Conditional market-timing models for mutual fund per

Transkrypt

Joanna Olbryś*
Conditional market-timing models for mutual fund performance evaluation1
Introduction
Performance evaluation of investment managers is a topic of considerable
interest to practitioners and academics alike. Superior performance may be
achieved through timing (macro-forecasting) and security selection (microforecasting) skills of portfolio managers. Fama suggested that a manager’s forecasting ability could be split into two separate activities [Fama, 1972]:
– microforecasting,
– macroforecasting.
Some researchers have developed models that allow the decomposition of manager performance into market-timing and selectivity skills. This began with the
work of Treynor and Mazuy [Treynor, Mazuy, 1966] and since then numerous
econometric techniques have been applied to this area ([Henriksson, Merton,
1981], [Jensen, 1968], [Henriksson, 1984], [Romacho, Cortez, 2006], [Ferson,
Schadt, 1996], [Ferson, Harvey, 1999]).
The main goal of this paper is a performance evaluation using unconditional and conditional models of timing and selectivity. We compare two methods: the unconditional Treynor & Mazuy (T-M) model [Treynor, Mazuy, 1966]
and the statistical procedure based on the Ferson & Schadt (F-S) conditional
model [Ferson, Schadt, 1996]. The market-timing and selectivity abilities of 15
equity open-end mutual funds have been evaluated for the period January 2003
– April 2009. For comparison, a bear market period from July 4, 2007 to Feb
17, 2009 has been investigated. The overall index of Warsaw Stock Exchange
companies (WIG index) fell from 66951.73 (July 4, 2007) to 21274.28 (Feb 17,
2009). It lost 68.22% during this period.
1. Unconditional and conditional models of timing and selectivity
The traditional performance measurement literature has attempted to distinguish security selection, or stock-picking ability, from market-timing, or the
ability to predict overall market returns. However, the literature finds that it is
not easy to separate ability into two such dichotomous categories. Traditional
unconditional T-M [Treynor, Mazuy, 1966] or H-M [Henriksson, Merton, 1981]
models, in addition to their strong assumptions about how managers use their
abilities, have taken the view that any information correlated with future market
returns is superior information [Ferson, Schadt, 1996, p. 434]. Conditional
models of timing and selectivity assume a semi-strong form of market effi*
1
dr, Wydział Informatyki, Politechnika Białostocka, [email protected]
Zrealizowano w ramach pracy badawczej statutowej S/WI/3/2008
2
Joanna Olbryś
ciency. The idea is to distinguish “market-timing” based on public information
from market-timing information that is superior to the lagged information variables (the F-S model [Ferson, Schadt, 1996] or the F-H model [Ferson, Harvey,
1999]).
1.1. The Treynor-Mazuy (T-M) unconditional model
A classic unconditional market-timing model is the quadratic regression of
Treynor and Mazuy (T-M model) [Treynor, Mazuy, 1966]:
2
rP,t   P   P  rM ,t   P  rM ,t    P,t
(1)
where:
rP,t  R P ,t  R F ,t is the excess return on the portfolio P in period t ,
rM ,t  R M ,t  R F ,t is the excess return on market portfolio M in period t ,
R P,t is the one-period return on the portfolio P ,
R M ,t is the one-period return on the market portfolio M ,
R F ,t is the one-period return on riskless securities,
 P measures selectivity skills of the portfolio’s P manager,
 P is the systematic risk of the portfolio P ,
 P measures market-timing skills of the portfolio’s P manager,
 P,t is a residual term, with the following standard CAPM conditions:


E  P ,t   0; E  P ,t  P ,t 1  0;
If the portfolio manager has the ability to forecast security prices, the intercept
 P in equation (1) will be positive. On one hand, a passive strategy
(random buy-and-hold policy) can be expected to yield a zero intercept. On the
other hand, if the manager is doing worse than a random selection buy-and-hold
policy,  P will be negative.
If a mutual fund manager increases (decreases) the market exposure of the
portfolio prior to a market increase (decrease) then the portfolio return will be a
convex function of the market return and  P will be positive. The size of the
estimate ˆ P informs about the manager’s market skills.
Empirical results obtained using the T-M technique do not support the hypothesis that mutual fund managers are able to follow an investment strategy
that successfully times the return on the market portfolio. The results show neutral or negative performance for mutual fund managers for the period January
2003 – January 2008 in the case of 15 Polish equity open-end mutual funds
[Olbryś, 2008a]. In general, the fact that Polish managers are not really successful as market timers is consistent with most of the literature on mutual fund performance ([Henriksson, 1984], [Fletcher, 1995], [Kao, Cheng, Chan, 1998],
[Romacho, Cortez, 2006]).
Conditional market-timing models for mutual fund performance evaluation
3
1.2. The Ferson – Schadt (F-S) conditional model
Ferson and Schadt derive a conditional version of the Treynor-Mazuy
model [Ferson, Schadt, 1996, p 435]:
2
rP,t   P   P  rM ,t  ' P z t 1  rM ,t    P  rM ,t    P ,t
(2)
where:
rP,t is the excess return on the portfolio P in period t ,
rM ,t is the excess return on market portfolio M in period t ,
 P measures selectivity skills of the portfolio’s P manager,
 P is the systematic risk of the portfolio P ,
 P is the coefficient vector that captures the response of the manager’s beta to
the public information Z t 1 ,
Z t 1 is a vector of lagged instrumental variables for the information available at
time t  1 ,
z t 1  Z t 1  E Z  is a vector of the deviations of Z t 1 from the unconditional
means,
 P is the coefficient that measures the sensitivity of the manager’s beta to the
private market-timing signal,
 P,t is a residual term, with the following standard CAPM conditions:
E  P ,t   0; E  P ,t ,  P ,t 1   0;
The regression (2) may also be interpreted as an unconditional multiple
factor model, where the market index is the first factor and the product of the
market and the lagged information variables are additional factors.  P is a vector with dimension equal to the dimension of Z t 1 . The elements of  P are the
response coefficients of the conditional beta with respect to the information
variables Z t 1 . The term ' P z t 1  rM ,t  in equation (2) controls for the public
information effect, which would bias the coefficients in the original T-M model
(1). The new term in the model (2) captures the part of the quadratic term in the
T-M model (1) that is attributed to the public information variables. In the conditional model, the correlation of mutual fund betas with the future market return, which can be attributed to the public information, is not considered to reflect market-timing ability [Ferson, Schadt, 1996, p 435].
2. The dataset
We have studied monthly ordinary excess returns for 15 selected open-end
equity mutual funds from January 2003 to April 2009 (75 observations). As in
the previous studies that used monthly data, we have implicitly assumed that the
investors evaluate risk and return, and that mutual fund managers trade using a
one-month horizon. Table 1 records the names of the funds, along with summary statistics for the Jan 2003-Apr 2009 period.
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Joanna Olbryś
Table 1. Summary statistics for funds’ excess returns from Jan 2003 to Apr 2009
Standard
Mean
Minimum Maximum
Equity Funds
Deviation
[%]
[%]
[%]
[%]
1 Arka BZ WBK Akcji FIO
1.10
7.32
-26.88
19.90
2 BPH FIO Akcji
0.46
6.24
-19.47
15.61
3 Aviva Investors FIO Polskich Akcji 0.83
6.97
-24.78
17.31
4 DWS Polska FIO Top 25 Małych
0.22
7.33
-23.11
13.79
Spółek
5 DWS Polska FIO Akcji
0.24
6.33
-24.40
15.81
6 DWS Polska FIO Akcji Plus
0.41
6.33
-23.17
15.40
7 ING FIO Akcji
0.48
6.51
-20.09
17.76
8 Legg Mason Akcji FIO
0.80
6.45
-23.70
14.51
9 Millennium FIO Akcji
0.28
6.16
-21.89
15.46
10 Pioneer Akcji Polskich FIO
0.25
7.61
-27.05
22.35
11 PKO/CREDIT SUISSE Akcji FIO
0.18
6.64
-27.21
14.47
12 PZU FIO Akcji KRAKOWIAK
0.27
6.20
-22.51
15.65
13 SEB 3 – Akcji FIO
0.42
6.63
-25.29
20.20
14 Skarbiec – Akcja FIO
0.79
5.98
-20.75
15.61
15 UniKorona Akcja FIO
0.75
6.31
-19.84
16.58
Income Group Average
0.50
6.60
-23.34
16.69
Source: author’s calculations
The monthly returns on the index of Warsaw Stock Exchange companies
(WIG) are used as the returns on the market portfolio. The returns were obtained from www.bossa.pl . The monthly average of returns on 52-week Treasury bills are used as the riskless asset.
2.1. The predetermined information variables
Ferson and Schadt use a collection of public information variables that
previous studies have shown are useful for predicting security returns and risks
over time. The variables are: (1) the lagged level of the one-month Treasury bill
yield, (2) the lagged dividend yield of the CRSP value-weighted NYSE and
AMEX stock index, (3) a lagged measure of the slope of the term structure, and
(4) a lagged quality spread in the corporate bond market [Ferson, Schadt, 1996,
p 437]. In Poland, the suitable variables are:
1) Z 1,t 1 - the lagged monthly dividend yield of the WSE stock index (WIG),
2) Z 2,t 1 - the lagged monthly level of the 1M WIBOR,
3) Z 3,t 1 - the lagged monthly measure of the slope of the term structure; the
term spread is a difference between the average of 2-year Treasury bond
yield and the average of 10-year Treasury bond yield.
We assume that the lagged variables are readily available, public information
over our entire sample period.
Table 2 presents summary statistics for the lagged information variables.
Note that all variables demonstrate high values of variation coefficients.
Table 2. Summary statistics for lagged variables from Jan 2003 to Apr 2009
Variable
Mean
1
Z 1,t 1
2
3
5
Variation
Coefficient
42.89%
Minimum
Maximum
2.49%
Standard
Deviation
1.07%
1.16%
5.83%
Z 2,t 1
0.44%
0.07%
16.99%
0.31%
0.56%
Z 3,t 1
0.014%
0.043%
319.88%
-0.09%
0.09%
Figure 1. The lagged monthly dividend yield of the WSE stock index (WIG) from
Jan 2003 to Apr 2009
Figure 2. The lagged monthly level of the 1M WIBOR from Jan 2003 to Apr 2009
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Joanna Olbryś
Figure 3. The lagged monthly measure of the slope of the term structure from Jan
2003 to Apr 2009
In fact, the additional exogenous variables used in the conditional model
(2) are:

z1,t 1  rM , t  Z1, t 1  E Z1  rM , t ,

z 2, t 1  rM , t  Z 2, t 1  E Z 2   rM , t ,

z3, t 1  rM , t  Z 3, t 1  E Z 3   rM , t .
Fig. 4, Fig. 5 and Fig.6 present this data in the form of charts, respectively. We
have detected (based on Dickey – Fuller test) that the analysed series are stationary.
Figure 4. The lagged exogenous variable z1, t 1  rM , t from Jan 2003 to Apr 2009
Figure 5. The lagged exogenous variable z 2, t 1  rM , t from Jan 2003 to Apr 2009
Figure 6. The lagged exogenous variable z3, t 1  rM , t from Jan 2003 to Apr 2009
7
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Joanna Olbryś
3. Empirical results
Table 3 presents the results of the OLS estimates for the T-M parametric
tests. The DW-statistic values indicate that we have encountered some autocorrelation problems. Autocorrelated disturbances are present in the case of the
following funds: BPH FIO Akcji, Aviva Investors FIO Polskich Akcji, DWS
Polska FIO Top 25 Małych Spółek and Skarbiec – Akcja FIO. The critical values of the DW-test are: d L  1.571, dU  1.680 .To detect for heteroskedasticity we have used White’s test. The results show that the residuals are heteroskedastic only in the case of SEB 3 Akcji FIO. The LM-statistic of this fund
LM  33.67 is higher than the critical value  *2  11.07 .
Table 3. Unconditional T-M model (1) (period from Jan 2003 to Apr 2009)
Equity Funds
̂ P
ˆ P
ˆ P
R 2 DW LM AIC
1 Arka BZ WBK Akcji FIO 0.006* 0.943* -0.460 0.929 2.03 1.57 -373.3
2 BPH FIO Akcji
-0.0003 0.815* -0.314 0.949 1.63 10.21 -421.5
3 Aviva Investors FIO Pol0.004* 0.908* -0.614* 0.957 1.44 0.70 -417.6
skich Akcji
4 DWS Polska FIO Top 25
-0.002 0.850* -0.508 0.756 1.29 3.25 -279.9
Małych Spółek
-0.001 0.820* -0.613* 0.950 2.32 2.45 -420.4
0.0005 0.814* -0.557* 0.935 1.87 3.63 -401.6
Plus
7 ING FIO Akcji
-0.003 0.859* 0.145
0.954 2.10 4.41 -422.8
8 Legg Mason Akcji FIO
0.005* 0.830* -0.679* 0.941 1.72 7.78 -405.4
9 Millennium FIO Akcji
-0.0013 0.788* -0.428 0.917 1.98 1.83 -386.6
10 Pioneer Akcji Polskich -0.005* 1.005* -0.074 0.962 2.13 7.31 -412.9
FIO
11 PKO/CREDIT
SUISSE
Akcji FIO
12 PZU FIO Akcji KRAKOWIAK
13 SEB 3 – Akcji FIO
14 Skarbiec – Akcja FIO
15 UniKorona Akcja FIO
9
-0.0007 0.856* -0.822* 0.952 1.81 4.39
-417.0
-0.0007 0.799* -0.577* 0.941 1.75 4.87
-412.0
-0.0002 0.861* -0.475* 0.946 2.42 33.67 -408.1
0.0037 0.766* -0.367 0.918 2.53 4.60 -391.8
0.0015 0.827* -0.136 0.951 2.31 3.08 -422.2
*Significant at 5%
Source: author’s calculations (using Gretl)
The new, improved models have been estimated using the Cochrane – Orcutt procedure [Osińska, 2005], [Kufel, 2004]. The new model for SEB 3 Akcji
FIO has been evaluated using the WLS procedure [Kufel, 2004, p.121] to receive heteroskedasticity – corrected estimates. We have tested the normality of
the residuals in this case. Table 4 presents final results of the T-M parametric
tests.
The evidence is that all of the funds present significant estimates of the systematic risk ( ˆ P ) at 5% level. Almost every coefficient (except for that of Pioneer Akcji Polskich FIO) lies between 0 and 1. The mean estimate of this coefficient is 0.835. During the period investigated, the mean value of R-squared
was quite high: 0.934. Table 4 provides the evidence of negative market-timing
( ˆ P  0 ). The mean value of this coefficient is –0.426.
The empirical results show no statistical evidence that Polish equity funds’
managers have outguessed the market. We have also observed that only three
funds present significantly positive estimates of selectivity ( ˆ P  0 ).The mean
value of this coefficient is 0.001.
Table 4. Unconditional T-M model (1); heteroskedasticity- and autocorrelationcorrected estimates using the observations from the period Jan 2003 - Apr 2009
Equity Funds
2
̂
ˆ
ˆ
P
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Arka BZ WBK Akcji FIO
BPH FIO Akcji
Aviva Investors FIO Polskich Akcji
DWS Polska FIO Top 25 Małych Spółek
DWS Polska FIO Akcji
DWS Polska FIO Akcji Plus
ING FIO Akcji
Legg Mason Akcji FIO
Millennium FIO Akcji
Pioneer Akcji Polskich FIO
PKO/CREDIT SUISSE Akcji FIO
PZU FIO Akcji KRAKOWIAK
SEB 3 – Akcji FIO
Skarbiec – Akcja FIO
UniKorona Akcja FIO
The Group Average
0.006*
-0.0004
0.004
-0.0001
-0.001
0.0005
-0.003
0.005*
-0.0013
-0.005*
-0.0007
-0.0007
0.0006
0.004*
0.0015
0.001
P
0.943*
0.811*
0.886*
0.693*
0.820*
0.814*
0.859*
0.830*
0.788*
1.005*
0.856*
0.799*
0.817*
0.777*
0.827*
0.835
P
-0.460
-0.288
-0.632*
-0.483
-0.613*
-0.557*
0.145
-0.679*
-0.428
-0.074
-0.822*
-0.577*
-0.402
-0.378
-0.136
-0.426
R
0.929
0.951
0.961
0.809
0.950
0.935
0.954
0.941
0.917
0.962
0.952
0.941
0.931
0.925
0.951
0.934
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Joanna Olbryś
*Significant at 5%
Table 5 presents summary results received based on the conditional model
(2). The elements of ' P  1P  2 P  3P  are the response coefficients of the
conditional beta with respect to the lagged regressors: X 1  z1,t 1  rM ,t ,
X 2  z 2,t 1  rM ,t , X 3  z 3,t 1  rM ,t .
Table 5. Conditional F-S model (2); heteroskedasticity- and autocorrelationcorrected estimates using the observations from the period Jan 2003 - Apr 2009
Equity Funds
̂ P
ˆ P
ˆ1P
Arka BZ WBK
0.006* 0.940* 1.770
Akcji FIO
2 BPH FIO Akcji 0.0001 0.791* 1.442
Aviva Investors
3 FIO Polskich
0.005
0.861* 1.367
Akcji
DWS Polska
4 FIO Top 25
0.001
0.648* -0.932
Małych Spółek
DWS Polska
5
-0.0005 0.798* 3.258
FIO Akcji
DWS Polska
6
0.001
0.792* 3.060
FIO Akcji Plus
7 ING FIO Akcji -0.003 0.861* -1.094
Legg Mason
8
0.006* 0.805* 1.887
Akcji FIO
Millennium FIO
9
-0.001 0.795* -2.250
Akcji
Pioneer Akcji
10
-0.005* 0.957* 12.64*
Polskich FIO
PKO/CREDIT
11 SUISSE Akcji
-0.0004 0.831* 7.241*
FIO
PZU FIO Akcji
12
-0.0002 0.780* 2.440
KRAKOWIAK
SEB 3 Akcji
13
-0.0001 0.839* 6.311*
FIO
Skarbiec Akcja
14
0.004* 0.776* -0.344
FIO
UniKorona
15
0.001
0.856* -2.532
Akcja FIO
The Group
0.001
0.822 2.284
Average
*Significant at 5%
1
AIC
(OLS)
ˆ 2 P
ˆ3P
48.71
-61.11 -0.223
-89,6
5.563
-0.621* 0.957 1.98 -426.4
-87.7* 1.010
-0.942* 0.966 2.16 -418.5
-108.5 156.5
-1.014* 0.821 2.39 -274.6
-3.24
ˆ P
R2
DW
0.931 2.16 -369.4
-86.05 -0.557* 0.956 2.02 -421.2
-38.75 -13.19 -0.700* 0.939 1.90 -400.5
-24.56 33.89
0.023
0.955 2.13 -418.0
-99.43 96.87
-1.160* 0.952 1.83 -414.5
-62.31 -121.2 -0.507
0.918 2.04 -382.0
161.9* 187.6* 0.328*
0.977 1.98 -442.5
136.7* 29.94
0.961 1.89 -425.9
-0.396
-41.14 -16.29 -0.721* 0.945 1.77 -410.4
-18.42 -60.59 -0.413* 0.964 2.02 -425.9
-34.51 -98.61 -0.373
0.926 2.04 -386.9
-22.77 -8.81
-0.202
0.955 2.06 -416.5
-18.90 3.04
-0.490
0.942 -
-
The evidence regarding the mean value of R-squared is similar to that from
the model (1) in Table 4. During the period investigated, the mean value of the
R-squared was slightly higher and equal to 0.942. All of the funds present sig-
Conditional market-timing models for mutual fund performance evaluation 11
nificant estimates of the systematic risk ( ˆ P ) at 5% level. Each coefficient lies
between 0 and 1. The mean value of this coefficient is equal to 0.822.
We have used Cochrane – Orcutt procedure to correct autocorrelated error
terms in the case of eight funds, and Table 5 reports final empirical results from
the conditional F-S models. To detect for heteroskedasticity we have used
White’s test. Although not reported in the paper, the results show that for all of
the funds the LM-statistics have been lower than the critical value  *2  21.03 ,
so we have no grounds for rejecting the null hypothesis that the residuals are
homoskedastic.
Additionally, we have used the VIF test to detect for multicollinearity.
The major undesirable consequence of multicollinearity is that the variances of
the OLS estimates of the parameters of the collinear variables are quite large.
The inverse of the correlation matrix is used in detecting for multicollinearity.
The diagonal elements of this matrix are called variance inflation factors VIFi .
One interpretation is that it is a measure of the amount by which the variance of
the ith coefficient estimate is increased (relative to no collinearity) due to its
linear association with the other explanatory variables. As a rule of thumb, for
standardized data, a VIFi  10 indicates harmful collinearity. In the investigated sample of equity funds’ F-S models, no multicollinearity has been found.
For selected equity funds, the factors X 1  z1, t 1  rM , t , X 2  z 2,t 1  rM ,t
and X 3  z 3,t 1  rM ,t included in equation (2), do not seem to have an important
role in explaning mutual fund excess returns. In fact, only one fund (Pioneer
Akcji Polskich FIO) exhibits significant estimates of ˆ1P , ˆ 2 P , ˆ3 P coefficients. We have used Akaike Information Criterion (AIC) to compare T-M
models (see Table 3) and F-S models (see Table 5). Lower values of the AIC
index indicate the preferred model, that is, the one with the fewest parameters
that still provides an adequate fit to the data. The evidence is that only in the
case of seven funds, the regressors addition caused a little decrease in the AIC
index. To sum up, the lagged variables Z 1,t 1 , Z 2,t 1 , Z 3,t 1 are not very useful
for improving the quality of the market-timing models for Polish equity openend mutual funds.
4. Empirical results in a bear market period
The period from July 4, 2007 to Feb 17, 2009 was a bear market period.
The overall WIG index fell from 66951.73 (July 4, 2007) to 21274.28 (Feb 17,
2009). It lost 68.22% during this period (Fig. 7). We have studied monthly ordinary excess returns for 15 selected open-end equity mutual funds in this period
(20 observations).
Figure 7. The WIG index in the period from July 4, 2007 to Feb 17, 2009
12
Joanna Olbryś
Table 6 reports final empirical results of the unconditional T-M tests in the
bear market period. Although not reported in the paper, the values of DWstatistic show evidence that residuals are autocorrelated in the case of five
funds: Arka BZ WBK Akcji FIO, BPH FIO Akcji, DWS Polska FIO Akcji,
PZU FIO Akcji KRAKOWIAK and SEB 3 – Akcji FIO. To detect for heteroskedasticity we have used White’s test. The results have shown that the residuals are heteroskedastic in the case of BPH FIO Akcji and Millennium FIO
Akcji. Hence, we have used the Cochrane – Orcutt procedure to correct autocorrelated error terms and the WLS procedure to receive heteroskedasticity – corrected estimates. We tested the normality of the residuals in this case.
Table 6 provides the evidence of negative, but not significant markettiming ( ˆ P  0 ), in the case of 14 funds (except BPH FIO Akcji). The mean
value of this coefficient is -0.361. All of the funds present significant estimates
of the systematic risk ( ˆ P ) at 5% level. The mean estimate of this coefficient is
0.843 and it is almost equal to this in Table 4. Note that the poor quality of the
models can be attributed to small sample size.
Table 6. Unconditional T-M model (1) in a bear market period from July 2007 to
Feb 2009
Equity Funds
̂ P
ˆ P
ˆ P
R2
1
2
3
4
5
6
7
8
Arka BZ WBK Akcji FIO
BPH FIO Akcji
Aviva Investors FIO Polskich Akcji
DWS Polska FIO Top 25 Małych Spółek
DWS Polska FIO Akcji
DWS Polska FIO Akcji Plus
ING FIO Akcji
Legg Mason Akcji FIO
0.006
-0.004*
-0.003
-0.036*
0.006
-0.008*
-0.014*
0.005
0.911*
0.955*
0.986*
0.694*
0.972*
0.828*
0.629*
0.857*
-1.310
0.740*
-0.107
-0.479
-0.289
-0.393
-0.724
-0.545
0.942
0.982
0.987
0.765
0.953
0.950
0.943
0.965
9
10
11
12
13
14
15
Millennium FIO Akcji
Pioneer Akcji Polskich FIO
PKO/CREDIT SUISSE Akcji FIO
PZU FIO Akcji KRAKOWIAK
SEB 3 – Akcji FIO
Skarbiec – Akcja FIO
UniKorona Akcja FIO
The Group Average
-0.007*
-0.008*
-0.007
-0.005*
-0.005
-0.0015
-0.002
-0.004
0.848*
1.053*
0.851*
0.878*
0.786*
0.710*
0.864*
0.843
-0.056
-0.067
-0.931
-0.107
-0.930
-0.621
0.123
-0.361
0.998
0.977
0.954
0.984
0.965
0.874
0.959
0.957
*Significant at 5%
Conditional F-S models (2) have not been estimated in the bear market period from July 2007 to Feb 2009 because of their low quality (see Table 5) and
small sample size. Misspecifying the timing function may cause violations of
regression assumptions in unknown and possibly time-varying ways, so that
standard corrections for heteroskedasticity and serial correlation may not fully
capture the effect of these violations on the standard errors of regression coefficients. Such models may generate false evidence of market-timing abilities.
Conclusion
In this paper we have examined the usefulness of the unconditional T-M
and the conditional F-S models for the investment managers’ performance
evaluation. While Ferson’s and Schadt’s empirical investigations of conditional
market-timing models are adequate to illustrate that the use of conditioning information is important, they do not advocate using them to evaluate managers in
practice [Ferson, Schadt, 1996, p.453]. The evidence on Polish market shows
that the quality of the conditional models is rather low (Table 5). Probably the
selected lagged variables are not very appropriate for timing and selectivity
modelling and it seems to be the main reason why these models are not better in
comparison with the unconditional versions.
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Joanna Olbryś
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Zastosowanie warunkowych modeli market-timing do oceny wyników funduszy inwestycyjnych
Streszczenie
Pierwszy parametryczny model market-timng (tzw. wyczucia rynku) zaproponowali w
1966 roku Treynor i Mazuy (model T-M). Technika market-timing zarządzania portfelem polega na wyborze momentu dokonania inwestycji oraz czasu jej trwania w oparciu
o krótkoterminowe oczekiwania cenowe, na podstawie obserwacji całego rynku (przewidywanie w skali makro). W odpowiedzi na zapotrzebowanie praktyków pojawiły się
w literaturze przedmiotu modele wspomagające ocenę jakości zarządzania portfelem
pod kątem analizy umiejętności w zakresie stosowania technik market-timing. Celem
artykułu jest porównawcza analiza empiryczna umiejętności wyczucia rynku przez zarządzających portfelami OFI akcji z wykorzystaniem parametrycznego modelu T-M
oraz warunkowego modelu F-S Ferson’a i Schadt’a (1996). Badaniem objęto grupę 15
funduszy akcji na rynku polskim w okresie styczeń 2003 – kwiecień 2009.
16
Joanna Olbryś
Performance evaluation of investment managers is a topic of considerable interest to
practitioners and academics alike. Superior performance may be achieved through timing (macro-forecasting) and security selection (micro-forecasting) skills of portfolio
managers. The main goal of this paper is a performance evaluation using unconditional
and conditional models of timing and selectivity. We compare two methods: the unconditional Treynor & Mazuy (T-M) model [Treynor, Mazuy, 1966] and the statistical procedure based on the Ferson & Schadt (F-S) conditional model [Ferson, Schadt, 1996].
The market-timing and selectivity abilities of 15 equity open-end mutual funds have
been evaluated for the period January 2003 – April 2009.

Conditional market-timing models for mutual fund per

Transkrypt

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