A Big Data approach to understand Central Banks Big Data Spain 2018

Transcription

1 A Big Data approach to understand Central Banks / 1 A Big Data approach to understand Central Banks Big Data Spain 2018 November 2018

2 A Big Data approach to understand Central Banks / 2 Summary 01 Why is the use of NLP important in economics and Monetary policy? The data and methodology 02 Understanding Central Banks: What, How and Who is talking (or writing) about?

3 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

4 A Big Data approach to understand Central Banks / 4 Why is the use of NLP important in economics and Monetary policy? Text as a key source of information to enrich economic analysis 80% 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 The potential use of textual information and text sources improves the understanding of economic and financial systems It helps us to understand what drives monetary policy decisions

5 A Big Data approach to understand Central Banks / 5 20% of used data 80% of available data

6 The data and methodology From Extraction to Sentiment Analysis A Big Data approach to understand Central Banks / 6 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 Text filtering Indexing to quantify text in lists of term counts Create the Document-term matrix Weighting matrix Analysis and Machine learning Topics extraction (LDA) Clustering Modelling (STM and DTM) Apply sentiment dictionaries Semantic analysis and classification Clustering Case-folding Factorization (SVD)

7 The data and methodology Analyzing central banks communication: Examined documents A Big Data approach to understand Central Banks / 7 Information extraction 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

8 The data and methodology Analyzing central banks communication: Cleaning and transforming the text A Big Data approach to understand Central Banks / 8 Pre-Processing and text parsing Transformation Working with text in numbers Create the Document-term matrix Weighting matrix Factorization Extracting and organizing the data Extract words Identify parts of speech Stopword Removal Case-folding Preparing it for the analysis Text filtering Indexing to quantify text in lists of term counts Converting it into numbers Stopword Removal Stemming Tokenization and multi-word tokens

9 The data and methodology Analyzing central banks communication: Dynamic topic models A Big Data approach to understand Central Banks / 9 Text mining and NPL Latent Dirichlet Allocation (LDA) and Dynamic Topic Model (DTM)

10 The data and methodology Analyzing central banks communication: Sentiment analysis A Big Data approach to understand Central Banks / 10 Text mining Sentiment analysis and NPL Loughran and McDonald (2011) FED Financial Stability dictionary (2017) Positive words Negative words Positive words Negative words Positive words Negative words Positive words Negative words achieve progress bankruptcy fallout benefit stabilize bottleneck imbalance efficiency strength corrupt monopolize outperform versatility downgrade stagnant benefit improve adverse escalate enhance upgraded challenge stagnation stabilise smooth deteriorate vulnerability favorable strengthened downgrade worsen Average tone = Positive words Negative words Total words

11 Main outputs Analyzing the Central Bank of Turkey, European Central Bank and Federal Reserve A Big Data approach to understand Central Banks / 11

12 Main outputs More than words: Getting the relation between words In the case of CBRT: A Big Data approach to understand Central Banks / 12

13 Main outputs their evolution over time A Big Data approach to understand Central Banks / 13 Most frequent words by year in the analyzed documents (the case of CBRT)

14 Main outputs as well as the topical content covered in the text A Big Data approach to understand Central Banks / 14

15 A Big Data approach to understand Central Banks / Understanding Central Banks: What, How and Who is talking (or writing) about

16 A Big Data approach to understand Central Banks / 16 We go Inside of the CB Reports to identify the topics using Machine Learning and Dynamic Topic Models. They can be different A Big Central Bank (ECB) A Central Bank of a EM Country as Turkey (CBRT) Economy EMU Integration Monetary Policy Global Flows Activity Financial Crisis Banking Union Quantitative Easing Monetary Policy Inflation 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 Source: BBVA Research

17 2006 jan 2007 feb 2008 feb 2009 feb 2010 feb 2011 feb 2012 feb 2013 feb 2014 feb 2015 mar 2016 sep A Big Data approach to understand Central Banks / 17 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 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% Economy Banking Union Standard MP EMU integration Financial crisis Non-standard MP Global Flows Labor Market Inflation Core Other Economic Activity Fiscal & Structural Policies Monetary Policy Source: BBVA Research Source: BBVA Research

18 A Big Data approach to understand Central Banks / 18 Networks are a useful tool to show the interconnectedness & complexity helping us to understand How the Central Banks talk.. Monetary Policy in the North (ECB) and in the EM (Turkey): Complexity and interconnectedness (Networks) Pre Lehman ( ) Financial Crisis ( ) QE & Post Crisis ( ) Source: BBVA Research

19 Negative Negative Positive Positive Sentiment analysis reinforces the analysis by describing How the Central Bank talks ( tone ) A Big Data approach to understand Central Banks / 19 Turkey (CBRT) : Economic Activity & Inflation Tone (Tone economic activity and Inflation jn the MP Minutes) 3 Turkey (CBRT): Economic Activity & Employment Tone (Tone economic activity and employment jn the MP Minutes) Economic Activity Inflation Economic Activity Employment Source: BBVA Research

20 A Big Data approach to understand Central Banks / 20 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) Monetary Policy Statements 3 Monetary Policy Minutes Tightening Easing A more formal Statement More extensive and analytical Source: BBVA Research

21 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

22 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

23 1996-June 1997-August 1998-October 1999-December 2001-February 2002-April 2003-June 2004-August 2005-October 2006-December 2008-February 2009-April 2010-June 2011-August 2012-October 2013-December 2015-February 2016-April 2017-June 2018-August Dovish Hawkish A Big Data approach to understand Central Banks / 23 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) Inflación -0.8 Tasa de paro BBVA Fed Sentiment Index (12-month moving average, left) Fed. Funds Rate (right) Source: BBVA Research

24 Easing Tightening A Big Data approach to understand Central Banks / 24 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) General Index Greenspan General Index Bernanke General Index Yellen General Index Powell From a EM Crisis Reactive and tigtening (Mr Greenspan) To a Governor Managing the crisis (Mr Bernanke) To a Lady managing the Exit Strategy (Mrs Yellen) To a Normalization Policy (Mr Powell) Source: BBVA Research

25 You can find us at: A Big Data approach to understand Central Banks / 25 Thank you! Alvaro Ortiz

26 A Big Data approach to understand Central Banks / 26 A Big Data approach to understand Central Banks Big Data Spain 2018 November 2018