Testing the efficacy of Information Transmission:
Is equity style index better than sectoral index?
Wee-Yeap Lau1
Faculty of Economics & Administration and
University of Malaya Graduate School of Business (UMGSB)
University of Malaya
Kuala Lumpur
Email: wylau@um.edu.my
Lee Chin
Department of Economics,
Faculty Economics and Management
Universiti Putra Malaysia
Serdang
1
Corresponding author. No part of this paper can be reproduced without prior permission from
the authors.
Testing the efficacy of Information Transmission:
Is equity style index better than sectoral index?
Abstract
Equity style index has become popular in financial markets with the seminal paper on
attribution of fund performance and asset allocation by Nobel Laurette William Sharpe
(1992). While there is yet to be a study that links equity style index and leading economic
index, this paper hypothesizes that equity style index is better in transmitting economic
information compared to stock market sectoral index. As shown by statistical evidence,
equity style index is a better candidate than KLCI Industrial Index as a component in the
Malaysian Leading Economic Index (LEI). Notably, this study adds to another stylized
fact that equity style index has economic content and the ability to transmit information.
This further underscores the importance of equity style index as it is more sensitive to
detect the change in business cycle. The study provides support to review the components
of leading economic index.
Keywords : information transmission, economic content, equity style index, leading
economic index, emerging capital market, time series analysis, vector autoregression,
cross-correlation function
JEL classifications: G11, G18, G23, L51
2
INTRODUCTION
Equity style index has been widely used in the field of investment since the advent of
return-based style analysis by Sharpe (1992). The style index is used as a factor in
decomposing the fund returns to their respective allocation in different asset classes.
Many researchers have followed suit in their respective research like Fama and French
(1992, 1993, 1996), Sharpe et al. (1993), and Lakonishok et al. (1994). In addition,
International equity styles have also been studied by Sharpe et al. (1993) and Sinquefield
(1996).
The popularity of equity style indices is further propelled with the introduction of Style
Box by Morningstar in late nineties. In the money management industry, many equity
style indices have been created as benchmark for performance attribution, evaluation and
measurement. Various types of funds are also been classified using the equity style
indices created by different Asset Management Companies (AMC).
The study on the role of equity style index is important in the context of emerging capital
market. Without a proper recognition of its role, from investment product point of view,
investors lose out an opportunity to have additional investment products which focus on
different segments of the stock exchange. Likewise, fund managers would lack of the
chance to benchmark their performance if they intend to focus specifically on the growth
/ small and value / large segments of the stock exchange.
Interestingly, there is yet to be a study that links equity style index and stock index, or
equity style index and economic index. If equity style index is indeed that useful, it
should possess economic content that is justifiable for its usage in capital market. In this
study, firstly, we intend to link equity style index to leading economic index and stock
market index. Secondly, we intend to examine the role of information transmission of
KLCI Industrial index --- a stock market sectoral index as compared to equity style
index. In this respect, we will use vector autoregression and cross-correlation function
techniques to further unravel the difference between the two.
As known by many, leading, coincident and lagging economic indexes are published by
Department of Statistics on monthly basis to gauge the business sentiment. They are
composite indexes as within each indexes, there are components that underpin the
measurement. For example, for leading economic index (LEI), there are eight leading
indicators used. Leading indicators inform users on where the economy is heading,
particularly for the forecasted period of six-month ahead. Among the earlier signs that an
ongoing expansion may start to decelerate is a sustained decline in the leading growth
rate.
On the other hand, coincident indicators inform users on the current state of the economy.
In contrast, lagging indicators inform users what had happened to the economy,
especially on performance of cyclical movements of the leading and coincident indicators.
In this respect, we examine the components of leading economic indexes as shown in
table 1.
3
Table 1 The Components of Leading, Coincident and Lagging Economic Indexes
Coincident Index Components
1 Index of Industrial Production
2 Real Gross Imports
3 Real Salaries and Wages, Manufacturing
4 Total Employment, Manufacturing
5 Real Sales, Manufacturing
6 Real Conributions, EPF
1
2
3
4
5
6
7
8
Leading Index Components
Real Money Supply, M1
KLSE Industrial Index
Real Total Traded: Eight Major Trading Partners
CPI for Services, Growth Rate (Inverted)
Industrial Material Price Index, Growth Rate
Ratio of Price to Unit Labour Cost, Manufacturing
Number of Housing Permits Approved
Number of New Companies Registered
1
2
3
4
5
Lagging Index Components
7-day Call Money, Rate
Real Excess Lending to Private Sector
Number of Investment Projects Approved
Number of Defaulters, EPF (Inverted)
Number of New Vehicles Registered
Source: Department of Statistics, Malaysia (December , 2003)
As shown in table 1, coincident, leading and lagging indices have six, eight and five
components respectively. These economic indicators are jointly developed by the
Department of Statistics, Malaysia and Center for International Business Cycle Research
(CIBCR) at Columbia University.2 Among the components, only KLSE Industrial Index
--- a stock market sectoral index is included in the leading economic index.
From economic perspective, the efficacy of information transmission role played by
leading economic index becomes questionable if its component is insensitive to the
change of business cycle. This would enable inaccurate signal on the part of data
collection and dissemination by Department of Statistics and Central Bank. If sectoral
index is replaced by equity style index, would the leading index become more sensitive to
change in the business cycle?
Henceforth, this paper intends to investigate the role played by equity style index in
transmitting information in capital markets, and justify on economic grounds that equity
2
Refer p.5 of the publication of Department of Statistics, Malaysia on “Malaysia
Economic Indicators: Leading, Coincident and Lagging Indices”, December 2008.
4
style index has its role to play in the process of information flow. Thus, further research
in the future can be done to identify steps that could further enhance the informational
efficiency among these indices.
The rest of this paper is arranged as follow: Section two discusses the prior studies on
leading, coincident economic indicators and their relevance with business cycle. This is
followed by a number of studies that utilize growth value style index in recent years.
Section three discusses data and methodology used. Section four reports the findings, and
final section concludes the study.
LITERATURE REVIEW
The initial study related to leading and coincident economic indices was undertaken by
Burns and Mitchell (1945) and their colleagues at the National Bureau of Economic
Research (NBER) in 1937 as part of research program on business cycles. The indicators
developed by them have been useful in constructing leading and coincident indexes that
are used for summarizing and forecasting macroeconomic activity. In other earlier studies,
Yeats (1973) reviews historical performance of the Federal Reserve Board Sensitive
Price Index (SPI). He finds that the SPI performs well as an indicator for business
conditions. Stekler et al. (1968) examines the two aspects of the forecasting record of the
index of 12 Leading Series (ILS). Their results indicate that ILS is a better indicator than
a quantitative predictor.
Stock and Watson (1989) revise those earlier indices using the tools of modern time
series econometrics. As a result, they have created three experimental monthly indexes:
an index of coincident economic indicators (CEI), an index of leading economic
indicators (LEI) and a Recession Index. They report that their experimental CEI tracks
the coincident index produced by the Department of Commerce (DOC), although the
methodology of producing these indexes differ. They relook into the definition of
coincident indicators (defined as the “reference cycle” by Burns and Mitchell (1946, p.
46)) as a broad-based swings in economic activity i.e. whether to allow swings in the
GNP defined as the reference cycle. They conclude that it is more appropriate to state that
the reference cycle reflects co-movements in a broad range of macroeconomic aggregates
such as output, employment and sales. Hence, they define the CEI as an estimate of the
value of a single unobserved variable, Ct as “the state of the economy”. They use
“dynamic factor” model for the coincident variables to measure the co-movements across
aggregate several time series. On the other hand, they propose a new LEI is the estimate
of the growth of the unobserved factor over the six months, using a set of leading
variables. They are more interested in the relative growth rather than the absolute level of
economy activity.3
Refer to p.355 of Stock and Watson (1989) “…The original NBER and DOC leading index can
be thought of as forecasts of the level CEI several months ...”
3
5
Diebold el al. (1989) evaluate the ability of the composite index of leading indicators to
predict business cycle turning points. They use formal probability assessment scoring
rules to turning points generated from the leading index via a Bayesian sequential
probability recursion. Layton et al. (1989) develop indicators that are combined to make
a composite coincident index of aggregate economy activity in the service sector. They
also construct a composite leading index and, they find that the index is able to give
advance warnings of major swings in the growth of the service sector. In another study,
Diebold et al. (1991) examine the ability of the composite index of leading economic
indicators to predict future movements in aggregate economic activity. They perform
real-time analysis i.e. partially revised data for the leading index along with recursive
out-of-sample forecasts. They find substantial deterioration of forecasting performance in
the real time framework. Filardo (1994) examines the differences in expansionary and
contractionary phases of business cycle. He finds that many of the economic variables
that determine the time-varying probabilities help to predict turning points.
Hamilton et al. (1996) find that the composite leading index (CLI) is useful for
forecasting gross national product, both in sample and in out-of-sample real-time exercise.
They find that better forecasts are provided by a simple linear relation between current
GNP growth and the growth rate of the CLI during the previous quarter along with an
error-correction term. Kim et al. (1998) study the business cycle turning points by
creating a new coincident index and testing the duration dependence based on a dynamic
factor model with regime switching. They find the new index was useful in practice and
both the features of the business cycle are relevant.4 Daniel (1999) examines the episodes
of banking system distress and crisis in a large sample of countries to identify which
macroeconomic and financial variables can be used as leading indicators. Birchenhall et
al. (1999) use logistic classification methods for identifying and prediction of postwar
U.S. business cycle expansion and contraction regimes as defined by NBER reference
turning-point dates.
Forni et al. (2001) develop a methodology for the construction of coincident and leading
indicators for Euro Area. They find that the coincident indicator is well forecasted by the
average leading variables. Maximo et al. (2002) propose an optimal filter to transform
Conference Board Composite Leading Index (CLI) into recession probabilities the US
economy. They analyze the accuracy of CLI in anticipating US output growth. They
confirm the usefulness of CLI, even in real-time analysis.
DATA & METHODOLOGY
This study uses three types of data. The first type of data used is Malaysia economic
indicators which consist of leading economic index (“LEI”), coincident economic index
(“CEI”) published by the Department of Statistics, Malaysia on month-to-month basis.
The sample used for this study starts from May 1997 to July 2007.
4
The two defining characteristics of business cycle are (i) co-movement among economic
variables through the cycle and (ii) non-linearity in the evolution of the business cycle, that is,
regime switching at the turning points of the business cycle.
6
The second type of data used is Morgan Stanley Capital International (MSCI) equity style
indices which consist of six style indices.5 They are MSCI Malaysia Standard Value Style
Index (“Value”) and MSCI Malaysia Standard Growth Style Index (“Growth”). Sample
used all style indices consists of monthly data from May 1997 to July 2007. There are
123 observations for each of this series.
The third type of data used is stock market indices which consist of two indices. They are
Kuala Lumpur Composite Index (“KLCI”) and KLCI Industrial Index (“Industrial”)
sourced from Kuala Lumpur Stock Exchange. Sample period is from May 1997 to July
2007, which consists of 123 observations.
Unit Root test
The augmented Dickey-Fuller (1979) test is an extension from the Dickey-Fuller (DF)
test by adding the lagged terms of the dependent variables to ensure that error terms are
not autocorrelated. The lag length is determined by choosing the value that minimizes the
Akaike Information Criterion (AIC), with the maximum set at 12.
The ADF test equation is
p
yt    t  yt 1    i yt i   t
(1)
i 1
where t =,1,2,…, n. n is the sample size and  t is the error term. Equation (3.1) is also
augmented with a time trend component to allow for possible presence of deterministic
time trend. The  t statistic does not follow the student-t distribution under H0. The
empirical distribution for the test is tabulated by Mackinnon (1996). If H0 is rejected, the
series yt is integrated of order 0 or I(0), and is stationary. Otherwise, the series is
integrated of order 1 or I(1).
The PP test equation is
yt    t  yt 1   t
(2)
The hypothesis to be tested is
H0:   0 versus H1:   0
Non parametric corrections are made to the test statistic to account for serially correlated
and heteroscedastic error term. The critical values are given by Mackinnon (1996). If H0
is rejected, the series yt is said to be stationary.
In order to ascertain the unit root test, stationarity tests have also been carried out. In this
instance, KPSS test by Kwaitowski, Phillips, Schmidt and Shin (1992) is used. Under the
5
Both “equity style indices” and “style indices” are used interchangeably in this study.
7
null hypothesis, the series yt is assumed to be stationary. On the contrary, under the
alternative hypothesis, yt is non-stationary. So that by default under the null the data will
appear stationary.
H : y  I(0)
0
t
H : y  I(1)
1
t
Granger’s Causality Test
Next, causality tests are used to assess the information content of leading indicators.
Granger’s (1969) test used within a bivariate context, states that if a variable x Granger
causes the variable y, the mean square error (MSE) of a forecast y based on the past
values of both variables is lower than that of a forecast that uses only past values of y.
Equation (3.3) shows the autoregression where the Granger causality test is carried out6:
p
p
i 1
i 1
yt      i yt i    i xt i   t
(3)
and testing the joint hypothesis
H 0 :  1   2  .....   p  0
H1 : At least one of the  i is not equal to zero
If the asymptotic chi-square test rejects the H0, then Granger causality from the leading
indicator x to the coincident indicator y is established. A significant test statistic indicates
that the leading indicator x has predictive value for forecasting movements in the chosen
coincident indicator y, over the information contained in the past of y.
Vector Autoregression (VAR) Model
VAR (p) Model (if Y and X are not co-integrated)
Yt =  0 +  1 X t +  t ………………..……. I (0)
 t = Yt -  0 -  1 X t .…………..................... I (0) (Regression is not spurious)
p
p
Yt = a1, 0 +  a1,1,i Yt i +  a1, 2,i X t i +  1,t
i 1
i 1
p
p
i 1
i 1
X t = a 2 , 0 +  a 2,1,i Yt i +  a 2, 2,i X t i +  2 ,t
Akaike Information Criterion (AIC) and Hannan-Quinn (HQ) information Criterion
are used to determine the appropriate lag structure of these models.

AIC= n u 2 t + 2k
6
(4)
However, differencing is only restricted to variables with unit roots.
8

2
HQ = n u t + 2k . ln(ln( n))
(5)

Where, u t =Residuals.
k = The number of parameters including intercept.
Next, we proceed to VAR model estimation, and followed by Multivariate Granger
Causality Test. The concept of causality proposed by Granger (1969) states that of X is
said to Granger-cause Y if Y can be forecasted better using past Y and X than just past Y.
In Granger’s causality test, the direction of causal effect between equity style indices and
stock indices, equity style indices and economic indicators, stock indices and economic
indicators is tested using restricted F test statistic as shown equation (6). Using optimal
lag length, the parameter of the model is tested with null hypothesis that there is no
Granger causality between two series. As proposed by Engle and Granger (1987):
F =
( RSS R  RSS U ) / p
RSS U /( n  2 p  1)
where,
(6)
RSSR = Residual Sum of Square for Restricted model
RSSU = Residual Sum of Square for Unrestricted model
P = The number of restricted parameters.
n
= Sample size.
Cross Correlation Analysis
In order to ascertain whether there is relationship the economic indicators and MSCI
Style Indices, the statistical methodology such as cross correlation analysis in equation (7)
is used.
rxy (k ) 
c xy (k )
c xx (0)c yy (0)
(7)
Where:
c xy
= sample cross covariance of the two series at lead k
c yy and c xx = autocovariances
The focus is on the bivariate correlations between two pairs of indices, where x refers to
the input or independent indicator, y refers to the output or dependent indicator. For
every pair of indicators, analysis is conducted for both directions.
9
RESULTS AND DISCUSSION
The descriptive statistics are shown as below. The data shown are at their level form.
Table 1: Descriptive Statistics
LEI
CEI
KLCI
INDUSTRIAL GROWTH
VALUE`
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
111.9146
107.2000
153.0000
80.90000
21.06235
0.274197
1.808670
103.0187
102.1000
122.7000
85.40000
10.75063
0.009767
1.996161
793.4977
779.2800
1373.710
302.9100
197.0823
0.552798
3.953978
1586.717
1450.980
2635.930
639.4400
426.7843
0.314005
2.584559
47.69187
46.50000
100.0000
22.20000
13.17089
1.399041
6.152321
106.3748
104.7000
200.7000
29.00000
32.75369
0.395683
3.680999
Jarque-Bera
Probability
8.815018
0.012185
5.166377
0.075533
10.92864
0.004235
2.905811
0.233890
91.05272
0.000000
5.586350
0.061227
Sum
Sum Sq. Dev.
13765.50
54121.93
12671.30
14100.27
97600.22
4738653.
195166.2
22221672
5866.100
21163.61
13084.10
130882.2
Observations
123
123
123
123
123
123
Figure 1: The Graphs of MSCI Style Benchmark and Economic Indicators
300
140.00
120.00
250
100.00
80.00
MSCI
Economic Indicators
200
150
60.00
100
40.00
50
20.00
0.00
Ma
yJu 97
Se l-97
p
No -97
v
Ja -97
nMa 98
r
Ma -98
yJu 98
Se l-98
p
No -98
v
Ja -98
nMa 99
r
Ma -99
yJu 99
Se l-99
p
No -99
v
Ja -99
n
Ma -00
r
Ma -00
yJu 00
Se l-00
p
No -00
v
Ja -00
nMa 01
r
Ma -01
yJu 01
Se l-01
p
No -01
v
Ja -01
n
Ma -02
r
Ma -02
yJu 02
Se l-02
p
No -02
v
Ja -02
nMa 03
r
Ma -03
y03
0
Coincident
Lead
Lag
Growth
Value
10
Figure 1 shows the trends of MSCI Value and MSCI Growth Indexes from May 1997 to
May 2003. MSCI Growth and Value Indices started at 100 points on May 30th, 1997 and
ended with 38.042 and 98.254 points respectively at the end of May, 2003. On the other
hand, the coincident, leading and lagging indices started at 177.2, 156.1 and 198.9 points
respectively on May 30th, 1997 and ended up with 211.06, 206.1 and 258 points
respectively at the end of May, 2003.
Unit Root Test Results
All series are transformed into natural logarithmic form before the unit root test is
conducted. Phillips-Perron (PP) tests for unit root are conducted on the logarithmic series
of the respective variable of leading economic index ( ln LEI ), coincident economic
index ( ln CEI ), Kuala Lumpur Composite Index ( ln KLCI ), KLCI Industrial Index (ln
Industrial), MSCI Malaysia Value Style Index ( ln Value ) and MSCI Malaysia Growth
Style Index ( ln Growth ).
The first model with a constant is a random-walk process with drift that will exhibit a
definite trend in the series when the coefficient of lagged dependent variable becomes
zero. The second model allows for a non-stochastic time trend. The PP test uses both the
models that allow for a constant, constant and deterministic trend. As shown in table 1,
under PP test, for model with constant, lnGrowth is stationary. For second model, PP test
has shown that lnKLCI , lnValue, lnGrowth and lnIndustrial are stationary with constant
and trend.
As for lnLEI and lnCEI, they are stationary after taking first difference. In this respect,
both series are stationary for both constant, constant and trend models. According to
Hallam and Zanoli (1993) and Obben (1998), the result from PP test is more preferred for
small sample study as the test is more powerful.
For KPSS test, for model with constant and trend, lnKLCI and lnValue do not rject Ho.
Hence, both are stationary. As for , lnGrowth and lnIndustrial, Ho is rejected at 10 percent level.
Since PP test has shown they are stationary, we follow the result from PP test. Finally, lnLEI and
lnCEI are stationary after taking first difference.
Granger Causality Results
We proceed to forming vector autoregression model (VAR) and conduct Granger’s
Causality test for first model without equity style indexes. Lag length criteria is checked
and the model is at its optimal lag length one. However, as shown in table 3, none of the
series shows any causality with significant result.
Next, a second model is formed by taking into account both MSCI value style and growth
style indexes. Based on the VAR model in table 4, looking at the last column, the
asymptotic Granger Chi-squared statistics are statistically significant at one per cent for
growth style index to granger cause LEI, KLCI, KLCI industrial index and value style
index.
11
Table 2: Unit Root and Stationary Test Results
Variable
Level
lnKLCI
lnValue
lnGrowth
lnLead
lnCoincident
lnIndustrial
No trend
-1.844057
-1.031342
-2.87750*
0.575757
-0.164779
-1.296994
Phillips-Perron
First difference
Trend
-3.4451**
-3.3930*
-3.5635**
-2.915043
-2.277873
-3.9622**
No trend
Trend
11.0601***
11.9745***
11.1320***
11.9553***
Kwiatkowski-Phillips-Schmidt-Shin
Level
First difference
No trend
0.719254**
1.022061***
0.303683
1.302649***
1.261710***
0.958564
Trend
0.109003
0.045785
0.162654*
0.085903
0.068268
0.121535*
No trend
0.157068
0.064358
The asterisks *, ** and *** denote statistical significance at 1%, 5% and 10% level respectively. Critical values are based
Kwiatkowski,Phillips,Schmidt and Shin (1992). For Phillips-Perron test, critical values are based on Mackinnon (1996).
Trend
0.066795
0.057711
Table 3: Granger Causality test results based on VAR for four series
Variables
Dependent
Variables
 lnLEI
 lnLEI
 lnCEI
lnKLCI
lnIndustrial
0.753179
( 0.385)
0.024698
(0.875)
0.00092
( 0.975)
2.205876
( 0.137)
1.05520
(0.304)
 lnCEI
0.75106
( 0.386)
lnKLCI
0.01126
(0.915)
0.28708
( 0.592)
lnIndustrial
0.23210
(0.6300
0.13766
(0.710)
0.79283
(0.373)
0.42272
( 0.515)
*, ** and *** denote statistical significance at 1%, 5% and 10% level respectively
All estimates are asymptotic Granger Chi-squared statistics. Values in parentheses are p-values.
Table 4: Granger Causality test results based on VAR for six series
Variables
Dependent
Variables
 lnLEI
 lnLEI
 lnCEI
lnKLCI
0.6733
( 0.411)
5.3055
(0.021)**
0.1937
(0.659)
2.1431
(0.143)
6.9721
( 0.008)***
2.0411
(0.153)
1.0169
( 0.313)
0.5246
( 0.468)
1.4344
(0.231)
0.0263
(0.871)
4.0266
(0.044)**
13.296
(0.000)***
1.4744
(0.225)
9.6592
( 0.002)***
lnIndustrial lnValue
 lnCEI
0.2285
( 0.632)
lnKLCI
1.2049
(0.272)
0.5046
(0.477)
lnIndustrial
0.5931
(0.441)
0.2799
(0.596)
5.4337
(0.019)**
lnValue
1.558129
(0.211)
0.5709
( 0.449)
8.5005
( 0.003)***
0.2154
( 0.643)
lnGrowth
0.7319
(0.392)
0.5453
(0.460)
7.7909
(0.005)***
0.1424
(0.706)
lnGrowth
14.075
(0.000)***
3.0108
(0.083)*
*, ** and *** denote statistical significance at 1%, 5% and 10% level respectively
All estimates are asymptotic Granger Chi-squared statistics. Values in parentheses are p-values.
Figure 2: Short-run Causal relationship
LEI
Growth Style
KLCI
Industrial
Value Style
The relationship from table 4 is summarized in figure 1. It is found that there is unidirectional causality from growth style to LEI as well as growth style to KLCI Industrial
Index. There is also bidirectional relationship between growth style index and KLCI as
well as between growth style index and value style index. It can be seen growth style
index contains economic information that precedes leading economic index. This lends
support that equity style index should be included as a component of leading economic
index.
Table 5: Cross-correlation between LEI and KLCI Industrial Index
14
We further examine the cross-correlation between KLCI Industrial Index and LEI. As
shown in table 5, the variance of the cross-correlation coefficient under the null
hypothesis of zero correlation is approx 1/n where n is the length of the series. On the
assumption that the coefficients are also asymptotically normal, the approximate critical
values at the 5% level are ± 2 /√n. As observed, KLCI Industrial Index is leading LEI
from t + 2 to t + 11 with the strongest lead at t + 6 with positive correlation of 0.2740. 7
In contrast, for cross-correlation between growth style index and LEI in table 6, Growth
style index is leading LEI from t + 5 to t + 13, with the strongest lead at t + 8 with
positive correlation of 0.3034. Hence, it can be concluded that growth style index is a
better candidate as predictor for LEI.
Table 6: Cross-correlation between LEI and Growth Style Index
Taking a closer look at the figure 3(a) on the left, the cross-correlation between LEI and
Industrial Index is relatively positive at zero lag. The diagram also shows the relationship
is non-proportional in nature.
For figure 3(b) on the right, it can be observed that LEI and growth style is not correlated
at zero lag, and the correlation between input variable at time t and output variable at t + i
7
Based on the calculation of ±
2 /√n, the critical value is 0.18107.
15
is more proportional as time increases. This reaffirms the earlier conclusion as growth
style index is better in terms of proportionality.
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25
0.00
0.00
-0.25
-0.25
-0.50
-0.50
-0.75
-0.75
-1.00
-1.00
-10
0
Lag
Cross-correlogram
10
Cross-correlations of dLEI and Growth
Cross-correlations of dLEI and Industrial
Figure 3(a) and 3(b): Cross-correlogram between LEI and KLCI Industrial Index
(left) Cross-correlation between LEI and Growth Style Index (Right)
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25
0.00
0.00
-0.25
-0.25
-0.50
-0.50
-0.75
-0.75
-1.00
-1.00
-10
-5
0
Lag
5
10
Cross-correlogram
CONCLUSION
This study has managed to unravel the missing link among there groups of indices. It
shows that equity style index like growth style is more sensitive to new information in
the capital market as compared to sectoral index like KLCI industrial index. The
implication of this study can be manifold. Firstly, equity style index is more sensitive,
hence a better candidate to be a component of leading economic index that helps to detect
turning point in business cycle. Secondly, this study adds another stylized fact that equity
style index has economic content and the ability to transmit information far from what is
recorded in literature. This underscores the third point that equity style index can be used
for mutual fund classification of which has been a practice in advanced financial markets
as compared to emerging markets. It is right time for the relevant authority to review the
components of the economic indexes with respect to their efficacy in transmitting
information.
ACKNOWLEDGEMENT
The authors would like record their appreciation for funding under FRGS Universiti
Malaya. The authors also would like to thank Dr. Sieh Lee Mei Ling, Head of The
Service Research and Innovation Centre (ServRI), Faculty of Business & Accountancy,
University of Malaya for her kind support for this research.
REFERENCES
Birchenhall, C.R., Jessen, H., Osborn D.R. and Simpson, P. (1999). Predicting U.S.
Business-Cycle Regimes, Journal of Business & Economic Statistics, 17( 3): 313-323.
16
Burns, A.F. and Mitchell, W.C., (1945). Measuring Business Cycles, National Bureau of
Economic Research, NY.
Central Bank of Malaysia (various issues). Monthly Statistical Bulletin. Central Bank
Publication, Kuala Lumpur.
Coggin, T.D. and Trzcinka, C.A. (2000). A Panel Study of U.S. Equity Pension Fund
Manager Style Performance. Journal of Investing, (Summer):6-12.
Daniel C. H. and Pazarbasioglu, C. (1999). Determinants and Leading Indicators of
Banking Crisis: Further Evidence. IMF Staff Paper, 46(3):257-258.
Diebold, F.X. and Rudebusch, G. D. (1989). Scoring the Leading Indicators. Journal of
Business, 62(3): 603-610.
Diebold, F.X. and Rudebusch, G. D. (1991). Forecasting Output With the Composite
Leading Index: A Real-Time Analysis. Journal of the American Statistical Association,
86(415): 603-610.
Department of Statistics Malaysia. (2003, 2007, 2008). Malaysia Economic Indicators:
Leading, Coincident & Lagging Indices, Government Printers, Kuala Lumpur.
Dickey, D.A. and Fuller, W.A. (1979). Distribution of the Estimators for Autoregression
Time Series with a Unit Root. Journal of the American Statistical Association, 74:427-31.
Fama, E. F. and French, K. R. (1992). The Cross-Section of Expected Stock Returns.
Journal of Finance, 47(2): 427-465.
Fama, E. F. and French, K. R. (1993). Common risk factors in the Returns on Stocks and
Bonds. Journal of Financial Economics, 33(1): 3-56.
Fama, E. F. and French K.R. (1996). Multifactor Explanations of Asset Pricing
Anomalies. Journal of Finance, vol.47, pp. 426-465.
Filardo, A.J. (1994). Business-Cycle Phases and Their Transitional Dynamics. Journal of
Business & Economic Statistics, 12(3):299-308.
Forni, M., Hallin, M., Lippi, M., and Reichlin, L. (2001). Coincident and Leading
Indicators for the Euro, The Economic Journal, 111(471):C62-C85.
Granger, C.W.J. (1988). Causality, Cointegration, and Control. Journal of Economic
Dynamics and Control, 12:551-59.
Hallam, D. and Zanoli, R. (1993). Error-correction Models and Agricultural Supply
Response. European Review of Agricultural Economics, 20, 155-166.
17
Hamilton, J.D. and Perez-Quiros, G. (1996). What do the Leading Indicators Lead? The
Journal of Business, 69(1):27-49.
Kim, C-J. and Nelson, C.R. (1998). Business Cycle Turning Points, a New Coincident
Index, and Tests of Duration Dependence Based on a Dynamic Factor Model with
Regime Switching. The Review of Economics and Statistics, 80(2):188-201.
Lakonishok, J., Shleifer, A. and Vishny, R. (1994). Contrarian Investment, Extrapolation
and Risk. Journal of Finance, 49(5): 1541-1578.
Lau, W.Y. (2006). Investment Style of Mutual Funds: How is it Useful In
Communicating Economic Trends to Investors? Osaka Economic Papers, 55(4): 139-156.
Layton, A.P. and Moore, G. H. (1989). Leading Indicators for the Service Sector. Journal
of Business & Economic Statistics, 7(3):379-386.
Maximo C. and Perez-Quiros, G. (2002). That is what the Leading Indicators Lead.
Journal of Applied Econometrics, 17:61-80.
Osterwald-Lenum, M. (1992). A note with Quantiles of the Asymptotic Distribution of
the Maximum Likelihood Cointegration Rank Test Statistics, Oxford Bulletin of
Economics and Statistics, 54, 461-472
Schwert, G.W. (1987). Effects of Model Specification Tests for Unit Root in
Macroeconomic Data. Journal of Monetary Economics, 20,73-103.
Sharpe, W.F. (1988). Determining A Fund’s Effective Asset Mix. Investment
Management Review, (December): 59-69.
Sharpe, W.F. (1992). Asset Allocation: Management Style and Performance
Measurement. Journal of Portfolio Management, 18(2): 7-19.
Sharpe W.F., Capaul, C. and Rowley, I. (1993). International Value and Growth Stock
Returns, Financial Analyst Journal, (January/February): 27-36.
Sinquefield R. (1996). Where are the Gains from International Diversification. Financial
Analyst Journal, (January/February):8-14.
Steckler, H. 0. (1968). Forecasting with econometric models: an evaluation.
Econometrical, XXXVI (July-October), pp 437-463.
Stock, J.H. and M.W. Watson (1989), New Indexes of Coincident and Leading Economic
Indicators, NBER Macroeconomics Annual, Vol. 4.
Yeats, A.J. (1973), An Evaluation of the Predictive Ability of the FRB Sensitive Price
Index, Journal of the American Statistical Association, Vol. 68, No. 344, pp. 782-787.
18