1741_2014-1_how alternative indices are revolutionising investor behavior

8/9/2019 1741_2014-1_how Alternative Indices Are Revolutionising Investor Behavior

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Research Note Series1/2014


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Research Note Series 1/2014 2

During the second half of the 20th century, a number of NobelPrize-winning research studies supplied the theoretical foun-

dation for the triumph of indexing based on market capitalisa-tion. With reference to the efcient-market hypothesis, it wasdeduced that rational investors with mean-variance prefer-ences should invest “only” in the broad market and should re-frain from making active investment decisions on a quest foralpha.

In recent years, academics and practitioners have increas-ingly launched successful attacks on the theory of the rationalinvestor and the efcient-market hypothesis. Alternatives havebeen put forth that are better able empirically and theoreticallyto explain stock market returns. The traditional single factor

capital asset pricing model (CAPM) has since been supplantedby multifactor models that explain expected stock returns not just by means of market beta, but also by means of a variety ofsystematic factors. These multifactor models lay the theoreticalfoundation for alternative indexing methods.

The nancial industry has since picked up on these nd-ings, and indexing methods are being launched that speci-cally attempt to capture the individual factors that explainstock returns. These new indices should not be understoodas active strategies, but rather as rule-based alternatives to aconventional capitalisation-weighted index. An index imper-

atively must feature transparency, broad diversication, am-ple liquidity and low implementation costs. Alternative indi-ces meet those criteria and present a coequal alternative tocapitalisation- weighted indices.

Alternative indices can be employed along different stagesof the investment process. They can be used to evaluate theperformance of active portfolio managers, and they enable de-liberate cost-efcient investments to be made in individual sys-tematic factors that are tailored to specic strategic and tac-tical investment needs. As the array of alternative indices onoffer grows, their importance for investors as a portfolio build-

ing block is becoming ever more essential. For this reason, 1741Asset Management Ltd – drawing on its longstanding experi-

ence and expertise in the eld of alternative indexing – has de-veloped the 1741 Switzerland Index Series of eight indices for

the Swiss equity market. The eight indices consistently coverthe spectrum of relevant systematic factors and enable inves-tors to specically capture individual risk premiums. The mainfocus of this research note is to introduce these indices andsubject them to an empirical test. In addition, case studies arepresented in order to point out potential tangible ways to putthese indices to use.

F R O M T H E S I N G L E FA C TO R M O D E L TO T H E

A LT E R N ATI V E I N D E X A T H E O R E T I C A L O U T L I N E

The publication of the Capital Asset Pricing Model (CAPM)

around 1964 sounded the start of the victory parade for indexingbased on market capitalisation. 1 Below is a brief refresher on themain cornerstones of the model:

1. The CAPM is a single factor model:– Investors receive compensation in the form of a higher ex-

pected return only for holding systematic risk.– Expected equity returns (above the risk-free interest rate)

are a function of a single systematic factor: the market riskpremium. 2

2. A capitalisation-weighted market portfolio is a mean- variance

efcient portfolio, i.e. it is the portfolio with the highest sharperatio.

For investors this implies one simple dictate: Hold only a marketportfolio! Over the subsequent decades, most of the CAPM’sunderlying assumptions and its theoretical foundation werequestioned and empirically tested. This research revealed thatscaled broad-market beta as a sole systematic factor inade-quately explains the expected equity return. Additional factorsexist that systematically explain equity returns. Analogously tothe CAPM, the expected return in such multifactor models is

depicted as a linear combination of exposures to multiple sys-tematic factors. 3

1741 SWITZERLAND INDEX SERIES HOW ALTERNATIVE INDICES ARE

REVOLUTIONISING INVESTOR BEHAVIOUR


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The move from one to multiple systematic factors is a sig-nicant step: In a multifactor world, a capitalisation-weightedmarket portfolio is not necessarily mean-variance efcient. Inorder to build an efcient portfolio, the investor must accord-ingly also take into consideration the rest of the systematic

risks. One of the best-known and most commonly employedmultifactor models is the Fama-French three factor model(FF3FM) 4, which adds in the systematic factors “value” (i.e. anenterprise’s valuation level) and “size” (i.e. the absolute size ofeach enterprise’s market capitalisation) alongside the marketfactor. Juxtaposing the FF3FM against the CAPM instantly re-veals a practical application possibility for multifactor models– using them to isolate alpha from beta. The classical CAPMregression (R t

ei = α i + β im R t em + ε t

i ) yields an “alpha”, a system-atic performance differential between a portfolio and a (scaled)capitalisation-weighted index. But if one uses the FF3FM re-

gression (R t ei

= α i + β imR t em

+ β iValue R t eValue

+ β iSize R t eSize

+ ε t i

) to eval-uate portfolio performance, the return is explained by meansof three different factors. The far-reaching consequence of thisis that it turns parts of the alpha (which is often considered aproxy for manager skill) into beta (i.e. exposure to a system-atic factor). This distinction is by no means purely semantic,because whereas the asset management industry often chargeshigh fees for purported alpha, beta can usually be obtained lessexpensively. Why should one pay high fees for a return that iseasy to replicate?

The search for systematic factors goes far beyond just

“value” and “size”. In recent years a variety of systematic fac-tors have been identied that can help to explain expected re-turns. Each factor, in turn, can be encapsulated by a separatealternative index. An investor can selectively exploit the riskpremium on individual factors by investing in the correspond-ing alternative index. Drawing on earlier research, 5 alternativeindices can be classied into ve different beta categories. Eachindex category targets different systematic factors and exhibitsa specic risk/return prole. Chart 1 illustrates the connectionbetween equity return, systematic factors and beta category.

CHART 1: BETA CATEGORIES AND SYSTEMATIC FACTORS

Systematic factors

Small size

StructuralfactorsLarge Size

Liquidity

Growth

Return-basedfactors

Value

Momentum

Quality

Volatility Risk

Beta category

Marketfactor

β1 – price-based

β2 – price-agnostic

β3 – fundamental-based

β4 – risk-based

β5 – return-based

Source: 1741 Asset Management Ltd. For information on beta categorisation, see Gander, Leveau& Pffner (2012)

1 7 4 1 S W I T Z E R L A N D I N D E X S E R I E S A LT E R N AT I V E

I N D I C E S I N T H E S W I S S E Q U I T Y M A R K E T

The usefulness of alternative indices is obvious in theory. Inpractice, however, it is sometimes an extremely challengingtask to nd suitable alternative indices. For one thing, the al-ternative indices available on the market frequently lack trans-parency. This is not a trivial matter because in reality, an alter-native index lacking transparency hardly differs from an activefund – the investor cannot reconstruct with sufcient certaintyhow its performance is achieved. The uncontrolled methodol-

ogy proliferation by some index providers likewise appearsproblematic. Alternative indices often blend together multi-ple systematic factors and thus do not allow investors to selec-tively seize distinct individual risk premiums. A classic exampleof this is value indices based on a preselection of value stocksthat are subsequently weighted in the index in accordance withtheir respective market caps. This type of methodology blendsthe market beta with the value beta and makes it impossible toisolate the value premium. It potentially will even mix unde-sired factor exposure into the investor’s portfolio, thus under-mining his investment decision.

These are serious drawbacks, and they make working withalternative indices unnecessarily complicated for investors. In


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the course of its research in the eld of alternative indexingmethods, 1741 Asset Management Ltd has developed eight al-ternative indices for the purpose of making the best-known in-dividual systematic factors investable for investors by renderingthem methodologically consistent and comparable across differ-

ent weighting formulas.

CHART 2: OVERVIEW OF 1741 SWITZERLAND INDEX SERIES

Source: 1741 Asset Management Ltd. For information on beta categorisation, see Gander, Leveau& Pffner (2012)

Beta category Index Information source

β1 – price-based Cap Weighted Market capitalisationper company

β2 – price-agnostic Equal Weighted Number of co mpanies: N

β3 – fundamental-based Accounting Based Fundamental data(absolute) per company

β4 – risk-based Minimum VolatilityRisk Parity

Price data

β5 – return-based Value MomentumQuality

Fundamental (relative)and price data impliedreturn expectations

These indices can be used by investors as a benchmark for evalu-ating the performance of their stock portfolios (e.g.: Is the value

manager worth the money spent?), and they make it possible toinvest in alternative indices on the Swiss equity market. Theycover all ve beta categories and their best-known underlyingsystematic factors. Individual indices or multiple indices com-bined are suitable for use depending on the investor’s needs. Thenext section of this research note takes a closer look at the 1741Switzerland Index Series’s investment universe and its construc-tion as well as its weighting methodology.

D E F I N I T I O N O F I N V E S T M E N T U N I V E R S E A N D

C O N S T R U C T I O N M E T H O D O L O G Y

All stocks traded on the SIX Swiss Exchange form the base in-vestment universe. A company must have a free oat greater

than 20% and an average daily share trading volume greaterthan CHF 1 million to be considered for inclusion into the 1741Switzerland Index Series. From the remaining pool of compa-nies, the 50 largest ones by market capitalisation are selected atthe end of each quarter to be the next quarter’s constituents for

all of the alternative indices. The size of a company is not meas-ured by its closing market cap on the last day of the quarter, butinstead is determined by its moving average over the precedingsix months so that an undesired uctuation of index constituentsfrom one quarter to the next is avoided. 6

Chart 3 illustrates how the method chosen to dene theuniverse affects the return of the index weighted by marketcapitalisation. It compares the traditional Swiss PerformanceIndex (SPI) with the 1741 Cap Weighted Index over time. De-spite their different number of underlying constituents, theyhave performed almost identically with an annualised return

differential of just 10 basis points.Since all of the indices are representative of the market,

they all reect the general movement of the broad market.Whether it is a cap weighted, minimum volatility, accountingbased or momentum index, they all provide diversied invest-ment exposure to the Swiss stock market.

All eight indices are based on the same universe and differonly in terms of their respective weighting formulas. A detaileddescription of the universe denition and the individual meth-

CHART 3: PRACTICALLY IDENTICAL PERFORMANCE

50

100

150

200

250

1998 2000 2002 2004 2006 2008 2010 2012

1741 Switzerland Cap Weighted +4.6% p.a.Swiss Performance Index (SPI) +4.5% p.a.

1741 Cap Weighted Index: Based on 1741 universe. SPI: Based on SWX universe.Source: Calculations by 1741 Asset Management Ltd. Data for period from 31 December 1997 to31 December 2013.


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odologies employed is beyond the scope of this research note,but interested readers can access information on the individualindex methodologies at www.1741am.com. Such methodologi-cal transparency is absolutely imperative if alternative indicestruly are to gain traction in the future as a genuine alternative

to the traditional index weighted by market capitalisation.The availability of the data required for the respective in-

dex calculations (fundamental data, price data, trading volume,etc.) allows a calculation starting point set at 31 December1997. The resulting calculation period encompasses a numberof completely different market phases: the tech bubble, the2000s bull run, the subprime crisis, and the sovereign debt cri-sis and subsequent recovery. A historical analysis of the risk/return characteristics allows inferences to be drawn about theperformance of the indices under widely different scenariosand leads to robust ndings, in our opinion.

E M P I R I C A L O B S E R VAT I O N O F T H E I N D I V I D U A L I N D I C E S

In the sections that follow, we examine the attributes of theindividual components of the 1741 Switzerland Index Seriesfrom return, risk and diversication perspectives and take alook at their behaviour under different market scenarios. Theunderlying index time series have been rechecked and veriedfor accuracy by an external index calculator. 7 All of the follow-ing calculated risk/return ratios and transaction data are basedon these ofcially calculated index time series, which can bedownloaded from Bloomberg and other market data suppli-

ers.8

Without giving away too much in advance, it turns out thatno single index is superior to the others in all aspects and cir-cumstances; a different index or a combination of different in-dices present themselves depending on the investor’s objectiveand market scenario.

Perspective: Return

The returns of the individual indices and other pertinent dataare shown in Table 1. When one considers that around 99.8%of all Swiss stock-market ETFs replicate market-capitalised in-dices, one might be surprised to learn that the cap weighted

index is the one that generates the lowest annualised return.The minimum volatility and risk parity indices, which focus on

risk, score much better in terms of their returns even thoughthey mainly concentrate on minimising or dispersing indexrisk. However, it’s the return-focused indices that historicallyexhibit the highest return. The momentum and quality indiceswere the only ones to generate a return above 8% per annum,

and the value index as well exhibits a bit more than a 3 percent-age point annual outperformance relative to its cap weightedcounterpart.

Perspective: Risk

The indices explicitly focused on risk management do not justminimise ex-ante volatility; they also exhibit by far the lowestannualised ex-post volatilities and betas (as measured againstthe cap weighted index). The minimum volatility index alsostands out with the lowest maximum drawdown.

Relative risk can be judged from two different angles. A look

at tracking error relative to the cap weighted index reveals thataside from the accounting based index, the rest of the indicesdwell in a relatively narrow band between 7.9% and 10.7%. Therespective information ratios range from 0.08 (accounting basedindex) to 0.45 (momentum index). The return-focused Beta 5 in-dices generally score the best here. However, the results changewhen tracking error is examined in comparison to the averagefor the alternative indices. When viewed from this angle, the riskparity and equal weighted indices exhibit the lowest trackingerror, and the value and cap weighted indices the highest. Theresulting information ratio is negative for the cap weighted, ac-

counting based and equal weighted indices while the momentumand quality indices again score the best.

Perspective: Diversication

A good way to illustrate the potential diversication effect isto look at the correlation matrix of relative returns (Table 2).The low and frequently negative correlations indicate that adiversication effect can be obtained by investing in multipleindices. Notably, all of the indices except the accounting basedindex exhibit a negative correlation to the cap weighted index.The high correlation between the cap weighted and the ac-

counting based indices implies that both tap exposure to verysimilar systematic factors.


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Chart 4 shows the resulting diversication effect. Employinga very simple diversication strategy that, rather than investing100% in the cap weighted index, invests increasingly in a com-posite index consisting of all of the alternative indices, doublesthe sharpe ratio. This means that benecial diversication can

be obtained not just by investing in multiple actively managedfunds, but also by investing in alternative indices.

Perspective: Market scenarios

The preceding analyses concentrated on the long-term charac-teristics of the indices. Equally interesting, though, is the ques-tion of how the individual indices behave under different marketscenarios. Table 3 displays their average monthly returns bro-

ken down by rising, sideways-trending and falling market pe-riods. As one would expect, the return-focused indices deliver

CapWeighted

EqualWeighted

AccountingBased

MinimumVolatility

Risk Parity Value Momentum Quality

Cap Weighted 1

Equal Weighted –0.55 1

Accounting Based 0.81 –0.26 1

Minimum Volatility –0.27 –0.32 –0.58 1

Risk Parity –0.56 0.09 –0.77 0.81 1

Value –0.35 0.40 0.08 –0.38 –0.20 1

Momentum –0.38 0.13 –0.51 –0.04 0.12 –0.31 1

Quality –0.38 0.13 –0.46 0.09 0.16 0.16 0.16 1

TABL E 2: CO RR ELAT ION MAT RIX OF RE LAT IVE RE TURN S

Source: Analysis by 1741 Asset Management Ltd. Calculations based on relative returns for period from 31 December 1997 to 31 December 2013 (in CHF)

CapWeighted

EqualWeighted

AccountingBased

MinimumVolatility

Risk Parity Value Momentum Quality

Return, p.a. 4.6% 6.6% 5.0% 7.5% 7.1% 7.7% 9.6% 8.4%

Impact of 5 bps TC, p.a. –0.02% –0.04% –0.02% –0.07% –0.06% –0.09% –0.13% –0.06%

Volatility 18.8% 17.9% 20.8% 12.4% 15.0% 20.3% 18.4% 17.1%

Beta 1 0.88 1.09 0.59 0.72 0.93 0.83 0.80

Tracking error 1 n/a 8.4% 4.1% 9.8% 7.9% 10.7% 10.7% 9.1%

Tracking error 2 6.1% 2.6% 6.8% 6.1% 3.3% 6.8% 6.7% 4.7%

Sharpe ratio 0.19 0.31 0.18 0.51 0.40 0.32 0.46 0.42

Information ratio 1 n/a 0.24 0.08 0.28 0.28 0.27 0.45 0.40

Information ratio 2 neg. neg. neg. 0.07 0.01 0.08 0.38 0.27

Maximum drawdown –53.8% –64.2% –59.9% –48.9% –54.8% –64.3% –56.1% –58.8%

TABL E 1: HI STO RICAL RI SK/ RE TURN OVER VIE W

Source: Analysis by 1741 Asset Management Ltd for the period from 31 December 1997 to 31 December 2013 (in CHF)1 Calculated relative to Cap Weighted Index2 Calculated relative to the average for the alternative indices


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disproportionate returns in rising markets, whereas the risk-focused indices perform better in falling markets. In rangeboundmarkets, focusing on specic systematic factors only marginallypays off; all eight alternative indices deliver similar returns.

Rising-markets

Sideways-trendingmarkets

Falling-markets

Cap Weighted 4.6% 0.5% –6.0%

Equal Weighted 5.3% 0.7% –6.6%

Accounting Based 5.2% 0.5% –6.8%

Minimum Volatility 3.9% 0.6% –4.2%

Risk Parity 4.5% 0.7% –5.5%

Value 6.0% 0.5% –6.9%

Momentum 5.1% 1.0% –5.8%

Quality 5.0% 1.0% –6.1%

TABLE 3: RE TURNS UN DE R DI FFERENT MA RKE T SCENAR IOS

Source: Analysis by 1741 Asset Management Ltd. Calculations based on relative returns. Time period: 31 December 1 997 to 31 Decem ber 2013 (in CHF)

APPLICATION EXAMPLES OF USES OF ALTERNATIVE INDICES

What are good examples of practical uses for alternative indi-ces? Three specic application possibilities are outlined below:

C A S E S T U D Y 1 : S T R AT E G I C P O S I T I O N I N G

With the advent of alternative approaches to indexing, inves-tors are starting to ask themselves which index is the right onefor their personal investment needs. Personal investment objec-tives inevitably lead to the use of different indices. As outlined insection 2, substantial risk and return differences are empiricallyobservable. Investors can exploit those differences. There is asuitable specic alternative index for each investment objective.Empirical experience suggests that investors whose goal is tomaximise long-term returns should turn to return-focused indi-ces that capture value, quality and momentum premiums. Theseindices record the highest return over the long term, though

the enhanced return potential is also accompanied by greaterrisk. Investors with a low appetite for risk are better served byminimum volatility and risk parity indices, which explicitly takethe risk attributes of individual stocks into account when settingthe index weightings. Chart 5 uses three simple portfolio combi-nations to illustrate this long-term effect.

CHART 5: EXAMPLES OF PORTFOLIO COMBINATIONS

50%50%100% 50%

17%

17%

17%

Tradit ional Risk reduction Returnmaximisation

Return, p.a. 4.6% 6.1% 6.8%

Volatility 18.8% 15.6% 18.3%

Sharpe ratio 0.24 0.39 0.37

MCAPMVVALMOMQUA

Source: Calculations by 1741 Asset Management Ltd. Data for period from 31 December 1997

to 31 December 2013 (in CHF). MCAP = Cap Weighted, MV = Minimum Volatility, VAL = Value,MOM = Momentum, QUA = Quality

CHART 4: DIVERSIFICATION PAYS OFF

0.20

0.30

0.40

0.50

100% 80% 60% 40% 20% 0%90% 70% 50% 30% 10%

Weight of Cap Weighted Index

Sharpe ratio

Cap WeightedIndex

Source: Calculations by 1741 Asset Management Ltd. 100% signifies a 100% investment in anindex weighted by market capitalisation, 0% signifies a 100% investment in a composite index ofalternative indices (1/7 invested in each of the 7 alternative indices); time period: 31 December1997 to 31 December 2013 (in CHF).


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C A S E S T U D Y 2 : TA C T I C A L P O S I T I O N I N G

If an investor holds a specic opinion about the stock market’sdirection, alternative indices can be used to help build a portfo-lio that promises the best return under the investor’s expectedmarket scenario. The empirical evidence presented in section 2

indicates that return-focused indices exhibit the highest returnsin bull markets while risk-focused indices minimise expectedlosses in bear markets. An investor who is convinced about animpending bull market should accordingly build an index port-folio composed of value, quality and momentum indices. Chart 6graphically lays out the case:

CHART 6 : T IMING BULL AND BEAR MARKETS

Good markets Bad markets

Good timing (Investor X) +318% –47%

Bad timing (Investor Y) +203% –57%MCAP portfolio +192% –53%

0

100

200

300

400

500

Good timing

Bad timingMCAP portfolio

Definition of “good timing” and “bad timing”: Most attractive, or conversely, least attractive indexportfolio in each market phase.Good markets: 12 March 2003 to 31 May 2007. Bad markets: 1 June 2007 to 9 March 2009.Source: Analysis by 1741 Asset Management Ltd

If Investor X, for example, correctly foresees the great bullmarket rally from March 2003 to June 2007 and puts his predic-tion into action in the form of a portfolio of equally weightedreturn-focused indices (34% momentum, 33% quality, 33%

value), he achieves a performance of +318% over this period(or +40% p.a.). In contrast, Investor Y, who opts for a combina-

tion of cap weighted and minimum volatility indices, earns a re-turn of just +30% p.a., which works out to a relative annualisedunderperformance of 10 percentage points.

The opposite holds in a bear market. If Investor X reas-sesses the situation in June 2007 and comes to the conclusion

that a signicant bear market is imminent and thus prudentlyswitches to a defensive index portfolio (50% cap weighted, 50%minimum volatility), during the credit market crisis from June2007 to March 2009 he loses just 47% instead of 57%. InvestorX, who is skilled at timing the market, would have achieved aperformance of +122% over the entire period from March 2003to March 2009 while a classic cap weighted investor would haveearned a return of just +39%. By comparison, Investor Y, who isnot adept at timing the market, switches from a defensive port-folio to a more aggressive one in June 2007 and earns a returnof +31% for the same six-year period, which is only marginally

lower than the performance achieved by the cap weighted in-vestor. So compared to the classic cap weighted index, selec-tively employing alternative indices provides substantial out-performance potential for those investors who want to time bulland bear markets without altering the equity allocation in theirportfolios.

C A S E S T U D Y 3 : E VA L U AT I N G T H E P E R F O R M A N C E O F A N

A C T I V E F U N D M A N A G E R

Alternative indices serve not just as an aid for strategically andtactically managing the composition of a stock portfolio, but

also help to evaluate active fund managers. Chart 7 comparesthe live track record of one of the best-known and most suc-cessful Swiss value managers with that of the 1741 SwitzerlandCap Weighted Index and the 1741 Switzerland Value Index.

Two things stick out: If an investor uses the cap weightedindex as the performance benchmark, he or she might drawfalse conclusions about the manager’s performance contribu-tion. The outperformance from February 2002 to October 2008looks impressive, but the subsequent underperformance untilMarch 2009 is equally disillusioning. Did the fund manager rstdo an excellent job and afterwards a terrible job? If the relative

performance is judged using the value index, which is the moreappropriate benchmark in view of the manager’s systematic


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factor exposures, then the picture changes. The long outper-

formance phase was not achieved solely through astute stock-picking on the part of the manager, but was also driven by thefund’s value bias. The same applies to the underperformancephase in 2008/2009. Another striking observation is that thetransparent, publicly accessible value index stands up well in acomparison with active value funds. In the example presented,the value index actually even delivered a slightly superior re-turn over the entire observation period.

Irrespective of whether the return is slightly better, equallygood or mildly inferior, it is the transparency and the consist-ent capturing of factor premiums that make alternative indices

a compelling alternative to conventional active managers. Astraightforward, rule-based, transparent investment approachthat replicates the bulk of the relative performance simply andinexpensively compensates the investor for any forfeit of man-ager skills.

We have limited ourselves to outlining three possible waysto use alternative indices in the investment process. Besidesemploying them as a benchmark for an active manager or usingthem to strategically position a stock portfolio or to tacticallymanage it, there are many other application possibilities for al-ternative indices throughout the entire portfolio construction

process.

Q U I N T E S S E N C E

After several decades of preeminence, the dominance of mar-ket cap weighted indexing is beginning to crumble. Successfultheoretical and empirical attacks have been launched againstthe bastion of cap weighted indexing. Today numerous alterna-

tive indices are crowding onto the market. Empirical evidenceshows that none of these indices is superior to any of the oth-ers in all aspects and circumstances. Their suitability is muchmore determined by an investor’s personal investment objec-tives. Different indices present themselves for use dependingon the investment need. However, the wild proliferation ofindexing methodologies and the often decient transparencyneedlessly complicate working with alternative indices in ac-tual practice. 1741 Asset Management Ltd developed the 1741Switzerland Index Series to address this. Composed of eightalternative indices featuring heretofore unparalleled transpar-

ency and methodological consistency, the 1741 Switzerland In-dex Series offers investors a user-friendly instrument that canbe utilised to prot from the systematic factor premiums on theSwiss equity market.

F O O T N O T E S

1 Sharpe, W. 1964. "Capital Asset Prices: A Theory of Market Equilibrium underConditions of Risk". Journal of Finance, vol. 19, no. 3, 425-442.

2 Formula: E(Rei )=β im E(Rem )

3 Formula: E(Rei )=β im γ m+∑ j β ij γ j , where γ j is the risk premium in reference to anidentiable systematic risk, j .

4 Fama, E., K. French. 1993. “Common Risk Factors in the Returns on Stocks andBonds”. Journal of Financial Economics, vol. 33, no. 1, 3-56.

5 Gander, P., D. Leveau, and T. Pffner. 2012. “Categorization of Indices: Do AllRoads Lead to Rome?”. Journal of Index Investing, Winter 2012, 12-17.

6 Compared against the Swiss Performance Index (SPI), the universe deter-mined this way exhibits a historical cap coverage of approximately 95% onaverage.

7 The official calculator of the 1741 Index Series is Solactive Ltd, one of theleading providers of nancial-market indices. So lactive Ltd currently calcula-tes more than 900 indices across all asset classes for more than 90 clients inEurope, North America and Asia. Approximately USD 20 billion is currentlyinvested in indices calculated by Solactive.

8 The corresponding tickers are listed in the appendix.

CHART 7: PERFO RMANCE EVALUATION

0

50

100

150

200

250

300

350

1997 2000 2003 2006 2009 2012

Indexed (in CHF)

Source: Calculations by 1741 Asset Management Ltd based on net returns for period from31 December 1997 to 31 December 2013.

Prominent Swiss value fund1741 Switzerland Value Index1741 Switzerland Cap WeightedIndex


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A P P E N D I X

I N D E X M E T H O D O L O G Y

1741 Cap Weighted IndexThe constituents of the 1741 Switzerland Cap Weighted Index

are weighted on the basis of their total market capitalisation. Oneach quarterly rebalancing date, each company’s market capi-talisation is set in relation to the aggregate market capitalisationof all companies in the 1741 Switzerland Index Series universe.

1741 Equal Weighted IndexOn each quarterly rebalancing date, each of the companies inthe 1741 Switzerland Equal Weighted Index is given an identicalweighting of 2%.

1741 Accounting Based Index

On each quarterly rebalancing date, the constituents of the 1741Switzerland Accounting Based Index are weighted on the ba-sis of their absolute values for four fundamental metrics (bookvalue, earnings, revenue and dividends). The average over thelast three years is applied for each of those metrics.

1741 Minimum Volatility IndexOn each quarterly rebalancing date, the weights of the constitu-ents of the 1741 Switzerland Minimum Volatility Index are setsuch that the overall index risk – dened as historical 6- and12-month volatility – is minimised. The maximum weight of a

company on the rebalancing date is 10%.

1741 Risk Parity IndexOn each quarterly rebalancing date, the weights of the constitu-ents of the 1741 Switzerland Risk Parity Index are set such thatthe risk contributions of each index constituent are all identical.Risk is dened as historical 6- and 12-month volatility. The maxi-mum weight of a company on the rebalancing date is 10%.

1741 Momentum IndexOn each quarterly rebalancing date, the constituents of the 1741Switzerland Momentum Index are each weighted on the basisof the strength of their momentum. The momentum calculationis based on each constituent’s return over the prior nine and

twelve months excluding the last one. An aggregate ranking isdetermined for each company, and a weighting function assignseach one a weight. The maximum weight of a company on therebalancing date is 10%.

1741 Quality IndexOn each quarterly rebalancing date, the constituents of the 1741Switzerland Quality Index are weighted on the basis of their at-tractiveness with regard to the systematic factor “quality”. Threequality metrics are examined: return on equity, net prot mar-gin and debt-to-equity ratio. An aggregate ranking is determined

for each company and a weighting function assigns each one aweight. The maximum weight of a company on the rebalancingdate is 10%.

1741 Value IndexOn each quarterly rebalancing date, the constituents of the 1741Switzerland Value Index are weighted on the basis of their at-tractiveness with regard to the systematic factor “value”. Fourvalue metrics are examined: price/book value, price/earnings,price/sales and dividend yield. An aggregate ranking is deter-mined for each company, and a weighting function assigns each

one a weight. The maximum weight of a company on the rebal-ancing date is 10%.


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Index Bloomberg Ticker Reuters ISIN

1741 Switzerland Cap Weighted Price Index .1741CHCWP DE000SLA0ZZ7

1741 Switzerland Cap Weighted Net Total Return Index 1741CHCW Index .1741CHCWN DE000SLA0CQ5

1741 Switzerland Cap Weighted Gross Total Return Index .1741CHCWG DE000SLA0CS1

1741 Switzerland Equal Weighted Price Index .1741CHEWP DE000SLA0CU7

1741 Switzerland Equal Weighted Net Total Return Index 1741CHEW Index .1741CHEWN DE000SLA0CV5

1741 Switzerland Equal Weighted Gross Total Return Index .1741CHEWG DE000SLA0CX1

1741 Switzerland Accounting Based Price Index .1741CHABP DE000SLA0CY9

1741 Switzerland Accounting Based Net Total Return Index 1741CHAB Index .1741CHABN DE000SLA0CZ6

1741 Switzerland Accounting Based Gross Total Return Index .1741CHABG DE000SLA0DD1

1741 Switzerland Minimum Volatility Price Index .1741CHMVP DE000SLA0DE9

1741 Switzerland Minimum Volatility Net Total Return Index 1741CHMV Index .1741CHMVN DE000SLA0DF6

1741 Switzerland Minimum Volatility Gross Total Return Index .1741CHMVG DE000SLA0DG4

1741 Switzerland Risk Parity Price Index .1741CHRPP DE000SLA0DH2

1741 Switzerland Risk Parity Net Total Return Index 1741CHRP Index .1741CHRPN DE000SLA0DJ81741 Switzerland Risk Parity Gross Total Return Index .1741CHRPG DE000SLA0DK6

1741 Switzerland Value Price Index .1741CHVP DE000SLA0DL4

1741 Switzerland Value Net Total Return Index 1741CHV Index .1741CHVN DE000SLA0DN0

1741 Switzerland Value Gross Total Return Index .1741CHVG DE000SLA0DQ3

1741 Switzerland Momentum Price Index .1741CHMP DE000SLA0DR1

1741 Switzerland Momentum Net Total Return Index 1741CHM Index .1741CHMN DE000SLA0DS9

1741 Switzerland Momentum Gross Total Return Index .1741CHMG DE000SLA0DU5

1741 Switzerland Quality Price Index .1741CHQP DE000SLA0DV3

1741 Switzerland Quality Net Total Return Index 1741CHQ Index .1741CHQN DE000SLA0DW1

1741 Switzerland Quality Gross Total Return Index .1741CHQG DE000SLA0DX9

OVERVIEW OF INDICES


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