HSBC Portfolio Risk Analyser

HSBC James Capel AustraliaMember HSBC Group

HSBC James Capel: Portfolio research 1

November 1997 Portfolio research

Portfolio Analyser

Risk analysis of Australian equitiesThe HSBC James Capel Portfolio Analyser is tailored to the unusual structureof the Australian sharemarket – the high concentration of mining companies.It consists of a spreadsheet that analyses the risk of a portfolio of Australianequities.

The Portfolio Analyser highlights the overall risk of a portfolio against anumber of different benchmarks and, importantly, the sources of this riskin terms of both stocks and sectors.

The style views approach enables assessment of the portfolio using morethan one paradigm. This allows investors to better allocate stocks tomatch their views of sector groupings, or the risk of the portfolio.

The key benefit of the Portfolio Analyser over rival products is its ability tointerrogate the engine, rather than being presented with a “black–box”approach.

Contact+61-3-9229+ Ext

Colin Ritchie 3572

[email protected]

November 1997 Portfolio Analyser

2 HSBC James Capel: Portfolio research

ContentsOverview – getting to know portfolio risk ....................................................................... 3

Entering a portfolio ........................................................................................................... 4

Summary statistics............................................................................................................ 5

Overall break-down of tracking variance ........................................................................ 6

Sensitivity analysis of tracking variance ......................................................................... 7

Stocks outside the portfolio ........................................................................................................9

Stock contributions to tracking variance....................................................................... 10

Tracking error – popular misconceptions ..................................................................... 11

Tracking error & residual error....................................................................................... 13

Beta & residual error ....................................................................................................... 13

Vasicek beta adjustment ...........................................................................................................14

Stock statistics ................................................................................................................. 15

Price-earnings ratio, coefficient of variation & dividend yield...............................................15

Slicing & dicing................................................................................................................ 16

Sector analysis – ASX break-down...........................................................................................16

Size analysis ...............................................................................................................................17

Value growth analysis ...............................................................................................................18

Industrial portfolio plot..............................................................................................................18

Cluster analysis ..........................................................................................................................19

Pairs .................................................................................................................................. 19

Spearmans correlation.................................................................................................... 21

Style views ....................................................................................................................... 22

Engine............................................................................................................................... 23

Tracking error of portfolio returns relative to a benchmark...................................................23

Estimating the covariance matrix of the residual returns of stocks in the index .................24

Portfolio Analyser – Benchmark menu.....................................................................................25

Australian Research Team contacts .............................................................................. 26

The screen dumps illustrated on page 1 and throughout this document are for illustrative purposes. Data shown is calculated as at thedate appearing “on screen”, 30 September 1997.



Overview – getting to know portfolio riskThe Portfolio Analyser contains a series of worksheets to help equity managersform a new portfolio, or better understand aspects of an existing portfolio. Itconcentrates on the overall risk of the portfolio that is represented by the beta ofthe portfolio relative to the benchmark. Not only does the analyser give a break-down of stocks’ contributions to the portfolio beta, it also highlights the impacton tracking variance of stocks selected both within the portfolio, and thoseexcluded from the portfolio – the active portfolio.

The beta of the portfolio is the measure of the expected sensitivity tomovements in the benchmark – the overall risk of the portfolio. The betaexplains most of its expected returns. However, unless the portfolio is identicalto the benchmark, a slight tracking deviation exists.

The tracking deviation is analysed and attributed between different stocks andsectors. The Portfolio Analyser will, for example, highlight those stocks thatmake up most of the likely volatility in the excess returns of the portfolio againstthe benchmark. In particular, this volatility may be due to stocks excluded fromthe portfolio. (Not only is the inclusion of a stock in a portfolio an activedecision, but so too its exclusion.)

Industry break-up of the sharemarket

Resources25%

Industrials75%

The Australian sharemarket is markedly different to overseas sharemarkets dueto its high concentration of resources companies. At the end of September 1997,resources companies represented 24.9% of the market capitalisation of theASX’s All Ordinaries index. Given that the resources stocks’ relative share pricereturns tend to move in opposite directions to the industrial stocks’ relativereturns, this may lead to unintentional “bets” occurring in the portfolio. Also,companies that may be classified as industrial stocks can display positivecovariance with resources stocks. For instance, ICI Australia and Simsmetal bothpresent high correlations with resources stocks due to the nature of the driversof their respective businesses.

In most instances, an experienced equity manager will be aware of the areas orstocks that might generate surprises in the portfolio’s returns. The purpose ofthe Portfolio Analyser is to highlight unintentional bets that may have emergedin an Australian portfolio.

Tracking deviation analysedand attributed to stocks andsectors

Resources companiesrepresent 24.9% of theAustralian market



Entering a portfolioThe portfolio is entered into the Portfolio analysis worksheet. This can be donemanually, or stocks and weightings can be cut and pasted from an existingproduct. You are also able to start with a base portfolio, clear an existingportfolio ready to enter a new one, or re-base your portfolio stock weights toadd to 100% – via the Benchmark menu (see the insert “Portfolio Analyser –Benchmark menu” on page 25 for more details.) The company name columnwill automatically show whether the stock entered exists in the database. If theportfolio includes some cash, a separate security code can be entered as CASH,accompanied by the appropriate weighting.

When you have finished entering the portfolio, select a different benchmark viathe Benchmark menu and/or push the [RUN] button. Once the [RUN] button hasbeen pushed all the worksheets will be updated, yielding summary statistics andgraphs related to your portfolio.

Portfolios can be cut andpasted to/from existingproducts

HSBC James Capel - Portfolio Analyser Date: 30-Sep-97

ASX code weight Company name Summary Statistics - Benchmark All Ordinaries100.0%

1 AMC 2.4% Amcor Portfolio Benchmark2 ANZ 7.4% ANZ Banking Group Beta 1.05 1.003 BHP 14.1% BHP Tracking error p.a. 2.8% 0.00%4 BIL 2.8% Brambles Industries Tracking variance 0.078% 0.00%5 BOR 2.1% Boral - All Ordinaries volatility 13.3%6 CBA 6.9% Commonwealth Bank7 CCL 4.1% Coca-Cola Amatil Price-earnings ratio8 CML 3.2% Coles Myer 1997 17.7 18.39 CSR 2.5% CSR 1998 16.2 16.110 FBG 2.2% Foster's Brewing Group 1999 14.1 14.211 FLCBS 0.4% Fletcher - Building 12 FLCES 0.5% Fletcher - Energy Earnings growth13 FLCFS 0.4% Fletcher - Forest 1998 9.2% 13.1%14 FLCPS 0.5% Fletcher - Paper 1999 14.6% 13.4%15 LLC 3.6% Lend Lease Corp16 NAB 13.1% National Australia Bank Earnings uncertainty17 NCP 6.0% News Corporation coefficient of variation18 NCPDP 4.6% News Corporation preferred 1998 9.4% 10.0%19 PDP 1.7% Pacific Dunlop 1999 8.8% 10.0%20 RIO 5.8% Rio Tinto21 WBC 6.7% Westpac Banking Corp Dividend yield22 WMC 3.2% WMC 1997 3.4 3.623 WOW 2.2% Woolworths 1998 3.7 3.924 WPL 3.8% Woodside Petroleum 1999 4.2 4.42526 Dividend growth27 1998 7.9% 8.9%28 1999 12.7% 11.4%2930 Overall breakdown of Tracking variance31



Summary statisticsThe first box details the summary risk and valuation statistics. In order they are:

Beta: this measures the expected average sensitivity to a movement in thebenchmark returns. A figure of 1.10 means that the portfolio is 10% moresensitive to a movement in the benchmark. For example, if the benchmark risesby 10% the portfolio would, on average, rise by 11%. The beta of a portfolioaccounts for most of its expected volatility around the index. The beta of theportfolio is derived from the individual stocks’ betas which, in turn, have beenadjusted using a Vasicek adjustment (see the insert “Vasicek beta adjustment”on page 14 for more details).

Tracking error: although the beta explains most of the volatility of a portfolioaround the index, a slight residual error usually remains. This residual error iscaused by stock-specific events (say, an operational failure) or specific industryevents (eg, the removal of a fuel rebate) that is not related to the overall marketeffects.

The tracking error incorporates this residual error by measuring the standarddeviation of the excess returns of the portfolio (for more details on the actualcalculation refer to the section titled “Engine” on page 23.) A figure of say, 2%,suggests that the portfolio will display returns, in the majority of instances(65%), in a band of plus or minus 2% around the difference between the meanreturn of the portfolio and the benchmark. Note: refer to the section titled“Tracking error –popular misconceptions” on page 11.

Tracking variance: this figure is the tracking error squared. In analysing thetracking error of the portfolio against the benchmark, the tracking variance isused because mathematically only variance can be added and subtracted – notstandard deviations (tracking error).

Price-earnings ratio: the current price of each stock is divided by the actualand expected EPS estimates for the calendar years detailed to derive the price-earnings ratio (PE) for each stock. These PE ratios are then market-weighted andportfolio-weighted (by weighting the reciprocal of the PE ratio, the earningsyield) to measure respective PE ratios.

Earnings growth: the earnings growth of the market and the portfolio are theimplied percentage growth rates in the PE ratio of the portfolio and market.

Earnings uncertainty: the standard deviation of the consensus estimates ofthe mean EPS for each stock is divided by its mean EPS. This has the effect ofstandardising a level of uncertainty in the EPS estimates for each stock –normally referred to as the coefficient of variation (CV). These CVs are thenmarket and portfolio-weighted for each calendar year.

Dividend yield: the dividends of each of the stocks are divided by the currentprice of each stock to derive their respective dividend yields. These dividendyields are then market-weighted and portfolio-weighted to measure theirrespective dividend yields.

Dividend growth: the dividend growth of the market and the portfolio are theimplied percentage growth rates in the dividends of the portfolio and market.

Tracking variance is best formanipulating data



Overall break-down of tracking varianceIn analysing the tracking error of the portfolio against the benchmark, thetracking variance is used because, mathematically, only variances can be addedand subtracted. The chart below provides a quick snapshot of the level of extrarisk flowing into the portfolio because certain stocks, or groups of stocks, tend tomove together – either in the same or opposite directions. This extra risk isreferred to as the “sector effect”, and it is a more general in its application thanjust looking at the sector exposures as defined by the ASX.

Overall break-down of tracking variance

0.00% 0.02% 0.04% 0.06% 0.08%

Stock specificeffect

Sector effect

Total

The remainder is the risk flowing from the respective stock weightings. This is afunction of how heavily weighted in the portfolio the stock is relative to thebenchmark, and/or the volatility of the stock’s returns relative to the benchmark.

The ratio between specific variance and the “sector effect” can be used as ameasure of the managers strategy. For instance, if the intended strategy is oneof “stock picking”, then the expectation would be for a small fraction of the riskbeing due to the “sector effect”.

Sector effect shows theextra risk in the portfolio –certain stocks tend tomove together



Sensitivity analysis of tracking varianceThe chart below shows the sensitivity of the tracking variance to changes in theweightings of stocks in the portfolio. From the total portfolio of stocks the top 10diversifying stocks and the bottom 10 least-diversifying stocks are displayed.The term “diversifying” means that an increase in the relative weighting of astock in the portfolio will reduce the tracking variance of the portfolio.Conversely, an increase in weighting of the least-diversifying stocks willincrease the tracking variance and, in turn, the tracking error of the portfolio.These sensitivity measures are a helpful guide to index funds or low-trackingfunds that need to tightly control the tracking error around the benchmark.

As an example of this sensitivity analysis, we’ll assume that the tracking error ofthe portfolio is too high. Looking at the chart below, the most-diversifying stockin the portfolio is WMC. Buying more WMC will reduce the tracking variance.However, increasing the weighting of one stock means that the weighting ofanother stock will need to fall. The best pick would be to reduce the weighting ofThe News Corporation (NCP).

-0.0080% -0.0060% -0.0040% -0.0020% 0.0000% 0.0020% 0.0040% 0.0060% 0.0080%

WMC

RIO

BHP

FBG

PDP

FLCFS

FLCPS

FLCBS

FLCES

CSR

NCP

NCPDP

WBC

WPL

ANZ

CBA

CCL

CML

NAB

LLC

Most diversifying

Least diversifying

The actual calculations would run as follows:

Firstly, the current tracking error is a low 2.8%. The tracking variance is 0.078% –this is the term that is used to calculate the new tracking variance and ultimatelythe tracking error (see the section “Tracking variance” on page 5 for details).

Sensitivity analysishighlights the most-diversifying and least-diversifying stocks



A 1% increase in the relative weighting in WMC should decrease the trackingvariance by 0.00604% (see the previous chart.) Similarly, a 1% decrease in theweighting of NCP will reduce the tracking variance by 0.00754%.

Therefore, the final tracking variance should be 0.07785% minus 0.00604% (theincrease in WMC) and minus 0.00754% (the decrease in NCP) which equals0.06427%. This equates to a tracking error, the standard deviation, of 2.535%. Inother words, the changes in the relative weightings of WMC and NCP havereduced the tracking error from 2.790% to 2.535%.

Another way to calculate the new tracking error would be to perform theoperation on the spreadsheet, as shown below:

Summary Statistics - Benchmark All Ordinaries

Portfolio BenchmarkBeta 1.05 1.00Tracking error p.a. 2.560% 0.00%Tracking variance 0.066% 0.00%

As expected, the tracking error has dropped. But it is a little higher than expectedusing the numbers derived from the sensitivity analysis. The reason for the slightdifference is that the sensitivity analysis uses the “instantaneous” change in therelative weights of each stock. It is roughly equal to a 1% change in the relativeweights of each stock in the portfolio. (see the end of the section titled “engine”on page 23 for the calculation of “sensitivity to tracking variance”).



Tracking variance – stocks outside the portfolioThe previous example only dealt with the contribution of stocks in the portfolio tothe tracking variance. However, stocks within the benchmark, but not in theportfolio, also contribute to variance. Hence, the top 10 most-diversifying stocksand bottom least-diversifying stocks in the benchmark can also be examined.

With the press of a button these stocks can be viewed. The chart on the previouspage can also be viewed in tabular form. The table below shows the contributionof the top 10 most-diversifying stocks and the bottom 10 least-diversifying stocksfrom the total benchmark to tracking variance.

Sensitivity analysis of tracking variance

115 CRT -0.0156%114 EMP -0.0153%113 CRI -0.0148%112 CMX -0.0132%111 SVR -0.0128%110 KGM -0.0124%109 MMC -0.0122%108 SBM -0.0120%107 AWA -0.0118%106 QNI -0.0117%

1 NCP 0.0075%2 NCPDP 0.0074%3 WBC 0.0069%4 WPL 0.0065%5 ANZ 0.0060%6 CBA 0.0055%7 CCL 0.0054%8 CML 0.0051%9 NAB 0.0047%

10 LLC 0.0042%

As can be seen from the table above, a 1% increase in Consolidated Rutile (CRT)has a far greater effect on decreasing variance, than the previous example of a1% increase in WMC. At the same time, NCP remains the least-diversifying stock.Therefore, the final tracking variance should be 0.07785% minus 0.0156% (theincrease in CRT) and minus 0.00754% (the decrease in NCP), which equals0.05474%.

This equates to a tracking error of 2.340%. In other words, the changes in therelative weightings of Consolidated Rutile (CRT) and NCP have reduced thetracking error from 2.790% to 2.340%.

Show benchmark stocks



Stock contributions to tracking varianceThe “Contribution of stocks to the tracking variance” chart displays thecontribution of the top 15 stocks to the tracking variance of the portfolio. Asmaller portfolio of nine stocks is shown below for clarity.

NAB12% NCP

10%

WBC9%

ANZ9%

CBA7%NCPDP

7%WPL5%

CCL4%

CML3%

Remainder33%

This break-up of the tracking variance is different to the sensitivity of thetracking variance to each stock. A stock may represent a large slice of the overalltracking variance of the portfolio, yet there may be other stocks that cangenerate a bigger change in the tracking error from a 1% change in their relativeweighting in the portfolio.

Normally, the two tables are related but there can be instances, such as anoverweighting or underweighting in the larger capitalisation stocks, when theydiffer. Some stocks due to the way they covary with other stocks on the portfoliomight subtract tracking variance from the total portfolio. Negative contributionsare still highlighted in the pie chart but are displayed near the remainder.

Also, only stocks in the portfolio are displayed in the pie chart. It is quite possiblethat there are other stocks in the market that affect the tracking error more thanthe companies in the portfolio. For a complete listing of all the stocks in thebenchmark and their contribution to the tracking variance refer to the worksheet“Risk rankings”.

The table on the next page is an extract from the “Risk ranking” worksheet. Hereyou can not only see the contribution of tracking variance of stocks within thephysical portfolio, but also those within the active portfolio. That is, stocks withinthe benchmark, that are actively excluded from the portfolio. Of interest, in thisexample, is the fact that Comalco (CMC) is the seventh-largest positivecontributor to tracking variance through its exclusion from the portfolio. Theportfolio, in this case, being the 20 Leaders index, against the All Ordinaries asthe benchmark.

Tracking variance break-updiffers from the sensitivityof tracking variance toeach stock



Contribution of all stocks in the benchmark to Tracking variance

Ranking ASX % contributioncode

1 NAB 11.6%2 NCP 9.6%3 WBC 9.3%4 ANZ 8.9%5 CBA 7.4%6 NCPDP 7.3%7 CMC 6.4% Not in Portfolio8 WPL 5.1%9 CCL 4.2%

366 WFT -1.4% Not in Portfolio367 SGB -2.6% Not in Portfolio368 WMC -7.1%369 RIO -8.3%370 BHP -14.7%

Tracking error – popular misconceptionsIt is worth discussing some popular misconceptions about tracking errors ofportfolios. Tracking errors measure the volatility of the excess returns of aportfolio. They are often viewed as a measure of the potential returns that a fundmanager can add to a portfolio.

For example, a particularly low tracking error is perceived to flow from aportfolio that closely tracks the index returns. In fact, fund managers runningactive portfolios are often criticised if their tracking errors are too low.Conversely, fund managers with high tracking errors are perceived to beaggressive, and have the potential to add a significant excess return to theportfolio.

But how correct are these notions?

Depending upon a critical assumption, they are misleading. The criticalassumption is the expected return from the portfolio after taking into account thesensitivity of the portfolio to the market index.

The excess returns of a portfolio can be shown to be a function of threeelements. Firstly:

excess returns = returns of portfolio minus returns of market index.

Now the sensitivity of the portfolio against the market, or beta, accounts for themajority of its return. So the returns of the portfolio could be rewritten as:

returns of portfolio = a constant returnplus beta of the portfoliotimes returns of the indexplus a random residual return.

E[r] = rP - rM

rP = αα P + ββP*rM + εε



Therefore, the excess returns of the portfolio are:

excess returns = a constant returnplus (beta of the portfolio – 1)times returns of the indexplus a random residual return.

In other words, the excess returns of a portfolio against the index are determinedby:

a constant return (that reflects the return added by a fund manager overtime);

the beta of the portfolio; and

a random residual return due to specific company or industry effects.

The average excess return is simply:

average excess return = a constant returnplus (beta of the portfolio – 1)times average return of the index.

(The random residual return disappears given that, on average, it is equal to 0.)

Now, the important point is that the tracking error is the standard deviation ofthe excess returns around this average. To be precise, the tracking variance (thestandard deviation squared) is equal to:

tracking variance = (beta of the portfolio - 1)2

times variance of the index returnsplus variance of residual returns.

For more details on actual calculation refer to the “Engine” spreadsheet.

When the beta of the portfolio is equal to 1.0, the same as the market index, theaverage excess return collapses to the constant return or value-added by thefund manager. Therefore, the tracking variance is the dispersion of returnsaround this constant return. In theory, a fund manager could exhibit a markedlylow tracking error, but an extremely high constant return over the index. In thiscase, it would be incorrect to assume that a low tracking error equates to anindex-type performance, unless, it is assumed that the fund manager’s returnswill, on average, be equal to the index.

Similarly, a high tracking variance is only one part of the equation. The otherpart is the average expected returns that are likely to be added by the fundmanager.

E[r] = αα P + (ββP-1)*rM + εε

Average excess returnE = αα P + (ββP-1)*rM

VP = (ββP-1)2*VM + Vεε

Tracking variance is onepart of the equation – theother is average expectedreturn added by the fundmanager



Tracking error & residual errorHow does the tracking error relate to the residual error of the stock and theportfolio?

Following on from the previous section, the tracking variance incorporates themeasure of the residual variance, plus the beta of the portfolio. The reason thatthe beta of the portfolio enters into the equation is that the tracking error isdefined as the volatility of the excess returns of the portfolio around thebenchmark. Whereas the residual variance is the volatility of the portfolio returnsaround the beta-adjusted benchmark returns (or the line of best fit).

If the portfolio displays a beta of 1.0, that is, the same as the benchmark, theresidual error and the tracking error are equivalent (VP = (βP-1)2*VM + Vε , if βP =1.0 then VP = Vε). However, as the beta moves away from 1.0 the trackingvariance increases above the residual variance. The tracking error could bevisualised as the deviation of the stocks or portfolio returns in the graph below,from another line of best fit which has a slope of 1.0 (the benchmark beta).

The risk space (either residual or tracking variance) can be selected on the firstspreadsheet, “Portfolio analysis”, by choosing the Risk space option under theBenchmark menu. The full Benchmark menu has been detailed in the shadedinsert on page 25.

-10

-8

-6

-4

-2

0

2

4

6

8

-15 -10 -5 0 5 10 15

Beta =slope of line

Stock returns

Benchmark returns

Residual error = standard deviation of residuals

Beta & residual errorThe beta is calculated using the preceding 48 months of returns of each stock(including dividends) against the accumulation indices of each benchmark. Thebeta provides a measure of the average sensitivity of each stock to a movementin the benchmark. Note that the beta has been constructed using a Capital AssetPricing model where the returns from the risk-free rate have been subtractedfrom the monthly returns of each stock (the 90-day bank bill accumulation indexhas been used).



Finally, the betas have been adjusted for each stock, using a so-called Bayesianadjustment. This adjustment helps to provide a better estimate of the futurebeta of the stock. The adjustment is known as a Vasicek adjustment andbasically pulls the beta back towards 1.0, (the market beta) if the estimationerror in the beta is higher than other stocks’ betas (see the insert “Vasicek betaadjustment” below for more details).

The residual error of each stock is the standard deviation of each stock’s returnsaround the beta-adjusted market returns. The residual error and beta of a stock isgraphed on the page opposite.

The beta of the stock can be pictured as the line of best fit between the stockreturns and the market returns (before the Vasicek adjustment). The moresensitive the stock to the market, the higher the beta and the more steeplyinclined is the slope of the line. The residual error is a measure of the dispersionof the “unexplained” part of the returns around the line of best fit (or beta).These errors are assumed to fall in a normal bell-shaped distribution around theline of best fit. The residual error is measured in terms of one standarddeviations given that this captures the majority of the residual returns.

Vasicek beta adjustment

The beta of a stock is an important component in the calculations of the risk-adjusted returns. Originally, betafactors were simply estimated from past data by least-squares regression procedures. The least-squarestechnique consists of fitting a linear relationship between monthly rates of return of a stock and the marketindex so that the sum of squared differences between the stock’s actual returns and forecast returns areminimised.

The Vasicek beta adjustment provides a better estimate of the future beta of a stock. It does this byincorporating some additional information outside the sample of returns of the stocks and the market index.This additional information is the fact that the beta of the market must be 1.0. This information is combinedwith the sample estimator, the least-squares estimator of the beta, using Bayesian statistics.

The formula for the adjustment is detailed below;

Betaì= 1/m2 + Betai/s2

1/m2 + 1/s2

where:Betaì is the Vasicek adjusted beta of stock iBetai is the least squares estimator beta of stock is is the standard error of Betai

m is the market weighted average of all the standard errors of betai

For an example, assume a stock’s unadjusted beta is 1.50 with a low degree of accuracy, indicated by a 0.60standard error. This compares to a market beta and standard error of 1.0 and 0.36, respectively. The adjustedbeta would be:

BetaÌ = (7.7 + 4.2) = 11.9 = 1.13 (7.7 + 2.8) 10.5

In other words, a beta of 1.13 provides a better estimate of the “underlying beta”.

For more information refer to "A note on Cross-Sectional Information in Bayesian Estimation of SecurityBetas", Journal of Finance 28 (December 1973), pp1233-1239.

Betas are modified via aVasicek adjustment



Stock statisticsThis spreadsheet details a number of risk and valuation statistics for each stock.This is a report page and no information needs to be entered on thisspreadsheet.

HSBC James Capel - Portfolio Analyser

ASX code Company name weight Price Index Beta Residual30-Sep-97 weight error p.a.

Total 100.0% Portfolio 54.02% 1.05 2.8%

1 AMC Amcor 2.4% 8.69$ 1.31% 1.106 11.6%2 ANZ ANZ Bank 7.4% 11.28$ 3.99% 1.010 17.1%3 BHP BHP 14.1% 16.08$ 7.61% 1.144 13.7%4 BIL Brambles Industries 2.8% 28.75$ 1.50% 1.084 14.8%5 BOR Boral 2.1% 4.18$ 1.13% 0.927 14.6%6 CBA Commonwealth Bank 6.9% 17.04$ 3.72% 0.969 13.5%7 CCL Coca Cola Amatil 4.1% 14.77$ 2.19% 1.015 25.1%

The first table, as shown above, simply lists the stocks entered from the portfolioanalysis page. The next table lists the share price, benchmark index weight, thebeta and tracking error (see the section “Beta and residual error” on page 13 formore details).

Price-earnings ratio, coefficient of variation & dividend yieldThe remaining tables on the Stock analysis page, shown below, list the price-earnings ratio (PE), the coefficient of variation (CV), and the dividend yield (DY)for each stock in the portfolio.

1997 PE 1998 PE 1999 PE 1998 CV 1999 CV 1997 DY 1998 DY 1999 DY

17.7 16.2 14.1 9.4% 8.8% 3.4 3.7 4.2

21.6 17.8 14.9 7.2% 9.0% 4.4 4.6 4.7 13.1 12.4 11.5 4.6% 6.9% 4.3 5.0 5.6 19.0 16.8 14.4 11.6% 14.2% 3.2 3.2 3.8 26.4 22.8 20.2 3.1% 3.8% 2.6 2.7 2.7 27.0 19.5 15.2 9.6% 8.8% 3.6 3.8 4.3 12.9 12.9 11.9 2.7% 4.1% 6.0 6.5 7.1 44.7 40.6 35.3 9.5% 12.0% 1.3 1.4 1.5

The PE ratio is defined as the current price divided by the estimated earningsper share (EPS) for each calendar year.

The CV measures the degree of dispersion of analysts’ estimates around themean EPS. Low CV figures indicate that analysts’ forecasts are tightlyclustered around the mean – implying lower earnings risk.

The dividend yield is defined as the estimated dividend per share divided bythe current price for each year.



Slicing & dicingThis spreadsheet slices up the tracking variance of the portfolio into differentrisk perspectives. These risk perspectives may change over time and are afunction of how investors view the Australian sharemarket. The advantage ofthis sort of technique is that we are not bound by a certain risk model orparadigm in analysing the Australian sharemarket. For example, stocks could beclassified into a “bottom-up” risk classification, perhaps by balance sheet andearnings risk. However, at other times, style classification may be moreimportant – growth versus value stocks. By using different views of risk we areunlikely to be surprised by the emergence of a new clustering of stocks in theportfolio.

The risk perspectives are based on two different techniques – a classificationtechnique and a factor analysis. The first classifies stocks by eithermembership or non-membership of certain groupings or sectors. There arethree different views of the portfolio risk based on this method:

ASX sector break-down;

small and large-capitalised segmentation; and

value and growth views.

The other method looks at the correlations of different stocks’ returns. If twostocks display similar return profiles, irrespective of their ASX sectorclassification, it might suggest that they are being affected by certain commonfactors. By unravelling the correlations of all the stocks, a number of commonfactors can be identified as driving the returns of stocks.

Using a factor analysis of the correlations of all stocks, the Australian marketdivides neatly into basically industrial stocks and resources stocks. These sectorgroupings are different to the ASX break-down – for example ICI tends to displaya return profile similar to resources stocks. Further, these sector groupings arenot just based on membership or non-membership of the sectors. A loading orsensitivity for each stock is calculated for each factor.

An advantage of this factor analysis is that the stocks’ returns reveal potentiallynew groupings in the Australian market – which would not be evident using asimple classification system based on a certain risk model or paradigm.

Sector analysis – ASX break-downThe chart opposite compares the portfolio against the ASX sector groupings andweightings. Additionally, the tracking variance is attributed between the differentsectors and graphed in the bar chart.

If the beta is different to 1.0 then part of the tracking error will be due to thehigher or lower sensitivity of the portfolio against the index. This extra risk isclassified as “market risk” because a higher or lower beta is equivalent to takingan increased or decreased bet on the market.

Another use of this spreadsheet is in analysing the sectors weights in differentbenchmarks. For example, what are the two largest sector weightings in the ex-ASX 100 index? The answer: Property Trusts and Miscellaneous Industrials!

Slicing the trackingvariance into different riskperspectives



HSBC James Capel - Portfolio Analyser

1. Sector Analysis - ASX breakdown % contributionASX code Sector Portfolio Index Difference to tracking variance

1 GOLD 0.0% 2.6% -2.6% 9.4%2 METALS 3.2% 5.3% -2.1% 16.1%3 DIVRES 19.9% 12.0% 7.9% -20.0%4 ENERGY 4.4% 5.1% -0.8% 6.9%5 UTILITIES 0.0% 0.9% -0.9% 0.6%6 DEVCON 3.6% 3.8% -0.2% 3.5%7 BLDMAT 5.0% 4.4% 0.5% 4.8%8 ALCTOB 2.2% 1.8% 0.4% -0.8%9 FOODHH 4.1% 3.4% 0.6% 2.7%

10 CHEMIC 0.0% 1.1% -1.1% 2.9%11 ENGIN 0.0% 0.7% -0.7% 0.9%12 PAPER 3.3% 1.9% 1.4% 0.4%13 RETAIL 5.4% 3.8% 1.6% 4.1%14 TRANSP 2.8% 3.0% -0.2% 4.2%15 MEDIA 10.6% 9.0% 1.6% 19.9%16 BANKS 34.0% 21.6% 12.5% 35.3%17 INSUR 0.0% 2.8% -2.8% -1.2%18 TELECOM 0.0% 0.2% -0.2% 0.3%19 INVFIN 0.0% 2.0% -2.0% 0.6%20 PROPTY 0.0% 4.8% -4.8% -3.3%21 HEALTH 0.0% 1.2% -1.2% 2.4%22 MISIND 0.0% 1.5% -1.5% 0.9%23 DIVIND 1.7% 4.5% -2.8% 3.5%24 TOUR 0.0% 2.6% -2.6% 0.9%

cash 0.0% 0.0% 0.0% 0.0%MARKET RISK (for tracking error) 5.0% 1

100.0% 100.0% 100.0%

GOLD

METALS

DIVRES

ENERGY

UTILITIES

DEVCON

BLDMAT

ALCTOB

FOODHH

CHEMIC

ENGIN

PAPER

RETAIL

TRANSP

MEDIA

BANKS

INSUR

TELECOM

INVFIN

PROPTY

HEALTH

MISIND

DIVIND

TOUR

-30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% 40.0%

GOLD

METALS

DIVRES

ENERGY

UTILITIES

DEVCON

BLDMAT

ALCTOB

FOODHH

CHEMIC

ENGIN

PAPER

RETAIL

TRANSP

MEDIA

BANKS

INSUR

TELECOM

INVFIN

PROPTY

HEALTH

MISIND

DIVIND

TOUR

Risk analysis by sector

Size analysisThe next view of the risk divides the portfolio into large and small capitalisationstocks. These are further sub-divided into industrials and resources. Again, thetracking variance is attributable across these different sectors. The definition of asmall capitalisation is a company classified in the so-called ex-ASX 100 index.

Risk Analysis by size% contribution

2. Size Analysis Portfolio Index Difference to tracking varianceIndustrials Large Top 100 72.6% 64.2% 8.3% 72.9%Industrials Small Ex 100 0.0% 10.8% -10.8% 9.9%

Resources Large Top 100 27.4% 22.3% 5.2% 5.3%Resources Small Ex 100 0.0% 2.7% -2.7% 7.0%

cash 0.0% 0.0% 0.0% 0.0%MARKET RISK (for tracking error) 5.0%

100.0% 100.0% 100.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

LargeIndustrials

SmallIndustrials

LargeResources

SmallResources

One area to closely monitor in the Australian market is the risk from the smallresources stocks. Many of these companies are gold stocks that can displaysignificant volatility.



Value growth analysisStocks have also been classified into a value, growth or other category. A“value” stock denotes a company that is selling on a low price relative to itsexpected earnings, cash flow or net tangible assets. Additionally, thesecompanies tend to sell on persistently low PE multiples – such as the banks. Thefull list of the stocks included in the value group (and any other grouping) isdetailed in the “Style views” spreadsheet.

Risk Analysis by style3. Value|Growth Analysis % contribution

Portfolio Index Difference to tracking varianceIndustrials Value 36.2% 26.4% 9.8% 40.0%Industrials Growth 21.0% 18.9% 2.1% 31.3%Industrials Other 42.8% 54.7% -11.9% 23.7%

MARKET RISK (for tracking error) 5.0%100.0% 100.0% 100.0%

0.0%5.0%

10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%

ValueIndustrials

GrowthIndustrials

OtherIndustrials

The “growth” label is applied to those stocks which tend to sell on a persistentlyhigh share price relative to their current earnings, cash flow or net tangibleassets. However, they are not just the opposite of a value stock. Companies mayexhibit high PE ratios because current or expected earnings have collapsed. Thedistinguishing trait of a growth stock is its strong comparative advantage againstits competitors – such as Coca-Cola Amatil.

Industrial portfolio plotThe final chart brings together some of the results of the previous risk tables.

4. Industrial portfolio plot - value|growth & large|small% contribution Risk profile by style and size for industrials

Portfolio Index Difference to tracking varianceIndustrials Large 72.6% 64.2% 8.3% 72.9%Industrials Growth 21.0% 18.9% 2.1% 31.3%Industrials Small 0.0% 10.5% -10.5% 9.8%Industrials Value 36.2% 26.4% 9.8% 40.0%

0.0%

20.0%

40.0%

60.0%

80.0%Large

Growth

Small

Value

For the industrial stocks in the portfolio the style is mapped against thecapitalisation bias. For example, is most of the risk coming from value andsmaller stocks? This analysis is particularly useful in assessing differenteconomic environments. In the Australian market, smaller capitalisation stockstend to perform better in a strengthening economy. Further, value stocks attractmore interest when the economy improves. In times of earnings downgradesand economic uncertainty, a more defensive portfolio would be positioned in thetop right-hand quadrant of growth and larger capitalisation stocks.



Cluster analysisThis table adopts the factor analysis technique and looks at the most importantfactor in the Australian market – the industrial/resources split

A weighted score is provide for the cluster. The more positive the number fromthe industrial/resources factor, the more “industrial” like is the portfolio.Conversely, a negative figure would indicate a portfolio of stocks that acts likethe resources companies. The industrial/resources split has always been themost important decision in the Australian market (at least in terms of the AllOrdinaries index) and continues to dominate the correlation of stocks.

4. Cluster analysisFactor exposures

Industrials/ResourcesPortfolio exposure 0.265Index exposure 0.167

0.098Positive figures = Industrial type portfolioNegative figures= Resource type portfolio

PairsThis page lists all the significant correlations between pairs of different stocks –both significantly positive and negative correlations.

This information is useful in looking for arbitrage opportunities in the market.For example, two companies’ share prices and dividend returns might moveclosely together – perhaps due to a similar sector exposure. If their share pricereturns move out of sync, for no apparent reason, we have the basis for apossible pairs trading opportunity.

Alternatively, pairs correlations might suggest that certain other stocks outsidethe portfolio would provide a better exposure than the current stock in theportfolio (due to better value fundamentals).

Industry Index wgt ASX code ASX code Spearmans correlation Industry Index wgtPROPTY 0.16% APF AAD 0.400 TOUR 0.08%METALS 0.90% CMC AAD 0.429 TOUR 0.08%INVFIN 0.02% EQK AAD 0.542 TOUR 0.08%DEVCON 0.03% MDC AAD 0.455 TOUR 0.08%BLDMAT 0.03% WFI AAD 0.426 TOUR 0.08%METALS 0.07% WSL AAD 0.469 TOUR 0.08%BANKS 0.05% BEN ABC 0.491 BLDMAT 0.07%

The key data is in the middle of the table – two columns labelled ASX code and athird column listing the correlation coefficient (Spearmans correlation). Theother columns are simply to sort the data by a number of criteria – ASX code,index weight and industry. There are arrows above each column which whenselected will list a menu of different sector, ASX codes, or index weights. Forexample, a listing of bank correlations and stocks above 1% index weight wasperformed – see overleaf.

The more positive theindustrial/resources factor,the more “industrial” likethe portfolio



Industry Index wgt ASX code ASX code Spearmans correlation Industry Index wgtDIVRES 7.61% BHP ANZ -0.403 BANKS 3.99%BANKS 3.99% ANZ CBA 0.572 BANKS 3.72%BANKS 3.99% ANZ NAB 0.468 BANKS 7.05%DIVRES 3.13% RIO ANZ -0.414 BANKS 3.99%BANKS 3.72% CBA ANZ 0.572 BANKS 3.99%DIVRES 7.61% BHP CBA -0.424 BANKS 3.72%DIVRES 3.13% RIO CBA -0.412 BANKS 3.72%METALS 1.72% WMC CBA -0.612 BANKS 3.72%BANKS 3.72% CBA NAB 0.510 BANKS 7.05%BANKS 7.05% NAB ANZ 0.468 BANKS 3.99%BANKS 7.05% NAB CBA 0.510 BANKS 3.72%DIVRES 3.13% RIO NAB -0.448 BANKS 7.05%METALS 1.72% WMC NAB -0.586 BANKS 7.05%BANKS 3.63% WBC ANZ 0.561 BANKS 3.99%BANKS 3.63% WBC CBA 0.428 BANKS 3.72%

Not surprisingly, most of the major banks are highly correlated to each other’sreturns (remember, this is after the market effect has been stripped out of thereturns for each of the stocks). ANZ Banking Group is positively correlated toCommonwealth Bank of Australia to the tune of 0.572. But just how significant is0.572? The maximum correlation is +1.0 (stocks move exactly in tandem) and theminimum -1.0 (stocks move in exactly the opposite direction). Only thesignificant correlation figures have been picked in the sense that there is only a5% chance that these correlations are a “fluke” or random occurrence.

An interesting point from the above table is the strong negative correlation (thatis, they move in opposite directions) between many of the larger banks andmining stocks.



Spearmans correlationVariance and correlation provide a measure of linear association. They aresensitive to outliers and may indicate association where none exists.

The Pearson’s product moment correlation coefficient uses residual returns. TheSpearmans correlation coefficient is based on the ranking of stocks’ returns.This reduces the effect of outliers in the correlation. The correlation measure inthe HSBC James Capel Portfolio Analyser is calculated using a Spearmanscorrelation statistic.

To demonstrate the effects of outliers on the Pearson’s correlation coefficient,assume that the following five-monthly returns were exhibited for two stocks.

Stock A Stock B1 1% 2%2 2% 3%3 4% 3%4 5% 4%

Correlation statistic 0.8944

The correlation between the stocks is high at 0.89. Further we will assume thatthis correlation reflects the majority of returns. However, there will always beone unusual return month that upsets the figures. Say stock B exhibits a 10% fallin period 3. The new numbers are displayed below.

Stock A Stock B1 1% 2%2 2% 3%3 4% -10%4 5% 4%

Correlation statistic -0.2508

The correlation statistic has now reversed to -0.25.

A better way to handle these rogue numbers is to use a more robust technique –the Spearmans correlation coefficient.

The Spearmans correlation is based on the ranking of stocks’ returns. Instead ofusing the returns for each period, a simple ranking is performed from 1 (thelowest) to 4 (the highest). This means that rogue numbers simply affect theranking of the return not the absolute number. Continuing on the above example(with the rogue minus 10% figure), the ranking of the stocks would be:

Stock A Stock Brankings rankings

1 1 22 2 33 3 14 4 4

Correlation statistic 0.4000

The correlation statistic falls, but only to 0.40. When the sample of numbers andrankings are larger, the correlation statistic is not overly disturbed by outlyingfigures.

The Spearmans correlation coefficients are used in the factor analysis for theSlicing & Dicing of the tracking variance.

Correlation provides ameasure of linearassociations betweenstocks



Style viewsThe “Style views” spreadsheet simply lists every stock in the All Ordinariesindex with its categorisation in the ASX sector, small/large capitalisation andvalue/growth.

INDUSTRIAL/RESOURCE SIZE VIEW VALUE/GROWTH VIEWSVIEW INDUSTRIAL RESOURCES INDUSTRIALS

LARGE SMALL LARGE SMALL VALUE GROWTHTOP 100 EX 100 TOP 100 EX 100

AAA 0 1 0 0 0 1 0 0AAD 1 0 0 1 0 0 0 1ABC 1 0 0 1 0 0 0 0ABF 0 1 0 0 0 1 0 0ABG 1 0 0 1 0 0 0 0ACH 1 0 0 1 0 0 1 0ADB 1 0 0 1 0 0 1 0ADZ 1 0 0 1 0 0 0 0AFF 1 0 0 1 0 0 0 0

A part of the spreadsheet is displayed above. A ‘1’ indicates membership of theparticular sector and ‘0’ non-membership. These columns of numbers are thenused to pick out the relevant tracking variance contribution on the “Slicing &Dicing” spreadsheet.

Apart from listing all the stocks that make up a particular sector, it can also beused as a template to derive other sector perspectives of the Australiansharemarket that more closely follow the investor’s perspective of risk.



EngineThe engine is where all the calculations are performed. The tracking or residualerror is calculated here and the sensitivity and contribution of each stock to thetracking/residual variance. The workings of this sheet is for the aficionados ofportfolio analysis.

The calculations are based on a so-called “statistical model” which decomposesthe market residual covariance matrix into a smaller number of factors – usingfactor analysis. The covariance matrix is based on a more robust estimator ofthe correlations – the Spearmans correlation. The tracking variance and residualvariance is calculated using the formulae in the shaded boxes below.

Tracking error of portfolio returns relative to a benchmark

Variance (tracking returnsi) = (wp-βp.wi)` Σ (wp - βp.wi) + (βp-1)2. s2i

(1x1) (1xn) (nxn) (nx1) (1x1)

where:n = the number of stocks in the indexwp = the weights of each stock in the portfolio – a nx1 vector of weightswi = the weights of each stock in the index – a nx1 vector of weightsβp = a scalar representing the beta of the portfolio relative to the indexs2

i = the variance of the index (per annum) – a scalarΣ = the covariance matrix of the residual returns of stocks in the index – an nxnmatrix. This is estimated using a factor analysis of the Spearmans correlationmatrix.

The residual variance drops the last term:

Variance (residual returnsi) = (wp-βp.wi)` Σ (wp - βp.wi)(1x1) (1xn) (nxn) (nx1)

The tracking error or residual error (the standard deviation) is simply the squareroot of the above terms.

The estimation of the correlation matrix is a critical part of the calculation of thetracking/residual variance. The correlation matrix is decomposed into a smallernumber of factors that represent most of the variation in the correlations of theresidual returns of stocks.

If the correlation matrix is not collapsed into a smaller number of factors thespreadsheet would be overwhelmed by the number of calculations. Using the AllOrdinaries index, there are 350-plus different securities which means a matrix of350 times 350 – that is, 122,500 figures.

A principal components analysis of the correlation matrix of the residual returnsof every stock in the index reveals that 15 factors capture most of the variation inthe matrix – roughly 75%. These 15 factors and the loadings of each stock oneach factor are detailed in the middle of the spreadsheet.

Covariance matrix isdecomposed using factoranalysis



In reducing the correlation matrix to these 15 factors, specific risk that presentsas covariance is reduced. This leads to a correlation matrix that better reflectsthe future. That is, it has less noise from past stock specific events than justusing a historical covariance matrix.

The sensitivity of the tracking/residual variance to a change in each stock’sweighting is calculated by differentiating the tracking variance with respect toeach stock’s weighting in the portfolio:

Sensitivity of tracking variance = δ

δ

[( . )` ( . ) ( ) . ]w B w w B w B s

wp p i p p i p i

p

− − + −Σ 1 2 2

Estimating ΣΣ, the covariance matrix of the residual returns, ej, ofstocks in the index

ej = Rj - Rf - (aj + bj •Ri )(mx1) (mx1) (mx1) (mx1)

where bj= the Vasicek adjusted beta for each company, j, based on a sample of48 months

Ri = the monthly logged returns (including dividends) for the indexRj = the monthly logged returns (including dividends) for each stockRf = the monthly logged returns of the accumulation index of the 90 day

bill rate

Correlation (e1 , e2 , e3 .... ,en) = ΩΩ = an nxn Spearmans correlation matrix of residual returns for each stock, e j

Using factor analysis – principal components technique, 15 factor loading vectorsare extracted from ΩΩ = (λ1λ1` + λ2λ2` + λ3λ3` + ... λ15λ15`) + Ψ(nxn) (nxn) (nxn)where λk = the nx1 eigenvectors scaled by their respective eigenvaluesΨ = the residual matrixHowever, 15 factor loadings are not enough to specify the full communalities ofeach stock – only 75% on average is estimated. Therefore, the diagonals of theresidual matrix are added to the matrix of multiplied eigenvectors – see below.

Ω` = (λ1λ1` + λ2λ2` + λ3λ3` + ... λ15λ15`) + Ψ`

where Ψ` is a diagonal matrix of the residual terms and Ω` is the estimatedcorrelation matrix.The final step is to convert the estimated correlation matrix, Ω`, into a covariancematrix

Σ = σ σ` • Ω`(nxn) (nxn) (nxn)

where σ is a vector of nx1 standard deviation of each stock’s residual returns.

Fifteen factors capture 75%of the correlation matrix



Portfolio Analyser – Benchmark menu

The Portfolio Analyser workbook utilises the EXCEL© menu structure for a series of functions. Choosing theBenchmark menu which sits between the Window and Help menu, the following options are available:

Table of contents – an unique mechanism for navigating through the Portfolio Analyser.

Risk space – changes between displaying tracking error and residual error. See “Tracking error andresidual error” on page 13 for more details.

Set portfolio – offers you the ability to set a starting portfolio based on the supplied benchmarks. Onceyou have this opening portfolio, you can change the weights or add and remove stocks to suit yourinvestment strategy. Two additional menu items appear below “Set portfolio”. One allows you to<clear> all stocks and weights, so that you can start a portfolio again. The other is detailed below.

Re-base existing stock to 100% – the Portfolio Analyser has been designed to ease the exchange ofstocks and weights between itself and other products – such as HSBC James Capel’s Investment Edge©.1

Hence you may copy a group of stocks and their market capitalisations from another product into thePortfolio Analyser. This menu item will then convert those market capitalisations into percentageweights that add up to 100%. That is, it will re-base the stocks to 100%.

Speed – the Portfolio Analyser caters for portfolios that contain all the stocks in the All Ordinaries index.However, most portfolios will be of a much smaller size. This function allows you to reduce the size ofdata arrays and hence yield performance increases in re-calculations, particularly if you have anolder/slower PC.

Picking the benchmarkThis is the most important part of the items in this menu. A series of benchmarks are made available forportfolio comparison. The overall risk of the portfolio is measured in terms of its expected sensitivity tomovements in the benchmark, the so-called beta of the portfolio. Once the benchmark is selected, thesummary statistics line will change to label the new benchmark (top right-hand corner of spreadsheetunder [Run] button).

Domestic Benchmarks Overseas benchmarksAll Ordinaries All Resources FT Australian index20 Leaders ASX Top 100 MSCI Australian index2

50 Leaders Ex-ASX 100All Industrials Property Trusts

1. Investment Edge© is an evolutionary and detailed database on Australian listed companies. It represents a novel and dynamicapproach to analysis of Australia’s major publicly listed companies.

2. The MSCI Australian index is only available as a benchmark through analysis undertaken by HSBC James Capel.



Australian Research Team contactsMelbourne Sydney BrisbaneFax: (03) 9229 3577 Fax: (02) 9255 2555 Fax: (07) 3223 7890

Andrew Dalziel Head of Research (07) 3229 6199Chris Bedingfield Property Trusts (02) 9255 2676Justin Blaess Property Trusts (02) 9255 2681Stephen Burns Property Trusts (02) 9255 2643Nick Caley Insurance (02) 9255 2473James Casey Food & Retail (03) 9229 3669Jeff Emmanuel Banking (03) 9229 3685Bill Etheridge Gold (02) 9255 2586Kiera Grant Small Companies (02) 9255 2680Amos Hill Assistant Economist (03) 9229 3584Nola Hodgson Media (03) 9229 3658Michael Kirby Commodities (03) 9229 3607Peter Lucas Alcohol, Tourism & Leisure (03) 9229 3591Amanda Miller Small Companies (07) 3229 6199Andrew Perks Energy (03) 9229 3676Kate Prendiville Property Trusts (02) 9255 2569Michael Saba Equity Derivatives (03) 9229 3641Umit Safak RIO, MIM, NBH, CMC, PAS, ABF, SVR (03) 9229 3560Kessada Sawyer Small Companies (03) 9229 3665Mark Skocic Food & Retail (03) 9229 3599Stuart Smith Energy & Utilities (03) 9229 3570Adam Spowers Telecommunications & Pay TV (02) 9255 2554Andrew Sutherland Diversified Industrials, Engineering & Chemicals (03) 9229 3574John Syropoulo Global Mining (London) (171) 336 4389Peter Taubman Equity Derivatives (03) 9229 3662Mario Traviati Energy (03) 9229 3569Marcus Tuck Strategist & Chief Economist (03) 9229 3589Cherie Zanette Banking (02) 9229 3686

Colin Ritchie Portfolio Research & Quantitative Analysis (03) 9229 3572Geoff Thomas Research Data (03) 9229 3687Martin Summons Research Editor (03) 9229 3609



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HSBC Portfolio Risk Analyser