Funds - Australian Centre for Financial Studies

Evaluation of the Performance of Australian Retail Funds

Using Time-Varying Alphas and Betas

Tariq Haque

1

Department of Finance

The University of Melbourne

Melbourne, Australia

ABSTRACT

Jensen (1968) used an unconditional CAPM model to evaluate fund performance, which was subject to criticism because of the assumption that a fund’s exposure to common risk factors was constant through time. Later, Ferson and Schadt (1996), and Christopherson et al. (1998) extended the traditional unconditional model to develop conditional models that incorporate publicly available information to enable a fund’s time varying exposure to risk factors to be modelled. In this paper, we have estimated these conditional time varying beta, and time varying alpha and beta models based on a number of alternative benchmarks and public information variable sets in order to evaluate Australian retail fund performance over the period June 1981 to December 2001. The data used is extracted from the Morningstar

Australian Funds Database. Our results indicate that the conditional alphas are more negative than unconditional alphas which is contradictory to the previous findings of Sawicki and Ong

(2000), Ferson and Schadt (1996) and many others. The public information variables used are both statistically and economically significant, and control for biases in the unconditional models. It is thus expected that our conditional alpha estimates which in addition are free from survival bias, would provide more accurate estimates of the alphas obtained by

Australian retail funds than has been previously documented.

1 I am a Senior Tutor and a Ph.D Research Student in the Department of Finance, The University of Melbourne,

Parkville, Victoria, Australia 3052. For correspondence please use my E-mail: tariq@unimelb.edu.au

1

1.0

INTRODUCTION

Obtaining accurate measures of fund performance remains an unsolved problem despite many attempts by academics and practitioners. The traditional CAPM is usually used to evaluate fund performance, and is known to suffer from biases. This is because it is based on the assumption that a fund’s exposure to risk is constant over time. But, in reality a fund’s exposure to risk does vary over time, and hence any estimates based on the traditional unconditional model are likely to produce biased results. However, Breen et al. (1989) argued that public information could be used to control such biases. It is now well known that fund returns are predictable and using public information variables such as interest rates and dividend yields can be used to model time variation in risk premia.

2

Chen and Knez (1996), Ferson and Schadt (1996), and Christopherson et al. (1998) strongly push for the use of conditional models on two grounds: (i) to avoid problems with traditional measures, which cannot deal with the dynamic behaviour of fund returns; and (ii) to avoid analysis of the trading behaviour of managers, which are also very complex and dynamics.

The conditional models are widely used in the USA and Europe to evaluate fund performance, but are not commonly employed in Australia.

3

The main purpose of this paper is to evaluate retail funds by estimating fund alphas, using conditional time varying beta, and time varying alpha and beta models that incorporate public information.

In this paper, we have investigated monthly returns data for 75 surviving and collapsed funds, randomly selected from the Morningstar Australian Funds Database and that were also in existence for some period in the interval from June 1981 to December 2001. We have estimated two traditional unconditional models as well as eight conditional models based on

Australian and USA benchmarks and Australian and USA information variables. Our results indicate that Australian funds should be evaluated against an Australian benchmark, but that either Australian or USA information variables can be used to estimate conditional models on the grounds of goodness of fit. As a result, we only concentrate our discussion of conditional models that use an Australian benchmark with Australian or USA public information variables. Indeed, introduction of these information variables changes the estimated performance of many funds. There are more negative than positive unconditional alphas, which is consistent with Jensen (1968), Ferson and Schadt (1996) and many others. More importantly, our conditional models produce even more negative alphas than seen in the unconditional models, for our sample data, which is contradictory to many previous studies such as Sawicki and Ong (2000), Ferson and Schadt (1996) and many others

4

. Our main result is that for Australian investors with an Australian benchmark there are relatively many funds focusing on investment in Australian stocks only that have negative rather than positive alphas. We still believe that our alpha estimates are more accurate than any other previous

Australian estimates on the grounds that our estimates are survival bias free, and obtained from conditional time varying beta, and time varying alpha and beta models and based on a

2 A number of authors such as Ferson and Harvey (1991), Fama and French (1992), Evans (1994) and many others support this proposition.

3 See Sawicki and Ong (2000).

4 Note that these authors used only surviving a fund, which implies survivorship bias, and probably because of this their estimated alphas are shifted to the right.

2

number of alternative benchmarks with various public information sets and with a new and comprehensive data set.

The outline of the paper is as follows. A review of the literature on the evaluation of retail funds is given in Section 2. The data used in the present study is described in Section 3. Some methodological issues and development of conditional models are provided in Section 4 and

Section 5 respectively. Empirical illustrations are presented in Section 6, while some concluding remarks and limitations are made in the final section.

2.0

REVIEW OF LITERATURE

A brief review of literature on the evaluation of investment funds is presented in this section.

Measurement of fund performance has a long history to find the answer to the question of whether fund managers can deliver returns in excess of appropriate benchmarks. Research into fund performance began with the early work of Cowley (1933). The subject did not attract much theoretical attention until Markowitz (1952), which has further regained its importance following the works of Treynor (1965), Sharpe (1966), Jensen (1968), Grinblatt and Titman (1992, 1993), Brown and Goetzman (1995), Elton et al. (1993, 1996a, 1996b),

Malkiel (1995), Ferson and Schadt (1996), Gruber (1996), Carhart (1997), Ferson and Schadt

(1996), Cai et al. (1997), Christopherson, Ferson and Glassman (1998), Blake and

Timmermann (1998), Edelen (1999), Zheng (1999), Wermers (2000), Dalhlquist et al. (2000),

Brown et al. (2001), and Otten and Bams (2002).

Investigations of Australian fund performance started with Bird, Chen and McCrae (1983), who analysed 380 Australian superannuation funds and their managers (15 managers), using

Treynor (1965), Sharpe (1966) and Jensen (1968) risk-adjusted measures. They found that for the entire period the superannuation funds performed poorly compared to the benchmark, being the Australian market portfolio, and only one fund manager had a positive Jensen’s alpha whose significance was not reported. Robinson (1986) examined the investment performance of 67 Australian unit trusts and 9 mutual funds for the period 1969 to 1978, using the Treynor, Sharpe and Jensen risk-adjusted performance measures and found that the average performance of funds outperformed the market index for the period under consideration. While Sinclair (1990) focused on the market-timing ability of 16 Australian pooled superannuation funds over the period June 1981 to December 1987 using the market timing-models of Henriksson and Merton (1981) and concluded that 15 of the 16 funds had perverse market-timing ability.

Recently, many authors have analysed fund performance in Australia among whom Vos,

Brown and Christie (1995), Brown and Goetzmann (1995), Hallahan (1999), Orr (1999),

Sawicki and Ong (2000), Sawicki (2000, 2001), Gallagher (2001), Frino et al. (2003), Drew and Noland (2000), Drew and Stanford (2001a, 2001b, 2003), Cameron and Hall (2003),

Bilson et al. (2005) are important. The main findings of these studies are that there is a high correlation between fund performance measures, and that funds generally fail to outperform the market.

All these studies are based on some basic assumptions. First, the Asset Pricing model assumed gives rise to expected returns for the assets available to portfolio managers. Second, the literature pertaining to traditional fund performance assumes that the user of a performance measure holds unconditional expectations, meaning the use of any information by managers in their trading activities may lead to incorrect measurement of fund

3

performance. Thirdly there is an assumption about the functional form for the factor sensitivities of a managed portfolio.

All these assumptions will be relaxed and robust results will be provided, using the conditional time-varying alpha, and conditional time-varying alpha and beta models based on various sets of public information and benchmarks from a sample of Australian retail equity funds that is free of survivorship bias. .

3.0 DATA

The funds data is taken from the Morningstar Australian Funds Database. Initially all retail funds in existence on 31 st

December 2001 and whose asset allocation to Australian equities exceeded 90% were extracted 5 .

We then selected only one fund for each fund manager

6

, since funds under the same manager have similar asset allocations by definition and probably would invest in similar stocks. So these funds should have similar return series. The fund selected, for a given fund manager, for a given group was chosen by using the RAND (.) function in Microsoft Excel. The funds thus selected constitute the surviving funds used in this research provided those funds have at least

24 months of observations.

7

To avoid survivorship bias, we also consider collapsed Australian retail-equity funds. The

Morningstar database has the asset allocation of all collapsed (Australian retail-equity) funds at the time of their collapse. We examined funds that collapsed in the period from June 1981 to December 2001, and selected those that at the point of collapse had allocation to Australian equities exceeding 90%.

8

We again chose one collapsed fund out of all (collapsed) retail funds for each fund manager using the randomization algorithm described earlier. Again, funds with less than 24 months of observations are excluded.

3.1 Calculation of the Australian One-month Risk-Free Rate

We have calculated the Australian monthly risk-free rate as follows. r f , t

 ln( 1

 r a , f , t

)

12

(1) where r a,f,t is the annualised yield on 90-day Bank Accepted Bills at time t, as reported by the

Reserve Bank of Australia (RBA) on its website, which is consistent with Sawicki and Ong

(2000).

9

5 To consider both retail and equity funds would be erroneous as the two groups offer similar investment

strategies and hence would have similar pre-expense returns, but differing post-expense returns due to

the relatively higher investment fees for retail-fund investment.

6 There were often several funds for the same fund manager within a given group.

7 This is consistent with Sawicki and Ong (2000) and Christopherson et al. (1998)

8 We assume this asset allocation is representative of the fund’s investment strategy throughout its

existence.

9 RBA Website: www.rba.gov.au

; risk free rate can also be obtained by dividing quoted annualized yield by

12.

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3.2 Calculation of Fund Returns

The database gives monthly values in Australian dollars, for an investment of $10,000 in the fund, at its inception, after allowing for dividend distributions and management fees. We compute the returns for each of these funds as follows. r p , t

 ln ( v t v t



1

) x 100

 r f , t

(2) where v t

is the value at time t of an investment of $AUD10,000.00 in the fund at its inception and r p,t

is the continuously compounded excess return to investing in this fund from month t-1 to t , which is consistent with Sawicki and Ong (2000).

10

Descriptive statistics of monthly fund returns of all surviving, and collapsed funds are presented in Table 1.

Table 1 shows that the mean and median returns for the collapsed funds are lower than returns to the corresponding surviving funds. The surviving funds have higher returns and lower standard deviations and coefficients of variation, indicating that surviving funds have definitely performed well compared to collapsed funds. The Skewness and Kurtosis for both types of funds are respectively negative and positive, indicating the returns of both fund types are far from normality.

Table 1: Descriptive statistics of monthly fund returns for Australian

Survival and Collapsed funds.

*

Sample statistics

AUSTRALIA

**

Surviving Collapsed

Mean

Median

Maximum

Minimum

1.10

1.22

11.57

-42.13

0.85

0.89

16.88

-46.17

Std. Dev.

Skewness

Kurtosis

C.V

4.80

-3.19

30.57

4.36

53

4.87

-3.55

37.32

5.83

23

Number of Funds

Observations 228 246

*

**

Portfolios for these two groups are formed, by averaging for each month, the returns of all funds, in a given group, for which a return was available.

The minimum monthly return for both groups was observed in October

1987. The p-value for the Jarque-Bera normality test is zero for both fund groups. Kurtosis values are not excess kurtosis.

3.3 Return to the Australian and USA Market Portfolios

Excess returns, in Australian Dollars, to the Australian Market portfolio are calculated as follows:

10 Campbell, Lo, McKinlay (1997, pp 11-12) suggests that simple returns should be used for testing asset-pricing models and continuously-compounded returns for tests on the long-run time series properties of asset returns.

This suggests simple returns could also be used instead of continuously compounded returns. However most published papers on funds management use continuously compounded returns.

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r m , t

 ln( m t m t



1

) x 100

 r f , t

(3) where r f,t is the Australian one-month risk-free rate at time t, as defined in Equation (1) and m t is the level of the ASX S&P 200 Accumulation Index at time t as reported by the RBA on its website. For the US (world) market portfolio, m t is the level of the US (MSCI World) Index assuming dividend reinvestment in the index is the average of the maximum and minimum possible.

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The long term US (World) market return is calculated in U.S. dollars (USD), which is then converted to Australian dollars using the formula given in equation (4) below.

We then subtract the Australian risk-free rate to obtain the excess return, in AUD to the US

(world) market portfolio.

3.3.1 Conversion to Australian Dollar From the US (World) Market Portfolio

The return in Australian dollars to the US (world) portfolio is calculated by the following formula.

AUD return = ( 1

 s )( 1

 r w

)



1 (4) where: s

 s t



1

( AUD / USD ) s t

( AUD

 s t

( AUD

/ USD )

/ USD ) is the percentage monthly change from month t to (t+1) in the exchange rate quoted as

Australian dollars per US dollar, and r w

is the return in US dollar terms to the US (world) market portfolio. Descriptive statistics on the returns to the factor portfolios are shown in

Table 2.

Table 2 shows that the Australian market risk premium has been very low over the period

June 1981 to December 2001. From these observations one can say that for Australianinvesting funds, the betas with respect to excess returns to the Australian market portfolio should be relatively high but this is multiplied by a very slight average excess return to the

Australian market portfolio and the high Australian risk-free rate means the average excess return of these funds is also not expected to be especially high. Thus the possibility of large alphas for these funds is expected to be low.

3.4

The Information Variables

3.4.1

Australian Information Variables:

We have used two Australian public information variables, which are given below.

(1) The Australian one-month risk-free rate, which is defined in equation (1) earlier; and

(2) The 12-month dividend yield on the ASX S&P 200 Index, as reported by the

RBA on its website.

11 The US (MSCI World) Index data was obtained from www.msci.com/equity/index.html

. Details on assumptions regarding dividend reinvestment are also provided there.

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3.4.2 USA (World) Information Variables

We have also used two US (world) public information variables for our analysis, which are given below.

(1) The U.S. one-month risk-free rate: we use the monthly yield on one-month

Treasury bills issued by the United States Government, as reported on the

CRSP RISKFREE files; and

(2) The 12-month dividend yield on the (U.S) S&P 500 Index. This is computed from the S&P 500 with- and without- dividend return series, obtained from the

CRSP database. These variables are not converted to Australian dollar equivalents as described in equation (4). Descriptive statistics on the information variables are shown in Table 3 below.

The Australian risk-free rate is relatively higher than the corresponding U.S. risk-free rate with a higher standard deviation and co-efficient of variation. The important issue with respect to information variables is how fund managers react when observing the levels of these variables. Also the Australian dividend yield has been higher than the United States dividend yield with a lower standard deviation and co-efficient of variation.

Table 2: Descriptive Statistics for Returns to the Factor Portfolios

*

Sample

Statistics

Mean

Median

**

Risk-free

0.85

0.75

Maximum 1.78

Minimum 0.35

Australian

Market

0.13

0.40

13.82

-55.70

US

(World)

Market

0.43

0.38

13.58

-14.87

Std. Dev.

Skewness

Kurtosis

C.V

0.39

0.34

1.67

0.46

5.79

-3.70

36.67

44.54

4.61

-0.08

3.72

10.72

Observations 247 247 247

* The statistics presented in this table are percent per month, and for: the one-month Australian risk-free rate; for excess returns, to the Australian S&P ASX200 Accumulation Index and excess AUD returns to the US (MSCI

World) Index (please see note below). Other return series are for monthly observations from June 1981 to

December 2001.

** The return to the US (MSCI World) Index is calculated by assuming dividend re-investment is the average of (i) the dividend re-investment which would be achieved by a resident in the company’s home country, and (ii) the dividend re-investment which would be achieved by first subtracting with-holding tax at the rate applicable to nonresidents of the company’s home country. Kurtosis values have not had three subtracted from them.

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Table 3 Descriptive Statistics for the Information Variables *

Australian

Risk-Free Rate

U.S. Risk-Free

Rate

Australian

Dividend Yield

Mean

Median

Maximum

0.85

0.75

1.78

0.50

0.45

1.25

4.10

3.89

6.79

U.S. Dividend

Yield

3.07

3.03

6.33

Minimum

Std. Dev.

Skewness

Kurtosis

**

0.35

0.39

0.34

1.67

0.14

0.20

1.22

2.12

2.49

0.84

1.02

0.75

1.10

1.26

0.21

-0.57

CV 0.46

Observations 247

0.40

247

0.21

229

0.41

247

* All information variables are expressed in percent. The Australian one-month risk-free rate is calculated from the annualised Australian 90 Day Bank-Accepted-Bill rate; the U.S. one-month risk-free rate is the (monthly) yield on a Treasury

Bill issued by the United States Government. Annualised dividend yields, observed at monthly intervals are for the

Australian S&P ASX200 Accumulation Index and for the (US) S&P500. All statistics, other than for the Australian

Dividend Yield cover monthly observations from June 1981 to December 2001, and for the Australian monthly Dividend

Yield are from December 1982 to December 2001.

** The reported kurtosis values are not excess kurtosis values; they have not had three subtracted from them.

4.0 METHODOLOGY

4.1

Justification of Using Conditional Models

In general, the intercept term or alpha in a regression model is used to measure fund performance. A fund is judged as performing well, or not well depending on whether its alpha is positive or negative respectively. Traditionally an ‘alpha’ is calculated by subtracting the product of a fixed ‘beta’ and the average excess return of a benchmark portfolio over some period, from the average excess return to the fund over the same period. This is an unconditional alpha. In practice, often expected returns and risk premia vary over time, and so the use of unconditional method to estimate ‘alpha’ is likely to produce biased and unreliable estimates.

Chen and Knez (1996), Ferson and Schadt (1996) and Christopherson et al. (1998) used conditional models to avoid problems arising from the use of unconditional models, and found that the results obtained from conditional models are better than unconditional models.

These authors used ‘public information’ such as dividend yields and interest rates to control for biases raised from the unconditional models. The conditional model is better than the unconditional model, because it can incorporate the dynamic behaviour of returns, as well as the dynamic and trading behaviour of fund managers both of which are not adequately captured in traditional unconditional models.

In the past, Sawicki and Ong (2000) used such conditional models to evaluate the performance of managed funds in Australia, using one factor with four additional variables

(three public information variables and one dummy variable) and found that the computed alphas are higher than the corresponding alphas from the unconditional models. Following the study of Christopherson et al. (1998) and others, we have also used time-varying conditional alpha and beta models, which to my knowledge have not been used in Australia to evaluate

Australian retail funds, although such models have been used extensively overseas to evaluate

8

fund performance. We have used two types of time varying conditional models to evaluate retail funds in Australia, both using ‘public information’ to see how it differs from unconditional estimates. More importantly, we use these models to illustrate the intuitive appeal and the empirical importance of conditional models in the evaluation of retail funds.

4.2.

Benchmarks

The performance of a fund is generally measured by subtracting the fund’s average excess return from the product of its beta and the average excess return to the benchmark portfolio.

Hence, the overall performance of the fund to some extent depends on the choice of benchmark. Investigators have their own choice of benchmark in order to evaluate the performance of funds. Also managers with different style classifications may have different benchmarks. Ferson, Kandel and Stambaugh (1987), Harvey (1989), Ferson and Schadt

(1996) and many others have shown that measures of fund performance can be highly sensitive to the specification of an inefficient benchmark. Roll (1978, 1980a, 1980b) has shown that small variations in the benchmark can have a large impact on alphas. While,

Grinblatt and Titman (1994) indicated that measures of performance can be sensitive to the choice of the benchmark. Therefore, it is important to investigate the sensitivity of the results to alternative benchmarks.

Most Australian studies such as Sawicki and Ong (2000), Bilson et al. (2005), Drew et al.

(2003) and others have only used an Australian benchmark and excess returns in Australian dollars to the Australian Market portfolio as defined in equation (3). We have used an

Australian benchmark, as well as a US benchmark to see the sensitivity to the choice of the benchmark. It is quite relevant in today’s world, since the US economy is open and everyone is interested to compare his/her investment performance with what could be earned by investing in the US.

4.3.

Survival Bias

Survival bias occurs when the sample includes only those funds that have continued to survive, but ignores, those funs that fail to survive to the end of the study period. This is because the performance of those funds that collapse before the end of the study period is ignored. Survival bias creates a number of potential problems, viz., (i) it affects the perceived average level of performance, and (ii) it also affects the apparent persistence in performance

[Brown et al. (1992)]. Obviously, ignoring poor performing funds that withdraw from the market leads to an upward bias in the performance of the surviving fund sample relative to all funds. The present study is free from survival bias because we extracted data from the

Australian Funds Database, which provided information on both surviving and collapsed

Australian retail equity funds over the study period.

Thus, it is expected that the present study will provide more accurate measures of fund performance that incorporate more sophisticated conditional methods, using conditional timevarying beta, and conditional time-varying alpha and beta models for Australian and USA benchmarks with more up to date public information data. Our results will be based on one factor time-varying alpha and beta models, which will then be compared with the one-factor time varying beta model as well as the unconditional model to see the difference due to the use of the more sophisticated time-varying conditional alpha and beta model, and to see the sensitivity of the performance of the funds to using different benchmarks.

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5.0

THE MODELS

Ferson and Schadt (1996), Christopherson et al. (1998) and others have extended the traditional unconditional model developed by Jensen (1968) to take into account public information and how they can improve conventional measures of fund performance. The mathematical formulation of the unconditional, conditional beta, and conditional alpha and beta models for one factor only are given below.

5.1 Single Factor Unconditional Model

12 r p , t

  p

  p r m , t

 u p , t

(5)

Where r p,t

is the fund’s Australian dollar monthly excess return, r m,t

is the Australian dollar excess return to the market portfolio,

α p is intercept term, which is popularly known as the alpha of the fund,

β p is the portfolio’s market beta and u p,t

is the error term which follows normal distribution with mean zero and variance σ 2 . Note that the excess market portfolio, r m,t is the Australian market portfolio when we consider Australian benchmarks, and the World market portfolio when we consider World benchmarks.

Jensen (1968) considered this unconditional alpha as a measure of abnormal performance, using a proxy for the market portfolio proxy as the benchmark and further assuming that the

CAPM model holds.

5.2.

Conditional Single Factor Time Varying Beta Model.

Jensen (1968) developed the unconditional model (5) on the assumption that the beta remains stable over time, but in reality fund managers do change exposure to the market factor over time and hence the estimated parameters obtained from such a model can be misleading. To avoid these problems, Ferson and Schadt (1996) extended the original Jensen model, by incorporating lagged public information which was also used by a number of other authors such as Cochrane (1996), Jagannathan and Wang (1996), etc. The mathematical formulation of a conditional single factor model can be expressed as follows. r p , t

  p

  p

( Z t



1

) r m , t

 u p , t

, (6a) where:

 p

( Z t



1

)

 

0 , p

 

1 , p

' z t



1

;

Z t-1

is a n by 1 vector of information variables, assuming there are n information variables, at time t-1 ; z t-1

is a n by 1 vector of those information variables, demeaned, by subtracting the long-term unconditional mean for those variables from the level of those information variables at time t-

1 ;



1 , p

' is a 1 by n vector of sensitivities of the fund’s market beta, to changes in the n information variables;

12 This is the model used by Jensen (1968).

10

r p,t

is the fund’s monthly excess return, r m,t

is the excess return to the market portfolio, α p is the alpha of the fund,

β p is the portfolio’s market beta and u normal distribution with mean zero and variance

σ 2

. p,t

is the error term and follows

Therefore the final one factor conditional beta model can be expressed as follows. r

  p

 

0, r p ,







1,

' z p t



1

 r

 u (6b)

Note that

 p

in equation (6b) differs from equation (5) if



1 , p

is nonzero. The coefficient



0 , p

is the ‘average beta,’ or the fundamental beta of the fund. With a single benchmark portfolio r m , t

and k information variables in the vector z t



1

, equation (6b) is a regression where a fund’s monthly return is the dependent variable and there are (k + 1) independent variables plus a constant term. Note that equation (5) is a special case of equation (6b) in the absence of any public information variables. This can be verified by testing a null hypothesis that H o

:



1 , p

= 0 (a null vector) against the alternative hypothesis H

1

:



1 , p



0.

5.3. Conditional Single Factor Time-Varying Alpha and Beta Models

This is the same as the one-factor time-varying beta model but we allow for the possibility that the fund manager uses more information than just the public information variables used in estimating the one-factor time-varying beta model. If the fund manager acted on such additional information correctly, we would expect to observe a positive alpha for the next reporting period and this positive alpha which was generated by the action of altering the fund’s beta at the end of the last time period could be considered to be a function of the level of the information variables at the time the beta alteration was made. Christopherson et al.

(1998) developed the time-varying alpha and beta model as a linear function of the information variables. In particular a fund’s alpha at a point in time is the fund’s (constant) fundamental alpha adjusted for its sensitivity to deviations in the information variables from their long-term unconditional means. Here funds with significant, positive coefficients in the

 '

1 , p vector in Equation (7b) correctly use more than just the public information variables. For simplicity, we only look at the fundamental alpha (the average of the time series of alphas) and if this is significant, we claim that it is a worthwhile addition to the current investment portfolio. r p , t

  p

( Z t

)

  p

( Z t

) r m , t

 u p , t

, (7a) where:

 p

( Z t

)

 

0 , p

 

1 , p

' z t



1

,

And:

 p

( Z t

)

 

0 , p

 

1 , p

' z t



1

,

If we insert the values of  p

( Z t

) and  p

( Z t

) into equation (7a), we can write the final single factor time varying alpha and beta model as follows. r p , t

 

0 , p

 

1 , p

' z t



1

 

0 , p r m , t

 

1 , p

'

 z t



1 r m , t



 u p , t

(7b)

11

Note that equation (6b) is a special case of equation (7b) when equation (5) is a special case of equation (7b) if both vectors z t



1 z t



1

is a null vector, while

and

 z t



1 r m , t



are null vectors; which can be tested with the estimates of regression coefficients. We have conducted the Wald test to avoid the problem of heteroskedasticity, using the technique of Newey and

West (1987).

6.0

EMPIRICAL ILLUSTRATIONS

We have estimated the following single factor regression models for each of the surviving and collapsed funds in our sample individually and also with an equally weighted portfolio of (i) all surviving funds, (ii) all collapsed funds, and (iii) all surviving and collapsed funds in our analysis, using the Ordinary Least Squares (OLS) method.

1. Unconditional model: Australian Benchmark with Australian Information r p , t

 

0 , p

  p r m , t ( A , A )

 u p , t

,

2. Unconditional model: USA Benchmark with USA Information r p , t

 

0 , p

  p r m , t ( U , U )

 u p , t

,

3.

Conditional time-varying beta model: Australian Benchmark with Australian

Information. r

 

0, p

 

0, p r

, ( , )





1, p





  r 

 



2, p



 

Dividend t



1

 r 

  u

4.

Conditional time-varying beta model: Australian Benchmark with USA Information. r

 

0, p

 

0, r p , ( , )





1, p

  r r





2, p



Dividend t



1

 r

 u

5.

Conditional time-varying beta model: USA Benchmark with Australian

Information. r

 

0, p

 

0, r p , ( , )





1, p r





2, p



Dividend t



1

 r

 u

6. Conditional time-varying beta model: USA Benchmark with USA Information. r

 

0, p

 

0, p r

, ( , )





1, p r





2, p



Dividend t



1

 r

 u

7. Conditional time-varying alpha and beta model: Australian Benchmark with

Australian Information. r

 

0, p

 

1, p r

,



1( A A )

 

2, p





Dividend t



1



,

2, p



 

0, p r

, ( , )



Dividend t



1

 r

( , )



 u



1, p

 r

,



1

 r

( , )

12

8. Conditional time-varying alpha and beta model: Australian Benchmark with USA

Information. r

 

0, p

 

1, p r

,



1( , )

 

2, p

Dividend t



1



,



 

0, p r

, ( , )





1, p r



 

Dividend t



1

 r

2, p

 u

9.

Conditional time-varying alpha and beta model: USA Benchmark with Australian information. r

 

0, p

 

1, p r

,



1( , )

 

2, p





Dividend t



1



,





Dividend t



1

 r

2, p

 

0, r p , ( , )



 u



1, p r

10.

Conditional time-varying alpha and beta model: USA Benchmark with USA

Information. r

 

0, p

 

1, p r

,



1( , )

 

2, p





Dividend t



1



,



2, p

 

0, r p , ( , )



Dividend t



1

 r

(



 u



1, p

,



1 r

The estimated parameters of the relevant models are now discussed. First, we discuss the results of the traditional unconditional models, models 1 and 2, based on Australian and USA benchmarks respectively, which are presented in Table A1 in the Appendix. The main results from these estimated models are as follows. The adjusted coefficient of determination ( R

2

) is used to judge the fitting performance of the various models discussed above, because each model has same dependent variable, but a differing number of independent variables. The value of R

2

of the model 1 is considered to be very high (varies between 0.50 to 0.99) for all individual funds with the exception of only four out of 75 funds whose R

2

falls below 0.378.

But the R

2

is generally very low for all individual survival and collapsed funds for model 2, which is based on the US benchmark. This implies that model 2 does not fit our data well.

Table 1 shows that the choice of US benchmark has a large impact on alpha compared to

Australian benchmark. For example, there are 40 negative alphas out of 75 funds of which 10 are significantly negative for model 1, but the corresponding figures are respectively 25 and zero for model 2, based on a US benchmark. Overall it may be concluded that it is better to use a local (Australian) benchmark when evaluating Australian (local) funds rather than a US

(foreign) benchmark on the grounds of goodness of fit. Hence from now on, we will concentrate our discussion only on the results of those models that are based on an Australian benchmark (that is Models 1, 3, 4, 7 and 8). A summary of the estimated parameters and relevant statistics for these models are provided in Table 4.

13

(Insert Table 4 here)

13 Results of models 5, 6, 9 and 10, which are based on US benchmark are not presented or discussed here on the grounds of poor fit. However, these results can be provided to interested readers on request.

13

Table 4 shows that there are 35 positive and 40 negative alphas out of 75 funds of which 4 and 10 are respectively significantly positive and negative based on one tailed t-test at 5% level of significance. All betas however are significantly different from zero for model 1, implying that excess returns from the Australian market portfolio (in AUD$) has a significant effect in explaining the Australian excess fund returns.

We now would like to discuss the alpha and beta estimates obtained from models 3 and 4 and presented in the Appendix in Table 2. These models are time varying beta models, based on

Australian and US information respectively. The US information is used here on the assumption that the Australian economy and Australian fund managers follow what is happening in the US economy. The purpose of these models is to see whether fund returns are related to lagged public information.

The Adjusted coefficient of determination ( R

2

) obtained from models 3 and 4 are presented in the Appendix in Table 2, which shows that both models fit the data well. The ( R

2

) values are generally very high for both models, mostly varying from 0.70 to 0.90 with a few exceptions. These are substantially higher than many previous studies such as Christopherson et al. (1998). These values are also slightly higher than from the unconditional model, model

1, which is consistent with the findings of Ferson and Korajczyk (1995), Ferson and Schadt

(1996), Christopherson et al (1998), and Sawicki and Ong (2000). This table also shows that for about 80% of funds, the market portfolio has a significant effect on fund return. Also for about 30% of funds, the interaction variables of the market portfolio with public information are significant. However, the overall F-statistic for these models shows that all variables included in models 3 and 4 are significantly different from zero at the 1% level, indicating that the additional public information does have a significant impact in explaining fund returns. As a result the marginal explanatory power of conditional models 3 and 4 has increased, and resulted in higher R

2

values than from the unconditional model. We have also conducted an F-test to test for the joint significance of the interaction variables only. The result can be seen in Table 4 (in the Partial F  -column) and shows that for about 50% of funds, the interaction variables are jointly significantly different from zero, indicating that additional public information variables can explain excess fund returns. This is consistent with the findings of Ferson and Schadt (1996), and Sawicki and Ong (2000), who showed the joint significance and the importance of a risk-free rate and dividend yields in helping to explain excess fund returns.

More importantly, it is observed from Table 2 that the number of negative conditional alphas obtained from models 3 and 4 is higher than the number of negative unconditional alphas which is contrary to the findings of Sawicki and Ong (2000), and Ferson and Schadt (1996).

These authors found that the conditional alphas are on average higher than unconditional alphas, while Ferson and Warther (1996) pointed out that these differences indicated a positive correlation between expected market returns and the flow of new money into funds over time together with a negative relation between new money flows and fund betas.

Table 3 in Appendix presents the results of the estimated parameters of models 7 and 8, the time varying conditional alpha and beta models, based on Australian and US information respectively with an Australian benchmark. It is clear from this table that the variables pertaining to a time-varying alpha are not significantly different from zero for most funds, implying that alpha is not generally varying over time. Table 4 shows that for approximately

16% of funds the variables pertaining to a time-varying alpha are significantly different from

14

zero. This indicates that alphas estimated from our time varying alpha and beta models, models 7 and 8, are generally constant over time. Furthermore, a partial F test on the variables pertaining to a time-varying alpha shows that these variables are not significantly affecting the estimates of alphas. However, Partial F tests, for these models, on the variables pertaining to a time-varying beta indicate that for more than 50% of individual funds, these variables are significantly different from zero. This can be interpreted as saying that the time varying beta model is more appropriate than the time varying alpha and beta model for our data. Also note that Sawicki and Ong (2000) found that for approximately 50% of funds, the variables pertaining to a time-varying beta were significantly different from zero while Ferson and

Schadt (1996) found this to be the case for 75% of funds in their sample.

Table 4 shows that under all models, there are relatively few funds with alphas significantly different from zero. For the unconditional model, there are only 10 significantly negative and four significantly positive significant alphas compared to 13 significantly negative and one significantly positive alpha for each of models 3 and 4 (conditional beta models based on an

Australian benchmark portfolio with Australian and US information respectively) at the 5% level of significance, out of 75 funds. The shift in the distribution of (significant) alphas to the left is contrary to the findings of Ferson and Schadt (1996) and Sawicki and Ong (2000). In fact, Sawicki and Ong evaluated 97 Australian managed funds and found two significantly negative and nine significantly positive funds based on an unconditional model compared to two significantly negative and 11 significantly positive alphas based on a conditional model.

This shows that our conditional beta models produce more negative alphas compared to traditional measures, whereas Sawicki and Ong (2000) showed that their conditional model produce more positive rather than negative alphas compared to unconditional models. Their results suffer from survivorship bias and may result in their distribution of alphas being shifted to the right relative to the true distribution. This is because survival bias shifts the distribution of alphas to the right [Ferson and Schadt (1996)].

When we investigate model 7, which is based on an Australian benchmark with Australian information, it shows that there are eight significantly negative and nine significantly positive alphas compared to ten significantly negative and four significantly positive alphas from the unconditional model, model 1. This shows that the conditional time-varying alpha and beta model produces more positive alphas than does the unconditional model. But model 8, which is based on US information gives rise to 14 significantly negative and three significantly positive alphas, indicating that like models 3 and 4, model 8 also produces more negative than positive alphas compared to the unconditional model. Thus we have found that for three of the four conditional models, the number of significantly negative alphas is greater than in unconditional model 1, which is contradictory to the findings of Sawicki and Ong (2000) and

Ferson and Schadt (1996).

Now, we would like to test whether the conditional alphas are significantly different from the unconditional alphas, using the traditional parametric paired t-test and the non-parametric

Wilcoxon matched-paired test. The results are presented in the Table 5 below.

15

Table 5: Comparison of Conditional and Unconditional Alphas *

Wilcoxon Z-Score

Models t-values

Survival Collapsed

3-1 -2.38 -0.76

(0.021)

4-1 -4.73

(0.453)

-3.42

All

-2.28

(0.026)

-5.51

Survival

-1.93

(0.054)

-4.08

Collapsed

-0.96

(0.338)

-3.55

(0.000)

7-1 0.14

(0.889)

(0.003)

0.43

(0.670)

(0.000)

0.31

(0.757)

(0.000)

-0.67

(0.506)

(0.000)

-0.80

(0.426)

8-1 -2.65

(0.011)

4-3 -1.79

(0.078)

-0.94

(0.356)

-1.84

(0.081)

-2.75

(0.007)

-2.43

(0.018)

-3.15

(0.002)

-2.30

(0.022)

-0.92

(0.357)

-1.78

(0.076)

All

-2.22

(0.027)

-5.45

(0.000)

-0.98

(0.326)

-3.20

(0.001)

-2.83

(0.005)

7-3 0.24

(0.815)

8-3 -2.59

(0.012)

7-4 0.29

(0.774)

8-4 -2.57

(0.013)

8-7 -3.11

(0.003)

0.50

(0.620)

-0.93

(0.366)

0.69

(0.498)

-0.85

(0.403)

-1.17

(0.254)

0.43

(0.672)

-2.71

(0.008)

0.56

(0.575)

-2.66

(0.010)

-3.27

(0.002)

-0.96

(0.339)

-3.05

(0.002)

-0.98

(0.328)

-3.00

(0.003)

-4.27

(0.000)

-0.96

(0.338)

-0.83

(0.408)

-1.09

(0.277)

-0.55

(0.581)

-1.64

(0.101)

-1.30

0.193)

-3.05

(0.002)

-1.37

(0.170)

-2.81

(0.005)

* p-values are presented in parentheses ( ).

In the paired t-test, the Null and Alternate Hypotheses are:

H

0

: The number of significant Conditional Alphas are the same as the number of significant

Unconditional Alphas;

H

1

: The number of significant Conditional Alphas differs from the number of significant

Unconditional Alphas.

Results of the paired t-tests indicate that the conditional alphas obtained from models 3, 4 and 8 are significantly different from the alphas from the unconditional model 1 at the 5% level of significance when all funds in the sample are considered. These results are confirmed by the nonparametric

Wilcoxon Matched-Paired test. There is no significant difference between conditional and unconditional alphas for collapsed funds except when conditional alphas are computed using model 4.

More interestingly, we have also compared pair-wise conditional models to investigate whether there are any significant differences among estimated alphas obtained from different conditional models.

Surprisingly, the results of table 5 show that alpha estimates obtained from various conditional models are generally significantly different from each other except for model 3 against model 7 and model 4 against model 7. Note that our result (model 3 and model1) is against the findings of Sawicki and Ong

(2000) who found that conditional alphas are not significantly different from the unconditional alphas when a paired t-test is used for surviving funds only, since their sample consisted of surviving funds only. They did however find a significant difference between conditional and unconditional alphas when a nonparametric Wilcoxon test was used.

16

We have also conducted a Binomial test to compare the distribution of conditional and unconditional alphas. Here the null hypothesis is that the probability of a positive Alpha is

50%, which is to be tested by the estimation of the probability that the observed proportion comes from a population with alpha centred at zero. The binomial test results are shown in the

Table 6 below.

Table 6: Binomial Test: Proportion of Negative Alphas

Models Number of

Negative

1 40

Alphas

3 43

4 45

All Funds (75)

Proportion of Negative

Alphas

0.53

0.57

0.60 t-values

-0.516

-1.212

-1.732

Number of

Negative

Alphas

26

29

30

Surviving Funds (53)

Proportion of

Negative

Alphas

0.49

0.55

0.57 t-values

0.146

-0.728

-1.019

7 41

8 49

0.55

0.65

-0.866

-2.598

28

35

0.53

0.66

-0.437

-2.330

The above Table 6 shows that neither the unconditional nor the conditional models except model 8 give rise to a proportion of negative alphas that differs from 50% at the conventional 5% level of significance. However, it clearly shows that there are more observations are in the left tail. The distribution of the t-ratios shifts even further to the left when conditional models are used. 57% of all funds under Model 3 (an Australian benchmark with Australian information) have negative alphas. Of these 13 are significantly negative and only one is significantly positive. The t-statistics are adjusted for autocorrelation, using the Newey-West covariance matrix. We also observe similar results when the estimates are heteroskedasticity consistent (using a White correction), as well as when the estimates are obtained from the normal Ordinary Least Squares Method. Finally, the conditional alphas obtained from model 8 are significantly different from a proportion of 50% at the 5% level of significance.

We know that unconditional alphas may be subject to error due to omission of public information.

The binomial test produces negative t-statistics, and it is clear from Table 5 that most significant alphas are negative rather than positive.

7.0 CONCLUSIONS

Some concluding remarks and limitations of this study are made in this section. In this paper, we made an attempt to estimate fund alphas, using a time varying beta, and a time varying alpha and beta model. To my knowledge, the latter models have never been used to evaluate retail funds in Australia. In fact, we have estimated alphas using two unconditional models and eight conditional models based on Australian and USA benchmarks and Australian and

US public information variables. Our results show that Australian funds should be evaluated based using an Australian benchmark, but both Australian and US public information can be used to estimate alpha on the grounds of goodness of fit. The USA benchmark is considered to be inappropriate when evaluating Australian funds. Hence, our analysis to evaluate

Australian retail funds is mainly concentrated on one unconditional and four conditional

17

models. We have used public information in the estimation of the conditional models, and it is seen that the estimated performance of many funds changes when compared with the unconditional model. We observed that conditional models produce more negative alphas than unconditional models for our sample data. Statistical tests indicate that the conditional alphas are significantly lower than the unconditional alphas. More importantly, further statistical tests indicate that the distribution of alpha is shifted to the left using conditional models relative to the non-conditional model, which is contradictory to the findings of

Sawicki and Ong (2000).

More importantly, for the first time, we have estimated the conditional time varying alpha and beta model to evaluate Australian retail funds, using an Australian benchmark with Australian and US public information. It shows that the variables pertaining to a time-varying alpha are not significantly different from zero, indicating that alpha is not time varying for most

Australian retail funds. Thus, it is shown in this study that the time varying beta model is more appropriate than the time varying alpha and beta models for our sample data at least.

Thus, our alpha estimates based on a time varying beta model are more appropriate and these are even more negative than previous Australian estimates probably because our estimates are free from survivorship bias. Also they are obtained from a number of alternative benchmarks with Australian and US public information variables and with a new and comprehensive data set.

There are many limitations associated with the present study. First, we have used a simple linear function to model the time-variation in betas and this may not be appropriate. The specification of this functional form is an empirical issue. However the linear functional form used is attractive because it can nicely be expressed in a linear regression form, and can be estimated using the widely used Ordinary Least Squares (OLS) technique. Thus, the conditional models should be evaluated using other relevant functional forms and this is left for further study.

This study only considers those retail funds with allocation to Australian equities exceeding

90%, but ignores those funds with, for example, allocation to international equities exceeding

90% and mixed funds that have exposure to both Australian and international equities. As a result, alpha estimates obtained from the current study for Australian retail funds may not be entirely indicative of the ability of the entire population of fund mangers. More importantly, we have only estimated single factor conditional and unconditional models, which might not be enough to capture all relevant factors in explaining fund returns. Hence, a multifactor conditional and unconditional model should be estimated. Also, to get more insight, it would be interesting to evaluate wholesale funds, using conditional models. Studying performance persistence using conditional model would also be interesting as well as incorporating the

Treynor and Mazuy (1966) market-timing model into the estimated models through the addition of an additional regressor, that being the square of the return to the market portfolio, r

2

. These are directions for further research, and are left for future studies.

Finally, one simple but useful extension to the present study would be to reproduce the analysis using weekly and quarterly data. This is because the results reported in this paper may be affected by the data frequency used. Further persistence in fund performance may be present in monthly data, but may not be present in weekly or quarterly data. This is an interesting task left for further study.

18

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JASSA , 2 , 32-35

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Paretz, P. D., (1976), “The measurement of mutual fund performance in Australia”,

The

Securities Institute Journal JASSA , 3 , 8-10

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Harvard

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21

APPENDIX: Alpha and Beta Estimates by Various

Conditional and Unconditional Models

Table A1: Alpha and Beta Estimates for Australian

Surviving and Collapsed Funds: Unconditional Models

Australian

Surviving

Funds

XSF17

XSF18

XSF19

XSF20

XSF21

XSF22

XSF23

XSF24

XSF25

XSF26

XSF27

XSF28

XSF29

XSF30

XSF31

XSF32

XSF33

XSF34

XSF35

XSF36

XSF37

XSF38

XSF39

XSF40

XSF1

XSF2

XSF3

XSF4

XSF5

XSF6

XSF7

XSF8

XSF9

XSF10

XSF11

XSF12

XSF13

XSF14

XSF15

XSF16

*

#



-0.21

*

MODEL 1: Australian Benchmark with

Australian Information t(



)

 t(



)

R

2 Obs. 

-3.07 0.77 51.75 0.901 197 -0.14

MODEL 2: USA Benchmark with t(



)

USA Information

-0.48



0.29 t(



)

1.79

R

2

0.08

Obs.

197

0.003 0.024 0.841 29.69 0.831 228

0.152 0.868 0.560 4.989 0.619 191

0.42

# 1.921 0.468 4.048 0.521 186

-0.34

* -1.96 0.943 22.23 0.822 226

0.1 0.31 0.47 3 0.17 228

0.16 0.59 0.35 3.76 0.15 191

0.46

-0.15

1.62

-0.35

0.27

0.36

4.05

1.98

0.1

0.08

186

226

-0.08 -0.25 0.941 10.48 0.642 187

0.018 0.135 0.871 35.68 0.847 194

0.013 0.126 0.921 25.02 0.869 167

0.208 1.415 0.755 20.46 0.749 209

-0.04

0.04

-0.07

0.1

0.19 0.8

0.42

0.46

1.68

2.49

0.37 5.8

0.07

0.15

187

194

0.16 167

0.27 0.83 0.37 2.71 0.12 209

0.047 0.382 0.874 18.79 0.815 217

-0.01 -0.14 0.720 17.67 0.838 167

0.086 0.853 0.734 10.94 0.893 180

-0.06 -0.45 0.655 25.74 0.854 183

-1.29

* -2.00 1.251 9.300 0.435 190

0.11

0.11

0.08

0.01

0.31

0.59

0.32

0.03

0.34

0.35

0.45

0.35

2.04

8.38

3.64

2.44

0.08

0.22

0.19

0.14

217

167

180

183

-1.14 -1.28 0.45 1.43 0.03 190

0.08 0.23 0.36 1.7 0.08 191

0.34 0.91 0.18 1.61 0.04 49

0.26 0.79 0.45 4.55 0.2 88

0.004 0.058 0.967 32.31 0.933 191

0.009 0.042 0.768 10.02 0.816 49

0.145 1.163 1.003 36.78 0.919 88

-0.17

* -1.90 0.897 27.16 0.892 63

0.24

# 1.762 0.972 33.76 0.910 76

-0.22 -0.62 0.979 5.637 0.648 27

-0.30 -1.60 0.753 8.362 0.656 40

0.047 0.320 0.868 10.16 0.664 81

0.358 1.583 0.747 10.13 0.460 147

0.005 0.033 0.875 25.57 0.930 24

0.29

# 1.667 0.701 9.826 0.638 90

-0.32

* -2.80 0.780 20.74 0.846 53

-0.36 -1.56 0.988 19.48 0.791 69

-0.24

* -1.99 1.070 53.98 0.961 33

0.043 0.342 0.898 40.59 0.954 58

-0.61

* -2.61 0.960 7.600 0.803 26

-0.01

0.39

0.26

0.17

0.29

0.41

0.23 0.4

0.35 1.34 0.41 4.98 0.24 90

-0.2

-0.15

0.14

0.18

-0.03

1.25

0.57

0.58

1.04

1.37

-0.57

-0.33

0.36

0.47

0.39

0.46

0.17

0.26

0.38

0.4

0.1

0.26

0.38

0.37

0.37

4.05

4.61

1.21

3.34

4.01

5.75

0.68 -0.03 24

2.38

4.05

3.37

4.11

0.19

0.23

-0.01

0.07

0.14

0.16

0.09

0.13

0.1

0.18

63

76

27

40

81

147

53

69

33

58

-0.03 -0.06 0.25 2

0.5 1.24 0.35 2.75

0.02

0.1

26

44

0.21 0.71 0.51 5.76 0.2 110

0.23 0.52 0.25 2.34 0.04 28

0.180 1.053 0.960 10.61 0.800 44

-0.14

* -1.93 1.036 24.55 0.892 110

-0.24 -1.51 1.058 26.22 0.949 28

0.108 0.885 0.982 30.26 0.914 92

-1.23 -1.47 1.099 5.769 0.112 168

-0.08 -0.34 0.89 16.62 0.804 44

-0.09 -0.36 0.866 15.84 0.892 26

0.008 0.107 0.856 26.29 0.841 166

-0.06 -0.18 0.728 5.559 0.382 56

0.039 0.359 0.936 16.79 0.834 101

0.25

-1.02

0.8

-1.15

0.43

0.21

0.22 0.49 0.3

0.19 0.7

0.22 0.76 0.48

4.56

0.9

6.46

0.19

0

0.2

92

168

2.62 0.08 44

0.41 0.84 0.18 1.39 0 26

0.32 5.12 0.13 166

0.15 0.35 0.14 0.87 0 56

101

Indicates significantly negative at 5% level of significance (one-tailed test).

Indicates significantly positive at 5% level of significance (one-tailed test).

22

Australian

Surviving

Funds

XSF41

XSF42

XSF43

XSF44

XSF45

XSF46

XSF47

XSF48

XSF49

XSF50

XSF51

XSF52

XSF53

Average Sur @ .

Funds

EW + Surviving

Funds

Table 1: Continued.



MODEL 1: Australian Benchmark with

Australian Information

T(



)



T(



)

R

2 Obs. 

0.140 0.993 0.894 22.64 0.875 77

-0.11 -0.93 0.821 17.09 0.824 91

0.55

# 1.854 0.700 9.476 0.556 79

MODEL 2: USA Benchmark with t(



)

USA Information

 t(



)

R

2 Obs.

0.31 1.07 0.42 4.27 0.22 77

-0.03 -0.14 0.41 6.29 0.21 91

0.75

# 1.89 0.25 3.41 0.08 79

0.544

-0.12

0.063

-0.01

-0.07

0.029

0.141

-0.10

0.840

-1.39

0.533

-0.10

-0.63

0.095

1.017

-0.69

1.009

0.845

0.949

0.943

0.643

0.437

0.999

0.841

8.554

30.36

16.06

37.95

15.25

3.270

36.07

21.81

0.526

0.952

0.830

0.928

0.814

0.378

0.937

0.870

47

95

66

70

83

172

113

67

-0.03 -1.62 0.995 230.8 0.999 38

-0.13 -0.45 0.836 8.287 0.590 53

0.84

-0.09

1.14

-0.3

0.28

0.5

2.21

6.43

0.03

0.29

47

95

0.24 0.76 0.42 4.34 0.19 66

0.14 0.42 0.39 4.46 0.18 70

0.13 0.61 0.24 4.07 0.13 83

-0.02 -0.06 0.28 3.62 0.08 172

0.36 1.01 0.46 5 0.17 113

0.07 0.19 0.32 3.28 0.14 67

0.47

-0.06

1.37

-0.13

0.27

0.42

3.11

4.53

0.06

0.15

38

53

-0.05 -0.23 0.86 24.05 0.77 107.9 0.137 0.467 0.346 3.498 0.121 107.9

-0.02 -0.18 0.80 48.16 0.91 228 0.13 0.40 0.33 2.41 0.10 228

Australian Collapsed Funds

XSF54

XSF55

XSF56

XSF57

XSF58

XSF59

XSF60

XSF61

XSF62

XSF63

XSF64

XSF65

XSF66

XSF67

XSF68

XSF69

XSF70

XSF71

XSF72

XSF73

XSF74

XSF75

Average

Colla @@ Funds

EW + Collapsed

Funds

0.09 0.59 0.95 24.11 0.89 71

-0.85 -1.27 1.14 6.86 0.25 132

0.27

-0.64

0.07

-0.09

-0.99

-0.2

-0.02

-0.53

-0.4

*

0.13

-0.07

-0.19 -0.45 0.739 12.96 0.621 85.64 0.12 -0.10 0.289 2.42 0.09 85.64

-0.13

*

1.3

-0.54

0.29

-0.18

-1.87

-0.13

-1.38

-3.18

0.32

-0.56

-1.10

0.83

0.88

0.87

0.77

1.13

0.32

-0.24

0.48

0.59

0.87

0.76

15.55

0.06 0.31 0.53 7.3

9.78

13.12

5.34

19.78

6.9

-2.09

10.66

16.51

22.11

17.59

0.75

0.7

0.51

0.83

0.49

0.7

0.39

0.08

0.75

0.6

0.79

0.84

167

50

46

0.05 0.16 0.78 8.22 0.81 98

33

-0.13 -1.01 0.81 10.7 0.71 81

44

0.27 1.49 0.93 15.8 0.82 32

99

-0.74 0.28 3.98 0.12 110

72

-0.24 -1.26 0.83 16.85 0.77 83

54

0.14 0.57 0.92 16.48 0.75 71

73

-0.45 -0.98 1.05 9.61 0.44 81

66

-0.54 -1.44 0.89 21.79 0.66 114

126

-0.06 -0.43 0.65 25.68 0.85 181

246

0.05 0.12 0.49 4.49 0.24 71

-0.63

0.22 0.69 0.49 2.95 0.14 167

0.24

-0.91 -0.63 0.63 1.27 0.07 46

-0.12

0.35 0.82 0.18 1.81 0.01 33

0.08

-0.01 -0.01 0.17 1.28 0.01 44

0.76

-0.43 -1.1

0.24

0

0

#

-1.07 -1.2

-0.14

0.08 0.39 0.25 4.32 0.19 72

-0.23

-0.36 -1.36 0.22 5.43 0.21 73

-0.03 -0.04 0.17 0.83 0.01 66

-0.62

-0.11 -0.37 0.35 5.14 0.15 126

-0.15

-0.82

0.68

-0.16

0.31

1.95

-0.49

-0.68

0.53

0

-0.88

0

-0.49

0.12

0.18

0.3

0.36

0.3

0.57 1.35 0.07 99

0.16

0.36

-0.15 -1.58 0.05 54

0.51

0.14

0.22

0.35

0.30

0.71

2.91

0.96

4.41

3.1

1.75

3.43

4.28

1.08

0.88

2.43

2.10

0

0.1

0.04

0.16

0.08

0.04

0.16

0.17

0

0.01

0.14

0.08

132

50

98

81

32

110

83

71

81

114

181

246

Average of All

Funds

Equally

Weighted of all

Funds

-0.09 -0.29 0.83 20.80 0.728 111.4 0.06 0.30 0.329 3.18 0.113 101.4

3

-0.01 -0.10 0.77 31.87 0.89 246 -0.05 -0.15 0.32 2.44 0.10 246

@: Sur. = Surviving; @@: Colla. = Collapsed; and EW + = Equally Weighted

23

Table A2: Alpha and Beta Estimates for Australian Surviving and Collapsed Funds: Conditional Time Varying

Beta Models (Models 3 & 4) Using Australian Benchmark With Australian and USA Information

Australian

Surviving

Funds

 t(



)



0 t(



0

)



1 t(



1

)



2

T(



2

) Prob.

Over-all

F

Prob.

Partial

F p

 i

R

2

XSF1

XSF2

XSF3

XSF4

XSF5

XSF6

XSF7

XSF8

XSF9

XSF10

XSF11

XSF12

XSF13

XSF14

XSF15

XSF16

XSF17

XSF18

XSF19

XSF20

XSF21

XSF22

XSF23

XSF24

XSF25

XSF26

-0.22

*

-0.23

*

0

0.05

0.04

-3.21 0.76

-3.25 0.76

0.05 0.85

0.48

0.28

0.85

0.7

0

0.31

0.01

1.63

0.49

0.61

0.29

-0.27

*

-0.33

*

1.46

-1.67

0.4

0.87

-1.98 0.95

-0.02 -0.06 0.76

-0.08 -0.27 0.93

-0.01 -0.08 0.92

39.82 -0.09 -1.57 -0.01 -1.19

38.39 -0.01 -1.03 -0.02 -0.54

19.2 -0.24 -3.11 -0.01 -0.16

40.71 0.06

14.63 -0.73

1.16

-8.7

-0.15

0.15

-1.88

4.64

5.82 -0.09

16.17 -0.56

-2.3 -0.18 -3.82

-4.7 0.16 7.2

4.38 -0.08 -2.27 -0.13 -2.1

27.39 -0.07 -0.92 -0.13 -4.62

24.27 0 0.06 -0.11 -2.13

13.24 -0.02 -0.14 -0.22 -5.49

8.31 0.01 0.22 -0.09 -1.14

23.88 -0.18 -2.24 0.05 1.4

28.66

15.87

19.78

22.4

0.04

0.03

1.8

0.12

0 -0.13

-0.25 -2.86

-0.13

-0.08

-0.1

0

-3.51

-1.17

-2.36

0.13

0.04

0.01

0.33

0.07

-0.01 -0.1

0.2 1.29

0.19

0.07

0.08

1.28

0.58

0.6

0.87

0.93

0.88

0.77

0.73

0.83

0.88

-0.04 -0.57 0.71

-0.06 -0.96 0.64

0.02 0.21 0.8

25.15 -0.02 -0.89 -0.11 -2.07

26.25 -0.1 -1.08 -0.07 -2.43

21.24 0.04 1.54 -0.15 -3.35

12.28 -0.12 -0.47 -0.08 -0.88

14.06 -0.03 -1.46 -0.11 -2.69

30.08 -0.28 -3.14 0.08 3.84

0 0.04 0.7

-0.05 -0.41 0.6

-0.06 -0.5

-1.24

* -1.97

0.63

1.14

-1.32

*

0.03

0.02

0.01

0.01

0.14

0.14

-0.2

*

-0.21

*

0.22

-2.03

0.43

0.31

0.05

0.04

0.99

1.27

0.93

0.98

0.57

0.32

0.7

1.09 0.77

-1.97 0.84

-2.19 0.88

1.6 0.77

12.79 -0.05 -1.52 -0.08 -2.27

18.78 -0.13 -1.02 -0.07 -3.81

26.57

5.66

8.72

48.42

32.02

1.11

0.19

7.9

21.23

3.52

4.57

4.51

0.21 1.61 1.01

-0.24 -0.71 2.71

-0.3

-0.42

*

-0.36

*

0.05

0.04

0.23

-0.76

-2.08

-1.65

0.32

0.24

0.94

4.21

0.61

-0.08

-0.03

0.51

0.55

7.88

4.2

1.15

0.92

-0.06

-0.1

2.19

5.38

0.02

0.12

-0.06

0.11

0.01

-0.37

-0.05 -0.57

-0.91 -2.46

-0.07

-0.33

-0.08

-0.65

-0.06

0.03

-0.01 -0.06

-1.32 -1.13

-0.19

-2.61

1.14

0.38

-0.5

1.98

0.84

-0.4

-3.47

-0.52

-2.73

-1.49

-1.09

0.03

-2.08

-3.04

-0.18

-0.13

0.26

-0.05

0.01

-0.06

-3.68

-0.94

1.01

-3.86

0.59

-0.15

-0.2 -0.23

0.1 0.82

-0.09

0.13

0.08

0.11

0.09

2.57

1.85

0.6

-0.25

0.29

-3.55

0.82

0.67

1.07

1.05

7.51

1.01

1.25

-0.37

0.78

-0.15 -2.52 -0.07 -0.43

-0.91 -1.99 0.21 1.4

0.28 1.16 0.59

-0.01 -0.04 0.6

-0.03 -0.16 0.56

0.14 0.74 0.27

5.73

1.43

0.51

1.01

-0.03 -0.67 -0.16 -2.01

-0.52 -0.68 -0.08 -0.38

-0.04 -0.78 -0.12 -0.21

-2.15 -1.98 0.8 2.45

0.26

-0.26

*

1.47 0.51 3.67

-2.12 -0.48 -0.57

-0.37

* -2.87 0.46 1.18

-0.22

-2.26

-3.5 0.13 1.86

-1.2 -0.42 -2.73

-0.11 -1.52 -0.07 -0.26

Estimates based on USA information are presented in BOLD . @

*

#

0

0

0


Indicates significantly positive at 5% level of significance (one-tailed test).

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.24 0.9

0.15 0.9

0 0.84

0.02 0.84

0 0.74

0 0.7

0 0.64

0 0.58

0 0.84

0.02 0.83

0 0.68

0.51 0.64

0.01 0.85

0 0.85

0.04 0.87

0.03 0.88

0.01 0.75

0 0.76

0.04 0.82

0 0.82

0 0.86

0 0.87

0 0.91

0 0.91

0 0.87

0 0.87

0.63 0.43

0.56 0.44

0 0.94

0.42 0.93

0.91 0.81

0.75 0.81

0.01 0.92

0 0.92

0.7 0.89

0.03 0.89

0.29 0.91

0.44 0.91

0 0.75

0.1 0.65

0.21 0.67

0.04 0.67

0 0.69

0.01 0.67

0.07 0.47

0.11 0.47

0.79 0.92

0.63 0.93

0.05 0.67

0 0.66

0.01 0.86

0.12 0.85

DW-

Statistics

2.18

1.92

1.78

1.8

1.78

2.93

2.9

1.4

2.12

2.52

2.51

2.32

2.36

2.2

2.73

2.75

2.07

2.11

2.02

2.08

1.85

1.86

2.06

2.02

2.01

2.03

2.04

2.14

2.14

2.14

2.12

1.58

1.98

2.38

2.05

2.16

2.93

2.91

2.1

1.5

1.65

1.63

2.78

2.83

1.64

2.07

1.95

1.99

2.44

2.36

2.68

2.63

24

Table 2: Continued.

Australian 

Surviving

Funds

XSF27

XSF28

XSF29

XSF30

XSF31

XSF32

XSF33

XSF34

XSF35

XSF36

XSF37

XSF38

XSF39

XSF40

XSF41

XSF42

XSF43

XSF44

XSF45

XSF46

XSF47

XSF48

XSF49

XSF50

XSF51

XSF52

XSF53

Average all

Sur @ . Funds

EW + all Sur @

Funds t(



)



0 t(



0

)



1 t(



1

)



2 t(



2

)

-0.3 -1.33 0.87

-0.34 -1.47 0.67

-0.24

* -1.96 1.31

-0.23

* -1.83 1.64

0.05 0.33 0.66

1.36

1.14

7.22

1.9

4.4

0.1 0.07 -0.28 -0.87

-0.03 -0.73 -0.17 -0.49

0.35 1.18 0.12 0.95

0.03 0.71

-0.55 -1.5

0.28

0.01

0.64

0.06

0.03

-0.65

*

0.23 0.72

-3.16 1.19

4.98

1.16

-0.77

* -2.91 -3.19 -1.4

0.09 0.55 -0.54 -1.03

0.05

-0.1

-0.14

*

0.27

-1.17

1.32

0.92

-1.79 0.97

-0.24 -1.38 1.76

1.64

8.24

-0.05

-1.14

-1.75

-0.64

-0.06

1.05

-0.72

1.79

-0.34 -2.68 -1.93 -1.68

-4.32 -4.54 0.54 1.66

-0.18 -3.12 0.41 0.92

-0.07 -0.17 -0.15 -1.05

10.68 -0.02 -0.54 -0.03 -0.46

5.16 0.82 1.44 0.53 2.25

-0.25 -1.44 1.68

0.1

0.1

-1.41

*

0.73

0.84

-1.68

0.63

0.69

0.94

0.95

5.68

0.01

-1.06

0.11

-2.48

0.34

0.12

0.38

0.85

14.97 -0.05 -2.44 -0.15

5.89 -1.07 -1.18 0

-5.5

-0.01

-1.38

*

-0.1

-0.17

-1.68

-0.4

-0.69

0.88

-0.06 -0.11

0.68

5.47

1.32

-0.08

-0.13

-0.05

-0.01

-0.31

-0.54

-0.6

-0.12

1.23

-2.9

0.78

0.82

2.09

-4.11

16.84 -0.42 -2.45

20.22

-0.06

-2.02

-0.14 -3.19

0.57

-0.57

-1.77

-0.34

-0.12

-1.41

-0.55

0.13

0.67

-0.18 -5.76 -1.91 -4.98

0

0.51

0.14

0.04

0.18

0.09

-0.09

1.58

-3.36

0.08 0.21

-0.06 -0.2

-0.65

-1.84

-0.09 -0.75 0.89

-0.62

-1.72

4.12

-2.28

-0.26

-0.97

-1.02

-2.39

-1.47

-0.62

-1.28

0.58

-1.27

-2.42

2.96

0.05 0.4

0.15 1.01

0.16

1.1

1.02

0.79

0.99

-0.12 -0.97 0.44

-0.12 -0.98 0.57

0.5

# 1.67 0.43

0.52

# 1.69 0.81

0.44 0.69 1.31

0.45 0.66

-0.12 -1.6

2.76

0.92

-0.12 -1.46 0.96

0.09 0.63 1.4

0.06

0.01

0.48

0.11

1.29

1.02

-0.01 -0.12 0.81

-0.03 -0.28 0.36

-0.05 -0.45 0.36

-0.19 -0.66 0.58

-0.13 -0.42 0.37

0.17 1.21 0.81

0.13 0.94 0.83

-0.13 -0.89 1.05

-0.19 -1.36 1.28

-0.03

* -1.68 1

-0.03

* -1.8 1.03

-0.18 -0.56 0.23

-0.22 -0.73 0.62

-0.07 -0.338 0.746

-0.08 -0.377 0.708

2.13

14.7

13.8

3.96

4.04

8.32

5.87

1.88

2.17

13.2

3.45

5.81

9.39

2.33

4.73

36.45

9.52

0.26

0.88

9.81

9.24

5.31 0.05 0.76 -0.02 -0.23

4.37 -0.18 -0.31 -0.04 -0.32

6.02 0.08 2.19 -0.02 -0.24

2.22 -1.15 -1.38 0.13 0.5

4.79 -0.11 -2.21 -0.04

1.36 -1.09 -1.19 0.32

2.49 -0.04 -0.57

1.48 -0.89 -0.47

0.12

1.04

-0.5

0.94

0.56

2.09

-0.03 -0.33

0.2 0.74

1.05

0

1.45

-0.03

0.02 1.11

1.32 1.5

0.09

-0.19

1.33

-0.68

0.07

0.3

-0.03 -0.76 -0.06 -0.67

-0.64 -1.31 -0.06 -0.37

-0.07 -2.31 -0.12 -1.12

-0.68 -5.67

-0.09 -2.17 -0.13 -1.76

-0.29 -0.77 -0.13 -1.45

-0.07 -2.91 -0.03 -0.58

0.36 0.35

-0.59

-0.06

1.99

0.67

-0.11 -3.49

0 -0.03

0 -0.1

-1.76 -1.04

-0.18 -1.78

-1.23

-1.04

0.14

-0.09

0.18

0.1

0.4

0.139

0

0.02

0.26

0.07

-0.066

0.67

-0.62

6.81

0.5

2.38

0.14

0.33

0.44

0.17

0.424

-0.935

-0.02

-0.02

-0.20

-0.22

0.81

0.80

49.36

50.91

-0.18

-0.01

-4.82

-0.66

0.003

-0.04

0.28

-1.73

Prob.

Over-all

F

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Prob.

Partial

R

2

F p

 i

0.03 0.83

0.47 0.55

0.79 0.55

0.12 0.53

0.3 0.52

0.32 0.95

0.37 0.95

0.32 0.83

0.08 0.83

0.79 0.93

0.56 0.93

0.29 0.82

0.01 0.82

0 0.5

0.01 0.43

0.04 0.94

0.02 0.94

0.81 0.87

0 0.88

0.99 1

0.83 1

0.53 0.58

0.23

0.21

0.22 0.59

0.78

0.78

0.69 0.79

0.73 0.79

0.48 0.96

0.77 0.96

0.29 0.95

0.17 0.95

0 0.83

0.02 0.84

0 0.84

0 0.82

0.3 0.89

0.66 0.89

0.09 0.95

0.82 0.95

0 0.92

0 0.92

0.02 0.12

0.06 0.11

0.18 0.8

0.01 0.81

0.79 0.88

0 0.91

0.03 0.85

0 0.85

0.25 0.38

0.03 0.44

0.01 0.85

0.61 0.83

0.75 0.87

0.1 0.88

0.09 0.83

0

0.05

0.91

0.91

DW

Statistics

2.14

2.13

2.08

2.10

2.74

2.92

2.91

2.45

2.61

1.75

1.8

2.47

2.43

1.71

1.72

1.52

1.24

2.78

2.37

1.59

1.64

1.48

1.43

1.78

1.76

2.67

2.04

2.02

2.32

2.25

1.46

1.36

2.7

2.33

2.5

2.52

2.92

2.91

1.68

1.92

1.65

1.6

2.43

1.72

1.46

1.45

1.95

1.86

1.95

2.66

2.74

2.7

2

1.98

1.71

1.93

2.3

2.26

25

Table 2: Continued

Australian 

Collapsed

Funds

XSF54

XSF55

XSF56

XSF57

XSF58

XSF59

XSF60

XSF61

XSF62

XSF63

XSF64

XSF65

XSF66

XSF67

XSF68

XSF69

XSF70

XSF71

XSF72

XSF73

XSF74

XSF75

Average

Colla @@

Funds

EW +

Collapsed

Funds

Average of All

Funds

Equally

Weighted of all

Funds

0.08 0.47 0.64

0.08 0.49 0.81

-0.96 -1.48 1.07

-1

0.2

-1.53

0.94

0.94

0.87

0.14

0

0.78

0.01

0.79

0.54

-0.09 -0.41 0.34

-0.52 -0.46 0.72

-1.1

0.28

0.03

0.1

-0.92

0.89

0.09

0.39

1

0.75

0.9

-0.2

25.51

6.81

2.4

2.54

-0.08

-0.04

-0.09

-0.08

-3.24

-0.11

-1.21

-0.1

-0.08

-0.17

-0.12

-0.15

-1.68

-2.24

-0.36

-1.05

20.39 -0.41 -2.06 -0.91 -2.91

15.15

28.78

-0.49

-0.23

0.03

-1.47

-1.14

1.23

-2.15

-0.15

-0.25

-0.62

-5.24

-4.59

-2.54

0.09

-0.11

-0.11

-0.15

0.32

-0.76

-0.82

-0.31

0.38

-0.01

0.24

2.62

-0.16 -0.34 4.03

0.19 0.89 0.6

0.23

-0.94

*

1.19

-1.8

0.93

1.01

-1.11

* -1.96 1.12

-0.32 -1.13 0.23

4.29

9.68

6.75

7.22

23.3

0.34

-0.02

0.69

3.92

3.18

1.37

4.82

17.84

22.25

2.89

-0.99 -1.69 0.13 0.62

0.07 1.02 -0.19 -4.97

-0.52 -0.74 -0.07 -0.34

-0.11 -1.27 -0.15 -0.54

-0.38 -3.35 0.03 0.94

0 0.04 -0.28 -0.53

-2.27 -2.11 0.18 0.61

-0.14

3.23

-2.7 -0.23 -1.08

1.69 0.72 1.78

0.32

-1.7

0.09

0.2

1.85

-0.79

1.54

1.02

0.8 -0.15

2.28

0.94

-0.9

0.72 -0.12 -2.63

-0.06 -1.16 -0.08 -0.48

-0.68 -1.93 0.09 0.63

-0.29 -1.07 0.29

0.05 0.28 0.49

-0.04 -0.21 0.3

-0.39

* -1.99 0.55

4.08

3.76

2.21

4.01

-0.24 -1.24 0.88

-0.41 -1.08 0.12

9.07

1.1

-0.61 -1.41 -0.24 -1.46

0.22 0.85 1.16 4.21

0.14

-0.51

*

0.56 0.92

-4.36 0.45

-0.48

* -4.44 0.65

-0.51 -1.04 1.32

6.11

9.87

9.44

2.33

-0.57 -1.17 1.56

-0.01 -0.02 0.4

0.1 0.22 0.57

-0.59 -1.56 0.8

4.08

3.59

18.41

8.51

-0.61 -1.44 0.85

-0.13 -1.01 0.8

-0.1 -0.83 0.84

-0.05 -0.38 0.6

-0.06 -0.46 0.63

-0.20 -0.575 0.706

-0.26 -0.664 0.851

18.47

13.11

20.56

18.7

26.52

6.979

11.03

-0.03 -0.74 -0.28 -1.91

0.86

0.03

-1.65 -2.98 0.69

-0.09 -1.23 0.15

0.39

-0.09 -1.22

1.15

-0.02 -0.42 0.07

-0.46 -2.68 0.08

0.05

0.42

-0.03

-0.41

1.75

0.8 -0.34 -2.02

1.25

1.15

1.67

0.22

-0.3

-0.41

0

-0.39

-0.56

0.2

-2.34

3.14

2.24

-2.49

-0.01

-1.58

0.58

1.38

-4.24

0.32

-0.14 -1.12

0.65 1.51

0.5

-0.06

1.59

-1.28

0

0.47

0.01 0.13 0.84

1.5 -0.01 -0.12

-0.5 0.21

-2 0.11

1.61

1.52

-0.01 -0.29 -0.07 -1.16

-0.13 -1.03 -0.07 -3.79

0.02

-0.165

-0.31

1.09

-0.59

-0.393

-0.18

0.033

-0.058

-3.7

-0.625

-0.997

-0.15

-0.19

-0.112

-0.137

-0.06

-0.07 t(



)

-1.37

-1.79

-0.408

-0.46

-0.63

-0.90



0

0.74

0.79

0.734

0.75

0.79

0.79 t(



0

)



29.96

25.22

8.982

9.77

48.13

70.17

1

-0.04

-0.02

-0.463

-0.048

-0.144

-0.02 t(



1

)



-0.49

-1.40

-1.04

-0.85

-3.13

-2.48

2

-0.09

-0.08

0,108

-0.063

-0.03

-0.06

T(



2

-4.61

-2.66

0.116

-0.953

-2.55

-3.30

) Prob.

Overall F

0

0

0

0

0

0

0

0

0

0

0.05

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Prob.

Partial

R

2

F p

 i

0.66 0.75

0 0.79

0 0.84

0.75 0.43

0.28 0.44

0.27 0.6

0.6 0.59

0.31 0.66

0.28 0.66

0.12 0.79

0.145

0.168

0.28 0.79

0 0.87

0 0.87

0.64

0.64

0.02 0.9

0 0.9

0.06 0.25

0.07 0.25

0 0.76

0 0.77

0 0.81

0 0.8

0.41 0.5

0.02 0.57

0 0.86

0 0.85

0.05 0.84

0.73 0.82

0.1 0.74

0.02 0.74

0 0.5

0.04 0.51

0.65 0.81

0.2 0.81

0.03 0.71

0.22 0.7

0 0.15

0 0.16

0.07 0.4

0.05 0.41

0.01 0.79

0.08 0.78

0.03 0.18

0.16 0.09

0.28 0.75

0

0

0.21

0.198

0

0

0.86

0.86

0.74

0.74

0.92

0.91

DW

Statistics

1.95

1.95

1.6

2.26

2.29

1.79

1.87

1.89

1.98

1.9

1.53

2.16

2.15

2.04

1.98

1.63

1.65

1.61

2

1.99

2.15

2.23

1.64

1.52

1.95

1.87

2.05

1.94

0.99

1.25

2.5

2.46

1.49

1.91

2.24

2.17

1.82

1.81

2.29

2.34

2.1

2.11

2.43

2.41

1.9

1.77

2.16

2.10

2.08

2.08

2.07

2.07

26

Australian

Surviving

Funds

Table A3: Alpha and Beta Estimates for Australian Surviving and Collapsed Funds:

Conditional Time Varying Alpha and Beta Models (Models 7 & 8) Using



0

Australian Benchmark with Australian and USA Information



1



2



0



1



2

Prob. Prob.

Overall Partial

F

Prob.

Partial

Prob.

Partial

F  i

XSF1

XSF2

-0.22

*

-0.23

*

-0.08

-0.02 -0.43

0.03 0.76 -0.09 -0.01

0.04 -0.09 0.76 -0.02 -0.01

0.16 0.85 -0.22 -0.01

0

0

0

F 

I

0.97

0.37

0.52

F  i

0.53

0.14

0.01

0.29

0.14

0.01

XSF3

XSF4

0.05

0.04

0.04

0.27

-0.17

-0.32

0.85

0.7

0.05

-0.74

-0.15

0.15

-0.15 -0.05

0.42

# 1.18

-0.2

-0.56

0.48

0.62

-0.09

-0.6

-0.18

0.16

0

0

0

0

0.35

0.47

0.36

0.15

0.04

0

0

0

0.02

0

0

0

XSF5

XSF6

0.09 -0.33 0.4 0.39 -0.08 -0.14

-0.23 0.91 -0.89 0.88 -0.11 -0.13

-0.37

*

-0.07

-0.04 -0.02 0.94 0 -0.11

0.1 -0.96 0.76 -0.05 -0.23

0

0

0

0

0.01

0

0.87

0.06

0

0

0.08

0

0

0

0.02

0

XSF7

-0.63 -0.34 -0.26 0.92 0.01 -0.09

-0.02 -0.06 -0.03 0.92 -0.18 0.05

0

0

0.14

0.94

0.19

0.02

0.52

0.02

XSF8

XSF9

-0.01 0 -0.12 0.87 0.04 -0.13

-0.05 -0.35 0.2 0.93 0.04 -0.09

0.01

0.25

0.15

1.04

-0.27

-0.55

0.88

0.77

-0.01

-0.3

-0.09

0

0

0

0

0

0.73

0.68

0.02

0.19

0

0.04

0.01

0

0

0.03

0.03

0

XSF10

XSF11

XSF12

XSF13

XSF14

XSF15

XSF16

XSF17

XSF18

XSF19

XSF20

XSF21

XSF22

XSF23

XSF24

XSF25

XSF26

0.21 0.07 -0.13 0.74 -0.02 -0.1

0.05 -0.28 -0.04 0.83 -0.09 -0.07

0.05 0 -0.09 0.88 0.04 -0.15

-0.21

* -0.92 0.33 0.7 -0.12 -0.08

-0.08

0.14

0

1.04

-0.01

-0.49

0.63

0.81

-0.03

-0.3

-0.11

0.08

-0.08 -0.04 -0.05 0.69 -0.05 -0.08

-0.01 0.45 -0.37 0.6 -0.15 -0.07

-0.15 -0.01 -0.14 0.63 0.02 -0.17

-1.02 2.99 -1.53 1.15 -0.01 -0.14

-1.64

* -0.66 0.93 1.25 -0.04 0.24

0.05

0.08

0.21

0.06

-0.03

-0.03

0.93

0.98

0.1

0.01

-0.05

0.02

0.95 3.99 -1.07 0.49 -0.7 0.01

-6.29

* -0.19 -3.39 1.26 -0.02 0.3

0.36

0.14

0.19

1.52 -0.69 0.7 -1.01 0.15

0.14 -0.14 0.74 -0.08 -0.1

1.58 -0.42 0.82 -0.42 0.17

0.01 0.16 -0.02 0.86 -0.1 0.08

-0.07 -0.87 0.08 0.78 -0.59 0.1

-0.05

-1.92

0.34

0.75

-0.5

-2.6

1.02

2.59

-0.09

-0.15

0.13

2.47

-16.97

-0.69

-0.2 -8.96 5.31 -0.03 2.47

1.93 -1.41 0.61 -1.55 0.71

-8.7 -0.27 -4.37 1.5 -0.13 0.56

-0.13 -0.37 -0.08 -0.01 -2.57 0.29

-0.25

0.44

-0.04

0.78

0.22

1.17

0.02

2.61

-0.41

-0.66

-0.41

-0.31

0.54

0.53

0.57

0.41

-0.17

-0.99

-0.04

-0.88

-0.04

0.23

-0.17

-0.16

-8.33 -0.18 -4.39 1.08 -0.05 0.17

0.52

1.05

#

0.13

0.34

0.69

0.23

0.24

0.44

-2.18

-0.26

0.77

0.13

-0.08 1.33 -0.58 -0.49 -2.36 -0.38

-3.74

* -0.01 -1.94 0.55 -0.12 -0.01

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.06

0.48

0.44

0.16

0.22

0.88

0.02

0.51

0.46

0.25

0.27

0.75

0.47

0.17

0.81

0.56

0.75

0.14

0.98

0.04

0.59

0.04

0.28

0.33

0.55

0.94

0.48

0.35

0.41

0.23

0.17

0.41

0.25

0.37

0.02

0.08

0.02

0

0.41

0.07

0.55

0.01

0

0

0.49

0.25

0

0.63

0.32

0.01

0.15

0.01

0

0

0

0

0

0.22

0.08

0.1

0

0.01

0.12

0.16

0.45

0.44

0.06

0

0

0

0.7

0

0

0.56

0.02

0.47

0.05

0

0

0.64

0.62

0

0.42

0.7

0

0.04

0.01

0

0

0

0

0

0.09

0.05

0.06

0.02

0.06

0.13

0.09

0.52

0.39

0.11

0

0.01

0.15

@

*

#

Estimates based on USA information are presented in BOLD .


Indicates significantly positive at 5% level of significance (one-tailed test)

R

2

0.6451

0.8492

0.8513

0.8722

0.8793

0.7581

0.7561

0.8191

0.8203

0.8597

0.8634

0.9156

0.9071

0.8692

0.9003

0.9

0.8353

0.8392

0.7388

0.6971

0.6432

0.5954

0.8487

0.8261

0.6884

0.8741

0.4383

0.4378

0.9349

0.9325

0.8142

0.8195

0.9214

0.9224

0.8871

0.8908

0.9067

0.9146

0.7376

0.6507

0.6546

0.6582

0.6783

0.667

0.4662

0.4724

0.919

0.9275

0.6638

0.6651

0.858

0.8543

DW-

Statistics

2.12

2.52

2.52

2.36

2.37

2.34

2.19

1.95

2.06

2.04

2.15

2.14

2.21

2.16

2.74

2.77

2.08

2.12

2.03

2.1

1.9

1.94

2.17

2.02

2.07

1.65

1.7

1.65

2.84

2.89

1.65

1.66

2.06

1.8

1.82

1.8

2.94

2.92

1.55

2.43

2.05

2.12

2.93

2.92

2.1

2.08

2.06

2.07

2.46

2.42

2.74

2.76

27

Au Australian

Sur Surviving

Funds

XSF27

XSF28

XSF29

XSF30

XSF31

XSF32

XSF33

XSF34

XSF35

XSF36

XSF37

XSF38

XSF39

XSF40

XSF41

XSF42

XSF43

XSF44

XSF45

XSF46

XSF47

XSF48

XSF49

XSF50

XSF51

XSF52

XSF53

Average all

Sur @ . Funds

EW + all

Surviving

Funds

Table 3: Continued



0



1



2



0



1

2.27

#

-0.18

8.57 -1.47 0.74 -0.44

0.37 -0.25 0.64

-1.14 -0.35 -0.96 1.27

-0.07

0.33

1.52

0.63

0.04

1.26

0.1

1.33

0.03

4.87

0.87

0.04

-0.02

-0.02

1.58

0.62

0.72

0.87

0.03

-0.65

-0.05

-1.76

-11.26 -0.07 -5.67 2.49 -0.36

2.62

# 5.74 0.18 -0.7 -4.71

-1.1

0.48

#

0.23

1.64

-0.85

-0.08

1.52

0.88

-0.2

-0.19

0.16

0.59

0.1

0.69

0.1

0.72

0.94

1.74

-1.57 -0.06 -0.67 1.75

0.52 2.07 -0.69 0.61

-0.03

0.77

0.01

-1.2



2

-0.15

-0.15

0.08

0.25

0

-0.06

0.94

-1.53

0.54

0.54

-0.13

-0.04

0.54

0.38

0.17

Prob.

Overal l F

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Prob.

Partial

F  i

0.01

0.42

0.31

0.91

0.77

0.97

0.15

0.02

0.14

0.46

0.11

0.38

0.88

0.97

0.43

Prob.

Partial

F  i

0.02

0.71

0.43

0.98

0.4

0.32

0

0

0

0

0.12

0.61

0.29

0.97

0.01

Prob.

Partial

F  i

0.83

0.5

0.64

0.84

0.19

0.26

0

0

0

0

0.25

0.49

0.19

0.82

0

R

2

0.8035

0.7864

0.9573

0.9562

0.9519

0.9529

0.8149

0.8462

0.8388

0.8175

0.8915

0.8888

0.9469

0.9407

0.9182

DW-

Statist ics

0.2 0.12 -0.06 0.67 -0.06 -0.16 0 0.55 0 0 0.9209 1.61

-1.43 -0.09 0.26 0.95 -1.04 -0.01 0 0.91 0.1 0.02 0.1048 2.43

-1.61

* -0.76 1.15 0.86 -0.05 -0.36 0 0.32 0.12 0.05 0.1125 2.46

3.36

# 6.27 1.17 0.24 -2.42 -0.14 0 0.09 0.04 0.1 0.8032 1.9

3.46

2.81

0.35 1.71 0.38 -0.17

5.08 1.16 0.93 -0.02

-0.11

0.1

0

0

0.12

0.48

0.01

0.71

0.01

0.91

0.8064 1.82

0.8787 1.57

0.41

-0.01

0.19 0.14 2.88 -0.2

0.22 -0.01 0.79 -0.41

0.05 0.02 0.05 0.82 0

-2.92 -4.79 -1.48 -0.4 -1.89

-4.01

-0.32

0.22 -2.5 -1.5

0.46 -0.77 0.92 -0.94

-0.75

* -0.23 -0.34 1.08

-0.54 -3.05 0.89 0.82

-0.29

0.07

0.02

-0.39 -0.12 -0.25 1.04

0.08 1.84 -0.98 0.44

0.1

-1.26

-1.89

0.09

-0.09

-0.53

-1.08

0.59

0

-0.11

-0.01

0.19

-0.05

0.45

0

0

0

0

0

0

0

0

0

0

0

0

0.42

0.82

0.77

0.58

0.24

0.06

0.16

0.13

0.42

0.08

0.24

0.05

0

0.02

0.01

0.41

0.04

0.02

0.32

0.38

0.02

0.02

0.01

0.09

0

0.03

0

0.38

0.02

0.01

0.53

0.62

0.03

0.15

0.16

0.16

0.9018

0.8449

0.8455

0.3617

0.4353

0.8498

0.8345

0.8738

0.8738

0.8299

0.831

0.5818

1.85

2.5

2.44

1.72

-0.37

3.53

#

0.12 -0.33 0.56 -0.11

9.24 -0.89 0.21 -1.82

1.75

# -0.29 1.11 0.68 -0.02

-7.69

* -8.57 -6.49 1.65 -0.51

-19.38

* -0.5 -10.73 5.37

-0.49

* -1.32 0.24 0.93

-0.56

* -0.14 -0.24 0.99

0.03

0.27

0.03

-0.53 -1.89 0.25 1.44 1.45

-0.79

0.33

0.09 -0.61 1.39

1.5 -0.48 1.01

0.06

0.21

-0.19 0 -0.11 0.82 -0.03

-0.18 -0.43 0.02 0.37 -0.59

-0.22

-0.63

*

-0.97

*

-1.27

*

0.06

-3.18

-0.17

-0.5

0.38

0.92

-0.07

-2.55 -0.05 0.55 -0.71

-0.58 -0.22 0.34 -0.09

0

-0.94

* -0.29 -0.48 0.91 -0.04

1.65

# 5.74 -0.91 0.96 -0.02

-0.05 0.25 -0.15 1.27 -0.13

-0.23 -0.32 -0.09 1 0.01

0.03

1.24

2.44

-0.02

0.1

-0.21

0.2

-0.06

-0.05

-0.06

-0.11

0.17

-0.14

-0.15

0

0.18

0.41

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.13

0.01

0.07

0.34

0.34

0.56

0.38

0.2

0.82

0.97

0.8

0

0

0

0.09

0.03

0.33

0.43

0.23

0.01

0.14

0.39

0.65

0.45

0.1

0.4

0.8

0.62

0.02

0

0

0

0.03

0.09

0

0.77

0.97

0.01

0.06

0.22

0.31

0.32

0.21

0.89

0.67

0.36

0.03

0

0.01

0.11

0.3

0.67

0

0.98

0.5555

0.5901

0.5524

0.9516

0.9549

0.8241

0.8253

0.9256

0.9246

0.8154

0.9445

0.877

1.72

1.7

1.52

1.8

1.84

2.68

1.27

2.79

2.75

2.94

2.95

2.5

2.64

1.75

2.67

2.74

2.71

1.99

0.8209 1.96

0.5421 1.87

0.4802 1.88

0.9485 1.74

1.65

2.12

0.8828 1.94

0.9988 2.04

-0.62 -0.04 -0.29 1.06

-3.23 -7.47 0.17 0.35

0

-1.4

0.89 -0.13 0.75 0.59 -0.16

0.04

0.21

0.04

0

0

0

0.18

0.37

0.84

0.44

0.31

0.24

0.91

0.7

0.43

0.9989 2.04

0.5717 2.37

0.5763 2.28

2.52

2.58

2.58

2.95

2.93

1.72

1.93

1.7

2.21

2.06

2.3

2.26

1.47

1.35

2.72

-0.02

-1.56

0.775

-0.03

-0.431

-0.80

0.74

1.06

-0.663

-0.58

0.148

0.016

0

0

0.368

0.40

0.176

0.174

0.232

0.205

0.788

0.78

2.19

2.17

0.008

-0.034

0.504

-0.02

-0.40

0.001

0.82

0.80

-0.20

-0.007

0.003

-0.04

0

0

0.007

0.90

0

0.21

0

0.05

0.91

0.91

2.13

2.10

28

Collaps ed

Funds

XSF54

XSF55

XSF56

XSF57

XSF58

XSF59

XSF60

XSF61

XSF62

XSF63

XSF64

XSF65

XSF66

XSF67

XSF68

XSF69

XSF70

XSF71

XSF72

XSF73

XSF74

XSF75

Average

Colla @@

Funds

EW +

Collapsed

Funds

Average of

All Funds

EW + of all

Funds

Table 3 continued



0



1



2



0



1

0.6 2.26 -0.69

0.26

0.36

0.15 -0.02

6.45 -2.34

-0.89 -0.34

0.54 2.69

1.07

-1.07

0.6 -1.21

0.78 0.06

1.11 -0.55

0.95 -0.1

0.88 -0.43

0.11 0.05

-0.77 -3.36

-1.28

* -0.59

-0.2

0.42

1.17

2.13 -6.65 -0.24

-2.58 -0.99 2.52

0.88 -1.84 -0.27

0.79

0.49

0.37

0.63

0.98

0.74

-0.08

-0.16

-0.06

0.17

-0.42

-0.15

0.05 0.05 -0.08 0.9 0.03

-2.24 -1.81 -2.05 -0.25 -1.46

3.64 0.15 1.82

-0.7 -0.86 -0.51

-0.79 0.03 -0.49

-3.02 -3.68 -1.93

-16.7

* -1.26 -8.26

-0.06 0.3 -2.13

0.24

0.06

5.81

0

-2.16

0.31 -0.14

2.69 3.3

0.31

0.61 -1.85

-0.35 -0.18 -0.46

-0.76 0.91 -0.71

0.83 0.92 -5.02

-0.36 -0.74 0.02

0.06

1.05

#

-0.27

0.49

0.31

4.72

0

3.37

-1.43

-1.49

-0.63

-0.69

1.02

1.02

0.15

0.16

1.15 -0.05

0.22 -0.71

0.27 -0.04

0.5 0.73

0.32 0.03

0.49 -1.92

-0.01 -0.02

0.22 0.54

0.21

-0.77

0.87

0.14

-0.1

0.35

-0.56 -0.08 -0.28 -0.24 -0.09

-0.05 1.33 -2.07 1.27 1.43

-1.26 -0.48

-0.55

* -0.92

-0.7

0.13

-0.81

* -0.22 0.57

1.24 10.95 -4.62

1

0.44

0.63

1.28

0.01

-0.47

0.05

-0.28

-1.64

-0.15

1.52

#

0.42 -1.15

0.09 0.22

0.13 -2.31

-0.26 -0.75 -0.36

0.35 0.26

-0.27 -0.65

-1.7

0.3

-0.16 -0.02 -0.13

-0.01 0.44 -0.37

-0.16 -0.02 -0.14

1.66

0.39

0.58

0.81

-0.17

0.69

0.03

0.46

0.86 -0.02

0.8 -0.4

0.84 -0.01

0.6 -0.15

0.63 0.02



2

Prob.

Overa ll F

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.01

0.16

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

-0.36

0.19

-0.19

0.76

2.54

1.45

-0.11

-0.12

-0.1

0.09

-0.23

-0.23

-0.28

0.77

0.15

-0.41

-0.03

-0.43

0.1

0.08

-0.55

0.5

0.58

-0.06

0.12

-0.01

0.2

0.11

-0.07

-0.07

-0.18

0.2

-0.2

-0.05

-0.18

0.03

-0.07

-0.15

-0.33

-0.21

-0.95

-0.15

-0.25

-0.71

-0.08

0.94

-0.12

-0.18

-0.036

-1.375

-0.03

-0.07

0.58

-0.078

0.58

0.06

0.718

-0.046

0.55

0.01

-0.96

-0.71

-0.49

-0.10

-0.588

-0.777

-0.45

-0.03

0.70

0.94

-0.21

-0.027

0.75

0.79

0.727

1.023

0.79

0.79

-0.06

-0.015

-0.53

-0.049

-0.17

-0.018

0.07

0.018

-0.08

-0.08

0.126

0.01

-0.03

-0.06

0

0.01

0

0

0

0.002

0

0

Prob.

Partial

F  i

0.07

0.19

0.01

0.08

0

0

0

0.09

0.03

0.79

0.15

0.03

0.02

0.01

0.23

0.18

0.08

0.4

0.02

0.29

0

0

0.1

0

0

0.13

0

0

0

0

0.71

0.06

0

0

0.09

0.3

0.55

0.18

0.41

0.26

0.24

0.63

0

0

Prob.

Partial

F  i

0.01

0.21

0.01

0.26

0

0

0.13

0.2

0.01

0.75

0.14

0.07

0.01

0

0.39

0.07

0.02

0.14

0.22

0.73

0

0

0.55

0

0.12

0.08

0

0

0

0

0.37

0.02

0

0

0.07

0.19

0.37

0.71

0.42

0.34

0.16

0.29

0

0

Prob.

Partial

F  i

0.54

0.37

0.08

0.58

0.05

0

0.52

0.36

0.67

0.28

0.32

0.61

0.02

0.3

0.76

0.36

0.8

0.02

0.19

0.04

0.01

0.05

0.28

0.91

0.09

0.43

0.18

0.46

0.75

0.03

0.38

0.72

0.94

0.01

0.54

0.03

0.68

0.02

0

0.58

0.51

0.42

0.94

0.63

0.327

0.42

0.123

0.164

0

0.69

0.356

0.409

0

0.91

0

0

0

0

0.161

0.171

0.136

0.184

0

0

0.204

0.20

0

0

R

2

0.643

0.644

0.8043

0.737

0.7374

0.4847

0.5658

0.8089

0.7992

0.7067

0.7281

0.1427

0.1918

0.4224

0.4089

0.788

0.772

0.1809

0.0598

0.7651

0.7487

0.8002

0.8489

0.4541

0.4317

0.5829

0.5879

0.6613

0.6636

0.7915

0.7886

0.8695

0.8747

0.8947

0.8992

0.2507

0.2431

0.7784

0.768

0.8389

0.8403

0.495

0.5649

0.8628

0.8486

0.8319

DW-

Statistics

2.01

2.02

0.868

0.85

0.746

0.743

2.14

2.13

0.92

0.91

2.21

2.11

2.15

2.07

2.21

2.06

2.16

1.64

1.69

2.29

2.35

1.84

1.23

2.52

2.49

1.53

1.79

2.11

1.88

1.93

1.99

2.08

1.96

2.39

2.41

1.9

1.85

2.3

2.42

2.12

2.17

2.44

2.41

1.94

1.79

1.68

1.61

2.02

2.02

2.29

2.24

1.98

1.92

1.99

1.91

2.07

1.94

1.08

29

*

Models

Model 1

Model 3

Model 4

Model 7

Model 8

35

(4)

32

(1)

30

(1)

34

(9)

26

(3)

Table 4: Summary of the Estimated Parameters and Relevant Statistics for Various Estimated Models

Status of Number of Funds

*

Based on



0 , p

Number of Funds significantly Different from

Zero: Univariate t-test

Number of Funds significantly different from zero: Joint Test

Positive Negative Neutral



1 , p



2 , p



0 , p



1 , p



2 , p

Overall

F

Partial

F 

Partial

F 

Partial

F  , 

- - 75 - - 75 - - - 40

(10)

43

(13)

45

((13)

41

(8)

49

(14)

(61)

(61)

(61)

(58)

(58)

-

-

12

8

-

-

14

11

56

60

53

58

20

23

19

21

22

24

22

24

75

75

75

74

-

-

17

10

38

39

37

36

-

-

39

32

Number of Significant funds are presented in parentheses ( ).

30

XSF41

XSF42

XSF43

XSF44

XSF45

XSF46

XSF47

XSF48

XSF49

XSF50

XSF51

XSF52

XSF53

XSF25

XSF26

XSF27

XSF28

XSF29

XSF30

XSF31

XSF32

XSF33

XSF34

XSF35

XSF36

XSF37

XSF38

XSF39

XSF40

XSF14

XSF15

XSF16

XSF17

XSF18

XSF19

XSF20

XSF21

XSF22

XSF23

XSF24

Table A5: Funds Examined in this Study

Fund Identifier

XSF1

Fund Name

AMP Investment Bond - Australian Share

XSF2

XSF3

XSF4

XSF5

ANZ - Equity Trust No 1

AXA NMFM - NM Equity Imputation Fund

BT - Australian Share Fund

Challenger - GrowthLink Trust

XSF6

XSF7

XSF8

XSF9

XSF10

XSF11

XSF12

XSF13

Dresdner RCM - Australian Equities Trust

HSBC - Imputation Growth Trust

Invia - High Asset Trust

Merrill Lynch - Equity Fund

Merrill Lynch - Growth Fund

Nat Aust Super Bond - Equities

Portfolio Partners Inv Trust - Shares

Tower Pers Super Bond - Ethical Growth Series 1

Tyndall - Aust'n Sharemarket Enhanced Fund

AMP No 2 Pooled Super Fund - Direct Investment

Advance Super & RO - Australian Shares

AM Trustees Pooled Super - Australian Equities (E)

AMP Flex Lifetime Super - INVESCO Aust'n Equity

ANZ - Australian Imputation Trust

AXA-NM R Dir A Pens - AXA Ws A Eqty Industrials

BT Lifetime Super Emp - CFS Australian Share

CFM Retirement - Australian Shares Fund

Challenger - SafeLink Trust

Citigroup - Citi Australian Shares NEF

Col Master ADF - ING Australian Share Super

Commonwealth PensionSelect - Australian Shares

Connelly Temple A Pension - Australian Shares

Credit Suisse Private Inv - Australian Shares

Fiducian - Australian Shares Fund

Hedge Funds - Australian Blue Chip Fund A Class

ING Life Flex Retire Ann - Aust'n Shares NEF

INVESCO - Australian Share Fund

IOOF Flexi Trust - Australian Equities NEF

LifeTrack Cashback Pension - Aust'n Equities

Lowell - Australian Sharemarket Fund

Macquarie - Leaders Imputation Trust

Merrill Lynch Super - Australian Shares Class B

MLC Masterkey Unit Trust - Australian Share Fund

Nat Aust Flexible Income Plan - Equity

Norwich A Pension - Australian Shares

NRMA Personal Inv - Australian Shares Growth Trust

Parker Asset - Enhanced Leaders Trust

Perpetual's Investor Choice - Smaller Cos Share

Portfolio Partners Inv Trust - Emerging Shares

Royal & Sun Alliance Superbond NEF - Aust Shares

STL - Premium Equity Fund

Suncorp Metway Investment - Australian Shares Fund

Tower Prestige Investment - Ethical Growth Series2

Tyndall - Australian Share Value Fund

UBS - Australian Share Fund

United Investment Service - Aust'n Shares

Vanguard - Index Australian Shares Fund

Westpac PPSA - BT Wholesale Australian Share Fund

31

XSF54

XSF55

XSF56

XSF57

XSF58

XSF59

XSF60

XSF61

XSF62

XSF63

XSF64

XSF65

XSF66

XSF67

XSF68

XSF69

XSF70

XSF71

XSF72

XSF73

XSF74

XSF75

AM Cashback Annuity - Australian Equities

AMP - Gold Trust

ANZ - Australian Leaders Trust

Bain IMS Sup Employer - Aust Equities

BSL Hi-Yield Equity Trust

Capita Third Universal Flexible Trust

Citicorp Pers Sup&RO - Australian Shares

Col PSL Master Super - Maqhs Aust'n Enhanced Eqtys

County Direct - Smaller Companies Units

Hartley Poynton - Dividend Income Trust

J B Were - 2nd Gold & Natural Res Trust

Liberty Life Prime Inv - Performance

Lifeplan Pers Super - Balanced Growth

Macquarie Trustee's Choice - Aust Eqtys

McIntosh Growth and Guarantee Fund

Merc Mutual - Australian Share Fund No 2

National Aust Investment Bond - Equity

Norwich Rollover - Resources DEF

Occidental Occibonds Private Resources

Potter Warburg Resources Trust

Prudential - Equity Imputation Fund

Tower Super - Ethical Growth Series 1

32

Funds - Australian Centre for Financial Studies

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