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The role of News Analytics in financial engineering: a review and the road ahead

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Title: The role of News Analytics in financial engineering: a review and the road ahead


1
The role of News Analytics in financial
engineering a review and the road ahead 
  • Gautam Mitra
  • 7 December 2011 London

2
Outline
  • Introduction
  • What Why How.
  • A commercial
  • News data
  • Data sources
  • Information Contents/Metadata
  • Summary Information/Views
  • Information/modelling architecture
  • Models and Applications
  • Abnormal Returns
  • News Enhanced Trading Strategies
  • Risk Control
  • Case studies
  • Risk Control
  • News Analytics Toolkit
  • Momentum study
  • Summary Conclusion

3
WHAT News analytics a working definition
  • News analytics refers to the measurement of the
    various qualitative and quantitative attributes
    of textual news stories. Some of these attributes
    are sentiment, relevance, and novelty.
    Expressing news stories as numbers permits the
    manipulation of information in a mathematical
    and statistical way
  • lt Taken from Wiki gt
  • A news story is about an event

4
WHY the research problem the business problem
  • The world of financial analytics is concerned
    with three leading problems.
  • ( i ) Pricing of assets in a temporal setting
  • ( ii ) Making optimum investment decisions- low
    frequency or optimum trading decisions- high
    frequency
  • ( iii )Controlling risk at different time
    exposures

5
Howthe message
  • Finance industry focuses on three major
    applications
  • gt High frequency Trading strategies
  • gt Low frequency Investment strategies
  • gt Risk control
  • By increasing the information set with quantified
    news the legacy models for the above applications
    can be enhanced
  • Knowledge from three disciplines are required
  • gt Information engineering
  • gt AI Knowledge Engineering
  • gt Financial Engineering

6
Introduction
  • News
  • Market Environment
  • Sentiment
  • Behavioural finance lt greed..fear..irrational
    exuberance gt
  • Wall Street 1
  • Wall Street 2 gt money never sleeps

7
Introduction
  • neo classical models for choice or decision
    making
  • Trading Strategies/ Decisions
  • Investment Decisions
  • Risk Control Decisions

8
R D Challenge ? Identify Killer Application
Introduction
  • Smart investors rapidly analyse/digest
    information.
  • News stories/announcements.
  • Stock price moves (market reactions).
  • Act promptly to take trading/investment
    decisions.
  • Can a machine act intelligently(AI) to compete or
    outsmart humans ?

9
Commercial
  • Read
  • The Handbook of News Analytics in Finance
  • By Gautam Mitra and Leela Mitra
  • lt for an instant understanding ...! gt
  • lt or look up http//www.bis.gov.uk/foresight/our-
    work/projects/current-projects/computer-trading
  • The Future of Computer Trading in Financial
    Markets
  • Our report Automated analysis of news to compute
    market sentiment its impact on liquidity and
    trading...Gautam Mitra , Dan DiBartolomeo, Ashok
    Banerjee, Xiang Yu.

10
Outline
  • Introduction
  • What Why How.
  • A commercial
  • News data
  • Data sources
  • Information Contents/Metadata
  • Summary Information/Views
  • Information/modelling architecture
  • Models and Applications
  • Abnormal Returns
  • News Enhanced Trading Strategies
  • Risk Control
  • Case studies
  • Risk Control
  • News Analytics Toolkit
  • Momentum study
  • Summary Conclusion

11
News data Data sources
  • Which Asset classes....?
  • FX- Currency
  • Commodities
  • Fixed income (Bonds)
  • Stocks (Equities)
  • Wall Street proverb
  • Stocks are stories bonds are mathematics

12
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13
News data Data sources
  • Traders High Frequency
  • Fund Managers Low Frequency
  • Desktop
  • Market Data
  • NewsWire
  • Web lt blogs, twitter, message boards gt
  • Data WareHouse
  • DataMart

14
News data Data sources
  • Sources of news/informational flows (Leinweber)
  • News Mainstream media, reputable sources.
  • Newswires to traders desks.
  • Newspapers, radio and TV.
  • Pre-News Source data
  • SEC reports and filings. Government agency
    reports.
  • Scheduled announcements, macro economic news,
    industry stats, company earnings reports
  • Web based news
  • Social media Blogs, websites and message boards
  • Quality can vary significantly
  • Barriers to entry low
  • Human behaviour and agendas

15
News data Data sources
  • Financial news can be split between
  • Scheduled news (Synchronous)
  • Unscheduled news (Asynchronous, event driven)
  • Scheduled news (Synchronous)
  • Arrives at pre scheduled times
  • Much of pre news
  • Structured format lt XML..XBRL gt
  • Often basic numerical format
  • Typically macro economic announcements and
    earnings announcements

16
News data Data sources
  • Unscheduled news (Asynchronous, event driven)
  • Arrives unexpectedly over time
  • Mainstream news and social media
  • Unstructured, qualitative, textual form
  • Non-numeric
  • Difficult to process quickly and quantitatively
  • May contain information about effect and cause of
    an event
  • To be applied in quant models needs to be
    converted to an input time series

17
Information contents/Metadata
  • Key Attributes include
  • Entity Recognition
  • Relevance
  • Novelty
  • Events categories
  • Sentiment
  • Preanalysis ? extracts/computes/mines these
    attributes and using text analysis and
    AI-classifiers sentiment scores are created This
    is the (news) metadata
  • Also the news flow/the intensity influences the
    resulting sentiment

18
Information/modelling architecture
Mainstream News
metadata
Pre-News
Pre-Analysis (Classifiers others)
Analysis
Consolidated Data mart
Updated beliefs, Ex-ante view of market
environment
  • Entity Recognition
  • Relevance
  • Novelty
  • Events
  • Sentiment Score

Web 2.0 Social Media
  • Quant Models
  • Return Predictions
  • Fund Management / Trading Decisions
  • Volatility estimates and risk control

News Flow/Intensity
(Numeric) financial market data
Information value chain Data
information knowledge Data ? analysis ? Data
mart ? quant models
19
Analysis ..synthesis ..miningentity recognition
  • Identify entities such as companies in news
    stories using point-in-time sensitive
    information
  • Short names
  • Long names
  • Common abbreviations
  • Common misspellings
  • Securities identifiers
  • Subsidiaries

20
Analysis ..synthesis ..mining relevance
  • Calculate the relevance of a story to a given
    company
  • Mentions in the text
  • Positioning in the story (headline vs. last
    paragraph)
  • Total number of companies mentioned
  • Detect roles played by companies in the story
  • Represent the context numerically

21
Analysis ..synthesis ..mining novelty
  • Is the news story "new" or novel?
  • Elementize the various characteristics of a news
    story
  • Distinguish between similar vs. duplicate stories
  • Define a time window between stories

Example Toyotas Vehicle Recall (news flow in
the first 30 minutes)
22
Analysis ..synthesis ..mining event categories
  • Company news and events are categorized
  • Identify actionable events
  • The more detailed the event, the better
  • Differentiate between scheduled vs. unscheduled
    news events
  • Distinguish between explanatory or predictive
    inputs

23
Analysis ..synthesis ..miningsentiment
24
Summary information and views
  • Thomson Reuters News Analytics  
  • Equity coverage and available data
  • Coverage
  • Equity
  • All equities ............................34,037
    (100.0?)
  • Active companies ................32,719 (96.1)
  • Inactive companies............. 1,318 (3.9)
  • Equity coverage by region
  • Americas ...............................14,785
  • APAC .....................................11,055
  • EMEA.......................................8,197
  • Equity Coverage Updates Bi-weekly updated for
    recent changes (de-listings, MA, IPOs).
  • History Available from January 2003 (history
    kept for delisted companies symbology
  • changes tracked).

RavenPack News Analytics Equity Coverage by
Region All equities...............................
....28,279 (100) Americas ......................
.............11,950 (42.24) Asia
............................................8,858
(31.31) Europe................................
...... 5,859 (20.71) Oceania
....................................436
(5.08) Africa ..................................
.......186 (0.66) For the most updated list
of supported companies download the companies.csv
file at https//ravenpack.com/newsscores/ Histori
cal Data Data format Comma separated values
(.csv) files Date/Time info In Universal
Coordinated Time (UTC) Archive Range Since Jan
1, 2005 Archive Packaging Monthly .csv files
compressed in .zip files on a per year basis
25
Summary information
  • Other suppliers
  • Deutsche Boerse lt Alpha Flash gt
  • Bloomberg Black box newsfeed
  • Dow Jones Elementized Newsfeed

26
Summary information and views
  • Tetlock et al. event study shows information
    leakage

27
Average Stock Price Reaction to Negative News
Events
Summary information and views
Source Macquarie Quant Research May 2009
28
Average Stock Price Reaction to Positive News
Events
Summary information and views
Source Macquarie Quant Research May 2009
29
Summary information and views
Illustration of Seasonality (Hafez, RavenPack)
30
RavenPack Sentiment Scores
31
Reuters NewsScope Sentiment Engine
32
Outline
  • Introduction
  • What Why How.
  • A commercial
  • News data
  • Data sources
  • Information Contents/Metadata
  • Summary Information/Views
  • Information/modelling architecture
  • Models and Applications
  • Abnormal Returns
  • News Enhanced Trading Strategies
  • Risk Control
  • Case studies
  • Risk Control
  • News Analytics Toolkit
  • Momentum study
  • Summary Conclusion

33
Model Applications (abnormal ) Returns
  • Traders and quant managers identify and exploit
    asset mispricings before they correct generate
    alpha
  • News data can be used
  • Stock picking and generating trading signal
  • Factor models
  • Exploit behavioural biases in investor decisions

34
Model Applications (abnormal ) Returns
  • Stock picking and generating trading signal
  • Sentiment reversal as buy signal J Kitterell
    uses a sequence of
  • P, N scores as a means of testing
    sentiment reversal.
  • Momentum strategy enhanced by news sentiment
    scores
  • Macquarie research also Sinha reports
    results with Thomson Reuters data.

35
Model Applications (abnormal ) Returns
  • Behavioural biases
  • Odean and Barber (2007) find evidence individual
    investors have a tendency to buy attention
    grabbing stocks.
  • Professional investors better equipped to assess
    a wider range of stocks they are less prone to
    buying attention grabbing stocks
  • Da, Engleberg and Gao also consider how the
    amount of attention a stock received affects its
    cross-section of returns.
  • Use the frequency of Google searches for a
    particular company as a measure of attention.
  • Find some evidence that changes in investor
    attention can predict the cross-section of
    returns.

36
Model Applications (abnormal ) Returns
  • Stock picking and generating trading signal
  • Li (2006) simple ranking procedure
  • identify stocks with positive and negative
    sentiment
  • 10 K SEC filings for non-financial firms 1994
    2005
  • Risk sentiment measure count number of times
    words
  • risk, risks, risky, uncertain, uncertainty and
    uncertainties
  • appear in management discussion and analysis
    section
  • Strategy long in low risk sentiment stocks
  • short in high risk sentiment stocks
  • reasonable level returns
  • Leinweber (2010) event studies based on Reuters
    NewsScope Sentiment Engine

37
News Enhanced Algorithmic Trading
  • Information/modelling architecture
  • Modelling architecture
  • Pre-trade Post trade Analysis
  • Characterize asset behaviour/dynamics by
  • Asset Price/Return
  • Asset (Price) Volatility
  • Asset (Price) Liquidity
  • Construct trading models using these measures

38
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39
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40
Applications Risk management
  • Traditionally historic asset price data has been
    used to estimate risk measures.
  • ex post retrospective measures
  • fail to account for developments in the market
    environment, investor sentiment and knowledge
  • Significant changes in the market environment
  • Traditional measures can fail to capture the true
    level of risk
  • (Mitra, Mitra and diBartolomeo 2009
    diBartolomeo and Warrick 2005)
  • Incorporating measures or observations of the
    market environment in risk estimation is
    important

41
EQUITY PORTFOLIO RISK (VOLATILITY) ESTIMATION
USING MARKET INFORMATION AND SENTIMENT
  • Leela Mitra
  • Co-authors Gautam Mitra and
  • Dan diBartolomeo .
  • Sponsored by

42
Case study Outline
  • Problem setting
  • Model description
  • Updating the model using quantified news
  • Study I
  • Study II
  • Discussion and conclusions

43
Introduction background
  • Tetlock et al. (2007) note there are three main
    sources of information
  • Analyst forecasts
  • Publicly disclosed accounting variables
  • Linguistic descriptions of operating environments
  • If first two are incomplete third may give us
    relevant information
  • Tetlock et al. (2007) introduce news to a
    fundamental factor model

44
Problem setting
  • Three main types of factor models
  • Macroeconomic use economic variables as factors
    (Chen, Ross and Roll Sharpe)
  • Fundamental based on firm specific
    (cross-sectional) attributes (BARRA and
    Fama-French)
  • Statistical factors are unobservable and
    derived via calibration, often orthogonal.
  • Differ on sources of risk (uncertainty) can be
    shown to be rotations of each other.

45
Problem setting
  • Need for models to update risk structure as
    environment changes
  • diBartolomeo and Warrick (2005) update covariance
    estimates using option implied volatility
  • Traders respond quickly in an intelligent fashion

46
Model description
  • An extension of diBartolomeo Warrick(2005)
  • In two parts
  • Basic statistical factor model
  • Factor variance estimates are updated for changes
    in option implied volatility

47
Model description
  • We construct a statistical factor model using
    principal component analysis to find orthogonal
    factors
  • Update the asset variances using option implied
    volatility data

48
Model description
  • For each asset for which we have option
    implied volatility data
  • We wish to identify the new factor variances
    and asset specific variances
  • implied by updated asset variances
  • Solve this set of simultaneous equations to
    derive the values, subject to some further
    conditions

49
Model description
  • Further conditions
  • Allow for structure that is expected of
    principal component factors
  • Assume factor variances do not decline
    substantially from one period to the next
  • Similarly assume asset specific variances do not
    decline substantially from one period to the next

50
Study I
  • Period 17 January 2008 to 23 January 2008
  • EURO STOXX 50
  • Market sentiment worsened
  • Option implied volatility measures surged
  • Few key events
  • Large interest rate cut
  • George Bush announced stimulus plan
  • Soc Gen hit by Jerome Kerviel rogue trader
    scandal

51
Study I
  • Portfolio volatility from option implied model
  • is higher than basic model
  • rises significantly on 21 January

52
Study II
  • Over 2008 markets fell
  • Loss of liquidity in credit markets and banking
    system
  • Many banks suffered bankruptcy or propped up
  • September and October 2008 Volatility for
    financial firms particularly high
  • Lehman Bankruptcy
  • Lloyds takeover of HBOS
  • Restrictions on short selling of financials

53
Study II
  • 18 September 2008 to 24 September 2008
  • Dow Jones 30
  • Portfolio of three finance stocks
  • Bank of America, CitiGroup and JP Morgan Chase
  • Equal weight on each stock
  • Portfolio of three non-finance stocks
  • Johnson Johnson, Kraft Foods and Coca Cola
  • Equal weight on each stock
  • Can the model predict impact in one sector?

54
Study II
55
Study II
56
Information/modelling architecture
Mainstream News
metadata
Pre-News
Pre-Analysis (Classifiers others)
Analysis
Consolidated Data mart
Updated beliefs, Ex-ante view of market
environment
  • Entity Recognition
  • Relevance
  • Novelty
  • Events
  • Sentiment Score

Web 2.0 Social Media
  • Quant Models
  • Return Predictions
  • Fund Management / Trading Decisions
  • Volatility estimates and risk control

News Flow/Intensity
(Numeric) financial market data
Information value chain Data
information knowledge Data ? analysis ? Data
mart ? quant models
57
News Analytics Toolkit
58
Momentum Study
  • RSI (Relative Strength Indicator) with a 15 day
    timeframe
  • U closenow - closeprevious if up period, 0
    otherwise
  • D closeprevious - closenow if down period, 0
    otherwise
  • RS EMA(U,n) / EMA(D,n)
  • EMA n-period Exponential Moving Average
  • RSI 100 100 / (1 RS)
  • Asset Universe FTSE100 and CAC40
  • Daily market data from Jan 2005 to Jan 2011
  • Portfolio Selection
  • Ranked by the RSI Momentum Indicator
  • Long only, equally weighted
  • Calendar rebalancing frequency every 60 or 90
    working days
  • Transaction Cost 0.2
  • Number of assets in portfolio 10 for FTSE100, 5
    for CAC40

59
Momentum Study
  • News enhanced Momentum Strategy
  • News provided by RavenPack News Score 1.5
  • Revised Ranking including Market Data and News
    Data
  • Companies are ranked according to average
    sentiment score
  • Only news with Relevance 75 and within the
    previous 15 days are considered
  • Momentum ranking and news ranking are combined
    with equal weights between news sentiment score
    and RSI score
  • Companies with no news in the period are
    considered to have an average sentiment score of
    50 (neutral sentiment)

60
Momentum Study
  • FTSE 100, 90 days rebalancing

61
Momentum Study
  • CAC 40, 90 days rebalancing

62
Momentum Study
  • FTSE 100, 60 days rebalancing

63
Momentum Study
  • CAC 40, 60 days rebalancing

64
Summary discussions
  • Applications of (semi-)automated news analytics
    in finance are growing in importance.
  • Pay back can be substantial to
  • Investment Managers
  • Traders
  • Internal Risk Auditors
  • Regulators

65
Summary discussions
  • Knowledge and Skills from three different
    disciplines
  • Information Systems.
  • Artificial Intelligence.
  • Financial Engineering quantitative
    modelling(including behavioural finance).
  • are required in various degrees to progress the
    field/make substantial impact.

66
Thank you....
  • Thank you for your attention
  • Comments and Questions please
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