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International Journal of Financial Economics

Vol. 2, No. 2, 2014, 58-72

Stock Market Price Behavior and Macroeconomic Variables in

Nigeria: An Error Correction Analysis

Onyemachi Maxwell Ogbulu1, Zeph Chibueze Abaenewe

2, Patrick Nwaeze Nnamocha

3

Abstract

This study examines stock market price behavior and selected key macroeconomic variables in Nigeria

within the period (1986-2011.) The co-integration and error correction technique (ECM), impulse response

function(IRF) as well as variance decomposition(VDC) test techniques were employed to investigate the

relationship between stock market prices and selected key macroeconomic variables. The study reveals that

there is co-integration relationship between share prices and the selected macro economic variables,

indicating long run relationship between stock prices and the specified macroeconomic variables. The

findings of the study also show insignificant negative relationship between the NSE All share index (ASI)

and index of industrial production (IDP) contrary to a priori expectation. The negative relationship can be

attributed to high cost of energy generation and distribution in Nigeria during the period under review, a

situation which has affected the industrial sector adversely. The results of the variance decomposition of ASI

to shocks emanating from FXR, IDP and MRR show that ASI own shocks remain the dominant source of

total variations in the forecast error of the variables. However, we recommend that adequate attention be paid

to solving energy generation problem in Nigeria which invariably will reduce cost of industrial production

and enhance profit margin of industries as well as support further investment in the sector.

Key words: Macroeconomic Variables, Stock Market Price, Unit Root Test, Co-integration, Error correction

model (ECM), Impulse Response Function (IRF) and Variance Decomposition (VDC).

1. Introduction

In recent times many finance and economic researchers have made attempts to examine the relationship

between stock market activities and macro economic variables. These studies tend to analyze how stock

prices react to changes in macro economic variables. However, a good number of these studies had focused

more on developed economies and developed stock markets. For example, Fama (1981), Mandelker and

Tandon, (1985), Bulmash and Trivoli (1991), Schwert (1990), Mukherjee and Naka (1995) among others

have examined these relationships in developed economies. Some of the individual macro economic

variables examined by these scholars include inflation, real activity, interest rate and money supply and their

impact on share prices.

Furthermore, Chen et al (1986), Fama and French (1989), as well as Cheung and Ng (1988) had

examined the relationship between stock prices and a wide range of economic variables in developed

economies. According to Liu and Shrestha (2008) studies on long term relationship between macro economic

variables and stock market prices are all in the developed countries and little is known about the relationship

between stock prices and macro economic variables in emerging markets. On another note, scholars in the

past had concentrated on finding the relationship between corporate fundamentals and stock market price

behavior. According to London Stock Exchange (2008), news on such factors as the economy, company

1Department of Banking and Finance, Abia State University, Uturu, Abia State, Nigeria

2Department of Banking and Finance, Abia State University, Uturu, Abia State, Nigeria

3 Department of Economics, Imo State University, Owerri, Imo State, Nigeria

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59

news, analysts’ reports, press recommendations, technical reports etc all have the potential of influencing

share pricing.

Looking at the roles of stock market activity especially in capital formation for investment, it is

worthwhile to establish its relationship with the major indicators of an economy. It is globally acknowledged

that stock market activities are taken as a potent barometer for measuring the state of being of an economy or

its future growth. (Meristem Securities, 2008). Furthermore, investment in common stocks or securities,

according to Nnamocha and Nwobi (2001), generally constitute a barometer to measure economic

development just as stock prices serve as an indicator to measure economic and political conditions in a

country. In financial market literature, the stock market has ordinarily been expressed as an indicator of the

economy. According to Ajao and Oseyemon (2010), many believe that large decreases in stock prices are

reflective of future recession, whereas large increases in stock prices suggest future economic growth.

Theoretical reasons for why stock prices might predict economic activity include the “traditional

valuation model” of stock prices and the wealth effect model. (Comincioh, 1996). The traditional valuation

model of stock prices suggests that stock prices reflect expectations about the future economy and therefore

predict the economy. The wealth effect model contends that stock prices lead economic activity by actually

causing what happens to the economy. To establish the relationship between stock market price and

economic variables properly has a lot of implications for any economy and this has motivated this study.

The above notwithstanding, in most countries of the world, the issue of stock market activities, its

capitalization and liquidity dominate the stories in pages of national dailies. Most often, the performance of

the daily stock market activities is seen to reflect the direction of the economy, though in the short run. But

this is not the only index to measure economic performance. Both stock market activities and economic

variables, such as gross domestic products (GDP), money supply, inflation rate, interest rate, exchange rate,

real activities, trade openness, foreign reserve etc are also used to measure the performance of the economy,

although, it is often argued that macro-economic variables are exogenous to stock market activities because

they are external to the stock market model and hence may not in any way have influence on stock prices.

It should be noted that quite a few studies on the stock price-macroeconomic variables link have been

carried out in Nigeria when compared to developed economies with different scholars having different views

in their findings. Some of these studies posit that stock market activities are positively and well correlated

with macro economic variables while some hold the view that stock market prices are negatively and not

strongly correlated with macro economic variables. The differences in their results and findings perhaps

depend on the econometric tool employed and the time frame of data applied. Thus, there is as yet no

consensus on the relationship between stock market prices and macroeconomic variables and the debate

continues.

In the light of the above therefore, there is the need to use, within the Nigerian context, a more recent,

comprehensive and updated data to investigate the problem posed by these questions. Are stock market

prices in Nigeria driven or influenced by macro economic variables? What is the relationship between stock

prices and macro economic variables like foreign exchange rate, industrial production and interest rate in an

emerging economy such as Nigeria? What are the plausible explanations for the relationships, if any?

2. Review of Related Literature

For many years, the economic impact of macro-economic variables such as money supply, inflation,

interest rate, industrial production and exchange rate, has been debated in the economic literature. The debate

and the interest it has generated have regained even more popularity in the wake of recent share market price

volatility. According to Liu and Shrestha (2008), numerous studies have analyzed how stock prices react to

changes in macro economic variables. However, most of these studies are all based on stock markets in

developed economies.

Be that as it may, there have been some few attempts to study this relationship in the developing

economies such as Nigeria. Such studies that have attempted to relate stock market activities and the

O. M. Ogbulu et al.

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Nigerian economy include Emenuga (1996), Amadi et al (2002), Nwokoma (2002), Ajao and Oseyomon

(2010) as well as Ogbulu (2009). For example, Emenuga (1996) and Nwokoma (2002) in their separate

studies found relatively insignificant relationship between stock market activities and the macroeconomic

variables. Amadi et al (2002) disclosed that the relationship between macroeconomic variables and stock

market activities in Nigeria are consistent with some studies outside Nigeria and conform to theoretical

postulations. Ajao and Oseyomon (2010) examined the predictive content of some leading economic

indicators on stock prices employing the Ordinary Least Squares (OLS) technique of model estimation. The

study revealed that expected stock prices/returns proxied by Nigerian Stock Exchange (NSE) All Share Index

(ASI) are positively correlated with Gross Domestic Product (GDP), Inflation rate, Money Supply, Industrial

Production Index and negatively correlated with Interest rate. They specifically stated that the relationship

between stock returns and macroeconomic variables can be increasing, decreasing or flat depending on the

model parameter.

Stock Prices and Exchange Rate

Exchange rate is the ratio of the number of units of one currency that are exchangeable for a unit of

another. Exchange rate is a product of a country’s external trade and directly relates to the balance of

payments. The external value of each currency is presumably reflected in the country’s economic conditions

in general and the purchasing power of the currency relative to that of other currencies in particular.

Many scholars have posited that there are few reasons why the relationship between stock prices and

exchange rates should be established. According to Dimitrova (2005), foreign exchange rate may affect

decisions about monetary and fiscal policies. In addition, the link between the stock and foreign exchange

markets may be used to predict the path and evolution of exchange rate changes. If this is the case, it will

benefit multinational corporations in managing their exposure to foreign contracts and exchange risks as well

as stabilizing their earnings (Dimitrova, 2005). Another reason for the study of the relationship is that

currency is more often being included as an asset in investment portfolios. Therefore, knowledge of the

relationship between currency rates and other assets in a portfolio is vital for the optimal management of

asset portfolios.

However, exchange rate movements will always affect a company’s profit especially when exchange

rate is high against a firm’s host country’s currency. The cost of imported production materials (expenditure)

will affect the cash flows and also the operating profit to the magnitude of importation made during a firm’s

accounting period. This invariably will reduce the dividend pay off ability of the firm.

According to Liu and Shrestha (2008), there is an exchange risk for holding foreign currency

denominated bonds and equities. A devaluation of the currency against foreign currencies increases exports

and improves cash flow and the ability to pay dividends for firms that rely on exports. In the case of firms

whose currencies are devalued (rise in exchange rate), their stocks would experience a general fall in price

especially in a country that is heavily import-dependent (Ogbulu, 2009).

On the other hand, Dimitrova (2005) has stressed that the effect of exchange rates on stock prices is

quite inconclusive as there are conflicting views in support for both a positive and a negative relationship.

Hence there is no theoretical consensus on what exactly is the true relationship between stock prices and

exchange rate changes. Aydemir and Demirhan (2009) hold the view that country-specific empirical studies

may show different results for different economies for this relationship. The reasons adduced for these

differences include the time period over which data are collected, econometric models used and economic

policies which differ from country to country and from time to time.

Other studies such as Aggarwal (1981), Solnik (1987) and Smith (1992) found significant positive

relationship between stock prices and exchange rates. Some other scholars who found significant negative

relationship between these variables in their studies include Soenen and Hennigar (1998) and Mohammad et

al (2009). According to Muhammed and Rasheed (2002) portfolio balance models of exchange rate

determination postulate a negative relationship between stock prices and exchange rates and that causality

runs from stock prices to exchange rates. In this model, individuals hold domestic and foreign assets

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61

including currencies on their portfolio. Exchange rates play the role of balancing the demand for and supply

of assets. An increase in domestic stock prices leads individuals to demand more domestic assets. To buy

more domestic assets, local investors would sell foreign assets that are relatively or less attractive now,

causing local currency appreciation. An increase in wealth due to a rise in domestic asset prices will also

lead investors to increase their demand for money, which in turn raises domestic interest rates. This again

leads to appreciation of domestic currency by attracting foreign capital.

On the other hand, and according to Aydemir and Demirhan (2009), the asset market approach to

exchange rate determination states a weak or no association between exchange rates and stock prices and

treats exchange rate to be the price of an asset (price of one unit of foreign currency),

Meanwhile, Ogbulu and Ndubuisi (2009) accepted the hypothesis that there is a significant long run

positive relationship between exchange rate movements and stock prices in Nigeria. Furthermore, they found

that foreign exchange rate (FXR) changes granger cause changes in stock prices. This means that a

unidirectional causality runs from exchange rate movements to stock prices

Industrial Production and Stock Prices

Index of Industrial production index (IDP) is an economic indicator that is released monthly, quarterly

or annually. It measures the amount of output from the manufacturing, mining, electric and gas industries.

Investors can use the IDP of various industries to examine the growth in the respective industry, a rise in the

value of the index indicating an increase in the overall performance of the industry.

Therefore, according to Liu and Shrestha (2008), the firm’s decision on the amount of dividend to pay

and the growth rate of dividend payout is affected by the general economic conditions as characterized by

industrial production. Two scholars in their separate studies observed that the growth rate of industrial

production is a determinant of future stock prices. These include Chen et al. (1991) and Schwert (1990).

Dividend paying ability and its growth is a function of output performance of the firm such that good and

quality output increases cash inflow of a firm which can translate to profit. From the foregoing, it is expected

that stock prices should have positive relationship with industrial production. In addition, Erdogan and

Ozlale (2005) explained that increased production leads to higher revenues and profits for the firms, together

with high volume of cash-flows which as a result raises stock returns. Mohammad et al (2009) also found

positive relationship between stock prices (in the Karachi Stock Exchange) and industrial production.

On the other hand, Gharan et al (2009) established in their study that a negative relationship exists

between industrial production and stock market returns. This result was attributed to oil price shocks and

structural changes in the United States. Other studies that had also established negative relationship between

stock market index and macro economic variables include Brahmasrene et al (2007) who had earlier found

negative relationship between stock market index and the macroeconomic variables during the period of

financial crises in Thailand. Nwokoma (2002) in a related study found that interest rate as represented by 3-

month commercial bank deposit rates and industrial production have long run relationship with stock market

prices.

Furthermore, Liu and Shrestha (2008) show in their findings that the co-integration relationship does

exist between stock prices and macro-economic variables in highly speculative Chinese stock market and

that more importantly, in the long run, their performance is positively related. In another study, Ray and

Vani (2011), considered the monthly data of several economic variables like national output, fiscal deficit,

interest rate, inflation, exchange rate, money supply and foreign institutional investment in the Indian market

between 1994 and 2003. Their paper applied the modern non linear technique like VAR and Artificial Neural

Networks and the results show that certain variables like Interest rates, Output, Money supply, Inflation rate

and Exchange rate have considerable influence on the stock market movement in the considered period,

while others have negligible impact on the stock market.

Nevertheless, other scholars such as Mohammad et al (2009) opine that changes in macro-economic

variables cannot be used as a trading rule by investors to consistently earn abnormal profits in the stock

market. This, they hold, is the result of their empirical study on the relationship between macroeconomic

O. M. Ogbulu et al.

62

variables and stock prices in the Karachi Stock market using such statistical steps asdescriptive statistics, unit

root tests and auto regressive integrated moving average model testing. Similarly, Mohiuddin et al (2008), in

their study on the relationship between macroeconomic variables and stock prices in the Dhaka Stock

Exchange, using ordinary multiple regression analysis found out that there is no significant relationship

between stock prices and any of the macroeconomic factors included in their study which include inflation

rate, exchange rates, interest rate, money supply and production index. Again, the study by Ali et al (2010)

on causality relationship between macro-economic indicators and stock exchange prices in Pakistan, reveal

that the performance of macro- economic indicators cannot be used to predict stock prices; and moreover,

that stock prices in Pakistan do not reflect the macro-economic conditions of the country. The authors

employed the ADF unit root tests as well as the Johansen’s co-integration and Granger causality tests with

data running from 1990-2008.

However, in a study by Asaolu and Ogunmuyiwa (2010), Granger causality test did not confirm any

relationship between average share price and macroeconomic variables in Nigeria although, the study

established a long run relationship between the two. Riman et al (2008) also observed in their study that the

bi-directional causality between market performance and economic growth was not evident in the case of

Nigeria.

In a similar study employing co-integration and causality tests, Brahmasrene et al (2007) established

negative impact of industrial production index and foreign exchange rate on stock market index in Thailand.

Interest Rates and Stock Prices

Interest rate is an economic variable that usually dictates credit affordability for investment in an

economy. Low interest rate encourages investment, while high interest rate discourages investment. Interest

rates have a wide and varied impact on the economy. Prevailing interest rate shows the direction of

investment in financial assets. When there is an increase in interest rate (a restrictive monetary policy), the

intention is to reduce money in circulation. In this case, any increase in the rate of interest discourages

borrowing. Borrowing of money becomes expensive which affects how consumers and businesses spend

their money. This is because an increase in the interest rate increases a company’s expenses and lowers

earnings.

In relation to the stock market, an increase in interest rate tends to make the stock market a slightly less

attractive place for investment. People borrow to invest in the stock market with the belief that stock market

returns will be higher than the interest rate. However, any change in the interest rate affects the investors

required rate of return. The relationship between interest rate and stock prices is therefore expected to be

negative in all cases. According to Mohammad et al (2009), an increase in interest rate increases the

opportunity cost of holding money either in stock or interest-bearing securities. Increase in interest rate can

cause increase in cost of production which invariably can cause a deterioration in company profit and

dividend paying ability thereby reducing the prices of shares.

Liu and Shrestha (2008), Bulmash and Trivoli (1991), Mukherjee and Naka (1995) all document

negative relationship between interest rate and stock prices in the long run. In addition, Ogbulu (2010), using

ADF and P-P unit root tests, co-integration, ECM and Granger causality tests finds that there is a negative

long run relationship between interest rates and stock returns in Nigeria. He also documents a uni-directional

causality running from interest rates to stock returns.

Based on the reviewed related literature, there exist some levels of disagreement on the opinion of

scholars. This has been attributed, in part, to the nature and type of data employed in various studies (whether

time series, cross-sectional or pooled and whether annual, quarterly, weekly or other high-frequency data) as

well as the dynamism in the macroeconomic variables of interest.

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3. Research Methodology

This study relied mainly on annual secondary data sourced from the Central Bank of Nigeria (CBN)

Statistical Bulletin, National Bureau for Statistics (NBS) and the Stock Exchange Fact Book, covering the

period 1986 to 2011. The data involved in this study include stock market index, foreign exchange rate,

index of industrial production and interest rate proxied by minimum rediscount rate (monetary policy rate) as

prescribed by the CBN from time to time in the conduct of its monetary policy.

The justification for the use of the stock market index (ASI) can be seen from the fact that it is a value-

weighted index and also all-inclusive of all equity stocks (but not ALL SECURITIES) quoted on the

Nigerian stock market and hence it is to that extent, a sufficient representation of the activities on the

Nigerian stock market. The index of industrial production represents real economic activity in the industrial

sector of the economy. Foreign exchange rate (FXR) represents the interaction between the domestic

economy and the international economy while the minimum rediscount rate (MRR) represents monetary

policy instrument in the economy. The choice of the period of the study (1986-2010) was motivated by the

fact that the period accounted for much of the developments in the Nigerian Stock market in Nigeria such as

the liberalization of the economy inclusive of the stock market, the foreign exchange market as well as

deregulation of pricing and allocation of resources in the economy.

The following analytical techniques would be applied in the analysis of data. These include the

Augmented Dickey Fuller (ADF) and Philips-Perron (PP) unit root tests, Johansen’s co-integration test,

impulse- response function (IRF) and variance decomposition (VDC) tests using E-views statistical

package.

Unit Root Test

According to Ogbulu (2009), in carrying out country-specific and time series analysis of data in

financial econometrics, it is important to examine the stationarity properties of the time series data.

Furthermore, Koirala (2009) cited Nelson and Plosser (1982) and Chowdhury (1994) who posited that there

exist unit roots in most macroeconomic time series. The findings that many macro- economic time series

may contain a unit root has spurred the development of the theory of non-stationary time series analysis

(Koirala, 2009). Engle and Granger (1987) pointed out that a linear combination of two or more non-

stationary series may be stationary. In order to avoid the problem of spurious regressions and inconsistency

of the parameter estimate, we employed the Augmented Dickey- Fuller (ADF) and Philips- Perron unit root

tests on the dependent and independent variables. Another reason for the adoption of ADF tests is that the

ADF test is considered superior to Dickey- Fuller test because it adjusts appropriately for the occurrence of

serial correlation (Ogwuru and Ewubare, 2009). In all, the ADF and Philips- Perron tests were applied to

check for the stationary status of the data.

Co-Integration Test

It is often said that co-integration is a means for correctly testing the relationship between two variables

having unit roots (integrated of order 1). The Johansen’s co-integration test was applied to check the co-

integration between and among the variables. There are different methods of testing for co-integration but

Jung and Seldon (1995) state that the Johansen co-integration test is more valid as there is no need of prior

knowledge of the co-integration vectors in cases when they are unknown. According to Koirala (2009), the

Johansen (1998) method of testing for the existence of co-integrating relationships has become standard in

the econometrics literature because of its superiority over other alternatives. According to Engle and

Granger (1987), a set of variables Yt is said to be co-integrated of order (d,b) denoted Yt ≈ C1(d,b) if all

components of Yt are integrated of order d or b (b and d ˃ 0) and there exists a vector β = (β1, β2,…,βn) such

that a linear combination β’Yt = β1Y1t + β2Y2t +…+βnYnt is integrated of order (d,b).

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Model Specification

Given the above discussion, the model for the study is specified thus:

ASI = (FXR, IDP, MRR) ……………………………….. (3)

Where

ASI - All Share Index

FXR - Foreign Exchange Rates

IDP - Index of Industrial Production

MRR - Minimum Rediscount Rate

The above model is estimated linearly in the form of an equation as thus:

ASI = o + 1FXR + 2IDP+ 3MRR+ u ……………………… (4)

µ = stochastic variable or the error term

4. Data and Analysis

The aggregate data of the variables in our model from the year 1986 to 2011 are as presented in Table 1

in the Appendix at the end of the paper.

Model Estimation Results and Discussion

We present in this section the results of the estimated models.

Table 2: Augmented Dickey Fuller Unit Root Test Summary Results

Variable ADF test

statistic Critical values

Order of

Integration

ASI -4.700927

1% = -3.7497

5% = -2.9969

10%= -2.6381

I(1)

FXR -3.201092

1% = -3.7497

5% = -2.9969

10%= -2.6381

I(1)

IDP -3.365022

1% = -3.7497

5% = -2.9969

10%= -2.6381

I(1)

MRR -5.390657

1% = -3.7497

5% = -2.9969

10%= -2.6381

I(1)

Authors’ computation from data in Table.1

The results of both Augmented Dickey Fuller and Phillips-Perron unit root tests indicate that the four

variables ASI, FXR, IDP and MRR are all stationary at first difference. Therefore, following Dritsakis and

Adamopouslos (2004), these variables can be co-integrated as well, if there are one or more linear

combinations among the variables that are stationary. Also, the unit root test conducted on the residuals is all

integrated of order zero. The Engle-Granger (1987) two-step approach to co-integration requires that the

variables be integrated of order 1(1) and the residuals of 1(0).

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Table 3: Phillips-Perron Unit Root Test Summary Results

Variable PP test statistic Critical values Order of

Integration

ASI -5.298841

1% = -3.7343

5% = -2.9907

10%= -2.6348

I(1)

FXR -4.529193

1% = -3.7343

5% = -2.9907

10%= -2.6348

I(1)

IDP -6.556068

1% = -3.7343

5% = -2.9907

10%= -2.6348

I(1)

MRR -7.168406

1% = -3.7343

5% = -2.9907

10%= -2.6348

I(1)

Authors’ computation from data in Table1

Table 4: Augmented Dickey-Fuller Unit Root Test on Residual (ECM)

Variable ADF Test Statistic Critical Values Order of Integration

Residuals

-4.136678

1% = -3.7343

5% = -2.9907

10% = -2.6348

I(0)

Authors’ computation from data in Table1

Table 5: The Johansen (1991) Test Summary Result for Co-integration

Sample: 1986 – 2011

Included Observation: 23

Test Assumption: Linear deterministic trend in the data

Series: ASI, FXR, IDP, MRR

Eigen Value Likelihood Ratio 5% Critical Value 1% Critical Value Hypothesized No. of

CE(S)

0.879438

0.424241

0.345031

0.021934

71.59897

22.94045

10.24295

0.510107

47.21

29.68

15.41

3.76

54.46

35.65

20.04

6.65

None**

At most 1

At most 2

At most 3

*(

**) denotes rejection of the hypothesis at 5% (1%) significance level

LR tests indicates 1 co-integrating equation at 5% significance level

Table 5 presents the result of the Johansen co-integration test. The results indicate that there is at most

one (1) co-integrating equation. This implies that there is one linear combination of the variables that is

stationary in the long run.

O. M. Ogbulu et al.

66

Table 6 presents the Error Correction Model (ECM). This is estimated using the VECM below. The

VECM (p) form is written as

where is the differencing operator, such that .

It has an equivalent VAR (p) representation as thus:

Table 6: Parsimonious ECM Result

Dependent Variable: D(ASI)

Method: Least Squares

Sample (adjusted): 1989 – 2011

Included Observations: 23 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob.

C 788.9400 1702.622 0.463368 0.6498

D(ASI(-1)) 0.354859 0.221835 1.599656 0.1305

D(FXR) -102.4421 103.0921 -0.993695 0.3361

D(IDP) -94.40701 200.7166 -0.470350 0.6449

D(IDP(-2)) 286.9257 208.8237 1.374009 0.1896

D(MRR) -769.1350 421.5689 -1.824459 0.0881

D(MRR(-1)) 263.7019 389.9823 0.676189 0.5092

ECM(-1) -0.829141 0.240600 -3.446135 0.0036*

R-squared 0.582197 Mean dependent var 891.1752

Adjusted R-squared 0.387222 S.D. dependent var 8674.283

S.E. of regression 6790.242 Akaike info criterion 20.75257

Sum of squared resid 6.92E+08 Schwarz criterion 21.14752

Log likelihood -230.6545 F-statistic 2.986008

Durbin-Watson stat 2.315927 Prob(F-statistic) 0.035719 Authors’ computation

Table 6 above presents the results of the parsimonious error correction model conducted to further

analyze the long run relationship between stock market prices and macro economic variables and also to

capture the short run deviations of the parameters from the long run equilibrium by incorporating period

lagged residuals.

The parsimonious model of the ECM produced the expected negative sign and the estimate was

statistically significant at 1 percent with value -0.829141. This suggests the validity of long run equilibrium

relationship among the variables of the estimated model. The implication of the coefficient of the ECM is

that short run disequilibrium in stock market price behavior is corrected at a speed of approximately 83

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67

percent per annum once the equation is shocked. The adjusted R2 values indicated that 38.72% of the

variations in ASI could be explained by the independent variables – the macro economic variables. The F

statistic of 2.986008 is statistically significant at 5 percent level indicating that the explanatory variables are

jointly and significantly strong in influencing stock market price movements. The Durbin-Watson statistic of

2.3 suggests absence of autocorrelation among the variables. The coefficient of foreign exchange rate is

negative and insignificant at 5 percent level in the model. Muhammed and Rasheed (2002) had postulated a

negative relationship between foreign exchange rates and stock prices. The coefficient of IDP is negative

though insignificant at 5 percent. The negative relationship between IDP and stock market price index is not

in line with the apriori expectation of positive relationship. The Minimum rediscount rate (MRR) also shows

a negative relationship and is statistically insignificant at 5 percent level. The negative relationship between

MRR and stock prices in the study is consistent with apriori expectation.

Table 7: Impulse Response to One S.D. Innovations

Response of ASI:

Period ASI FXR IDP MRR

1 5367.432 -2166.061 -509.9760 -1636.751 (774.722) (1191.89) (1147.79) (1120.80) 2 2245.901 -712.9352 1736.178 -868.9355 (1122.31) (1313.24) (1269.42) (1197.55) 3 101.6741 1400.290 2260.735 -205.1888 (1025.92) (1161.49) (1170.78) (991.439) 4 102.2744 2495.191 1347.674 783.1246 (975.743) (1189.76) (1068.90) (846.552) 5 158.6958 2384.838 1031.907 728.5680 (941.409) (1080.50) (996.099) (837.683) 6 -237.9161 2076.503 1124.474 167.8821 (780.451) (1000.86) (931.854) (737.343) 7 -198.3355 1940.161 923.4629 93.84473 (732.080) (957.503) (963.882) (693.1620 8 151.7534 1720.889 680.1670 212.8729 (661.354) (879.411) (989.813) (684.982) 9 203.7640 1466.408 704.8976 118.2100 (538.046) (781.360) (954.967) (652.247) 10 79.21188 1361.110 761.7739 24.59796 (419.663) (730.374) (850.318) (587.252) Source: Authors’ computation

Tables 7 and 8 present the extension of our analysis of the stock market price behavior and

macroeconomic variables in Nigeria by employing the impulse response function and the variance

decomposition techniques. Specifically, the two methods allow us to investigate the dynamic effects of

Foreign exchange rates (FXR), industrial production Index (IDP) and minimum rediscount rate (MRR) on

stock prices (ASI) over the long run period (Cheng and Vijverberg, 2012) in Ogbulu and Torbira (2012).

Runkle (1987) as well as Gujarati and Porter (2009) as cited in Ogbulu and Torbira (2012) stressed that

impulse respond function (IRF) traces out the response of the dependent variable in VAR system to shocks in

the error terms both in the current and future periods.

O. M. Ogbulu et al.

68

Table 8: Variance Decomposition Test Results

Variance Decomposition of ASI:

Period S.E. ASI FXR IDP MRR

1 6036.570 79.05924 12.87541 0.713705 7.351649 2 6764.753 73.97728 11.36338 7.155278 7.504067 3 7272.280 64.03149 13.54026 15.85543 6.572819 4 7845.509 55.03345 21.74891 16.57385 6.643794 5 8298.210 49.22921 27.70006 16.36120 6.709528 6. 8632.577 45.56542 31.38187 16.81506 6.237652 7 8898.682 42.93068 34.28667 16.90135 5.881292 8 9092.798 41.14511 36.42025 16.74697 5.687670 9 9240.222 39.89130 37.78590 16.79881 5.523994 10 9371.312 38.79021 38.84569 16.99288 5.371219

Source: Authors’ computation

In a nutshell, Table 7 basically, presents the results of the impulse response estimates to one standard

deviation innovations in each of the four variables in the VAR system for a period of ten years into the

future. The figures in parenthesis are the standard errors and the ordering of the variables is as shown in

Table 7 above- that is ASI --------- FXR --------- IDP ------- MRR. The Impulse response of ASI to own

shock shows a positive value 5367.432 in the first year, 102.2744 in the 4th year and 79.21188 in the tenth

year. While the impulse response of ASI to shocks coming from FXR, IDP and MRR in the 1st year are (-

2166.061), (-509.9760) and (-1636.751) respectively. FXR was negative in the 1st and 2

nd year becoming

positive from the 3rd

to the 10th year in a fluctuating order. IDP was negative in the first year, became

positive in the 2nd

year through to the 10th year.

The results of the variance decomposition analysis (VDC) are presented in Table 8 above. A careful

study of the table indicates that “own shocks” (ASI) represent the dominant source of variation in the

forecast errors of the variables. For instance, in the variance decomposition of ASI, “own shocks” contribute

79.04% in the 1st year with other variables FXR, IDP and MRR constituting 12.88%, 0.71% and 7.35%

respectively. In subsequent years, “own shocks” continues to decline on annual basis to 38.79% in the tenth

year; while shocks coming from FXR increases continually from 12.88 percent to 38.85 percent in the 10th

year as well as IDP to 16.99 % and MRR started decreasing from the 3rd

year to the tenth year.

Discussions

The co-integration test revealed that there is a co-integration relationship between share prices and

macro economic variables. The results indicate that there is at most one (1) co-integrating equation. This

implies that there is one linear combination of the variables that is stationary in the long run. The result is in

line with the findings of Liu and Shrestha (2008) that discovered the evidence of co-integration relationship

between stock prices and macro economic variables in China. Also, in a similar work by Asaolu and

Ogunmuyiwa (2010), their study on an econometric analysis of the impact of macroeconomic variables on

stock market movement in Nigeria revealed a long run relationship between average share price and macro

economic variables.

The error correction model established insignificant negative relationship between the all share index

and index of industrial production contrary to apriori expectation earlier cited of positive relationship. This

negative relationship can be attributed to high cost of energy generation and distribution in Nigeria. The

energy generation and distribution problem have put Nigerian industries on high cost of production including

the high foreign exchange rate. This problem exposes Nigeria industries to low output performance and

profit. This result is in agreement with the findings of Gharan et al (2009) who established in their study that

a negative relationship exists between industrial production and stock market returns. They attributed their

result to oil price shocks and structural changes in the United States. This finding is also in line with that of

International Journal of Financial Economics

69

Brahmasrene et al (2007) who established negative impact of index of industrial production (IDP) and

foreign exchange rate (FXR) on stock market index in Thailand. Our finding is also buttressed by the

observation of Ayodele (1998) that the agency responsible for supply of electricity is faced with numerous

problems since 1996 such as providing adequate electricity for operations in Nigeria. He further asserted that

this situation has made NEPA (now PHCN) to revert to the adoption of rationing and shedding device thus

creating uncomfortable man- made electricity imbalance in the country. This situation has affected the

industrial sector. The negative relationship also observed between foreign exchange rate and stock market

prices is not in doubt. Dimitrova (2005) had stressed that effect of exchange rates on stock prices is quite

inconclusive as there are views in support for both a positive and negative relationship. Aydemir and

Demirhan (2009) hold the view that empirical studies for a specific economy may show different results for

this relationship. The reason adduced for these differences can be explained by the time period used for data,

econometric models used and economic policies of countries. MRR exhibits negative relationship with stock

prices. This is in line with the apriori expectation. Bulmash and Trivoli (1991), Mukherjee and Naka (1995),

Liu and Shrestha (2008) as well as Ogbulu (2010) among others had documented negative relationship

between stock prices and interest rates.

5. Conclusion

This study examined the co-integration relationship between stock market price behaviour and

macroeconomic variables in Nigeria. The macroeconomic variables were represented by foreign exchange

rate (FXR), index of industrial production (IDP) and interest rate (MRR), while the stock market prices

were represented by all share index of the Nigerian stock exchange (ASI). The study employed robust

econometric analysis techniques such as the Augmented Dickey Fuller (ADF) and Phillips-Perron unit root

tests, the Johansen co-integration test, the error correction model (ECM) as well as impulse response function

(IRF) and variance decomposition (VDC) to analyze the relationship between the selected macroeconomic

variables and stock market prices. The study confirms that long run relationship exist between stock prices

and the selected macroeconomic variables in Nigeria. The negative relationship between stock prices and

industrial production in Nigeria revealed by this study calls for attention especially the high cost of energy

generation which affects the performance of industries during the period under review. This study

recommends that adequate attention be paid to solving energy generation problems in Nigeria which

invariably will reduce cost of production and enhance profit margin of industries as well as support and

attract investment in the sector.

References

Aggarwal, R. (1981), “Exchange Rates and Stock Prices: A Study of U.S. Capital Market Under Floating

Exchange Rates,” Akron Business and Economic Review, 7-12

Ajao, M.A. and E.P. Oseyomon (2010), “The Predictive Content of Some Leading Economic Indicators on

Stock Prices, Journal of Research in National Development, Vol. 8, No.1 June.

Ali, I. Rehman, K. U, Yilmaz, A.K., Khan, M.A. and Afzal, H. (2010), “Causality Relationship between

Macro-economic Indicators and Stock Exchange Prices in Pakistan,” African Journal of Business

Management, Vol. 4(3), 312-319, March. http://www.academicJournals.org

Amadi, S.N., Onyema, J.I. and Odubo (2002), “Macroeconomic Variables and Stock Prices: A Multivariate

Analysis” Africa Journal of Development Studies, Vol.2, No. 1, 159 - 164

Asaolu, T.O. and Ogunmuyiwa, M.S. (2010), “An Econometric Analysis Of The Impact Of Macro-

Economic Variables On Stock Market Movement In Nigeria,” Asian Journal Of Business

Management 3(1) 72-78

Aydemir, O. and Demirhan, E. (2009), “The Relationship between Stock Prices and Exchange Rates:

Evidence from Turkey,” International Research Journal of Finance and Economics, Issue 23

www.eurojournals.com/finance.htm

O. M. Ogbulu et al.

70

Ayodele, A.S. (1998), “Energy Crises in Nigeria: The Case of Energy Market”, CBN Bullion, 22(4), 19-24

Azzopardi F. (2004), “Exchange Rates and Hedging Instruments,” www.e:/e-journal.html

Brahmasrene, Tantatape, Jiranyaku and Komain (2007),”Cointegration and Causality between Stock Index

and Macroeconomic Variables in an Emerging Market,” Academy of Accounting and Financial

Studies Journal, http:/www.allbusiness.com/economy.

Bulmash, S.B., Trivoli, G.W. (1991), "Time Lagged Interactions BetweenStock Prices And Selected

Economic Variables," Journal of PortfolioManagement, Vol. 17 pp. 61-67

Comincioli, B. (1996), “The Stock Market as Leading Indicator: An Application of Granger Causality,” The

Parkplace Economist, Vol. 4, (No.1), 13 http//digital commons.iwu.edu/parkplace/vol.14/Iss1/13

Chen, N.F., Roll, R and S. Ross (1986), “Economic Forces and the Stock Market”, Journal of Business,

Vol.59, No.3, 383-403

Cheung, Y., Ng, L.K. (1998), “International Evidence on Stock Market and Aggregate Economic Activity”,

Journal of Empirical Finance, Vol.5, 281-296.

Dimitrova, D. (2005), “The Relationship between Exchange Rates and Stock Prices: Studies in a Multivariate

Model”, Issues in Political Economy, Vol. 14, August.

Dritsakis, N and Adamopoulos, A. (2004), “Financial Development and Economic Growth in Greece: An

Empirical Investigation with Granger Causality Analysis”, International Economic Journal, 18: 547-

559.

Emenuga, C. (1996), “Macroeconomic Factors and Returns on Equities: Evidence from the Nigerian Capital

Market, in S. Mensah (Massachusetts:Rector Press): 86- 96.

Erdogan, E.and Ozlale, U. (2005), “Effects of Macro Economic Dynamics on Stocks Returns: The Case of

Turkish Stock Exchange Market”, Journal of Economic Cooperation, 26 : 69-90.

Engle, R and Granger, C.W.S. (1987), “Co-integration And Error Correction: Representation, Estimation

And Testing”, Econometrica, 55: 251-276.

Fama, E.F. (1981), “Stock Prices, Expected Returns and Real Activity”,America Economic Review, Vol. 71:

251-76

Fama, E.F. and French, K.R. (1989), “Business Conditions and Expected Returns on Stocks and Bonds”,

Journal of Financial Economics, Vol. 25(1): 23-49.

Gharan, G, Kumar K., Matiur R., Parayitam and Satyanarayana (2009), “Influences of Selected Macro-

Economic Variables on U.S Stock Market Returns and their Predictability Over Varying Time

Horizons”, Academy of Accounting and Financial Studies Journal,Vol.13,Source Issue,1, Jan.

Jung, C. and B. Seldon (1995), “The Macroeconomic Relationship between Advertising and Consumption”,

Southern Economic Journal, 61(3), 577-58.

Koirala, J. (2009) “Stock Market Development and Economic Growth: Evidence from Underdeveloped

Nation (Nepal)” A Research Proposal Submitted to Faculty Members, Economics Department,

Tribluva University, Nepal.

Liu, M.H. and Shrestha, K.M. (2008), “Analysis of Long Term Relationship between Macro-Economic

Variables and Chinese Stock Market: A Heteroscedastic Co-integration”, Journal of Managerial

Finance, Vol. 34, No. 11, 744-755.

London Stock Exchange (2008), “What Factors Influence A Share Price?” London exchange.com/en-

gb/price/news/education. Retrieved 10th November, 2008

Mandelker, G.and Tandon, K. (1985), “Common Stock Returns, Real Activity, Money and Inflation: Some

International Evidence”, Journal of International Money and Finance, Vol.4, 267-286.

International Journal of Financial Economics

71

Meristem Securities Limited (2008), “The Nigerian Stock Market in 2007: Review and Prospects”

www.meristemng.com Retrieved 18th November, 2009

Mohammad, S.D., Hussain, A. and Ali A. (2009), “Impact of Macro Economics Variables on Stock Prices:

Empirical Evidence in Case of KSE (Karachi Stock Exchange)”, European Journal of Scientific

Research, Vol.38, No.1, 96-103 http://www.eurojournals.com

Muhummad, N and Rasheed, A ( 2002), “Stock Prices and Exchange Rates: Are They Related? Evidence

from South Asian Countries”, The Pakistan Development Review, 41(4): 535-550

Mohiuddin, Alam, D. and Shahid, A.I. (2008), “An Empirical Study of the Relationship between

Macroeconomic Variables and Stock Price: A Study on Dhaka Stock Exchange”

http://www.orp.aiub.edu mohiuddiniba@yahoo.com

Mukherjee, K. and Naka, A. (1995), “Dynamic Relation between Macro-Economic Variables and Japanese

Stock Market: An Applicationof A Vector Error Model”, Journal of Financial Research, Vol.18:

223-37.

Nishat, M. and Shaheen, N. (2004), “Macroeconomic Factors and Pakistani Equity Market”, Department of

Finance and Economics, Institute of Business Administration, Karachi, Pakistan

Nnamocha, P.N. and Nwobi, (2001), “Share Prices As an Indicator of Economic Growth and Political

Stability”, Journal of Business and Finance, Imo State University, Owerri Vol. (4): 45-48

Nwokoma, N.I. (2002), “Stock Market Performance and Macroeconomic Indicators Nexus in Nigeria: An

Empirical Investigation”, Nigerian Journal of Economic and Social Studies, Vol.44 (2): 231-251.

Ogbulu, O.M. and Ndubuisi, P. (2009), “Stock Price and Exchange Rate Movements: The Case of Nigeria”

Journal of Finance, Banking & Investment, Abia State University,Uturu Vol. 2 No.1, pp. 1-15

Ogbulu, O.M. (2009), “Capital Market Development and Economic Growth in Nigeria: Application of

Cointegration and Causality Tests”, Journal of Finance, Banking & Investment, Abia State

University, Uturu. Vol.3, No.1, April, 1-19

Ogbulu, O.M. and Torbira, L.L. (2012), “Monetary Policy and Transmission Mechanism: Evidence from

Nigeria”, International Journal of Economics and Finance, Vol.4, No. 11, 122-133.

Ogwuru, H.O.R. and Ewubare (2009), “Exchange Rate Dynamics and Current Account Balance in Nigeria”,

Journal of Finance, Banking & Investment, Abia State University, Uturu.Vol. 3, No.1:74-85

Ologunde A.O., Elumilade, D.O. and T.O. Asaolu (2006), “Stock Market Capitalization and Interest Rates in

Nigeria”, www.eurojournals.com/finance html.

Ray, P. and Vani, V. (2011), “What Moves Indian Stock Market: A Study on the Linkage with Real

Economy In the Post –Reform Era, prantikray@hotmail.com, vina@fsm.ac.in Retrieved on 26 April,

2011

Riman, H.B., Esso, I.E.and E. Eyo (2008), “Stock Market Performance and Economic Growth in Nigeria: A

Causality Test Investment, Global Journal of Social Sciences, Vol.7, No.2: 85-91

Schwert, G.W. (1990), “Stock Returns and Real Activity: A Century of Evidence”, Journal of Finance,

Vol.45(4) :1237-57.

Smith, C. (1992), “Equities and the UK Exchange Rates”, Applied Economics , 24: 327-335.

Soenen, L.A. and Hennigar, E.S (1998), “An Analysis of Exchange Rates and Stock Prices: The U.S.

Experience Between 1980 and 1986”, Akron Business and Economic Review, 7-16

Solnik, B. (1987), “Using Financial Prices to Test Exchange Rate Models: A Note,” Journal of Finance, 42 :

141-149.

Van Wijnbergen, S. (1985), “Trade Reform, Aggregate Investment and Capital Flight: On Credibility and the

Value of Information,” Economic Letters, 19 (4), 369-372

O. M. Ogbulu et al.

72

Appendix:

Table 1: Aggregate Time Series Data of the Variables

YEAR ASI MRR FXR IDP

% N/$

1986 163.8 10 2.0206 103.5

1987 190.9 12.75 4.0179 122.1

1988 233.6 12.75 4.5367 108.8

1989 325.3 18.50 7.3916 125.0

1990 513.8 18.50 8.0378 130.6

1991 783.0 14.50 9.9095 138.8

1992 1107.6 17.50 17.2984 136.2

1993 1,548.8 26.0 22.0511 131.7

1994 2,205.0 13.50 21.8861 129.2

1995 5,092.2 13.50 21.8861 128.8

1996 6,992.1 13,50 21.8861 132.5

1997 6,472.1 13.50 21.8861 140.6

1998 5,889.9 14.31 21.8861 133.9

1999 5,397.9 18.0 92.6934 129.1

2000 8,111 13.50 102.1052 138.9

2001 10,963 14.31 111.9433 144.1

2002 11,740.8 19.00 120.9702 145.2

2003 21,222.8 15.75 129.3565 147.0

2004 23,844.5 15.0 133.5004 151.2

2005 24,085.8 13.0 129.00 158.8

2006 33,189.3 12.25 128.71 156.4

2007 57,990.22 8.75 117.97 150.5

2008 31,450.78 9.82 132.56 152.7

2009 20,827.17 7.44 148.88 149.8

2010 24,770.52 6.08 150.30 127.9

2011 20,730.63 8.90 153.86 137.9 Source: CBN Statistical Bulletin (Various Issues), CBN Annual Report

(2011) National Bureau for statistics (NBS) publications and Nigerian Stock

Exchange Factbook( Various Issues).