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Proceedings of Applied International Business Conference 2008
DOES THE STOCK MARKET LEAD THE ECONOMIC ACTIVITY- APPLICATION OF
COINTEGRATION AND ERROR CORRECTION MODEL
S. Varalakshmi ψ
Pondicherry University, India
Abstract
The stock market has traditionally been viewed as an indicator or predictor of the economy. Those who
support the market’s predictive ability argue that the stock market is forward-looking, and current prices
reflect the future earnings potential, or profitability, of corporations. Since stock prices reflect expectations
about profitability, and profitability is directly linked to economic activity, fluctuations in stock prices are
thought to lead the direction of the economy. If the economy is expected to enter into a recession, the stock
market will anticipate this by bidding down the prices of stocks. Since 1990 the Indian stock market has
witnessed only rise in the sensex of Bombay Stock Exchange. Some of the major contributing factors for
the sensex to reach its peak level are liberal export-import policy, the infotech boom and high market
confidence of increased FII investment and strong corporate results. In this context this paper makes an
attempt to evaluate stock prices as a leading indicator of economic activity. Cointegration tests and Vector
error correction models are used to estimate relationships between stock prices and the economy, and to
see if they are consistent with theory. This study explores the following questions. 1. Does the stock market
lead the real economy 2. Does the stock market Granger cause the real economy and 3. Whether there
exists a co integration between the real economy which is measured by Real GDP , exchange rate and the
stock prices, interest rate and inflation rate.
Keywords: Cointegration; Unit root; Real economic activity; Stock prices; Exchange rate; Inflation; Vector
error correction model.
JEL Classification Codes: F30; G10.
1. Introduction
The establishment of Bombay National stock exchange ,India (BSE) in 1986, and the large momentum it
has gained since then, has provoked considerable academic curiosity about the causal relationships between
BSE and the country’s economic growth. Stock exchanges are expected to accelerate economic growth by
increasing liquidity of financial assets, making global risk diversification easier for investors, promoting
wiser investment decisions by saving-surplus units based on available information, forcing corporate
managers to work harder for shareholders interests, and channeling more savings to corporations.
Levine (1991), and Benchivenga & Smith & Starr ( 1996) emphasize the positive role of liquidity provided
by stock exchanges on the size of new real asset investments through common stock financing. Investors
are more easily persuaded to invest in common stocks, when there is little doubt on their marketability in
stock exchanges. This , in turn, motivates corporations to go to public when they need more finance to
invest in capital goods.The second important contribution of stock exchanges to economic growth is
through global risk diversification opportunities they offer. Saint-Paul (1992),Deveraux&Smith (1994) and
Obstfeld (1994) argue quite plausibly that opportunities for risk reduction through global diversification
make high- risk-high return domestic and international projects viable, and , consequently, allocate savings
between investment opportunities more efficiently. Stock prices determined in exchanges ,and other
publicly available information help investors make better investment decisions. Better investment decisions
ψ
Corresponding author. S. Varalakshmi. Faculty of Economics, Kanchi Mamunivar Centre for Post
Graduate Studies, Pondicherry University- Pondicherry- 605013- India.Email: [email protected]
Proceedings of Applied International Business Conference 2008
by investors mean better allocation of funds among corporations and, as a result, a higher rate of economic
growth.
Finally, stock exchanges are expected to increase the amount of savings channeled to corporate sector.
Levine & Zervos applied regression analysis to the data compiled from 41 countries for the years 1976
through 1993 to see the relationships between financial deepening and economic growth. The level of
development of stock exchange as a financial deepening indicator measured by a composite index
combining volume, liquidity and diversification indicators. Economic growth indicator was the real growth
rate in per capita GDP. Levine and Zeros reported a very strong positive correlation between stock market
development and economic growth. The most interesting aspect of this study was the decrease in the
statistical significance of other financial deepening variables after stock market development index was
included in regression equation. According to the authors stock market development was more influential
than other financial deepening indicators on the growth of the economy.
2. Stock market development and exchange rate
From the microeconomic level standpoint, the exchange rate is seen as influencing the value of domestic
and multinational companies, and the research undertook in this area deals with the issue of domestic
economies’ exposure to exchange rate risk. Fluctuations in exchange rates can significantly have an effect
on firm value, as they influence the terms of competition, the input and output prices, and the value of
firm’s assets and liabilities denominated in foreign currencies. Consequently, all firms’ prices may react
sooner or later to changes in the exchange rates. Kim (2003) investigates the existence of long-run
equilibrium relationships among the aggregate stock price, industrial production, real exchange rate,
interest rate and inflation rate in the United States, applying Johansen’s cointegration methodology. Dong
et. al (2005) examined six emerging Asian countries over 1989 and 2003 and found no cointegration
between their exchange rates and stock prices, but they detected bidirectional causality in Indonesia, Korea,
Malaysia and Thailand. Except for Thailand, the stock returns show significantly negative relation with the
contemporaneous change in the exchange rates, which implies that currency depreciations generally
accompany fall in stock prices. Ibrahim (2000) studies the interactions between the foreign exchange
market and the stock market in Malaysia and his results indicate that despite the lack of a long run
relationship between the exchange rate measures and stock prices in bivariate cointegration models, there is
evidence of such long-run relations in multivariate models that include M2 money supply and foreign
reserves. Pacific Basin countries were studied by Phylaktis and Ravazollo (2005), which examine the longrun and short-run dynamics between stock prices and exchange rates and the channels through which
exogeneous shocks impact on these markets by using cointegration methodology and multivariate Granger
causality tests.
The purpose of this study is to investigate whether current economic activities in India can explain stock
market indices by using a cointegration test and a Granger causality test from a vector error correction
model. The cointegration test and the vector error correction model illustrate that stock price indices are
cointegrated with a set of macroeconomic variables—that is, the production index, exchange rate, call
money rate that is, short term interest rate and inflation provide a direct long-run equilibrium relation with
each stock price index. However, the stock price indices are leading indicators for economic variables,
which is consistent with the previous findings that the stock market rationally signals changes in real
activities.
3. Stock market and developing economies
A growing number of developing countries have recognised the useful role that stock markets can play in
enhancing the efficiency of domestic financial systems. Stock markets can usefully complement and
compete with the banking sector, thereby reducing the cost of capital for borrowers. They also permit a
diversification of company ownership, more efficient risk sharing, and a healthier financial structure of
corporations by improving their debt/equity ratios. The opportunities which stock markets offer investors
for diversifying their portfolios also help lower the risk premium component in the cost of capital.
Secondary equity markets also help in matching the long-term horizon of borrowers with the short-term
liquidity preference of investors. Through the stock price mechanism, a more effective allocation of
investment might also result, as poor management of listed companies may have large effects on the price
at which the market values a firm. Furthermore, listing on stock markets implies disclosure of information
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Proceedings of Applied International Business Conference 2008
to investors; this will encourage firms to improve accounting standards and make management more
transparent. The recent history of developing economies adds other objectives that stock markets can
usefully serve.
Government policies promoting privatisation and debt equity swaps, for example, can hardly be
implemented outside the framework of a stock market. In addition, stock markets can become an important
channel to raise external finance, as debt finance becomes less available as a consequence of the debt crisis.
The establishment of stock-markets in developing countries and the opening of them to foreign security
houses as well as foreign portfolio investors can be viewed as a part of global liberalisation trend.
Progressive deregulation of financial markets both internally and externally in the firm’s profits are directly
linked to the behavior of the real economy, stock prices will be affected by expectations about the future
economy. For example, if investors expect the economy to enter into recession, then expected profits will
be diminished and stock prices will decrease in value. On the other hand, if investors anticipate economic
growth, then expected profits will improve and stock prices will increase. Thus, investors have an interest
in predicting the future real economy. And, if they are somewhat successful in their predictions, then stock
price movements will lead the direction of the economy.
A change in recent experience, then, can cause investors to change their expectations about the future real
economy, which then causes them to bid up or down the prices of stocks. To the extent that these models
are true, the economy may also lead the stock market. The "wealth effect" from fluctuations in stock prices
is another theoretical argument for why stock prices might lead the economy.
In summary, according to fundamental valuation models, stock prices depend on expectations about the
future economy. Therefore, expected changes in the real economy
cause the values of stock prices. According to the wealth effect, however, changes in stock prices cause the
variation in the real economy. It is important to point out that, while the causation in the two theories is
different, both theories suggest that the stock market predicts the economy.
4. Research methodology
The research we undertook employs of data over the period of 1979 – 2006 period in India. The data
collected are the growth of GDP (deflated) , call money rate ( interest rate) exchange rates of Rupee in
terms US Dollar, Consumer price index( inflation) and BSE indices( Bombay Stock exchange index). All
data were collected from the Hand book of statistics on Indian economy, Reserve Bank of India.. To track
the performance of the Indian stock exchange we used the values of the Bombay stock exchange sensex .
Primary data is collected from Bombay stock exchange by their daily and weekly indices
Our research objective was directed towards the detection of significant interactions between the real GDP(
Gross Domestic Product) ,stock indices, exchange rate of Rupee in terms of US Dollar, inflation and
interest rate . The study variables are transformed into natural logarithms. We developed our analysis by
using two types of analysis: a co integration test and vector error correction model.
The Johansen-Juselius cointegration procedure is based on the maximum likelihood estimation in a VAR
model, and calculates two statistics – the trace statistic and the maximum Eigenvalue – in order to test for
the presence of r cointegrating vectors.The trace statistics tests the null hypothesis that there are at most r
cointegrating vectors against the hypothesis of r or more cointegrating vectors. The maximum Eigenvalue
statistics tests for r cointegrating vectors against the hypothesis of r+1 cointegrating vectors. The JohansenJuselius procedure considers all variables included in the cointegration test as being endogeneous and
therefore it avoids the issue of cointegrating vector normalization on one of the variables or of imposing a
unique cointegrating vector, as implied in the Engle-Granger test. Besides its ability to determine the
number of cointegrating vectors, the Johansen-Juselius procedure is generally considered to have more
power than the Engle-Granger test.
Vector error correction model
Statistically, the presence of cointegration excludes non-causality between the variables under
consideration. Therefore, if two variables are found to be cointegrated, then there must be causality in the
Granger sense between them, either uni-directionally or bidirectionally. In such a case, the Granger test can
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Proceedings of Applied International Business Conference 2008
be correctly specified by including in the equation referring to two cointegrated variables and error
correction (EC) term, representing the residuals from the cointegrating regression. The general form of the
equation we used is
k
∆ ln Yt = c +
∑
δ i ∆ ln Yt −i +
i =1
k
∆ ln X t = g +
∑
k
∑ β ∆ ln X
i
t −i
+ λ1 EC y / x + ε t
t −1
i =1
θ i ∆ ln Yt −i +
i =1
k
∑ γ ∆ ln X
i
t −i
+ µ1 EC x / y + ω t
i =1
t −1
where Yt and X t are two cointegrated variables, while EC ty−1/ x and EC tx−/1y are the residuals from the
cointegrating regressions where Yt was the dependent variable and X t the independent variable and vice
versa. In such a test, the EC term indicates the adjustment of the dependent variable to the lagged
deviations from the long-run equilibrium path. If the coefficient attached to the EC term is statistically
significant, it means that the dependent variable adjusts towards its long-run level. For this test we also
used the various information criteria to indicate for the number of lags to be introduced in the regression.
ADF unit root tests
Before specifying any cointegration test, we test for unit root in the GDP, interst rate, exchange rate,
inflation and Stock indices at levels, as well as in first differences. Table 1 exhibits the results of the
Augmented Dickey-Fuller (ADF) terms of t-statistic for the variables at levels and first differences with
intercept and trend . The ADF tests involved the estimation of the following regression
∆X t = α + β t + δX t −1 +
k
∑ ∆X
t −i
+ ωt
i =1
where X t is the variable under consideration.
Table 1 Shows that Dickey fuller regression is employed for Log GDP, Log interest, Log exchange rate,
Log CPI and Log Stock index. The results reveal that all the t statistics values of the regressions with
intercept and no trend are higher than the Mackinnon critical values of -2.9970. Therefore all variables are
stationary at level and significant at 1 percent level. The t statistics of regression with intercept and trend
are higher than critical value of -3.6119 and all variables are stationary at levels.
Table 1: The Dickey-Fuller regressions
With intercept and DF Results with
no trend
and trend
t Statistics
95%critical value
t Statistics
Log GDP
-4.59 *
-2.9970
-4.8987*
Log interest
-5.4763*
-2.9907
-5.4105 *
Log exchange rate -3.144 *
-2.9907
-3.8526 *
Log CPI
-5.052*
-2.9907
-4.939*
Log Stock index
-4.1308*
-2.9907
-4.0933*
Note: * denotes significant at 1 percent level.
Variables
DFResults
Intercept
95%critical value
-3.6219
-3.6119
-3.6119
-3.6119
-3.6119
The Table 2 shows the results of Augmented Dickey Fuller regression for the study variables. The t
statistics for all variables for the regressions with intercept and no trend except log exchange rate are
significant at one percent level and stationary. The t statistics for all variables except log stock index for the
regressions with intercept and trend are significant at 1 percent level and stationary.
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Proceedings of Applied International Business Conference 2008
Variables
Log GDP
Log interest
Log exchange rate
Log CPI
Table 2: The augmented Dickey-Fuller regressions
ADF Results with intercept and no trend
ADF Results with intercept and trend
t Statistics
95%critical value
t Statistics
95%critical value
-3.5703(3) *
-2.9970
-4.9717* (3)
-3.6219
-5.5657(1)*
-2.9907
-5.5680 *
(1)
-3.6119
-2.0954(3)
-2.9907
-2.7147 (3)
-3.6119
-3.5802(1)*
-2.9907
-3.4969(1)*
-3.6119
Log Stock index
-3.2625(1)*
-2.9907
-3.2561(1)
Note: * denotes significant at 1 percent level. Lag values are given in parentheses.
-3.6119
Co integration test- Johansen- Juselius
Consequently, we proceed with the development of the Johansen-Juselius cointegration test (1990) with
unrestricted trend and intercepts for the study variables.
The Tables 3 and 4 reveal the results of Johansen – Juselius cointegration test both trace and eigen value
tests with unrestricted trend and intercepts between GDP, interest rate, exchange rate, inflation and stock
index.. Based on the Johanson’s maximum eigen value test the null hypothesis of no co integration (r=0 ) at
5 percent significant level is rejected as the test statistics 83.19 and 33.45 are sufficiently higher than the
critical value. The test reveals that the study variables are cointegrated of order two. The LR test based on
trace value reveal that the variables are integrated of order one as the t statistics 137.24 is higher than the
critical value. Hence based on maximum eigen value test and trace test there is cointegration between the
variables.
Table 3: Cointegration LR test based on maximal eigenvalue of the stochastic matrix
Null
Alternative
Statistic
95% Critical Value
90% Critical Value
r=0
r=1
83.19 *
37. 07
34.16
r<= 1
r=2
33.45 *
31.00
28.32
r<= 2
r=3
12.03
24.35
22.26
r<= 3
r=4
7.15
18.33
16.28
r<= 4
r=5
1.40
11.54
9.75
Note: * denotes significant at 1 percent level.
Table 4: Cointegration LR test based on trace of the stochastic matrix
Null
Alternative
Statistic
95% Critical Value
90% Critical Value
r= 0
r> = 1
137.24 *
82.23
77.55
r<= 1
r> = 2
54.05
58.93
55.01
r<= 2
r> = 3
20.59
39.33
36.28
r<= 1
r>= 4
8.56
23.83
21.23
r<= 2
r> = 5
1.40
11.54
9.75
Note: * denotes significant at 1 percent level.
Table 5 shows the results of vector error correction model for log GDP and shows change in GDP depends
on the change in interest rate, exchange rate , inflation and stock market indices.The ECM value 0. 896 is
significant at 1 percent level. The dependent variable GDP will increase in the next period to restore
equilibrium. F statistics 15.10 is also significant at one percent level.. Hence based on vector error
correction model the casual relationship exists between GDP and stock indices. Hence the stock market
lead economic activity in India.
The vector error correction model for log interest shows change in log interest depends on the change in
exchange rate, inflation , GDP and stock market. The ECM value -4.152 with one lag is significant at 1
percent level. The dependent variable interest rate decreases to restore equilibrium. F statistics 112.22 is
also significant at 1 percent level .The error correction models for exchange rate, inflation and stock index
are not showing significant ECM values. Hence, based on the vector error correction model stock market
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Proceedings of Applied International Business Conference 2008
influences the GDP and interest rate while stock market is not caused by Economic Growth. There exists a
unidirectional causality between GDP and Stock market and Interest rate and Stock market
Table 5: Results of vector error correction model
Ecm(-1)
F stat
R¯2
0.896
15.10*
0.5204
(4.56*)
∆ Log interest
-4.152
112.22*
0.0.895
(-13.95) *
∆ Log Exchange rate
0.155
4.89
0.237
(2.51)
∆ Log CPI
0.058
0.462
0.037
(0.22)
∆ Log Stock index
(0.167)
0.314
0.05
(0.68)
Notes: * denotes significant at 1 percent level. Figures in parentheses show t ratios.
Dependant variable
∆ LGDP
D.W
2.263
1.687
4.89
2.35
1.71
The results suggest that stock prices do Granger cause economic activity. That is, the stock market does
predict the economy. It is important, therefore, to review the theories that are consistent with the stock
market as a leading economic indicator. One possible explanation for why stock prices predict the economy
is that stock prices actually cause what happens to the economy. This would be consistent with the wealth
effect. According to this argument, fluctuations in stock prices raise and lower wealth, which in turn, raises
and lowers aggregate consumption. As a result, economic activity is affected by fluctuations in the stock
market. , the significant EC term indicates the adjustment of the dependent variable GDP to the lagged
deviations from the long-run equilibrium path.
Another possible explanation for why stock prices "Granger cause" economic activity is that the stock
market is forward- looking. If investors are truly forward- looking, then stock prices reflect expectations
about future economic activity. If a recession is anticipated, for example, then stock prices reflect this by
decreasing in value. Since the results indicate that the stock market improves the prediction of economic
activity, and if we assume that the stock market is forward- looking, then investors’ expectations about the
future economy are fairly accurate.
5. Conclusion
The purpose of this paper is to evaluate the stock market as a leading economic indicator and explore
causal relationships between stock prices and the economy. This project used Co integration test to test
long run relationship between the study variables and vector error correction model to test causality and the
long run relationship between the variables for the study period 1979 to 2006. Our results indicated a co
integration relationship between the variables. The variables are transformed into natural logarithams. We
found that while stock prices Granger-caused economic activity, the GDP does not Granger cause the stock
market. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock
market and changes in the real economy are relatively short.
One issue that needs further exploration is the actual reason for the causality relationship between the stock
market and economic activity. Is the causality relationship more consistent with the wealth effect or with
the forward- looking nature of the stock market?
This study provides further research in this area to evaluate where expectations about the future economy
are coming from. In conclusion, the results of this study reveal that the stock market does help predict the
future economy. Although it may not be surprising to find that fluctuations in economic activity may be
preceded by changes in stock prices, our finding that changes in GDP are"Granger-caused" by changes in
stock prices is important in that it provides additional support for the leading economic role of the Indian
stock market.
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Proceedings of Applied International Business Conference 2008
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