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www.jrc.ec.europa.eu
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
𝑆𝑡 = 𝛼 + 𝛽1𝑆𝑡−1 + 𝛽2𝑊𝑡 + 𝛽3𝑊𝑡−1 + 𝜀𝑡
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 Resultsweb
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
Royal 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