Does the inflation rate affect the performance of the stock market ...

Emerging Markets Review
Ž . 2 2001 263279
Does the inflation rate affect the
performance of the stock market The case
of Egypt
Mohammed Omran a , John Pointon b,
a Arab Academy for Science and Technology and Maritime Transport, College of Management and
Technology, Alexandria, Egypt
b Uniersity of Plymouth Business School, Drake Circus, Plymouth, Deon PL4 8AA, UK
Received 1 September 2000; received in revised form 10 April 2001; accepted 17 April 2001
Abstract
The intention of this paper is to examine the impact of the inflation rate on the
performance of the Egyptian stock market. Particular attention is paid to the effects of the
rate of inflation on various stock market performance variables, in terms of market activity
and market liquidity. From the co-integration analysis through error correction mechanisms
Ž ECM ., significant long-run and short-run relationships between the variables are found,
implying that the inflation rate has had an impact upon the Egyptian stock market
performance generally. 2001 Elsevier Science B.V. All rights reserved.
JEL classifications: C12; C22; E44; G10; N25; O11
Ž .
Keywords: Inflation rate; Stock market; Egypt; Co-integration and error correction mechanism ECM
1. Introduction
Countries around the world have achieved significant reductions in the general
rate of inflation. For emerging economies, controlling inflation has been a high
priority, and there needs to be an evaluation of economic reform on emerging
Corresponding author. Tel.: 44-1752-232-865; fax: 44-1752-232-493.
Ž .
E-mail address: john.pointon@pbs.plym.ac.uk J. Pointon .
1566-014101$ - see front matter 2001 Elsevier Science B.V. All rights reserved.
Ž .
PII: S 1 5 6 6 - 0 1 4 1 0 1 00020-6

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279
stock markets. This paper focuses upon Egypt as an important example of a
successful economic experiment within the Middle East region. Since inflation has
tended to have a negative impact on stock market performance, such economic
policies have benefited the stock markets. Many academic investigations into the
impact of inflation have concentrated upon the effects on returns to investors. It
should be mentioned that the market index in Egypt was established in late 1993.
This means that to analyze statistically the impact on returns is not sensible for
such a short period of time. However, other aspects of stock market performance,
such as market activity and liquidity have been neglected. Yet, it is particularly
important for policy-makers to look at market activity and liquidity, since these
give indicators of the market’s attraction as a channel for investment and a source
of finance for businesses
When Egypt started its economic reform program by late 1990, the inflation rate
had been targeted to be under control in order to create an attractive environment
for investment. With regard to this program, Egypt has witnessed major and radical
changes in its economic climate. The aim of this program was to increase the
growth rate of the economy. This objective is not likely to be achieved without
increasing the level of investment. In turn, this investment can be obtained through
creating a strong stock exchange market that is capable of attracting local and
foreign investment. In fact, it was vital for the Egyptian economy to depress the
high inflation rate in the first stage of the economic reform program period. In
turn, the government chose to restrict the demand for goods and services by
putting the inflation rate under control in the short-term, since the other alternative,
which represented an increase in the supply of goods and services, usually,
needs a long time. In light of this, the Egyptian government introduced treasury
bills in 1991 followed by treasury bonds Ž 510 years.
in 1995. In the meantime, the
rates of interest liberalized and real interest rates became positive, and in turn, all
these procedures helped in absorbing the level of liquidity. As a conclusion, the
liquidity growth declined from over 27% in 19901991 to only 8.7% in 19971998
Ž Central Bank of Egypt 1998 ..
On the other hand, the decline in the budget deficit, combined with a relatively
stable exchange rate, assisted in decreasing the rate of inflation.
This paper will focus upon examining the impact of the inflation rate on the
stock market activity and liquidity in Egypt. In turn, we will consider previous
empirical studies on inflation and stock prices and returns, the background
literature, the hypotheses, and the data set and then move on to set out the
methodology based on co-integration analysis. The results of the analysis will then
follow, before the summary and conclusions.
2. Literature review
The impact of inflation on the Egyptian stock market does not appear to have
been the subject of prior study. However, Omran and Pointon Ž 2000.
examined the
cost of capital in Egypt based on a sample of 109 companies, although their

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279 265
investigation was a cross-sectional study, rather than a time series analysis. Also,
their main focus was upon variations across industries. However, they observed
that, based upon an international comparison of 41 countries, Egypt has a very
high cost of equity exceeded only by Peru, Pakistan and Columbia. They note that
Egypt is a fairly new emerging market, despite its very early beginnings, so there
may be a high perceived risk, but that even the Treasury bill rate was high,
approximately 9% in 1998. In turn, the treasury bill rate is likely to reflect to some
extent the inflation rate, in that investors demand maintaining their purchasing
power. The experience of a high cost of capital suggests that inflationary effects
may have had an impact on the performance of the individual firms.
It is therefore, of intrinsic importance to examine the impact of inflation on the
stock market in Egypt. The results have relevance to other markets, such as the
recent experience in Turkey.
In theory, there is a case to support the view that since the rate of inflation
means an increase in the general level of prices, and since common stocks can be
considered as capital goods, then the stock prices should move with the general
level of prices. So, when the general inflation rate increases, common stocks should
also increase to compensate investors for the decrease in the value of money. In
this framework, it is expected that there is a positive relationship between the
inflation rate and stock prices. However, early empirical studies demonstrated a
negative relationship between the inflation rate and stock returns Žsee, Lintner,
1975; Bodie, 1976; Jaffe and Mandelker, 1976; Nelson, 1976; Fama and Schwert,
1977 .. In fact, the inverse relationship between a higher inflation rate and lower
common stock prices according to Feldstein Ž 1980a.
results from basic features of
US tax laws, particularly, historic cost depreciation and the taxation of nominal
capital gains. This is also reinforced by others studies Žsee, e.g. Feldstein and
Summers, 1979; Feldstein, 1980b, 1982; Summers, 1981a,b; Pindyck, 1984; Fama,
1981 .. Dokko and Edelstein Ž 1987.
examined this relationship in the US market by
using the Mundell Ž 1963. wealth-effect hypothesis, and the Darby Ž 1975.
tax-effect
hypothesis. The results of their study indicated that a negative relationship exists
between the level of expected inflation and the expected real stock market returns.
Chen et al. Ž 1986.
used monthly data for the period 19581984 to test the impact
of the inflation rate on stock prices. In fact, they defined three variables related to
the inflation rate: expected inflation; the change in expected inflation; and unanticipated
inflation, and found a significantly negative relationship between the
inflation variables and stock prices. Similarly, Chen and Jordan Ž 1993.
found the
same result for the same variables.
Benderly and Zwick Ž 1985. and Titman and Warga Ž 1989 ., however, suggested
there exists a structural relationship between the inflation rate and stock returns
arising from the real balance effect pertaining only to a period of adjustment
rather than to a long-run equilibrium.
In an Italian study, Bottazzil and Corradi Ž 1991.
investigated the variability of
the risk premium in the stock market over the period 19781989 and found that
the acceleration of the inflation rate is negatively related to stock prices. They
added:

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279
‘Since equities are claims against physical assets, whose returns are not affected by inflation, they
should be considered as an ideal hedge against inflation’.
ŽBottazzil and Corradi Ž 1991 ., p. 338 ..
On the other hand, other empirical studies have examined this issue in the shortand
long-term. For example, Boudoukh and Richardson Ž 1993.
used annual data
on inflation, stock returns and short- and long-term interest rates over the period
18021990. Covering both the UK and US markets, the data were obtained from
Siegel Ž 1992. and Schwert Ž 1990 .. To look at the contemporaneous relationship
between the inflation rate and the stock market, the study regressed 1-year stock
returns on the 1-year inflation rate, and 5-year stock returns on the 5-year inflation
rate. The results of this study revealed a negative relationship between the
inflation rate and stock returns in the short-term, but in a long-horizon, this
relationship tended to be positive.
Again, Boudoukh et al. Ž 1994.
investigated the cross-sectional relationship
between expected inflation and the industry sorted stock returns. Using monthly
data for the period 19531990, sorting the firms into 22 industry sectors and using
regression analysis, they found that the direction of relationship between expected
inflation and the industry group is linked to cyclical movements in industry output
and, specifically, stock returns of cyclical industries co-vary negatively with expected
inflation, while the non-cyclical industries co-vary positively. They also
found a negative relationship for short horizons and a positive relationship for long
horizons.
In fact, all the above empirical studies focused on the relationship between the
inflation rate and stock returns and prices in the developed countries, especially
the UK and the US. In turn, little is known about the impact of inflation rates on a
broader menu of countries.
In this framework, Asprem Ž 1989. and Wasserfallen Ž 1989.
explored the relationship
between macroeconomic variables, and stock prices and asset portfolios in
European countries. They found a negative relationship between the inflation rate
and stock prices. Also, Najand and Rahman Ž 1991.
argued that the volatility of
inflation increases the volatility of stocks, thus in turn causing a higher required
rate of return on stocks, which means a fall in stock prices. Erb et al. Ž 1995.
examined the interaction between the inflation rate and both the time-series and
cross-section of expected stock returns in 41 developed and emerging equity
markets. The result of this study confirmed the negative time-series relationship
between realized inflation and realized asset returns when examined country-bycountry.
The study found that the negative relationship is maintained when longer
horizon returns are examined, otherwise, when this study investigated the relationship
between long-term inflation and long-horizon asset returns, it did not find a
positive relationship between both variables. Hence, this suggested that international
equity returns fail to serve as an inflation hedge, even if the equities are held
over long horizons. Furthermore, on a country-by-country basis, equity returns do
not serve as an inflation hedge. Other studies, however, have indicated that the
relationship between the inflation rate and international stock returns tend to be

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279 267
positive in the long horizons ŽBoudoukh and Richardson, 1993; Boudoukh et al.,
1994 ..
However, from most of the empirical studies shown above, it can be concluded
that, contrary to economic theory and common sense, a significant negative
relationship between the rate of inflation and stock returns is found.
It is important to observe that these empirical studies have concentrated mainly
on stock prices and returns as indicators of stock market performance. However, it
can be argued that the changes in the inflation rate may also affect other aspects of
stock market performance, such as market activity and market liquidity, in turn this
paper will examine these neglected aspects.
3. Hypotheses
Five main stock market activity variables have been identified, namely, the value
of trade, the volume of trade, the number of transactions, the number of traded
companies and the value of new issues Ž including capital increases .. Additionally,
the total value traded to market capitalization and the volume of shares traded to
the volume of shares listed have been identified as referring to market liquidity.
This leads to two main hypotheses that can be formally stated.
H1: the market activity increases as the inflation rate decreases. The above
hypothesis includes several sub-hypotheses as follows:
H11: the value of trade increases as the inflation rate decreases.
H12: the volume of trade increases as the inflation rate decreases.
H13: the number of transactions increases as the inflation rate decreases.
H14: the number of traded companies increases as the inflation rate
decreases.
H15: the value of new issues Ž including capital increases.
increases as the
inflation rate decreases.
H2: the market liquidity increases as the inflation rate decreases. The above
hypothesis can, in turn, be divided into sub-hypotheses as follows:
H21: the total value traded to market capitalization increases as the
inflation rate decreases.
H22: the volume of shares traded to the volume of shares listed increases
as the inflation rate decreases.
4. Data set
The data of this paper cover the period from 19801981 to 19971998, which
incorporates time periods prior to and after the introduction of the economic
reform program. The Central Bank of Egypt, the Egyptian Cabinet Information

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and Decision Support Center and the International Monetary Fund have been
consulted as the sources for the inflation data series. Egypt’s Capital Market
Authority and the Central Bank of Egypt were the source of these data. Because of
limitations in the availability of data, annual figures are used in the analysis. This
means that an important caveat to the results is that a longer period of study, or
more frequent observations, may perhaps have provided different implications.
However, the importance here is to investigate broad inflationary effects upon
market performance in terms of activity and liquidity. Nevertheless, although these
are arguably crucial to the smooth functioning of the stock markets, the results
need to be considered within the context of the data set.
5. Methodology: co-integration analysis
In the analysis of time series data, both long-run and short-run relationships
often co-exist. The dynamics of both long- and short-run effects can be modeled by
introducing error correction mechanisms Ž ECMs.
to enable a simultaneous evaluation
of both processes Ž Banerjee et al., 1986; Engle and Granger, 1987 .. But first,
there should be a test for the order of integration Ž Dickey and Fuller, 1979 .. The
basic approach is to run a simple linear model of values at time t regressed against
values of the same variable at time t 1. This is clearly an autoregression, since
the variable is regressed upon its previous value. In the regression, if the coefficient
of the variable at time t 1 is less than 1, then the variable at time t is said
to be integrated of order zero. The test is known as a unit root test. The predicted
value of the variable at time t, according to the product of the actual value at time
t 1 and the autoregressive coefficient, is likely to differ from its actual value.
These error terms through time may be related, i.e. there may be an autocorrelation
in the error process. To help overcome this Dickey and Fuller Ž 1981.
use a
multiple regression, instead of a simple regression, containing extra components to
represent changes in earlier values of the variable for selected lags. These lags
should be relatively small in order to save some degrees of freedom. The corresponding
tests are known as augmented DickeyFuller Ž ADF.
unit root tests.
If the underlying variable is not integrated of order zero then a new model is set
up that is based upon the same procedure, except that the change in the value of
the variable from time t 1 to time t is regressed against the change in the
variable from t 2tot 1. This is known as first differencing. If the autoregressive
coefficient is less than 1, then the variable is integrated of order 1. The same
process is repeated, if necessary, to test for higher orders of integration. In this way
if the raw data are not stationary, then they are first differenced, or second
differenced until stationarity is achieved.
In this paper, the ADF unit root tests were performed to determine the order of
integration for both the inflation rate and the chosen stock market performance
variables using the Personal Computer Generalized Instrumental Variables Estimators
Ž PCGIVE. Version 8.0 Ž Doornik and Hendry, 1994 .. Table 1 reveals the
results of the augmented Dickey-Fuller unit root tests for the order of integration.

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279 269
Integrations of order zero were not found, and so the variables had to have a
higher order of integration. On first-differencing, it was found that they were each
integrated of order one.
The capital market variables were first log-transformed to help linearize the
data. For the number of traded companies and the value of new issues Žincluding
capital increases.
a lag of 1 year was used in the multiple regressions. The ADF
statistic, for the number of transactions was significant at the 5% level. The
remaining variables were significant at the 1% level.
As to the inflation rate, the raw data were used without a log transformation.
Again, the order of integration was one, with an ADF statistic significant at the 1%
level.
The next step was to determine whether the variables, that were integrated of
the same order, exhibited a co-integrating relationship. This means that they
cannot drift too far apart through time Ž Dickey et al., 1991 .. For example, the
inflation rate was found to have the same order of integration as the log-value of
each stock market variable. Thus, simple regressions can be performed. For
example, the logarithm of the value of trade at time t is regressed against the rate
of inflation at time t. From this regression the residuals are saved. This means that
differences between each actual observed logarithm of the value of trade and the
respective estimated logarithm of the value of trade Ž from the regression.
are saved
as a vector.
The changes in the residuals between adjacent points in time are defined as the
error correction mechanism Ž ECM .. These changes are tested to determine their
Table 1
Augmented DickeyFuller unit root tests for stock market variables and the inflation rate
Selected Transformation Order of Selected ADF
variable of variable integration lags statistic
Value of trade Log One Zero 2.306
Volume of trade Log One Zero
2.89
Number of Log One Zero
2.12
transactions
Number of Log One One
3.176
traded
companies
Value of new Log One One
2.967
issues Žincluding
capital increases.
Total value traded Log One Zero
3.89
to market
capitalization
Volume of shares Log One Zero
2.80
traded to volume
of shares listed
Inflation rate None One Zero
3.285
denotes critical at 5%, denotes critical at 1%.

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279
order of integration using the same procedure as earlier ŽCharemza and Deadman,
1992 .. If the ECM is integrated of order zero, then the variables are co-integrated
Ž Engle and Granger, 1991 .. Conversely, the Granger Representation Theorem
points out that co-integrated variables must have an error correction representation
Ž Engle and Granger, 1987 .. But changes in the dependent variable can also
reflect disequilibria between the dependent and independent variables ŽDolado et
al., 1990 ..
Therefore, given a stationary residual in the long-run static equation, the next
stage is to regress the difference in, say, the change in the value of trade from
t 1 to t against: the change in the value of trade from t 2 to t 1, and
similarly for earlier lags; as well as the difference in the inflation rate from t 1to
t, and similarly for earlier lags; and also the error correction mechanism at time
t 1 Ž see, for example, Thomas, 1997 .. The result is a first-differenced autoregressive
distributed lag model with an error correction mechanism.
A simplification search is carried out in a systematic manner involving the
gradual elimination of apparently unimportant lagged variables. To reduce the
general model, a two-tailed t-test with a 10% level of significance will be used to
eliminate non-significant variables until no further reductions are feasible. Strictly,
general-to-specific models will be reduced using the criteria shown above, up to the
point where the right-hand side of the EC model contains at least one differenced
independent variable Ž for the inflation rate.
and the lagged ECM, which represents
the basic form of the EC model. For each bivariate relationship, once the final
version from EC models have been specified, various diagnostic tests are run in
order to test for the power of the models Ž see Appendix A ..
6. Results and analysis
6.1. Modeling the impact of the inflation rate upon market actiity through error
correction models
The ADF unit root tests indicated that the inflation rate and all market activity
variables have the same order of integration, that is, these variables are integrated
of order 1. In turn, static long-run regressions have been performed using ordinary
least squares Ž OLS.
to test for co-integration relationships between the variables.
The outputs of this analysis are given in Table 2, which summarizes the results of
this test.
The results from ADF unit root tests upon the residuals, from each bivariate
static long-run equation given in Table 2, indicated clearly that the residuals from
the five static long-run equations are integrated of order zero, suggesting that the
variables in each bivariate relationship are co-integrated, that is, there is a long-run
relationship between these variables. Additionally, the long-run relationships
between these market activity variables and the inflation rate are negative. However,
since ADF unit root tests have a low power, EC models can support or refute
the co-integration relationship between the variables explaining both long- and

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279 271
Table 2
Static long-run models for the impact of the inflation rate upon market activity variables
2
Variables Coefficient S.E. t-Prob. F-Prob. R
y1,t
Constant 9.8830 0.74919 0.0000
Inflation 23.747 3.7568 0.0000 0.0000 0.7140
y2,t
Constant 5.7633 0.44887 0.0000
Inflation 17.842 2.2508 0.0000 0.0000 0.797
y3,t
Constant 13.968 0.70823 0.0000
Inflation 25.161 3.5514 0.0000 0.0000 0.7583
y4,t
Constant 6.3300 0.33344 0.0000
Inflation 7.3525 1.6720 0.0004 0.0004 0.547
y5,t
Constant 10.395 0.45794 0.0000
Inflation 20.228 2.2963 0.0000 0.0000 0.829
Notes: y1 value of trade; y2 volume of trade; y3 number of transactions; y4 number of
traded companies; y value of new issues Ž including capital increases ..
5
short-run relationships simultaneously. With regard again to the data series, they
indicated that there is a slow response of most stock market performance variables
to the changes in the inflation rate, in turn both dependent and independent
variables will be lagged up to 3 years in the EC models. Clearly, this may give more
information about the best model, which can represent the best relationship
between the variables. So, the chosen model has been decided, taking into
consideration the probability of the F statistic, the number of insignificant variables
in the equation, mainly, ECM and the independent variable, the R 2 and also
the degrees of freedom. The EC models, in turn, will explain both the long- and
short-run relationship simultaneously, as the ECM coefficients are expected to
capture the adjustments of differenced-dependent and independent variables towards
long-run equilibrium, whereas the coefficients on differenced-dependent and
independent variables are expected to capture the short-run dynamics of the
model. The results from the EC models can be seen in Table 3.
As shown in Table 3, the selected inflation rate models, which treat market
activity as a dependent variable, contain significant ECMs without exception. The
ECMs were significant at the 1% level for the first four variables: the value of
trade; the volume of trade; the number of transactions; and the number of traded
companies. The ECMs were significant at the 5% level for the value of new issues
Ž including capital increases ..
The inflation rate model of the relationship with the value of trade contains a
significant ECM without a lag for the differenced inflation rate and a lag of 1 and 3
for the differenced autoregressive variable. With regard to the sign of the coefficients
of the independent variable from the static long-run equation given in Table
3, it was found to be negative, suggesting that there is a negative long-run
relationship between the variables. In the meantime, the differenced inflation rate
without a lag was significant at the 10% level, suggesting a short-run relationship
between the variables. In addition, the differenced value of trade with a lag of 1

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and 3 years was significant at the 5 and the 10% level, respectively, implying that
the value of trade may be affected in the short run by its previous performance.
Since the ECM indicated a significant effect as well as the differenced independent
variable, the hypothesis, which stated that the value of trade increases as the
inflation rate decreases, cannot be rejected, implying a negative long- and short-run
relationship between the variables.
With regard to the inflation rate model of the relationship with the volume of
trade, the results showed that this model contains a significant ECM with a lagged
differenced inflation rate of 1 and 3 years and a lagged differenced autoregressive
variable of 1 year, that is confirming the co-integration relationship found in the
previous analysis. The lagged differenced-dependent variable for 1 year was significant
at the 10% level, suggesting that the value of trade may be affected in the
short-run by its performance in the previous year. In addition, the coefficient of the
Table 3
Specific error correction models for the impact of the inflation rate upon market activity variables
Dependent Autoregressive Independent Error-correction R
variable variables variables mechanism Ž F-prob. .
Ž differenced. Ž differenced. Ž differenced.
or constant
Ž. Ž
.
Value of Value of Inflation t ECM t 1 66%
trade Ž. t
trade Ž t 1. Constant Ž 3% .
2
Value of
Ž .
trade t 3
Volume of Volume of Inflation Ž t 1. ECM Ž t 1. 57%
trade Ž. t
trade Ž t 1.
Inflation Ž t 3. Ž 7.5% .
Constant
Ž
. Ž
.
Number of Number of Inflation t 1 ECM t 1 74%
trans. Ž. t
trans. Ž t 2. Inflation Ž t 3. Ž 3% .
Number of
trans. Ž t 3.
Constant
Number of Number of Inflation Ž. t ECM Ž t 1. 82%
traded traded
Constant Ž 0.0% .
cos. Ž. t cos. Ž t 1.
Ž. Ž
.
Value of Value of new Inflation t ECM t 1 80%
new
issues Ž t 2. Constant Ž 0.0% .
issues Ž. t
Value of new
issues Ž t 3.
denotes 10% level of significance; denotes 5% level; and denotes 1% level.

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279 273
differenced inflation rate lagged for 3 years was significant at the 10% level
suggesting a short-run relationship between the variables. Since the inflation rate
showed a negative coefficient in the long-run static regression, the hypothesis,
which stated that the volume of trade increases as the inflation rate decreases,
cannot be rejected, that is, there is a negative long-run and short-run relationship
between the variables.
Concerning the number of transactions, the results showed that this model
contains a significant ECM with a lagged differenced inflation rate of 1 and 3
years, and a lagged autoregressive-dependent variable of 2 and 3 years. The
coefficient of the differenced autoregressive variable lagged for 2 years was
significant at the 10% level suggesting that there is a short-run relationship
between the number of transactions and the performance in the previous years. In
the meantime, the coefficients of the differenced inflation rate with a lag of 1 and 3
years were significant at the 10 and 5% level, respectively, suggesting a short-run
relationship between the variables. Since the inflation rate showed a negative
coefficient in the long-run static regression, the hypothesis, which stated that the
number of transactions increases as the inflation rate decreases, cannot be rejected,
implying a negative long- and short-run relationship between the variables.
The inflation rate model of the relationship with the number of traded companies
contains a significant ECM with a lag of 1 year for the differenced autoregressive
variable and a lag of zero for the differenced inflation rate. However, the
model indicated that the autoregressive variable lagged for 1 year was significant at
the 1% level, which means that the number of traded companies may be affected
in the short-run by its performance in the previous year. In the meantime, the
coefficient of the differenced inflation rate without a lag indicated an insignificant
effect, that is, there is no short-run relationship with the number of traded
companies. Since the coefficient from the static long-run equation showed a
negative sign, in this case, the hypothesis, which stated that the number of traded
companies increases as the inflation rate decreases, cannot be rejected, indicating a
negative long-run relationship between the variables.
Lastly, the inflation rate model, which incorporates the value of new issues
Ž including capital increases.
indicated a significant ECM with a lag of 2 and 3 for
the differenced autoregressive variable and without a lag for the differenced
inflation rate. However, the model indicated that the differenced autoregressive
variable lagged for 2 and 3 years was significant at the 10 and 5% level, respectively,
which means that the value of new issues Ž including capital increases.
may
be affected in the short-run by its performance in the previous years. In the
meantime, the coefficient of the differenced inflation rate without a lag was
significant at the 10% level, implying a short-run relationship between the variables.
In addition, the coefficient from the static long-run equation indicated a
negative sign, which means that the relationship between the variables is negative.
In turn, the hypothesis, which stated that value of new issues Žincluding capital
increases.
increases as the inflation rate decreases, cannot be rejected, indicating a
negative long- and short-run relationship between the variables.

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In conclusion, the results indicated that the inflation rate has a significant
impact on the market activity in Egypt as all the five variables indicated a
significant negative long-run relationship. As well, the inflation rate seems to have
a significant short-run relationship with all market activity variables except for the
number of traded companies. In all cases, the overall fit of the selected inflation
rate models of market activity was good with R 2 ranging from 0.57 to 0.82.
Therefore, the hypothesis, which stated that the market activity increases as the
inflation rate decreases, cannot be rejected, implying a negative relationship
between the variables.
6.2. Modeling the impact of the inflation rate upon market liquidity through error
correction models
Since the results from the ADF unit root tests indicated that the inflation rate
and the market liquidity variables were shown to be integrated of the same order,
that is, these variables are integrated of order 1, static long-run regressions were
performed using ordinary least squares Ž OLS .. The outputs of this analysis are
given in Table 4, which summarizes the results of this test.
The residuals from the static long-run equations as described in Table 4 were
integrated of order zero, suggesting that the variables in each bivariate relationship
are co-integrated, that is, there is a long-run relationship between these variables,
which is negative as identified in the regressions. However, EC models can
represent the co-integration relationship between the variables explaining both a
long- and short-run relationship simultaneously. Table 5 shows the final model for
each lag.
The inflation rate models, which targeted market liquidity as the dependent
variable, contain significant ECMs at the 1% level.
The inflation rate model of the relationship with the total value traded to market
capitalization confirmed a co-integration relationship between the variables as the
ECM was significant with a lag of 1 for the differenced autoregressive variable and
a lag of 3 for the differenced inflation rate. However, the coefficient of the
Table 4
Static long-run models for the impact of the inflation rate upon market liquidity variables
2
Variables Coefficient S.E. t-Prob. F-Prob. R
y Constant 1.4716 0.36802 0.0010
6,t
Inflation 7.7040 1.8454 0.0007 0.0007 0.52
y Constant 1.6273 0.20597 0.0000
7,t
Inflation 8.8736 1.0328 0.0000 0.0000 0.822
Notes: y total value traded to market capitalization; and y volume of shares traded to volume
6 7
of shares listed.

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279 275
Table 5
Specific error correction models for the impact of the inflation rate upon market liquidity variables
Dependent Autoregressive Independent Error R
variable variables variables correction Ž F-prob. .
Ž differenced. Ž differenced. Ž differenced.
mechanism
Ž . Ž .
Total value Total value Inflation t 3 ECM t 1 60%
traded to traded to
Constant Ž 2% .
market
market
cap. Ž. t cap. Ž t 1.
Ž
. Ž
.
Volume of Volume of Inflation t 1 ECM t 1 83%
shares traded shares traded Inflation Ž t 3. Ž 0.0% .
to volume to volume
Constant
listed Ž. t listed Ž t 1.
denotes 5% level of significance; denotes 1% level of significance.
2
differenced autoregressive variable lagged for 1 year was significant at the 5% level
suggesting that the total value traded to market capitalization can be affected by its
previous performance in the short-run. In the meantime, the coefficient of the
differenced inflation rate lagged for 3 years were significant as well at the 5% level.
Hence, there is a short-run relationship between the variables. As the coefficient of
the independent variable from the static long-run equation has a negative sign,
then there is a negative relationship between the variables. In turn, the hypothesis,
which stated that the total value traded to market capitalization increases as the
inflation rate decreases, cannot be rejected, indicating a long- and short-run
relationship between the variables.
The inflation rate model of the volume of shares traded to the volume of shares
listed showed a significant ECM with a lag of 1 year for the differenced autoregressive
variable and a lag of 1 and 3 years for the differenced inflation rate. However,
the coefficient of the differenced autoregressive variable lagged 1 year was significant
at the 1% level, suggesting that the volume of shares traded to the volume of
shares listed can be affected by previous performance in the short-run. In the
meantime, the coefficients of the differenced inflation rate lagged for 1 and 3 years
were significant as well at the 5% level. Hence, there is a short-run relationship
between the variables. With regard to the sign of the coefficient of the independent
variable from the static long-run equation, it is found to be negative, suggesting a
negative relationship between the variables. In turn, it can be concluded that the
hypothesis, which stated that the volume of shares traded to the volume of shares
listed increases as the inflation rate decreases, cannot be rejected, indicating a
long- and short-run relationship between the variables.
It is noticeable that the value of R 2 was 0.60 and 0.83% for the inflation rate
models of the total value traded to market capitalization and the volume of shares
traded to the volume of shares listed, respectively, reflecting a good fit for both

276
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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279
models. Since the two variables, which represent market liquidity were cointegrated
with the inflation rate, the hypothesis, which stated that the market
liquidity increases as the inflation rate decreases, cannot be rejected, indicating a
negative long-run relationship between the variables.
7. Summary and conclusions
An examination has been made of short- and long-run relationships between the
inflation rate and the performance of the Egyptian stock market, in terms of
market activity and liquidity. It needs to be pointed out that fairly complex
econometric techniques have been applied to a very restricted data set. As such it
would be advisable to place only a qualitative interpretation on the results, rather
than to place confidence in, e.g. the size of the coefficients or the number of lags.
With this in mind, the results indicated that there is a negative relationship
between inflation and market activity and liquidity. The results revealed an
expected behavior for the stock market response to the decrease in the inflation
rate, and the results regarding overall performance seem to be consistent with the
literature review, which stated that there is an inverse relationship between the
inflation rate and both stock returns and prices.
However, it needs to be pointed out that the focus of this study is on market
activity and liquidity, not returns and prices. In fact, the news about the level of
inflation can depress or encourage the stock markets. In the case of Egypt, the
inflation rate decreased sharply after the introduction of the economic reform
program due to tight fiscal and monetary policy. The decrease in the inflation rate
may give a good sign to investors to invest in the stock market, as it means that
there will be an expansion in the business sector, in turn, the returns of companies
will increase. In the meantime, with a decrease in the inflation rate, it is expected
that interest rates will decrease as well, and this will encourage investors to
establish new firms and to find the required finance with less cost. As a conclusion,
all stock market variables benefited significantly from the changes in the inflation
rate.
From this analysis shown above, it can be concluded that the inflation rate,
clearly, has had an impact upon stock market performance in terms of market
activity and market liquidity. In fact, this relationship was negative and in the longand
short-run for all market activity and market liquidity variables except for the
number of traded companies, in which case this relationship was in the long-run
only.
Acknowledgements
The authors would like to acknowledge helpful comments from Nick Wiseman,
Paul Bishop, Jon Tucker, the editor and the anonymous referee.

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279 277
Appendix A. Diagnostic tests for the chosen EC model
D.V. Right-hand Coefficient P-Value Diagnostic tests
side variables
y Cons 0.87297 0.0010 AR 0.08303 0.7806
1,t
y
0.57236 0.0466 ARCH 0.02520 0.8783
1,t1
y
0.42636 0.0835 Normality 0.26568 0.8756
1,t3
x
11.003 0.0534 RESET 0.10933 0.7494
1,t
ECM-1 0.50837 0.0071
y Cons 0.57825 0.0023 AR 1.3328 0.2816
2,t
y
0.51337 0.0595 ARCH 0.17179 0.6909
2,t1
x 8.8720 0.1310 Normality 2.9563 0.2281
1,t1
x
10.085 0.0475 RESET 1.7369 0.2240
1,t3
ECM-1
0.64436 0.0219
y Cons 0.35656 0.1134 AR 0.27569 0.6158
3,t
y
0.54023 0.0648 ARCH 0.066711 0.8048
3,t2
y 0.50614 0.1165 Normality 1.2548 0.5340
3,t3
x
12.179 0.0937 RESET 2.4268 0.1632
1,t1
x
1,t3 15.877 0.0350
ECM-1
0.65750 0.0044
y Cons 0.29625 0.0000 AR 0.005367 0.9451
4,t
y
0.70230 0.0005 ARCH 3.697e 05 0.9953
4,t1
x 1.1663 0.3016 Normality 0.2843 0.8675
1,t
ECM 1 0.30157 0.0001 RESET 0.37992 0.5710
y Cons 0.37160 0.0037 AR 0.90351 0.3697
5,t
y
0.39786 0.0526 ARCH 1.4088 0.2740
5,t2
y
0.50150 0.0197 Normality 0.067784 0.9676
5,t3
x 3.3942 0.0899 RESET 3.1565 0.1152
1,t
ECM-1 0.19476 0.0387
y Cons 0.41978 0.0063 AR 0.89262 0.3694
6,t
y
0.72641 0.0166 ARCH 0.00153770.9681
6,t1
x
8.2057 0.0683 Normality 0.10688 0.9480
1,t3
ECM 1 0.86499 0.0043 RESET 0.51822 0.4899
y
1.2692 0.0003 ARCH 1.2869 0.2940
7,t1
x
1.2692 0.0219 Normality 0.43934 0.8028
1,t1
x
8.2434 0.0180 RESET 0.54136 0.4829
1,t3
ECM-1
1.4250 0.0041
y7,t
Cons 0.29473 0.0052 AR 1.1809 0.3088
Notes: denotes 10% level of significance; denotes 5% level of significance; denotes 1%
level of significance; D.V., Dependent Variable; AR, Autocorrelation; ARCH, Autoregressive Conditional
Heteroscedasticity; and RESET, model mis-specification. y1 value of trade; y2 volume of trade;
y number of transactions; y number of traded companies; y value of new issues Ž
3 4 5
including
capital increases .; y6 total value traded to market capitalization; y7 volume of shares traded to
volume of shares listed; and x1
inflation rate.

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M. Omran, J. Pointon Emerging Markets Reiew 2 2001 263279
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