While these are positive moves, there are still improvements that need to be made in high speed mobile data and security as heavy data usage will require more 4G/LTE antennas and IT infrastructure across the region.
– Shafiq Arghandiwal, Director of Technology, Matthews Asia
Identify beneficiaries of telecom and IT infrastructure companies in Asia
Part I
Following our previous session during which we determined the period of time and what cohort was suitable for our analysis, I sought to develop tools I could use to observe the relationships between growth/contraction in emerging economies relative to developed economies with more precision. With the data set I arranged from session 1.1, I began to structure our data such that I could dynamically observe growth relationships on the basis of which cohorts I would like to use and what time period I would like to observe. Like many sessions where I found myself getting too far/obsessive with assembling my sheet in a clean and efficient manner, I was able to scrape together a suitable first suite of tools that will be useful going forward.
Figure 1.2.1: Correlation Matrix: Regression Coefficient Observing the Relationship in GDP Growth/Contraction between Countries in Emerging Economies v. Countries in Developed Economies; 1970 – Present
I’m fond of correlation heat maps because they’re a lot easier on the eyes than a standard multivariate regression chart. I formatted my sheet such that high regression coefficients appeared in red, those closer to the median in white, and low levels of correlation in blue. Additionally, I originally assembled my data in such a way that each country was ordered alphabetically, but eventually decided to group countries by region. It proved a wise choice, as we can see above, from a very high level of analysis, that many European regions did not share a statistically significant relationship with those economies in the emerging world; primarily because these countries have fared fairly poorly in the last few decades.
Figure 1.2.2: Isolated Regression Analysis: Relationship between Growth/ Contraction of GDP in Indonesia v. Developed Economies from 1970-Present
When I first began to organize my experiment, my primary focus involved isolating countries which grew on trajectories in line with those in the developed world. I am less concerned about generalizations that can be made following my analysis, as I am more interested in observing the “tails” of a given bell curve.
Above represents the regression coefficient of Indonesia versus the developed economy cohort. From a functional standpoint, this graph “zooms” in on the correlation matrix. I put it together primarily to allow myself not to focus when observing countries on an individual basis.
Figure 1.2.3: Analysis of Global Median; Relationship Between Growth/Contraction in GDP in Emerging Economy v. Developed Nation Cohort; 1970-Present
Finally, I also assembled a summary chart that illustrates the median correlation in growth between emerging economies and developed economies. With this tool, we are able to observe which countries generally grow (or contract) in/out of line with developed economies. Our global median in our original sample set is very high, primarily because we’re observing a relatively longer period of time. In the next section, I isolate the time period of analysis to reflect what I had deemed appropriate in session 1.1: the period from 2008 to the present day, excluding China and Venezuela.
Part 2
At this point in time, I was interested in observing the period 2008-present for two reasons. The first derives from our session previously, deeming the period one with relatively little noise by which confounding variables interefering with our data set were minimized. Additionally, the period 2008 – the present represents a relatively turbulent time in the financial markets. Following the second most severe recession of the century, the trajectories by which economies recovered from the crisis are interesting to observe, especially considering the peaks in the equities markets we’ve been experiencing in the US.
Figure 1.2.4: Correlation Matrix: Regression Coefficient Observing the Relationship in GDP Growth/Contraction between Country in Emerging Economy v. Country in Developed Economy; 2008 – Present
Isolating our period of analysis, the most obvious point of observation illustrates that the relevant trends continuing from 1970 – the present are more pronounced when we isolate the time period. It is necessary to note; however, that this statement cannot be held as an overarching generalization – the period is one involving many confounding factors, including the exclusion of certain countries at different periods of times, or inclusion when countries have enough data.
Nonetheless, it is possible to observe above, that countries seemed to grow in line with one another an a regional basis, with Europe involving most of the laggards, representing a negative relationship with most emerging economies. The most fascinating trend I would like to follow up on is the lack of relationship between Asian countries and Eastern European countries (top right quadrant). I will follow up on which industries are relevant in each region, which I will expand on on a later date.
Figure 1.2.5: Isolated Regression Analysis: Relationship between Growth/ Contraction of GDP in Indonesia v. Developed Economies from 2008-Present
As we can observe in figure 1.2.5, the global median dips significantly when we isolate our point of analysis. This is very exciting, primarily because it represents that emerging economies do not grow and contract in line with developed economies, and that other variables may affect growth, which I will look to further research on a later date.
Figure 1.2.6: Isolated Regression Analysis: Relationship between Growth/ Contraction of GDP in Indonesia v. Developed Economies from 2008-Present
Finally, we come to our most isolated point of analysis – this time, from the perspective of Indonesia. Not surprisingly, Italy, Spain, Greece, and Portugal remain the least correlated with the trajectory of India’s growth. With Indonesia, it will also be useful to observe its relationship with Indonesia. It is important to note that Indonesia shares similar growth trajectories to those of Australia and New Zealand. I will follow up on this later.
Follow Up
It is now possible to pinpoint which emerging economies to delve in to in order to understand which sectors/industries are primary drivers of growth in this countries. It is helpful to observe the relationships in growth between emerging economies and developed economies because developed economies can be tertiary and even primary indicators of the prospects of growth within a country. With this data, I plan on assembling the following next:
I am rather excited to begin logging point three, but it is necessary to continue assembling the foundation for my analysis to ensure that I am not missing data moving forward.
For the first portion of my analysis, I am interested in determining the correlation between growth in emerging ecnomies vis a vis developing countries.
Figure 1.1.1: % Increase / (Decrease) in Size of Cohort vs. Population Size: Emerging Market Cohort
From the onset, I understood that it would not be appropriate to compare countries on a group by group basis. The reasoning for this is because my primary focus of analysis involves emerging nations. This creates a problem because countries are commonly added or subtracted from this cohot for varying reasons (no reported data, countries were too small, countries became too big), so it was necessary to cleanse the data I had on hand. For this experiment, I divided countries by subgroup; dividing them in to 4 cohorts. There is overlap amongst each cohort, but it will be appropriate for analysis later on.
I defined countries as 1) emerging 2) developed 3) g7 4) eu. I wanted to isolate the periods of time by which countries were added or subtracted from each cohort from the time period November 1970 until the present day (or more precisely defined as CQ2 2014). I accessed the information by downloading world GDP by country on a quarterterly basis on Factset.
As we can see in figure 1.1, from the period in the middle of the 1990’s to today, many countries were added to this cohort. I quickly chose to isolate my focus of analysis from mid 2000 onward.
Figure 1.1.2: Mean and Median GDP of Emerging Market Countries
At this point in time, it is very obvious that country additions/ subtractions had a tremendous impact on our specified points of analysis. There is a sharp drop in median/average GDP for the emerging markets cohort right before CY 2013.
I dug further, to observe the absolute variance between the countries with the highest GDP and the countries with the lowest GDP:
Figure 1.1.3: Variance in GDP in Emerging Market Nations, Min to Max
The disparity was only further exacerbated when we analyzed the cohort on an absolute, rather than normalized basis (observing the avg/median).
Observing the rate of change of average and median GDP on a more isolated basis, it becomes clear that this cohort is unsuitable for our analysis. Recall that our primary objective here is to understand the relationship between growth in developed nations vs. growth in emerging market nations.
Figure 1.1.4: Rate of Change of Average/Median GDP in Emerging Market Nations
Upon further analysis, I realized that the skew in my data derived from the removal of China and Venezuela from the categorization of Emergin Market Countries. I set out to “cleanse” my data and omitted the two from my next point of analysis.
Figure 1.1.5: Treatment of Emerging Markets Cohort; Removal of China and Venezuela
Figure 1.1.5a: % Increase / (Decrease) in Size of Cohort vs. Population Size: Emerging Market Cohort
Figure 1.1.5b: Mean and Median GDP of Emerging Market Countries
Figure 1.1.5c Variance in GDP in Emerging Market Nations, Min to Max
Figure 1.1.5d: Rate of Change of Average/Median GDP in Emerging Market Nations
Following the removal of China and Venezuela, we achieved a much more suitable cohort of analysis. We cleaned our data by holding constant a few variables, the primary variable being additions/substractions of countries within our data set with the capacity to skew our data distinctly. By isolating the period of time of our analysis, we were able to control for this variable. Additionally, by removing significant outliers within our data set allowed us to “normalize” our population. Most notably, a comparison of figures 1.1.5b and 1.1.5d accurately surmise that our treatment was effective.
Next steps
The purpose of our experiment is to run a regression on the rate of growth/(conraction) of emerging markets vs. developed economies. I’d like to determine by what degree growth in developed nations preceed growth/contractions in emerging nations. From an elementary perspective, this is an obvious correlation. However, by isolating our study, it may be possible to determine/identify various relationships including:
The first is relevant because the ability to identify this relationship may present a signficant market opportunity. If it is possible to identify the lag/lead time of an occurence in a developed nation, it may represent a significant signal to add/remove positions from various emerging economies.
Second, identifying countries that grow independent of developed nations represent significant opportunity for long term growth and minimized risk following uncontrollable macroeconomic factors.
Additionally, the purpose of this introductory treatment is to isolate special regions to observe later on. Recall that the next part of this analysis involves parsing out ETFs by country to develop a framework to follow important industries within each country to follow. By understanding these relationships from a macro perspective, it will be possible to gain greater context for how different “levers” in emerging markets may react relative to the broader economy.