Performance and Performance Persistence of ‘Ethical’ Unit Trusts in the UK

Journal of Business Finance & Accounting, 34(7) & (8), 1327–1344, September/October 2007, 0306-686Xdoi: 10.1111/j.1468-5957.2007.02006.x

Performance and PerformancePersistence of ‘Ethical’ Unit Trusts in the

UK

Alan Gregory and Julie Whittaker∗

Abstract: We examine performance, and persistence in the performance, of UK ‘ethical’ orSRI funds and find that performance appears to be time-varying, showing that conclusions onperformance itself are influenced by whether a static or time varying model is employed. Givenevidence that many UK funds which claim to be international in nature may exhibit home biasin their portfolio allocations, we also propose a new measure for performance of internationalfunds that allows for this and show that such recognition has important implications for theconclusions drawn with respect to these funds. We find evidence that supports persistence inperformance, particularly at longer time horizons. There is some evidence that for domesticfunds, past ‘winning’ SRI funds outperform ‘losing’ SRI funds to a greater extent than theircontrol portfolio counterparts.

Keywords: ethical unit trusts, performance, persistence

1. INTRODUCTION

Increasingly market choices are being made that reflect attitudes to social issues.1

Within financial markets, investors can give some expression to their social concernsby investing only in companies that meet certain ethical criteria. However, investingin such a manner requires additional information and ethical unit trusts, now morecommonly referred to as SRI funds, provide a means for individuals to invest sumsaccording to ethical criteria without personally engaging in detailed company research.

∗The authors are respectively from the Centre for Finance and Investment (Xfi) and the Department ofManagement at the School of Business and Economics, University of Exeter. They would like to thankcolleagues and seminar participants at the University of Exeter for their comments on an earlier versionof this paper, Richard Harris and Ian Tonks for helpful suggestions on test statistics, and Maria Michoufor her assistance with updating the Fama French and Momentum factors used in this paper and for theDatastream information on dead unit trust returns. They would also like to thank the editor, Peter Pope,and an anonymous referee for comments on an earlier draft. The usual caveats with respect to errors andomissions apply. (Paper received December 2005, revised version accepted January 2007. Online publicationApril 2007)

Address for correspondence: Alan Gregory, Centre for Finance and Investment (Xfi), School of Businessand Economics, University of Exeter.e-mail: [email protected] See for example, Harrison, Newholm and Shaw (2005).

C© 2007 The AuthorsJournal compilation C© 2007 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UKand 350 Main Street, Malden, MA 02148, USA. 1327

1328 GREGORY AND WHITTAKER

The market for these funds is growing. Within the UK, for most years during the decade1989–1999, the value of SRI unit trusts grew at a faster annual rate than the whole unittrust market. In 2003 it was valued at a minimum of £4.2 billion, an increase of over25% since 2000 (EIRIS, 2005).

Although some investors in SRI funds are willing to accept lower returns for theirmoral stance (Lewis and Mackenzie, 2000; and Webley, Lewis and Mackenzie, 2001), theperformance of SRI funds vis-a-vis non-SRI funds remains of interest. First, growth inthe SRI sector will require funds to appeal to a wider group of investors than those witha combination of strong principles and an income level that can support potentiallysuboptimal returns. Second, the effects of ethical criteria on financial performancehave relevance for company strategy. Third, there is intellectual curiosity in consideringthe performance of funds chosen from a restricted universe of companies. Initially,ethical funds were developed through a negative screening process resulting in theexclusion of stocks of companies engaged in, for example, gambling, tobacco, andhuman rights abuses. Then choice of stocks began to be made on the basis of positivecriteria too, such as the inclusion of companies which engage in environmentalmanagement. This positive screening permitted the inclusion of larger companieswhich, because of their diversity, were typically more likely than small companies tobe excluded by negative screening. Therefore, although still restrictive, the universefor SRI funds has been enlarged.

Previous empirical work on SRI unit trusts in the UK (Luther, Matatko and Corner,1992; Luther and Matatko, 1994; Mallin, Saadouni and Briston, 1995; and Gregory,Matatko and Luther, 1997) has attempted to understand whether performance canbe explained by ethical criteria or by other factors such as fund size and composition.With regard to composition of funds the main area of interest was company size, sincelarger firms were more likely to breach ethical criteria when negative screening criteriawere paramount. Gregory et al. (1997) conducted comparative work using time seriesand cross sectional regressions, which also included risk adjusted benchmarks. Theirresults tentatively suggested that SRI funds did not perform as well as other funds,but that this could be explained by their greater exposure to ‘small firms’ risk ratherthan ethical criteria per se. This approach has recently been extended by Kreander,Gary, Poer and Sinclair (2005) to look at the performance of ethical funds in fourEuropean countries. Their work uses size-adjusted benchmarks for each country andfinds conclusions broadly similar to those of previous UK based studies. Empirical workon US SRI funds by Hamilton, Jo and Statman (1993) found no significant differencein performance compared with conventional funds. This result was endorsed by Reyesand Grieb (1998), although with cointegration testing they also found that SRI fundsdid not share a common underlying time trend with other funds. Geczy, Stambaughand Levin (2003) compared optimal portfolios of funds with SRI objectives and thosewithout, concluding that SRI constraints are costly when fund managers are skilled.Bauer, Koedijk and Otten (2005), working with US, UK and German data foundlittle significant difference in the performance of SRI and conventional funds, whilediscerning that older SRI funds performed better than younger ones, indicative of alearning effect.

In this paper, we build on some interesting innovations in Bauer et al. (2005). Thatpaper looked at differences in style between ethical and ordinary mutual funds, andalso investigated performance of international funds. First, we use UK style portfoliosthat, by construction, are free of any survivorship bias. Second, we propose a very

C© 2007 The AuthorsJournal compilation C© Blackwell Publishing Ltd. 2007

PERFORMANCE OF ‘ETHICAL’ UNIT TRUSTS 1329

different measure for performance of international funds that picks up ‘home bias’ ininvestment allocations. It turns out that the use of such a performance metric changesthe finding in Bauer et al. (2005) of outperformance by UK SRI funds. Third, we showthat performance appears to be time-varying, and that conclusions on performanceitself are influenced by whether a static or time varying model is employed. Fourth,as in Blake and Timmerman (1998) and Fletcher and Forbes (2002) we examinepersistence in fund performance and find results supportive of those in Otten andBams (2002). However, we find there is some evidence of a difference in performancepersistence between SRI and non-SRI funds, and that, as in Fletcher and Forbes (2002)conclusions on the degree of persistence appear to depend on the performance metricchosen. Overall, our findings do not suggest that ethical investors lose out comparedto ‘ordinary’ investors, but absolute returns are low and the style exposure of SRI fundslooks very different to that of ordinary funds.

2. DATA

For each year from January 89 to December 02, we collected returns for all 32 ethical(or SRI) unit trusts that had existed in the UK up until that point, whether alive ordead.2 To construct a control group of funds, for every SRI unit trust we selected acharacteristic-matched portfolio of 5 non-SRI unit trusts. The matching criteria were:(1) the fund should have a start date within 6 months of the SRI unit trust with which it ismatched, and; (2) the fund should be in the same AUTIF category. Where this approachresulted in more than 5 matching funds being available, random draws were made toselect the 5 funds.3 Where (1) could not be met, the start date was relaxed to within 9months, and in a small number of cases when even this became problematic, matchingwas done with a fund in a similar but not identical AUTIF category.4 We did not matchon size given the evidence in Gregory et al. (1997) and Kraender et al. (2005), as thiswould necessarily have involved a trade-off with the start date criterion. The wholeprocess of matching funds was a lengthy one and was accomplished by consulting theFT Unit Trust Yearbooks. From these it was possible to identify the funds with the sameAUTIF category, and then find the start date.

Using a portfolio of funds rather than the matched pairs analysis of Mallin et al.(1995), Gregory et al. (1997) and Kreander et al. (2005) overcomes the inherentproblem of survivorship bias implied in a matched pairs sample. This is potentiallyimportant, as the most striking element of summary data is that 29.93% of our controlsample die before the end of the sample period5 compared with the relatively low rateof demise for SRI funds. Over this same period, only four out of the 32 SRI fundsdied (12.5% of the sample). Blake and Timmerman’s (1998) sample (admittedly for alonger period, 72–95) shows a non-survival rate of 41%, so the low failure rate of SRIfunds is remarkable in this context. Returns data for the funds are from S&P Micropaland Datastream. Whilst S&P Micropal is a useful source of information and returns for

2 Note: Bauer et al. (2005), who are also careful to control for survivorship bias in their sample of SRI funds,have an identical number of ethical funds.3 Typically the number of funds that satisfied these criteria were not large. Our original research designtried to find 10 matched non-SRI funds for each SRI fund, but such a large control set proved infeasible.4 Excluding these few funds makes no qualitative difference to any of our results.5 Bauer et al. (2005) using a slightly shorter period (1990-2000) and different matching criteria report asimilar figure, with 28% of their whole sample disappearing before the end of the measurement period.



live funds, it was necessary to supplement this with returns data from Datastream, asthis database includes data on both dead and live funds. Factor returns (describedbelow) are calculated as described in Gregory, Harris and Michou (2003) and areconstructed using Datastream for market capitalisation and book-to-market ratios, withmonthly returns being drawn from the London Business School LSPD CD.

Our first tests involve calculating average returns, wealth relatives and risk-adjustedreturn measures. Here our tests are based on the SRI unit trusts (UTs) and theircharacteristic-matched funds of UTs. Our later tests involve looking at persistence inperformance, and for these tests we need to look at individual funds rather than funds-of-funds, as portfolio effects in funds of funds are likely to mean that variation inperformance is less than would be observed in individual unit trusts.

3. MEASURING UNIT TRUST PERFORMANCE

The first approach to measuring performance is to use either a three factor or fourfactor model. The standard Fama-French three factor model has factors which arethe excess return on the market Rmt − Rft , the returns on a size factor SMBt (thedifference between the returns on a portfolio of small companies and a portfolio oflarge companies) and a book-to-market factor HMLt (the difference in returns on aportfolio of high book-to-market companies and low book-to-market companies). Thefour factor model adds a momentum factor MOMt , which is the difference in returnsbetween a ‘winner’ portfolio and a ‘loser’ portfolio, formed on the basis of the previous12 months’ returns. The 4-factor model6 can be described as:

Rpt − R f t = αp + βp(Rmt − R f t ) + γ1pSMBt + δpHMLt+λpMOM + εpit. (1)

This model can be estimated for each fund or portfolio p over the full t data periods,when under null hypothesis of no abnormal performance the αp coefficient should beequal to zero. The appropriateness of such a model when evaluating fund returns isworthy of some discussion. The US literature, whilst not uncontentious on this point,seems broadly supportive of a risk-based interpretation of the first three factors in sucha model.7 The UK evidence is a little more mixed, with, for example, Gregory, Harrisand Michou (2003, Tables 9–11) showing that although future GDP and consumptiongrowth are related to HML return individually, the relationship becomes (marginally)insignificant in the presence of the SMB and market factors. By contrast, the latter arealways significant in predicting GDP and consumption growth, and unlike the HMLfactor they are also significant in predicting future investment growth.8 Nonetheless,there appears to be at least a partial risk based explanation for returns to HML, evenif research suggests there may be anomalous elements in returns to value investing(Strong and Xu, 1997; and Gregory et al., 2003). However, the well-documented returns

6 The 3 factor model amounts to a special case of the 4-factor model with the coefficient on MOM constrainedto equal zero.7 Space reasons do not allow a detailed discussion of this matter, but empirical support can be found inpapers such as Liew and Vassalou (2000) and Campbell and Vulteenaho (2004), whilst recent theoreticalarguments for such an interpretation include Zhang (2005).8 A positive relationship should exist if returns to the factor have positive covariance with the future state ofthe economy (e.g., Cochrane, 2001).



to momentum have yet to be convincingly explained in terms of risk, although a recentpaper by Liu (2005) provides evidence supportive of US momentum returns beingrationally-priced liquidity risk. No such investigation appears to have been completedin respect of UK stock returns. Whilst there is a clear case for the use of a threefactor benchmark, we remain somewhat sceptical on whether a four factor model isappropriate in the determination of a risk-adjusted return. Nonetheless, we includeresults from this model for the purposes of comparison with other published work,although we emphasise that we place most weight on the results from the three factormodel.

A potential problem with the approach to calculating abnormal returns using thesefactor models is that it imparts some look-ahead bias if funds are required to survivefor a certain minimum number of months in order to be incorporated into the sample.Besides the obvious problem that such a bias presents for any investigation, it couldhave particularly serious implications for a study using a small number of funds wheresurvivorship differs between the SRI and non-SRI sub-samples. For that reason, weestimate equation (1) by regressing an equally-weighted portfolio of excess returns onthe unit trusts in calendar time. As Blake and Timmermann (1998) point out, thisavoids making any assumption about the cross-section relationship of returns betweenfunds. This approach is analogous to that proposed for event tests by Fama (1998) andMitchell and Stafford (2000). The alternative, which again avoids any look ahead bias,is to run equation (1) for individual funds on a rolling basis for months t=T to t=T +36, then use the stored regression coefficients to calculate the abnormal performancefor month T + 37. In these regressions we require the fund to have a minimum of18 months prior returns in order to estimate the coefficients for month T + 37. Thisrolling regression approach can also be employed for calendar time portfolios. Theregressions are run for successive over-lapping 36 month periods starting in January1989. To test the hypothesis that the Jensen alphas and other coefficients from theserolling regressions are significantly different from zero requires a t-statistic that is robustto the overlapping nature of the data, with the relevant test statistic being estimatedby the Newey-West procedure allowing for the 35 overlapping observations. We alsoseparate our sample into two equal periods (with 66 months in each) and estimateWald statistics to test for performance differences between first and second halves,once again allowing for overlapping observations using a Newey-West procedure.9

We try alternative approaches to time-varying performance by employing both theTreynor-Mazuy test for market timing skill, and by estimating Ferson and Schadt (1996)style conditional models using the CAPM, three and four factor models. Since none ofthese approaches to timing result in any changes in our conclusion on performanceand risk exposures, we do not report these results, although a full set is availablefrom the authors on request. Note that these are conceptually different tests fromthe rolling regression tests described previously. Under the conditional model, time-varying exposures occur in response to publicly available information. The markettiming model is silent on whether changes in risk exposure are in response to publiclyavailable information, but measures the success of timing through the factor returnsquared terms. By contrast, the rolling regressions simply allow factor exposures tochange through time, with no priors on what these changes may or may not be inresponse to.

9 The authors are grateful to Richard Harris for these suggestions on significance testing.



The above are perfectly reasonable models for estimating domestic fund perfor-mance, but 12 out of our 32 SRI funds are ‘international’. One option is to simplyrun the above regressions using a suitable world index. However, inspection of theinvestment strategies of these funds shows evidence of home bias and considerablevariation in the degree of this bias between the funds. This home bias can be measuredby extending the four factor model in (1) to incorporate home bias not only in theexposure to the domestic market, but also in the domestic, SMB, HML and MOMfactors. We investigate various proxies for the world factors, with the most successfulappearing to be the use of the MSCI World index and the US factors from KennethFrench’s web page, translated into sterling returns. We then measure home bias foreach factor as (HOMEFACTOR – WORLDFACTOR), which we describe as (Rw − Rm),SMBd, HMLd and MOMd respectively, giving the following model:

Rpt − R f t = αp + βwp(Rwt − R f t ) + γw1pSMBwt + δwpHMLwt+λwpMOMw

+ βwp (Rwt − Rmt ) + γ h1pSMBd + δh pHMLdt+λhpMOMd + εpit. (2)

In addition to testing for evidence of fund out-performance, we also examine theconsistency or persistence of fund performance. We perform two types of test for bothdomestic and international funds. Our first test is based upon the test for differencesin performance described in Carpenter and Lynch (1999). This involves ranking fundsinto quartiles10 on the basis of a prior ranking period return. The performance of thesefunds is then tracked in subsequent evaluation periods. We use symmetrical rankingand evaluation periods here, reporting results for 1, 3, 6, 12 and 36 month ranking andsubsequent evaluation periods. The appropriate t-test is described in Carpenter andLynch (1999, p. 349) and involves the calculation of a Newey-West standard error withan appropriate number of lags. The first important advantage of this approach given therelatively short period during which SRI funds have existed is that it allows overlappingtime periods to be used, and hence longer period returns can be evaluated. Second,Carpenter and Lynch (p. 367) show that in the absence of survivorship bias this test‘appears to be the best specified under the null hypothesis of no persistence, and one ofthe most powerful’ against the alternatives they consider. In terms of time horizon, theseauthors (p.372) find this test is 100% powerful at the 36 month horizon. Our secondgroup of tests make use of the contingency table tests common in the literature (for aUK case applied to unit trusts, see Fletcher and Forbes, 2002), and involve classifyingfunds as winners or losers in each of two consecutive time periods (which can be 1month, 3 months, 6 months or 12 months.11) We compute the number of combinationsand the related statistics described in Carpenter and Lynch (1999) and Fletcher andForbes (2002). The results from these rather less powerful tests are broadly consistentwith, though generally weaker than, those obtained under the preferred Carpenter andLynch (1999) t-test described above, but are not reported here for reasons of space.12

10 Note that whilst Carpenter and Lynch (1999) use deciles, the small number of SRI funds (20 domesticand 12 international) does not realistically allow us to use anything other than quartile ranking. In a UKcontext both Fletcher and Forbes (2002) and Blake and Timmerman (1998) also employ quartiles. Theformer (fn. 23, p.485) note that similar results are obtained if decile portfolios are used in the analysis.11 Because these tests require observations from non-overlapping periods, there is insufficient data tocalculate any meaningful figures for 36 month holding periods.12 An extended version of this paper including these persistence tests is available at: http://www.xfi.ex.ac.uk/



Table 1Basic Statistics Based Upon Absolute Returns for SRI and Non-SRI Funds

Mean Final Wealth AnnualisedMonthly Annualised Per Unit Standard DeviationReturn% Return% Invested of Return%

Panel A: Domestic FundsSRI 0.62 7.75 2.487053382 13.76Non- SRI 0.67 8.35 2.591774833 15.59Rm (FTASI) 0.83 10.48Difference −0.05 −0.56Rf 0.59 7.26

Panel B: International FundsSRI 0.54 6.64 2.102313177 14.95Non- SRI 0.62 7.72 2.434036369 14.68Rm (MSCI World) 1.22 15.65Difference −0.08 −1.01Rf 0.59 7.26

Notes:The table shows mean monthly and annualised returns, terminal value per £1 invested and annualisedstandard deviation of returns for an equally weighted calendar time portfolio of SRI and non-SRI funds,together with the market return (FTASI for domestic funds, Morgan-Stanley MSCI returns in sterling forinternational funds) and the risk free return (UK Treasury Bill return).

Our final tests, in keeping with Blake and Timmerman (1998, Table V) and Fletcherand Forbes (2002, Table 3), are regression-based tests on the monthly returns fromprior year quartile portfolios.

4. RESULTS

Our first finding (Table 1) reports results based upon the absolute performance ofthe funds on a calendar time portfolio (with equal weightings) basis. These record adisappointing level of performance for all funds relative to market benchmarks. Fordomestic funds, the SRI funds under-perform non-SRI funds by 0.56% p.a., but havelower absolute risk (13.8% standard deviation versus 15.6% for the non-SRI funds).The wealth-relative figures show the cumulated value of the calendar funds at the endof December 2002. The performance of the international funds are particularly poor,and here the SRI funds under-perform the control group of non-SRI funds by around1% p.a.. Furthermore, the risk of these funds is marginally higher than that of thecontrol group (14.95% standard deviation versus 14.68%).

In Table 2 we report results from a basic Jensen alpha model run in calendar time,for the three and four factor models. We also report results for ‘hedge’ portfolios whichare long in SRI funds and short in non-SRI funds. The first message from the resultsis that none of the alphas for the domestic UTs are significant under either model.Performance is marginally negative for both SRI and control group funds, but neversignificantly so. Furthermore, there are no differences in the alphas between the twogroups of funds under either of the models. Our results are not qualitatively differentfrom those of Bauer et al. although our alphas are considerably closer to zero. Thisprobably reflects differences in our market indices (FTASI versus Worldscope) and factorconstruction, which, in contrast to those based upon Worldscope figures, are based on



Table 2Basic Jensen Model Coefficients for SRI and Non-SRI Unit Trusts

SRI Non-SRI Hedge

Coefficient T-test Coefficient T-test Coefficient T-test

Panel A: Coefficients for Domestic Funds3-factorRm − Rf 0.805 28.37 0.974 46.67 −0.169 −7.10SMB 0.246 6.70 0.164 6.08 0.082 2.65HML −0.039 −1.16 0.059 2.36 −0.098 −3.44Constant (Alpha) −0.001 −0.45 −0.001 −1.25 0.001 0.56Adj R2 0.835 0.930 0.328

4-factorRm − Rf 0.816 27.64 0.970 44.47 −0.154 −6.27SMB 0.258 6.84 0.160 5.73 0.098 3.12HML −0.025 −0.70 0.054 2.06 −0.078 −2.66MOM 0.059 1.34 −0.020 −0.60 0.079 2.15Constant (Alpha)T −0.001 −0.64 −0.001 −1.16 0.000 0.26Adj R2 0.836 0.930 0.342

Panel B: Coefficients for International Funds3-factor, extendedHOME (=Rm − Rw) 0.498 11.29 0.446 11.94 0.052 1.18Rw − Rf 0.858 33.11 0.900 41.02 −0.041 −1.58HOMESMB (=SMB − SMBw) 0.196 5.68 0.057 1.94 0.139 4.01SMBw 0.464 11.10 0.214 6.05 0.250 5.95HOMEHML (=HML − HMLw) −0.004 −0.12 0.021 0.79 −0.025 −0.78HMLw −0.046 −1.23 0.008 0.27 −0.054 −1.45Constant (Alpha) −0.001 −1.24 −0.001 −1.02 0.000 −0.37Adj R2 0.891 0.919 0.203

Notes:The table shows the coefficients from calendar time regressions on equally weighted portfolios forall SRI and non-SRI control funds. Regression equations are given by expressions (1) and (2) in the text. Fordomestic funds the factors are the excess return on the market Rmt − Rft , the returns on a size factor SMBt(the difference between the returns on a portfolio of small companies and a portfolio of large companies)and a book-to-market factor HMLt which is the difference in returns on a portfolio of high book-to-marketcompanies and low book-to-market companies. The four factor model adds a momentum factor MOMt,the difference in returns between a ‘winner’ portfolio and a ‘loser’ portfolio, formed on the basis of theprevious 12 months’ returns. The model for international funds calculates these factors for global portfolios(for precise definitions see text), but in addition controls for home bias in each factor as (HOMEFACTOR– WORLDFACTOR), which we describe as (Rw − Rm), SMBd, and HMLd respectively. Results are shown forthree and four factor models for domestic funds, and for the extended version of the three factor modelfor international funds. The columns present results for the SRI, non-SRI and ‘hedge’ portfolio, which is azero net investment fund long in the SRI funds and short in the non-SRI control funds.

Datastream figures including those from the dead companies file, which should avoidany survivorship bias being present in our factors. We find that there are significantdifferences in style/factor exposures. First, SRI funds have lower market exposures.Betas are close to unity for the control group, but significantly less than unity for the SRIfunds. The difference in betas is highly significant under both models. Second, the size-exposure is significantly positive for both groups, but the SRI funds have significantlygreater exposure to this factor. The SRI funds have an insignificant negative exposureto the HML factor, whereas the non-SRI group have a significant positive exposure; thedifference between the groups is significantly negative. Last, whilst neither group of



funds has significant exposure to the momentum factor, we find that SRI funds have asignificantly greater exposure to the factor than non-SRI funds. Although our findingsin respect of differences in market risk, SMB and HML factor loadings are similar to thoseof Bauer et al., the absolute factor loadings on SMB, HML and MOM are different inmagnitude and tend to be less significant for both groups. In addition, our finding ondifferences in momentum exposure contrast to theirs.

For the international funds, our approach reveals significant home bias in exposuresto market and SMB factors for SRI and non-SRI funds, although the significancelevel is slightly over 5% for the latter. Both groups also have significant exposure tointernational market and SMB factors. However, there is no significant exposure toHML, either domestically or internationally, for either group. Extending the analysisto a four factor model adds nothing, and exposure to the momentum factor (homebias and international) is always insignificant, so we do not report the results here.13

We also show negative but insignificant alphas of around 0.1% per month for bothSRI and non-SRI funds. The only significant factors on the ‘hedge’ fund are that bothinternational SMB exposure and home bias in SMB exposure are positive, which is inline with the finding from the domestic analysis where it was illustrated that ‘ethical’funds have greater exposure to size effects. Our results stand in some contrast to Baueret al. (Table 5, p. 1761) who report insignificant positive alphas, significant HML andmomentum exposures, and significant differences on all factor exposures between SRIand non-SRI groups.

The results in Table 2 are confirmed by our unreported tests described abovethat incorporate both market timing and conditional factor models. An alternativeapproach which allows for time-varying performance is to estimate regressions ona rolling basis. Of course, a sub-period analysis could be used (as in Bauer et al.)but running rolling regressions allows for continual variation in factor exposure andperformance. A further advantage is that the following month’s abnormal performancecan always be estimated for individual funds (as well as calendar portfolios) basedupon the prior 36 month estimates of their factor exposures. The results in Table 3show the means of the time-varying factor exposures, together with the Newey-Westtest p-values described above. These results reveal differences from those reported inTable 2. Perhaps the most important is that although rolling regressions confirm thatneither SRI nor non-SRI funds exhibit significant out-performance, the results revealthat domestic SRIs have significantly better performance than non-SRI funds usingeither the three factor or four factor models. Consistent with the findings of Baueret al., the Wald test result shows that this significantly superior performance arises inthe second period rather than the first. The ‘hedge change, second period’ alpha isroughly 0.2% per month better than the first period alpha, whichever model is used.Turning to the factor exposures, the qualitative results for market and SMB exposuresare unchanged, but non-SRI funds have a significant exposure to HML at only the10% level under the three factor model, and exposure to the momentum factor ofSRI funds is significantly positive at the 10% level. All conclusions on differences infactor exposures (i.e. the hedge fund exposures) are unchanged under the rollingregressions. However, the Wald tests show that there are significant changes in thehedge fund exposure to market risk and WML between first and second periods.14

13 See footnote 12.14 Changes in respect of the SMB factor are only significant under the 3-factor model.



Table 3Time-varying Coefficients

Rm − Rf SMB HML Alpha

Panel A: Domestic 3 FactorSRI mean 0.778917 0.352202 −0.06794 0.000972SRI p 0.000 0.000 0.187 0.219Non-SRI mean 0.96034 0.17844 0.044741 −0.0005Non-SRI p 0.000 0.000 0.082 0.157Hedge Mean −0.18142 0.173762 −0.11268 0.001468Hedge p 0.000 0.002 0.006 0.0004732Hedge change, 2nd period 0.1623931 0.1227636 −0.0392513 0.0019134Hedge change p 0.000 0.070 0.486 0.0000

Rm − Rf SMB HML MOM Alpha

Panel B: Domestic 4 FactorSRI mean 0.795184 0.381075 −0.04341 0.085172 0.001029SRI p 0.000 0.000 0.254 0.092 0.268Non-SRI mean 0.962347 0.156682 0.037392 −0.04283 −0.00031Non-SRI p 0.000 0.000 0.023 0.355 0.433Hedge Mean −0.16716 0.224393 −0.08081 0.128 0.001342Hedge p 0.000 0.000 0.031 0.000 0.0002577Hedge change, 2nd period 0.1609086 0.0399318 −0.0632784 0.0396967 0.0021641Hedge change p 0.000 0.567 0.191 0.042 0.0000

Rw − Rf SMBw HMLw Home SMBd HMLd Alpha

Panel C: International Extended 3 Factor ModelSRI mean 0.508197 0.857764 0.293561 0.542898 −0.03286 −0.01283 −0.00042SRI p 0.000 0.000 0.000 0.000 0.175 0.575 0.613Non-SRI mean 0.460533 0.927012 0.072141 0.219392 0.036887 0.086299 −0.00255Non-SRI p 0.000 0.000 0.001 0.000 0.313 0.064 0.000Hedge Mean 0.047664 −0.06925 0.22142 0.323506 −0.06975 −0.09913 0.002135Hedge p 0.103 0.003 0.000 0.000 0.068 0.024 0.141Hedge change, 0.127196 −0.060097 −0.009535 0.027723 0.095421 0.029652 0.003064

2nd periodHedge change p 0.000 0.060 0.893 0.700 0.120 0.712 0.1491

Notes:The panels show the mean coefficients from calendar time regressions on equally weighted portfolios forall SRI and non-SRI control funds estimated on a rolling 36 month basis. Regression equations are givenby expressions (1) and (2) in the text. For domestic funds the factors are the excess return on the marketRmt − Rft , the returns on a size factor SMBt (the difference between the returns on a portfolio of smallcompanies and a portfolio of large companies) and a book-to-market factor HMLt which is the differencein returns on a portfolio of high book-to-market companies and low book-to-market companies. The fourfactor model adds a momentum factor MOMt, the difference in returns between a ‘winner’ portfolio anda ‘loser’ portfolio, formed on the basis of the previous 12 months’ returns. The model for internationalfunds calculates these factors for global portfolios (for precise definitions see text), but in addition controlsfor home bias in each factor as (HOMEFACTOR – WORLDFACTOR), which we describe as (Rw − Rm),SMBd, and HMLd respectively. Results are shown for three and four factor models for domestic funds, andfor the extended version of the three factor model for international funds. The rows present results for theSRI, non-SRI and ‘hedge’ portfolio, which is a zero net investment fund long in the SRI funds and short inthe non-SRI control funds, together with p-values for a Newey-West t-test for significance. The final two rowsin each column report the change in the ‘hedge’ portfolio coefficients between the first and second half ofour test period, together with a Wald test for differences.



This suggests that relative to non-SRI funds, SRI funds appear to significantly increasetheir exposure to market risk and momentum in the second period.

Within the international funds, important differences again arise compared withthose reported in Table 2. First, non-SRI funds now significantly under-perform, byaround a quarter of one percent a month, although the hedge results reveal nosignificant difference in performance between SRI and non-SRI funds. Second, ona time-varying basis both SRI and non-SRI portfolios have significant positive exposureto international HML. Third, SRI funds exhibit significantly less exposure to home HMLbut more exposure to international HML. Last, our Wald tests reveal significant changesin exposure to the world market and size factors through time. Taken as a whole,our results in Table 3 suggest that it is important to allow for changes in style/factorexposure in performance measurement tests. These changes can come about eitherbecause individual funds change their portfolios, or because the mix of funds withinthe portfolio changes.

We now consider persistence in performance. Given the evidence on time-varyingperformance and factor loadings, we use rolling risk exposure factors in these tests forboth three and four factor model analyses. In addition, we look at what conclusions maybe drawn if we use absolute performance to rank trusts. Turning to domestic funds first,we report results in Table 4 for the entire sample of SRI and non-SRI funds together,and the SRI and non-SRI funds ranked separately.15 The panels show the results forthe differences in returns between upper and lower pre-performance ranked quartileportfolios with the t-test for differences described above. Panel A, describes the resultsfrom the Fama-French three factor (FF3F) model. Here, we find weakly significant (atthe 10% level) negative persistence in performance at the one month ranking andevaluation horizon, suggesting reversal in performance at this horizon. This findingmirrors that found at the same horizon for UK personal pension funds in Gregory andTonks (2005). Note, however, that this result is strengthened in the non-SRI sample,but there is no evidence of any reversal effect amongst ethical funds. At the threemonth horizon we see no persistence whatsoever, but from 6 months onwards positivepersistence starts to occur. This is present at 6 month (weakly) and 36 month horizonsfor SRI funds, but at 6, 12 (weakly) and 36 month horizons for non-SRI funds. The36 month horizon test seems to be preferred by Carpenter and Lynch (1999), and atthis horizon the difference in performance between upper and lower quartile SRI unittrusts is roughly double that of their non-SRI control group counterparts (0.396% permonth compared to 0.196% per month).

In Table 4, Panel B, we report results from the four factor or ‘Cahart’ model. Theresults are broadly similar to those reported under the three factor model, exceptfor the fact that performance persistence at the 6 month horizon is less significant.However, at the 12 month horizon persistence significance is greater for the non-SRIsample, whilst at the 36 month horizon SRI persistence is similar but the non-SRIgroup of funds exhibit strengthened persistence. Thus these tests, although ratherdifferent from those in Fletcher and Forbes (2002), do not suggest that persistence inperformance disappears under a four factor model.

Fletcher and Forbes (2002, Tables 1 and 2) show that conclusions with respectto performance can differ substantially depending on whether funds are ranked

15 This separate analysis is similar to that employed in Blake and Timmerman (1998, Table V).



Tab

le4

Perf

orm

ance

Pers

iste

nce

Test

s,D

omes

ticFu

nds

Ran

king

/Fu

llT-

test

SRI

T-te

stN

on-S

RI

T-te

stEv

alua

tion

Sam

ple

for

Sam

ple

for

Sam

ple

for

Peri

odD

iffer

ence

,D

iffer

ence

Diff

eren

ce,

Diff

eren

ceD

iffer

ence

,D

iffer

ence

Qu

–Q

l%Q

u–

Ql%

Qu

–Q

l%

Pan

elA

:Per

form

ance

Dif

fere

nce

Tes

tsB

ased

onP

revi

ous

Per

iod

Upp

eran

dL

ower

Qua

rtile

Per

form

ance

,Usi

ngth

eFa

ma-

Fren

chT

hree

Fact

orM

odel

1M1M

−0.3

48−1

.89

∗−0

.134

−0.8

8−0

.394

−2.1

5∗∗

3M3M

0.00

00.

000.

020

0.14

0.02

10.

156M

6M0.

259

2.87

∗∗∗

0.25

21.

82∗

0.25

12.

67∗∗

∗

12M

12M

0.23

62.

15∗∗

0.09

81.

120.

203

1.90

∗

36M

36M

0.30

39.

02∗∗

∗0.

396

5.19

∗∗∗

0.19

61.

99∗∗

Pan

elB

:Per

form

ance

Dif

fere

nce

Tes

tsB

ased

onP

revi

ous

Per

iod

Upp

eran

dL

ower

Qua

rtile

Per

form

ance

,Usi

ngth

eC

ahar

tFou

rFa

ctor

Mod

el1M

1M−0

.298

−1.7

2∗

−0.0

41−0

.27

−0.3

58−2

.00

∗∗

3M3M

0.02

00.

140.

058

0.42

0.03

90.

296M

6M0.

171

2.03

∗∗0.

221

1.47

0.15

61.

89∗

12M

12M

0.22

52.

64∗∗

∗0.

090

0.90

0.21

22.

89∗∗

∗

36M

36M

0.35

914

.50

∗∗∗

0.39

74.

08∗∗

∗0.

307

3.40

∗∗∗

Pan

elC

:Per

form

ance

Dif

fere

nce

Tes

tsB

ased

onP

revi

ous

Per

iod

Upp

eran

dL

ower

Qua

rtile

Per

form

ance

,Usi

ngA

bsol

ute

Ret

urns

1M1M

0.21

61.

080.

297

1.43

0.14

30.

763M

3M0.

129

0.68

0.29

51.

390.

115

0.69

6M6M

0.27

51.

79∗

0.42

52.

58∗∗

∗0.

192

1.29

12M

12M

0.10

60.

690.

184

1.03

0.09

80.

6736

M36

M0.

059

0.72

0.17

70.

87−0

.065

−1.2

0

Not

es:

Fund

sar

era

nked

into

quar

tiles

byab

norm

alpe

rfor

man

ce(o

rab

solu

tepe

rfor

man

cein

the

case

ofPa

nel

C)

inth

epr

evio

uspe

riod

and

the

subs

eque

ntab

norm

alpe

rfor

man

ce(o

rabs

olut

epe

rfor

man

ce,P

anel

C)

calc

ulat

edfo

rthe

follo

win

gpe

riod

.The

diff

eren

cebe

twee

npr

ior-

rank

edup

perq

uart

ile(Q

u)an

dlo

wer

quar

tile

(Ql)

perf

orm

ance

fund

sist

hen

eval

uate

dfo

rthe

subs

eque

ntpe

riod

,with

rank

ing

and

eval

uatio

npe

riod

sran

ging

from

1m

onth

(1M

1M)

to36

mon

ths(

36M

36M

).T

heT

-test

isa

t-sta

tistic

fort

hedi

ffer

ence

inpe

rfor

man

ceth

atal

low

sfor

over

lapp

ing

obse

rvat

ions

(see

text

).∗∗

∗ ,∗∗

,∗de

note

sign

ifica

nce

atth

e1%

,5%

and

10%

leve

lsre

spec

tivel

y.



and evaluated on the basis of absolute performance or performance relative to abenchmark.16 Accordingly, in Table 4, Panel C, we examine the effect of looking atabsolute returns (the excess returns on the fund minus the risk free return) as opposedto benchmarked returns. The quartile difference results reported are quite striking,and show that significant persistence in performance all but disappears when absolutereturns are used in both ranking and evaluation. The one exception is that there nowappears to be significant persistence in performance for SRI funds at 6 month horizons.At any longer horizon, the difference in returns between upper and lower quartiles isreduced (for example, for SRI funds this falls from 0.396% per month to 0.177% permonth) and becomes insignificant.

In Table 5 we report our results for the 12 international SRI funds and their non-SRI controls using the extended three factor model described in (2). The results inPanel A show less support for persistence than is seen in the domestic sample. Forthe full sample, weak significance is found only at the 36 month horizon. For theSRI sub-sample, there is negative persistence (i.e. reversal of performance) at the onemonth horizon, but no persistence in performance beyond that. In particular, the36 month horizon returns are insignificantly negative. By contrast, at the 36 monthhorizon international non-SRI funds exhibit persistence in performance not dissimilarfrom their domestic counterparts. When international fund performance is measuredin absolute terms (Table 5, Panel B) no significant persistence exists at any horizon.

Our final group of tests replicate the Cahart (1997)-style tests in Blake andTimmerman (1998, Table V) and Fletcher and Forbes (2002, Tables 3 and 4). Thesetests are also quartile tests, but unlike those reported above do not allow overlappingholding period returns to be evaluated. Instead, portfolios are formed based upon theprevious 12 month returns,17 using the previous 12 month excess returns (‘absolute’returns), abnormal returns from the three factor model, and abnormal returns fromthe four factor model. The excess returns for the month following portfolio formationcan then be estimated by applying the appropriate factor model (we use both threeand four factor models in the case of ranking by absolute prior returns). Note that thisapproach does not utilise individual fund ‘rolling’ abnormal returns as in the tests inTable 4, Panels A and B, but instead calculates the factor exposures for the calendar-timequartile portfolios. The regression methodology assumes these are fixed, which may bea questionable assumption given our evidence for our sample reported in Table 3 ontime-varying factor exposures.18 However, we report these results both for comparisonwith that of previous research and as a robustness check.19

In Panels A to B of Table 6 we show the results for domestic funds20 from thethree factor and four factor benchmark models respectively where funds are rankedby absolute performance in the previous 12 months. Under the three factor model,

16 In the case of their Table 2, this was the FTASI return.17 Note that Blake and Timmerman rank on the basis of prior 24 month abnormal performance.18 The returns could be modelled using a conditional Ferson and Schadt (1996) approach. However, our(unreported) results for these models does not suggest that variation in factor exposures occurs in responseto the Ferson-Schadt conditioning variables.19 Carpenter and Lynch (1999) did not directly investigate the power of such tests. However, they reportthat t-tests for differences in ranked portfolios that do not use overlapping observations are well-specifiedand powerful tests, although not as powerful as the t-tests for overlapping observations which we employ inTables 4 and 5.20 Note that we only report results for the domestic quartile portfolios, as F -tests for the regressions for theinternational SRI funds failed to be significant at conventional levels.



Tab

le5

Perf

orm

ance

Pers

iste

nce

Test

s,In

tern

atio

nalF

unds

Ran

king

/Fu

llT-

test

SRI

T-te

stN

ON

-SR

IT-

test

Eval

uatio

nSa

mpl

efo

rSa

mpl

efo

rSa

mpl

efo

rPe

riod

Diff

eren

ce,

Diff

eren

ceD

iffer

ence

,D

iffer

ence

Diff

eren

ce,

Diff

eren

ceQ

u–

Ql%

Qu

–Q

l%Q

U–

QL

%

Pan

elA

:Per

form

ance

Dif

fere

nce

Tes

tsB

ased

onP

revi

ous

Per

iod

Upp

eran

dL

ower

Qua

rtile

Per

form

ance

,Usi

ngth

eE

xten

ded

Thr

eeFa

ctor

Mod

el1M

1M−0

.231

−0.9

9−0

.590

−2.5

4∗∗

−0.1

39−0

.55

3M3M

0.26

51.

600.

054

0.41

0.23

51.

456M

6M0.

252

1.32

0.21

21.

450.

175

1.12

12M

12M

0.14

81.

130.

079

0.46

0.15

71.

5936

M36

M0.

215

1.76

∗−0

.112

−0.8

40.

226

5.11

∗∗∗

Pan

elB

:Per

form

ance

Dif

fere

nce

Tes

tsB

ased

onP

revi

ous

Per

iod

Upp

eran

dL

ower

Qua

rtile

Per

form

ance

,Usi

ngA

bsol

ute

Ret

urns

1M1M

0.29

01.

38−0

.254

−1.2

30.

382

1.64

3M3M

0.09

80.

650.

100

0.51

0.10

10.

656M

6M0.

044

0.26

0.02

50.

140.

029

0.18

12M

12M

0.00

90.

060.

209

1.36

−0.0

23−0

.11

36M

36M

−0.1

02−1

.13

−0.1

08−0

.83

−0.1

08−1

.14

Not

es:

Fund

sar

era

nked

into

quar

tiles

byab

norm

alpe

rfor

man

cein

the

prev

ious

peri

odan

dth

esu

bseq

uent

abno

rmal

perf

orm

ance

calc

ulat

edfo

rth

efo

llow

ing

peri

od.

The

diff

eren

cebe

twee

npr

ior-

rank

edup

per

quar

tile

(Qu)

and

low

erqu

artil

e(Q

l)pe

rfor

man

cefu

nds

isth

enev

alua

ted

for

the

subs

eque

ntpe

riod

,w

ithra

nkin

gan

dev

alua

tion

peri

ods

rang

ing

from

1m

onth

(1M

1M)

to36

mon

ths

(36M

36M

).T

heT

-test

isa

t-sta

tistic

for

the

diff

eren

cein

perf

orm

ance

that

allo

ws

for

over

lapp

ing

obse

rvat

ions

(see

text

).∗∗

∗ ,∗∗

,∗de

note

sign

ifica

nce

atth

e1%

,5%

and

10%

leve

lsre

spec

tivel

y.



Tab

le6

Reg

ress

ion

Test

sB

ased

Upo

nPr

ior-

peri

odR

anki

ngof

Perf

orm

ance

,Dom

estic

Fund

s

Fund

:Al

lSR

IFun

dsN

on-S

RIF

unds

Pan

elA

:Abs

olut

eR

etur

n3F

alph

a3F

alph

a3F

alph

ap-

valu

e3F

alph

a3F

t3F

alph

ap-

valu

e3F

alph

a3F

t3F

alph

ap-

valu

eQ

u0.

0029

1.86

∗0.

0038

1.83

∗0.

0021

1.51

Ql

−0.0

021

−1.4

6−0

.002

5−1

.64

−0.0

020

−1.4

7D

iff

0.00

502.

51∗∗

0.00

632.

94∗∗

∗0.

0041

2.21

∗∗

Pan

elB

:Abs

olut

eR

etur

n4F

alph

a4F

t-te

st4F

alph

ap-

valu

e4F

alph

a4F

t-te

st4F

alph

ap-

valu

e4F

alph

a4F

t-te

st4F

alph

ap-

valu

eQ

u0.

0023

1.54

0.00

311.

530.

0017

1.24

Ql

−0.0

016

−1.1

9−0

.002

3−1

.53

−0.0

015

−1.1

7D

iff

0.00

402.

15∗∗

0.00

542.

56∗∗

0.00

331.

84∗

Pan

elC

:3F

abno

rmal

Ret

urn

3Fal

pha

3Ft-

test

3Fal

pha

p-va

lue

3Fal

pha

3Ft-

test

3Fal

pha

p-va

lue

3Fal

pha

3Ft-

test

3Fal

pha

p-va

lue

Qu

0.00

161.

150.

0017

0.78

0.00

070.

57Q

l−0

.001

2−0

.97

−0.0

028

−2.0

1∗∗

−0.0

014

−1.1

2D

iff

0.00

271.

75∗

0.00

442.

36∗∗

0.00

211.

34

Pan

elD

:4F

Abn

orm

alR

etur

n4F

alph

a4F

t-te

st4F

alph

ap-

valu

e4F

alph

a4F

t-te

st4F

alph

ap-

valu

e4F

alph

a4F

t-te

st4F

alph

ap-

valu

eQ

u0.

0012

0.88

0.00

170.

810.

0003

0.25

Ql

−0.0

012

−1.1

7−0

.003

1−2

.24

∗∗−0

.000

8−0

.71

Dif

f0.

0024

1.74

∗0.

0048

2.61

∗∗∗

0.00

110.

8

Not

es:

Qu

and

Ql

deno

tepr

ior

perf

orm

ance

quar

tiles

(Qu

ishi

ghes

t,Q

lis

low

est)

.D

iff

is‘w

inne

r’(h

ighe

stqu

artil

epr

ior

perf

orm

ance

)m

inus

‘lose

r’(l

owes

tqu

artil

epr

ior

perf

orm

ance

)po

rtfo

liope

rfor

man

ce.P

rior

perf

orm

ance

isba

sed

upon

prio

r12

-mon

thab

solu

tere

turn

sin

Pane

lsA

and

B,p

rior

12-m

onth

abno

rmal

retu

rns

from

the

Fam

a-Fr

ench

thre

efa

ctor

mod

elin

Pane

lC

,and

prio

r12

-mon

thab

norm

alre

turn

sfr

omth

eC

ahar

tfo

urfa

ctor

mod

el(F

ama-

Fren

chfa

ctor

spl

usm

omen

tum

)in

Pane

lC.S

ubse

quen

texc

ess

retu

rns

are

then

regr

esse

don

the

Fam

a-Fr

ench

thre

efa

ctor

retu

rns

inPa

nels

Aan

dC

,and

the

Cah

artf

our

fact

orre

turn

sin

Pane

lsB

and

D.A

llre

gres

sion

sre

sults

repo

rted

have

sign

ifica

ntF

-test

sat

the

1%le

vel.



we find evidence of persistence in the ‘difference’ portfolio, with weak evidence ofout-performance in the upper-quartile portfolio. In general, our results look strongerfor the SRI sub-sample, with significant positive abnormal returns (at the 10% level)for the ‘winner’ portfolio and almost significant under performance for the ‘loser’portfolio. The difference in performance is highly significant. In passing, we note thatthese SRI funds exhibit significant exposure to small firms, and that in Blake andTimmerman (1998, Table V, Panel B) it is smaller company funds in particular thatappear to exhibit similar performance characteristics.21 The non-SRI sample show nosignificant abnormal returns in the individual quartile portfolios, though the differencein returns is significant, suggesting that winners continue to outperform losers. In PanelB of Table 6 we show that under a four factor model none of the individual quartileportfolios exhibits evidence of significant abnormal performance. To some degree, thisis similar to the result reported in Table 4 of Fletcher and Forbes (2002). However, thedifference in returns between upper and lower quartile funds is significant for all ofthe full sample and SRI and non-SRI sub-samples. Under either metric the differencein returns between top and bottom quartile performances is greater for the SRI samplethan the non-SRI sample, which is in line with our results in Table 4.22

In Panel C we investigate the effect of prior ranking and evaluation under the threefactor model. Under this model, the full sample exhibits weak persistence, althoughneither upper nor lower quartiles have significant out-performance. By contrast, thelower quartile of the SRI sub-group show clear underperformance, and the differencebetween upper and lower quartile performance is a significant 0.44% per month. Nosuch persistence is present in the non-SRI control group. Prior ranking and evaluatingon the basis of the four factor model (Panel D) shows a consistent result. Whilst theoverall abnormal returns and the returns for the non-SRI sub-sample become closerto zero and remain insignificant, the abnormal returns for the lower quartile of theSRI sub-group become slightly more negative and the significance of the difference inreturns between ‘winners’ and ‘losers’ becomes larger.

Taken as a whole, our findings on persistence suggest that investors in domesticfunds should be concerned with long-term past performance relative to a risk-adjustedbenchmark when attempting to draw conclusions about future performance, and thatat long horizons this appears to generate somewhat larger differences in return forSRI funds than for the control group of funds. However, we emphasise that whilstthey may appear economically significant, these differences are dependent on themethod used to investigate persistence, and with small samples we cannot ascertainunambiguously whether these larger abnormal returns are significantly different fromtheir non-SRI controls.23 Persistence seems to be present at longer horizons usingboth t-test for differences and regression tests. By contrast, we find weaker evidence ofpersistence in performance when absolute returns are used to rank and evaluate funds.These conclusions with respect to long-horizon persistence in performance amongstSRI funds do not appear to carry through to international funds, although the small

21 Our portfolios are equally-weighted, hence the comparison with Table V, Panel B, of the Blake andTimmerman results.22 We also re-ran these regressions using a simple CAPM benchmark, with the same conclusions.23 In particular, for our overlapping period t-test for differences, a Newey-West adjusted t-test for differencesbetween SRI and non-SRI funds, which involves going long is the ethical ‘difference’ portfolio (i.e. upperquartile minus lower quartile), and short in the non-SRI ‘difference’ portfolio, yields a value of t = 1.16.



number of such funds in existence with any long history of returns makes any definitiveconclusion on such performance persistence tentative.

5. CONCLUSIONS

Using UK style portfolios that are free of any survivorship bias we have examinedperformance and persistence in performance of UK ‘ethical’ or SRI funds, andcompared them to a control group of non-SRI funds. In addition, given the evidencethat many UK funds which claim to be international in nature may exhibit homebias in their portfolio allocations, we have proposed a measure for performance ofinternational funds that picks up this ‘home bias’, and we further show that suchrecognition has important implications for conclusion drawn on performance. Theseinitial tests show that SRI funds have less exposure to HML, but greater exposureto SMB and momentum factors. However, neither SRI nor non-SRI funds exhibitsignificant under performance on a risk/style adjusted basis, despite comparativelypoor absolute performance. In addition, we show that performance appears to betime-varying, and that conclusions on performance itself are influenced by whether astatic or time-varying model is employed. Last, we have examined persistence in fundperformance and find evidence that supports persistence. There are some differencesin performance persistence between SRI and non-SRI funds, but, as in Fletcher andForbes (2002) conclusions on the degree of persistence appear to depend on theperformance metric chosen. Nonetheless, it is unambiguously the case that domesticpast ‘winner’ SRI funds outperform ‘loser’ SRI funds at 36 month horizons on a risk-adjusted basis. In conclusion, although absolute returns are low and the risk/styleexposure of SRI funds, particularly domestic funds, looks very different to that ofordinary funds, our findings do not suggest that ethical investors lose out compared toordinary investors. Additionally, our results provide some evidence that ethical investorscan enhance risk/style adjusted investment performance in domestic funds by investingin past ‘winners’ and avoiding past ‘losers’.

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Documents

Performance and Performance Persistence of ‘Ethical’ Unit Trusts in the UK