important banks NTTS March 11, 2015 www.jrc.ec.europa.eu - - PowerPoint PPT Presentation

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Does web anticipate stocks? Analysis for a subset of systemically important banks

NTTS March 11, 2015

‘… people sometimes trade on noise as if it were information’ (Black, 1986)

Rationale Where can we find the “noise” In the web

• 1. Is web buzz able to lead stock behavior (for banks)?
• 2. Are stock movements sensitive to the geo-tagging of

the web buzz

Steps of the experiment

• 1. Scrape the web
• 2. Look for texts containing given keywords
• 3. Extract the mood of each text (sentiment analysis)
• 4. Analyse the relationship between the mood and

stock movements

Scrape the web

JRC- Europe Media Monitor

Born in 2002 Scrapes more than 10,000 RSS feeds/HTML pages from 4000 media websites worldwide (EU+US sources are 1500 - from 140 for Germany, to 50 for Spain) retrieves about 200.000 new news articles per day Keywords based (over 1500 categories) Works in 60 languages (sentiment analysis in 14 languages) Real time: scrapes the net every 10 minutes, 24h/day Attracts up to 1,2 Million hits per day

http://emm.newsbrief.eu

Novelty with respect to existing literature

• 1. First to analyse banks (objective: anticipate turbulences)
• 2. Multilingual sentiment (usually literature on English web

texts)

• 3. Full control of sources (usually literature works with a

limited number of texts from a source)

• 4. Geo-tagging: first to analyse which information matters

Added value

More on Nardo et al. 2015 Journal of Economic Surveys

Analysis

Web and stock daily data: from Dec. 2013 to April 2014 (overall

100 days of trade)

Subset of 10 banks (Barclays, BBVA, BNP Paribas, Crédit Agricole,

Deutsche Bank, HSBC, Royal Bank of Scotland, Santander, Société Générale and Unicredit)

Each combination of 8 stock prices variables, 12 web buzz variables, 4 set of sources (with different geo-tagging), various stock markets. The relationship between stock data and web news is analysed via

• cross-correlation function,
• Granger causality (rank-sum test)
• Factor and Cluster analysis

Results

Cross correlation Average (10 banks): between 0.33 and 0.37 at lag δ=0

EU stock exchanges

Results

Location of information matters

Average correlation between number of web-texts and various stock variables NYSE

Effects of geo-tags

For each bank we estimate the equation: Estimation for 4 sources of web buzz (W):

• 1. Sources located in EU+US
• 2. Sources located in EU
• 3. Sources all over the world (ALL)
• 4. Sources located in the country where the banks has

headquarters (country) For each estimated model we calculate the % change in the model fit (R2) using option 4 as baseline

𝑇𝑢 = 𝛽 + 𝛾1𝑇𝑢−1 + 𝛾2𝑋

𝑢 + 𝛾3𝑋 𝑢−1 + 𝜁𝑢

Results

EU-US vs Country All vs Country EU vs Country Barclays 24.1% 23.0% 21.8% BBVA 21.2% 5.2% 17.1% BNP Paribas 24.6% 29.8% 23.9% Crédit Agricole 3.3% 1.3% 2.5% Deutsche Bank 22.4% 24.7% 22.0% HSBC

• 26.5%
• 26.9%
• 28.9%

Royal B. Scotland 27.5% 29.7% 29.5% Santander 4.8%

• 0.2%

3.5% Société Générale 11.1% 2.4% 6.1% Unicredito 14.1% 14.9% 11.6% sources

Results

Results

web anticipates stocks gain wrt uniformed investor key variable anticipa ted where is the key informa Barclays BBVA

4% prices EU+USA

BNP Paribas Crédit Agricole

4-9% volatility EU+USA

Deutsche Bank

5-7.5% prices and volumes EU+USA

HSCB yal Bank of Scotland

5% prices EU+USA

Santander Société Générale

5% prices EU+USA

Unicredito

4-11% prices and volumes EU+USA

On average web does not Granger cause Stock But we find good Results for individual banks

The project is on going future planning:

• Analyse the weekly averages (to eliminate some

noise) for 25 banks

• Investigate general trends in the Euro area (via

alert setting)

• More ambitious: text mining on keywords

michela.nardo@jrc.ec.europa.eu