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JOURNAL OF RESEARCH IN NATIONAL DEVELOPMENT VOLUME 8 NO 1, JUNE, 2010


THE PREDICTIVE CONTENT OF SOME LEADING ECONOMIC INDICATORS ON STOCK PRICES

Mayowa Gabriel Ajao

E-Mail:mayourwah@yahoo.com
and

Ehi Patrick Oseyomon
Faculty of Management Sciences, University of Benin, Benin City
E-Mail:poseyomon@yahoo.com

 

Abstract
Within the framework of a standard discounted model, we examine the predictive content of some leading economic indicators to future stock prices in Nigeria. An Ordinary Least Square (OLS) regression analysis is applied in order to model the long term relationship between macroeconomic variables (GDP, inflation, interest rate, money supply, exchange rate and industrial production index) and stock prices (All shares index) in Nigeria. Our estimation results reveal a significant positive relationship between stock market returns  and all these macroeconomic variables except the interest rate which had a negative relationship with stock prices during the period under consideration (1984-2006). These findings will help investors and portfolio managers deepen their understanding of the risk return relationship, pricing of macroeconomic risk as well as diversifications implications in Nigerian stock market. Besides, policy makers may play a major role in influencing the expected risk premium and volatility on stock markets through use of macroeconomic policy.

Keywords: Macroeconomics, all shares index, money supply, industrial production index.


Introduction
There is a consensus among macroeconomists and finance theorists that stock market prices are driven by macroeconomic variables, the so called “fundamentals” in the economy. Moreover, it is also agreed that the linkage is two ways that is, feedback exists between the stock market and real activity. The question of whether the stock market can predict the economy has been widely debated. 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 firms. 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.

Several studies have investigated the relationship between stock prices and level of economic activities in developed countries. Studies on non- US markets have mostly been based on the Chen, Roll and Ross (1986) approach Hamao (1988) tested the Japanese market and found strong pricing evidence except for the case of Japanese monthly production. Martinez and Rubio (1989) used Spanish data and found no significant pricing relationship between stock returns and macroeconomics variables. Poon and Taylor (1991) are also unable to explain stock returns in the UK by factors used by Chen et al (1986). More recently, Kaneko and Lee (1995) have re-examined the US and Japanese markets by employing the Chen et al (1986) factors to evaluate the effects of systematic economic news on stock market returns. Since there exists a long-term relationship between the changes in stock prices and leading economic variables as indicated by many finance experts. Fama (1981) documents a strong positive correlation between common stock returns and real economic variables like capital expenditures, industrial production, real GNP, money supply, inflation and interest rates. Chen et al (1986) found that changes in aggregate production, inflation, short term interest rates and risk premium are the economic factors which explain and predict stock prices. The characteristics which all stock market have in common is the uncertainty which is related with the short and longterm future state. This feature is undesirable for the investor but it is also unavoidable whenever the stock market is selected as the investment tool. The best that one can do is to try to reduce this uncertainty. Stock market prediction (or forecasting) through macroeconomic variables is one of the instruments in the process.
 
The stock market prediction task divides researcher and academics into two groups: those who believe that we can devise mechanisms to predict the market and those who believe that the market is efficient and whenever new information comes up the market  absorbs it by correcting itself, thus there is no place for prediction. Furthermore, they believe that the stock market follows a random walk which implies that the best prediction you can have about tomorrow’s value is today’s value.

The finance and economic literatures have been devoted to studies on the relationship between stock market returns and macroeconomic variables in developed economies, with little or no attempt in unfolding this relationship in a developing economy like Nigeria. This study therefore attempts to examine the relationship (if any) between economic fundamentals and stock prices in Nigeria.

Related literature
The significance of economic fundamentals using the arbitrage pricing theory (APT) of Ross (1976) has been well documented. Studies by Fama (1981), Chen et al (1991)and Ferson and Harvey (1991) have documented a significant relationship between stock returns and real economic variable such as industrial production, real GNP, interest rates, inflation and money supply. Harvey (1995) investigates the influence of oil prices, world industrial production, world inflations rate, world equity return and the return on a foreign currency index on emerging market returns. Sill (1995) documents that the industrial production output, T-bill rate and inflation are statistically significant in explaining the US stock market excess returns. In addition, the conditional variance-covariances of macroeconomic factor are important drivers of the conditional stock return volatility.

In the stock market literature, the stock market has traditionally been viewed as an indicator of the economy. Many believe that large decreases in stock prices are reflective of a 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”. The traditional valuation model of stock prices suggests that stock prices reflect expectations about the future economy and can therefore predict the economy. The wealth effect contends that stock prices lead economic activity by actually causing what happens to the economy (Comincioh, 1996).

Macroeconomic variables analysis
As in other related studies the macroeconomic variables included in this study are hypothesized to act as joint proxy for a set of latent variables that determine excess returns on stocks. An important point to take note is that these set of variables does not capture all economic risk, but it does include macroeconomic variables that are generally regarded as the more important variables that affect excess return on stocks. Based on “simple and intuitive financial theory” supported by relevant literatures and dictated by availability of data, stock market returns are expected to relate to changing trends in the economic and business conditions as reflected in the variation of the following variables: growth in Gross Domestic Product (GDP), Industrial Production Index (IPI), Unexpected Inflation (INF), Interest Rates (INT), Money Supply Growth (MS) and changes in Exchange Rate (EXR). These variables have the additional appeal that they are all somewhat “exogenous” in the sense that they come from outside the stock markets. Besides, these variables have been adopted in past research of similar nature. The main economic justifications for the inclusion of the macroeconomic variables are briefly explained below.

Growth rate in GDP
Equity returns are a function of the future cash flow stream that is highly dependent upon future economic conditions. There is evidence that current stock return levels are positively related to future levels of real activity as measured by GDP. The variable growth rate in GDP is computed as the geometric means different between successive years seasonally adjusted gross domestic product. GDP is a measure of all currently produced final goods and services valued at market price and this is an aggregated value of all the industries in an economy. Since the stock market is a significant asset of a nation’s economy the economic growth should reflect the market condition. Consequently, the GDP growth could have predicative power to stock returns. During period of high economic growth, there is confidence within the economy and this would stimulate demand for products and services. Accordingly, growth in GDP is expected to have a positive influence on stock market returns. On the contrary in periods of economic downturn accompanied by high economic volatilities, investors confidence on the prospect of the economy may be dampened and as a consequence, associated with a lower expected returns on investment assets and capital. Hence we would expect the direction and significance of the relationship between GDP and stock market returns to be determined empirically.

Growth rate in industrial production output
The industrial production output is a measure of the production sector of an economy and also indicates the national economic growth. This measure reflects the activities of all the industries in an economy. Fama (1981) documents a relationship between concurrent measures of US stock returns and industrial production that is positive and significant. Therefore, a priori, the industrial production output is expected to be related to stock market returns.

Inflation rate
Inflation has been defined as the persistent and appreciable rise in the general price level. Its variation has impact on economic activities because it affects both aggregate demand and supply. It is generally measured by changes in Consumer Price Index (CPI) which measures the retail prices of a fixed “market basket” of several thousand of good and services purchased by households. Inflation is usually separated into two parts. The unexpected inflation is usually defined as the different between the actual and expected rate. Ferson and Harvey (1991) argued that unexpected inflation could be “source of economic risk and as a result a risk premium would be added if the stock of firms has different exposure to unexpected inflation.

Fama and Schwert (1977) present evidence that stock prices are negatively related to both the expected and the unexpected component of consumer price index. It is believed that common stock should be a hedge against inflation. However, Brueggeman et al (1984), discovered that real estate sock fail to hedge inflation. So also securitized real estate has been found to provide no effective hedge against inflation. Increase in the rate of inflation reduces stock prices because of the interaction of inflation with the tax system. During inflationary period, it is general believed that investors undervalue corporate stock because they fail to consider capital gain on corporate debt.  So far, the extant literature provides no clear answer on the impact of unexpected inflation on asset returns; in light of the lack of agreement between the theory and evidence, it is difficult to predicted the direction of relationship between stock returns and unexpected inflation, but there is a general believe that a negative relationship exist between inflation rate and stock prices.
Interest rates
This economic indicator is selected here because it would have effects on both the future cash flow of firms and discount rate. Interest rates are important for affordable access to credit by investing firms and consuming households in an economy. A low interest rate stimulates investment by making credit available easily and cheaply with implication for capacity utilization and profitability. Higher interest rate would increased debt service of firms and reduce future net income, it can also affect investment activities of companies in financing real estate investment and development. In a high interest rate environment, interest rate is thus expected to negatively affect excess returns on stock. On the contrary, higher interest rates will increase the income to investors in money market funds and then in turn stimulate the economy and stock market. So far, empirical evidence regarding the direction and significance of interest rate impacts on stock market returns, real estate markets, and securitized real estate has been mixed (Ling and Naranjo (1997), Lizieri and Satchell (1997), Devaney (2001).

In a recent study, Liow and Huang (2006) find that real estate stocks are generally sensitive to changes in the long term and short term interest rates and to a lesser extent, their conditional volatilities. However, there are disparities in the magnitude as well as direction of sensitivities in interest rate level and volatility across different stock markets and under different market conditions. Smith (1990) in his study of US economy found that stock prices rises after (sometimes before) the federal reserves announces cut in the interest rate. Amadi and Odubo (2002) found that interest rate has a significant influence on stock price variation in Nigeria. As in most related study, the prime lending rates are used as a proxy of interest rate movement in this study.

Money supply
There exists economic rationale to include money supply as a relevant macroeconomic factor. First, changes in money supply will alter the equilibrium position of money, thereby altering the composition and price of assets in an investor’s portfolio, second, changes in money supply may impact on real economic variables and having a lagged influence on stock and property stock returns. Both of these mechanisms suggest a positive relationship between changes in money supply and stock market returns. However, increases in money supply may also give rise to a greater inflation uncertainty and thus can have an adverse impact on stock markets. In particular, excessive growth in money supply may lead to an inflationary environment and in turn lower stock prices because of higher expected discount rates required. The excess returns would therefore be negatively affected (Liow, Ibrahim and Huang (2006). In this study, growth rate in money supply is taken to be the geometric mean difference between successive years money supply, represented by M2 which is broad measure of money in an economy.

Exchange rates
Exchange rate is the price unit of a given currency in relations to other currencies; it is a product of a country’s external trade and directly related to the balance of payment. The performance and profitability of firms that are major importers and exporters are considerably affected by the exchange rate of the local currency to major currencies of the world. According to the Purchasing Power Parity (PPP), exchange rates will adjust to reflect relative inflation levels and exchange rate risk will not be separately priced. However, in the short-to-medium term deviations, PPP imply that exchange rate risk must be borne by investors. For example, an appreciation of local currency relative to the US $ is expected to decrease exports and profits and lead to lower economic growth. The appreciation of local currency would therefore be negatively associated with the returns on stock (Liow etal 2006). In a study by Akinnifesi (1987) on the relationship between exchange rate and stock prices in Nigeria, the result indicated that stock prices and depreciating naira are positively related implying that the higher the level of depreciation of the naira, the higher the stock prices. Amadi and Odubo (2002) study on the estimate of exchange rate on stock price behaviour also confirm a positive relationship between these variables. Exchange rate is measured as naire to US $ in this study.

Stock markets and prediction
In literature, a number of different methods have been applied in order to predict stock market returns. However, prior to applying these methods in predicting the market, some parameters need to be considered. These parameters are in form of theories that guide the investors in applying the appropriate prediction methods.

Investment theories
An investment theory suggests what parameters one should take into account before placing his (or her) capital on the market. Traditionally the investment community accepts two major theories: the firm foundation, and the castles in the Air. Reference to these theories allows us to understand how the market is shaped or in other words how the investors think and react. It is sequence of ‘thought and reaction” by the investors that defines the capital allocation and thus the level of the market.

There is no doubt that the majority of the people related to stock markets is trying to achieve profit. Profit comes by investing in stocks that have a good future (short or long term future). Thus what they are trying to accomplish one way or the other is to predict the future of the market. But what determines this future? The way that people invest their money is the answer; and people invest money base on the information they hold. Therefore we have the following scheme.

                                    information ® investor ® market level

The factors that are under discussion on this scheme are: the content of the information component and the way that the investor reacts when having this information.

On the other hand according to the “Castles in the Air” theory the investors are triggered by information that is related to other investors behaviour. So for this theory the only concern that the investor should have is to buy today at lower price and sell tomorrow at a higher price, no matter what the intrinsic value of the firms is.

Prediction methods 
The prediction of the market is without doubt an interesting task. In the literature, there are a number of methods applied to accomplish this task. These methods use various approaches, ranging from highly information ways (e.g. the study of a chart with the fluctuation of the market) to more formal ways (e.g. linear or non-linear regressions). We have categorized these techniques as follows:

  • Technical Analysis Methods
  • Fundamental Analysis Methods
  • Traditional Time Series Methods
  • Machine Learning Methods

The criterion   to this categorization is the type of tools and the type of data that each method is using in order to predict the market. What is common to these techniques is that they used to predict and thus benefit from the market’s future behaviour.

Technical analysis
“Technical analysis is the method of predicting the appropriate time to buy or sell a stock used by those believing in the castles-in-the-air view of stock pricing”. The idea behind technical analysis is that share prices move in trends dictated by the constantly changing attributes of investors in response to different forces. Using technical data such as price, volume, highest and lowest prices per trading period, the technical analyst uses charts to predict future stock movements. Price charts are used to detect trends, these trends are assumed to be based on supply and demand issues which often have cyclical or noticeable patterns. According to Smith (1990) technical analysts engage themselves in studying changes in market prices, the trading volume and investors’ attitude the purpose is to make excess return from information asymmetry. From the study of these charts trading rules are extracted and used in the market environment. The chartists (technical analysts) believe that the market is only 10 percent logical and 90 percent psychological. The chartist’s belief is that a careful study of what the other investors are doing will shed light on what the crowd is likely to do in the future. This is a very popular approach used to predict the market which has been heavily critized. The major point of criticism is that the extraction of trading rules from the charts is highly subjective because different analysts might extract different trading rules by studying the same charts. Although it is possible to use this method to predict the market on daily basis, but its subjective character limit its wide usage.

Fundamental analysis
“Fundamental analysis is the technique of applying the tenets of the firm foundation theory to the selection of individual stocks”. The analysts that use this method of prediction use fundamental data in order to have a clear picture of the firm (industry or market) they will choose to invest in. They are aiming to compute the ‘real” value of the asset that they will invest in and they determine this value by studying variables such as the growth, the dividend payout, the interest rates, the risk of investment, the sales level, the tax rates and so on. Their objective is to calculate the intrinsic value of an asset (e.g. of a stock). To determine the intrinsic value of an equity stock the security analyst must forecast the earnings and dividends expected from the stock and choose a discount rate which reflects the riskiness of the stock. This is what is used in fundamental analysis perhaps the most popular method used by investment professionals. The fundamental analysts believe that the market is defined 20 percent by logical and 10 percent by physiological factors.

Traditional time series prediction
The traditional time series prediction analyses historic data and attempts to approximate future values of a time series as a linear combination of these historic data. In econometrics there are two basic types of time series forecasting: univariate (simple regression) and multivariate (multiple regression). These types of regression models are the most common tools used in econometrics to predict time series. The way they are applied in practice is that firstly a set of factors that influence (or more specific is assumed that influence) the series under prediction is formed. These factors are the explanatory variables (xi) of the prediction model; then a mapping between their values xit and the values of the time series yt (y is the to be explained variable) is done; so that pairs (xit, yt) are formed. These pairs are use to define the importance of each explanatory variable in the formulation of the to be defined variable. In other words the linear combination of xi that approximates in an optimum way y is defined. Univariate models are based on one explanatory variables (I=1) while multivariate models use more than one variable (I >1). Regression models have been used to predict stock market time series.

Machine learning methods
Several methods for inductive learning have been developed under the common label “machine learning”. All these methods use a set of samples to generate an approximation of the underlying function that generated the data. The aim is to draw conclusions from these samples in such a way that when unseen data are presented to a model, it is possible to infer the to be explained variable from these data. The common method are “The Nearest Neighbour” and “The Neural Networks” techniques. Both methods have been applied to market prediction; particularly for neural networks there is a rich literature related to the forecast of the market on daily basis.

Data characteristics and methodology
In the empirical analysis of this study, we used annual data for the period of 1984-2006. The major data used is the Nigerian Stock Exchange All Shares Index and some selected macroeconomics variables (the economic choice and justification of these selected macroeconomics are described above). The Nigerian stock exchange all shares index is a capitalization weighted index of all the equities stock traded on the Nigerian stock exchange. The index was developed with a base value of 100 as of January 1984. It consists of all listed equities on the stock exchange. The justification for the inclusion of NSE, All Shares Index is because the index is sufficiently representative of the Nigerian stock market, since it accounts for approximately 100% of total trading volume.

As in other related studies, the macroeconomic variables included in this study are hypothesized to act as joint proxy for a set of latent variables that determine stock prices. An important point to note is that this set of variables does not capture all economic risk but it does include macroeconomic variables that are generally regarded as the more important variables that affect excess return on stocks. Based on “simple and intuitive financial theory” supported by relevant literatures and dictated by availability of data stock prices and returns are expected to relate to changing trends in the economic and business conditions as reflected in the variation of the following variables: Gross Domestic Product (GDP), Industrial Production Index (IPI), Inflation (INF), Interest Rate (INT) Money Supply (MS) and Exchange Rate (EXR). These variables have the additional appeal that they are all somewhat “exogenous in the sense that they come from outside the stock market.

In this study, data will be processed by running a regression to verify in quantitative terms how the explanatory variables impact on the value of the dependent variable. To achieve this Ordinary Least Square (OLS) techniques of model estimation was employed. The concepts of OLS is often used to described statistically the behaviour of time series that satisfy some longrun equilibrium situations.

Model specification
The equation/model to be specified is based on the assumption that All Shares Index behaviours can be explained by the values of macroeconomic variables, the model to be estimated can be stated as follow:
ASI = ¦(GDP, INF, INT, MS, IPI, EXR)
Where
ASI = All shares index
GDP=Gross Domestic Product
INF = Inflation
INT = Interest Rate
MS = Money Supply
IPI = Industrial Production Index
EXR = Exchange Rate.

The above model is specified linearly in the form of an equation as follow:
ASI = β0 + βiGDP + β2INF + β3INT + β4Ms+ β5IPI + β6EXR + e



Table I: All shares index and leading economic indicators

Year

All Shares Index

          GDP

Inflation Rate

Interest Rate

Money Supply

Industrial Production Index

Exchange Rate

 

 

        N'M

        %

      %

       N'M

 

 

1984

100

59,622.50

39.6

13

21.6

91.6

0.7649

1985

127.3

67,908.60

5.5

11.75

23.82

100

0.8938

1986

163.8

69,147

5.4

12

24.59

103.5

2.0206

1987

190.9

105,222.90

10.2

19.2

29.99

122.1

4.0179

1988

233.6

139,085.30

38.3

17.6

42.78

108.8

4.5367

1989

325.3

216,797.50

40.9

24.6

46.22

125

7.3916

1990

513.8

267,550.00

7.5

27.7

64.93

130.6

8.0378

1991

783

312,139.80

13

20.8

86.15

138.8

9.9095

1992

1,107.60

532,613.80

44.5

31.2

129.09

136.2

17.2984

1993

1,543.80

683,869.80

57.2

36.09

198.52

131.7

22.0511

1994

2,205.00

899,863.20

57

21

266.955

129.2

21.8861

1995

5,092.00

1,933,211.60

72.8

20.79

318.76

128.8

21.8861

1996

6,992.00

2,702,719.10

29.3

20.86

370.33

132.5

21.8861

1997

6,440.50

2,801,972.60

8.5

23.32

429.73

140.6

21.8861

1998

5,672.70

2,708,430.90

10

21.34

525.64

133.9

21.8861

1999

5,266.40

3,194,023.60

6.6

27.19

699.73

129.1

92.6934

2000

8,111.00

4,537,640.00

6.9

21.55

1,036.08

138.9

102.1052

2001

10,963.10

4,685,912.20

18.9

21.34

1,315.87

144.10

111.94

2002

12,137.70

5,403,006.80

12.9

29.7

1,490.46

145.20

120.97

2003

20,137.70

6,947,819.90

14

22.47

1,862.60

147

129.3565

2004

23,844.50

11,411,066.90

15

20.62

2,067.05

151.20

133.50

2005

24,085.80

14,610,881.50

17.9

19.47

2,814.85

158.80

132.15

2006

33,189.30

18,564,594.72

8.2

18.43

2,814.85

163.50

128.6516

Source: Central Bank of Nigeria, Statistical Bulletin, 2006 Edition
Nigerian Stock Exchange quarterly Bulletin  

The regression equation is
ASI = 4377.82 + 1.098GDP + 14.365INF – 109.186INT + 2.612Ms +53.956IPI + 16.621EXR +e

 

 

Table II: The calculated R, R-square and adjusted R-square.


Variables entered

R

R-Square

Adjusted R-Square

Std Error of the Est.

GDP, INF, INT, Ms, IPI, EXR

0.987

0.974

0.964

1781.4275

This table shows a multiple coefficient of determination R = 98.7 and R2 of 97.4. This indicates a very good model fit.

Table III: Model summary result


Change Statistics

Durbin-Wastson

R-square change

F-change

dfi

df2

Sig. f. change

1.824

0.974

98.680

6

16

0.000

           
Table IV: ANOVA Result
Model              Sum of Squares                     df           Mean Square    F                      Sig.
Regression                   1.88 E + 09                  6          313160125.41      98.680                    000
Residual                       50775741                    16        3173483.799
Total                            1.93E+09                     22                               


With F value of 98.680 and P value of 0.00 the regression ANOVA indicates that the regression variables have significant effect on the response variable. This is also confirmed by R2 of 97.4


Table V Betta Coefficients and T-test Result

 

Model

Unstandardized coefficients

Standardized coefficients

 

t

 

Sig

β

Std Error

Beta

(constant)
GDP
INF
INT
MS
IPI
EXR

4377.822
1.098E-03
14.365
-109.186
2.612
53.956
16.621

4779.68
0.000
22.057
107.540
3.480
50.126
28.295

 

0.585
0.030
-0.068
0.251
0.103
0.094

-0.916
2.641
0.651
-1.015
0.751
1.076
0.587

0.373
0.018
0.524
0.325
0.464
0.298
0.565

Table VI: Residuals Statistics

 

Minimum

Maximum

Mean

Std. Deviation

N.

Predicted value
Residual
Std. Predicted value
Std Residual

-151.4316
-3822.3330
-0.813
-2.146

32417.363
4194.7349
2.712
2.355

7357.6870
4.350E-13
0.000
0.000

9241.6074
1519.2064
1.000
0.853

23
23
23
23


Interpretation of regression results/findings
A total of 23 annual observations of the Dependent Variables (ASI) and independent (macroeconomics) variables were used in this study covering the period 1984 to 2006; and the results of the model estimation are shown in the above tables. From the model estimation, the dependent variables, All Shares Index (ASI) have an autonomous value of 4377.82 and a positive relationship with all the explanatory variables except the interest rate with which ASI has an inverse or negative relationship with. This signify that each of the macroeconomic/explanatory variables (except interest rate) contributed to the growth of All Shares Index and impact positively on stock prices return during the period under consideration (1984-2006).

This first statistics R is the multiple correlation coefficients between all the explanatory variables and the dependent variables. From table II above, the value of R is 0.987, and the R2 is 0.974. This is the co-efficient of determination which is frequently used to described the goodness of fit or the amount of variance explained by a given set of explanatory variables. In this study, R-square value is 0.974, which indicates that 96% of the variance in the dependent variable is explained by the independent variables while the remaining 2.6% is explained by the elements not included in the model but are taken care of by the stochastic error term e. The result of the Analysis of Variances (ANOVA) as shown in table IV was done on multiple regression data which produced a T-ratio value of 98.688 which was significant at 0.05 alpha level (t-ratio: 98.68, df of 6/16) the independent variables (GDP, INF, INT, Ms, IPI and EXR) passed the overall significant test (t-test) this is an indication that none of the estimated coefficient is equal to zero and that there is a linear relationship between the dependent and explanatory variables. The Durbin-Watson test of 1.824 which can be approximated to 2 suggest the absence of serious correlation beyond what is induced by a first or second order auto regressive process; so the result of this study indicates that there is probably not any serious autocorrelation in the residual. Thus the model fit the data relatively well.

Considering the T-test only the GDP pass the significance with the value of 2.641 when compared with the critical value of 1.71 under the 5% level of significance using the one  tail test. Other explanatory variables though contributed positively to all shares index but were not statistically significant at 5% level.

Conclusion
Several findings and implications are derived from the model result. Expected stock prices/returns aggregated by All Share Index (ASI) are positively correlated with Gross Domestic Product (GDP) Inflation Rate (INF), Money Supply (Ms), Industrial Production Index (IPI) and negatively correlated with Interest Rate (INT). Specifically, the relationship between stock market return and macroeconomics variables can be increasing, decreasing or flat depending on model parameters. The estimated results also suggest that the volatility of stock market returns is dynamically related to the variances of the macroeconomics variables.

Our estimation results are able to reveal a significant relationship between stock market returns and macroeconomics variables. Many other studies [Mukherjee and Naka, (1995); Masih and Masih, (1996); Kwon Shin and Bacon, (1997); Nasseh and Strauss, (2000); and Liow, Ibrahim and Huang, (2006)] have examined the impact of several macroeconomic variables on stock markets in both developed and emerging economies. Most studies find that these macroeconomic variables have significant influence on the stock market and/or the existence of a longrun relationship between these macroeconomic variables and stock prices returns. However, the influences of the macroeconomic risk factors on the expected return risk premium in terms of direction and significance do vary across the economies studied. Consequently the result imply that there are opportunities for risk diversification in stock markets and have some practical implications. The relationship between stock market return and the macroeconomic indicators may also provide useful information for government policy makers in regulating the relevant economic and financial variables. Although this study has important implications as far as the magnitude of the impact of macroeconomic variables on the Nigerian Stock Exchange, the research could by enhanced by considering social and political factors not included in the study (This is left for future research).

In conclusion the main thrust of this paper is an empirical investigation of the relationship between the expected stock market returns and some major macroeconomics factors as reflected in the general business and financial conditions in Nigerian context. The results and findings of this study corroborated the findings of other studies in similar areas. Although it is now well recognized that stock market returns react to fluctuations in macroeconomic  variables; any definite prediction of the relationships between the expected risk factors is difficult if not impossible. However our results will help investors and portfolio managers deepen their understanding of the risk-return relationship, pricing of macroeconomic risk as well as diversification implications in Nigerian stock market. Additionally, policy makers may play a major role in influencing the expected risk premium and volatility on stock markets through the use of macroeconomic policy.

Recommendations
From the findings of this study, the following recommendations are hereby suggested for policy implications.

  1. Since there exists a feedback linkage between stock market return and real economic activity. Policy makers and regulators should monitor closely the level of activity in the nation stock market in order to use it to know the next direction in which economic reforms will be focused toward the actualization of macroeconomic objectives.
  2. The predictive ability and forward-looking potential of stock market returns should not be solely relied on at all by investors to form their future expectations. There are a number of factors which influence investors’ expectations that are not being derived by simply looking at the past trend in the economy to form expectations about future activity.
  3. The negative relationship between stock market returns and interest rates revealed by this study need to be seriously considered by the financial system regulators. There suppose not to be significant difference between stock market and money market returns so that activity in one segment of the market will not negatively affect the other segment.
  4. Policy makers should play major role in influencing the expected return risk premium and volatility on stock markets through the use of macroeconomic policy.

 

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