This document discusses how trade data can provide more reliable economic indicators than traditional economic estimates. It notes that trade data contains a large amount of granular information across countries, products, and time that can be used to generate forecasts. The document describes how a company called Complete Intelligence uses trade data and machine learning to generate consistent economic and financial forecasts with confidence intervals. They aim to provide reliable forecast data to help monitor markets and evaluate strategies.

1. From Insufficient Economic
Data to Economic Big Data
How Trade Data is redefining Reliability in Economic
Indicators for Markets
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2. Economic Data or Economic
Estimates?
• Economic data vs Financial data
• How many revisions did major USA economic
indicators have, over the last 3 months?
• Who uses this data: Traders, Quants, Macro funds,
Economists, Media, Corporates and Government
institutions
• GDP (as a case):
• Market sensitivity to GDP figures (first report vs
estimate) is quite high
• Left or Right side of the equation
• Way forward – alternative measures
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3. Economic Data or Economic
Estimates?
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Source: http://www.tradingeconomics.com/united-states/news

4. Trade Data – the internals
• Trade Data: More Economic Data and Less Economic
Estimate
• Speciality of Trade Data - Depth and Granularity
• Country x 200 Trading partners x 2 Export/Import x 1400
products x 7 years of monthly data
• 25 million data points per country
• Quarter million time series per country
• Data Structure
• Real world functionality
• At 50 countries, there are 11 million real world time series
• How do we use this?
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5. Trade Data – DB sample
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* Sample figures,
not actuals

6. Trade corridors
• Trade corridor as a mathematical
function
• Correlations:
• Equity Indices:
• S&P500 (0.96), FTSE100 (0.90), DJIA
(0.96), Nikkei225 (0.95), NASDAQ
Composite (0.96)
• Commodities:
• Oil (0.86), Steel (0.84), Aluminium (0.90)
• Currencies:
• EURUSD (0.85), USDJPY (0.94), USDCNY
(0.97)
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* Correlations at HS2 Trade level
* Time period over 6 years

7. Trade Data - applications
• How companies use this data:
• Deeper understanding of Supply Chains
• Understanding of Trade of products
• Using Trade forecasts
• Sales Forecast and analytics
• Hedging Risk in Treasuries
• Forecast of currencies and commodities
• Banks, an exception – how they use this
information
• Global Trade picture
• Estimation of Trade Finance
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8. Forecasting – a bird’s eye view
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Trade Database
Economic
forecast
Statistical
forecast
Forecast Trade
Database
Asset Data
(Macro/Currency/
Commodity)
Shortlising
mechanism
(Statistical)
Certainty of
Forecast
Asset Forecast
Economic
forecast
Statistical
forecast
• Trade Database at HS4, while
Forecast Trade DB used as input for
shortlisting at HS2
• Calculations PER Asset forecast
• Period assumed: since 2010-Jan
• Refresh rate = 1 month

9. Machine learning
• Algorithms used have machine learning capabilities
• Previous decisions are stored
• Economic and Financial decisions and data over the last 7 years are stored
• New data
• New data is absorbed as per changes in Economic conditions, Financial
price movements as well as new Trade Data at a highly granular level
• Data is processed using New Data along with previous decisions
• Cycle is repeated once every month, for all Economic
indicators, Financial assets and Trade Data
• Each forecast asset has not only an accurate forecast, but
comes with a confidence level for the prediction
• Back-testing indicates lower error rate with higher confidence level
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10. CompleteIntel – what we do
• Data Analytics
• Trade Data
• Market Data
• Economic and Financial Forecasting
• Supply Chain Analysis
• Business Structure:
• Consulting
• B2C Economic Forecasting
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11. Who hasn’t used a Google search to get that one piece of data for a presentation? Everyone does it. We all try to locate the exact data we
need and get it for free, if possible.
Unfortunately, most data are either old, inconsistent or not forecasted. Historical data is pretty easy to find, but it’s tough to get data that is
consistent, affordable, regularly updated and comparable across countries.
Complete Intelligence uses open data sources and sophisticated big data modelling engines to deliver reliable, frequent, forecast data that
can be used to monitor markets, develop your own models, assess economic and industry events, evaluate corporate strategy and more.
We have a single global model, which ensures consistency across countries and time series. It also allows us to account for the
interdependencies in the global economy. If an economic event happens in Germany, how do you know it’s accounted for in the China
forecast? If oil demand surges in India, how do we know it’s accounted for in a global oil price forecast? You can’t unless it’s done with an
integrated global model. Like the one we use.
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everyone. www.completeintel.com
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This document contains confidential IP of Complete Intelligence and its client. No part of it may be circulated,
quoted, or reproduced for distribution outside the Client without prior written approval from Complete Intelligence;
For questions write to: info@completeintel.com