Google-Next-Madrid-BBVA-Research inv.pdf

June 2017
Big Data at BBVA Research
using BigQuery
Tomasa Rodrigo
Google Cloud Next

Click here to modify the style of the master title
Big Data at BBVA Research using BigQuery
Summary
What is GDELT and how BigQuery helps us to exploit it
01
Geopolitical analysis
02
Economic and digital analysis
03

01
What is GDELT and how
BigQuery helps us to exploit it

What is GDELT?
4
… georeferenced across
the entire planet…
… including over 300 events
around the world and more than
30000 themes…
…and collecting emotions using
some of the most sophisticated
algorithms
Open database of human
society from every corner of the
globe dating back to 1979 …
Global Database on Events Location and Tone

Why do we use BigQuery?
5
Really fast dealing with Big
Databases
Easy to use using SQL
Open access
Complex data analysis
Flexibility and scalability
Combination of historical
data with real time data

Our working proccess
6
GDELT
(Global Database
on Events,
Location
and Tone)
Clean,
Aggregate
& transform
the data
Fuse,
visualize
& analyze
the data
BigQuery
Data
Storage
(SQL)

Why is it important?
7
Among other things we
can focus in news
intensity, geographic
density of events and
emotions across the
world
Novel data-driven
computational
approaches as needed
to enable a new era in
which these data can be
used to study “real life”
at population scales
At its core, we analyze
geopolitical, political,
social and economic
questions using
quantitative data-
driven methods rather
than qualitative
introspection

What is GDELT and how BigQuery helps us to exploit it
Our products
Political, Geopolitical Social Indexes
(Political Indexs)
Color Maps NAFTATopics
(Nafta Project)
Politics & Financial Networks
(Political Netwoks)
Mix Hard data & Sentiment & VAR models
(CBSI and TurkeySentiment Indexes)
Geographical AnalysisHousing Prices
(sentiment on Housing Prices)
Measuring Sentiments
(sentiment Analysis on Economyand Society)
Financial Stability & Macroprudential
(ECB & FED FS index byFED Board)
8

Tracking Geopolitics on real time
is useful to identify the main hot spots and potential spillovers
9
Source: www.gdelt.org & BBVA Research
Conflict Intensity Map May 2017
(Number of conflicts/ Total events)

From an historical perspective…
10
BBVA Research World Protest and
Conflict Intensity Index 1979-2017
World Protest Intensity Map
1979- 2017
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17
USA
UK
Norway
Sweden
Austria
Germany
France
Netherlands
Italy
Spain
Belgium
Ireland
Portugal
Greece
Poland
Czech Republic
Hungary
Bulgaria
Romania
Croatia
Turkey
Russia
Ukraine
Georgia
Kazakhstan
Moldova
Azerbaijan
Armenia
Morocco
Algeria
Tunisia
Libya
Egypt
Israel
Jordan
Syria
Iraq
Iran
UAE
Bahrain
Qatar
Oman
Saudi Arabia
Mexico
Brazil
EM
Europe
&
CIS
Developed
Markets
N.
Africa
&
Middle
East
M
Protests Conflict

… to the main hot spots
11
BBVA Research Refugees Flows Map
in 2015-17
Number of media citations about refugees’ inflows and outflows
BBVA Research Asia Conflict
Intensity Index 2008-17

Social unrest events across the world: Cairo, Istanbul and Hong Kong cases
Protest events
Geopolitical analysis
…at the exact geolocation

Spanish perception around the world according to the media
Negative tone
Positive tone
Neutral tone
Sentiment analysis: the case of Spain

Contagion effects of China’s slowdown
14
Chinese slowdown: media perception and country network
Oman
Qatar
Iran
Kazakhstan
Russia
U.A.E.
Iraq
Nicaragua
Saudi Arabia
Mexico
Chile
Dominican R.
Brazil
Bolivia
Ecuador
Venezuela
Peru
Panama
Argentina
Spain
Austria
Ukraine
Israel
Greece
Poland
Belgium
Czech Republic
Italy
Netherlands
Finland
Ireland
Iceland
Portugal
Hungary
Yemen
Sri Lanka
Macau
Indonesia
Philippines
Taiwan
Cambodia
Pakistan
Turkey
Brunei
N. Zealand
Burkina Faso
Singapore
Thailand
Malaysia
Zimbabwe
Uganda
Nigeria
Zambia
Congo
Mozambique
Kenya
Sweden
Angola
E. Guinea
Ethiopia
South Africa
France
US
UK
Japan
Australia
Canada
S. Korea India
Switzerland
Germany
Hong Kong
China

Measuring Chinese uncertainty
15
Chinese Vulnerability Sentiment Index (CVSI): components and evolution
-3
-2
-1
0
1
2
3
Mar-15
Apr-15
May-15
Jun-15
Jul-15
Aug-15
Sep-15
Oct-15
Nov-15
Dec-15
Jan-16
Feb-16
Mar-16
Apr-16
May-16
Jun-16
Jul-16
Aug-16
Sep-16
Oct-16
Nov-16
Dec-16
Jan-17
Feb-17
Mar-17
Apr-17
7 days mov.avg 30 days mov.avg
3%
dev aluation
Economic work
conf erence
Neutral Area +- 0.5 std
1st
stock market
crash
"Black Monday "
2nd
stock
market crash,
trade halted
f or 3 day s
PMI f alls
to 4-Y low
NPC meeting
RMB enters IMF’s
SDR basket
Declining
Sentiment
(Higher
Vulnerability)
Improv
ing
Sentiment
(Lower
Vulnerability)

Turkey GDP & Economic Sentiment
(%YoY and media economic sentiment in Turkish and English)
Systematic bias!
Narratives matter: language bias

Media Sentiment Digital Index
17
Media sentiment digital index and components
-2
-1,5
-1
-0,5
0
0,5
1
1,5
2
2,5
Mar-15
Apr-15
Apr-15
May-15
Jun-15
Jun-15
Jul-15
Aug-15
Aug-15
Sep-15
Oct-15
Nov-15
Nov-15
Dec-15
Jan-16
Jan-16
Feb-16
Mar-16
Above
average
ON
average
Below
average -2,4
-1,4
-0,4
0,6
1,6
2,6
apr-15
may-15
jun-15
jul-15
aug-15
sep-15
oct-15
nov-15
dec-15
jan-16
feb-16
mar-16
Digital Economy
-2,4
-1,4
-0,4
0,6
1,6
2,6
apr-15
may-15
jun-15
jul-15
aug-15
sep-15
oct-15
nov-15
dec-15
jan-16
feb-16
mar-16
Fintech
-2,4
-1,4
-0,4
0,6
1,6
2,6
apr-15
may-15
jun-15
jul-15
aug-15
sep-15
oct-15
nov-15
dec-15
jan-16
feb-16
mar-16
Digital Regulation
Emerging Asia
US
Europe Confidence Interval for Emerging Asia

You can find us at:
18
Media sentiment digital index and components
BBVAResearch webpage
LinkedIn
Twitter

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