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 .

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Table of Contents1.Introduction....................................................................................................................3

2.Literature Review............................................................................................................42.1 Evolution of Asset Pricing Models.........................................................................................42.2 Fund Performance measures.................................................................................................62.3 Mutual Fund Performance....................................................................................................6

U.S Market.....................................................................................................................................7U.K Market.....................................................................................................................................7

2.4 Selectivity and market timing in UK Investment Trusts..........................................................82.5 Fund concentration and performance...................................................................................9

3. Data description.............................................................................................................93.1 Share price analysis over time.............................................................................................11

4. Methodology................................................................................................................134.1 Overall Performance...........................................................................................................134.2 Market Timing....................................................................................................................14

5.Empirical Results...........................................................................................................155.1 Selectivity Performance ().................................................................................................155.2 Market Timing Performance ()..........................................................................................165.3 Sensitivity to the market ()................................................................................................175.4 Market and Idiosyncratic risk..............................................................................................175.5 Sharpe Ratio’s.....................................................................................................................18

6. Discussion.....................................................................................................................186.1 Selectivity Performance......................................................................................................186.2 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

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1.IntroductionSince the first publicly listed fund of Foreign and Capital in 1868, Investment trusts have

played a fundamental role in financial intermediation across the world. A type of mutual fund, an

Investment trust raised funds by issuing shares on the stock market and then investing the proceeds

into a portfolio of assets. Significantly growth in the operating number of funds has meant fund

managers have had to strive to deliver competitive returns. It has been widely documented that

investors benefit from managerial skills as well as diversification, cost advantages and liquidity

intermediation when investing in Investment trusts. These skills are related to selectivity ability in

picking successful 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

results were achieved on UK funds (Blake and Timmermann, 1999; Blak et al 1999) Although, the UK

fund management industry is responsible for an excess of $5.5tr 1, to the best of 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 whether managers from UK Investment trusts generate positive alphas and thus show evidence

of selectivity ability. Whilst also accounting for market timing ability through the gamma term

supplied 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

pricing models and mutual fund performance. This is followed by a description of the data and chart

analysis. The next section provides an explanation of the empirical models and methodology. This is

followed by empirical results on all estimates measures. Section 6 presents discussion on the

empirical findings in the context of other literature. Section 7 discusses the limitations of the study

1 Annual Reports and Accounts year end 2015, The Investment Association (2016), www.theinvestmentassociation.org .[accessed: 2/09/16]

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and future 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 Review2.1 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’s model explains how an investor selects a portfolio at time t-1 that produces

a 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

given the expected return and variance, portfolio’s both minimize the variance of the portfolio

return and maximize expected return.

Sharpe (1964) and Lintner (1965) developed two key assumptions to the Marrkowitz’s mean-

variance framework. The first is complete agreement: Given market clearing prices at t-1, investors

agree on the joint distribution of asset returns from t-1 to t. The second assumption states that there

is borrowing and lending at a risk-free rate, which is the same for all investors and is independent of

amount. Combined with the work of Black (1972) who formed the CAPM, explained how the

expected return on a stock is determined by the risk-free interest rate and a risk premium, which is a

function of the stock’s responsiveness to movements in the market. The latter is classified as the

beta coefficient, arguably the main component which is heavily used among fund managers in the

financial markets.

A majority of the earlier empirical tests of CAPM give support to its specification that beta is

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)

show the beta coefficient was statistically insignificant. Black et al (1972) used time series regression

analysis to show how the intercept is significantly different from zero and its time varying properties,

which violate market efficiency and the original model. Later Roll (1977) supplied further criticism, 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

efficiency of the selected proxy portfolio. More recently, Bartholdy and Peare (2003) concluded that

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

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macroeconomic risk factors which cannot be diversified against. Although the APT benefited the

CAPM in being less restrictive and explaining greater proportion of security returns, several

drawbacks exist. Unlike the CAPM, the APT does not reveal the identity of priced factors. 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, claiming miss-specification in the CAPM between 1963 and 1990. Similarly, there has

been evidence of this for European and Japanese markets (Capaul et al, 1993). Within the mutual

fund literature, 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

by three risk factors. These factors include the CAPM’s excess market return, size factor 2 and book-

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/M revealed no relation. Comparably, Porras

(1998) found B/M insignificant, although found size to be insignificant using cross-sectional

regression analysis. Nonetheless, studies post the millennium have found the two additional factors

significant in explaining returns across Australia, Canada, Germany, Japan, the UK and US (Maroney

et al, 2002; Drew et al, 2003).

Given the popularity of 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 merit of the three-factor model is its ability to capture information relating to a wide 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-factor explained 5%. Likewise, Sourmere et al (2013) finds 11 out of 28 company stocks satisfy

2 The size factor (SMB) is a zero-investment portfolio that is long on small capitalization stocks and short on bi capitalization stocks.3 The book-to-market factor (HML) is a zero-investment portfolio that is long on high book - to-market (B/M) stocks and short on low B/M stocks.4

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the CAPM model, and 10 satisfy the three-factor model. These empirical findings are encouraging to

this papers 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 investors utilising a strategy based on recent momentum5 to generate abnormal returns.

Soon after Carhart (1997) examined the persistence in stock returns of mutual funds in US equity

markets firstly using an augmented multifactor model to account for momentum. The limited

literature surrounding the validity of this factor mainly support 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 momentum factor to be significant for the Hong Kong Market. Lai and Lau (2010) highlight

the 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 measuresThe evolution of fund performance measures has been parallel with the growth of asset

pricing models. The introduction of the CAPM by Sharpe(1964) and Lintner (1965) led to the arrival

of the “three indicies” from Sharpe (1966), Treynor (1965), and Jenson (1968). All three of these

models were derived simply from the CAPM model; The Sharpe ratio is based on the reward to

volatility trade-off and formulates the ratio between average returns earned in excess of the risk-

free rate per unit of volatility. The Treynor Ratio from Treynor (1965) is of close format to the Sharpe

ratio, however defines the reward-to-volatility ratio in relation to each unit of the CAPM beta risk. In

comparison, the Jensen (1968) alpha measures refers to the intercept determined from the CAPM

regression of excess portfolio returns on the excess market returns.

Jensen’s alpha has been the predominant measures used in fund performance valuation.

Essentially, it holds a stable position because it represents the intercept when excess fund returns

are estimated against either the market index, book-to-market ratio, size or momentum factors. In

relation to the efficient market hypothesis, the alpha term indicates market efficiency on the basis of

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 PerformanceThe UK and US have two of the most developed and largest fund management industries in

the world. Total U.S mutual fund assets being in excess of $15.7 trillion 6 whilst UK funds totalling

5 This strategy involves buying stocks which have performed well in the past year, whilst selling recent poor performing stocks. 6 2016 Investment Company Fact Book, A review of Trends and Activities in the U.S Investment Company Industry, 56th edition. Investment Company Institute, [accessed 2/09/16]

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over $7.3 trillion7 in managed funds. Given the large amount of data available for meaningful

analysis, much of the academic literature has focused on funds in these market. Although more

recently there have been new focus on European and Australian industries.

U.S MarketStudies 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

funds using the single index model but over a longer period than Jensen (1968). Results over the

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 abovementioned studies had used standard conventional statistical techniques,

however there has been a recent influx of new methods to measure fund performance more

accurately. Namely, Kosowski et al (2006) and Fama and French (2010) adopt the bootstrap

technique to calculate alpha and its corresponding test statistic. This method aims to separate

managerial skill from luck since the standard statistical technique does not account for presence of

luck or the non normality properties in alpha. Applying this measure, Kosowski et al (2006)

demonstrated that only a minority from the analysed 2118 US mutual funds posses stock picking

skills. Moreover, using FDR (False Discovery rate)8 approach, Scaillet et al (2010) found 75% of US

funds exhibit a zero alpha based on returns, with a few showing evidence of genuine skill.

U.K MarketResearch 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 CRSP 9 database which holds

information on both dead and live funds. As a result, a large portion of UK studies have been subject

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 False Discovery Rate (FDR) is a measure to provide a simple way to calculate the number and the proportion of funds with truly positive and negative performance in any portion of the tails of the cross-sectional alpha distribution. 9 Center for Research in Security Prices – Provider of historical stock market data. Maintains some of the largest and most comprehensive proprietary historical databases in stock market research. Researchers rely on the CRSP for accurate, survivor bias-free information.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 cause skewness in results because only successful companies are included.

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Results on UK mutual funds has tended to yield similar results to those discovered in the US.

Fletcher (1997) examines fund performance using Henriksson and Merton (1981) to decompose

performance into stock picking and market timing. The results suggest that on average managers

from UK unit trust exhibit positive stock selectivity and negative market timing. Using the same

dataset in a later study, Fletcher (1997) finds no significant evidence that UK unit trust outperform

the 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 period of 1978 to 1998. They show that UK managers net of expenses are unable to

outperform the market, thus coinciding with the US findings.

Studies using measures beyond the conventional standard statistical techniques have also

developed on to UK data. Cuthbertson et al (2008) applies the approach of Kosowski et al (2006) to a

survivor-bias free sample of 842 UK equity unit trusts. Results from this bootstrapping technique

show the average alpha of funds is negative but statistically insignificant. These results lie consistent

with Blake and Timmermann (1998). In a subsequent study using the same survivor-bias free

dataset, Cuthbertson et al (2010) replicates a similar FDR methodology as Scaillet et al (2010).

Results suggests 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 TrustsThe literature closely linked with this paper have concluded that UK Investment trusts have

not on average been able to out-perform the market. Bal and Leger (1997) analyse 92 funds over the

period 1975 to 1993 using Jensen’s alpha and the Sharpe Ratio. Even without correction for

transaction costs, funds on average did not generate significant alpha’s. They also show that the

choice of variance or covariance risk (Sharpe and Treynor measures) matters very little. In addition,

they find evidence of perverse market timing from the 90’s onwards. This trend intersects with the

start of the dataset used in this paper, and thus will be compared against in later discussion.

Correspondingly, Leger (1996) observed insignificant alphas and negative timing performance for

one in three trusts of a sample of 72 funds. Whilst highlighting the strong negative correlation

between manager timing and selectivity. Bangassa (1999) adds further support to these results, but

identifies fund styles in Japan, North America and Europe generates significant perverse timing

practices. Moreover, Cuthbertson (2009) showed that only 1% of funds demonstrated positive

market timing at 5% level, while 19% of funds exhibited negative timings. More recently, Bangassa et

al (2012) examined selectivity and timing performance of 218 UK Investment Trusts. They conclude

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that international funds show some favourable selectivity ability, while domestic funds show better

timing ability.

2.5 Fund concentration and performancePositive fund performance can be attributed to a wide selection of factors related to

managerial characteristics. Though there has been an avenue of research on the relationship

between portfolio composition and performance. Kacperczyk et al (2005) claimed investment ability

is more evident among managers who hold portfolios concentrated in a few industries. Likewise,

other related studies have showed that focused managers outperform their more broadly diversified

counterparts (Baks et al, 2006; Hujj and Derwall, 2011). Others have attributed focused investment

strategies and 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 findings suggest that

should there be presence of abnormal performance in this paper, one would expect the fund to be

concentrated on a few industries.

3. Data descriptionThe data used in this study consists of monthly returns calculated as the percentage change

in share price of 10 UK Investment Trusts. Data was extracted from Thomson Reuters DataStream

over the examined period of 1 January 1995 to 1 June 2016.

Often termed as Closed-End funds, Investment trusts sell a fixed amount of units to investors

at the time of offer. They do not issue additional units in response to demand, instead they are listed

on the stock market for investors to purchase. These listed units act essentially as company stock

and their prices are determined by demand and supply forces. Unlike other investment funds, shares

in Investment trusts can be purchased and sold at prices above or below the Net Asset Value

(combined value of all assets the trusts hold). A share price lower than the NAV is said to be trading

at a discount, in comparison to a price that is above the NAV, the shares trading at a premium.

In selecting data, we incorporate an approach that only accounts for surviving funds. Firstly,

a search of UK investment trusts on the DataStream database was taken over the specified period.

Secondly, all resulting funds were imported to an excel spread sheet. Next, all non-surviving funds

that have ceased operations/terminated over the observation period are dropped and this was

evidence by a constant share price. Fund attrition has been the consequence of poor fund

performance over time or a judgement from management that market value of fun is sufficiently

small and thus it is no longer rational to maintain the fund (Elton et al,1996). This process of fund

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selection generated a remaining sample of 10 UK Investment Trusts. The details of these trusts can

be found in the appendix (see table 2), which also outlines each trust Investment Objectives.

Empirical research in Grinblatt and Titman (1989), Brown et al (1992), and Brown and

Goetzmann (1994) on survivorship bias may suggest implications in our study. For example, Malkiel

(1995) finds that surviving funds consistently have higher mean returns than non-surviving funds.

We aim to analyse historical fund data with the intention of providing future fund value prediction,

which would not be relevant to terminated funds. Nonetheless, we consider this bias with caution

our interpretations and conclusions.

To construct an appropriate CAPM model for the model we also retain dataset for the return

on a risk-free asset and the market return. The annualized US three-month Treasury Bill rate is used

to represent the risk-free return across the observation period. Deducting this value from fund

returns provides us with values that represent each funds excess returns. The market proxy used in

this study is the FTSE All-Share Index returns. This is considered the best performance measure of

the London equity market and captures 98% of UK’s market capitalization. It is the most suitable

index for analysis 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 selectivity and performance,

we retain information on the asset allocation of each fund. Data relating to portfolio composition is

collected from the Annual Reports of corresponding Investment trusts. Taking the most recent

annual report, we assume that funds maintain an approximately constant industry concentration

throughout 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 industry groups (see Table 3). The application of this classification in this study enables us

to evaluate 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 have betas of greater than 1.12 The defensive super sector includes industries that are relatively immune to economic cycles. These industries provide services that consumers require in both good and bad times, such as healthcare and utilities. In general, the stocks in these industries have beta of less than 1.13 The sensitive super sector includes industries which ebb and flow with the overall economy, but not severely so. Sensitive industries fall between the defensive and cyclical industries as they 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 have betas that are close to 1.

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The descriptive statistics are used to describe the basic features of the dataset and are

presented in Table 1. Data has been separated into monthly returns and excess returns. Initial values

shows that for all funds average monthly returns were positive, but are all negative for monthly

excess returns.

3.1 Share price analysis over timeFigure 1 shows a plot of the monthly share prices of each Investment Trust from January

1995 to June 2016. Most of the assessed funds follow a similar pattern, however certain trusts

shares show high volatility level. Low performers include E and A funds. Medium performers

includes funds 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 years leading up to 2000 we can observe relative correlation between a majority of

the funds. However, funds with investment objectives in Asia and Japan suffered an episode 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.

Between the months leading up to the millennium and 2001 Lazard World (H/I) trust and

more evidently Shroder UK Mid Cap (J) experienced a steep spike in their share price’s. This can be

justified by the speculative ‘dot com bubble’ which saw a rapid rise in the equity markets through

exponential investment in internet-based companies. Both funds price peaked in December 2000

and then suffered a steep decline 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 likely to have held a portfolio heavily exposed to technology and consumer stocks, in

comparison to the other funds.

From 2003 all examined trusts show some recovery from the dot com crisis, and all share

prices display steady growth until mid-2007. Similarly, to the spike seen in 2000 Shroder Mid-Cap

and Lazard World funds outgrow the other funds. However, ORYX International (C) and Shroder

Income (D) both follow similar growth. We detect a disparity between these four funds and the

remaining five funds during 2007 to 2008. The period has been labelled by financial commentators

as the ‘run-up’ to the US housing bubble, fuelled by extraordinary low interest rates and the

reallocation of investment from the stock market into the housing market. In August 2007 the UK

stock market suffered extreme volatility as a result heightened fears in the interbank market amid

fears of exposure to high-risk US Mortgages. This was followed by the UK Bank Northern Rock being

nationalised in February 2008 and the collapse of Lehmen Brothers in September 2008.

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Subsequently, assets across all classes declined in value and this is indicated with the steep fall in

funds shares between 2008 and 2009. We observe the least affected funds to this crisis were Atlantis

Japan (A) and Shroder Japan (E). Interestingly, the graph highlights that in fact Atlantis Japan

experienced a decline well before the crisis and other funds.

During the aftermath of the financial crisis of 2008 all funds share’s show positive growth

and a majority recover to their pre-crisis value by 2012. Notably, we observe rapid growth in Shroder

Mid-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 pattern but at slower rate. Interestingly, we also observe the increasing growth of ORYX

International (C) as well as Shroder Mid Cap (J) and Shroder Income (D).

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4. Methodology4.1 Overall Performance

This study uses the unconditional single-factor performance measure: Jensen’s (1968) alpha.

This measure is the intercept term estimated through the the regression model:

R¿−Rft=α i+ βi (Rmt−Rft )+ϵ i (1)

where α i is the risk adjusted abnormal return from the single index model, R¿ is the return on fund i over period t, R ft is the return on the 5 year US Treasury yield adjusted to constant maturity, Rmt is

the return on the FTSE ALL-SHARE Index, β i the factor sensitivity of differences in fund returns and

the risk free rate, and ϵ i is the error term with the following properties: E(ϵ i ,t)=0 , Var (ϵi , t)=σ ϵi , t2 ,

Cov (ϵi , t , rm,t )=Cov (ϵ i ,t , ϵ j , t )=0.

The following hypothesis are tested with this model:

H 0: α i=¿ 0

H a: α i≠ 0

where the null hypothesis suggests fund i does not out or under-perform the relative market proxy,

the alternate hypothesis indicates fund i either out or under-preforms to the relative market proxy.

Statistical significance is measured using critical values using a two-tailed test at 5% significance

level. Although for discussion purposes we also consider coefficients that generate a high test-

statistic but are marginally insignificant.

The intercept term identifies whether fund managers have superior stock selection abilities.

Alpha generation is achieved through selecting securities that result in ϵ i>0. A statistically significant

positive alpha indicates the fund manager has the ability to forecast future security prices. An alpha

term that does not statistically differ from zero implies the manager mimics the composition of a

reference market benchmark. A statistically significant negative alpha terms suggest the fund

manager performs poorer than a naïve strategy of random selection. In the context of

compensation, the annualized Jensen’s alpha is the maximum mount of money an investor should

be willing to pay a fund per year. Subsequently, this allows us to contribute to the literature

surround pay performance in mutual funds.

In addition, we also assess and compare the risk of each fund. Under CAPM the risk is

divided into two components. These are formally expressed as products of the standard deviation in

returns:

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σ i2=[ β ]2σm

2 +σ u ,i2

The first part [ β]2σ m2 indicates the market (or systematic) risk. This type of risk arises due to

fluctuations in the market as a whole and cannot be eliminated through diversification. This is also

interpreted in terms of a high R2 of the regression model (1). We pay special attention to this value

in each funds model in our results analysis. The second part σ u ,i2 is referred to as specific (or non-

systematic) risk. This is often attributable to managerial competence with the fund. In contrast to

systematic risk this type of risk is idiosyncratic and thus can be eliminated through successful

portfolio 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.

Another measure for risk-adjusted returns that we use is the Sharpe ratio. This performance

method was developed by Sharpe (1966) under its birth name, the reward-to-variability ratio. Over

the years it has gained significant popularity in the finance and now operates an industry standard

for 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=Ri−R fσ i

where Rp is the mean return to portfolio i during the evaluation period, R f is the mean risk free rate

of return and σ i 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

estimating this measure over different time horizons to gauge its variability under different

economic conditions and to provide further analysis on fund manager selectivity.

4.2 Market TimingOverall 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 addition of a quadratic term to equation (1) in order to account for non-linearity in the

function of market return, and is as follows:

R¿−Rft=α i+ βi (Rmt−Rft )+γ i (Rmt−R ft )2+ϵ i (2)

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where γi is the risk adjusted measure of market timing ability of fund i, and ϵ i is an error term with

the following properties: E(ϵ i ,t)=Cov ( ϵ i ,t , rm, t )=Cov (ϵ i ,t ,Rm,t2 )=0

The following hypothesis is tested using this model:

H 0: γi=¿ 0

H a: γi≠ 0

where the null hypothesis states that manager in fund i exhibits no positive or negative market

timing ability. The alternate hypothesis states that manager in fund i exhibits significant positive or

negative timing abilities. Market timing ability is reflected by greater market exposure when the

excess market 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 Results5.1 Selectivity Performance ()

Table 4 presents the results of applying the unconditional single-index model (1) to each

individual fund in our sample over the time period. The values for Jensen’s (1965) alpha indicates 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-distribution critical values using 2-tailed laws. However, we also pay

attention to all alpha’s that contain a test-statistic greater than +-1.5 and lie marginal to the 5%

critical value.

Estimations show only three out of the ten funds generate a positive alpha term. Although,

within this three only one fund generates a statistically significant positive alpha at a 5% level. Fund

B 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 relative to the the FTSE All Share over the observation period. Which

provides evidence that based on the unconditional single factor model, the fund manager from fund

B exhibited superior stock selectivity over the period. Whereas the remaining positive funds F and J

generate alpha terms that do no significantly differ from 0 at 5% level, with alpha values of 0.0036

and 0.0079, respectively. Nonetheless, these values are relatively high and cause a marginal upheld

in the null hypothesis of no abnormal fund performance. This could be sensitive to changes in

observation timeframe or used model. Thus, we may infer to a smaller degree that fund managers in

funds F and J have shown stock selectivity skills over the observation period.

15

The estimations in Table 1 also shows that seven out ten funds generate negative alpha

values. Unlike the positive alpha sample, all of the negative alpha are statistically insignificant at 5%

level. Thus for funds A, C, D, E, G, H, and I, the null hypothesis of no abnormal fund performance is

upheld. This implies that based on the unconditional single model, fund managers in these seven

funds have 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 suggests a marginal upheld in the null hypothesis that is sensitive to changes in observation

period or 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 displays the results in applying the unconditional TM(1966) model to each individual

fund over the observation period. The values for gamma indicates the fund managers market timing

ability. In testing the null hypothesis that 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

terms from the TM (2) model and the single index model (1) is made to assess the impact of

accounting for market timing.

Coefficient estimates show that only two out of ten funds generate a positive gamma value.

Although these coefficients are not significant at a 5% level and hence the null hypothesis of no

market timing ability is upheld. Funds D and F generate gamma values of 0.98 and 1.03, respectively,

with t-statistics of 1.82 and 1.91, respectively. This implies that based on the unconditional TM

model, fund managers in D and F exhibit neither superior or perverse market timing abilities over

the observation period. Howbeit, these values are marginal to the 5% critical value of 1.96. This may

indicate tests against the null hypothesis are sensitive to research design. On this basis, results give

partial evidence that fund managers in D and F have shown positive market timing skills.

Subsequently, the majority of funds display negative gamma values. Table 5 shows that eight

from the ten funds generate a negative gamma. Although of this eight, only three are significant to a

5% level. Fund G yields a gamma term of -2.85, Fund H of -1.91, and Fund I with -1.65. Notably, these

are in fact all significant to a 1% level. This means that the null hypothesis that fund managers have

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

16

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

this is branded ‘omitted variable bias’, which suggests estimates may capture the effect of important

omitted variables and thus provide a biased estimate. Table 5 shows that two alpha estimates have

changed considerately as a result of the inclusion of market timing. Firstly, the alpha term on Fund B

has increased from 0.0097 to 0.011. With a negative gamma estimate, one can infer that the

omission of market timing was causing some downward bias on the stock selectivity measure

(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 by FTSE ALL share excess returns. Given the nature of Investment trusts and fund

manager’s objectives to track market indices, one would expect β values for most of the sample to

approximately 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 β values in Table 4 indicate that all the fund returns in our sample move

closely with the FTSE ALL share index. The fund beta’s over the observation period range from 0.665

for fund 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 seven funds of A, C, D, E,

G, H, and I yield a beta less than 1. These funds are less sensitive to market movements and behave

similar to a ‘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 riskTable 1 also contain information regarding each model’s explanatory power in the R2

values. These represent the percentage of Investment trust returns that is explained by variations in

the FTSE ALL share index. Statistically this is a measure of how well the data are to the fitted

17

regression line, however in the financial context this measure is interpreted as 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 unexplained by variations in the

FTSE ALL share index is referred to as non-systematic or idiosyncratic risk. This can be attributable to

managerial competence yet in comparison to the systematic risk, this type of risk can be eliminated

by portfolio 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

lowest idiosyncratic risk. This is evident through a R2 value of 0.7489. In comparison, Fund C

contains the lowest market risk of the sample and hence the highest idiosyncratic risk. This is evident

through a R2 value of 0.2335.

5.5 Sharpe Ratio’sTable 6 presents the calculations for each funds Sharpe ratio over several examined periods.

In the period of 1995 to 2016 all funds generated a negative ratio. This implies that the 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 per unit of risk. Examining the funds across consecutive inclusive 5-year

periods of 2000 – 2004 and 2005 – 2009, showed that all funds generated negative Sharpe ratios

over these separate periods. Notably, these align with our expectations given the dot come bubble

in 2001 and the credit crisis in 2008. Albeit, based on these ratio’s, the best performing funds over

the 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.

In the most recent 5-year period of 2010 to 2014 inclusive, all funds generated positive

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 rewarded for taking additional

risk, in comparison to the first few years into the millennium.

6. Discussion6.1 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

most funds have not been able to outperform the market, and thus the average fund manager

18

across this sample does not possess stock selectivity skills. Howbeit, Fund B remains the anomaly to

this in generating a significant alpha. These results show consistency with earlier studies that

document the insignificance in alpha values of UK Investment Trusts (Bal and Leger 1996; Bangassa

1999). Similarly, our results that nine out of the ten analysed fund’s generate insignificant alpha’s

follows Cuthbertson et al (2010), who found 75% of UK mutual funds neither underperform not

outperform their benchmarks. Nonetheless, these results show some contradiction to studies that

have found evidence of under-performance on a risk adjusted basis by the average fund manager

(Blake and Timmermann, 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 observation period shows consistency with Cuthbertson et al (2008), who finds

stock picking ability in 5% to 10% of top performing UK equity mutual funds. This result also follows

findings from US studies of Kosowski et al (2006) and Barras et al (2005), who find strong evidence of

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 ability and 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. This could partially explain Fund B’s estimated alpha, since the fund primarily invests

in equities of companies across Asia and far eastern countries bordering the Pacific Ocean (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 provides further evidence of strong

managerial competency. This is shown for the timeframe of 2005 to 2009 in Table 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, we may infer that in terms of risk-adjusted returns, Fund B outperformed the

other funds throughout the years surrounding the 2008 Financial Crisis. This may potential explain

why the fund generated the only significant positive alpha. This is also evident of Fund’s F and J, who

both have 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

19

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

industries with the cyclical super sector. Stocks within these industries are generally more volatile

than the market, containing beta’s greater than 1. Linking this with the alpha term, we may infer

that the manager from Fund B has utilised cyclical and volatile stocks to generate abnormal

performance. This relation may be attributed to luck or stock picking skills, as obvious from the

significantly positive 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 concentration has the potential to explain

superior selectivity ability, as evidenced by a positive alpha. Fund managers may want to hold a

concentrated portfolio if they predict growth in certain industries over others, or if they have

superior information to select profitable stocks14. This inference is supported by Kacperczyk et al

(2005), who found that investment ability is more evident among managers who hold portfolios

concentrated in a 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 timingThe estimates from the unconditional TM model do not provide evidence in support of fund

managers exhibiting positive market timing over the observation period. Results show that no funds

generate significantly positive gamma, and three out ten fund generated a significantly negative

gamma. This coincides with recent research from Cuthbertson et al (2009), who found that only 1%

of UK funds demonstrate positive timing ability and 19% of funds exhibit negative timing and on

average miss-time the market. These results also support findings that market timing abilities in fund

managers has diminished over time into the 90’s (Bal and Leger, 1996). In addition, our results follow

Leger (1996), who found that one in three trusts posses negative timing abilities. Likewise, Bangassa

(1999) also found evidence of perverse timing practices in 72 UK Investment trusts over a 15-year

period. Nonetheless, our results contradict his findings that fund styles associated with Japan, North

America and Europe are the only funds to generate significant gamma’s. In our results, we observe

14 Levy and Livingston (1995) conclude that fund managers that have superior information should hold a relatively concentrated portfolio, under the mean-variance framework. Van Nieuwerburgh and VeldKamp (2005) conclude that increasing returns to scale in market learning should cause optimal under-diversification.

20

no significant gamma values for Japanese styled funds such as Fund A (Atlantis Japan) and Fund E

(Shroder Japan).

Attempts at market timing in portfolio management comprise tactical asset allocation, using

financial derivatives, or rebalancing. Figure Evidence for significant perverse timing was found for

fund’s G, and H. Figure 7 and Figure 8 show these funds contain a heavy concentration of investment

in the financial services sector compared to all other funds. This may suggest that these fund

managers have not been tactical with their asset allocation strategies over the observation period.

Other studies that have found differences in results whilst using a higher frequency dataset.

This avenue of results sheds potential weakness on our selected data type in this study. Banagassa et

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 funds exhibit significant

timing ability more often in daily data. Goetzmann et al (2000) argue that monthly frequency might

fail to capture the contribution of manager’s timing activities to fund returns since decisions

regarding market exposure are likely made more frequently than months. In light of this, our results

on market timing could be inaccurate.

7. Limitations & Future ResearchOne of the main issues with this paper stems from the data selection. This paper uses

surviving investment trusts monthly returns only and ignores funds that have terminated over the

observation period. As discussed earlier, this can lead to biases in estimated coefficients. Malkiel

(1995) implied that the dataset used in this study will overstate the returns to fund investors.

According to Gregory et al (2007) a problem in calculating abnormal returns using factor model is

that it incorporates look-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 majority of studies have used samples that

contains both surviving and non-surviving funds to counteract this bias. Leite et al (2009) argued that

this issue could have serious implications for studies using a smaller number of funds. To this end,

the results in this study must be interpreted with some precaution. An avenue of extension from this

paper would be to record non-surviving funds into the sample, with the view of comparing non-

surviving alphas with the surviving fund alphas.

Another limitation relates to the frequency of the dataset. This study collected the monthly

share price of 10 UK Investment trust from January 1995 to June 2016 to measure both stock picking

and market timing skills. However, empirical research in Bollen and Busse (2001) and Goetzmann et

al (2000) support the use of daily returns when analysing mutual fund performance. They argue that

21

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 market timing skills. A

responsive approach to this issue and in the context of future research in this area is to use fund

daily returns to measure market timing skills.

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

disregard information on the changing nature of the economy and can incorrectly measure alpha,

beta and gamma. In reality, fund managers respond to market information using dynamic strategies

which often means varying alpha’s and beta’s. Ferson and Schadt (1996) shows that conditional

models outperform unconditional ones. Thus, this papers results of alpha and gamma should be

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 assumption that fund composition remains approximately constant over time is likely

no to hold. This study uses the most recent available Annual Reports and MorningStar 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

managers move away from risky industries or when a fun appoints a new manager. On this account,

the inferences regarding stock picking skills in the concentrated funds relative to the diversified

funds will lack validity.

8. Investors ImplicationsTests of the performance of mutual funds are important for investors choosing between

active and index funds. In this study, results suggest on average managers from UK investment trust

are were not able to out perform the market. From an investors point of view, this provides

discouraging information for the likelihood of generating profit through holding UK Investment

trusts. Moreover, tests of performance of mutual funds supply information on the question of the

validity of the Efficient Market Hypothesis in the Investment Management Industry. The results

derived in this paper in fact lend support to the notion that no fund managers are able to beat the

market. However, estimations show that Shroder Asia Pacicific fund (B) is an exception.

In testing for the presence of managerial skills, this study provides some implications relating to

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

22

Lazard World Trust (H/I) may take actions to revaluate their technical and predictive models that are

responsible for switching between asset classes.

9. ConclusionThis study evaluates the performance of 10 UK Investment trusts over the period January

1995 – 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 that Investment trusts on average do not outperform the market. This is

evidenced by only 1 fund generating a truly positive alpha. Estimations for market timing indicate

that 3 trusts exhibit truly negative gammas. Results were consistent with relevant UK literature in

Cuthbertson et al (2008) and Bangassa et al (2012) as well as complying with US findings in Kosowski

et al (2006) and Barras et al (2005). Together this advocates that managerial skill is more attributable

to stock picking as oppose to market timing abilities.

In addition, Sharpe ratios were estimated for different time frames between the observation

period detailed above. Results for this measure suggest the reward-to-volatility in Investment Trust

was persistently negative between 2000 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’s enabled an insight into the relationship between generated alphas and portfolio

concentration. We observe that Shroder Asia Pacific (the only fund to generate a significantly

positive alpha) holds a more concentrated portfolio relative to most other funds. This association is

consistent 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

23

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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.

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Porras, D. and Griswold, M., 2000. The value line enigma revisited. Quarterly Journal of Business and 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.

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Treynor, J, and K Mazuy. "Can mutual funds outguess the market." Harvard business review 44.4 (1966): 131-136.

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Data SourcesDatastream. (2012) Thomson Reuters Datastream. [Online]. Available at: Subscription Service (Accessed: 17/07/2016)

Lazard Asset Management (2016). The Lazard World Trust Fund Annual Report (2016). Retrieved from www.theworldtrustfund.com

Morningstar. (2016, August 17) Atlantis Japan Growth Fund. Retrieved from Morningstar Investment Research database.

New City Investment Managers (2016). CQS New City High Yield Fund Ltd Interim Report (2015). Retrieved from www.ncim.co.uk

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Shroders UK. (2016). Shroder Income Growth Fund plc. 2015 Annual report and Accounts. Retrieved from www.shroders.co.uk

Shroders UK. (2016). Shroder Japan Growth Fund plc. 2015 Annual report and Accounts. Retrieved from www.shroders.co.uk

Shroders UK. (2016). Shroder Mid Cap fund plc. 2015 Annual report and Accounts. Retrieved from www.shroders.co.uk

Shroders UK. (2016). Shroder UK Gowth Fund plc. 2015 Annual report and Accounts. Retrieved from www.shroders.co.uk

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APPENDIX TABLESTable 1.

Descriptive statistics of monthly returns and monthly excess returns of each Investment Trust, Risk-Free rate and FTSE All Share index. Observation Period 1 January 1995 to 1 June 2016.

Monthly Returns Monthly Excess ReturnsFund 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 0.5255B 247 0.0082 0.082 -0.253 0.451 0.5845 7.5309 -0.0255 0.0873 -0.3208 0.3884C 255 0.009 0.059 -0.297 0.3897 0.0035 12.746 -0.0256 0.0658 -0.3137 0.3846D 255 0.008 0.044 -0.171 0.153 -0.0284 4.4616 -0.0265 0.0506 -0.2384 0.1396E 257 0.0044 0.071 -0.174 0.333 0.4552 4.7102 -0.0304 0.0769 -0.2304 0.2709F 257 0.0063 0.057 -0.212 0.2298 -0.1012 5.363 -0.0285 0.0632 -0.2588 0.2241G 257 0.0069 0.0666 -0.389 0.2298 -1.1081 11.732 -0.02796 0.0709 -0.4258 0.2634H 257 0.0055 0.055 -0.235 0.19 0.8412 5.7642 -0.0293 0.0604 -0.2723 0.1872I 257 0.0056 0.05599 -0.2398 0.232 -0.707 6.1886 -0.0293 0.0617 -0.2723 0.2268J 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

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Table 2.

Presents the code used for the 10 UK Investment Trusts that are analysed in this study. Information relating fund investment objectives has also been extracted from the Annual Reports and Prospectus’ of the funds.

Code Fund Name Investment ObjectiveA Atlantis Japan Growth Fund Invests in undervalued growth companies across the market cap range including some cyclical growth

companies that can do well over the longer terms and that look cheap in terms of valuation. The Fund's objective is to achieve long term capital appreciation through an actively managed portfolio of equity and equity related investment issued by companies listed in Japan.

B Shroder Asia Pacific Growth Fund plc

The Company's principal investment objective is to achieve capital growth through investment primarily in equities of companies located in the continent of Asia (excluding the Middle East and Japan), together with the Far Eastern countries bordering the Pacific Ocean, with the aim of achieving growth in excess of the MSCI All countries Asia excluding Japan Index in Sterling terms (Benchmark) over the longer term.

C ORYX International Growth Fund

The investment objective of the company is to seek to generate consistently high absolute returns whilst maintaining a low level of risk for shareholder. The company principally invests in small and mid-size quoted and unquoted companies in the United Kingdom and United States. The Investment manager targets companies that have fundamentally strong business models, but where there may be specific factors which are constraining the maximization or realization of shareholder value, which may be realized through the persuit of an activist shareholder agenda by the Investment Manager.

D Shroder Income Growth Fund plc

The Company's principal investment objectives are to provide real growth of income, being growth of income in excess of the rate of inflation, and capital growth as a consequence of the rising income.

E Shroder Japan Growth Fund plc

The Company's principal investment objective is to achieve capital growth from an actively managed portfolio principally comprising securities listed on the Japanese stock markets, with the aim for achieving growth in excess of the TSE First Section Total Return over the longer term.

F Shroder UK Growth Fund plc The principal investment objective of the Company is to achieve capital growth predominantly from investment in UK equities, with the aim of providing a total return in excess of the FTSE All-Share Index. The company invest in a relatively concentrated portfolio of between 35 and 65 stock principally selected for their potential to provide shareholders with attractive returns relative to the FTSE All-Share Index. The portfolio is invested primarily in listed UK equities. It may include convertible securities, and equity-related derivatives may be used

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for efficient portfolio management purposes. Stocks are predominantly constituents of the FTSE 350 Index.G CQS New City High Yield Fund

LtdTo provide investors with a high dividend yield and the potential for capital growth by investing mainly in high yielding fixed interest securities.

H Lazard World Trust Fund Seeks to achieve long-term capital appreciation by investing primarily in companies whose shares trade at a discount to their underlying Net Asset Value. The Fund measures its performance principally against the MSCI All Countries World Index, although Lazard Asset Management LLC (the 'Manager') seeks to achieve the highest possible risk-adjusted returns and the allocation of the Fund's assets will normally diverge substantially from the Index, in particular in relation to its weighting in the US markets which historically has been relatively low.

J Shroder UK Mid Cap Fund plc The Company's investment objective is to invest in Mid Cap equities with the aim of providing a total return in excess of the FTSE 250 (ex-Investment Companies) Index.

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Table 3.

Present the MorningStar Global Equity Classification. This classification is used to produce several portfolios asset allocation.

Super Sector Industry Definition

Cyclical

Basic MaterialsCompanies that manufacture chemical, building materials and paper products. This sector also includes companies engaged in commodities exploration and processing. Companies in his sector include Arcelot Mittal, BHP Billiton and Rio Tinto.

Consumer Cyclical

This sector includes retail stores, auto and auto parts manufacturers, companies engaged in residential construction, lodging facilities, restaurants and entertainment companies. Companies in this sector include Ford Motor Company, McDonald's and News Corporation.

Financial servicesCompanies that provide financial services which includes banks, savings and loans, asset management companies, credit services, investment brokerage firms, and insurance companies. Companies in this sector include Allianz, J.P Morgan Chase and Legg Mason.

Real estateThis sector includes mortgage companies, property management companies and REITs. Companies sin this sector include Kimco Realty Corporation, Vornado Realty Trust and Westfield Group.

Defensive

Consumer Defensive

Companies engaged in the manufacturing of food, beverages, household and personal products, packaging, or tobacco. Also includes companies that provide services such as education & training services. Companies in this sector include Philip Morris International, Procter & Gamble and Wal-Mart Stores.

HealthcareThis sector includes biotechnology, pharmaceuticals, research services, home healthcare, hospitals, long-term care facilities, and medical equipment and supplies. Companies in this sector include Astra Zeneca, Pfizer and Roche Holding.

UtilitiesElectric, gas, and water utilities. Companies in this sector include Elctricite de France, Exelon and PG&E Corporation.

Sensitive

Communication services

Companies that provide communication services using fixed-line networks or those that provide wireless access and services. This sector also includes companies that provide internet services such as access, navigation and internet related software and services. Companies in this sector include AT&T, France Telecom and Verizon Communications.

EnergyCompanies that produce or refine oil and gas, oil field services and equipment companies, and pipeline operators. Companies in this sector include BP, ExxonMobil and Royal Dutch Shell.

IndustrialsCompanies that manufacture machinery, hand-held tools and industrial products. This sector also includes aerospace and defence firms as well companies engaged in transportation and logistics services. Companies in

32

this sector include 3M, Boeing and Siemens

Technology

Companies engaged in this design, development, and support of computer operating systems and applications. This sector also includes companies that provide computer technology consulting services. Also includes companies engages in the manufacturing of computer equipment, data storage products, networking products, semi-conductors, and components. Companies in this sector include Apple, Google and Microsoft.

Table 4.

Results of applying the unconditional single-index CAPM model (1) to sample of 10 UK Investment Trusts over the period of 1 January 1995 to 1 June 2016.

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

33

* = significance level of 5%, ** = significance level of 1%.

34

Table 5.

Results of applying the TM model (2) to the sample of 10 UK Investment Trusts over the period of 1 January 1995 to 1 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

* = significant at 5% level, ** = significance at 1%

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Table 6.

Presents the results of applying the Sharpe ratios across the 10 Investment Trusts. The time frames examined include 2000 to 2004, 2005 to 2009, and 2010 to 2014 as well as the overall observation period estimates of 1995 to 2016. These examined windows represented an inclusive 5-year duration.

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

36

37

APPENDIX FIGURESFigure 1.

Plots the monthly share price of the 10 analysed Investment Trusts over period January 1995 to June 2016.

3470

034

820

3494

335

065

3518

635

309

3543

135

551

3567

435

796

3591

636

039

3616

136

281

3640

436

526

3664

736

770

3689

237

012

3713

537

257

3737

737

500

3762

237

742

3786

537

987

3810

838

231

3835

338

473

3859

638

718

3883

838

961

3908

339

203

3932

639

448

3956

939

692

3981

439

934

4005

740

179

4029

940

422

4054

440

664

4078

740

909

4103

041

153

4127

541

395

4151

841

640

4176

041

883

4200

542

125

4224

842

370

4249

1

0

100

200

300

400

500

600

700

800

900

1000

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)

(Source: Thomson Reuters DataStream [Accessed: 17/07/17])

38

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

39

Figure 4: ORYX International (C) Asset allocation

Figure 5: Shroder Income (D) Asset allocation

Figure 6: Shroder Japan (E) Asset allocation

Cyclical Sensitive Defensive

Cyclical Sensitive Defensive

Basic Materials

Consumer cyclical

Financial Services

Communication Services

Energy

Industrials

Technology

Consmer defensive

Healthcare

Utilities

Basic Materials

Consumer cyclical

Financial Services

Reals estate

Industrials

Technology

Consmer defensive

Healthcare

40

Figure 7: Shroder UK (F) Asset allocation

Figure 8: CQS New City High Yield (G) Asset

allocation

Figure 9: Lazard World Trust (H/I) asset allocation

Basic Materials

Consumer cyclical

Financial Services

Reals estate

Communication Services

Energy

Industrials

Technology

Consmer defensive

Healthcare

Utilities

Basic MaterialsConsumer cyclical Financial ServicesReals estateCommunication ServicesEnergyIndustrialsTechnologyConsmer defensiveHealthcareUtilities

Cyclical Sensitive Defensive

Cyclical Sensitive Defensive

Basic MaterialsConsumer cyclical Financial ServicesReals estateCommunication ServicesEnergyIndustrialsTechnologyConsmer defensiveHealthcareUtilities

Cyclical Sensitive Defensive

Cyclical Sensitive Defensive

41

Figure 10: Shroder Mid Cap (J) Asset allocation

Figure to Figure 10 display each Investment trust asset allocation compiled from information at the Morningstar Website and Annual Reports of each fund. Investments are arranged through guidance from the MorningStar Global Equity Classification. The left hand side charts display the portfolios composition in terms of super sectors. The right hand side charts display the portfolios compositions in terms of associated industries. Information on composition is retained through the most recent and available asset allocation source. This paper assumes that fundamental composition of each portfolio remains approximately constant over the funds life.

Cyclical Sensitive Defensive

Basic Materials

Consumer cyclical

Financial Services

Communication Services

Energy

Industrials

Technology

Consmer defensive

Healthcare

Utilities

42

43