1. 1
MSc Banking and Finance
Research Project: 926N1
Candidate Number: 143347
Do Mutual Funds outperform the market? An analysis of the Stock-Picking
and Market Timing Ability in Fund Managers from leading UK Investment
Trusts
Abstract
This study examines the selectivity and timing performance of 10 UK Investment trusts over
the period January 1995 to June 2016 using a combination of Jensen, Sharpe, and Treynor
measure. Results show little evidence of outperformance against the FTSE All Share index.
Only 1 fund showed evidence of superior stock selectivity, whilst no funds showed evidence
of superior market timing. Consistent with other studies, this paper also highlights the
positive association between portfolio concentration and performance in mutual funds.
Acknowledgement
I would like thank my dissertation advisor Dr Bruce Hearn of the School of Business and Management at the
University of Sussex .

2. 2
Table of Contents
1.Introduction............................................................................................................................3
2.Literature Review...................................................................................................................4
2.1 Evolution of Asset Pricing Models .......................................................................................4
2.2 Fund Performance measures...............................................................................................6
2.3 Mutual Fund Performance ..................................................................................................6
U.S Market............................................................................................................................ 7
U.K Market ........................................................................................................................... 7
2.4 Selectivity and market timing inUK Investment Trusts ........................................................8
2.5 Fund concentration and performance .................................................................................9
3. Data description....................................................................................................................9
3.1 Share price analysis over time........................................................................................... 11
4. Methodology.......................................................................................................................13
4.1 Overall Performance......................................................................................................... 13
4.2 Market Timing.................................................................................................................. 14
5.Empirical Results..................................................................................................................15
5.1 Selectivity Performance ()............................................................................................... 15
5.2 Market Timing Performance ()......................................................................................... 16
5.3 Sensitivity to the market ().............................................................................................. 17
5.4 Market and Idiosyncratic risk............................................................................................ 17
5.5 Sharpe Ratio’s .................................................................................................................. 18
6. Discussion ............................................................................................................................18
6.1 Selectivity Performance.................................................................................................... 18
6.2 Market timing................................................................................................................... 20
7. Limitations & Future Research ...........................................................................................21
8. Investors Implications.........................................................................................................22
9. Conclusion............................................................................................................................23
Bibliography.............................................................................................................................24
Data Sources............................................................................................................................28
APPENDIX TABLES ...................................................................................................................29
APPENDIX FIGURES .................................................................................................................38

3. 3
1.Introduction
Since the firstpubliclylistedfundof ForeignandCapitalin1868,Investmenttrustshave played
a fundamental roleinfinancial intermediationacrossthe world.A type of mutualfund,anInvestment
trust raised funds by issuing shares on the stock market and then investing the proceeds into a
portfolio of assets. Significantlygrowth in the operating number of funds has meant fundmanagers
have hadtostrive todelivercompetitive returns.Ithasbeenwidelydocumentedthatinvestorsbenefit
from managerial skills as well as diversification, cost advantages and liquidity intermediationwhen
investinginInvestmenttrusts.These skillsare relatedtoselectivityabilityinpickingsuccessful stocks
and timing ability in accurately forecasting future market movements. Variability in such skills are
evidenced in fund manager’s strategy and asset allocation.
Most studies on US mutual funds suggest little or no superior performance, but stronger
evidence of underperformance (Lakonishok et al 1992, Grinblatt et al 1995, Cahart 1997). Similar
resultswere achievedonUK funds(Blake and Timmermann,1999; Blak et al 1999) Although,the UK
fundmanagementindustryisresponsible foranexcessof $5.5tr1
, to the bestof our knowledge most
research on selectivity and market timing ability has used out-of-date dataset. With little research
been done that surpasses 2010. On this account, the papers look to provide some fresh conclusions
on whethermanagersfromUKInvestmenttrustsgenerate positive alphasandthusshow evidence of
selectivityability.Whilstalsoaccountingformarket timingabilitythroughthe gamma termsupplied
by Treynor and Mazuy (1966). With these underlying objectives, this paper aims to discuss the
following research questions:
Do UK Investment Trusts generate positive abnormal performance relative to the market?
Do fund managers in UK Investment trust possess superior selectivity and market timing skills?
Is Investment ability more evident for fund managers who hold portfolios concentrated in a few
industries?
The structure of this paper is as follows. Firstly, an analysis of relevant literature on asset
pricingmodelsandmutual fundperformance.Thisisfollowedbya descriptionof the data and chart
analysis. The next sectionprovides an explanationof the empirical models and methodology. This is
followedbyempirical resultsonallestimatesmeasures.Section6presentsdiscussiononthe empirical
findingsinthe contextof other literature.Section7 discussesthe limitationsof the study and future
1 Annual Reports and Accounts year end 2015,The Investment Association (2016),
www.theinvestmentassociation.org .[accessed: 2/09/16]

4. 4
research. Section 8 present a brief summary of the investor implications to corporate policy of our
findings. Finally, this if followed by concluding remarks.
2.Literature Review
2.1 Evolution of Asset Pricing Models
The origins of the CAPM stem from the work of Sharpe (1964) and Lintner (1965), whilst
Markowitz (1952) and Tobin(1958) laid the model’s foundations through the mean-variance
algorithm.Markowitz’smodelexplainshow aninvestorselectsaportfolioattime t-1thatproducesa
stochastic return at t. While assuming investors are risk averse and only consider the mean and
variance of their investment return. Thus, investors choose “mean-variance-efficient” portfolios, as
giventhe expectedreturnandvariance,portfolio’sbothminimize the varianceof the portfolioreturn
and maximize expected return.
Sharpe (1964) and Lintner(1965) developedtwokeyassumptionstothe Marrkowitz’smean-
variance framework. The first is complete agreement: Given market clearing prices at t-1, investors
agree on the jointdistributionof assetreturnsfromt-1to t. The secondassumptionstatesthatthere
is borrowingandlendingat a risk-free rate,whichisthe same for all investorsandis independentof
amount.Combinedwiththe workof Black(1972) whoformedthe CAPM,explainedhow theexpected
return on a stock is determinedbythe risk-free interestrate and a risk premium, whichisa function
of the stock’s responsiveness to movements in the market. The latter is classified as the beta
coefficient,arguablythe maincomponentwhichisheavilyusedamongfundmanagersinthe financial
markets.
A majorityof the earlierempirical testsof CAPMgive supportto its specificationthatbetais
the only explanatory factor in explaining cross sectional portfolio returns (Lintner, 1965; Douglas,
1968). However, in later research support for the model has weakened. Fama and MacBeth (1973)
showthe betacoefficientwasstatisticallyinsignificant.Blacketal (1972) usedtime seriesregression
analysistoshowhowthe interceptissignificantlydifferentfromzeroanditstime varyingproperties,
whichviolate marketefficiencyandthe original model.LaterRoll (1977) suppliedfurthercriticism,in
claiming the proxies used to compose the market portfolio are not reflective of the portfolio of
invested wealth.Thus using any other portfolio as opposed to the true market portfolio tests the
efficiencyof the selectedproxyportfolio.More recently,BartholdyandPeare (2003) concludedthat
any correctly used proxy will always generate biased estimates for expected returns.
As empirical research documenting the flaws of CAPM grew, a wave of alternative asset
pricing models arrived. The Arbitrage Pricing Theory (APT) by Ross (1976) presented a multi-factor
model which allows an asset returns to have many systematic risk measures. These refer to

5. 5
macroeconomic risk factors which cannot be diversified against. Although the APT benefited the
CAPMinbeinglessrestrictiveandexplaininggreaterproportionof securityreturns,several drawbacks
exist.Unlike the CAPM,the APT doesnot reveal the identityof pricedfactors.Therefore,demanding
users to reasonably estimate the factor sensitivities.Further studies on asset pricing have identified
numerous variables beyond the market beta that explain stock returns, termed ‘anomalies’. These
include market capitalization (Banz, 1981), earnings to price ratio (Basu, 1983) and book-to-market
ratio (Rosenbeg et al, 1985). Fama and French (1992) confirm these anomalies explain returns,
claimingmiss-specificationinthe CAPMbetween1963 and 1990. Similarly,there hasbeenevidence
of thisforEuropeanandJapanese markets(Capaul etal,1993).Withinthe mutual fundliterature,the
APT framework has been applied in studies including Connor et al (1991) and Fletcher (1997), who
both conclude that on average trusts in the UK and US do not outperform the market benchmark.
In response to the poor performance and anomalies of the CAPM, Fama and French (1993)
developed the three-factor asset pricing model. In this model excess portfolio returns are explained
bythree riskfactors.These factorsincludethe CAPM’sexcessmarketreturn,sizefactor2
andbook-to-
market factor3
. Early tests of the three-factor model by Fama and French (1995) show that only
market and size factors help explain returns, though B/Mrevealed no relation. Comparably, Porras
(1998) found B/Minsignificant,althoughfoundsizetobe insignificantusing cross-sectionalregression
analysis. Nonetheless,studies post the millennium have found the two additional factors significant
in explainingreturnsacrossAustralia,Canada,Germany,Japan, the UK and US (Maroney et al, 2002;
Drew et al, 2003).
Given the popularityof the single index by financial professionals today,there is an ongoing
controversial debate between the CAPM and the three-factor model. Blanco (2012) favoured the
three-factor model with respect to explaining expected returns in the American Stock Market,
providing support for the size and B/M factor inclusions. Similarly, Simpson et al (2008)4
found the
relative meritof the three-factormodelisitsabilitytocapture informationrelatingtoawide range of
economic indicators. On the other hand, numerous studies have yielded evenly sided results.
Bartholdy and Peare (2005) show that CAPM explained on average 3% of stock returns, whilst the
three-factorexplained5%.Likewise,Sourmere etal (2013) finds11 out of 28 companystocks satisfy
2 The sizefactor (SMB) is a zero-investment portfolio that is longon small capitalization stocks and shorton bi
capitalization stocks.
3 The book-to-market factor (HML) is a zero-investment portfolio that is longon high book - to-market (B/M)
stocks and shorton lowB/M stocks.

6. 6
the CAPMmodel,and10 satisfythe three-factormodel.These empirical findingsare encouragingto
thispapers chosen methodology to use the single-index CAPM to model Investment trust returns.
Following the arrival of the three-factor model,Jegadeesh and Titman (1993) explained the
case of investorsutilisingastrategybasedonrecentmomentum5
togenerate abnormalreturns.Soon
after Carhart (1997) examinedthe persistence instockreturnsof mutual funds inUS equitymarkets
firstly using an augmented multifactor model to account for momentum. The limited literature
surrounding the validity of this factor mainlysupport its inclusion. L’Her et al (2004) concluded that
the four-factor model was valid in the Canadian Market. Likewise, Lam and So (2009) found the
momentumfactortobe significantforthe HongKongMarket.Lai andLau(2010) highlightthe relative
strength of the model in explaining mutual fund returns in Malaysia. Unlu (2012) found consistent
results for the Irish Stock Exchange.
2.2 Fund Performance measures
The evolution of fund performance measures has been parallel with the growth of asset
pricingmodels.The introductionof the CAPMbySharpe(1964) andLintner(1965) ledtothe arrival of
the “three indicies”fromSharpe (1966),Treynor(1965), and Jenson (1968). All three of these models
were derivedsimplyfromthe CAPMmodel;The Sharperatioisbasedonthe rewardtovolatilitytrade-
off andformulatesthe ratiobetweenaverage returnsearnedinexcessof the risk-freerate perunitof
volatility. The Treynor Ratio from Treynor (1965) is of close format to the Sharpe ratio, however
definesthereward-to-volatilityratioinrelationtoeachunitof the CAPMbetarisk.Incomparison,the
Jensen(1968) alphameasuresreferstothe interceptdeterminedfrom the CAPMregressionof excess
portfolio returns on the excess market returns.
Jensen’s alpha has been the predominant measures used in fund performance valuation.
Essentially,itholdsastable positionbecauseitrepresentsthe interceptwhenexcessfundreturnsare
estimated against either the market index, book-to-market ratio, size or momentum factors. In
relationtothe efficientmarkethypothesis,the alphatermindicatesmarketefficiencyonthe basisof
either out or under-performance in fund returns. According to the efficient market hypothesis this
alpha term should not be significantly different from zero.
2.3 Mutual Fund Performance
The UK and US have two of the most developed and largest fund management industries in
the world.Total U.S mutual fundassetsbeinginexcessof $15.7 trillion6
whilstUKfundstotallingover
5 This strategy involves buyingstocks which haveperformed well in the pastyear, whilstsellingrecentpoor
performing stocks.
6 2016 Investment Company FactBook, A review of Trends and Activities in the U.S Investment Company
Industry,56th edition. Investment Company Institute, [accessed 2/09/16]

7. 7
$7.3 trillion7
inmanagedfunds.Giventhe largeamountofdataavailableformeaningful analysis,much
of the academic literature hasfocusedon fundsin these market.Althoughmore recentlythere have
been new focus on European and Australian industries.
U.S Market
Studies on US mutual funds suggest evidence of little or no superior performance. Earlier
research in the seminal paper of Jensen (1968) tests the abnormal performance on 115 funds over
1945 to 1964, and found no significant abnormal performance.Malkiel (1995) analysed US equity
fundsusingthe singleindex model butoveralongerperiodthanJensen(1968).Resultsoverthe 1971
to 1991 period here show the average alpha is statistically insignificant from zero. Other literature
from the US has indicated minimal superior performance, but more evidence of underperformance
(Daniel et al, 1997; Chevailler and Elliso, 1999; Wermer, 2000, Baks et al, 2001).
Most of the abovementionedstudieshadusedstandardconventional statistical techniques,
however there has been a recent influx of new methods to measure fund performance more
accurately.Namely,Kosowskietal (2006) and FamaandFrench(2010) adoptthe bootstraptechnique
to calculate alpha and its corresponding test statistic. Thismethod aimsto separate managerial skill
from luck since the standard statistical technique does not account for presence of luck or the non
normalitypropertiesinalpha.Applyingthismeasure,Kosowski etal (2006) demonstrated thatonlya
minority from the analysed 2118 US mutual funds posses stock picking skills. Moreover, using FDR
(False Discoveryrate)8
approach,Scailletetal (2010) found75% of US fundsexhibitazeroalphabased
on returns, with a few showing evidence of genuine skill.
U.K Market
Research in the UK has been more limited than the US. This is because dataset providers in
the UK are more commercially motivated in that they only offer information on active funds. In
comparison, academics in the US have benefited from access to the CRSP9
database which holds
informationonbothdeadand live funds.Asa result,a large portionof UK studieshave beensubject
to survivor-bias10
samples.
7 Annual Reports and Accounts year end 2015,The Investment Association (2016),
www.theinvestmentassociation.org .[accessed: 2/09/16]
8 The FalseDiscovery Rate (FDR) is a measure to providea simpleway to calculatethe number and the
proportion of funds with truly positiveand negative performance in any portion of the tails of the cross -
sectional alphadistribution.
9 Center for Research in Security Prices – Provider of historical stock marketdata. Maintains someof the
largestand most comprehensive proprietary historical databases in stock marketresearch.Researchers rely on
the CRSP for accurate,survivor bias-freeinformation.
10 Survivorship bias refers to the tendency for failed companies to be excluded from performance studies
based on the fact that they no longer exist. This can causeskewness in results becauseonly successful
companies areincluded.

8. 8
ResultsonUK mutual fundshas tendedto yieldsimilarresultstothose discoveredin the US.
Fletcher (1997) examines fund performance using Henriksson and Merton (1981) to decompose
performance intostockpickingandmarkettiming.The resultssuggestthatonaverage managersfrom
UK unittrust exhibitpositive stockselectivityandnegative markettiming.Usingthe same datasetin
a laterstudy,Fletcher(1997) findsno significantevidence thatUK unit trust outperformthe market.
Constant with this finding, Blake and Timmermann (1998) find evidence of under performance by
equity and balanced managed UK funds. Moreover, Quigley and Sinquefield (2000) use both CAPM
and three –factor model to analyse monthly returns on 752 UK equity based funds over a 20 year
periodof 1978 to 1998. They show that UK managers netof expensesare unable to outperformthe
market, thus coinciding with the US findings.
Studies using measures beyond the conventional standard statistical techniques have also
developedontoUK data.Cuthbertsonetal (2008) appliesthe approachof Kosowski etal (2006) to a
survivor-biasfreesampleof 842UKequityunittrusts.Resultsfromthisbootstrappingtechnique show
the average alpha of funds is negative but statisticallyinsignificant. These results lie consistentwith
Blake and Timmermann (1998). In a subsequent study using the same survivor-bias free dataset,
Cuthbertsonetal (2010) replicatesasimilarFDRmethodologyasScailletetal (2010).Resultssuggests
that the number of UK funds with truly negative abnormal performance significantly exceeds the
number of funds with truly positive abnormal performance.
2.4 Selectivity and market timing in UK Investment Trusts
The literature closely linked with this paper have concludedthat UK Investment trusts have
not on average beenable toout-performthe market.Bal andLeger(1997) analyse 92 fundsoverthe
period 1975 to 1993 using Jensen’s alpha and the Sharpe Ratio. Even without correction for
transactioncosts,fundsonaverage didnotgenerate significantalpha’s.Theyalsoshowthatthe choice
of variance or covariance risk(Sharpe andTreynormeasures) mattersverylittle.Inaddition,theyfind
evidence of perverse markettimingfromthe 90’s onwards.Thistrendintersectswiththe startof the
dataset used in this paper, and thus will be compared against in later discussion. Correspondingly,
Leger(1996) observedinsignificantalphasandnegative timingperformance forone inthree trustsof
a sample of 72funds.Whilsthighlightingthe strongnegativecorrelationbetweenmanagertimingand
selectivity.Bangassa(1999) addsfurther supportto these results,but identifiesfundstylesinJapan,
North America and Europe generates significant perverse timing practices. Moreover, Cuthbertson
(2009) showedthat only1% of fundsdemonstratedpositive markettimingat5% level,while 19%of
fundsexhibitednegative timings.More recently,Bangassaetal (2012) examinedselectivityandtiming
performance of 218 UK Investment Trusts. They conclude that international funds show some
favourable selectivity ability, while domestic funds show better timing ability.

9. 9
2.5 Fund concentration and performance
Positive fund performance can be attributed to a wide selection of factors related to
managerial characteristics.Thoughtherehasbeenanavenueof researchonthe relationshipbetween
portfolio composition and performance. Kacperczyk et al (2005) claimed investment ability is more
evidentamongmanagerswhoholdportfoliosconcentratedinafewindustries.Likewise,otherrelated
studieshave showedthatfocusedmanagersoutperformtheirmore broadlydiversifiedcounterparts
(Baks etal, 2006; Hujj and Derwall,2011). Othershave attributedfocusedinvestmentstrategiesand
outperformance to the case where managers exercise their informational advantages (Coval and
Moskowitz,1999,2000; Nanda et al, 2004). Whereas, Sapp and Yan (2008) find no evidence that
focused funds outperform diversified funds.These empirical findingssuggest that should there be
presence of abnormal performanceinthispaper,one wouldexpectthe fundtobe concentratedona
few industries.
3. Data description
The data used in this study consistsof monthlyreturns calculatedas the percentage change
inshare price of 10UK InvestmentTrusts.DatawasextractedfromThomsonReutersDataStreamover
the examined period of 1 January 1995 to 1 June 2016.
OftentermedasClosed-Endfunds,Investmenttrustssell afixedamountof unitstoinvestors
at the time of offer.Theydonot issue additionalunitsinresponsetodemand,insteadtheyare listed
on the stockmarketfor investorstopurchase.These listedunitsactessentiallyascompanystockand
their prices are determined by demand and supply forces. Unlike other investment funds, shares in
Investmenttrustscanbe purchasedandsoldatpricesabove orbelow the NetAssetValue(combined
value of all assetsthe trustshold).A share price lowerthanthe NAV issaidtobe tradingatadiscount,
in comparison to a price that is above the NAV, the shares trading at a premium.
In selectingdata,we incorporate anapproachthatonlyaccountsforsurvivingfunds.Firstly,a
search of UK investment trusts on the DataStream database was taken over the specifiedperiod.
Secondly,all resultingfundswereimportedtoanexcel spreadsheet.Next,allnon-survivingfundsthat
have ceased operations/terminated over the observationperiod are droppedand this was evidence
by a constant share price. Fund attrition has been the consequence of poor fund performance over
time or a judgementfrommanagementthatmarketvalue of funis sufficientlysmall andthusitis no
longer rational to maintain the fund (Elton et al,1996). This process of fund selection generated a
remainingsampleof 10UK InvestmentTrusts.The detailsof these trustscanbe foundinthe appendix
(see table 2), which also outlines each trust Investment Objectives.

10. 10
Empirical research in Grinblatt and Titman (1989), Brown et al (1992), and Brown and
Goetzmann (1994) on survivorshipbias may suggest implications in our study. For example, Malkiel
(1995) findsthatsurvivingfundsconsistentlyhavehighermeanreturnsthannon-survivingfunds.We
aimto analyse historical funddatawiththe intentionof providingfuture fundvalue prediction,which
would not be relevant to terminated funds. Nonetheless, we consider this bias with caution our
interpretations and conclusions.
To construct an appropriate CAPMmodel forthe model we alsoretaindatasetfor the return
on a risk-free assetandthe market return.The annualizedUS three-monthTreasuryBill rate is used
torepresentthe risk-freereturnacrossthe observationperiod.Deductingthisvalue fromfundreturns
providesuswithvaluesthatrepresenteachfundsexcessreturns. The marketproxyusedinthisstudy
is the FTSE All-Share Index returns. This is considered the best performance measure of the London
equitymarketandcaptures98%of UK’smarketcapitalization.Itisthe mostsuitableindexforanalysis
on index tracking funds such as investments trusts, unit trusts and exchange-traded funds. Whilst
being the predominant market benchmark in previous literature on UK Investments Trusts
performance.
In addition, and to gain a greater insight behind fund manager selectivityand performance,
we retain information on the asset allocation of each fund.Data relating to portfolio composition is
collectedfromthe AnnualReportsof correspondingInvestmenttrusts.Takingthe mostrecentannual
report,we assume thatfundsmaintainanapproximatelyconstantindustryconcentrationthroughout
their life, e.g each industry weighting remains roughly the same. The Morningstar Global Equity
Classification Structure is used to categorise and define asset allocations into three major economic
sectors; cyclical11
, defensive12
and sensitive13
. Within these ‘super sectors’ there the associated
industrygroups(see Table 3). The applicationof thisclassificationinthisstudyenablesustoevaluate
and compare each portfolio’s exposure to different sectors,whilst supporting understanding behind
abnormal volatility and correlation with major economic events.
11 The cyclical super sector includes industries significantly impacted by economic shifts.When the economy is
prosperous these industries tend to expand and when the economy is in a downturn these industries tend to
shrink.In general, the stocks in these industries havebetas of greater than 1.
12 The defensive super sector includes industries thatarerelatively immune to economic cycles.These
industries provideservices thatconsumers requirein both good and bad times, such as healthcareand
utilities.In general,the stocks in these industries havebeta of less than 1.
13 The sensitivesuper sector includes industries which ebb and flow with the overall economy, but not severely
so. Sensitiveindustries fall between the defensive and cyclical industries asthey are not immune to a poor
economy but they also may not be as severely impacted by a poor economy as industries in the cyclical super
sector. In general, the stocks in these industries havebetas that are closeto 1.

11. 11
The descriptive statistics are used to describe the basic features of the dataset and are
presentedinTable 1. Data has beenseparatedintomonthlyreturnsandexcessreturns.Initial values
showsthatforall fundsaveragemonthlyreturnswerepositive, butare all negativeformonthlyexcess
returns.
3.1 Share price analysis over time
Figure 1 showsaplotof the monthlyshare pricesof eachInvestmentTrustfromJanuary1995
to June 2016. Most of the assessedfundsfollow asimilarpattern,howevercertaintrustssharesshow
high volatility level. Low performers include E and A funds. Medium performers includesfunds such
as F, B, G and H. High performers where share price has grown at least six fold include J, D and C.
In the yearsleadingupto2000 we can observe relative correlationbetweenamajorityof the
funds.However,fundswithinvestmentobjectivesinAsiaandJapansufferedanepisode of decline in
share price. Shroder Asia (B), Shroder Japan (E) and Atlantis Japan (A) were exposed heavily to the
Asian financial crisis in July 1997 which saw several major companies including Nissan Mutual Life
Insurance and Yaohan a Japanese retailer go into bankruptcy.
Betweenthe monthsleadinguptothemillenniumand2001Lazard World (H/I) trustandmore
evidentlyShroderUKMidCap (J) experiencedasteepspike intheirshare price’s.Thiscanbe justified
by the speculative‘dotcombubble’whichsaw arapid rise inthe equitymarketsthroughexponential
investment in internet-based companies. Both funds price peaked in December 2000 and then
suffered a steepdecline until November 2001. Whereas, a majority of the remaining funds display a
smaller change in their price movement. This implies that Shroder Mid Cap and Lazard World Trust
are likelytohave heldaportfolioheavilyexposedtotechnologyandconsumerstocks,incomparison
to the other funds.
From 2003 all examined trusts show some recovery from the dot com crisis, and all share
pricesdisplaysteadygrowthuntilmid-2007.Similarly,tothe spike seenin2000 ShroderMid-Capand
Lazard World funds outgrow the other funds. However, ORYX International (C) and Shroder Income
(D) bothfollowsimilargrowth.We detectadisparitybetweenthese fourfundsandthe remainingfive
fundsduring2007 to 2008. The periodhasbeenlabelledbyfinancial commentatorsasthe ‘run-up’to
the US housingbubble,fuelledbyextraordinarylow interestratesandthe reallocationof investment
fromthe stockmarketintothe housingmarket.InAugust2007 the UK stockmarketsufferedextreme
volatilityas a result heightened fears in the interbank market amidfears of exposure to high-risk US
Mortgages.Thiswas followedbythe UKBank NorthernRock beingnationalisedinFebruary2008 and
the collapse of LehmenBrothersinSeptember2008. Subsequently,assetsacrossall classesdeclined
in value andthisis indicatedwiththe steepfall infundssharesbetween2008 and 2009. We observe

12. 12
the leastaffectedfundstothiscrisiswere AtlantisJapan (A) and ShroderJapan (E).Interestingly,the
graphhighlightsthatinfactAtlantisJapanexperiencedadeclinewell beforethe crisisandotherfunds.
Duringthe aftermathof the financial crisisof 2008 all fundsshare’sshow positive growthand
a majorityrecovertotheirpre-crisisvalueby2012. Notably,we observe rapidgrowthinShroderMid-
Cap (J) and Shroder Income (D) within this period. In contrast, a majority of the other funds such as
CQS New City (G), Shroder Asia Pacific (B), Shroder UK (F) and ORYX International (C) follow similar
patternbutat slowerrate.Interestingly,we alsoobserve the increasinggrowthof ORYXInternational
(C) as well as Shroder Mid Cap (J) and Shroder Income (D).

13. 13
4. Methodology
4.1 Overall Performance
This studyuses the unconditional single-factorperformance measure:Jensen’s(1968) alpha.
This measure is the intercept term estimated through the the regression model:
𝑅 𝑖𝑡 − 𝑅 𝑓𝑡 = 𝛼𝑖 + 𝛽𝑖( 𝑅 𝑚𝑡 − 𝑅 𝑓𝑡) + 𝜖𝑖 (1)
where 𝛼𝑖 isthe risk adjustedabnormal returnfromthe single index model, 𝑅 𝑖𝑡 isthe returnonfund i
overperiod t, 𝑅 𝑓𝑡 is the returnon the 5 year US Treasuryyieldadjustedtoconstant maturity, 𝑅 𝑚𝑡 is
the returnonthe FTSEALL-SHAREIndex, 𝛽𝑖 the factorsensitivityof differencesinfundreturnsandthe
risk free rate, and 𝜖𝑖 is the error term with the following properties: E(𝜖𝑖,𝑡) = 0 , Var(𝜖𝑖,𝑡) = 𝜎𝜖 𝑖,𝑡
2 ,
Cov( 𝜖𝑖,𝑡,𝑟 𝑚,𝑡) = Cov( 𝜖𝑖,𝑡,𝜖𝑗,𝑡) = 0.
The following hypothesis are tested with this model:
𝐻0: 𝛼𝑖 = 0
𝐻 𝑎: 𝛼𝑖 ≠ 0
where the null hypothesissuggests fund i does not out or under-performthe relative market proxy,
the alternate hypothesis indicates fundi either out or under-preformsto the relative market proxy.
Statistical significanceismeasuredusingcritical valuesusingatwo-tailedtestat5% significancelevel.
Althoughfordiscussionpurposeswe alsoconsidercoefficientsthatgenerate a hightest-statisticbut
are marginally insignificant.
The intercept term identifies whether fund managers have superior stock selection abilities.
Alphagenerationisachievedthroughselectingsecuritiesthatresultin 𝜖𝑖 > 0.A statisticallysignificant
positive alpha indicates the fund manager has the abilityto forecast future securityprices. An alpha
term that does not statistically differ from zero implies the manager mimics the composition of a
reference marketbenchmark.A statisticallysignificantnegative alphatermssuggestthe fundmanager
performs poorer than a naïve strategy of random selection. In the context of compensation, the
annualizedJensen’salphaisthe maximummountof moneyaninvestorshouldbe willingtopayafund
per year. Subsequently, this allows us to contribute to the literature surround pay performance in
mutual funds.
In addition,we alsoassessandcompare the riskof eachfund.UnderCAPMthe riskisdivided
intotwo components.These are formallyexpressedasproductsof the standard deviationinreturns:
𝜎𝑖
2
= [𝛽]2 𝜎 𝑚
2 + 𝜎 𝑢,𝑖
2

14. 14
The firstpart [𝛽]2 𝜎 𝑚
2 indicatesthe market (or systematic) risk.Thistype of riskarisesdue to
fluctuations in the market as a whole and cannot be eliminated through diversification. This is also
interpretedintermsof ahigh 𝑅2 of the regressionmodel(1).We payspecial attentiontothisvaluein
each funds model in our results analysis. The second part 𝜎 𝑢,𝑖
2
is referred to as specific (or non-
systematic) risk. This is often attributable to managerial competence with the fund. In contrast to
systematicriskthistypeof riskisidiosyncraticandthuscanbe eliminatedthroughsuccessfulportfolio
diversification. A well diversified portfolio containing a great number of assets with different
characteristics can cancel out such risk. We take the difference between 1 and 𝑅2, to estimate each
funds idiosyncratic risk.
Anothermeasure forrisk-adjustedreturnsthatwe use isthe Sharpe ratio. This performance
method was developed by Sharpe (1966) under its birth name, the reward-to-variability ratio. Over
the yearsithas gainedsignificantpopularityinthe financeandnow operatesan industrystandardfor
measuring risk-adjusted performance. Incorporating standard deviation of portfolio returns against
excess returns, the ratio has benefits in its comparability across fund categories as well its
independence of any choice of benchmark (Jagric et al 2007). The measure is given by:
𝑆𝑖 =
𝑅̅ 𝑖 − 𝑅̅ 𝑓
𝜎𝑖
where 𝑅̅ 𝑝 is the meanreturn to portfolio i duringthe evaluationperiod, 𝑅̅ 𝑓isthe meanrisk free rate
of return and 𝜎𝑖 is the standard deviation of the portfolio return. The higher the ratio, the better its
returns have been relative to the amount of investment risk. We replicate (Bal and Leger, 1996) in
estimatingthismeasureoverdifferenttime horizonstogauge itsvariabilityunderdifferenteconomic
conditions and to provide further analysis on fund manager selectivity.
4.2 Market Timing
Overall performance can be decomposed into fund managers stock selection and market
timing abilities. To measure whether fund managers have the ability to predict market movements,
we adopt an extended version of (1) from Treynor and Mazuy (1966) (TM hereafter). The model
includes the additionof a quadratic term to equation (1) in order to account for non-linearityin the
function of market return, and is as follows:
𝑅 𝑖𝑡 − 𝑅 𝑓𝑡 = 𝛼𝑖 + 𝛽𝑖( 𝑅 𝑚𝑡 − 𝑅 𝑓𝑡) + 𝛾𝑖( 𝑅 𝑚𝑡 − 𝑅 𝑓𝑡)
2
+ 𝜖𝑖 (2)
where 𝛾𝑖 is the risk adjusted measure of market timing ability of fund i, and 𝜖𝑖 is an error term with
the following properties: E(𝜖𝑖,𝑡) = Cov( 𝜖𝑖,𝑡,𝑟 𝑚,𝑡) = Cov( 𝜖𝑖,𝑡,𝑅 𝑚,𝑡
2 ) = 0
The followinghypothesisistestedusingthismodel:

15. 15
𝐻0: 𝛾𝑖 = 0
𝐻 𝑎: 𝛾𝑖 ≠ 0
where the null hypothesisstatesthatmanagerinfund iexhibitsno positiveornegativemarkettiming
ability.The alternate hypothesisstatesthatmanagerinfund i exhibitssignificantpositive ornegative
timingabilities.Markettimingabilityisreflectedbygreatermarketexposurewhenthe excessmarket
returns are higher and vice versa. A significant positive 𝛾𝑖 would indicate superior market timing
ability. When 𝛾𝑖 does not deviate significantly from zero, the fund manager cannot outguess the
market. A significantly negative 𝛾𝑖 implies perverse market timing from the manager.
5.Empirical Results
5.1 Selectivity Performance ()
Table 4 presents the results of applying the unconditional single-index model (1) to each
individualfundinoursample overthe time period. The valuesforJensen’s (1965) alphaindicates the
risk adjusted abnormal return in relation to the FTSE All Share market proxy, net of the manager’s
timing ability. In testing the null hypothesis of no abnormal fund performance on individual
Investment Trust, we apply t-distributioncritical values using 2-tailed laws. However, we also pay
attentiontoall alpha’sthatcontainatest-statisticgreaterthan+-1.5andlie marginal tothe 5% critical
value.
Estimations show only three out of the ten funds generate a positive alpha term. Although,
withinthis three onlyone fundgeneratesastatisticallysignificantpositivealphaata 5% level.FundB
has an alpha value of 0.0097 with a t-statistic of 2.19. This suggest that the fund B has experienced
positive abnormal performance relativetothe the FTSE All Share overthe observationperiod.Which
providesevidence thatbasedon the unconditional single factormodel,the fundmanagerfromfund
B exhibited superior stock selectivity over the period. Whereas the remaining positive funds F and J
generate alphatermsthatdonosignificantlydifferfrom0at5% level,withalphavaluesof 0.0036 and
0.0079, respectively.Nonetheless,these valuesare relativelyhighand cause a marginal upheldinthe
null hypothesis of no abnormal fund performance. This couldbe sensitive to changes in observation
timeframe orusedmodel.Thus,we may infertoa smallerdegree thatfundmanagersinfundsF and
J have shown stock selectivity skills over the observation period.
The estimationsinTable1alsoshowsthatsevenouttenfundsgeneratenegative alphavalues.
Unlike the positive alpha sample, all of the negative alpha are statistically insignificant at 5% level.
Thus for fundsA, C, D, E, G, H, and I, the null hypothesisof noabnormal fundperformance isupheld.
This impliesthatbasedon the unconditional single model,fundmanagersinthese sevenfundshave

16. 16
exhibited neither superior or inferior stock selectivity skills over the observation period. However,
funds C, D and G generate relatively high t-statistics of -1.71, -1.6 and -1,51, respectively. Which
suggestsamarginal upheldinthe null hypothesisthatissensitive tochangesin observationperiodor
model design. This also implies with less strength that fund mangers in fund C, D, and G may have
possessed inferior stock selectivity over the observation period.
5.2 Market Timing Performance ()
Table 5 displaysthe resultsinapplyingthe unconditional TM(1966) model to each individual
fundover the observationperiod.The valuesforgamma indicatesthe fundmanagers markettiming
ability. In testing the null hypothesisthat fund manager posses no market timing ability, we apply t-
distribution critical values using a 2 tailed rule. However, we also pay notice to gamma values that
generate a t-statistic that lies marginal to the 5% critical value. Furthermore, a comparison of alpha
termsfromthe TM(2) model andthe singleindexmodel (1) ismade toassessthe impactof accounting
for market timing.
Coefficientestimatesshowthatonlytwo out of ten fundsgenerate a positive gamma value.
Althoughthesecoefficientsare notsignificantata5% levelandhencethe null hypothesisof nomarket
timingabilityis upheld.FundsDand F generate gammavaluesof 0.98 and 1.03, respectively,witht-
statisticsof 1.82 and 1.91, respectively.Thisimpliesthatbasedonthe unconditionalTMmodel,fund
managersinD and F exhibitneithersuperiororperverse markettimingabilitiesoverthe observation
period. Howbeit, these values are marginal to the 5% critical value of 1.96. This may indicate tests
againstthe null hypothesisare sensitivetoresearchdesign.Onthisbasis,resultsgivepartial evidence
that fund managers in D and F have shown positive market timing skills.
Subsequently,the majorityof fundsdisplaynegative gammavalues.Table 5showsthat eight
from the tenfundsgenerate a negative gamma.Althoughof thiseight,onlythree are significanttoa
5% level.FundGyieldsagamma termof -2.85, FundH of -1.91, and FundI with -1.65. Notably,these
are in fact all significantto a 1% level. Thismeans that the null hypothesis that fund managershave
neither positive or negative market timing ability is decisively rejected. Hence,fund managers from
fund’s G, H and I have shown evidence of perverse market timing against the market. Whereas the
remaining five negative gamma funds of A, B, C, E and J show insignificant values to a 5% level. This
implies that fund managers within these funds exhibit neither superior or perverse marking timing
skills. Although,fund A has a gamma value which is marginal to the 5% critical value. Thus could be
subject to choice of methodology and research design.
In addition, the inclusion of the quadratic market term in the TM model highlights the
potential bias present in coefficients in the unconditional single index model. In econometric sense

17. 17
thisis branded‘omittedvariable bias’,whichsuggestsestimatesmaycapture the effectof important
omitted variables and thus provide a biased estimate. Table 5 shows that two alpha estimates have
changedconsideratelyasa resultof the inclusionof markettiming. Firstly,the alphatermon FundB
hasincreasedfrom0.0097 to0.011. Witha negative gammaestimate,one caninferthatthe omission
of markettimingwascausingsome downwardbiasonthe stockselectivitymeasure (alpha).Secondly,
the alpha value on Fund D has now become significant to a 5% level. The value has changed from -
0.004 to -0.005, implying that the omission of market timing was causing some upward bias on the
selectivity measure in model (1).
5.3 Sensitivity to the market ()
Table 4 also shows the estimated β values of each individual Investment trust in relation to
the FTSE All share market returns. This refers to the sensitivity of fund returns to market returns
measure byFTSE ALL share excessreturns. Giventhe nature of Investmenttrustsandfundmanager’s
objectivestotrackmarketindices, one wouldexpectβvaluesformostof the sample toapproximately
equal 1. Which implies fund returns have a one-to-one relationship with the market returns. A beta
value above 1 implies fund returns are more volatile than market, or termed by the financial
professionals as ‘aggressive’ stock. A beta value between 0 and 1 implies fund returns are less
responsive to market returns, generally termed ‘defensive’ stock.
The estimated βvaluesinTable4indicate thatall the fundreturnsinoursamplemove closely
withthe FTSE ALL share index.The fundbeta’soverthe observationperiodrange from0.665 forfund
C to 1.29 for fund B. Notably, only three out of the ten funds generate a beta value greater than 1,
these include Fund B, F and J. This indicates that these funds are more volatile than the market and
behave similar to an ‘aggressive’ stock. Whereas the remaining sevenfunds of A, C, D, E, G, H, and I
yielda betalessthan 1. These fundsare lesssensitive tomarketmovementsandbehave similartoa
‘defensive’ stock. In addition, the fund that tracks the FTSE All-Share the closest is fund I which
generated an alpha value of 0.967 over the observation period.
5.4 Market and Idiosyncratic risk
Table 1 alsocontaininformationregardingeachmodel’sexplanatorypowerinthe 𝑅2 values.
These representthe percentageof Investmenttrustreturnsthatisexplainedby variationsinthe FTSE
ALL share index. Statistically this is a measure of how well the data are to the fitted regression line,
however in the financial context this measure is interpretedas systematic or market risk. This arises
due to fluctuations in the market as a whole and cannot be eliminated by diversification. The
percentage of Investment trust returns that is unexplainedby variations in the FTSE ALL share index
is referred to as non-systematic or idiosyncratic risk. This can be attributable to managerial

18. 18
competence yet incomparisonto the systematicrisk,this type of risk can be eliminatedbyportfolio
diversification.
Values Table 4 and the unconditional single index model show that the highest 𝑅2 values is
generated in Fund’s D, F, H and I. This implies that these funds contain the greatest market risk.
Specifically,we observe the Fund F contains the highest market risk from the sample and hence the
lowestidiosyncraticrisk.Thisisevidentthrougha 𝑅2 value of 0.7489. In comparison,FundCcontains
the lowestmarketriskof the sample and hence the highestidiosyncraticrisk.Thisisevidentthrough
a 𝑅2 value of 0.2335.
5.5 Sharpe Ratio’s
Table 6 presentsthe calculationsforeach fundsSharpe ratio overseveral examinedperiods.
Inthe periodof 1995 to2016 all fundsgeneratedanegativeratio.Thisimpliesthatthe average excess
return over the 15 years was negative for all funds. Values show that Fund D had the most negative
value of -0.6125 and thus its portfolio returned the greatest underperformance per unit of risk.
Whereas, Fund J yielded the least negative value of -0.2795 and thus its portfolio returned the least
underperformance perunitof risk.Examiningthe fundsacrossconsecutive inclusive5-yearperiodsof
2000 – 2004 and 2005 – 2009, showed that all funds generated negative Sharpe ratios over these
separate periods. Notably, these alignwithourexpectationsgiventhe dotcome bubble in 2001 and
the creditcrisisin 2008. Albeit,basedonthese ratio’s,the bestperformingfundsoverthe respective
crises were Fund J and Fund B. This could be attributed to managerial competencies in each fund to
minimise portfolio losses during economic downturns.
Inthe mostrecent5-yearperiodof 2010to2014 inclusive,allfundsgeneratedpositive Sharpe
ratios. These ranged from 0.0485 for fund I to 0.2194 for Fund C. The results for this period provide
evidence that fund managers have become more positively rewardedfor taking additional risk, in
comparison to the first few years into the millennium.
6. Discussion
6.1 Selectivity Performance
The results in Table 4 provide evidence that a majority of fund mangers from the selected
Investment trusts have not generated significant alphas over the observation period. This suggests
mostfundshave notbeenable tooutperformthe market,andthusthe average fundmanageracross
this sample does not possess stock selectivity skills. Howbeit, Fund B remains the anomaly to this in
generatingasignificantalpha.These resultsshow consistencywithearlierstudiesthatdocumentthe
insignificance inalphavaluesof UKInvestmentTrusts(Bal and Leger1996; Bangassa1999). Similarly,
our resultsthatnine outof the tenanalysedfund’sgenerateinsignificantalpha’sfollowsCuthbertson

19. 19
et al (2010), who found 75% of UK mutual funds neither underperform not outperform their
benchmarks. Nonetheless,theseresultsshowsome contradictiontostudiesthathave foundevidence
of under-performance onariskadjustedbasisbythe average fundmanager(BlakeandTimmermann,
1998; Quigley and Sinquefield, 2000).
The finding of one significantly positive alpha in our sample supports the view that fund
managers contain superior selectivity skills relative to the market. The positive estimated alpha for
only Fund B over the observationperiodshowsconsistencywithCuthbertsonetal (2008), who finds
stock picking abilityin 5% to 10% of top performing UK equity mutual funds. This result also follows
findingsfromUSstudiesof Kosowski etal (2006) and Barras etal (2005), whofindstrong evidenceof
stock selectivity skills among top performing US funds. Moreover, this result for Fund B supports
Banagassa et al (2012), that international funds show some favourable selectivity abilityand their
commentary on the international diversification provides some relevance. They argue that
international funds can benefit from international diversified portfolios and greater stock returns in
global markets.ThiscouldpartiallyexplainFundB’sestimatedalpha,since the fundprimarilyinvests
in equitiesof companiesacrossAsiaandfar easterncountriesborderingthe PacificOcean (see More
recently, our results lend support to Verheyden and Moor (2015), who found only 6 out of 272 US
equity funds generated a positively significant alpha from 2004 to 2014. Conversely, they attribute
outperformance by fund manager’s ability to limiting losses in times of market inefficiency and by
profiting from subsequent learning effects. Hence, we may infer that managers in Fund B have
followed this strategy over the observation period.
In addition,the estimates of the Sharpe ratio for Fund B providesfurtherevidence of strong
managerial competency.Thisisshownforthe timeframeof 2005 to2009 inTable 6. Specifically,Fund
B generated the smallest negative ratio over this examined period. This suggests that each unit of
additional risk was associated with lower negative excess returns relative to all other funds in the
sample.Therefore, wemayinferthatintermsof risk-adjustedreturns,FundBoutperformedthe other
fundsthroughoutthe yearssurroundingthe 2008 Financial Crisis.Thismaypotential explainwhythe
fundgeneratedtheonly significantpositive alpha. Thisisalsoevident of Fund’sFandJ,whobothhave
positive alpha terms and have considerably smaller negative Sharpe ratios over this period. To this
end, we can confidently assume that superior stock selectivity over the 15-year period was more
apparent for firms with a smaller Sharpe ratio over the 2008 financial crisis. This also confirms the
positive relationship between alpha and Sharpe ratio.
Figure 2 presents Fund B’s asset composition. A majority of the fund’s investment are in
industrieswiththecyclicalsupersector.Stockswithintheseindustriesare generallymore volatilethan

20. 20
the market,containingbeta’sgreaterthan 1. Linkingthiswiththe alpha term, we may inferthat the
managerfromFundB has utilisedcyclical andvolatile stockstogenerateabnormal performance. This
relationmaybe attributedtoluckorstockpickingskills,asobviousfromthe significantlypositive alpha
estimate. Also, one can observe from Figure 2 that Fund B is the most heavily exposed to the real
estate industry.
Moreover, Fund B’s investment portfolio is more concentrated industries of technology,
consumer cyclical, real estate and financial services. Such concentrationhas the potential to explain
superior selectivity ability, as evidenced by a positive alpha. Fund managers may want to hold a
concentratedportfolioif theypredictgrowthincertainindustriesoverothers,orif theyhavesuperior
informationtoselectprofitable stocks14
.Thisinference issupportedby Kacperczyk etal (2005), who
foundthatinvestmentabilityismore evidentamongmanagerswhoholdportfoliosconcentratedina
few industries. Similarly,other studies have documented that focused fund managers outperform
their more broadly diversified counterparts (Baks et al ,2006; Huij and Derwall, 2011). Nonetheless,
our inference is less consistent with Sapp and Yann (2008), who do not support the view that fund
mangers holding focused portfolios have superior stock picking skills.
6.2 Market timing
The estimates fromthe unconditional TMmodel donot provide evidence insupportof fund
managersexhibitingpositive markettiming overthe observationperiod.Resultsshow thatno funds
generate significantly positive gamma, and three out ten fund generated a significantly negative
gamma.ThiscoincideswithrecentresearchfromCuthbertsonetal (2009),whofoundthatonly1%of
UK fundsdemonstrate positivetimingabilityand19% of fundsexhibitnegativetimingandonaverage
miss-timethe market.These results alsosupportfindingsthatmarkettimingabilitiesinfundmanagers
has diminished over time into the 90’s (Bal and Leger, 1996). In addition, our results follow Leger
(1996), whofoundthat one inthree trustspossesnegative timingabilities.Likewise,Bangassa(1999)
also found evidence of perverse timing practices in 72 UK Investment trusts over a 15-year period.
Nonetheless,ourresultscontradicthisfindingsthatfundstylesassociatedwithJapan,NorthAmerica
and Europe are the only funds to generate significant gamma’s. In our results, we observe no
significant gamma values for Japanese styled funds such as Fund A (Atlantis Japan) and Fund E
(Shroder Japan).
Attemptsat market timinginportfoliomanagementcomprise tactical assetallocation,using
financial derivatives,or rebalancing. Figure Evidence for significant perverse timing was found for
14 Levy and Livingston (1995) concludethat fund managers that have superior information should hold a
relatively concentrated portfolio,under the mean-varianceframework. Van Nieuwerburgh and VeldKamp
(2005) concludethat increasingreturns to scalein market learningshould causeoptimal under-diversification.

21. 21
fund’sG, andH. Figure 7 and Figure 8 show these fundscontainaheavyconcentrationof investment
inthe financialservicessectorcomparedtoall otherfunds.Thismaysuggestthatthese fundmanagers
have not beentactical withtheirassetallocationstrategiesoverthe observationperiod.
Otherstudiesthat have founddifferencesinresultswhilstusingahigherfrequencydataset.
Thisavenue of resultsshedspotential weaknessonourselecteddatatype inthisstudy.Banagassaet
al (2012) find domestic funds show some favourable market timing ability. Bollen and Busse (2001)
also highlight weakness in our measure of timing ability. They show mutual fundsexhibit significant
timing abilitymore often in daily data. Goetzmannet al (2000) argue that monthly frequencymight
fail tocapture the contributionof manager’stimingactivitiestofundreturnssincedecisionsregarding
marketexposure are likelymade more frequentlythanmonths.Inlightof this,our resultsonmarket
timing could be inaccurate.
7. Limitations & Future Research
One of the mainissues withthis paperstemsfromthe dataselection.Thispaperuses surviving
investmenttrusts monthlyreturns only andignoresfundsthathave terminatedoverthe observation
period. As discussedearlier, this can lead to biases in estimatedcoefficients. Malkiel (1995) implied
that the datasetused inthis studywill overstate the returnsto fund investors. AccordingtoGregory
et al (2007) a problemincalculatingabnormal returnsusingfactormodel isthatit incorporateslook-
ahead bias if funds are required to survive for a certain number of months. This empirical issue has
also been argued in earlier research (Grinblatt and Titman, 1989; Brown et al, 1992; Brown and
Goetzmann,1994). Though,a majorityof studieshave usedsamplesthatcontainsbothsurvivingand
non-survivingfundstocounteractthisbias. Leiteetal (2009) arguedthatthisissue couldhave serious
implicationsforstudiesusingasmallernumberof funds.Tothisend,the resultsinthisstudymustbe
interpretedwith some precaution. An avenue of extension from this paper would be to record non-
surviving funds intothe sample, withthe view of comparing non-surviving alphaswith the surviving
fund alphas.
Another limitation relatesto the frequencyof the dataset. This study collected the monthly
share price of 10 UK InvestmenttrustfromJanuary 1995 to June 2016 to measure bothstock picking
and market timing skills. However, empirical research in Bollen and Busse (2001) and Goetzmann et
al (2000) support the use of dailyreturnswhenanalysingmutual fundperformance.Theyargue that
manager’s decisions regarding market exposure are likely to happen more frequently than months.
This issue hinders the accuracy of this papers estimations on fund managers markettiming skills. A
responsive approachtothisissue andinthe contextof future researchinthisareaistouse funddaily
returns to measure market timing skills.

22. 22
Additionally, the use of the unconditional Jensen (1965) and TM (1968) models to measure
stock picking and market timing also pose limitations to this paper. These provide estimates that
disregardinformationonthechangingnature of theeconomyandcanincorrectlymeasurealpha,beta
and gamma.In reality,fundmanagersrespondtomarketinformationusingdynamicstrategieswhich
often means varying alpha’s and beta’s. Ferson and Schadt (1996) shows that conditional models
outperformunconditional ones.Thus,thispapersresultsof alpha and gamma shouldbe interpreted
with some caution. Nevertheless, this study will be of value to a novice investor in due to its
uncomplicated method’s used to analyse fund performance and managerial skills.
Finally,the assumptionthatfundcompositionremainsapproximatelyconstantovertime islikely
no to hold.Thisstudy usesthe most recentavailable Annual ReportsandMorningStar Trusts Data to
retain each funds asset allocation. Subsequently, we draw some inferences in associating fund
performance with the assumed asset allocation. However, portfolio holdings are likely change as
managersmove away from riskyindustriesorwhena funappointsa new manager.On this account,
the inferencesregarding stockpickingskills inthe concentratedfundsrelative tothe diversified funds
will lack validity.
8. Investors Implications
Testsof the performanceof mutual fundsare importantforinvestorschoosingbetween active
andindex funds.Inthisstudy,resultssuggestonaveragemanagersfromUKinvestmenttrustare were
not able to out perform the market. From an investors point of view, this provides discouraging
information for the likelihoodof generating profit through holdingUK Investment trusts. Moreover,
testsof performance ofmutualfundssupplyinformationonthe questionof thevalidityof theEfficient
Market Hypothesisinthe InvestmentManagementIndustry.The resultsderivedinthispaper infact
lendsupporttothe notionthatno fundmanagersare able tobeat the market.However,estimations
show that Shroder Asia Pacicific fund (B) is an exception.
In testing for the presence of managerial skills, this study providessome implications relatingto
the controversial performance related pay that fund managers are rewarded with. Based on the
results, on average fund managers from UK investment trust exhibit little or often perverse market
timing. This result would also be of value to chairmen, committee and high shareholders in UK
Investment trusts. Using these results, executives from both CQS New City High Yield Fund (G) and
Lazard World Trust (H/I) may take actions to revaluate their technical andpredictive modelsthatare
responsible for switching between asset classes.

23. 23
9. Conclusion
Thisstudyevaluatestheperformanceof 10UKInvestmenttrustsoverthe periodJanuary1995
– June 2016. The unconditional model of Jensen’s (1968) alpha and Treynor and Mazuy (1966) are
employed to investigate the presence of managerial stock picking and market timing skills. Results
provide evidence thatInvestmenttrustsonaverage donot outperformthe market.Thisisevidenced
by only 1 fund generating a truly positive alpha. Estimationsfor market timing indicate that 3 trusts
exhibittrulynegative gammas.Resultswere consistentwithrelevantUKliterature inCuthbertsonet
al (2008) andBangassa etal (2012) as well ascomplyingwithUSfindingsinKosowskietal (2006) and
Barras et al (2005). Togetherthisadvocatesthatmanagerial skill ismore attributabletostockpicking
as oppose to market timing abilities.
In addition,Sharpe ratioswere estimatedfordifferenttime framesbetweenthe observation
period detailed above. Results for this measure suggest the reward-to-volatility in Investment Trust
was persistentlynegative between2000 and 2009. However, became positive in the period beyond
2010. Interestingly, we found that the funds with the lowest negative ratio (best performers) over
2008 crises period were the funds which generated positive alphas.Thirdly,the collection of fund’s
asset allocation’senabled an insight into the relationshipbetween generated alphas and portfolio
concentration. We observe thatShroderAsiaPacific(theonlyfundtogenerateasignificantlypositive
alpha) holdsamore concentratedportfoliorelativetomostotherfunds.Thisassociationisconsistent
with work of Kacperczyk et al (2005), Baks et al (2006) and Huij and Derwall (2011). These results
propose the extending evidence that fund managers may exercise informational advantages in
Shroder Asia Pacific Fund. Therefore, confirming that investment ability is more present in
concentrated portfolios

24. 24
Bibliography
Andre, P., Kooli, M. and L'her, J.F., 2004. The long-run performance of mergers and acquisitions:
Evidence from the Canadian stock market.Financial Management, 33(4).
Baks, K.P., Metrick, A. and Wachter, J., 2001. Should investors avoid all actively managed mutual
funds? A study in Bayesian performance evaluation. The Journal of Finance, 56(1), pp.45-85.
Bal,Y.andLeger,L.A.,1996.The performance of UKinvestmenttrusts.ServiceIndustriesJournal,16(1),
pp.67-81.
Bangassa, K., 1999. Performance of UK investment trusts: 1980–1994.Journal of Business Finance &
Accounting, 26(9‐10), pp.1141-1168.
Bangassa, K., Su, C. and Joseph, N.L., 2012. Selectivity and timing performance of UK investment
trusts. Journal of international financial markets, institutions and money, 22(5), pp.1149-1175.
Banz, R.W., 1981. The relationship between return and market value of common stocks. Journal of
financial economics, 9(1), pp.3-18.
Barras, L., Scaillet, O. and Wermers, R., 2005. False Discoveries in Mutual Fund Performance:
Measuring Luck in Estimated Alphas, FAME Research Paper No. 163, University of Geneva.
Barras,L.,Scaillet,O.andWermers,R.,2010.False discoveriesinmutualfundperformance:Measuring
luck in estimated alphas. The Journal of Finance, 65(1), pp.179-216.
Bartholdy,J. and Peare,P., 2003. Unbiasedestimationof expectedreturnusingCAPM. International
Review of Financial Analysis, 12(1), pp.69-81.
Bartholdy, J. and Peare, P., 2005. Estimation of expected return: CAPM vs. Fama and
French. International Review of Financial Analysis, 14(4), pp.407-427.
Basu, S., 1983. The relationshipbetween earnings' yield, market value and return for NYSE common
stocks: Further evidence. Journal of financial economics, 12(1), pp.129-156.
Blake, D. and Timmermann, A., 1998. Mutual fund performance: evidence from the UK. European
Finance Review, 2(1), pp.57-77.
Blake,David.&AlanTimmermann,1998, Mutual FundPerformance:Evidence fromthe UK,European
Finance Review, 2: 57-77.
Blanco, B., 2012. The use of CAPMand Fama and French Three Factor Model: portfolios selection.
Bollen, N.P. and Busse, J.A., 2001. On the timing ability of mutual fund managers. The Journal of
Finance, 56(3), pp.1075-1094.
Brown, S.J. and Goetzmann, W.N., 1994, July. Attrition and mutual fund performance. In Journal of
Finance (Vol. 49, No. 3, pp. 1055-1056).
Brown, S.J., Goetzmann, W., Ibbotson, R.G. and Ross, S.A., 1992. Survivorship bias in performance
studies. Review of Financial Studies, 5(4), pp.553-580.

25. 25
Capaul, C., Rowley, I., and Sharpe, W.F., 1993, “International Value and Growth Stock Returns”,
Financial Analysts Journal, January/February, 27-36.
Carhart, M.M. "On persistence inmutual fundperformance." The Journal of finance 52.1 (1997): 57-
82.
Chevalier,J.andEllison,G.,1999.Are some mutual fundmanagersbetterthanothers?Cross‐sectional
patterns in behavior and performance. The journal of finance, 54(3), pp.875-899.
Connor,G. and R.A.Korajczyk(1986), `Performance MeasurementWiththe Arbitrage PricingTheory:
A New Framework for Analysis', Journal of Financial Economics, Vol. 15, pp. 373±94.
Coval, J.D. and Moskowitz, T.J., 1999. Home bias at home: Local equity preference in domestic
portfolios. The Journal of Finance, 54(6), pp.2045-2073.
Coval,J.D.andMoskowitz,T.J.,1999.The geographyof investment:Informedtradingandassetprices.
Cuthbertson, K., Nitzsche, D. and O'Sullivan, N., 2008. UK mutual fund performance: Skill or
luck?. Journal of Empirical Finance, 15(4), pp.613-634.
Cuthbertson, K., Nitzsche, D. and O'Sullivan, N., 2010. The market timing ability of UK mutual
funds. Journal of Business Finance & Accounting, 37(1‐2), pp.270-289.
Daniel,K.,Grinblatt,M.,Titman,S.andWermers,R.,1997. Measuringmutual fundperformance with
characteristic‐based benchmarks. The Journal of finance, 52(3), pp.1035-1058.
Douglas,W.W., 1968. Stimulus‐secretioncoupling:the conceptand cluesfrom chromaffinandother
cells. British journal of pharmacology, 34(3), pp.451-474.
Drew,M.E., Naughton,T.,&Veeraraghavan,M.(2003).FirmSize,Book-to-MarketEquityandSecurity
Returns:Evidence fromthe Shanghai StockExchange. Australian Journalof Management,28(2),119-
139.
Elton,E.J., Gruber,M.J. and Blake,C.R.,1996. Survivorbiasand mutual fundperformance. Reviewof
Financial Studies, 9(4), pp.1097-1120.
Fama, E.F. and French, K.R., 1992. The cross‐section of expected stock returns. the Journal of
Finance, 47(2), pp.427-465.
Fama,E.F. and French,K.R.,1993. Commonriskfactors in the returnson stocksand bonds. Journalof
financial economics, 33(1), pp.3-56.
Fama,E.F.andFrench,K.R.,1995.Size andbook‐to‐marketfactorsinearningsandreturns. TheJournal
of Finance, 50(1), pp.131-155.
Fama, E.F. and French, K.R., 2010. Luck versus skill in the cross‐section of mutual fund returns. The
journal of finance, 65(5), pp.1915-1947.
Fama, E.F. and MacBeth, J.D., 1973. Risk, return, and equilibrium: Empirical tests. The journal of
political economy, pp.607-636.

26. 26
Ferson,W.E.andSchadt,R.W.,1996. Measuringfundstrategyandperformanceinchangingeconomic
conditions. The Journal of finance, 51(2), pp.425-461.
Fletcher, J. (1995), `An Examination of the Selectivityand Market Timing Performance of UK Unit
Trusts', Journal of Business Finance & Accounting, Vol. 22, pp. 143±56.
Fletcher, J., 1997. An examination of UK unit trust performance within the arbitrage pricing theory
framework. Review of Quantitative Finance and Accounting, 8(2), pp.91-107.
Fletcher, J., 1997. An examination of UK unit trust performance within the arbitrage pricing theory
framework. Review of Quantitative Finance and Accounting, 8(2), pp.91-107.
Goetzmann,W.N.,Ingersoll Jr,J.andIvković,Z.,2000. Monthlymeasurementof dailytimers. Journal
of Financial and Quantitative Analysis, pp.257-290.
Grinblatt, M, S Titman, and R Wermers. "Momentum investment strategies,portfolio performance,
and herding: A study of mutual fund behavior." The American economic review (1995): 1088-1105.
Grinblatt, M. and Titman, S., 1989. Mutual fund performance: An analysis of quarterly portfolio
holdings. Journal of business, pp.393-416.
Henriksson,R.D.andMerton,R.C.,1981. Onmarkettimingandinvestmentperformance.II.Statistical
procedures for evaluating forecasting skills.Journal of business, pp.513-533.
Huij,J. andDerwall,J.,2008. “Hot Hands” inbondfunds. Journalof banking &finance,32(4), pp.559-
572.
Jagric, T., Podobnik, B., Strasek, S. and Jagric, V., 2015. Risk-adjusted performance of mutual funds:
some tests. South-eastern Europe journal of Economics, 5(2).
Jegadeesh,N.andTitman,S.,1993.Returnstobuyingwinnersandsellinglosers:Implicationsforstock
market efficiency. The Journal of finance, 48(1), pp.65-91.
Jensen, M.C., 1968. The performance of mutual funds in the period 1945–1964. The Journal of
finance, 23(2), pp.389-416.
Jensen,M.C.,Black,F.and Scholes,M.S.,1972. The capital assetpricingmodel:Some empirical tests.
Kacperczyk, M., Sialm, C. and Zheng, L., 2005. On the industry concentration of actively managed
equity mutual funds. The Journal of Finance, 60(4), pp.1983-2011.
Kosowski,R.,Timmermann,A.,Wermers,R.and White,H., 2006. Can mutual fund“stars” reallypick
stocks? New evidence from a bootstrap analysis. The Journal of finance, 61(6), pp.2551-2595.
Lai, M.M. and Lau, S.H., 2010. Evaluating mutual fund performance in an emerging Asian economy:
The Malaysian experience. Journal of Asian Economics, 21(4), pp.378-390.
Lakonishok,J,etal. "The structure and performance of the moneymanagementindustry." Brookings
Papers on Economic Activity. Microeconomics 1992 (1992): 339-391.
Lam, K., Li, F.K. and So, S., 2009. On the Validity of the Augmented Fama-French Four-Factor
Model. Available at SSRN 1343781.

27. 27
Leger,L., 1997. On investmenttrusts:performance,timingandselectivity.AppliedEconomicsLetters
4, 207–210.
Leite, P., Cortez, M.C. and Armada, M.R., 2009. Measuring fund performance using multi-factor
models: Evidence for the Portuguese market. International Journal of Business, 14(3), p.175.
Levy, A. and Livingston, M., 1995. Financial Markets, Institutions & Instruments: The Gains from
Diversification Reconsidered: Transaction Costs and Superior Information. Blackwell.
Lintner, J. "The valuation of risk assets and the selection of risky investments in stock portfolios and
capital budgets." The review of economics and statistics (1965): 13-37.
Malkiel, B.G., 1995. Returns from investing in equity mutual funds 1971 to 1991. The Journal of
finance, 50(2), pp.549-572.
Markowitz, H. "Portfolio selection." The journal of finance 7.1 (1952): 77-91.
Maroney, N. and Protopapadakis, A., 2002. The book-to-market and size effects in a general asset
pricing model: Evidence from seven national markets. European Finance Review, 6(2), pp.189-221.
Nanda, V., Wang, Z.J. and Zheng, L., 2004. Family values and the star phenomenon: Strategies of
mutual fund families. Review of Financial Studies.
Porras, D. and Griswold,M., 2000. The value line enigmarevisited. Quarterly Journalof Businessand
Economics, pp.39-50.
Quigley, G. and Sinquefield, R.A., 2000. Performance of UK equity unit trusts.Journal of Asset
Management, 1(1), pp.72-92.
Roll, R., 1977. A critique of the asset pricing theory's tests Part I: On past and potential testability of
the theory. Journal of financial economics, 4(2), pp.129-176.
Rosenberg,B.,Reid,K.andLanstein,R.,1985. Persuasive evidence of marketinefficiency. TheJournal
of Portfolio Management, 11(3), pp.9-16.
Ross, S.A., 1976. The arbitrage theory of capital asset pricing. Journal of economic theory, 13(3),
pp.341-360.
Sapp, T. and Yan, X.S., 2008. Security concentration and active fundmanagement:do focused funds
offer superior performance?. Financial Review, 43(1), pp.27-49.
Sharpe, W. F. "Capital asset prices: A theory of market equilibrium under conditions of risk." The
journal of finance 19.3 (1964): 425-442.
Simpson, M.W. and Ramchander, S., 2008. An inquiry into the economic fundamentals of the Fama
and French equity factors. Journal of Empirical Finance, 15(5), pp.801-815.
Soumaré, I., Aménounvé, E.K., Diop, O., Méité, D. and N'sougan, Y.D., 2013. Applyingthe CAPMand
the Fama–Frenchmodelstothe BRVMstock market. Applied FinancialEconomics, 23(4), pp.275-285.
Tobin, J., 1958. Liquidity preference as behavior towardsrisk. The review of economic studies,25(2),
pp.65-86.

28. 28
Treynor, J, and K Mazuy. "Can mutual funds outguess the market." Harvard business review 44.4
(1966): 131-136.
Treynor, J.L., 1965. How to rate mutual fund performance. Harvard Business Review, 43, pp.63-75.
Unlu, U., 2013. Evidence to support multifactor asset pricing models: The case of the Istanbul stock
exchange. Asian Journal of Finance & Accounting,5(1), p.197.
VanNieuwerburgh,SandVeldkamp,L,2005. InformationAcquisitionandPortfoliounder
Diversification. NYUWorkingPaperNo.FIN-04-025
Verheyden, T., De Moor, L. and Van den Bossche, F., 2015. Towards a new framework on efficient
markets. Research in International Business and Finance, 34, pp.294-308.
Wermers,R., 2000. Mutual fundperformance:An empirical decompositionintostock‐pickingtalent,
style, transactions costs, and expenses. The Journal of Finance, 55(4), pp.1655-1703.
Data Sources
Datastream.(2012) Thomson ReutersDatastream.[Online]. Available at:SubscriptionService
(Accessed:17/07/2016)
Lazard AssetManagement(2016). The Lazard World Trust FundAnnual Report(2016). Retrieved
fromwww.theworldtrustfund.com
Morningstar.(2016, August17) AtlantisJapanGrowthFund. RetrievedfromMorningstar
InvestmentResearchdatabase.
NewCityInvestmentManagers(2016). CQSNew CityHighYieldFundLtd InterimReport(2015).
Retrievedfromwww.ncim.co.uk
ShrodersUK. (2016). ShroderAsiaPacificFundplc.2015 Annual reportandAccounts.Retrievedfrom
www.shroders.co.uk
ShrodersUK. (2016). ShroderIncome GrowthFundplc. 2015 Annual reportandAccounts.Retrieved
fromwww.shroders.co.uk
ShrodersUK. (2016). ShroderJapanGrowth Fundplc.2015 Annual reportandAccounts.Retrieved
fromwww.shroders.co.uk
ShrodersUK. (2016). ShroderMid Cap fundplc.2015 Annual reportandAccounts.Retrievedfrom
www.shroders.co.uk
ShrodersUK. (2016). ShroderUK Gowth Fundplc.2015 Annual reportand Accounts.Retrievedfrom
www.shroders.co.uk

29. 29
APPENDIX TABLES
Table 1.
Descriptive statisticsof monthlyreturnsandmonthlyexcessreturnsof eachInvestmentTrust,Risk-Freerate andFTSEAll Share index.ObservationPeriod1
January1995 to 1 June 2016.
Monthly Returns Monthly Excess Returns
Fund N Mean Std.Dev Min Max Skewness Kurtosis Mean Std.dev Min Max
A 240 0.0066 0.088 -0.227 0.574 1.5684 11.2163 -0.0262 0.0936 -0.2983 0.5255
B 247 0.0082 0.082 -0.253 0.451 0.5845 7.5309 -0.0255 0.0873 -0.3208 0.3884
C 255 0.009 0.059 -0.297 0.3897 0.0035 12.746 -0.0256 0.0658 -0.3137 0.3846
D 255 0.008 0.044 -0.171 0.153 -0.0284 4.4616 -0.0265 0.0506 -0.2384 0.1396
E 257 0.0044 0.071 -0.174 0.333 0.4552 4.7102 -0.0304 0.0769 -0.2304 0.2709
F 257 0.0063 0.057 -0.212 0.2298 -0.1012 5.363 -0.0285 0.0632 -0.2588 0.2241
G 257 0.0069 0.0666 -0.389 0.2298 -1.1081 11.732 -0.02796 0.0709 -0.4258 0.2634
H 257 0.0055 0.055 -0.235 0.19 0.8412 5.7642 -0.0293 0.0604 -0.2723 0.1872
I 257 0.0056 0.05599 -0.2398 0.232 -0.707 6.1886 -0.0293 0.0617 -0.2723 0.2268
J 257 0.011 0.0857 -0.3101 0.462 0.3775 8.2159 -0.0243 0.0899 -0.3607 0.4059
US 3-month 258 0.03495 0.0241 0.0023 0.0736 -0.2646 1.4756 - - - -
FTSE All
Share
257 0.0068 0.04156 -0.13507 0.1215 -0.6252 4.0627 -0.028 0.0478 -0.1848 0.1166

30. 30
Table 2.
Presentsthe code usedforthe 10 UK InvestmentTruststhatare analysedinthisstudy.Informationrelatingfundinvestment objectiveshasalsobeen
extractedfromthe Annual ReportsandProspectus’of the funds.
Code Fund Name Investment Objective
A Atlantis Japan Growth Fund Investsinundervaluedgrowthcompaniesacrossthe marketcap range includingsome cyclical growth
companiesthatcan do well overthe longertermsandthatlookcheapin termsof valuation.The Fund's
objective istoachieve longtermcapital appreciationthroughanactivelymanagedportfolioof equityand
equityrelatedinvestmentissuedbycompanieslistedinJapan.
B Shroder Asia Pacific Growth
Fund plc
The Company'sprincipal investmentobjective istoachieve capital growththroughinvestmentprimarilyin
equitiesof companieslocatedinthe continentof Asia(excludingthe Middle EastandJapan),togetherwiththe
Far Easterncountriesborderingthe PacificOcean,withthe aimof achievinggrowthinexcessof the MSCIAll
countriesAsiaexcludingJapanIndex inSterlingterms(Benchmark)overthe longerterm.
C ORYX International Growth
Fund
The investmentobjective of the companyistoseekto generate consistentlyhighabsolute returnswhilst
maintainingalowlevel of riskforshareholder.The companyprincipallyinvestsinsmall andmid-size quotedand
unquotedcompaniesinthe UnitedKingdomandUnitedStates.The Investmentmanagertargetscompanies
that have fundamentallystrongbusinessmodels,butwhere there maybe specificfactorswhichare
constrainingthe maximizationorrealizationof shareholdervalue,whichmaybe realizedthroughthe persuitof
an activistshareholderagendabythe InvestmentManager.
D Shroder Income Growth Fund
plc
The Company'sprincipal investmentobjectivesare toprovide real growthof income,beinggrowthof income in
excessof the rate of inflation,andcapital growthasa consequence of the risingincome.
E Shroder Japan Growth Fund
plc
The Company's principal investmentobjective istoachieve capital growthfromanactivelymanagedportfolio
principallycomprisingsecuritieslistedonthe Japanesestockmarkets,withthe aimforachievinggrowthin
excessof the TSE FirstSectionTotal Returnoverthe longerterm.
F Shroder UK Growth Fund plc The principal investmentobjective of the Companyistoachieve capital growthpredominantlyfrominvestment
inUK equities,withthe aimof providingatotal return inexcessof the FTSE All-Share Index.The companyinvest
ina relativelyconcentratedportfolioof between35 and65 stock principallyselectedfortheirpotential to
provide shareholderswithattractivereturnsrelative tothe FTSEAll-Share Index.The portfolioisinvested

31. 31
primarilyinlistedUKequities.Itmayinclude convertible securities,andequity-relatedderivativesmaybe used
for efficientportfoliomanagementpurposes.Stocksare predominantlyconstituentsof the FTSE350 Index.
G CQS New City High Yield Fund
Ltd
To provide investorswithahighdividendyieldandthe potential forcapital growthbyinvestingmainlyinhigh
yieldingfixedinterestsecurities.
H Lazard World Trust Fund Seekstoachieve long-termcapital appreciationbyinvestingprimarilyincompanieswhose sharestrade ata
discounttotheirunderlyingNetAssetValue.The Fundmeasuresitsperformance principallyagainstthe MSCI
All CountriesWorldIndex,althoughLazardAssetManagementLLC(the 'Manager') seekstoachieve the highest
possible risk-adjustedreturnsandthe allocationof the Fund'sassetswill normallydivergesubstantiallyfromthe
Index,inparticularinrelationtoitsweightinginthe US marketswhichhistoricallyhasbeenrelativelylow.
J Shroder UK Mid Cap Fund plc The Company'sinvestmentobjective istoinvestinMidCapequitieswiththe aimof providingatotal returnin
excessof the FTSE 250 (ex-InvestmentCompanies) Index.

32. 32
Table 3.
Presentthe MorningStarGlobal EquityClassification.Thisclassificationisusedtoproduce several portfoliosassetallocation.
Super Sector Industry Definition
Cyclical
Basic Materials
Companiesthatmanufacture chemical,buildingmaterialsandpaperproducts.Thissector alsoincludes
companiesengagedincommoditiesexplorationandprocessing.CompaniesinhissectorincludeArcelot
Mittal,BHP BillitonandRioTinto.
Consumer
Cyclical
Thissector includesretailstores,autoandautoparts manufacturers,companiesengagedinresidential
construction,lodgingfacilities,restaurantsandentertainmentcompanies.Companiesinthissectorinclude
Ford Motor Company,McDonald'sand NewsCorporation.
Financial services
Companiesthatprovide financial serviceswhichincludesbanks,savingsandloans,assetmanagement
companies,creditservices,investmentbrokerage firms,andinsurance companies.Companiesinthissector
include Allianz,J.PMorganChase andLegg Mason.
Real estate
Thissector includesmortgage companies,propertymanagementcompaniesandREITs.Companiessinthis
sectorinclude KimcoRealtyCorporation,VornadoRealtyTrustandWestfieldGroup.
Defensive
Consumer
Defensive
Companiesengagedinthe manufacturingof food,beverages,householdandpersonal products,packaging,or
tobacco. Alsoincludescompaniesthatprovide servicessuchaseducation&trainingservices.Companiesin
thissectorinclude PhilipMorrisInternational,Procter&Gamble andWal-Mart Stores.
Healthcare
Thissector includesbiotechnology,pharmaceuticals,researchservices,home healthcare,hospitals,long-term
care facilities,andmedical equipmentandsupplies. Companiesinthissectorinclude AstraZeneca,Pfizerand
Roche Holding.
Utilities
Electric,gas,and waterutilities.CompaniesinthissectorincludeElctricitede France,ExelonandPG&E
Corporation.
Sensitive
Communication
services
Companiesthatprovide communicationservicesusingfixed-line networksorthose thatprovide wireless
access andservices.Thissectoralsoincludescompaniesthatprovide internetservicessuchasaccess,
navigationandinternetrelatedsoftwareandservices.CompaniesinthissectorincludeAT&T,France Telecom
and VerizonCommunications.
Energy
Companiesthatproduce orrefine oil andgas,oil fieldservicesandequipmentcompanies,andpipeline
operators.CompaniesinthissectorincludeBP,ExxonMobilandRoyal DutchShell.
Industrials

33. 33
Companiesthatmanufacture machinery,hand-heldtoolsandindustrialproducts.Thissectoralsoincludes
aerospace anddefence firmsaswell companiesengagedintransportationandlogisticsservices. Companiesin
thissectorinclude 3M, BoeingandSiemens
Technology
Companiesengagedinthisdesign,development,andsupportof computeroperatingsystemsand
applications.Thissectoralsoincludescompaniesthatprovide computertechnologyconsultingservices.Also
includescompaniesengagesinthe manufacturingof computerequipment,datastorage products,networking
products,semi-conductors,andcomponents.CompaniesinthissectorincludeApple,Google andMicrosoft.
Table 4.

34. 34
Resultsof applyingthe unconditional single-index CAPMmodel (1) tosample of 10 UK InvestmentTrustsoverthe periodof 1 January1995 to 1 June 2016.
* =
significance level of 5%, ** = significance level of 1%.
Fund alpha t(alpha) beta t(beta) 𝑅2
A -0.001 -0.18 0.94 8.71 0.2418
B 0.0097* 2.19 1.29 16.05 0.513
C -0.007 -1.71 0.665 8.78 0.2335
D -0.004 -1.6 0.824 19.8 0.6062
E -0.0037 -0.82 0.955 11.77 0.3519
F 0.0036 1.55 1.14 27.58 0.7489
G -0.0067 -1.51 0.759 9.51 0.2617
H -0.003 -1.03 0.939 17.73 0.552
I -0.002 -0.72 0.967 18.02 0.5601
J 0.0079 1.53 1.147 12.28 0.372

35. 35
Table 5.
Resultsof applyingthe TMmodel (2) tothe sample of 10 UK InvestmentTrustsoverthe periodof 1January 1995 to1 June 2016
Fund alpha t(alpha) beta t(beta) gamma t(gamma) 𝑅2
A 0.002 0.38 0.764 5.37 -2.62 -1.88 0.253
B 0.011* 2.43 1.21 11.38 -1.22 -1.16 0.5153
C -0.006 -1.42 0.61 6.11 -0.81 -0.82 0.2356
D -0.005* -2.05 0.89 16.26 0.98 1.82 0.6129
E -0.0032 -0.69 0.93 8.66 -0.38 -0.36 0.352
F 0.002 0.98 1.21 22.24 1.03 1.91 0.753
G -0.003 -0.72 0.57 5.48 -2.85** -2.78 0.283
H -0.0007 -0.24 0.812 11.77 -1.91** -2.81 0.5655
I -0.0001 -0.05 0.858 12.21 -1.65** -2.38 0.5697
J 0.0096 1.78 1.055 8.56 -1.39 -1.14 0.3749
* = significantat5% level,**= significance at1%

36. 36
Table 6.
Presentsthe resultsof applyingthe Sharpe ratiosacrossthe 10 InvestmentTrusts.The time framesexaminedinclude2000 to 2004, 2005 to 2009, and 2010
to 2014 as well asthe overall observationperiodestimatesof 1995 to 2016. These examinedwindowsrepresentedaninclusive5-yearduration.
Fund 1995 - 2016 2000 - 2004 2005 - 2009 2010 - 2014
A -0.3222 -0.4908 -0.4926 0.1775
B -0.3262 -0.4689 -0.2570 0.0835
C -0.4398 -0.7718 -0.3735 0.2194
D -0.6125 -0.8132 -0.6071 0.1921
E -0.4303 -0.5986 -0.5312 0.1346
F -0.5026 -0.8735 -0.3444 0.1122
G -0.4212 -0.3816 -0.6439 0.1474
H -0.5355 -0.8025 -0.3863 0.0682
I -0.5242 -0.7993 -0.3635 0.0485
J -0.2795 -0.3359 -0.3143 0.1986

37. 37

38. 38
APPENDIX FIGURES
Figure 1.
Plotsthe monthlyshare price of the 10 analysedInvestmentTrustsoverperiodJanuary1995 to June 2016.
(Source: Thomson Reuters DataStream [Accessed: 17/07/17])
0
100
200
300
400
500
600
700
800
900
1000
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
ATLANTIC JAP.GW.FD (A) SHRODER ASIA PACIFIC (B) ORYX INTERNATIONAL (C) SHRODER INCOME (D)
SHRODER JAPAN (E) SHRODER UK GROWTH (F) CQS NEW CITY HIGH YIELD (G) LAZARD WORLD TRUST (H)
LAZARD WORLD TRUST (I) SHRODER UK MID CAP (J)

39. 39
Figure 2: Atlantis Japan Fund (A) Asset allocation
Figure 3: Shroder Asia Pacific (B) Asset Allocation
Cyclical Sensitive Defensive
Basic Materials
Consumer cyclical
Financial Services
Reals estate
Communication
Services
Energy
Industrials
Technology
Consmer defensive
Healthcare
Utilities
Cyclical Sensitive Defensive
Basic Materials
Consumer cyclical
Financial Services
Reals estate
Communication
Services
Energy
Industrials
Technology
Consmer defensive
Healthcare

40. 40
Figure 4: ORYX International (C) Asset allocation
Figure 5: Shroder Income (D) Asset allocation
Cyclical Sensitive Defensive
Basic Materials
Consumer cyclical
Financial Services
Reals estate
Industrials
Technology
Consmer defensive
Healthcare
Cyclical Sensitive Defensive
Basic Materials
Consumer cyclical
Financial Services
Communication
Services
Energy
Industrials
Technology
Consmer defensive
Healthcare
Utilities

41. 41
Figure 6: Shroder Japan (E) Asset allocation
Figure 7: Shroder UK (F) Asset allocation
Basic Materials
Consumer cyclical
Financial Services
Reals estate
Communication
Services
Energy
Industrials
Technology
Consmer defensive
Healthcare
Utilities
Cyclical Sensitive Defensive
Basic Materials
Consumer cyclical
Financial Services
Reals estate
Communication
Services
Energy
Industrials
Technology
Cyclical Sensitive Defensive

42. 42
Figure 8: CQS New City High Yield (G) Asset allocation
Figure 9: Lazard World Trust (H/I) asset allocation
Cyclical Sensitive Defensive
Basic Materials
Consumer cyclical
Financial Services
Reals estate
Communication
Services
Energy
Industrials
Technology
Cyclical Sensitive Defensive
Basic Materials
Consumer cyclical
Financial Services
Reals estate
Communication
Services
Energy
Industrials
Technology
Consmer defensive
Healthcare
Utilities

43. 43
Figure 10: Shroder Mid Cap (J) Asset allocation
Figure toFigure 10 displayeachInvestmenttrustassetallocationcompiledfrominformationatthe
MorningstarWebsite andAnnual Reportsof eachfund.Investmentsare arrangedthroughguidance
fromthe MorningStarGlobal EquityClassification.The lefthandside chartsdisplaythe portfolios
compositionintermsof supersectors.The righthandside charts displaythe portfolioscompositions
interms of associatedindustries.Informationoncompositionisretainedthroughthe mostrecent
and available assetallocationsource.Thispaperassumesthatfundamental compositionof each
portfolioremainsapproximatelyconstantoverthe fundslife.
Cyclical Sensitive Defensive
Basic Materials
Consumer cyclical
Financial Services
Communication
Services
Energy
Industrials
Technology
Consmer defensive
Healthcare
Utilities

44. 44