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A Big Data approach to understand Central Banks / 1

A Big Data approach

to understand

Central Banks Big Data Spain 2018

November 2018

A Big Data approach to understand Central Banks / 2

Summary

Why is the use of NLP important in economics

and Monetary policy?

The data and methodology

Understanding Central Banks: “What”, “How”

and “Who” is talking (or writing) about?

A Big Data approach to understand Central Banks / 3

01 Why is the use of NLP important in

economics and Monetary policy?

The data and methodology

A Big Data approach to understand Central Banks / 4

of the total amount of web

pages on the internet is given

by textual or unstructured data

Text mining to extract meaning from

strings of letters

It helps us to understand

what drives monetary

policy decisions

The potential use of textual information and

text sources improves the understanding of

economic and financial systems

Why is the use of NLP important in economics and Monetary policy?

Text as a key source of information to enrich economic analysis

80%

A Big Data approach to understand Central Banks / 5

80% of available data

20% of used data

A Big Data approach to understand Central Banks / 6

The data and methodology

From Extraction to Sentiment Analysis

Information

extraction

Pre-Processing

and text parsing Transformation

Text mining

and NPL

Sentiment

analysis

Documents

Web pages

Extract words

Identify parts of

speech

Tokenization and

multi-word tokens

Stopword Removal

Stemming

Case-folding

Text filtering

Indexing to quantify

text in lists of

term counts

Create the

Document-term

matrix

Weighting matrix

Factorization

(SVD)

Analysis and

Machine learning

Topics extraction

(LDA)

Clustering

Modelling

(STM and DTM)

Apply sentiment

dictionaries

Semantic analysis

and classification

Clustering

A Big Data approach to understand Central Banks / 7

Statements / Press Releases Immediately after the meeting on monetary policy, a short report about the decision on

interest rates is released. If there’s a press conference, the president of the CB explains the

decision and answer questions from journalists

Minutes A more detailed document explaining the monetary policy decision containing an overview of

financial market, economic and monetary developments

Speeches Collection of speeches and articles by senior central bank officials published in the central

bank websites

The data and methodology

Analyzing central banks’ communication:

Examined documents Information extraction

A Big Data approach to understand Central Banks / 8

The data and methodology

Analyzing central banks’ communication:

Cleaning and transforming the text

Extracting and organizing the data Extract words

Identify parts of speech

Stopword Removal

Case-folding

Converting it into numbers Stopword Removal

Stemming

Tokenization and multi-word tokens

Preparing it for the analysis Text filtering

Indexing to quantify text in lists

of term counts

Working with text in numbers Create the Document-term matrix

Weighting matrix

Factorization

Pre-Processing and text parsing

Transformation

A Big Data approach to understand Central Banks / 9

The data and methodology

Analyzing central banks’ communication:

Dynamic topic models Text mining and NPL

Latent Dirichlet Allocation (LDA) and Dynamic Topic Model (DTM)

A Big Data approach to understand Central Banks / 10

Text mining

and NPL

𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐭𝐨𝐧𝐞 = 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑤𝑜𝑟𝑑𝑠 − 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑤𝑜𝑟𝑑𝑠

𝑇𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑑𝑠

benefit improve adverse escalate

enhance upgraded challenge stagnation

stabilise smooth deteriorate vulnerability

favorable strengthened downgrade worsen

Positive words Negative words

achieve progress bankruptcy fallout

benefit stabilize bottleneck imbalance

efficiency strength corrupt monopolize

outperform versatility downgrade stagnant

Positive words Negative words

The data and methodology

Analyzing central banks’ communication:

Sentiment analysis

Loughran and McDonald (2011)

FED Financial Stability dictionary (2017)

Positive words Negative words Positive words Negative words

Sentiment analysis

A Big Data approach to understand Central Banks / 11

Main outputs

Analyzing the Central Bank of Turkey, European Central Bank

and Federal Reserve

A Big Data approach to understand Central Banks / 12

Main outputs

More than words: Getting the relation between words…

In the case of CBRT:

A Big Data approach to understand Central Banks / 13

Main outputs

…their evolution over time…

Most frequent words by year in the analyzed documents (the case of CBRT)

2014 2015 2016 2017 2018

A Big Data approach to understand Central Banks / 14

Main outputs

…as well as the topical content covered in the text

A Big Data approach to understand Central Banks / 15

02 Understanding Central Banks:

“What”, “How” and “Who”

is talking (or writing) about

A Big Data approach to understand Central Banks / 16

A Big Central Bank (ECB)

We go Inside of the CB Reports to identify the topics using Machine

Learning and Dynamic Topic Models. They can be different…

Each word cloud represents the probability distribution of words within a given topic. The size

of the word and the color indicates its probability of occurring within that topic

A Central Bank of a EM Country as Turkey (CBRT)

Activity

Inflation

Global Flows

Monetary Policy

Economy EMU Integration

Banking Union Financial Crisis

Monetary Policy

Quantitative Easing

Source: BBVA Research

A Big Data approach to understand Central Banks / 17

Source: BBVA Research

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2 0 0

2 0 1

Global Flows Economic Activity

Labor Market Fiscal & Structural Policies

Inflation Core Monetary Policy

Other

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2 0 0

6 j a

2 0 0

7 f

e b

2 0 0

8 f

e b

2 0 0

9 f

e b

2 0 1

0 f

e b

2 0 1

1 f

e b

2 0 1

2 f

e b

2 0 1

3 f

e b

2 0 1

4 f

e b

2 0 1

5 m

a r

2 0 1

6 s

e p

Economy EMU integration

Banking Union Financial crisis

Standard MP Non-standard MP

Topics are dynamic and can change over time…and the picture

can change…particularly if important events hit the economy

European Central Banks: Evolution of Topics Central Bank Of Turkey: Evolution of Topics

Source: BBVA Research

A Big Data approach to understand Central Banks / 18

Pre Lehman (1999-2007) Financial Crisis (2007 -2012) QE & Post Crisis (2013-2018)

Monetary Policy in the North (ECB) and in the EM (Turkey): Complexity and interconnectedness (Networks)

Networks are a useful tool to show the interconnectedness &

complexity…helping us to understand “How” the Central Banks talk..

Source: BBVA Research

A Big Data approach to understand Central Banks / 19

-3

-2

-1

2 0

0 6

2 0

0 7

2 0

0 8

2 0

0 9

2 0

1 0

2 0

1 1

2 0

1 2

2 0

1 3

2 0

1 4

2 0

1 5

2 0

1 6

2 0

1 7

2 0

1 8

Economic Activity Inflation

-3

-2

-1

2 0

0 6

2 0

0 7

2 0

0 8

2 0

0 9

2 0

1 0

2 0

1 1

2 0

1 2

2 0

1 3

2 0

1 4

2 0

1 5

2 0

1 6

2 0

1 7

2 0

1 8

Economic Activity Employment

Sentiment analysis reinforces the analysis by describing

“How” the Central Bank talks (“tone”)

Turkey (CBRT) :

Economic Activity & Inflation Tone (Tone economic activity and Inflation jn the MP Minutes)

Turkey (CBRT):

Economic Activity & Employment Tone (Tone economic activity and employment jn the MP Minutes)

P o

s it

iv e

e g

a ti

v e

P o

s it

iv e

e g

a ti

v e

Source: BBVA Research

A Big Data approach to understand Central Banks / 20

-4

-3

-2

-1

2 0 0

2 0 1

Tightening

Easing

Monetary Policy “Statements” Monetary Policy “Minutes”

A more formal Statement… More extensive and analytical…

-4

-3

-2

-1

2 0 0

2 0 1

Through Sentiment Analysis we can check the monetary policy

stance… how “Tight” or “Ease” is the Wording of the reports

Central Bank of Turkey: Monetary Policy Sentiment (Standardized, estimated through Big Data LDA and STM Techniques from Minutes & Statements)

Source: BBVA Research

A Big Data approach to understand Central Banks / 21

And how the market rates react to the Central Bank changes in

monetary policy sentiment …

Response to Short term and Long term interest rates to positive/Negative changes in Sentiment CB Turkey (Response of interbank deposits rates and 2Y BondSwaps to mild and strong chnages in sentiment. Changes relative to t-1. T=event)

Bond Swaps Response to a Positive Change in Sentiment Bond Swaps Response to Negative Change in Sentiment

Source: BBVA Research

A Big Data approach to understand Central Banks / 22

Remember that in the case of Sentiment Analysis, we are using

unsupervised methods so…always cross-check for Robustness

Monetary Policy in Turkey:

Experts vs Algorithms (Sentiments fron LDA Algorithm and MP Surprises by

Demiralp et Al. 1=Hawkish, 0= Neutral, -1=Dovish)

Experts vs Algorithms in Turkey:

Size of Surprises & Sentiments (Sentiments fron LDA Algorithm and MP Surprises by

Demiralp et Al)

Source: BBVA Research

A Big Data approach to understand Central Banks / 23

Inflación

Tasa de paro 0

-0.8

-0.6

-0.4

-0.2

0.2

0.4

0.6

1 9 9

6 -J

u n

1 9 9

7 -A

u g u

s t

1 9 9

8 -O

c to

b e

1 9 9

9 -D

e c e

m b

e r

2 0 0

1 -F

e b

ru a ry

2 0 0

2 -A

p ri l

2 0 0

3 -J

u n

2 0 0

4 -A

u g u

s t

2 0 0

5 -O

c to

b e

2 0 0

6 -D

e c e

m b

e r

2 0 0

8 -F

e b

ru a ry

2 0 0

9 -A

p ri l

2 0 1

0 -J

u n

2 0 1

1 -A

u g u

s t

2 0 1

2 -O

c to

b e

2 0 1

3 -D

e c e

m b

e r

2 0 1

5 -F

e b

ru a ry

2 0 1

6 -A

p ri l

2 0 1

7 -J

u n

2 0 1

8 -A

u g u

s t

BBVA Fed Sentiment Index (12-month moving average, left)

Fed. Funds Rate (right)

H a w

k is

D o v is

Last… but not least … we are working on the Federal Reserve Board

(FED) Topics and Stance…

Federal Reserve Board (FED) Topics definition FED Hawkish/Dovish index and Fed Funds rate (Moving average)

Source: BBVA Research

A Big Data approach to understand Central Banks / 24

1 9 9 8

2 0 0 0

2 0 0 2

2 0 0 4

2 0 0 6

2 0 0 8

2 0 1 0

2 0 1 2

2 0 1 4

2 0 1 6

2 0 1 8

General Index Yellen

1 9 9 8

2 0 0 0

2 0 0 2

2 0 0 4

2 0 0 6

2 0 0 8

2 0 1 0

2 0 1 2

2 0 1 4

2 0 1 6

2 0 1 8

General Index Powell

1 9 9 8

2 0 0 0

2 0 0 2

2 0 0 4

2 0 0 6

2 0 0 8

2 0 1 0

2 0 1 2

2 0 1 4

2 0 1 6

2 0 1 8

General Index Greenspan

1 9 9 8

2 0 0 0

2 0 0 2

2 0 0 4

2 0 0 6

2 0 0 8

2 0 1 0

2 0 1 2

2 0 1 4

2 0 1 6

2 0 1 8

General Index Bernanke

From a EM Crisis

Reactive and tigtening…

(Mr Greenspan)

1987-2003

To a Governor

Managing the crisis…

(Mr Bernanke)

2016-2014

To a Lady managing

the Exit Strategy…

(Mrs Yellen)

2014-2018

To a Normalization

Policy

(Mr Powell)

2018-

…complementing our “What” and “How” the Central Banks talk

with “who” is talking…

General and Governor FED Hawkish/Dovish index by speaker over time (Moving average 12 months)

T ig

h te

n in

E a

s in

Source: BBVA Research

A Big Data approach to understand Central Banks / 25

You can find us at:

www.bbvaresearch.com

Alvaro Ortiz

Tomasa Rodrigo

@alvaroortiz1968

@TomasaRodrigo

Thank you!

A Big Data approach to understand Central Banks / 26

A Big Data approach

to understand

Central Banks Big Data Spain 2018

November 2018