277
model to estimate the estimated return during the window periods. The expected model can be developed with the Ordinary Least Square OLS regression techniqES using following
equation:
j i
Mj i
i j
i
R R
, ,
.
Notes: R
i,j
is the actual return of the security number-i during estimated period j, α
i
is the intercept for the security number-i,
β
i
is the slope coefficient which is the beta coefficient of the security number-i, R
Mj
is the market index return during the estimated period j calculated using the formula of R
Mj
= CI
j
–CI
j-1
CI
j-1
, with CI is the Composite Index , Є
i,j
is the residual error of the security number-i during the estimated period j calculated using market adjusted
model. The next step is the cumulative abnormal return CAR calculation using the following
formula:
t t
a a
i t
i
AR CAR
3 ,
,
Notes: CAR
i,t
is the cumulative abnormal return of the security number-i during the day number-t, which is the accumulated abnormal return AR of the security number-i after the
event period of t
+1
until t
+3
, AR
i,a
is the abnormal return for the security number-i during the day number-a, which begins during t
+1
days after event period until three days of event period t
+3
.
Earnings Shocks
According to Ho Sequeira 2007, earnings shocks are calculated by subtracting forecasted EPS from actual EPS. Then the difference is divided by the closing price during
the last day of the trading month before the date of the financial statement published. If the result is positive, the positive earnings shocks occur. Inversely, if the result is negative, then
the negative unexpected return occurs.
1
t
P FEPS
AEPS ES
Notes: ES is the earnings shocks, AEPS is the actual EPS, FEPS is the forecasted EPS.
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Size
Consistent with Edward, Benson Ohlson 1995, Skinner Sloan 2002, Lopez Rees 2001, Conrad, Cornel Landsman 2002, and Ho SeqESira 2007, this study uses the
natural logarithm ln of the closing price during the last day of the trading month before the earnings statement, multiplied by the sum of the outstanding stocks in accordance with the
financial statement published. This measurement is used as a company size proxy.
The Usage of Market Value Ratio against Intrinsic Value
Conrad, Cornel Landsman 2002 find that asymmetric reaction of the stock price against positive earnings shocks and negative earnings shocks is affected by economic conditions.
The study findings are based on the market level proxy during various economic conditions. High market level is defined as the periods when the economic conditions are profitable and
the wide-market news is favorable. Inversely, low market level is defined as the periods when the economic conditions are unprofitable and the wide-market news is unfavorable. To
define the market level, Conrad, Cornel Landsman 2002 used the proxy which is the difference of the ratio of the market price against the current earnings per share PE and
the average ratio of the market price against earnings per share during 12 months before. Unlike Conrad, Cornel Landsman 2002, the study conducted by Ho SeqESira
2007 used the ratio of the market price against intrinsic value PV as the proxy of the market level. In this study, Ho SeqESira state that the PV usage has a relative predictive
ability advantage than PE as reported by Frankel Lee 1998. Furthermore, PV uses more information available about the companies, such as current company value, current
expected return on equity ROE, current return on equity, two years and three years predicted earnings, which serve as the long term company growth prediction. Theoretically,
the measure of PV is better than any other measures. Francis, Olsson Oswald 2000 find that estimated abnormal earnings is more accurate and able to explain the variability of the
equity price better compared to the other variables, such as dividend and free cash flow. The method used to calculate intrinsic value V is EBO Residual Income Model. This
method has a better capability to calculate the intrinsic value of the stock Foster, Olsen, Shevlin, 1984; Bernard Thomas, 1989, 1990; Chan, Jagadeesh, Lakonishkok, 1996,
1999. The intrinsic value calculation formula uses EBO model period as described by Frankel Lee 1998 and Lee, Myers Swaminathan 1999 as follow.
2 ,
, 2
, 1
, ,
, 1
, ,
, 2
,
1 1
t i
t i
t i
t i
t i
t i
t i
t i
t i
t i
re re
FROE B
re re
FROE B
B V
1
279
At this equation 1,
t i
V
, 2
is two estimated EBO periods for the company i during the year of t;
t i
B
,
is the par value of each share for the company i during the year t;
t i
re
,
is the capital expenditure for the company i during the year t; and
t
i
FROE
,
is the annual forecasted ROE for the company i during the year or t during the period t+
during the period = 1 until = 12.
The input variables required to operate EBO model are forecasted ROE, current par value of each share and predicted value of each share, and company capital expenditure.
The forecasted ROE is calculated using the equation 2 as follow.
2
2 1
t t
t t
B B
FEPS FROE
2
Notes:
t
FROE
is the forecasted ROE during period t+ during period = 1 until = 2;
t
FEPS
is the forecasted EPS during period t+ during notaion = 1 until = 2;
1
t
B
is the par value of each share during t+
-1; and
2
t
B
is the par value of each share during t+
-2. The forecasted EPS during period t+
is estimated using one-year-ahead FEPS
t+1
and two-year-ahead FEPS
t+2
. The book value used in the EBO model is calculated using equation 3 as follow.
k FROE
B B
t i
t i
t i
1 1
, 1
, ,
3
Notes: B
i,t+
is the book value for the company i during the year t+ for =0 until =1;
FROE
i,t+
is the forecasted ROE for the company i during the year t+ for =0 until =1; and
k is dividend payment ratio. Because the dividend payment ratio is the percentage of net income paid in the dividend, estimation for certain company in k is calculated by dividing the
cash dividend paid during the last year with the net income EPS multiplied by outstanding share.
Lee, Myers, Swaminathan 1999 find that the inclusion of interest rate of various time, especially short term interest rate, can increase the V predictive power to predict stock
return. Consistent with Lee, Myers, Swaminathan 1999, this study uses a capital expenditure for certain company to calculate V using equation 4 as follow.
280
RP r
re E
t i
t f
t i
, ,
,
4
Notes: Ere
i,t
is the expected capital expenditure for the company i during the year t;
t i
,
is the beta for the company i during the year t; and RP is the market risk premium. Even
though the V is sensitive against interest rate selection, Lee, Myers, Swaminathan 1999 find that the effect of market risk premium selection against the V performance can be
ignored.
Market Level
The market price ratio between the stock against and intrinsic value PV is used as the market level proxy. Intrinsic value is calculated using Residual Income Model RIM
approach from Edward, Benson Ohlson 1995. After the intrinsic value of each stock is calculated, the next step is the calculation of market PV ratio for each period using the
following formula.
t N
i i
t t
i t
N V
E P
market V
P
t
,
5
where
t i
P
,
is the market price of the stock for the company i during period t;
i t
V E
is the estimated intrinsic value for the company i during the period t calculated using EBO model;
and
t
N
is the total sum for all companies during the period t. From the all observation periods, the period that have the lowest value of PV ratio is considered as the low market
level, whereas the period that have the highest value of PV ratio is considered as the high market level.
Hypothesis Testing
Ho Sequeira 2007 states that the stock price elasticity is measured generally using earnings reaction coefficient. Earning reaction coefficient is acquired from the regression of
excess return against earnings shocks for each company. At the following regression equation, the earnings reaction coefficient is the value of nilai a
1
.
1 1
2 2
1
_
j j
it it
j it
it it
Ind DUM
a SIZE
a ES
a a
CAR
R1
To show the asymmetric reaction against earnings shocks, two indicator variables are added to the regression equation, namely 1 up, which is valued as one for positive earnings
281
shocks and the others are valued as zero; 2 down, which is valued as one for negative earnings shocks and the others are valued as zero. The asymmetric impact can be shown
using the following second regression equation with b
1
and b
2
which each describes the positive and negative earnings shocks coefficient.
1 1
2 3
2 1
_
j j
it it
j it
it it
it
Ind DUM
b SIZE
b DOWN
ES b
UP ES
b b
CAR
R2
At those R1 and R2 equations,
it
CAR
is the sum of excess return during three days before and after the date of the earnings statement announced,
it
ES
is the earnings shocks calculated by subtracting actual EPS with forecasted EPS, then divide the result with the
closing price during the last trading day at the last month before the earnings statement,
it
SIZE
is the natural logarithm of the market price of the stock during the month before the earnings statement multiplied by the sum of the outstanding stock in accordance with the
financial statement published. The market price used in this calculation is the closing price during the last trading day at the last month before the statement issued.
it
UP ES
is the product of ES and indicator variable UP; and
it
DOWN ES
is the product of ES and indicator variable DOWN. An indicator variable named
it
Ind DUM
_
is a dummy variable added into the R1 and R2 equations to control the impact of industrial sector, which j
described the three industrial sectors analyzed. Each value of j,
it
Ind DUM
_
are equal to one when the company is within j industrial sector and zero if otherwise. All of the regression
equations are estimated among j-1 industrial sector to avoid dummy variable trap Gudjarati, 2003, with the formulation as follow.
1 1
2 3
2 1
_ ~
~
j j
it it
j it
it it
it it
Ind DUM
b SIZE
b DOWN
ES b
DOWN ES
UP ES
b b
CAR
R3
4. Data Analysis
This study assumes that investors in emerging market forecast future companies‘ earnings.
They, then, evaluate whether the actual companies‘ earnings meet their expectation. If the actual earnings exceed do not meet their expected earnings, then positive negative ES
occurs. It is rationally expected that the stock price will rise up go down if the earnings shocks are positive negative.
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Descriptive Statistics
This study uses three emerging stock markets. i.e., Indonesia, Malaysia, and the Phillipines. Initially, this study collects 1,170 firm-year samples. Due to data incompleteness, this study
finally uses 413 firm-year samples. The incompleteness is mainly due to the abscence of earnings announcements 211 firm-
years, firms‘ outstanding shares for the last two years 184 firm-years, closing price during the two last year 126 firm-years, earnings per share
during four year observations 185 firm-years, and others 51 firm-years. Table 1 shows descriptive statistics of CAR, UE, size and PV for full sample.
Table 1 shows descriptive statistics of CAR, ES, size and PV for full sample. The table also divides samples into positive and negative ESs. In the other perspective, this table
classifies the samples between manufacturing, and banking and financial industries. The CAR is the cumulative abnormal return during three days after the date of earnings
announcement. The mean of CAR for full sample equals 0.0038 and its standard deviation equals 0.1747. The mean of ES for full sample equals to 50.2882 with standard deviation as
600.9468. The samples that have ES less than zero ES 0 has mean of CAR which equals 0.0157 and standard deviation equals 0.1303. Meanwhile, the mean of ES equals -
69.1225 with standard deviation equals 236.5798. Otherwise, the sample with ES greater than zero ES 0 has mean of CAR which equals 0.0020 and standard deviation equals
0.1879. The mean of ES equals 118.3174 with standard deviation equals 725.1118. The sample of manufacturing industry has mean of CAR which equals 0.0061 and standard
deviation equals 0.1782. The mean of ES equals 56.0899 with standard deviation equals 626.6493. The sample of banking and finance industry has the mean of CAR which equals -
0.0206 and standard deviation equals 0.1314. The mean of ES equals -10.4681 with standard deviation equals 168.3254.
The descriptive statistics show relatively similar to studies by Ho Sequeira 2007, Conrad, Cornel δandsman β00β and other similar studies conducted by O‘Brien 1998;
Kang, O‘Brien Sivaramakrishna 1994. These studies show negative signs for ES that is less than zero and positive signs for ES that is greater than zero. These results indicate that
the analysis‘ forecast is mostly optimistic. The table also deduces that negative ES is responded in greater magnitude in comparison with positive ES, showed by greater CAR
which equals 0.0157 for negative ES compared to the CAR of positive ES which equals 0.0020. Meanwhile, the sample with negative ES has absolute ES which equals 69.1225,
compared to the positive ES which has absolute ES of 118.3174. -----------------------------------
