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A research addressing the use case and role of statistical trade
data in commodity trading.
Key points:
Traditional 'commodity' labels show a direct linkage with
Harmonised System codes (HS Codes)
Spot prices have high correlations to data concepts found
within statistical trade data
The structured extended history and multiple concepts found
within the Global Trade Atlas (GTA) data could be used in modelling
and forecasting
Case Study of Commodity Trading for
Financials
We at IHS Markit are constantly working towards developing new
insights and applications for our assets and solution.
Lately, we have been working towards better understanding the
relationship between bilateral trade data and financial markets
data as to establish if there are any statistical relationships
between the two datasets that might in turn prove insightful when
looking at commodity markets.
The research paper was devised to provide a fundamental base,
not only describing the bilateral trade data, but also trying to
answer the below questions:
Can Harmonised System codes (HS codes) be mapped against more
traditional 'commodity' labels?
Can any of the concepts found in statistical trade data be
correlated to spot prices?
Could the data be used within the scope of predictive analytics
to predict fluctuations in tradable commodity prices?
Commodity Mapping
Trade data as reported by National Statistical Authorities is
structured and organised under the Harmonised System (otherwise
referred to as HS Codes); this is a multi-layered coding system
introduced in 1988 and is now adopted by more than 200 countries.
These codes which are internationally standardised at the 6 digits
covering more than 5,000 different physical goods.
These HS Codes are made up of:
Chapters, of which there are 99. These make up
the first two digits and are broad 'buckets' of homogeneous
commodities (e.g. HS12: Oils, Seeds & Oleaginous Fruits)
Headings, of which there are more than 1,000.
These make up the second set of two digits which distinguishes
certain sub-types of goods (e.g. HS12.01: Soybeans, Whether Or Not
Broken)
Sub-headings, of which there are more than
5000. These make up the third set of two digits which further
distinguish sub-types under the header (e.g. HS12.01.90: Soybeans,
Other Than Seed)
This in all gives you a 6-digit code highlighting its
classification (e.g. HS 12.01 90). There are further
sub-classifications beyond that of 6 digits known as National Codes
or Tariff Lines used which are country specific in nature and
primarily used for customs purposes.
This type of coding is not necessarily easy to map against more
traditional commodity data used in the financial world, but our
market expertise and understanding of the data allows us to work
alongside our clients in interpreting and mapping the data to
commodities and industries. An example of this can be seen in
Figure 1 where we can identify key commodities and map them to a
single label.
The GTA has many different reporting concepts, ranging from
primary/secondary quantity, value, unit price by direction (import,
export, total trade), and trade partner. In the paper, we found
that unit prices were highly correlated to spot prices, with higher
correlations being found on the import/export directions depending
on the primary activity of that country (the US for example is a
huge exporter of soybeans therefore exports had higher statistical
significance).
One of the benefits of this type of data is that whereas spot
prices are very one dimensional unless combined with other
datasets, unit price concepts found in the Global Trade Atlas
database are multi-dimensional, and can be broken down by quantity,
value and trade partner - this in turn can allow for a better
understanding of how markets are affected by changes in quantity,
value by trade partner.
In Figure 2, we see how soybeans have been mapped as to
demonstrate the relationship between GTA's US exports of soybean to
that of the US soybean spot price. This yielded a 0.9R2.
US soybean, GTA export unit price (US$/T) vs soybean
spot price (US$/Bushel)
With such a relationship being established, we can look at
additional dimensions, such as month-on-month growth rates for the
two data sets to see if there is any lead-lag which might be
insightful when looking at building particular data models (Figure
3). Or perhaps explore how changes in volume and value by trade
partner affects the unit price, which might in turn provide
insights into how the spot market might react.
Figure 3: Month-on-month percent change of GTA unit price vs US
spot prices. Source: IHS Markit Global Trade Atlas
and Financial Markets
Due to the structured extended history (extends to more than 20
years in some instances) The mapped data can then go on to be used
in predictive modelling to as to gain actionable insights into how
the market might evolve going forward. Or even perhaps look at
additional financial concepts such as futures to see if any
significant statistical relationships exist.
Figure 4: ETS forecast of mapped GTA data against US spot
prices. Source: IHS Markit Global Trade Atlas
and Financial Markets - Historical
Global Trade Atlas is the world's most comprehensive database
of official bilateral trade data covering imports and exports by
quantity, value and unit price for over 96% of world trade.
Providing insight into all reported 'physical' commodities under
the Harmonised System, across all transportation modes for 101
countries
With over 20 years' worth of data available and covering about
5,000 commodities, the Global Trade Atlas is used by governments,
like the EU and US to assess trade, as well as corporations that
need an official source to quantify markets, monitor trends and
identify opportunities in emerging markets. This type of data is
also consumed by financial institutions whom need to fully
understand the macroeconomic landscape of a country and conduct
market evaluations
