An empirical investigation into the performance of UK pension fund managers

Transcription

1 An empirical investigation into the performance of UK pension fund managers By Andrew Clare, Keith Cuthbertson and Dirk Nitzsche, 1 Center for Asset Management Research Cass Business School, City University, London This version : 09 th January 2009 Abstract The UK s defined benefit pensions industry makes widespread use of pooled investment vehicles which are provided by a large number of fund management groups. In this paper we provide the first comprehensive performance analysis of these funds. Using data on 734 pooled funds, that had a combined value of just over 400bn at the end of 2007, ranging from UK equity to funds specialising in Pacific Basin equities, we found almost no statistically significant evidence that the managers of these funds generate alpha, or can time the market. With increasing numbers of UK fund managers purporting to be able to provide high alpha products to the UK s beleaguered pensions industry, our results do not give us great confidence that the solution to the widespread deficits lies in the hands of the UK s active institutional investment managers. Keywords: UK pooled pension fund performance, Henriksson and Merton, Treynor and Mazuy market timing test, non-parametric performance test. JEL Classification: C15, G11 1 Corresponding Author: Dr Dirk Nitzsche, Cass Business School, City University, 106, Bunhill Row, London, EC1Y 8TZ. Tel. : +44-(0) d.nitzsche@city.ac.uk. Electronic copy available at:

2 1. Introduction Much of the academic literature on performance measurement has focused on the persistence of returns or upon the market timing abilities of managers of US mutual funds. This work has its roots in the early work of Jensen (1968) and has been extended by Carhart (1997) and Wermers (2003), among many others. On the whole the results of the vast majority of the studies in this area suggest that managers of US mutual funds neither display market timing ability, nor can they perform on a consistent basis over time. In the UK, studies by Blake and Timmermann (1998), and by Giles, Wilson and Worboys (2002) find that positive performance persistence is absent amongst managers of UK mutual funds (known as unit trusts in the UK), though studies do indicate that negative performance persistence is common. Our focus in this paper is on the performance of managers of UK pooled pension fund assets. Far fewer studies with respect to this aspect of the investment management industry have been conducted. Given the importance of the UK s occupational pension industry, it is perhaps a little surprising that so little work has focused on the performance of the fund managers charged with managing these assets. According to the last ONS release on the issue, the market value of the longterm assets held by the UK s pension funds is just over 800bn, representing about 80% of the annual GDP of the UK economy. At the same time, the pension assets that comprise these pooled funds had a market value of around 400bn (BNY Russell Mellon CAPS survey). This means that they account for around half of all pension assets in the UK. The need to focus on this important industry is perhaps even more crucial at the moment, given that the vast majority of Defined Benefit scheme s are in deficit. Watson Wyatt recently estimated that the combined deficit of the UK s defined benefit pensions industry is 130bn, which implies that the average UK scheme is now facing a deficit of around 15%. Some studies have been conducted that have looked at the performance of individual pension schemes. Brown, Draper and McKenzie (1997) investigated the performance persistence of UK pension fund managers 2. Using a sample of As opposed to the unitised vehicles themselves that make up these assets which we study here. 1 Electronic copy available at:

3 individual company pension schemes between 1986 and 1992 and another consisting of 409 funds from 1986 to 1992 that retained the same manager over these samples, Brown et al concluded that there was limited evidence that the managers were able to achieve a persistent performance. A result, broadly in keeping with persistence studies conducted using alternative fund manager universes. Blake, Lehmann and Timmermann (1999) examined the asset allocation decisions of 364 individual, UK company pension schemes using data that spanned the period from 1986 to The criterion they used in identifying the sample was that each fund should have been managed by the same manager over this period, and that this manager should also have been responsible for the asset allocation of the fund over this uninterrupted period, in other words these were balanced mandates. Using this sample Blake et al found surprisingly little variation in the performance of these schemes, or in the strategic asset allocation decisions that they made over time. In addition they found that the vast majority of time variation in returns was due to the strategic asset allocation decisions, very little of the variation was due to stock selection. They concluded that the empirical regularities that they observed were most likely due to the legal and economic environments under which these managers operated, for example, by the fact that fees were paid on the basis of assets under management rather than on performance. Perhaps their most damning conclusion was that - most funds 3 would have been better off with their strategic asset allocation decisions placed in passive index funds rather than paying for active fund management. Using the quarterly returns on a much larger sample (2,175) of segregated UK pension schemes spanning the period from 1983 to 1997 Thomas and Tonks (2001) investigate the performance of UK equity portfolios managed by investment managers, in contrast to the performance of the balanced portfolios investigated by Blake et al. Thomas and Tonks conclusions were consistent with those of Blake et al. The variety of techniques used to assess the quality of fund performance all suggested a very narrow cross-sectional dispersion in returns, which suggested that 3 By which they meant company schemes. 2

4 the managers were all closet trackers. They also conclude that on the whole there were negative returns to both selectivity and to market timing. Finally, Tonks (2005) suggests that the results of Brown et al and of Blake et al, might suffer from survivorship bias, since both studies impose the restriction that the pension scheme examined should have the same manager over the sample period. Instead, Tonks looks at the performance of pension funds irrespective of whether the management changed over the 1983 to 1997 sample period used. Examining the performance of 2,175 UK equity funds over this sample, Tonks found evidence to suggest that there was indeed performance persistence at least at the one year horizon. In this paper we investigate the market timing ability of the UK s pension fund managers, but from a different perspective than previous studies. Rather than looking at the performance of individual pension schemes over time, we instead focus on the performance of the pooled investment funds offered by the UK s investment managers. The alternative focus of the paper reflects a change in the style of pension fund management over the last twenty years or so. Over this period pension funds have made increasing use of the pooled funds offered by investment managers. Because of this the performance of these funds is now of much more relevance to pension fund trustees than would have been the case during the sample periods studied by previous authors. In investigating this aspect of the industry it also means that we do not have to be concerned about changes in the fund management houses since, by definition, they do not change over time. In addition, our results encompass the turbulent equity market periods following the collapse of the high tech bubble in 2000, whereas data used in previous studies end prior to this period. The rest of this paper is organised as follows: in Section 2 we describe the methodology that we employ to investigate the market timing ability of UK pension fund managers; in Section 3 we describe the data used in the study; our results are presented in Section 4; and finally, we conclude the paper in Section 5. 3

5 2. Fund data and performance statistics Because pension funds are exempt from taxation in the UK the funds studied in this paper have been designed to match the specific legal and tax requirements of pension funds. They are not made available to retail investors, although they are made available to certain other institutional investors, such as charities. However, in essence these funds are analogous to the mutual funds made available to retail and other investors, although they are managed separately and are ring-fenced from all other assets that a fund manager might manage, either on their own behalf or for any third parties. The Russell Mellon CAPs survey is the industry-standard source for performance information for the UK s pooled pension fund sector, and the source of the data for this study. For a fund to be included in the database, it must be available to UK institutional investors. The survey monitors and provides quarterly performance information on the following equity funds: UK, North American, European (ex UK), Japanese, Pacific and Global/Overseas equities. The purpose of the Russell Mellon CAPs survey is to display the up-to-date performance of each of the various pooled fund by investment category. The returns in the survey are based upon timeweighted rates of return calculated on both a net and gross basis. Returns are calculated both net of fees from the fund offer prices and, where appropriate, income distributions as quoted by the participating fund management companies. Returns are also calculated gross of fees by adding the charges made against the fund back into the net performance record. However, since pension funds are interested in the returns net of fees, we use net rather than gross returns below. Finally, all the returns are denominated in sterling. The data consist of the quarterly returns on an initial sample of 734 pooled equity funds between March 1980 to December Of the 734 equity funds 459 were still available for investment at the end of our sample in December 2004, whereas 275 were classified as dead funds at this date. Only those funds with at least 12 consecutive observations (three years of data) were included in the study of market timing. 593 of the 724 funds met this criteria. The average number of observations 4

6 of funds with more than 12 observations was quarters, just over 10 years. Summary information about the sample is shown in table 1. [Table 1 HERE] The summary statistics represent the averages of individual excess fund returns. The quarterly mean excess return of all funds is % per quarter or 1.556% p.a. It varies considerably between different investment categories, ranging from % p.a. for Japan and 5.242% p.a. for UK Smaller Companies. The variation of the individual fund returns is quite large averaging for all funds. Overall our data also suggest that the fund returns are not normally distributed and have an average value for the normality test for 6.53 and being as high as for the Overseas Equity category. In addition to the summary statistics presented in table 1 we have also calculated a series of performance-related statistics for the funds in our sample to provide a fuller picture of the risk and return characteristics of these pooled investment vehicles. We believe that this is the first of such analysis of these investment vehicles. In our analysis we use all of the equity-based pooled funds, as can be seen in table 1, these invest in a range of geographic areas. For each geographic region we have used the appropriate MSCI index measured in pounds sterling as the benchmark. These results are reported in tables 2, 3 and 4. [Table 2 HERE] Table 2 details the average performance of these funds, relative to their benchmark over two, three and five year non-overlapping samples, and also relative to the rate of return on cash, represented by the return on a UK government three month T-bill. For some time periods and investment horizons these excess returns are positive, suggesting some substantial outperformance on average. However, these excess returns are not statistically significant; the standard deviation of these returns is large as reported in the last column of table 2. [Table 3 HERE] 5

7 In table 3 we report the results of CAPM-based regression analysis where the pooled fund returns have been regressed against their relevant benchmark index, over the full twenty year sample and over five and ten year sub-samples. We can see that very few funds generate a positive statistical alpha (less than 10% of the funds) as shown in column 4. Over the full sample for which we have uninterrupted data on 42 funds, we found that only 3 of these funds had an alpha which was both positive and statistically significant at conventional levels of confidence. In fact over each sample we found that almost as many funds produce a statistically significant negative alpha as do a positive and statistically significant one. These results are shown in column 5 of table 3. The final three columns of this table show the arithmetic mean alpha, beta and R-squareds for all funds from the regression estimates. The average alpha is 0.2% p.a. and is statistically significant from zero a 10% level of significance with a test statistic The alphas of four of the six subperiods are statistical significance at least at the 5% level of significance. The average beta is just under 0.96 for the whole sample period and does not change much for the different subperiods. The results in Tables 2 and 3 show that there is limited evidence that these pooled funds outperform their benchmarks. We extend this analysis now to see how persistent the returns on these funds are. Table 4 reports performance persistence results for these pooled pension funds based on contingency analysis. This analysis requires us to rank all funds by their performance in one period so that they can be divided into winners and losers. We then examine whether they remain as winners/losers over subsequent investment periods. We define a winner fund as one that beats its benchmark over the chosen horizon and a loser as one that underperforms its benchmark. The number of repeat winners ranges between 44% for the 20 quarter horizon to almost 52% for the 12 quarter investment horizon. Overall these results indicate that roughly 50% of the funds beat the benchmark, a statistic that one would expect by chance alone. 6

8 Brown and Goetzmann (1995) suggest the following log odds ratio as a measure of the significance of performance persistence: (1a) log-odds ratio = ln ( ww)( ll) ( wl)( lw) where w represents a winner fund (i.e. one that has outperformed its benchmark) and l a loser fund (i.e. one that has underperformed). The standard error of the log-odd ratio statistics can be calculated as: (1b) s.e. = ww ll wl 1 + lw The logs odds ratio is shown in column 5 of table 4. In column 5 we present the test statistic for the log odds ratio using the standard error shown above. This test statistic shows no evidence of performance persistence amongst any of the pooled pension funds. We do however find some evidence for persistence for some specific time periods. Column 6 reports the proportion of individual log odds tests that are statistically significant for specific investment horizon analysis. It suggests that there may be certain time periods, that is, certain market conditions when more funds do better than usual. 3. Parametric and Non Parametric Tests of Market Timing Standard statistical tests for market timing have been proposed by Henriksson and Merton (1981) (HM) and by Treynor and Mazuy (1966) (TM) for market timing 4. The tests are based on regression analysis and are extensions of the CAPM. They assume that the manager s timing ability is dependent upon the relevance of their information about the market. HM estimated the following model: (2a) r i,t+1 = α i + β i r m,t+1 + γ i D t+1 r m,t+1 + ε i,t+1 4 By market timing these researchers mean the ability of the manager to increase (decrease) the risk exposure of their fund as the market is rising (falling) within an asset class, rather than between asset classes, for example, by switching from equities into bonds etc. 7

9 whereas TM used the following regression model: (2b) r i,t+1 = α i + β i r m,t+1 + γ i (r m,t+1 ) 2 + ε i,t+1 where r i is the excess return of fund i, r m is the excess return on the market and D is a dummy variable which takes the value 1 if the excess return on the market is greater than zero, and 0 otherwise. The intuition behind these regression-based tests is that fund managers who do time the market, generally increase their exposure to the market prior to the market going up, and reduce exposure prior to a decline. This market timing ability is captured by the second term in equations (2a) and (2b). Abrevaya and Jiang (2005) and Jiang (2003) have suggested an alternative, nonparametric, test for market timing. Based on the CAPM, a fund manager with market timing ability would maintain a higher beta between period 2 and 3 compared to period 1 and 2, if r m,1 < r m,2 < r m,3 for any triplet {r m,1, r m,2, r m,3 }. The beta for only two observations is calculated as (r i,t2 r i,t1 )/ (r m,t2 r m,t1 ). This implies that when the triplets are ordered from the smallest excess market return to the largest value and the fund manager has market timing ability, one would expect that: r r i, t3 m, t3 r r i, t 2 m, t 2 > r r i, t 2 m, t 2 r r i, t1 m, t1 Using probabilities, a summary statistic of market timing ability can be expressed as follows (see Jiang, 2003): ri, t3 ri, t 2 ri, t 2 ri, t1 (3) θ = 2Pr > 1 rm, t3 rm, t 2 rm, t 2 rm, t1 θ measures the probability that the fund return forms a convex relationship with the market return. For a sample the analogue of θ is a U-statistic 5, with Kernel of order 3: 5 The test statistics of the triplet q follows a third order U distribution. Using the asymptotic results from this distribution it is possible to derive a test statistic which is asymptotically normally distributed. 8

10 (4) ˆ n rit, rit, rit, rit, θn = sign > 3 rmt, < r 1 mt, < rmt, r mt, r mt, 2 r mt, 2 r mt, 1 where n is the number of observations and sign(.) is the sign function taking the values -1, 0 or 1, if the argument is negative, zero or positive respectively. Abrevaya and Jiang (2005) derive a consistent estimator of the standard error of θˆ as: (5) 1 n 2 9 n ˆ σ ˆ = hz ( ˆ t, z, ) 1 t z θ n 2 t θ 3 n n t1= 1 2 t2< t3, t1 t2, t1 t 3 2 rit, rit, rit, rit, hz (, z, z) = sign > r < r < r rmt, r 3 mt, r 2 mt, r 2 mt, with t1 t2 t 3 m, t1 m, t2 m, t3 Jiang (2003) also shows that this unconditional test can be used for a conditional model where r i,t = f i (x) and r m,t = f m (x) and x are some macroeconomic variables which have some forecasting ability, such as the dividend yield, interest rates or the yield curve. Instead of using r i and r m in equations (4) and (5) we have to use the unexplained excess returns. For the baseline model which uses the unconditional excess returns we would estimate the following regression model: r(i) = a(i) + e(i). The unexplained excess returns are the residuals from a regression model where the excess returns are regressed on a number of explanatory variables, such as the dividend yield and short term interest rates. By using these returns instead of the unconditional excess returns we can test the success of market timing using riskadjusted returns. 4. Market Timing Results Tables 5 and 6 present the regression results from the Henriksson and Merton and Treynor and Mazuy parametric market timing models. All statistics are averages of all the individual regression results. Market timing is measured by the gamma coefficient, the coefficient on the squared excess return of the market (TM) or the The consistent estimator of the variance is given in equation (5). For a more detailed discussion of this test and test statistic see Abrevaya and Jiang (2005). 9

11 positive excess market return variable (HM). The average explanatory power of either one of those two market timing models is very high with over 87% of the variation explained by the excess market return and the market timing variable. However for individual funds the explanatory power can be as low as 25%. Comparing the R 2 of the market timing models with the CAPM we see only a slight improvement of the predictive power, less than 5%. The other statistics reported in tables 5 and 6, are very similar which indicates that there is not much to choose between these two different tests of market timing. The last two columns report the percentage of funds whose market timing coefficient is statistically significant at the 95% confidence level. Considering all equity funds the statistics are 4.89% and 3.54% for the Henriksson and Merton model for negative and positive significant gammas respectively. For the Treynor and Mazuy model the statistics are 7.59% (negative) and 4.05% (positive). Overall, we can conclude that there is not much evidence of successful market timing as these results are only slightly higher than what one would expect due to pure chance (2.5% using the normal distribution). [Table 5 HERE] [Table 6 HERE] The results from the non-parametric market timing test as suggested by Jiang (2003) are reported in table 7. The results for the unconditional model (i.e. excess returns) and the results from the conditional model, where the conditioning variable is the dividend yield are very similar. Using excess returns on 335 out of 593 funds reveals a significant market timing test statistic for only 8 funds at 95% confidence level. 258 funds had negative test statistics with 11 being statistically significant. This means that less than 5% are significant which indicate that the managers of these funds are not able to time the market. The results from the conditional model using the dividend yield as the conditioning variable are very similar to the results from the unconditional model funds have a positive test statistic and 272 a negative. The number of statistical significant funds are 17 (positive) and 6 (negative). This is almost the other way around than when returns have been used, but still within 95% confidence. 6 Alternative conditioning variables have also been used, such as short term interest rates and the yield spread. The results are qualitatively similar and therefore not reported here. They are available from the authors on request. 10

12 [Table 7 - HERE] Looking at the individual funds we find that only six funds have a positive θ value, using both the unconditional and conditional non-parametric market timing test. Three of those funds have a statistical significant gamma from both of the regression based tests. Another fund has a θ test statistic of 1.81 using the non parametric test and statistical significant statistics using the other 3 tests. All funds that reveal positive market timing using all 4 tests are UK equity funds. Four funds, with negative market timing ability, have statistical significant negative values using both versions of the non-parametric market timing test. Three of these funds also show statistical significant negative γ s using the parametric test. None of those funds belong to the UK Equity classification. Overall, our results for UK pension funds are very similar to the findings Jiang (2003), who similarly found virtually no evidence of significant market timing. Whatever it is that these fund managers are doing, they do not seem to be able to add value to the funds by timing the market. 5. Conclusions In this paper we have presented the first comprehensive performance analysis of the pooled pension funds offered to UK pension schemes in the UK. These vehicles comprise a significant proportion of total Defined Benefit (and Defined Contribution) pension assets and therefore they deserve attention. They are the UK pension world s equivalent of the retail sector s unit trusts. Although the performance of UK unit trusts has been undertaken in the past, the performance of their pension industry equivalents may differ for any number of reasons. For example, the returns (income and capital gains) from these unitised, pooled pension funds are not subject to tax. Furthermore, in many investment firms the retail and institutional funds are run by different managers. First, using a range of different methodologies and tests we find little evidence of positive performance persistence. The implication of this result is that pension schemes may be better off in the long-term investing in passive investment vehicles with their lower associated fees than in their active equivalents. That is, investing to 11

13 achieve beta and not paying for alpha which seems illusive. Second, using the non-parametric test for market timing ability suggested by Jiang (2003) we find virtually no evidence that that the managers of these pooled equity funds can time the market. Our results were not materially affected by the use of a conditional version of the test. These results were confirmed by the more traditional, regressionbased tests of market timing ability suggested by both Treynor and Mazuy (1966) and Henriksson and Merton (1981). Taken together these results suggest that pension fund trustees looking to enhance the value of scheme assets by choosing actively managed pooled investment vehicles rather than say passively managed equivalents, may ultimately be disappointed with the outcome. The ability to add alpha and to time the market is a crucial element of adding value to any investment portfolio. We find no evidence here that these pooled vehicles offer such return enhancement. 12

14 References Abrevaya, J. and W. Jiang, 2005, A Non Parametric Approach to Measuring and Testing Curvature, Journal of Business and Economic Statistics, Vol. 23 (1), pp Blake, D. and A. Timmermann, 1998, The Birth and Death Processes of Mutual, European Finance Review, Vol. 2, pp Blake, D., B. Lehmann and A. Timmermann, 1999, Asset Allocation Dynamics and Pension Fund Performance, Journal of Business, Vol. 72, pp Brown, S.J. and W.N. Goetzmann, 1995, Performance Persistence, Journal of Finance, Vol. 50 (2), pp Brown, G., P. Draper and E. McKenzie, 1997, Consistency of UK Pension Fund Performance, Journal of Business Finance and Accounting, Vol. 24, pp Carhart, M., 1997, On Persistence in Mutual Fund Performance, Journal of Finance, Vol. 52, pp Giles, T., T. Wilsdon and T. Worboys, 2002, Performance Persistence in UK Equity : An Empirical Analysis, Report No. D , Charles River Associates. Henriksson, R. and R.C. Merton, 1981, On Market Timing and Investment Performance: Statistical Procedures for Evaluating Forecasting Skills, Journal of Business, Vol. 54, pp Jensen, M., 1968, The Performance of Mutual in the Period , Journal of Finance, Vol. 23, pp Jiang, W., 2003, A Non Parametric Test of Market Timing, Journal of Empirical Finance, Vol. 10, pp Thomas, A. and I. Tonks, 2001, Equity Performance of Segregated Pension in the UK, Journal of Asset Management, Vol. 1, pp Tonks, I., 2005, Performance Persistence of Pension-Fund Managers, Journal of Business, Vol. 78 (5), pp Treynor, J., and K. Mazuy, 1966, Can Mutual Outguess the Market, Harvard Business Review, Vol. 44, pp Wermers, R., 2003, Is Money Really Smart? New Evidence on the Relation Between Mutual Fund Flows, Manager Behaviour, and Performance Persistence, Mimeo, University of Maryland. 13

15 Table 1: Summary Statistics Only funds with a minimum of 12 observations have been included in the analysis. The maximum number of observations available for each fund has been used. The whole sample period is from June 1980 to December 2004 (99 observations). All statistics are means values of the corresponding statistics for each fund. The number of funds used to calculate the mean are given in column 2. The normality test, reported in the last column is χ 2 distributed with 2 degrees of freedom. The critical value at 95% confidence is Fund Group Number of Mean Return Averages (excess return, quarterly data) SD Skewness Kurtosis Normality Test UK Equity UK Smaller Companies North America Europe (excl. UK) Europe (including UK) Japan Pacific (excl. Japan) Pacific (including Japan) Overseas Equity Global Equity ALL

16 Table 2 : Performance of Pension (1980Q1 to 2004Q4) This table reports the performance of all equity funds in the funds described in Table 1 over a range of non-overlapping investment periods. The performance benchmark for each fund is the appropriate MSCI country/region index. All returns are quarterly returns. The mean excess return in column 4 is calculated as the fund s return less the risk free rate. Column 6 reports the percentage as well as the absolute number of funds (in brackets) over each investment horizon that outperform their benchmarks, while column 7 reports the average standard deviation of fund returns in relative to their benchmark. Sample start Sample end Panel A : 2 year investment period Number of Mean Excess Return of Mean Fund Return less Benchmark Number of beating benchmark SD Fund Return less Benchmark 1980Q2 1982Q % (14) Q2 1984Q % (34) Q2 1986Q % (32) Q2 1988Q % (44) Q2 1990Q % (74) Q2 1992Q % (102) Q2 1994Q % (158) Q2 1996Q % (104) Q2 1998Q % (74) Q2 2000Q % (187) Q2 2002Q % (203) Q2 2004Q % (223) Sample start Sample end Panel B : 3 year investment period Number of Mean Excess Return of Mean Fund Return less Benchmark Number of beating benchmark SD Fund Return less Benchmark 1980Q2 1983Q % (18) Q2 1986Q % (29) Q2 1989Q % (34) Q2 1992Q % (106) Q2 1995Q % (136) Q2 1998Q % (88) Q2 2001Q % (162) Q2 2004Q % (212) Sample start Sample end Panel C : 5 year investment period Number of Mean Excess Return of Mean Fund Return less Benchmark Number of beating benchmark SD Fund Return less Benchmark 1980Q2 1985Q % (14) Q2 1990Q % (50) Q2 1995Q % (95) Q2 2000Q % (90)

17 Table 3 : Summary Statistics of CAPM Estimation This table reports mean values for summary statistics from CAPM regressions for different sample periods for the funds described in Table 1. The sample periods are indicated in column 1 and 2. In column 3 we report the number of funds available for analysis over each horizon; columns 4 and 5 report the proton of funds with significant positive and negative alphas respectively; while columns 6 7 and 8 report average alphas, betas and regression R-squareds respectively. The number reported in parenthesis below the mean of the alpha is the standard error of the statistic and the stars denote statistical significance at the 1% (***), 5% (**) and 10% (*) level. Panel A : 5 years period SMPL Start SMPL End Number of funds significant positive significant negative Mean alpha (std. error) Q Q (0.1142)* Q Q (0.0793)*** Q Q (0.0764) Q Q Mean Beta Mean R- square Panel B : 10 years period (0.0584)*** SMPL Start SMPL End Number of funds significant positive significant negative Mean alpha (std. error) Q Q (0.0913)*** Q Q Mean Beta Mean R- square Panel C : 20 years period (0.0671)** SMPL Start SMPL End Number of funds significant positive significant negative Mean alpha (std. error) Q Q Mean Beta Mean R- square (0.1226)* 16

18 Table 4 : Persistence of Pension Fund Performance (Contingency Tables) This table reports summary statistics based on contingency analysis of the funds described in Table 1. are categorized as being either winner or losers. A winner fund is one that beats its benchmark over the chosen horizon and a loser as one that underperforms its benchmark. Column 3 shows how many repeat winners there are over each horizon. Columns 4 and 5 present the logs odds ratio and the test statistic for this ratio, which tests for the presence of persistence in fund returns (see Brown and Goetzmann (1995)). Column 6 reports the proportion of log odds tests for each individual fund that are statistically significant for specific investment periods. Length of period of persistence Number of time periods Repeat Winners Overall Statistics Log Odds Ratio Test Statistic Number of significant periods measured by log odds ratio test 1 Quarter % 2 Quarters % 4 Quarters % 6 Quarters % 8 Quarters % 10 Quarters % 12 Quarters % 15 Quarters % 20 Quarters % 17

19 Table 5 : Parametric Market Timing Test (Henriksson and Merton Models) This table reports the results from the Henriksson and Merton parametric market timing test. All the regression results are averages using the individual regression results. We have used the longest available sample period for each fund for the estimation. Column 3 shows the average length (in quarters) for the different subgroups. The last two columns show the percentage of funds with a significant γ coefficient which indicated market timing. A minimum number of 12 observations were required in order for the fund to be included in the analysis. Fund Group Number of funds Number of obs. (quarters) Average Statistics α t α β T β γ t γ R 2 Neg. sig. γ (%) Pos. sig. γ (%) UK Equity UK Smaller Companies North America Europe (excl UK) Europe (including UK) Japan Pacific (excl Japan) Pacific (including Japan) Overseas Equity Global Equity All

20 Table 6 : Parametric Market Timing Test (Treynor and Mazuy Models) This table reports the results from the Treynor and Mazuy parametric market timing test. All the regression results are averages using the individual regression results. We have used the longest available sample period for each fund for the estimation. A minimum number of 12 observations were required in order for the fund to be included in the analysis. Fund Group Number of funds Number of obs. (quarters) Average Statistics α t α β t β γ t γ R 2 Neg. sig. γ (%) Pos. sig. γ (%) UK Equity UK Smaller Companies North America Europe (excl. UK) < Europe (including UK) Japan Pacific (excl. Japan) Pacific (including Japan) Overseas Equity Global Equity All

21 Table 7 : Non Parametric Market Timing Test This table reports the results from the non parametric test proposed by Abrevaya and Jiang (2005). Panel A shows the market timing statistic using the returns whereas panel B reports the results where the test is being performed on the residuals using a dividend yield model. Columns 4 and 6 report the number of funds which show a positive (negative) test statistic. The number of statistical significant funds are being reported in the following column (column 5 for positive funds and column 7 for negative funds). Fund Group Number of Mean θˆ # θˆ > 0 # sig. (5%) # θˆ < 0 # sig. (5%) Panel A : Returns UK Equity UK Smaller Companies North America Europe (excl. UK) Europe (including UK) Japan Pacific (excl. Japan) Pacific (including Japan) Overseas Equity Global Equity ALL Panel B : Residuals (using the dividend yield model) UK Equity UK Smaller Companies North America Europe (excl. UK) Europe (including UK) Japan Pacific (excl. Japan) Pacific (including Japan) Overseas Equity Global Equity ALL