Key fundamental factors and long-run price changes in an emerging market--a case study of Karachi Stock Exchange (KSE).
Irfan, Chaudhary Mohammad ; Nishat, Mohammed
This paper identifies the joint effect that multiple factors exert
on share prices in Karachi Stock Exchange (KSE), using annual balance
sheet data of listed firms during 1981 to 2000. Out of six
theory-suggested fundamental variables (dividend yield, payout ratio,
size, asset growth, leverage, and earning volatility), the highly
significant joint factors observed are payout ratio, size, leverage, and
dividend yield. Together, these four factors explain one-fourth
variation in share prices at KSE. The explanatory power of fundamental
factors is found to be different in the pre-reform period (1981-1990)
and the post-reform period (1991-2000). In the pre-reform period, three
factors, i.e., payout ratio, size, and dividend yield, explain about
half of the variation in share prices, whereas during the post-reform
period these factors explain about one-third of the variability in these
prices. The correct sign of size effect is only observed in the
pre-reform period.
1. INTRODUCTION
Share prices are the most important indicator readily available to
the investors for their decision to invest or not in a particular share.
Theories suggest that share price changes are associated with changes in
fundamental variables which are relevant for share valuation like payout
ratio, dividend yield, capital structure, earnings size of the firm and
its growth, [Wilcox (1984); Rappoport (1986); Downs (1991)]. Linter (1956) linked dividend changes to earnings while Shapiro valuation model
(1962) showed dividend streams discounted by the difference in discount
rate and growth in dividend should be equal to share price. This
predicts direct relation between pay out ratio and the price-earning
multiple. Conversely it means that there is an inverse relation between
pay out ratio and share price changes. Several event-based studies
established direct relation between share price changes and either
earnings or dividend changes [Ball and Brown (1968); Baskin (1989)].
Sharpe (1964) and Hamada (1972) suggested direct relation between share
price changes and capital structure. Beaver, Kettler and Sholes (1970)
showed that firms appear to pay less of their earnings if they have
higher earning volatility. This suggests payout ratio as relevant factor
for share price changes. Investigations of share price changes appear to
yield evidence that changes in fundamental variable(s) should jointly
bring about changes in share prices both in developed and emerging
markets. However, the actual fundamental factors found to be relevant
may vary from market to market. For example, changes in asset growth of
firms are significant in the case of Japanese shares while earnings
appear to be universally a relevant factor [Ariff, et al. (1994)].
However, it is widely agreed that a set of fundamental variables as
suggested by individual theories is no doubt relevant as possible
factors affecting share price changes in the short and the long-run
[Ariff and Khan (2000)].
The link between fundamental factors and share price changes has
been extensively investigated over short horizons but only few studies
attempted to model it over lengthy periods of time. Studies over short
windows commonly apply cross-sectional tests using event-based research
methodology. The studies examining this relation cross-sectionally or
inter-temporally are few, and these have one common feature i.e., the
fundamental factors used in a specific study are either one or two
although there is a long list of such factors. Furthermore, while price
revisions at the time of announcements of price relevant disclosures are
valid as announcement effects shown over short horizons, it is equally
important to test the effect over a lengthier period of time using data
over several years as measure of the variables [Ariff and Khan (2000)].
Black and Sholes (1973) support the idea, that the value of security
will be higher, the higher is the volatility of security's return.
The relation between dividend and earnings follows that greater the
volatility of earnings of a firm, the less is the likelihood of dividend
yield being changed by the firm's management. Hence earning
volatility is directly related to share price volatility.
Another relevant factor in affecting the share prices is the
capital structure of the firm. The level of debt financing by the firm
has impact on the value of firm's assets. Hamada (1972) and Sharpe
(1964) specify their theories regarding the capital structure. A
high-risk firm (a firm with debt) must generate high return consistent
with the investor's expected return. It follows that with higher
debt firm should have greater rate of change in its share price. Hence
capital structure (DA as debt to asset ratio) changes must be directly
related to the share price volatility. Modigliani and Miller (1958)
emphasised that in competitive capital markets the value of a firm is
independent of its financial structure. But if markets are imperfect (transaction cost, taxes, informational asymmetry, agency cost etc.)
then capital structure matters and influences the share prices.
Size of a firm does have effect on the valuation of the firm's
assets. Smaller stocks have higher average returns. Introduction of
size, as a multiplicative term to dividend, provides a significant
improvement in the explanation of share prices [Karathanassis and
Philiappas (1988)]. The size of the firm if captured through total
capital employed, is expected to influence the share prices positively
as large firms are better diversified than smaller ones and thus are
less risky [Benishy (1961)]. Atiase (1985) showed that as the size of
the firm increases, their share price volatility declines.
Ariff, et al. (1994) established the joint linear effect of six
firm specific variables for the three markets using data relating to samples of firms over 16 or more years in Japan, Malaysia and Singapore.
In general, the six variables are significantly related to share price
volatility in the three markets, although some were not significant in
particular markets. In the case of more analytically intensive Japanese
market, changes in the fundamental factors accounted for two-fifth of
the variation in share price volatility. The same was not the case in
the less analytically intensive developing markets of Malaysia and
Singapore. Obviously, larger portions of price variation appear not to
be explained by the variation in the six firm-specific fundamental
variables in the less developing markets. Perhaps, prices in the latter
two markets, it may be suggested, are more responsive to macroeconomic factors, which were not included in the cross-sectional tests.
Alternatively, investors in such markets are not pricing the shares on
the basis of fundamental factors, perhaps preferring to price on
speculative information. The US Investors are known to price the
securities much more on the basis of analysis made widely available by
the financial analyst community and the mass media. In another study
Ariff (2000) on a sample of hundred homogenous industrial firms, four
out of these six factors were found significant and explained two-third
of share price volatility over a window of twenty years for US market.
Karachi stock exchange is an important emerging capital market of
the region, among the developing countries. KSE is termed as high-risk
high return market where investors seek high-risk premium [Nishat
(1999)]. Few studies have attempted to analyse the long run behaviour of
the market [Nishat (1991)] and no work has been done to explore the
fundamental variables affecting the share prices. Factors affecting
share prices have been identified for the short run only [Nishat (1995);
Nishat and Saghir (1991)]. It is also important to study these factors
in the Pakistani context after the introduction of reforms, which
emphasised more towards openness to foreign investor and competition.
Under reforms emphasis has been on information disclosure by companies,
documentation, increasing role of brokerage houses and investment
companies which provide more feedback to investor. The objective of this
study is to investigate the joint effect fundamental factors may have on
share prices in the long run. It also attempts to see the impact of
these factors in two sub periods to assess the reform impact on the
share price changes. This paper is a modest first attempt in Pakistan in
this direction using the data of all the firms' (160) that are
continuously listed for the last 20 years.
The paper is organised into five sections. The second section
describes the econometric model and estimation methods. The data
description and variable construction is discussed in section three
followed by results and discussion in section four. Section five
provides summary and conclusion.
2. ECONOMETRIC MODEL AND ESTIMATION METHODS
Keeping in view these six factors an approximation of the relation
between share price volatility (PV) and the identified fundamental
variables namely dividend yield (DY), payout ratio (POR), leverage (DA),
asset growth (ASg), size of the firm (SZ) and earning volatility (EV)
can be given in the following form:
PV=f([DY.sup.-]; [POR.sup.-]; [DA.sup.+]; [ASg.sup.-]; [SZ.sup.-];
[EV.sup.+])
The directions of the predicted relation between share price
changes and the factors are indicated by the + or - signs placed as
superscripts above the symbols used for the factors. These factors will
be identified on an annual basis over a lengthy period of twenty years
to estimate a valid relation using all the continuously twenty year
listed companies at KSE from 1981 to 2000. The analysis utilised
cross-sectional least squares regression. First test involved regressing
the dependent variable PV against all the independent variables
separately. This provides a crude test of the relationship between
common stock volatility and the theory suggested fundamental variables
individually. Thus providing the impact of each variable on stock price
change if no other factor is considered.
In an attempt to find the collective impact of these six factors on
price variability all the factors are regressed against the dependent
variable PV. The test model can now be specified using observations per
firm 'j', where j = 1, ... 160 during t period where t = 1981
to 2000.
[PV.sup.jt] = [[lambda].sub.ot] + [[lambda].sub.1][(DY).sub.jt] +
[[lambda].sub.2][(POR).sub.jt] + [[lambda].sub.3][(DA).sub.jt] +
[[lambda].4][(EV).sub.jt] + [[lambda].sub.5][(ASg).sub.jt] +
[[lambda].sub.6][(SZ).sub.jt] + [[epsilon].sub.jt]
The expectation was that the DY, POR, ASg and SZ would be
negatively related to the PV whilst EV and DA would be positively
related to PV. The sign for the ASg and SZ and EV found to be contrary
to the theory.
The firms selected are heterogeneous in character ranging from big
firms as in energy sector to smaller ones as in textile sector. Because
of the presence of heteroscedasticity in the data we are using
Generalised Least Squares (GLS). The first set of estimation is from
running the regression using all the variables. Adding more variables
biases the researcher in favour of acceptance of the test model.
Therefore, it is necessary to carry out a selection procedure to choose
a parsimonious model. We use the Akaike selection statistical procedure
to eliminate errors that could arise from multi collinear independent
variables. The results after this selection, which is a step-wise
regression procedure, are presented separately.
3. DATA AND VARIABLES CONSTRUCTION
Price change (PV) is represented by a price volatility measure,
using extreme value method developed by Parkinson (1980) somewhat
similar to the standard deviation, but superior to the traditional
measure of standard deviation. Parkinson (1980) showed that the extreme
value method developed by him is 2.5 to 5 times superior as a measure of
variance in a variable. The year's high and low share prices of a
firm are used to calculate the rate of changes in prices over the test
period. This measure is appropriate to capture the changes in share
prices on an annual basis, and it is expressed as a rate of change
measure but it has greater amplitude than in case with the more common
standard deviation. Tests showed that this variable has produced
reliable results reported in other studies.
The six independent variables are measured on an annual basis, each
firm providing a total of twenty annual observations. Dividend yield for
the cross sectional regression test using the sample of firms will be
the averages of the dividend yields of individual firms, the average
being taken over the estimation years per firm.
Dividend Yield
This variable was calculated by summing all the annual dividends
paid to common shareholders and then dividing this sum by the market
value of the common stock. The average of all available years was
utilised.
Earning Volatility
First, the average of the total earnings to total asset ratios for
all years was obtained. Then, the average of the squared deviation from
the overall average was calculated. A square root transformation was
applied to the mean squared deviation to obtain estimates of standard
deviations.
Payout Ratio
Total cumulative individual company earnings and dividends were
calculated for all years. Payout is the ratio of total dividends to
total earnings of the firm. The use of this procedure means that the
problem of extreme value in individual years due to low or possibly
negative net income is reduced. Furthermore the payout ratio was set to
one in cases where total dividends exceeded total cumulative profits.
Size
The variable size was constructed in a form that reflects the order
of magnitude in real terms. The variable was constructed by taking the
average market of common stock for the period 1981 to 2000. The value
(in millions) of real size was averaged over the period.
Leverage
The ratio of long term debt to total assets was calculated. The
average of all years was used in the analysis.
Growth in Assets
The yearly growth rate was calculated by taking the ratio of the
change in total assets. The average over all years was used in analysis.
The behaviour of the variables over the long run as defined in an
earlier section is presented in Table 1 the descriptive statistics of
the data in overall, pre-reform and post-reform periods respectively.
All the firms that are continuously listed on the Karachi Stock Exchange
from 1981 to 2000 has been taken for the purpose. The annual data of
these firms is taken from the various issues of "balance sheet
analysis" published by State Bank of Pakistan. Price data has been
taken from the annual reports and annual publications of Karachi Stock
Exchange.
4. RESULTS AND DISCUSSION
Table 2 reports the test results of regression between price change
variable (PV) and each of the six independent variables. This generates
the kind of results most commonly found in existing literature
investigating one fundamental variable at a time. The results of this
regression shows that two variables Earning volatility (EV) and Asset
growth (ASg) are not significantly explaining the price volatility. The
other four variables are significant but not substantial enough to
explain a large portion of price variation. The explained variation
ranges from as low as 1 percent in case of asset growth to as high as 16
percent for payout ratio. Earning volatility, which is significant
factor in most of the markets, is insignificant in case of KSE.
Individually the highest impact is of pay out ratio, which is 16 percent
while lowest is of size if we consider the significant variables only.
Highest impact is of POR followed by leverage, dividend yield and size.
Contrary to theory, size has a sign that is opposite to that predicted
by theory in this simple regression test. This is due to market
imperfections as already reported in an earlier study on KSE [Nishat
(1999)]. Sign of earning volatility is also wrong theoretically but the
variable is statistically insignificant. Taken together these results
are consistent with prior studies for developing markets.
The joint effect on share price variable from the six variables is
tested running regression presented in Table 3. The explained variation
in the price is not very substantial, about 28 percent. However this is
consistent with the findings regarding the developing markets where firm
related factors are not priced fully. Rather speculative forces and
macro economic environment plays more dominant role [Ariff, et al.
(1994)]. Noticeably the largest impact comes from payout ratio
(coefficient of 0.26) followed by dividend yield, size and leverage
closely. Thus four of the six factors are statistically relevant for the
pricing of the shares in the Karachi stock exchange in this join test
using a lengthy period data.
To control the multicollinearity and to select only significant
variables, the procedure suggested by Mendenhall and Sincich (1989) is
used. The results shown in Table 4 are obtained by applying the
step-wise regression to remove the effect multiple factors may have on
the model's specificity. These results indicate that the
joint-effect of only four variables dominate this relationship. However
there are a number of important differences. The variables selected are
more reliable as the variable selection is carried out using step-wise
regression method of entering one variable at a time, then adding
variables only if the variable being added marks a substantial
difference to the results. Overall, four variables namely payout ratio,
size of the firm, dividend yield and leverage are statistically
significant in that order of importance as determined by the size of the
coefficient. Overall the model fit is significant with the F-ratio of
29.962. The explained variation by these four variables is 25.9 percent.
Our results are consistent with the results observed in less developed
markets of Singapore and Malaysia where goodness of fit obtained were
about 25 percent [Ariff, et al. 1994). Individual factors were found to
be significant, consistent with theoretical predictions, even in the
institutionally less developed markets. However, compared with the
results for developed markets the goodness of fit of the model is far
inferior. It is very likely that the stable economic environment in
developed markets coupled with their more analytically inclined
investing institutions drive prices more than 60 percent on the basis of
key fundamental variables. Such may not be the case of investors pricing
shares in the more volatile economic
environments in the less analytically intensive investment, dominated
by individuals trading on uninformed basis in the less developed capital
markets like Pakistan.
The results for pre-reform and post-reform periods are presented in
Tables 5 to 8. The significance of three fundamental factors is found to
be more prominent, namely pay out ratio, size and dividend yield. In
pre-reform period (1981-1990) as presented in Tables 5 and 6 the
explaining power of these variables is higher as compared to overall
study period. These three variables jointly explain more than two-fifth
of the price variation in share prices in the market. Also, the sign of
the size variable is different from the sign observed for the overall
results. In pre-reform period the negative sign of size shows that
larger firms were having lower volatility in their share prices while in
post reform period the positive sign indicates that the larger firms are
more volatile after the introduction of reforms. The results appear to
vindicate the intuition given by common belief that fundamental factors
drive share prices to change in the long run, though the power of model
to explain is the price variation is not as robust as found in case of
developed markets. This may be due to greater instability in the
economic environment as well as both insufficient institutional progress
in investment culture and the apparent lack of analytically intensive
market practices in Pakistan.
5. SUMMARY AND CONCLUSIONS
This study attempts to explain the price changes due to the six
theory-suggested fundamental variables (dividend yield, payout ratio,
size of the firm, leverage, earning volatility and asset growth) in
Karachi Stock Exchange during 1981 to 2000 using annual balance sheet
data. This paper identifies the joint-effect multiple factors exert on
share prices in the long run. The significant joint factors observed are
payout ratio, size, leverage and dividend yields. Together these four
factors explain one-fourth variation in share prices at KSE. The
explanatory power of fundamental factors is found to be different in
pre-reform (1981 to 1990) and post-reform (1991 to 2000) periods. In
pre-reform period three factors pay out ratio, size and dividend yield
explain more than two-fifth of the variation whereas during post reform
period these factors explained about only one-third of the variability
in prices. The correct sign of size effect is only observed in the
pre-reform period, which shows that market has become more volatile in
the post reform period, which made prices of larger firms more volatile.
The explanatory power of the variables has reduced in the post reform
era. The analysis indicates that variables other than fundamental
variables may be more important and relevant to explain the share price
variation in Pakistan and need further investigation.
Comments
* The study is a good attempt to measure the extent to which the
changes in the six factors (dividend yield, payout ratio, size of the
firm, leverage, earning volatility, and asset growth) explain the
changes or volatility in the stock prices of the companies.
* The results show that Dividend Yield, Payout Ratio, and Size of
the Firms have significant impact on price volatility. Results are not
robust though and do not necessarily support the theory.
* While comparing the pre- and post-reform period, the paper finds
that impact of fundamental factors is lesser in post-reform period.
Post-reform period is taken as after 1990s, whereas it is pertinent to
mention that major reforms in the capital market took place after 1999.
* Intuitively, we all know that KSE is a very narrow market
dominated by few speculators, who do not really take a lead from
financial results of companies and other information in the annual
reports. Therefore, even before reading the study, one could have
presumed that the study would show that most of the volatility in the
stock prices could not be attributed to the fundamental factors.
Supporting this perception, this is exactly what the study tries to
prove on the whole and there are no counter-intuitive results.
* The authors have used regression analysis to measures the effect
of these factors (independent variables) on the share price (dependent
variable). The [R.sup.2] or coefficient of determination of the
regression models is from 26 percent to 28 percent. This means that
about 26 percent to 28 percent of the volatility in the share prices of
the companies is explained by the six factors. That is, the six
fundamental factors do not explain about 72 percent to 74 percent of the
variability in the stock prices. * It would be important to point out
certain peculiar characteristics of KSE.
Trading in only few stocks, highest level of volume as compared to
capitalisation, thirty stocks dominate the whole market and actual
settlement is still not more than 10 percent.
* It is also pertinent to note that market only started looking at
fundamentals in "post-reform" period when foreign portfolio
managers came to Pakistan.
* To use regression, authors needed a large number of data points
for the same company. So they selected 160 companies, which were
continuously listed at KSE from 1981 to 2000. Now, we do know that
liquidity at KSE is concentrated to a high extent in scrips that were
NOT listed during the twenty years. This means that most of the
companies in the sample were highly illiquid. Price discovery is the
most important function of any market. For a company's stock price
to reflect its fundamentals, it should be a large capitalisation company
with a significant free float and good fundamentals so that it is
followed by a large number of analysts and investors. Moreover, the
stock ought to be liquid--Hubco for instance. The sample companies do
not fit this description. Therefore, to some people it would be
surprising, that in such a sample the model still tends to explain as
much as a quarter of the variability in the stock prices.
* News hits stock markets every day. Investors assess the impact of
the news on the profitability of the companies and then take buy, sell,
hold, and etc., decisions. The stock prices respond to these news items
every day as directed by the investors. The information on the six
fundamental ratios is coming from the annual reports, which were made
available to the investor only once a year. But the volatility in the
share price is of the whole year or at least not specific to the days,
in which Annual Reports were made available to them. Therefore, there is
a mismatch between relevant time period for the share price and the
fundamental ratios. The model would be appropriate if the annual reports
were the only information made available to the investors.
* Example: In May 2002, when there was a threat of Indo-Pak war,
prices collapsed across the board because the whole economy was under
threat. Later, when the war threat disappeared, prices rebound, war did
not happen, so the profitability of the companies was not affected. It
is important to take note of such extraordinary events that have
phenomenal impact on price volatility. Therefore, it would be advisable to exclude the May period while compiling results of the study. This
would keep the focus of study on relationship between volatility and
annual report based fundamental factors.
* That fundamentals like dividend yield determine the stock price
is only one way of looking at the stock prices. Another view (technical)
is that share prices respond to the demand and supply. For instance, for
the last month or so, the current bull-run at KSE is predominantly technical. More and more money and people are turning to KSE bidding up the prices of the stocks, even though no such news is coming every day
that would influence the fundamentals of the companies. Study focused on
fundamental factors in a highly speculative and narrow market like KSE.
It set out to prove quantitatively what we already knew but intuitively.
* Although, there were few capital market reforms carded out in
nineties but the actual reforms came in the period of 1999-2002. These
reforms addressed the structural issues of governance, transparency, and
efficiency. It would be interesting to see impact of fundamental after
these comprehensive reforms.
* Psychology of local investors is also critical. There are three
types Of investors, institutions, high net-worth speculative individuals
and small investors. I am afraid only institutions are the one who would
probably go for fundamentals. The remaining investors are either
speculators or possess bank depositors' mentality. In vesting in
stocks is a risk business and growth should be the objective. As some
say, it is 90 percent psychology and 10 percent fundamentals.
* I think a study should be conducted to see to what extent share
price volatility is explained by technical or non-Fundamental factors at
KSE.
Haroon Sharif
Securities and Exchange Commission of Pakistan, Islamabad.
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Chaudhary Mohammad Irfan is working towards the M. Phil. at the
Applied Economics Research Centre, University of Karachi. Mohammed
Nishat is Professor and Chairman, Department of Economics and Finance,
Institute of Business Administration, Karachi.
Table 1 Descriptive Statistics
Variable Min Max Mean Std
(a) Overall Period
PV 0.123 0.906 0.497 0.146
DY 0.000 0.162 0.041 0.032
EV 0.135 0.866 0.031 0.141
POR -0.533 1.858 0.265 0.304
SZ 0.718 8666.291 395.174 1107.773
DA 0.000 3.478 0.152 0.319
Asg -0.048 12.181 0.193 0.959
(b) Pre-Reform Period
PV 0.121 0.927 0.453 0.153
DY 0.000 0.217 0.054 0.043
EV 0.000 0.401 0.018 0.057
POR -0.326 3.716 0.326 0.043
SZ 0.495 2067.124 115.641 252.791
DA 0.000 1.333 0.132 0.172
Asg -0.155 7.883 0.134 0.108
(c) Post-Reform Period
PV 0.013 1.133 0.54 0.22
DY 0.000 0.165 0.0302 0.0346
EV 0.000 1.142 0.0422 0.1798
POR -1.401 1.72 0.204 0.3295
SZ 0.783 16692.99 674.71 2031.17
DA 0.000 6.875 0.172 0.58
Asg -1.74 23.297 0.245 1.8408
Where
PV : Price Volatility.
EV : Earning Volatility.
DY : Dividend Yield.
DA : Leverage.
POR : Payout Ratio.
ASg : Asset Growth.
SZ : Size of the Firm.
Table 2
Estimated Relation between Share Prices and Fundamental Variables:
Simple Regressions
Variables Coefficients t-value R-squared
DY -0.30 -3.94 0.09
EV -0.04 0.54 0.02
POR -0.40 -5.47 0.16
SZ -0.17 2.25 0.03
DA 0.32 4.29 0.10
Asg 0.10 1.30 0.01
[PV.sub.jt] = [[lambda].sub.ot] + [[lambda].sub.1]
[(DY).sub.jt] ... ... ... ... ... (2)
[PV.sub.jt] = [[lambda].sub.ot] + [[lambda].sub.1]
[(EV).sub.jt] ... ... ... ... ... (3)
[PV.sub.jt] = [[lambda].sub.ot] + [[lambda].sub.1]
[(POR).sub.jt] ... ... ... ... ... (4)
[PV.sub.jt] = [[lambda].sub.ot] + [[lambda].sub.1]
[(SZ).sub.jt] ... ... ... ... ... (5)
[PV.sub.jt] = [[lambda].sub.ot] + [[lambda].sub.1]
[(DA).sub.jt] ... ... ... ... ... (6)
[PV.sub.jt] = [[lambda].sub.ot] + [[lambda].sub.1] [(ASg).sub.jt]
Where
PV : Price Volatility.
EV : Earning Volatility.
DY : Dividend Yield.
DA : leverage.
POR : Payout Ratio.
ASg : Asset Growth.
SZ : Size of the Firm.
Table 3
Joint Effect of the Fundamental Variables on Share Price Variable
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1][(DY).sub.jt] +
[[lambda].sub.2][(POR).sub.jt] + [[lambda].sub.3][(DA).sub.jt] +
[[lambda].sub.4][(EV).sub.jt] + [[lambda].sub.5][(ASg).sub.jt] +
[[lambda].sub.6][(SZ).sub.jt] + [[epsilon].sub.jt]
Coefficients t-values
DY -0.231 -2.985
EV -0.044 -0.598
POR -0.266 -3.350
SZ 0.219 2.971
DA 0.192 2.547
ASg 0.073 1.009
R-squared = 0.28.
Adjusted R-squared = 0.25.
F- ratio = 10.07 D.W = 2.02.
Where
PV : Price Volatility .
EV : Earning Volatility.
DY : Dividend Yield.
DA : Average.
POR : Payout Ratio.
Asg : Asset Growth.
SZ : Size of the Firm.
Table 4
Parsimonious Test Results of Price to Fundamental Relation:
Step-wise Regressions
Variables Variables in Coefficient
Steps Entered Test Model (t-values) R-squared
1 POR -- -0.400 0.155
(-5.471)
2 SIZE POR -0.410
(-5.71)
SZ 0.193
(2.685) 0.187
3 DY POR -0.332
(4.449)
SZ 0.236
(3.308)
DY -0.232
(3.046) 0.22
4 DA POR -0.259
(-3.331)
SZ 0.2477
(3.526)
DY -0.220
(-2.947)
DA 0.203
(2.726) 0.25
F-ratio for step 4 is 29.96 significant at or better than 0.0001 level.
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1][(POR).sub.jt] ... (1)
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1][(POR).sub.jt] +
[[lambda].sub.2][(SZ).sub.jt] ... (2)
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1][(POR).sub.jt] +
[[lambda].sub.2][(SZ).sub.jt] + [[lambda].sub.3][(DY).sub.jt] ... (3)
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1][(POR).sub.jt] +
[[lambda].sub.2][(SZ).sub.jt] + [[lambda].sub.3][(DY).sub.jt] +
[[lambda].sub.4][(DA).sub.jt] ... (4)
Where
PV : Price Volatility.
DY : Dividend Yield.
POR : Payout Ratio.
SZ : Size of the Firm.
DA : Leverage.
Table 5
Joint Effect of the Fundamental Variables on Share Price
Variable in the Pre-Reform Era
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1][(DY).sub.jt] +
[[lambda].sub.2][(POR).sub.jt] + [[lambda].sub.3][(DA).sub.jt] +
[[lambda].sub.4][(EV).sub.jt] + [[lambda].sub.5][(ASg).sub.jt] +
[[lambda].sub.6][(SZ).sub.jt] + [[epsilon].sub.jt]
Coefficients t-values
POR -0.371 -5.994
SZ -0.474 -6.794
DY -0.427 -6.76
Asg -0.001 -0.015
DA -0.009 -0.128
EV 0.075 1.132
R-squared = 0.471 F = 22.797
Adj. R-squared = 0.450 DW = 1.89
Where
PV : Price Volatility .
DY : Dividend Yield.
EV : Earning Volatility.
POR : Payout Rado.
SZ : Size of the arm.
Asg : Asset Growth.
DA : Leverage.
Table 6
Parsimonious Test Results of Price to Fundamental Relation
for the Pre-Reform Period: Step-wise Regressions
Variables Variables in Coefficients Adj.
Steps Entered Test Model (t-values) R-squared
1 POR POR -0.359
(-4.839) 0.124
2 SIZE POR -0.397
(-5.838)
SIZE 0.392
(-5.773) 0.272
3 DY POR -371
(6.304)
SIZE 0.493
(-8.173)
DY -.442
(-7.341) 0.456
F-slat is 23.418 for the third step at or better than 0.0001.
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1]
[(POR).sub.jt] ... (4)
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1] [(POR).sub.jt] +
[[lambda].sub.2] [(SZ).sub.jt] ... (5)
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1] [(POR).sub.jt] +
[[lambda].sub.2] [(SZ).sub.jt] + [[lambda].sub.3] [(DY).sub.jt] ... (5)
Where
PV : Price Volatility.
POR : Payout Ratio.
SZ : Size of the Firm.
DY : Dividend Yield.
Table 7
Joint Effect of the Fundamental Variables on Share Price Variable for
the Post-Reform Period
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1][(POR).sub.jt] +
[[lambda].sub.2][(SZ).sub.jt] + [[lambda].sub.3][(DY).sub.jt] +
[[lambda].sub.4][(AS).sub.jt] + [[lambda].sub.5][(DA).sub.jt] +
[[lambda].sub.6][(EV).sub.jt] + [[epsilon].sub.jt]
Variables Coefficients t- values
POR -0.274 -3.284
SZ 0.375 5.371
DY -0.252 -3.069
Asg 0.054 0.802
DA 0.083 1.222
EV -0.129 2.033
R-squared : 0.403 F-slat : 17.117
Adj. R-squared : 0.38 D.W : 1.94
Where
PV : Price Volatility.
POR : Payout Ratio.
SZ : Size of the Firm.
DY : Dividend Yield.
ASg : Asset Growth.
DA : Leverage.
EV : Earning Volatility.
Table 8
Parsimonious Test Results of Price to Fundamental Relation in
the Post-Reform Period: Step-wise Regressions
Variables Variables in Coefficients Adj.
Steps Entered Test Model (t-values) R-squared
1 DY -- -0.475 0.221
(-6.761)
2 SIZE DY -0.441
(-6.687)
SIZE 0.325
(4.925) 0.321
3 POR DY -0.262
(3.202)
SIZE 0.392
(5.896)
POR -0.289
(-3.503) 0.367
F-slat is 31.514 for the third step at or better than 0.0001.
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1]
[(DY).sub.jt] ... (1)
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1] [(DY).sub.jt] +
[[lambda].sub.2] [(SZ).sub.jt] ... (2)
[PV.sub.jt] = [[lambda].sub.0t] + [[lambda].sub.1] [(DY).sub.jt] +
[[lambda].sub.2] [(SZ).sub.jt] + [[lambda].sub.3]
[(POR).sub.jt] ... (3)
Where
PV : Price Volatility.
POR : Payout Ratio.
SZ : Size of the Firm.
DY : Dividend Yield.