Transcript Newsflow - OptiRisk Systems

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
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.
Global Platform to service clients
 Leverage off the skills and
experience of regional teams
to service clients globally
 Service clients across all
areas of the investment
process
Alpha/Style Research
Portfolio Construction
Research
Risk Management
Custom Products
Quant Applications
Page 2
Academic Research

Many of the anomalies that quantitative investors systematically exploit are based on deep-seated
behavioural biases.
Academic literature points to both the market and analysts’ misreaction to new information due to:
– Delayed information diffusion
– Investors’ inattention
– Investors’ limited ability to process information instantaneously

Over and underreaction - explain momentum and reversal characteristics of different investor responses
to public and private signals.

Analyst forecast bias - links price momentum with earnings momentum, measuring analysts’ forecast
error and analysts’ underreaction to the continuation in returns.

Computation linguistic methods - natural language processing algorithms used to analyse the sentiment
of news stories in real time.
Source: Macquarie Research, Feb 2010
Page 3
Academic Research

DeBondt and Thaler (1990) - investor myopia; too much weight on new information, less weight on longterm averages

Daniel, Hirshleifer and Subrahmanyam (1998) - investors hold too strongly their own information and
discount public signals

Barberis, Shleifer and Vishny (1998) - conservatism and the representativeness heuristic. Investors change
their views and over/underreact to company earnings based on the past stream of realisations.

Hong and Stein (1999) - slow diffusion of information through two classes of traders with a differential in
processing news.

Hirshleifer, Lim and Teoh (2008) - Distracted investors fail to fully price new information.

Tetlock (2007) - linguistic content of media and finds that media pessimism predicts downward pressure and
a subsequent reversal

Tetlock, Saar-Tsechansky and Macskassy (2007) - proportion of negative works in a news story predicts
earnings and stock returns.
Source: Macquarie Research, Feb 2010
Page 4
Earnings Revisions Strategies

Historically, earnings revisions and earnings surprises tend to be followed by more revisions and surprises in
the same direction.

Stock prices tend to underreact initially to information then drift in the direction of the event over time
Revisions Breadth (upgrades vs downgrades)
LONG SPREAD (Top quintile - univ)
SHORT SPREAD (Univ - bottom quintile)
Revisions Depth (magnitude of EPS changes)
LONG SPREAD (Top quintile - univ)
SPREAD ASYMMETRY (LONG SPREAD - SHORT SPREAD)
SHORT SPREAD (Univ - bottom quintile)
SPREAD ASYMMETRY (LONG SPREAD - SHORT SPREAD)
80%
40%
30%
70%
30%
70%
20%
60%
20%
60%
10%
50%
10%
50%
0%
40%
0%
40%
-10%
30%
-10%
30%
-20%
20%
-20%
20%
-30%
10%
-30%
10%
-40%
0%
-40%
0%
-50%
-10%
-50%
-10%
-20%
-60%
40%
BREADTH REVISIONS
-60%
91
92
93
94
95
96
TOTAL SPREAD (Top-bottom) > 5%
97
98
99
00
01
02
SPREAD ASYMMETRY > 5%
Source: Factset, Macquarie Research, Feb 2010
03
04
05
06
07
SPREAD ASYMMETRY < -5%
08
09
80%
DEPTH
REVISIONS
-20%
91
92
93
94
95
96
TOTAL SPREAD (Top-bottom) > 5%
Page 5
97
98
99
00
01
SPREAD ASYMMETRY > 5%
02
03
04
05
06
07
SPREAD ASYMMETRY < -5%
08
09
Have I Got News For You?
Quantamentals May 2009
 Understanding news datasets
 Does news flow matter?
 What causes analysts to revise
earnings expectations?
 Does the informational content of
earnings signals vary with the
catalyst?
 News signals as an input into models
Page 6
Understanding News flow Datasets
400,000 news articles downloaded since 2001, cleaned sample of 60,000 based for large-cap Europe
Data Challenges:
i.
Timeliness of news – Key newswires, stock exchange statements, press releases from company websites,
national newspapers
ii.
Relevance of news – Company names mentioned in headlines/1st paragraph of text. Avoid double counting
iii.
Classification of news – Accounting-related versus strategic news
iv.
Independence of news – Mixed versus standalone events
v.
Informational content of news – Identifying good versus bad news (computational linguistics/market based)
Classification of news
Degree of news overlap
Accounting related news
Earnings
Trading Updates
Guidance
Financial Issues
Credit Rating news
Announcements of Earnings
Announcements of
Sales/Trading Statement
Conference Presentation
Calls
Sales/Trading Statement
Calls
Shareholder/Analyst Calls
Guidance/Update Calls
Buybacks
S&P Credit rating
changes
Restatements of Operating
Results
Annual General Meetings
Special/Extraordinary
Shareholders Meetings
Dividend
announcements
Debt Financing Related
100%
80%
Fixed Income Offerings
60%
Follow-on Equity
Offerings
Strategic news
M&A related
Restructuring Issues
Product Related
Corporate Governance
Other news
M&A Rumors and Discussions
Business Reorganizations
Client Announcements
Index changes
M&A Transaction
Announcements
Seeking
Acquisitions/Investments
Seeking Financing/Partners
Changes in Company
Bylaws/Rules
Discontinued
Operations/Downsizings
Lawsuits & Legal Issues
Product-Related
Announcements
Strategic Alliances
Executive Changes CEO
Executive Changes CFO
Executive/Board
Changes - Other
Seeking to Sell/Divest
Bankruptcy Related
Spin-Offs
Labor-related
Announcements
Business Expansions
Source: Factiva, Macquarie Research, FactSet, Feb 2010
40%
Address Changes
Ticker Changes
20%
0%
Earnings news
Earnings news
Page 7
Trading Updates
Trading Updates
Guidance news
Guidance news
Financial Issues
Other news
QUANTAMENTALS: HAVE I GOT NEWS FOR YOU? MAY ‘09
Data vendors

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
Source: Macquarie Research, Feb 2010
Page 8
Data vendors
Ravenpack’s News dataset
Source: Ravenpack, Macquarie Research, Feb 2010
Page 9
Understanding News flow Datasets

Majority of company news relates to earnings or guidance announcements

Coverage of news - depends on size of company, vendors data collection improved over time,
introduction of IFRS in 2005

Company can switch from being a news winner to a news loser several times a year. Event study return
profiles likely influenced by single news events than the accumulated reaction to multiple news
Categorising news flow by year
Categorising news flow by month
14,000
6,000
12,000
5,000
10,000
4,000
8,000
3,000
6,000
4,000
2,000
2,000
1,000
0
0
2001
2002
Corporate Governance
Guidance
Restructuring
2003
2004
Credit Ratings
M&A
Trading Updates
2005
2006
2007
Earnings
Other
Source: Factiva, Macquarie Research, FactSet, Feb 2010
2008
2009 YTD
Jan
Financial Issues
Product related
Feb
Corporate Governance
Guidance
Restructuring Issues
Page 10
Mar
Apr
May
Credit Ratings
M&A
Trading Updates
Jun
Jul
Earnings
Other news
Aug
Sep
Oct
Nov
Financial issues
Product Related
Dec
Does News flow Matter?

Hierarchy to news – earnings and guidance have greatest impact

Short-term momentum over days +1 to +5 followed by reversal

Credit ratings/earnings bad news important in downturn
3.0%
15%
Days -1:+1
Days +1:+5
Days +5:+10
2.5%
12%
Short-term
price reaction
to
good news
2.0%
1.5%
9%
1.0%
6%
0.5%
0.0%
3%
-0.5%
0%
-1.0%
2001
Earnings
Guidance
Credit Rating
news
Trading
Updates
Corporate Other news M&A related
Governance
Product
Related
Restructuring
Issues
Financial
Issues
1.5%
2002
Credit Rating news
2003
2004
Earnings
2005
Guidance
2006
2007
Corporate Governance
2008
2009
Restructuring
-15%
Days -1:+1
Days +1:+5
Days +5:+10
1.0%
-12%
0.5%
Short-term
price reaction
to
bad news
0.0%
-9%
-0.5%
-1.0%
-6%
-1.5%
-2.0%
-3%
-2.5%
-3.0%
0%
2001
-3.5%
Credit Rating Guidance
news
Earnings
Source: Factiva, Macquarie Research, FactSet, Feb 2010
Trading
Updates
Other news Restructuring Corporate
Issues
Governance
Product
Related
M&A related
Page 11
Financial
Issues
2002
Credit Rating news
2003
Earnings
2004
2005
Guidance
2006
2007
Corporate Governance
2008
2009
Restructuring
Revisions Clusters following News flow

Do earnings expectations change following certain news?

Majority of clusters form following earnings/financial news

Analysts take longer to digest strategic information
Revisions clusters by year
Revisions clusters by news type
800
Number of
News items
% of All
News
Accounting related news
Strategic news
29,061
30,238
49%
51%
Earnings
Trading Updates
Guidance
Corporate Governance
M&A related
Restructuring Issues
Product Related
Financial Issues
Credit Rating news
Other news
10,404
2,173
689
3,684
10,978
5,498
9,014
15,079
716
1,064
18%
4%
1%
6%
19%
9%
15%
25%
1%
2%
700
600
500
400
300
200
100
0
2001
2002
Corporate Governance
Guidance
Restructuring
2003
2004
Credit Ratings
M&A
Trading Updates
2005
2006
2007
Earnings
Other
Source: Factiva, Macquarie Research, FactSet, Feb 2010
2008
2009 YTD
Financial Issues
Product related
Page 12
% of News
% of all
with matched
revisions revisions
clusters
28%
58%
20%
42%
43%
25%
38%
19%
19%
21%
22%
18%
24%
18%
31%
4%
2%
5%
14%
8%
14%
19%
1%
1%
% of
Average
Revisions
duration of
made within
revisions
2 days of cluster (days)
News
44%
6.9
28%
6.7
56%
43%
47%
27%
30%
30%
26%
25%
37%
32%
7.3
6.3
6.4
6.5
6.4
6.7
7.0
6.5
6.6
6.3
Standard
deviation of
revisions
cluster (days)
4.7
5.1
4.7
4.7
4.5
4.9
4.8
5.3
5.3
4.5
5.2
4.9
Revisions Clusters following News flow

Earnings and financially related news receive the greatest attention

Correlation between short-term returns and SUE is 20% for earnings and guidance news
Average change in EPS estimates following news
40%
% Change in EPS post Good News
Average revision cluster returns following news
2.0%
% Change in EPS post Bad News
Good news
Bad news
35%
1.5%
30%
22%
22%
20%
19%
20%
1.0%
15%
15%
13%
13%
0.9%
0.8%
12%
0.5%
0.5%
10%
0.4%
0.4%
0.4%
0.4%
0.2%
0.2%
0.0%
0%
0.0%
-10%
-0.5%
0.0%
-0.1%
-12%
-20%
-19%
-0.7%
-13%
-16%
-16%
-17%
-0.2%
-0.3%
-0.3%
-0.4%
-0.6%
-0.6%
-0.8%
-1.0%
-20%
-20%
-30%
-1.3%
-1.5%
-33%
-1.3%
-33%
-40%
-2.0%
Financial
Issues
Earnings
Corporate
Governance
Guidance
Credit Rating Restructuring
news
Issues
Product
Related
Source: Factiva, Macquarie Research, FactSet, Feb 2010
Trading
Updates
M&A related Other news
Guidance
Page 13
Trading
Updates
Earnings
Financial Corporate
Issues Governance
Product Restructuring Unmatched
Related
Issues
clusters
M&A
Credit
Ratings
Other news
Exploiting News flow Strategies

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
Backtest summary statistics (daily, 2001-2009)
Annualised Return Annualised Return Annualised Return Annualised Volatility Information Ratio Maximum Daily
Turnover
Top Basket
Bottom Basket
(Long-Short)
(Long-Short)
(Long-Short)
Drawdown (Long-Short)
Earnings Momentum Strategy
News Momentum (all)
Hit Rate Average number of
stocks per basket
3.6%
7.3%
-4.0%
-7.2%
7.6%
14.5%
11.6%
7.8%
0.66
1.85
-3.3%
-2.8%
3933%
2463%
52%
56%
43
65
News Momentum (Accounting Related)
News Momentum (Strategic)
10.3%
4.3%
-6.4%
-8.5%
16.7%
12.8%
9.5%
9.5%
1.77
1.35
-3.8%
-4.2%
2646%
2835%
56%
54%
44
48
Combined Earnings and News Momentum
Earnings Momentum (filtered for news)
11.0%
8.0%
-12.7%
-7.6%
23.7%
15.6%
16.2%
8.3%
1.47
1.87
-6.0%
-2.9%
54%
55%
22
41
2.0
Information Ratios
1.9
News vs Revisions
600
1.8
1.8
1.6
500
1.5
1.4
400
1.2
300
0.8
0.7
200
0.4
100
0.0
Earnings
Momentum
(filtered for
news)
News
Momentum (all)
News
Momentum
(Accounting
Related)
Combined
Earnings and
News
Momentum
Source: Factiva, Macquarie Research, FactSet, Feb 2010
News
Momentum
(Strategic)
0
Jan-01
Earnings
Momentum
Strategy
Jan-02
Good news
Page 14
Jan-03
Bad news
Jan-04
Jan-05
Eq-weighted universe
Jan-06
Jan-07
Revision upgrades
Jan-08
Jan-09
Revision downgrades
Arming Models with industry-specific data
Quantamentals October 2009
 Industry-specific data and insights for
fundamental and quant investors

Monthly car sales, air passenger traffic and demand for
base metals

Forecast revenue and earnings either at the industry or
stock level
 Forecasting techniques

An economic-based approach - income and price
elasticity of product demand

Statistical-based approaches - time series properties of
indicators (Holt Winters and ARIMA models)
 Analysing Commodities demand and the
materials sector
 Airports and air passenger traffic
Page 15
Drivers of Corporate Earnings

Are returns driven by company fundamentals? If not, a technical approach may be better suited

How important is operating leverage? The greater a firm’s leverage, the greater the forecasting risk of a revenue
estimate impacting earnings

The importance of company-specific information and the degree of heterogeneity of firms?
Pharmaceuticals and Biotech driven by stock-specific information (e.g. patents),
Industry trends relevant in Autos, Capital goods and Materials
% returns explained by country, industry group, size and style
factors
Returns Dispersion
Select Data Series
Long Term Calculations
Return Dispersion (IndGrps)
Start Date: 31/01/2002
+ 1 SD
Highlighted dates
End Date: 31/08/2009
31/12/2008
LT Mean
31/08/2009
16%
14%
12%
10%
8%
6%
4%
2%
0%
Source: Macquarie Research, FactSet, Feb 2010
Page 16
Europe
Capital Goods
Technology Hardware & Equipment
Diversified Financials
Materials
Software & Services
Transportation
Banks
Consumer Durables & Apparel
Health Care Equipment & Services
Media
Commercial & Professional Services
Pharmaceuticals Biotechnology & Life
Sciences
Consumer Services
Insurance
Automobiles & Components
Retailing
Energy
Telecommunication Services
Food Beverage & Tobacco
Food & Staples Retailing
Utilities
Real Estate
Semiconductors & Semiconductor
Equipment
Household & Personal Products
- 1 SD
Forecasting sales: Airports and Passenger Traffic

Key driver for airports’ earnings is air passenger demand

Monthly statistics, highly correlated with GDP and have an 85% correlation with quarterly earnings
Global growth in passenger traffic and GDP
Passenger RPKs and global revenue growth
Air Freight Demand vs PMI Survey
Coefficient of Variation in Fraport’s 2010 forecasts
0.20
0.16
0.12
0.08
0.04
0.00
Net Income
Source: Macquarie Research, FactSet, Feb 2010
Page 17
EBITDA
Sales
Costs
Statistical models
Fraport’s Earnings vs Passenger Numbers
140
Quarterly Earnings (€ mn, LHS)
Quarterly passenger numbers (mn, RHS)
16
120
100
80
14
60
40
20
12
0
-20
-40
-60
10
04 04 04 04 05 05 05 05 06 06 06 06 07 07 07 07 08 08 08 08 09 09
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
Source: Macquarie Research, FactSet, Feb 2010
Page 18
Statistical models
ARIMA models
Comparison of models’ forecast accuracy
1mth ahead passenger forecasts
6.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1994
1995
1996
1997
1998
1999
2000
95% Confidence Interval
Mean Error
Mean Absolute Error
Mean Square Error
Root Mean Square Error
Theil's U-statistic
Source: Macquarie Research, FactSet, Feb 2010
Page 19
2001
2002
2003
2004
Forecast
2005
2006
2007
2008
Realised
Seasonal
ARMA
Holt
Winters
Income
Elasticity
(Rule-of-thumb)
Random
Walk
-0.02
0.26
0.13
0.35
0.98
-0.01
0.20
0.08
0.28
0.76
-0.06
0.29
0.15
0.39
1.06
0.03
0.27
0.16
0.40
1.00
Statistical models
Longer-term EPS forecasts (for FY08)
Short-term EPS forecasts
2.0
3.5
FY09 EPS
FY10 EPS
1.5
3.0
1.0
2.5
0.5
2.0
0.0
1.5
1.0
-0.5
0.5
-1.0
Mar-07
Jun-07
Sep-07
Dec-07
95% Confidence Interval
Mar-08
Jun-08
Forecast Quarterly EPS
Source: Macquarie Research, FactSet, Feb 2010
Sep-08
Dec-08
0.0
I/B/E/S consensus
Actual Quarterly EPS
Page 20
Macquarie's analyst
estimate
ARIMA passenger
forecasts input into
Macquarie's model
Holt-Winters passenger
forecasts input into
Macquarie's model
Exploiting dividend
uncertainty
Characteristics of dividend cutters
Opportunities for investors
Page 21
Characteristics of dividend cutters
Dividend Cutters have seen a greater incidence of news relating to distress
• 1/3 of firms had news regarding potential asset sales
• 1/5 of companies lowered earnings guidance/credit ratings downgraded
Frequency of negative news flow
T-statistic characteristics of dividend cutters
Dividend Yield
Daily stock volatility over past year
Book to Price (Historic)
Asset disposal news
Yoy change in shares outstanding
% Holding of Yield Investors
Debt to Assets
Incremental Capex to Assets
Corporate guidance downgrades
Cash conversion cycle
Historic earnings growth (5yrs)
Earnings certainty (IBES)
12mth Price momentum
Cashflow Revisions (Next 12mths)
EPS Revisions (Next 12mths)
DPS Revisions (Next 12mths)
-4
0
2
4
Note: A positive t-statistic indicates a greater characteristic value for dividend cutters vs
their sector peers. Sample based on 103 announcements since the start of 2008
Source: FactSet, Macquarie Research, March 2009
Source: Factiva, Macquarie Research, FactSet, Feb 2010
-2
Page 22
Momentum Seeking Attention
Quantamentals, Nov 09
Improving efficacy of price
momentum by combining with
abnormal trading volume and
news-flow signals
Page 23
MOMENTUM SEEKING ATTENTION, QUANTAMENTALS OCT 2009
Ravenpack News-flow Signal Definitions
Scope to create custom signal definitions
Score
Description
WLE
Identifies positive and negative words and phrases in articles on global equities
PCM
Identifies the sentiment of stories that are about global equity future earnings, developments and projections.
ECM
Specializes in short commentary and editorials on global equity markets
RCM
Focuses on corporate action announcements
VCM
Specializes in news stories about mergers, acquisitions and takeovers
NIP
Measures the degree of impact a news item has on the market he score is centered at 50. Values above or
below 50 indicate that the volatility of the stock price in the hours ahead will be higher or lower than the average
market volatility, respectively.
Company
Relevance
Score
Measures the relevance of the article to a particular company. Automated classifiers look for meaning by
detecting company specific events such as acquisitions, mergers, corporate actions, executive changes,
product launches/recalls, analyst ratings, among many others. The score is assigned by a text positioning
algorithm based on where the company is first mentioned (ie, headline, first paragraph, second paragraph, etc),
the number of references made in the text and the number of companies mentioned in the story.
Source: Ravenpack, Macquarie Research, Feb 2010
Page 24
MOMENTUM SEEKING ATTENTION, QUANTAMENTALS OCT 2009
Trading Volume and Momentum
Trading volume and momentum signals in combination add value and
support the ‘attention’ hypothesis
World 12-M Momentum and Volume (94 – 09)
World 1-M Momentum and Volume (94 – 09)
10%
14%
9%
13%
8%
8%
12%
6%
10%
4%
8%
8%
2%
6%
0%
5%
4%
-2%
2%
-4%
-5%
-6%
0%
High Momentum & Volume Less Low Momentum &
Volume
High Less Low Momentum
Source: Ravenpack, Factset, Macquarie Research, Feb 2010
High LessLow Volume
High Momentum & Volume Less Low Momentum &
Volume
Page 25
High Less Low Momentum
High LessLow Volume
MOMENTUM SEEKING ATTENTION, QUANTAMENTALS OCT 2009
News-flow Signal Performance
News-flow Signal Backtests (05 – 09)
News-flow Signal Yearly Performances (05 – 09)
20%
8.0%
6.9%
7.0%
14%
7.1%
13%
10%
9%
6%
5.0%
4.0%
4.0%
3.8%
3.0%
2.4%
2.6%
2.5%
2.0%
1.7%
1.1%
1.2%
1.1%
2.7%
1.6%
1.2%
7%
5% 6%
5%
4% 4%
3%
11%
10%
8%
7%
7%
4%
Good Less Bad News Portfolio Returns
Good Less Bad News Portfolio Returns
6.0%
8%
11%
3%
5% 5%
5%
8% 8%
6%
4%
2%
1%
0%
-1%
-2%
-5%
-10%
-17%
-20%
-17%
-20%
-20%
-18%
-20%
-19%
1.0%
-30%
0.0%
-31%
-0.2%
-0.4%
-1.0%
-40%
World
Russell 1000
Russell 3000
Pacific Ex Jp
Average News Score
Topix
S&P Australia
S&P Europe
Small
S&P Europe
Large
News Revisions
Russell 1000
Russell 3000
Pacific Ex Jp
2005
Page 26
Source: Ravenpack, Factset, Macquarie Research, Feb 2010
World
Page 26
2006
Topix
2007
2008
S&P Australia
2009
S&P Europe
Small
S&P Europe
Large
MOMENTUM SEEKING ATTENTION, QUANTAMENTALS OCT 2009
News-flow and Momentum
News-flow and momentum signals in combination add value and
support our view that understanding the motivations of trading is
important
World 12-M Momentum and News-flow (05 – 09)
World 1-M Momentum and News-flow (05 – 09)
7.0%
5.0%
6.6%
4.7%
4.5%
6.0%
4.0%
5.0%
3.5%
3.0%
4.0%
3.7%
2.5%
3.0%
2.0%
2.0%
1.5%
2.0%
1.1%
1.0%
1.1%
1.0%
0.5%
0.0%
0.0%
Good News & Strong Momentum Less Bad News
& Poor Momentum
High-Low Momentum
Source: Ravenpack, Factset, Macquarie Research, Feb 2010
Good News & Strong Momentum Less Bad News
& Poor Momentum
Good Less Bad News
Page 27
High-Low Momentum
Good Less Bad News
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
Outperform – expected return >+10%
Neutral – expected return from -10% to +10%
Underperform – expected <-10%
Volatility index definition*
This is calculated from the volatility of historic price
movements.
Financial definitions
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.
Added back: goodwill amortisation, provision for
catastrophe reserves, IFRS derivatives & hedging,
IFRS impairments & IFRS interest expense
Excluded: non recurring items, asset revals,
property revals, appraisal value uplift, preference
dividends & minority interests
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.
Macquarie First South - South Africa
Medium – stock should be expected to move up or
down at least 30-40% in a year.
Outperform – return > 10% in excess of benchmark return
Neutral – return within 10% of benchmark return
Underperform – return > 10% below benchmark return
Low–medium – stock should be expected to move up
or down at least 25-30% in a year.
Macquarie - Canada
Low – stock should be expected to move up or down at
least 15-25% in a year.
Outperform – return > 5% in excess of benchmark return
Neutral – return within 5% of benchmark return
Underperform – return > 5% below benchmark return
* Applicable to Australian/NZ stocks only
Macquarie - USA
Outperform (Buy) – return > 5% in excess of benchmark return
Neutral (Hold) – return within 5% of benchmark return
Underperform (Sell) – return > 5% below benchmark return
All "Adjusted" data items have had the following
adjustments made:
EPS = adjusted net profit /efpowa*
ROA = adjusted ebit / average total assets
ROA Banks/Insurance = adjusted net profit
/average total assets
ROE = adjusted net profit / average shareholders
funds
Gross 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 – 12 months
Note: Quant recommendations may differ from Fundamental Analyst
recommendations
Recommendation proportions – For quarter ending 30 September 2009
Outperform
Neutral
Underperform
AU/NZ
45.08%
39.77%
15.15%
Asia
54.02%
19.10%
26.88%
RSA
40.00%
45.00%
15.00%
USA
42.31%
43.36%
14.34%
CA
EUR
62.86% 43.61% (for US coverage by MCUSA, 0.35*% of stocks followed are investment banking clients)
31.90% 39.85% (for US coverage by MCUSA, 0.35% of stocks followed are investment banking clients)
5.24% 16.54% (for US coverage by MCUSA, 0.00% of stocks followed are investment banking clients)
28
Page 28
Analyst Certification: The views expressed in this research accurately reflect the personal views of the analyst(s) about the subject securities or issuers and no part of the
compensation of the analyst(s) was, is, or will be directly or indirectly related to the inclusion of specific recommendations or views in this research. The analyst principally responsible for
the preparation of this research receives compensation based on overall revenues of Macquarie Group Ltd ABN 94 122 169 279 (AFSL No. 318062 )('Macquarie') and its related
entities ('the Macquarie group') and has taken reasonable care to achieve and maintain independence and objectivity in making any recommendations.
Disclaimers: Macquarie Securities (Australia) Ltd; Macquarie Capital (Europe) Ltd; Macquarie Capital Markets Canada Ltd; Macquarie Capital Markets North America Ltd; Macquarie
Capital (USA) Inc; Macquarie Capital Securities Ltd; Macquarie Capital Securities (Singapore) Pte Ltd; Macquarie Securities (NZ) Ltd; and Macquarie First South Securities (Pty) Limited
are not authorised deposit-taking institutions for the purposes of the Banking Act 1959 (Commonwealth of Australia), and their obligations do not represent deposits or other liabilities of
Macquarie Bank Limited ABN 46 008 583 542 (MBL) or Macquarie Group Limited 94 122 169 279 (MGL). MBL does not guarantee or otherwise provide assurance in respect of the
obligations of any of the above mentioned entities. MGL provides a guarantee to the Monetary Authority of Singapore in respect of the obligations and liabilities of Macquarie Capital
Securities (Singapore) Pte Ltd for up to SGD 35 million. This research has been prepared for the general use of the wholesale clients of the Macquarie group and must not be copied,
either in whole or in part, or distributed to any other person. If you are not the intended recipient you must not use or disclose the information in this research in any way. Nothing in this
research shall be construed as a solicitation to buy or sell any security or product, or to engage in or refrain from engaging in any transaction. 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. There are
risks involved in securities trading. The price of securities can and does fluctuate, and an individual security may even become valueless. International investors are reminded of the
additional risks inherent in international investments, such as currency fluctuations and international stock market or economic conditions, which may adversely affect the value of the
investment. This research is based on information obtained from sources believed to be reliable but we do not make any representation or warranty that it is accurate, complete or up to
date. We accept no obligation to correct or update the information or opinions in it. Opinions expressed are subject to change without notice. No member of the Macquarie group accepts
any liability whatsoever for any direct, indirect, consequential or other loss arising from any use of this research and/or further communication in relation to this research.
Other Disclaimers: In Canada, securities research is prepared, approved and distributed by Macquarie Capital Markets Canada Ltd, a participating organisation of the Toronto Stock
Exchange, TSX Venture Exchange & Montréal Exchange. Macquarie Capital Markets North America Ltd., which is a registered broker-dealer and member of FINRA, accepts
responsibility for the contents of reports issued by Macquarie Capital Markets Canada Ltd in the United States and to US persons and any person wishing to effect transactions in the
securities described in the reports issued by Macquarie Capital Markets Canada Ltd should do so with Macquarie Capital Markets North America Ltd. Securities research is issued and
distributed by Macquarie Securities (Australia) Ltd (AFSL No. 238947) in Australia, a participating organisation of the Australian Securities Exchange; Macquarie Securities (NZ) Ltd in
New Zealand, a licensed sharebroker and New Zealand Exchange Firm; Macquarie Capital (Europe) Ltd in the United Kingdom, which is authorised and regulated by the Financial
Services Authority (No. 193905); Macquarie Capital Securities Ltd in Hong Kong, which is licensed and regulated by the Securities and Futures Commission; Macquarie Capital
Securities (Japan) Limited in Japan, a member of the Tokyo Stock Exchange, Inc., Osaka Securities Exchange Co. Ltd, and Jasdaq Securities Exchange, Inc. (Financial Instruments
Firm, Kanto Financial Bureau(kin-sho) No. 231, a member of Japan securities Dealers Association and Financial Futures Association of Japan); Macquarie First South Securities (Pty)
Limited in South Africa, a member of the JSE Limited and in Singapore, Macquarie Capital Securities (Singapore) Pte Ltd (Company Registration Number: 198702912C), a Capital
Markets Services licence holder under the Securities and Futures Act to deal in securities and provide custodial services in Singapore. Pursuant to the Financial Advisers (Amendment)
Regulations 2005, Macquarie Capital Securities (Singapore) Pte Ltd is exempt from complying with sections 25, 27 and 36 of the Financial Advisers Act. Clients should contact analysts
at, and execute transactions through, a Macquarie group entity in their home jurisdiction unless governing law permits otherwise. Macquarie Capital (USA) Inc., which is a registered
broker-dealer and member of FINRA, accepts responsibility for the content of each research report prepared by one of its non-US affiliates when the research report is distributed in the
United States by Macquarie Capital (USA) Inc. Macquarie Capital (USA) Inc. affiliate research reports and affiliate employees are not subject to the disclosure requirements of FINRA
rules. Any persons receiving this report directly from Macquarie Capital (USA) Inc. and wishing to effect a transaction in any security described herein should do so with Macquarie
Capital (USA) Inc. The information contained in this document is confidential. If you are not the intended recipient, you must not disclose or use the information in this document in any
way. If you received it in error, please tell us immediately by return e-mail and delete the document. We do not guarantee the integrity of any e-mails or attached files and are not
responsible for any changes made to them by any other person. Macquarie has established and implemented a conflicts policy at group level (which may be revised and updated from
time to time) (the "Conflicts Policy") pursuant to regulatory requirements (including the FSA Rules) which sets out how we must seek to identify and manage all material conflicts of
interest. Disclosures with respect to the issuers, if any, mentioned in this research are available at www.macquarie.com/research/disclosures. © Macquarie Group
Page 29