AnonymousMay 4, 2016 at 5:16 AM

    So much has changed in a decade. Good or bad?

    ReplyDelete
    Replies
    1. Lelo NxumaloMay 4, 2016 at 4:09 PM

      That is the purpose of the study. Its too early to say but the working hypothesis is that it depends. If greater connectedness has increased the risk of crisis contagion, then a dense network might not be ideal. If, however, it's led to more profitable and well-resourced companies, then it might be a good strategy for funds and other investors to allocate resources towards the most connected firms.
      I will be posting follow-up material as the analysis progresses.

      Delete

Post a Comment

Constructive feedback is always welcome. Thank you

Popular posts from this blog

Unemployment by state in the USA

Below is a visualization of unemployment rates by county using a powerful Python library called Bokeh . The two maps are for the states of Texas and the Commonwealth of Massachusetts. As the second largest economy in the United States (10th largest in the world), Texas shows interesting county variation in macroeconomic factors such as unemployment. According to Wikipedia , in 2015, Texas was home to six of the top 50 companies on the Fortune 500 list and 51 overall (third most after New York and California). The northern counties were least affected by the financial crisis of 2008/09. Dallas–Fort Worth–Arlington area encompasses 13 counties which constitute the economic and cultural hub of the region commonly called North Texas or North Central Texas. Bokeh Plot The least affected counties in Massachusetts were the southernmost tourist areas of Nantuckett and Dukes County. The ...
Post a Comment
Read more

Modeling Core PCE inflation: A dual approach

Today's release of the August 2025 Personal Consumption Expenditures (PCE) inflation data drew widespread media attention, with coverage highlighting both the persistence of inflation and its implications for Federal Reserve policy. Across outlets, analysts pointed to resilient consumer spending and income growth as signs of underlying economic strength, even as inflation remains above the Fed's 2% target. The consensus among media reports is that while inflation is not worsening, its stubbornness continues to challenge policymakers navigating a softening labor market and evolving rate expectations. To provide deeper insights into inflation's trajectory, I've developed a forecasting framework that combines two econometric approaches — ARIMA time series modeling and Phillips Curve analysis—to predict Core PCE inflation. This analysis presents a unique opportunity to validate my forecasting methodology against eight months of 2025 data. ...
Post a Comment
Read more

Malaysia at a Cross Roads: Diagnosing the Constraints to High Income Status

Malaysia at a Crossroads: Diagnosing Constraints to High-Income Status In 2008, Malaysia was recognized by the Growth Commission – a distinguished panel comprising 2 Nobel Prize Winning Economists and other leading development practitioners – as one of thirteen countries that sustained high growth in the post-war period. The 30-year stretch that caught the attention of the Growth Commission was between 1967 and 1997 when Malaysia grew at an average of 7.3% per year. This long stretch of growth was interrupted by periods of external shocks including the Volcker shock of 1986, the Asian Financial crisis in 1997/8, later the so-called Dot Com Bubble of 2001, and more recently the Global Financial Crisis of 2008. Despite these shocks, Malaysia remained resilient - formally earning the title "Upper Middle Income Country" in 1992. (See summary figure that breaks down the country's per capita growth story). As...
2 comments
Read more