Forum On News Analytics

Forum on News Analytics Forum on News Analytics applied to Trading, Fund Management applied to Trading, Fund Management and Risk Control and Risk Control

GAUTAM MITRA

Forum on News Analytics applied to Trading, Forum on News Analytics applied to Trading, Fund Management and Risk Control Fund Management and Risk Control


• Background and overview of news analytics in finance

• Sentiment classification

• News and abnormal returns

• News and volatility

• Industry insights, technology, products and services

• Directory of news analytics service providers

• Bibliography

DAN DI BARTOLOMEO


Incorporation of Quantified Incorporation of Quantified News into Portfolio Risk News into Portfolio Risk

AssessmentAssessment

Dan diBartolomeoDan diBartolomeoNorthfield and Brunel UniversityNorthfield and Brunel University

London, 2009London, 2009

Motivation for the Short Term Motivation for the Short Term Risk ForecastsRisk Forecasts

• Risk models for asset management (as distinct from trading Risk models for asset management (as distinct from trading operations) have traditionally focused on estimating portfolio risk operations) have traditionally focused on estimating portfolio risk from security covariance over time horizons of a year or morefrom security covariance over time horizons of a year or more– Suitable for long term investors such as pension fundsSuitable for long term investors such as pension funds

• Investment performance of asset managers is often evaluated Investment performance of asset managers is often evaluated over shorter horizons so they are interested in shorter term risk over shorter horizons so they are interested in shorter term risk assessment. Hedge funds and other portfolios with high portfolio assessment. Hedge funds and other portfolios with high portfolio turnover are even stronger in this preferenceturnover are even stronger in this preference

• The proliferation of high frequency trading and algorithmic The proliferation of high frequency trading and algorithmic execution methods have created demand for very short horizon execution methods have created demand for very short horizon risk assessmentrisk assessment

A Short ChronologyA Short Chronology

• A call from Blair Hull in 1996A call from Blair Hull in 1996

• diBartolomeo and Warrick (2005) in Linear Factor diBartolomeo and Warrick (2005) in Linear Factor Models in Finance, edited by Satchell and KnightModels in Finance, edited by Satchell and Knight

• ““Short Term Risk from Long Term Models”, Short Term Risk from Long Term Models”, Northfield research series, Anish Shah, 2007-2009Northfield research series, Anish Shah, 2007-2009

• ““Equity Portfolio Risk Using Market Information and Equity Portfolio Risk Using Market Information and Sentiment” by diBartolomeo, Mitra and Mitra, 2009Sentiment” by diBartolomeo, Mitra and Mitra, 2009

Simple Approach to Short Term Simple Approach to Short Term ModelingModeling

• The usual answer:The usual answer:– Increase the frequency of observations (daily or shorter)Increase the frequency of observations (daily or shorter)– Use a shorter sample periodUse a shorter sample period– Generally need different factors Generally need different factors

• There are serious problems with this approach at the There are serious problems with this approach at the individual security levelindividual security level– High degree of kurtosis in return distributions (well maybe?)High degree of kurtosis in return distributions (well maybe?)– Negative serial correlation due to short term reversal effectsNegative serial correlation due to short term reversal effects– Positive serial correlation on illiquid instrumentsPositive serial correlation on illiquid instruments– Asynchronous trading across time zones makes correlation Asynchronous trading across time zones makes correlation

estimation very difficultestimation very difficult

• Address “shocks” through a GARCH processAddress “shocks” through a GARCH process

What’s the Problem with What’s the Problem with High Frequency Data?High Frequency Data?• Financial markets are driven by the arrival of information in Financial markets are driven by the arrival of information in

the form of “news” (truly unanticipated) and the form of the form of “news” (truly unanticipated) and the form of “announcements” that are anticipated with respect to time “announcements” that are anticipated with respect to time but not with respect to content.but not with respect to content.

• The time intervals it takes markets to absorb and adjust to The time intervals it takes markets to absorb and adjust to new information ranges from minutes to days. Generally new information ranges from minutes to days. Generally much smaller than a month, but up to and often larger than a much smaller than a month, but up to and often larger than a day. That’s why US markets were closed for a week at day. That’s why US markets were closed for a week at September 11September 11thth..

• GARCH models don’t work well on announcementsGARCH models don’t work well on announcements– Market participants anticipate announcementsMarket participants anticipate announcements– Volume and volatility dry up as investors wait for outcomesVolume and volatility dry up as investors wait for outcomes– Reduce volatility into the announcement and boost it after the Reduce volatility into the announcement and boost it after the

announcement, so they are wrong twiceannouncement, so they are wrong twice

Some Surprising Things Appear Some Surprising Things Appear AnticipatedAnticipated• Lets look at a precipitous decline in the implied Lets look at a precipitous decline in the implied

volatility of options on LUVvolatility of options on LUV– All days in 2001 prior to September 7, average of .45 All days in 2001 prior to September 7, average of .45

with a s.d. of .13with a s.d. of .13– September 7, LUV implied = .22September 7, LUV implied = .22– September 10, LUV implied = .15September 10, LUV implied = .15– All days subsequent to September 17, average of .54 All days subsequent to September 17, average of .54

with a s.d. of .18with a s.d. of .18– September 10 is in bottom 1% of the universe in implied September 10 is in bottom 1% of the universe in implied

volatility, September 17 is 91volatility, September 17 is 91stst percentile percentile

• Could this be driven by fundamentals?Could this be driven by fundamentals?

Investor Response Investor Response to Information Flow to Information Flow

• Several papers have examined the relative market Several papers have examined the relative market response to “news” and “announcements”response to “news” and “announcements”– Ederington and Lee (1996)Ederington and Lee (1996)– Kwag Shrieves and Wansley(2000)Kwag Shrieves and Wansley(2000)– Abraham and Taylor (1993) Abraham and Taylor (1993)

• Jones, Lamont and Lumsdaine (1998) show a remarkable Jones, Lamont and Lumsdaine (1998) show a remarkable result for the US bond marketresult for the US bond market– Total returns for long bonds and Treasury bills are not different if Total returns for long bonds and Treasury bills are not different if

announcement days are removed from the data setannouncement days are removed from the data set• Brown, Harlow and Tinic (1988) provide a framework for Brown, Harlow and Tinic (1988) provide a framework for

asymmetrical response to “good” and “bad” news asymmetrical response to “good” and “bad” news – Good news increases projected cash flows, bad news decreasesGood news increases projected cash flows, bad news decreases– All new information is a “surprise”, decreasing investor confidence All new information is a “surprise”, decreasing investor confidence

and increasing discount ratesand increasing discount rates– Upward price movements are muted, while downward movements Upward price movements are muted, while downward movements

are accentuatedare accentuated

Our Approach is DifferentOur Approach is Different• Continue to use the existing risk models that are estimated Continue to use the existing risk models that are estimated

from low frequency return observationsfrom low frequency return observations

• Use new information that is not part of the risk model to adjust Use new information that is not part of the risk model to adjust various components of the risk forecast to short-term various components of the risk forecast to short-term conditionsconditions– Just ask yourself “How are conditions different now than they were on Just ask yourself “How are conditions different now than they were on

average during the sample period used for estimation?”average during the sample period used for estimation?”

• This approach has multiple benefitsThis approach has multiple benefits– We sidestep almost all of the statistical complexities that arise with use We sidestep almost all of the statistical complexities that arise with use

of high frequency dataof high frequency data– We get to keep the existing factor structure of the model so risk We get to keep the existing factor structure of the model so risk

reporting remains familiar and intuitivereporting remains familiar and intuitive– Since our long term and short term forecasts are based on the same Since our long term and short term forecasts are based on the same

factor structure, we can quickly estimate new forecasts for any length factor structure, we can quickly estimate new forecasts for any length time horizon that falls between the two horizonstime horizon that falls between the two horizons

– Can be applied to any of our existing modelsCan be applied to any of our existing models

One Form of Working with One Form of Working with “External Information”“External Information”• Risk estimates in our short term model of US Risk estimates in our short term model of US

equities have been conditioned for many years equities have been conditioned for many years based on analysis of stock option implied volatilitybased on analysis of stock option implied volatility– Every day we look at the implied volatility of options on all Every day we look at the implied volatility of options on all

US stocks. We keep a 30 day moving average of the ratio of US stocks. We keep a 30 day moving average of the ratio of implied volatility to historic volatilityimplied volatility to historic volatility

– If the implied volatility/historic ratio jumps because of an If the implied volatility/historic ratio jumps because of an information flow to the market (e.g. Bill Gates gets run over information flow to the market (e.g. Bill Gates gets run over by a bus), the specific risk of that stock is adjustedby a bus), the specific risk of that stock is adjusted

– If implied volatility ratio of many related stocks changes, If implied volatility ratio of many related stocks changes, the implied changes in factor variance are also made. Risk the implied changes in factor variance are also made. Risk forecasts change even for stocks on which no options tradeforecasts change even for stocks on which no options trade

– Requires non-linear optimization process for adjustmentsRequires non-linear optimization process for adjustments– See Chapter 12, by diBartolomeo and Warrick See Chapter 12, by diBartolomeo and Warrick Linear Linear

Factor Models in FinanceFactor Models in Finance, Satchell and Knight, editors , Satchell and Knight, editors (2005)(2005)

““Variety” as External Variety” as External InformationInformation• Solnik and Roulet (2000) examine the dispersion of Solnik and Roulet (2000) examine the dispersion of

country returns as a way of estimating correlations country returns as a way of estimating correlations between marketsbetween markets

• Lilo, Mantegna, Bouchard and Potters use the term Lilo, Mantegna, Bouchard and Potters use the term VarietyVariety to describe cross-sectional dispersion of stock returnsto describe cross-sectional dispersion of stock returns– They also define the cross-sectional dispersion of CAPM alpha as They also define the cross-sectional dispersion of CAPM alpha as

idiosyncratic varietyidiosyncratic variety (noted as v(t)) (noted as v(t))– They find that the average correlation between stocks is They find that the average correlation between stocks is

approximately: approximately:

C(t) = 1 / [1 + (vC(t) = 1 / [1 + (v22(t)/r(t)/rmm22(t) ](t) ]

• diBartolomeo (2000) relates periods of high cross-diBartolomeo (2000) relates periods of high cross-sectional dispersion to positive serial correlation in stock sectional dispersion to positive serial correlation in stock returns (i.e. momentum strategies working)returns (i.e. momentum strategies working)

Other Conditioning Other Conditioning InformationInformation• Estimates of volatility based on high/low/open/close Estimates of volatility based on high/low/open/close

information instead of the dispersion of returnsinformation instead of the dispersion of returns– Parkinson, Garman-Klass, Satchell-Wang, etc.Parkinson, Garman-Klass, Satchell-Wang, etc.

• Yield spreads for different classes of fixed income Yield spreads for different classes of fixed income securities provide an implied default rate and the securities provide an implied default rate and the potential for large negative skew in stock returnspotential for large negative skew in stock returns

• Implied distribution of asset returns given the implied Implied distribution of asset returns given the implied vols of options on market indices across strike pricesvols of options on market indices across strike prices

• Direct measures of information flow to investors, and Direct measures of information flow to investors, and investor attention that can create imbalances between investor attention that can create imbalances between supply and demand for a given stocksupply and demand for a given stock

What Makes People Buy or Sell a Particular Stock?• They WANT to trade the stock

– They believe the information that supports a valid forecast of abnormal future return

• They HAVE to trade the stock– They are trading to implement a change in asset allocation– They are trading to implement a cash versus futures arbitrage

trade on a stock index– They are a mutual fund or ETF sponsor responding to investor

cash flows in or out of the portfolio– They are hedge fund that is forced to transact because of a

margin call– They are forced to cover a short position by having the stock

called

The Potential for “Have To’s”

• We can fundamentally evaluate the potential for “have to” trades– Index arbitrage trades only occur with index constituents

and we know the open interest in futures– Short interest information is published– We know what big hedge and mutual funds have big

positions in particular stocks– We have somewhat out of date information on full

mutual fund holdings and cash flow statistics– We have fairly up to date information on ETF flows

The Potential for “Want To” Trades• Investors are responding to information, so just measure

variations in the volume of information about a particular stock over time

• Judge the magnitude of information flow of news text coming over services such as Dow-Jones, Reuters and Bloomberg– Ravenpack and Thomson Reuters offer real time statistical

summaries of the amount and content of text news distributed– Lexicons of over 2000 popular phrases are used to score the

content as “good news” or “bad news”

• Judge investor attention directly by measuring the number of Google and Yahoo searches on ticker symbols

Incorporating News Flows into Risk Incorporating News Flows into Risk AssessmentsAssessments

• diBartolomeo, Mitra and Mitra (2009) forthcoming in Quantitative Finance– Follow the diBartolomeo and Warrick mathematical framework– Allow the conditioning information set to include both option

implied volatility and variations in text news flows from Ravenpack (derived from Dow-Jones text feeds)

– Empirical tests on Euro Stoxx 50 during January 17-23, 2008 and Dow Jones 30 stocks September 18 to 24, 2008

– Evaluate both individual stocks, full index and financial/non-financial subset portfolios

• In all cases, inclusion of quantified news flows improved the rate of adjustment of risk estimates to time variation in volatility faster than implied volatility alone

Incorporating Investor Incorporating Investor AttentionAttention• Our next step will be to directly measure the degree of

investor attention to a stock– Judge investor interest directly by measuring the number of Google

and Yahoo searches on trading symbols– Avoid company names to eliminate product or service related

searches– Try it yourself with Google Trends

• Da, Engleberg and Gao (2009) have already documented a strong relationship between abnormal search frequency and price momentum

• Investor attention is not always a good thing– Bolster and Trahan (2009) document predictable price behavior in

stocks mentioned on the Jim Cramer television show– Clear strategy: wait two days, then short every stock mentioned

positively or negatively

Crucial RefinementCrucial Refinement• diBartolomeo and Warrick (2005), and diBartolomeo, diBartolomeo and Warrick (2005), and diBartolomeo,

Mitra and Mitra (2009) both assume that the full Mitra and Mitra (2009) both assume that the full impact of the conditioning information should applied impact of the conditioning information should applied to ex-ante risk estimatesto ex-ante risk estimates

• Shah (2008) introduces formal Bayesian framework for Shah (2008) introduces formal Bayesian framework for incorporating conditioning information into modelsincorporating conditioning information into models– Requirement for orthogonal factors is removedRequirement for orthogonal factors is removed– Non-linear optimization to “fit” the adjustments to correlated Non-linear optimization to “fit” the adjustments to correlated

factors is even more complexfactors is even more complex– Introduced into Northfield “near-horizon” models in May 2009Introduced into Northfield “near-horizon” models in May 2009– Reduces noise and allows for fitting to different time horizonsReduces noise and allows for fitting to different time horizons

Other Differences BetweenOther Differences BetweenLong and Short Horizon RiskLong and Short Horizon Risk

• Negative serial correlationNegative serial correlation– Daily overreactions & reversals, which cancel out Daily overreactions & reversals, which cancel out

over time, become significant e.g. under over time, become significant e.g. under leverageleverage

• Contagion / panicContagion / panic– Liquidity demands can drive up short-term Liquidity demands can drive up short-term

correlationscorrelations• Transient behaviorTransient behavior

– A long term model intentionally integrates new A long term model intentionally integrates new phenomena slowly: Is the future like the past or phenomena slowly: Is the future like the past or are we in and concerned about a present shift?are we in and concerned about a present shift?

• Lots more extreme events in the short-termLots more extreme events in the short-term– 3 std deviations contains less probability mass. 3 std deviations contains less probability mass.

99% VaR is farther away from the mean99% VaR is farther away from the mean

ConclusionsConclusions• The key to good short term risk assessment is The key to good short term risk assessment is

understanding how conditions now are different than understanding how conditions now are different than they usually arethey usually are

• A broad set of information other than stock A broad set of information other than stock characteristics and past returns are clearly useful in characteristics and past returns are clearly useful in improving risk estimatesimproving risk estimates

• Among the most useful sets of conditioning information Among the most useful sets of conditioning information appears to be summaries of textual news flows to appears to be summaries of textual news flows to investorsinvestors

• A rigorous Bayesian framework should be employed to A rigorous Bayesian framework should be employed to intelligently combine long term and short term intelligently combine long term and short term information setsinformation sets

ARMANDO GONZALEZ


ARUN SONI


JAMES CHENERY


INCOPRORATING NEWS ANALYSIS INTO TRADING AND INVESTMENT PROCESSESFORUM ON NEWS ANALYTICS

November 9, 2009

James Chenery

Business Development Manager

EXPLOITING NEWS CONTENT

• News is emerging as differentiated, value generating content set– Quant strategies – all trading frequencies

– Human decision support – especially with analytic enhancements

• Key uses– Speed – beat the humans, beat the machines

– Manage scale and scope of events affecting portfolio

– Risk management and loss avoidance

• NewsScope product line - Robust set of capabilities – Historical data to back test and build algorithms

– Real-time feeds for deployment, including ultra-low latency feed

– News Analytics which convert qualitative text into quantitative data

28

EXPLOITING NEWS CONTENT

• News flow is a good indicator of volume and volatility

• Pricing movements accompanied by news tend to be momentum in nature; those with a lack of news tend to reverse to average trends

• The market tends to overreact when there is a lot of news on something and under-react when there is a small quantity of news

• For other direction and magnitude signals, find cause:effect relationships

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MACHINE READABLE NEWS USE CASES

• Wolf detection / circuit breaker

• News flow algorithms

• Alpha generating signal

• Post trade analysis

• Stock screening tool

• Risk Management

• Compliance / Market abuse

• Fundamental research

• Trader decision support

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NEWSSCOPE PRODUCT PORTFOLIO

• NewsScope Archive

– Historical database of Reuters and select third-party market moving sources

• NewsScope Direct– Ultra-low latency feed of highly structured news and economic data

• NewsScope Analytics (aka NewsScope Sentiment Engine)

– Automated news analysis solution measuring sentiment, relevance, and novelty of text along with a host of other valuable metadata

• NewsScope Event Indices– Automated news analysis solution indicating when abnormal

amounts of news occur across various categories

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32

HOW TO MAKE MONEY WITH THE NEWSSCOPE ANALYTICS

Buy on good news

Sell on bad news

Outperform S&P500 by 5000 basis points over a 60 day period!

S&P1500 stocks in 2008; Daily items >50; Pos vs Neg >50%

33

Questions?

MARK VREIJLING

Forum on News Analytics applied to Trading, Fund Management and Risk Control

GANGADHAR DARBHA


GURVINDER BRAR


In preparing this research, we did not take into account the investment objectives, financial situation and particular needs of the reader. Before making an investment decision on the basis of this research, the reader needs to consider, with or without the assistance of an adviser, whether the advice is appropriate in light of their particular investment needs, objectives and financial circumstances. Please see disclaimer.

Exploiting news-flow signals

Macquarie Quantitative Research

Gurvinder Brar, Christian Davies, Adam Strudwick, Andy Moniz

[email protected]

Macquarie Capital (Europe) Ltd

Level 2, Moor House, 120 London Wall, London EC2Y 5ET

November 2009

The global presence of Macquarie Quant17 professional staff across the globe

Page 38

Australia (8) ResearchGeorge PlattJohn Conomos

Portfolio ProductsScott HamiltonBurke Lau

Quant ApplicationsConnah CutbushSimon RigneyGeorge FerizisCharles Lowe

Europe (4)Gurvinder BrarChristian DaviesAndy MonizAdam Strudwick

South Africa (1) Hannes Uys

Asia (2) Martin EmeryViking Kwok

Japan (2)Custom ProductsPatrick HansenAyumu Kuroda

Recent publications

Page 39

Monthly ‘Quantamentals’ report

Unwrapping value, Oct 08

Quality Control, Nov 08

When the tide turns, Dec 08

Style Outlook 2009, Jan 09

Exploiting dividend uncertainty, Feb 09

Do Technicals Add Value, Mar 09

Asymmetric Style, Apr 09

Have I got News for You? May 09

Positioning for Recovery, June 09

Preparing for regime changes, July 09

Spotting Growth, Sept 09

Arming Models with industry-specific data, Oct 09

‘Global Dynamics’ report

Focusing on earnings revisions, Oct 08

Beyond Minimum Variance, Jan 09

Asset Allocation: Spoilt for choice?, Apr 09

‘Risky Business’ report

Reality bites: Report on risk and implementation, Nov 08

Stopping losses, taking profits, Feb 09

Portfolio Turnover: Friend or Foe, June 09

Page 40

News-flow research in vogue

Source: Macquarie Research, November 2009

Academic literature The impact of public information on the stock market, 1994 The market impact of corporate news stories, 2004 More than words: Quantifying language to measure firms’ fundamentals, 2007 Equity portfolio risk (volatility) estimation using market information and sentiment, 2008 Investor inattention and Friday earnings announcements, 2008 In search of attention, 2009 Impact of news sentiment on abnormal stock returns, 2009

Data challenges Timeliness of news – Key newswires, stock exchange statements, press releases from

company websites, national newspapers Relevance of news – Company names mentioned in headlines/1st paragraph of text Classification of news – Accounting-related versus strategic news Independence of news – Mixed versus standalone events Informational content of news – Identifying good versus bad news (computational

linguistics/market based)

QUANTAMENTALS: HAVE I GOT NEWS FOR YOU? MAY ‘09

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Data vendorsQUANTAMENTALS: HAVE I GOT NEWS FOR YOU? MAY ‘09


Bloomberg - “Black Box Newsfeed” and “Black Box ECO Stats”. Low-latency delivery with 10,000+ daily corporate and economic headlines and text, 1500 category codes, 18month history

CapitalIQ – Website based search engine with categorized headlines, though without full text of article.

Dow Jones Elementized News feed - Low latency, tagged data feed (from Jan 2004). News categorized by corporate event

Dow Jones News and Archives - Text feed, 20+ years archive with identifiers, headlines & full stories. News items are not tagged into categories

Ravenpack - Sentiment scoring for traditional news wires (DJ News), internet sources (CNN Money) and blogs

Factiva.com – Website based search engine with categorized headlines and text

Page 42

Exploiting news flow strategiesQUANTAMENTALS: HAVE I GOT NEWS FOR YOU? MAY ‘09

Strategy PerformancesInformation ratios (2001-2009)


How to define the event? Companies may announce several news items over a month, should we react to all?

Informational content? Once we see an event, how do we systematically decide on its significance?

Holding period? Dealing with conflicting signals, excessive turnover and breadth of strategy

1.9 1.81.8

1.51.4

0.7

0.0

0.4

0.8

1.2

1.6

2.0

EarningsMomentum(filtered for

news)

NewsMomentum (all)

NewsMomentum(Accounting

Related)

CombinedEarnings and

NewsMomentum

NewsMomentum(Strategic)

EarningsMomentum

Strategy

0

100

200

300

400

500

600

Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09

Eq-weighted universe Revision upgrades

Revisions adjusted for good news Revisions adjusted for bad news

Revision downgrades

Page 43

Important disclosures:

Recommendation definitions

Macquarie - Australia/New Zealand

Outperform – return > 5% in excess of benchmark return Neutral – return within 5% of benchmark return Underperform – return > 5% below benchmark return

Macquarie – Asia/Europe

Outperform – expected return >+10%Neutral – expected return from -10% to +10%Underperform – expected <-10%

Macquarie First South - South Africa

Outperform – return > 10% in excess of benchmark returnNeutral – return within 10% of benchmark returnUnderperform – return > 10% below benchmark return

Macquarie - Canada


Macquarie - USA


Recommendation – 12 months

Note: Quant recommendations may differ from Fundamental Analyst recommendations

Volatility index definition*This is calculated from the volatility of historic price movements.

Very high–highest risk – Stock should be expected to move up or down 60-100% in a year – investors should be aware this stock is highly speculative.

High – stock should be expected to move up or down at least 40-60% in a year – investors should be aware this stock could be speculative.

Medium – stock should be expected to move up or down at least 30-40% in a year.

Low–medium – stock should be expected to move up or down at least 25-30% in a year.

Low – stock should be expected to move up or down at least 15-25% in a year.

* Applicable to Australian/NZ stocks only

Financial definitions

All "Adjusted" data items have had the following adjustments made:

Added back: goodwill amortisation, provision for catastrophe reserves, IFRS derivatives & hedging, IFRS impairments & IFRS interest expenseExcluded: non recurring items, asset revals, property revals, appraisal value uplift, preference dividends & minority interests

EPS = adjusted net profit /efpowa*ROA = adjusted ebit / average total assetsROA Banks/Insurance = adjusted net profit /average total assetsROE = adjusted net profit / average shareholders fundsGross cashflow = adjusted net profit + depreciation*equivalent fully paid ordinary weighted average number of shares

All Reported numbers for Australian/NZ listed stocks are modelled under IFRS (International Financial Reporting Standards).

Recommendation definitions – For quarter ending 30 September 2009

AU/NZ Asia RSA USA CA EUR

Outperform 45.08% 54.02% 40.00% 42.31% 62.86% 43.61%Neutral 39.77% 19.10% 45.00% 43.36% 31.90% 39.85%Underperform 15.15% 26.88% 15.00% 14.34% 5.24% 16.54%

Page 44

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