Asri S. W. and Heveadi —PriceEarningsRatio(PER)ModelConsistency
Gadjah Mada International Journal of Business
September 1999, Vol. 1, No. 2, pp. 85—97

PRICE EARNINGS RATIO (PER) MODEL
CONSISTENCY:
EVIDENCE FROM
JAKARTA STOCK EXCHANGE
Marwan Asri S. W.
Antonius N. Heveadi
Recently, stock valuation model using the earning multiplier approach (PER) is more popular among investors and analysts. This popularity has caused this model to seem to be the most perfect model among
other valuation models. In response to the fact above, this research tries to
give empirical evidence whether PER’s cross-sectional model can be used
in determining the fairness of stock price traded in Jakarta Stock Exchange.
Evaluation of the capability of PER’s cross-sectional model in determining the common stock price was conducted by developing three
regression models from different time periods, namely the years of 1995,
1996, and 1997. The regression models used in this research was the one
developed by Whitbeck-Kisor (1973). The model employed growth, dividend payout ratio (DPR), and standard deviation of growth (s-growth) as
independent variable.
This research was intended to test the consistency of the model in
assessing stock prices. The result of this research showed that each model
developed at different time periods, though with the same sample and
method, gave different results. The differences were in the significance
level and in the weight of influence of independent variables to the
corresponding dependent variables. As a stock valuation model, a regression model should perform consistently from period to period, so normal
PER of a stock could be predicted based on the model that was developed
by historical data.
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Gadjah Mada International Journal of Business, September 1999, Vol. 1, No. 2

The results of this research conclude that PER cross-sectional model
is inconsistent in determining the common stock to buy or to sell in shortterm. Nevertheless, the models can be helpful in finding the variable and to
set of the weight that determines the PER at a point of time. Stock valuation
using earning multiplier approach is appropriate if investors have an
assumption that the market taste and situation at the valuation period is the
same as the period when the model is developed.
Keywords: capital market; cross-sectional model; price earning ratio

Introduction
Searching for the “correct” method
to value common stock price varies from
the simple mechanical method to hypotheses of factors affecting stock price. According to the efficient market theory,
there is no simple mechanical method to
determine the best stock in a market. On
the other hand, the determinants of common stock price are quite easy to specify in
general terms. The price of common stock
is a function of the level of the company’s
earnings, dividends, risk, growth, and some
other factors. While it is easy to specify
these broad influences, the implementation of a system that uses these concepts to
successfully value or select common stock
is a difficult task. This problem has caused
the introduction of valuation models.
A valuation model is a mechanism
that converts a set of forecasts (or observations) on a series of economic or company
variables into a forecast of market value
for the company’s stock. Two kinds of
stock valuation models most often employed to analyze securities are:
- Discounted Cash Flow (DCF) Model or
earnings capitalization method, and
- Price Earnings Ratio (PER) Model or
earnings multiplication method
DCF that uses present value approach
tries to forecast the present value of a stock
using a certain rate of interest and the
return expected by stockholders. On the
86

other hand, PER approach estimates the
stock value by multiplying the return of
each stock by a certain earnings multiplier.
Although the usage of DCF model is
more popular among investors, the model
is only used by a few securities analysts
(Bing 1971). In the United States, PER
approach is used more often compared to
other methods which are based on dividends. However, the method based on
dividends is also increasingly being used
(Jones 1996). One of the approaches which
employed PER model is by determining
the factors affecting stock price as mentioned above and then measuring and determining how far the factors affect PER.
Therefore, the estimation of a stock PER
can be determined. The employment of
this cross-sectional regression analysis
method was very popular in the sixties and
now there is an indication that the usage of
the method is increasing (Elton & Gruber
1995).
The models are very helpful in determining factors affecting stock price in a
certain period. However, the models are
unsuccessful in determining which stock
should be sold or bought. According to
Elton and Gruber (1995), there are at least
three reasons why this happened, namely:
the changes of market taste, the input value
such as dividend and growth, and firm
effects are not covered in the models.
Although the usage of PER method
as a valuation model of common stock

Asri S. W. and Heveadi —PriceEarningsRatio(PER)ModelConsistency

price is more accepted, the ability of crosssectional PER model as a tool to choose
and select which stock should be bought
and sold is still unreliable. Therefore, it is
still needed to analyze and evaluate the
process of PER model formulation to understand the advantages and disadvantages
of the model.
This research is emphasized to test
the consistency of cross-sectional PER
model for different market conditions.
Elton and Gruber (1995) showed that different market conditions result in different
models. If the difference is significant, the
PER model can only be applied in situations and conditions where the model was
formulated. Based on the previous research,
this research is intended to prove the significance of effects of independent variables to the model tested.

The Indonesian Capital Market
in the Last Decade
Even though relatively young, the
Indonesian capital market has experienced
two different situations: bull and bear. The
term Bull Market refers to the condition
when the prices of stocks traded in the
capital market increase and result in active
market. On the contrary, Bear Market refers to the condition when the trading of
stocks is exhausted and the prices of stock
decrease. To understand when a market is
bull or bear, investors usually use the
market index as an indicator.
Bull Market and Bear Market definitions are easy to understand. But to determine the criterion when a market is categorized as bull market or bear market is
not simple. Atlas (1995) defined bull market as a period when the stocks index
increase 300 percent or more thoroughly
without correction of 33 percent or more.
However, Atlas’ definition is not simple to

categorize whether a market is bullish or
bearish.
For the Indonesian capital market,
Composite Stock Price Index (Index Harga
Saham Gabungan, IHSG) is an indicator
to determine whether it is bullish or bearish. Generally speaking, Indonesian capital market was bullish three times and
bearish four times. Bull market happened
during the year 1988 until the middle of
1990, year 1993, and 1996. Bear market
happened in 1979 to 1987, middle of 1990
to 1991, year 1994, and middle of 1997
until recently.

Stock Valuation Models
A valuation model can be viewed as
a black box that converts the forecast of
fundamental data of firms and/or economy
to a forecast or valuation of market price.
Below are two valuation models that are
the most often used:

1. Discounted Cash Flow Models
These models are based on a concept
that the value of a share of stock is equal to
the present value of the cash flow that the
stockholder expects to receive. Cash which
will be received by investors comes from
two sources: dividends and the value of
the stock when he or she sells it. The latter
one is also equivalent to dividend flow
which will be received by stockholders.
Therefore, the models are also called the
dividend discount models
An investor can hold a stock for an
unlimited period (n year) which is formulated:
•

P0 =

S (1 D+ r)
t

t=1

t

where,
P0 = Stock price in the year 0
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Gadjah Mada International Journal of Business, September 1999, Vol. 1, No. 2

Dt = Dividend received in the t-th year
r = The appropriate discount rate
Conceptually the formulation above
is understandable, but operationally it is
very difficult. The longer the time dimension used, the higher the uncertainty of the
estimation. Therefore simplification of the
model is needed.
The first simplification assumes that
return is constant every year and all returns are paid as dividends. Based on the
above assumption, the formulation of current stock price is:
P0 = E / r
or
P0 = D / r
The model is named as the zero growth
model because it is assumed there is no
growth of dividends.
Since the assumption above seems
unrealistic, other assumptions are used:
■ Some of the returns (b) are retained in
constant proportion
■ Retained earnings are reinvested and
result in return on equity (ROE) in the
amount of R.
■ Based on the above assumptions, EPS
(E) and dividends (D) increase in the
amount of bR, symbolized as g.
Henceforth, the stock price can be
determined using the formula:
P0 = D1 / ( r – g )
The model is called constant growth
model, based on the assumption that the
growth of earnings and dividends are constant. The model is adequate enough to
forecast stock intrinsic value for a firm
operating in maturity cycles. Otherwise,
for firms that operate in the growth cycle,
88

constant growth assumption is inappropriate. Consequently, multiple growth
model and three-period model appeared.

2. Price Earning Ratio Approach
There are various approaches using
PER. Huang (1987) stated that some approaches using PER basically can be categorized as follows:
■ Method 1
Dividing stock price by normalized
earnings or average earnings to calculate
P/E. The ratio is then compared to normal
P/E ratio of the stocks.
■ Method 2
Dividing stock price by estimated
earnings (the following one to three years)
to determine P/E ratio. Then, the ratio is
compared to normal P/E ratio of the stock.
■ Method 3
Comparing P/E ratio and earnings
growth of each stock to P/E and average
growth of the industry.
The two first approaches employ
normal P/E ratio concept of a stock. Normal P/E ratio of a stock is understandable
by determining factors affecting stock
price, such as earnings, growth, risk, time
value of money, etc. and followed by formulating the equation model of PER and
the factors. The model is then used to value
and justify the future PER of a firm.
Valuation model using PER approach
generally seems easier, simpler, and more
practical to be used by analysts and other
capital market players. The simplicity of
the model makes analysts often forget that
estimation of an uncertain future is needed
by the model. As a result, the models are
very helpful in determining factors affecting stock price in a certain time but unsuccessful in determining which stock should
be sold or bought. The theory which pro-

Asri S. W. and Heveadi —PriceEarningsRatio(PER)ModelConsistency

vides the basis for determining whether a
stock is undervalued or overvalued is that
market price will meet the theoretical price
before theoretical price changes. The reasons are: the changes of market taste, the
changes of data input, and the effect of
firm (Elton & Gruber 1995).
One of the reasons why market price
does not meet the theoretical price is that
the parameter used to determine the theoretical price had changed. The change is
caused by change of market taste. Market
taste always changes through time, often
rapidly and drastically. Research done by
Cohen, Zinbarg and Zeikel (1973) showed
that the effect of return growth of PER is
greater in the bull market compared to the
bear market. Gruber (1971) proved that
the effect of dividends, growth, and three
variables of risk (earnings instability, financial leverage, and size) to price always
changes from year to year.
Data input or value to determine theoretical price is obtained from historical
extrapolation or analyst expectation. The
value of the input such as return and growth
or expectation may change drastically any
time. The change of one of the variables
can change the stock theoretical price.
The actual price of a stock may always be above (or below) the theoretical
price. This can happen because there are
other factors uncovered in valuation models.

Determination of PER
Some of the factors affecting the PER
of a stock can be derived from the dividend
discount below:

The equation above shows that PER
is affected by rate of return growth (g), rate
of expected return (r), and dividend payout
ratio (d).
An empirical study supporting the
theory above was developed by WhitbeckKisor (1963). Using multiple regression
analysis, they employ three variables affecting PER: rate of earnings growth (g),
dividend payout ratio (DPR), and standard
deviation of growth (s growth). The model
was replicated by Chandra (1994)—the
last variable was replaced by standard
deviation of stock return (s stock return)—
using the stocks listed in Jakarta Stock
Exchange.

Empirical Models, Variables,
and Measurement Scale
Multiple regression based on the
model developed by Whitbeck-Kisor is
employed in this research. The model is:
PER = b0 + b1 g + b2 d + b3 s
g = rate of earnings growth
d = dividend payout ratio (DPR)
s = standard deviation of earnings growth
1. Price Earnings Ratio (PER)
PERt =

EPS0

2. Rate of Earnings Growth

S

d(1 + g)
=

r-g

EPSt

PERt : Price Earnings Ratio in the t-th
year
Pt
: Closing Price at the end of t-th year
EPSt : Earnings per share in the t-th year

4

P0

Pt

n=1

gt =

EPSt-n+1 - EPS1-n
EPSt-n
4
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Gadjah Mada International Journal of Business, September 1999, Vol. 1, No. 2

gt
: earnings growth in the t-th year
EPSt-n+1 : Earnings per share in the t-th
year
EPSt-n : earnings per share in the t-th
year
3. Dividend Payout Ratio (DPR)
DPSt

DPRt =

EPSt

DPRt : Dividend Payout Ratio in the t-th
year
DPSt : Dividend per share in the t-th year
EPSt : Earning per share in the t-th year
4. Standard Deviation of Rate of Earnings
Growth (s)
3

S (g - g)

2

st =

t-n

n=0

4-1

st : Standard deviation of earnings growth
in the year of t
gt-n : Actual growth in the year of t-n
g : Average earnings growth in the t-0 to
t-3

Data and Sample
The population of this research consists of stocks listed in Jakarta Stock Exchange (JSX). Data was obtained from
Jakarta Stock Exchange Statistic and Indonesian Capital Market Directory
(ICMD). Data and samples were obtained
from three different periods: 1995, 1996,
1997.
The criteria for the samples are as
follow:
1. The stocks must have been listed in JSX
for the three periods (1995, 1996, 1997).
2. EPS data of the stocks are available
from 1991 and forward. One of the
variables in this research is rate of return growth. The proxy of it is average
of return growth for the last four years.

Table 1. Descriptive Statistic of Each Variable
Mean

90

Standard
Deviation

Max

Min

Outliers
Top

Bottom

PER 95
Growth 95
DPR 95
s Growth 95

16.66
0.04
0.55
1.62

30.04
2.51
0.66
4.46

260.00
19.96
6.00
39.50

(86.84)
(16.63)
(0.37)
0.06

46.70
2.55
1.21
6.07

(13.38)
(2.47)
(0.12)
(2.84)

PER 96
Growth 96
DPR 96
s Growth 96

24.24
(0.17)
0.77
1.44

80.48
2.20
2.35
3.94

1.000.00
7.33
30.00
36.72

(53.70)
(18.83)
(1.48)
0.05

104.72
2.03
3.12
5.38

(56.25)
(2.37)
(1.58)
(2.50)

PER 97
Growth 97
DPR 97
s Growth 97

11.10
(1.21)
0.32
3.82

46.45
4.60
4.36
8.82

400.00
9.20
29.85
86.35

(87.50)
(43.31)
(43.42)
0.05

57.54
3.40
4.67
12.64

(35.35)
(5.81)
(4.04)
(4.99)

Asri S. W. and Heveadi —PriceEarningsRatio(PER)ModelConsistency

Therefore, EPS data is needed from at
least four years before 1995.
3. Outliers from all variables of each period are excluded from the sample. Outliers are defined as values that are outside of the two deviation standards
around the average of each variable.
(more than mean + 2 deviation standard
or less than mean –2 deviation standard).
From 283 stocks listed in JSX in
1997, only 237 have been listed from 1995
to 1997. From the amount above, only 177
have EPS from 1991. Mean and standard
deviation of each variable are shown in
Table 1.
The outliers in the Table 1 are excluded. As a result, the number of sample
analyzed are 119 stocks.

Analysis and Results
Three regression models are employed using the same amount of samples
(119) for the year of 1995, 1996 and 1997.
The comparisons of the results for each
year are shown in Table 2.

Table 2 shows that the F-statistic
value for the three models are pretty high,
higher than F-table value for a = 0.005
(0.5%), namely 4.5. This means that three
regression models can explain the changes
of PER based on the information of earnings growth, DPR, and deviation standard
of growth.
Statistic value of t, printed italic in
the Table 2 shows that the value is significant in a = 0.5 percent (t-table is 2.576),
except for s earnings growth in 1996 that is
significant in an a of 5 percent (t-table is
1.645). This means that in 1995, the independent variable that significantly affected
PER are growth and DPR, in 1996 DPR
and s growth, and in 1997 DPR only.
Because of the significant difference
of variable effects from one period to the
next, a model formulated in one period
cannot be used in the next. For example,
the model formulated in 1995 cannot be
used to predict PER value for 1996 although the independent variable value is
known (in this case growth, DPR, sgrowth). The reason is that in 1995, growth

Table 2. Comparison of Multiple Regression Results
PER = b0 + b1 g + b2 d + b3 s

Intercept
t-stat.
Growth
t-stat.
DPR
t-stat.
s Growth
t-stat.
F-statistic
SSE
R2
Adj-R2

Model I (1995)

Model II (1996)

Model III (1997)

6.422393
3.459
-6.322367
-2.767
8.945484
-2.767
1.506548
0.99
5.42971
7933.57827
0.12407
0.10122

9.016363
4.229
-2.439599
-0.784
13.218403
-0.784
-4.814653
-2.27
19.0072
14608.03331
0.33148
0.31404

3.080499
3.121
0.56824
0.639
7.657885
0.639
-0.457122
-0.871
37.15711
6151.47742
0.49221
0.47896
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Gadjah Mada International Journal of Business, September 1999, Vol. 1, No. 2

Table 3. Comparisons of Simple Regression Results
PER = b0 + b1 d

Intercept
t-stat.
DPR
t-stat.
F-stat.
SSE
R2
Adj-R2

Model I (1995)

Model II (1996)

Model III (1997)

7,112385
4,490
8,820307
2,779
7,72458
8496,37842
0,06193
0,05392

5,534378
3,645
14,045195
7,090
50,27052
15284,22249
0,30053
0,29456

1,917121
2,616
8,100322
10,227
104,58385
6396,50258
0,47198
0,46747

affected PER significantly and in 1996
growth did not affect it anymore. On the
contrary, s-growth that did not affect significantly in 1995, affected significantly
in 1996. In 1997, the variable that affected
PER significantly is DPR only.
Therefore, although each model
above can explain the effect of each independent variable to its dependent variable (PER) for each year, the three models
cannot be used to predict future PER.
Assume, the prediction of earnings, DPR,
and s-growth of a stock in 1998 are
known, normal PER for 1998 is also determinable. But it is difficult to determine which model will be used. The reason is that each model is very different.
One of the interesting things from the
table above is that DPR statistic value of t
always increased from 1995 to 1997. This
means that the effect of DPR to PER from
1995 to 1997 got more significant. Moreover the increase of statistic value of t is
followed by the increase of R2 value. This
means that the increase of DPR significance can better explain the change in
PER. Although DPR always affects significantly for all the models in this research, the analysis of whether DPR can
be used as the only variable for the three
models above is needed. Table 3 provides
92

the summary of comparisons of three regression models which use DPR as the
only independent variable.
Table 3 shows that all statistic value
of F or t is always significant in a = 1
percent. This means that DPR as an independent variable for the three regression
models can explain PER change for each
of the models. Although the table shows
that independent variable in the three models always affect significantly, it cannot be
concluded that the three models can predict the normal PER of a stock. The table
shows that each k-th coefficient (bk) in
each model is different from each other.
This means that DPR’s effect to PER in
1995 may be different to that of 1996 or
1997. If the difference is significant, normal PER resulted from the calculation of
the models will also be significantly different.
To understand whether each coefficient differs significantly or not, the
next step is determining the statistic value
of t to compare two coefficients. T-test
developed by Hartono and Ratnaningsih
(1997) is employed to compare two coefficients of two different regression models. The test is similar to the previous ttest. The difference is that t-statistic is
formulated as follows:

Asri S. W. and Heveadi —PriceEarningsRatio(PER)ModelConsistency

b$k(1) - b$k(2)

t=
SSE(1) + SSE(2)
●

df(1)

$
(bk(1))2 ● (df(1))
(t(1))2● (SSE(1))

Where,
bk(i) = k-th coefficient of i-th equation
t(i)
= t-statistic of i-th equation
(i)
= degree of freedom of i-th equadf
tion
(i)
SSE = sum square error of i-th equation
When t-statistic value is higher than
t-table or lower than negative value of ttable, it means that the two coefficients
differ significantly. The result of statistic
value of t to compare the two coefficients
is presented in the Table 4.
Statistic value of t printed bold in
table 4 is the significant value level of t for
a = 5 percent (t-table value is 1.645). This
means that the coefficients of the model
formulated in 1995 are relatively similar
to that formulated based on 1996 data.
Coefficients of the model formulated in
1996 differ significantly from that formulated based on 1997 data. Coefficients of
1997 model compared to those of 1995
model are only different in its intercept

+

$
(bk(2))2 ● (df(2))
(t(2))2 ● (SSE(2))

coefficients, but its DPR coefficient is
relatively similar.
The results of statistical analysis show
that the level of effect of factors always
changes from one period to another. Even
for the factor that always affects significantly, the level of its effect is different
from one period to the other. Actually,
insignificant difference is no matter. Unfortunately, the results of statistic analysis
above show that the difference of effect is
sometime very significant.
The results are consistent to that of
Gruber (1971) which showed that effect of
dividend, growth, and other three variables (instability of earnings, financial leverage, and size) to price always changes
from year to year. It is also consistent to
that of Cohen, Zinbarg, and Zeikel (1973)
which showed that the model formulated
when the market was bullish was different
to that when it was bearish.
The difference of model can be related to bullish and bearish condition of

Table 4. Coefficient Comparisons of Simple Regression Result
Variable being compared

t-statistic

Intercept 95 vs Intercept 96
Intercept 96 vs Intercept 97
Intercept 97 vs Intercept 95

0,6851
2,2673
(3,0913)

DPR 95 vs DPR 96
DPR 96 vs DPR 97
DPR 97 vs DPR 95

(1,2616)
3,0312
(0,2329)
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Gadjah Mada International Journal of Business, September 1999, Vol. 1, No. 2

the Indonesian Capital Market. In 1995
the market tended to be stable and IHSG
was closed at 513.847 or increased 9.2
percent compared to the former year. In
1996 IHSG was very volatile. The bull
market was until February 1997. From
February 1997 to June 1997 the market
was relatively stable. But from the middle
of 1997 to the end of 1997 the index
dropped drastically, making the market
bearish. The fact is that three market conditions used in this research was different
from each other. In other words, it can be
concluded that models formulated based
on different market conditions –although
the same sample and method were employed- result in different models.
However, it does not mean that the
model formulated based on the same market conditions will result in the same models. The difference of regression models
for a certain time differ to that for other
periods is caused more by the change of
market taste, not by market condition.
Market taste always changes through time.
It is possible that market taste is different
from one period to another period although
the market condition is not different. Besides, it is impossible for market conditions to be identical. Bull market in 1988
was different to that in 1993 or 1996. Bear
market in 1991 was different to that in
1997.
The results show that cross-sectional
regression analysis cannot be used to determine which stocks should be sold or
bought because the regression model cannot be used to predict normal PER value
that is needed to determine which stock is
overvalued or undervalued. However, the
cross-sectional regression analysis using
PER model can explain factors affecting
PER in a certain time or period.

94

Implication
The weaknesses and limitations of
present value approach caused PER model
to become a popular approach to value a
stock. Using PER model approach, investors can determine intrinsic value of a
stock by multiplying EPS of a previous
stock by earnings multiplier, normal PER.
Earnings multiplier or normal PER can be
determined by using cross-sectional regression analysis. The popularity of valuation models using the PER model approach with its affecting factors has
caused it to seem as if it is the most
suitable approach to value stock.
The result of this research show that
normal PER determined by using crosssectional regression analysis can be applied in the period when the model was
formulated. Intrinsic value of stock obtained by multiplying EPS of a previous
year by normal PER is actually inappropriate because normal PER here is historical, which may differ from current normal
PER. Moreover, the results above show
that in different periods, PER model resulted can be different although formulated using the same sample and method.
Miscalculation of intrinsic value can cause
mistakes in decision making.
Therefore, PER model approach
should only be used when investors assume that market situations and market
taste are similar both when evaluation
process is conducted and PER model is
formulated. Stock valuation using PER
model is appropriate when the assumptions are met. Therefore, similar to present
value approach, PER model is not free
from error because the existence of assumption or prediction in this approach.
Prediction, or whatever concerning the
future is an uncertain thing.

Asri S. W. and Heveadi —PriceEarningsRatio(PER)ModelConsistency

This research is only intended to remind readers that every valuation using
fundamental analysis or other approach is
not free from error. “No matter who does
the analysis, or how it is done, mistakes
will be made” (Jones 1996). Investors
must remember that stock valuation is
more an art than a science.

Conclusions and Suggestions
Cross-sectional regression analysis
of stocks listed in JSX using PER model
developed by Whitbeck-Kisor shows that
from year to year factors affecting significantly are various. Among the three variables used by Whitbeck-Kisor, only DPR
variable consistently affects significantly
for three periods of analysis. Further analysis shows that although DPR variable always affects significantly, the effect level
of DPR variable to PER is different.
Stock valuation using PER model
approach assumes that regression model
formulated based on historical data is a
mirror of regression model when valuation is conducted. Earnings multiplier used
to determine intrinsic value of stock based
on the EPS data of a previous year can be
obtained from the regression model. This
research which uses samples of 1995, 1996,
and 1997 show that regression model resulted from three periods is not always
similar. Therefore the assumption is not
appropriate.
Although the assumption is appropriate, PER model resulted from the crosssectional regression analysis cannot automatically be used to value the common
stock price. There are two other factors
that make the model unable to determine
whether stock is overvalued or undervalued, namely (1) input variable which always change and (2) firm effect uncovered
in the model.

The evaluation of cross-sectional PER
model as a valuation model to assess the
fair price of stocks traded in JSX in this
research show that PER model cannot be
used to determine the intrinsic value of a
stock. The model is unsuccessful in determining which stock should be sold or
bought. However the model is successful
in explaining factors affecting the PER of
a stock at a certain time.
The changes of market taste causing
different regression models in this research
are affected by conditions and situations
of capital market. Since three periods of
sample used in this research has different
market conditions and situations, the regression model resulted is also different.
Market in 1995, 1996, and 1997 is normal,
bullish, and bearish respectively.
Based on the result above, below are
suggestions for future research. First, this
research uses cross-sectional data in formulating the regression model. The results
show that from year to year the model
resulted are different because of different
market conditions. To solve this problem,
future research is suggested using poll
data (combination of cross-sectional and
time-series data).
Second, the results show that different regression models are resulted from
different market conditions and situations.
But this research has not investigated yet
whether the same market conditions and
situations will result in the same regression models. Research conducted by comparing the same market conditions and
situations will prove whether the difference of regression models is resulted by
different market conditions and situations
or by market taste which always changes
from year to year.
Finally, investors and analysts should
remember that there is no perfect valuation model. Stock valuation is more an art
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Gadjah Mada International Journal of Business, September 1999, Vol. 1, No. 2

than a science. Cross-sectional PER model
approach should only be used if investors
assume that market situations and taste

when the valuation is conducted is similar
to that when the PER model is formulated.

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