6 Characteristics of Companies in Emerging Markets

CEIBS MBA programme
CEIBS MBA programme

Emerging markets offer plenty of opportunities for investors. By opening themselves to international trade, the structure of these markets is dramatically altered. Foreign and local investments flood the economy with the aim of gaining enormous returns. A massive reallocation takes place and demand explodes.

As a result of these disruptions, the number of mergers and acquisitions grows exponentially. Under these circumstances, it becomes of crucial importance to understand the nature of companies in emerging markets. Such companies share many of the following characteristics:

1. Unreliable market measures:
When valuing publicly traded companies, we draw liberally from market-based measures of risk. To illustrate, we use betas, estimated by regressing stock returns against a market index, to estimate costs of equity and corporate bond ratings and interest rates to estimate the cost of debt. In many emerging markets, both these measures can be rendered less useful, if financial markets are not liquid and companies borrow from banks.

2. Currency volatility:
In many emerging markets, the local currency is volatile. This is the case in terms of what it buys of foreign currencies (exchange rates), as well as in its own purchasing power (inflation). In some emerging market economies, the exchange rate for foreign currencies is fixed. This is creating the illusion of stability, but there are significant shifts every time the currency is devalued or revalued. Furthermore, when computing risk free rates, the absence of long-term default free bonds in a currency denies us one of the basic inputs into valuation: the riskfree rate.

3. Country risk:
There is substantial growth in emerging market economies, but this growth is accompanied by significant macro economic risk. Hence, the prospects of an emerging market company will depend as much on how the country in which it operates does as it does on the company’s own decisions. Put another way, even the best run companies in an emerging economy will find themselves hurt badly if that economy collapses, politically or economically.

4. Corporate governance:
Many emerging market companies used to be family-owned businesses and while they might have made the transition to being publicly traded companies, the families retain control through a variety of devices – shares with different voting rights, pyramid holdings and cross holdings across companies. In addition, investors who challenge management at these companies often find themselves stymied by legal restrictions and absence of access to capital. As a consequence, changing the management at an emerging market company is far more difficult than at a developed market company.

5. Discontinuous risk:
The previously mentioned country risk referred to the greater volatility in emerging market economies and the effect that has on companies operating in these economies. In some emerging markets, there is an added layer of risk that can cause sudden and significant changes in a firm’s fortunes. Included here would be the threat of nationalization or terrorism. While the probability of these events may be small, the consequences are so dramatic that we ignore them at our own peril.

6. Information gaps and accounting differences:
It is not unusual for significant and material information about earnings, reinvestment and debt to be withheld in some emerging markets, making it more arduous to value firms in these markets. On top of the information gaps are differences in accounting standards that can make it difficult to compare numbers for emerging market companies with developed market firms.

Data Science Toolbox: How to use R with Tableau

Recently, Tableau released an exciting feature that enhances the capabilities of data analytics: R integration via RServe. By bringing together Tableau and R, data scientists and analysts can now enjoy a more comprehensive and powerful data science toolbox. Whether you’re an experienced data scientist or just starting your journey in data analytics, this tutorial will guide you through the process of integrating R with Tableau.

Step by Step: Integrating R in Tableau

1. Install and start R and RServe

You can download base R from r-project.org. Next, invoke R from the terminal to install and run the RServe package:

> install.packages("Rserve")
> library(Rserve)
> Rserve()

To ensure RServe is running, you can try Telnet to connect to it:


Protip: If you prefer an IDE for R, I can highly recommend you to install RStudio.

2. Connecting Tableau to RServe

Now let’s open Tableau and set up the connection:

Tableau 10 Help menu
Tableau 10 External Service Connection

3. Adding R code to a Calculated Field

You can invoke R scripts in Tableau’s Calculated Fields, such as k-means clustering controlled by an interactive parameter slider:

kmeans(data.frame(.arg1,.arg2,.arg3),' + STR([Cluster Amount]) + ')$cluster;
SUM([Sales]), SUM([Profit]), SUM([Quantity]))
Calculated Field in Tableau 10

4. Use Calculated Field in Tableau

You can now use your R calculation as an alternate Calculated Field in your Tableau worksheet:

Tableau 10 showing k-means clustering

Feel free to download the Tableau Packaged Workbook (twbx) here.

Connect and Stay Updated

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Blog post updates:

Gartner Positions Tableau as a Leader for the First Time in BI Magic Quadrant

Screenshot of Tableau's 2013 February Newsletter featuring: "Gartner Positions Tableau as a Leader in 2013 Magic Quadrant"
Screenshot of Tableau’s 2013 February Newsletter featuring: „Gartner Positions Tableau as a Leader in 2013 Magic Quadrant“

One of the most highly anticipated and highly regarded reviews of the business intelligence market was published a couple of days ago. Gartner released its 2013 iteration of the famous Magic Quadrant for BI and Analytics Platform (aka. Gartner BI MQ) – and Tableau was cited as a „Leader“ for the first time.

Congraulations team Tableau!

Transition from Academia to Capgemini: A New Chapter in Data and Analytics

CERN Main Auditorium: my transition from academia to Capgemini
CERN Main Auditorium: my transition from academia to Capgemini

After enjoying research for the last four years, especially during my time at CERN, I have made a significant decision. I have decided to resign from my postgraduate position and make a transition from academia to the exciting world of Capgemini. My passion for Data and Analytics remains strong and will be the core focus of my new role.

Capgemini: A New Adventure After Academia

Capgemini, one of the world’s largest consulting corporations, has caught my attention. Unlike many other consulting companies, Capgemini does not yet have a dedicated team to offer effective strategies and solutions employing Big Data, Analytics, and Machine Learning. This presents an exciting opportunity for me to contribute and innovate.

My Vision: Building a Data-Driven Future at Capgemini

I love these technologies and am confident in my ability to elaborate a business development plan to drive business growth. Through customer and market definition, my plan includes new services such as:

  • Data Science Strategy: Enabling organizations to solve problems with insights from analytics.
  • Consulting: Answering questions using data.
  • Development: Building custom tools like interactive dashboards, pipelines, customized Hadoop setup, and data prep scripts.
  • Training: Offering various skill levels of training, from basic dashboard design to deep dives in R, Python, and D3.js.

This plan also includes a go-to-market strategy, which I’ll keep under wraps for now. Stay tuned for a retrospective reveal in the future!

Reflecting on My Transition from Academia

Making this transition from academia to a corporate role has been a considered decision. As I previously shared in my reflection on my software engineering internship at SAP, the blend of technological challenges and team collaboration has always intrigued me. Joining Capgemini allows me to continue pursuing my passion for data in a dynamic business environment.

Conclusion: Exciting Times Ahead

This transition from academia to Capgemini marks a thrilling new chapter in my career. I look forward to leveraging my expertise in Data and Analytics to contribute to Capgemini’s growth and innovation.

Follow my journey as I explore the intersection of data, technology, and business. Connect with me on Twitter and LinkedIn.

CERN Photographs featured in Gallery curated by Yahoo

John Ellis at CERN
John Ellis at CERN

Two of my photos taken at CERN are featured in the CERN Gallery curated by the Yahoo Editorial:

Physics projects don’t get any bigger than this. The active European Organization for Nuclear Research, aka CERN, formed in 1954 and is headquartered in Geneva, Switzerland, employs thousands of world-class scientists on the forefront of breakthrough research. Its claim to fame is unmatched as the origin of the World Wide Web and creator of underground 17-mile-long particle accelerator called the Large Hadron Collider. Here, see photos of the many aspects of an international institution that may discover a way to move faster than the speed of light and how our universe was pieced together.

[flickr_gallery user_id=“47399036@N07″ id=“72157630498907364″]