The Nigerian Journal of Business and Management Sciences, Volume 2, No. 2, September,

2018 ISSN: 2639-7308 Pp…

MACROECONOMIC INDICATORS AND STOCK MARKET PERFORMANCE IN NIGERIA

BY

Ogbebor, Peter Ifeanyi (PhD)1 & Okolie, Onyeisi Romanus.2;

1 Department of Banking & Finance, School of Management Sciences, Babcock University,

Ilisan Remo, Ogun State, Nigeria2 Department of Accounting, Ambrose Alli University, Ekpoma, Edo State, Nigeria

*Corresponding Author:

ogbebor.peter@yahoo.com Tel:234-803-772-7142

Abstract

The postulation that the link between financial development and economic growth has gravitated

mainly towards one direction: economic growth and development is not invariant to dis-

equilibrium from the stock market has dominated finance literature. This study, however, defers

by studying the effects of macro-economic factors on the stock market in Nigeria for the period:

1993 – 2017. A time series regression analysis involving the use of modern econometrics

approach were employed to establish the effects of gross domestic product (GDP), inflation

(INF), industrial production index (IPI), unemployment rate (UR), interest rate (IR) and

exchange rate (ER) on all share index (ASI) in the stated period. Results from the findings were

mixed as inflation (INF), industrial production index (IPI), unemployment rate (UR), interest

rate (IR) and exchange rate (ER) had negative relationship with the all share index (ASI). The

effects of Gross domestic product (GDP) on the All Share Index was positive but in the short-

run. The same trend was observed in the long-run except that in addition to gross domestic

product (GDP), exchange rate (ER) had a positive relation. Based on this, we conclude that

equilibrium exists among the variables, thus, economic growth and development had significant

effects on stock market performance in the period covered by this study. We, therefore,

recommend, amongst others, that economic growth and development should be pursued in

Nigeria with more vigour in order to further enhance the performance of the stock market.

Keywords: Financial Development, Economic Growth, Stock Market, Macro-economic Variables,

Asset Prices.

SECTION ONE:

INTRODUCTION

The relationship between macro-economic indicators and stock market is considered important

due to the synergy between them. The stock market plays an important role in the financial

intermediation process, hence, efforts have been made by governments through policy reforms

to expand the capacity of the stock market to make more significant contributions to the

endogenous growth model. This theme forms the major crucible in the International Monetary

Fund (IMF)’s framework for macro-economic development of the least developed countries

(LICs). IMF (2012) asserts that particular concern arises given the fragile nature of the

economies of LICs especially their vulnerability to external shocks and further pointed out that

where the policy space and instruments to mitigate these shocks are constrained, growth and

welfare costs can be large. On his part, Alenoghena (2014) asserts that keen observers are of

the opinion that well-functioning stock markets increase economic efficiency, investment and

growth.

One of the main challenges facing a good number of emerging nations is the availability of

finance to fund innovative projects, hence, industrialization has remained stunted in these

economies and even more pathetic is the issue of under-development in majority of these

economies. Some of these countries with natural resource endowments such as oil have not

achieved the desired lift in economic growth as expected and their economies have remained

rooted to the base of modern industrial and technological development. The argument of

Quixima and Almeida (2014) is that the growth of a natural resource dependent economy is

exogenous, hence, it is unlikely that the development of the domestic financial sector has a

significant effect on overall growth. This pre-supposes that some generally adopted macro-

economic variables used in the literature as explanatory variables to explain stock price

performance may be inappropriate in the case of Nigeria as the economy is dominated by

hydrocarbon revenue. But the stock market has been touted as a veritable source of financial

intermediation through which finance for sustainable development can be mobilized. Citing

extant literature. Ume, Nelson and Onwumere (2014) point out that the depth or shallowness

of the financial intermediation platform is expected to be of essence to any economy because

financial deepening (FD) engenders economic growth through the demand and supply channels

in the economic system either as supply-leading or demand-following.

The overview has made it imperative to expand the scope of current research efforts on the

relation between macro-economic factors (gross domestic product, inflation rate, industrial

production index, unemployment rate, interest rate and exchange rate) and stock market

performance in Nigeria. Also, majority of the studies on the relationship between the stock

market and economic growth in Nigeria has been in one direction: how the stock market has

impacted economic growth {(Odo, Anoke, Onyeisi & Chukwu, (2017), Hassan, Babafemi &

Jakada (2016), Taiwo, Alaka & Afieroho (2016), Aigbovo & Izekor (2015), Njogo & Ogunlowore

(2014) Yadirichukwu & Chigbu (2014), Maduka & Onwuka (2013)}. This study defers by studying

how macro-economic factors have impacted the stock market in Nigeria. This is in line with the

argument by Pagano (1993) which highlights the significance of financial development on

growth and vice versa. Similarly, Quixima and Almeida (2014) argue that economic growth also

leads to increased demand for credit that should support the development of the financial

sector. In the case of asset prices, Chen, Roll and Ross (1986) are of the view that they are

commonly believed to react sensitively to economic news.

The objective of this research, therefore, is to determine whether macro-economic factors have

impacted stock market performance in Nigeria since stock market liberalization in 1993.

Following from the above, the hypothesis tested is: there is no significant relation between

macro-economic factors and stock market performance in Nigeria.

SECTION TWO: LITERATURE REVIEW

CONCEPTUAL ISSUE

The relationship between macro-economic indicators and stock returns is important due to the

fact that macro-economic factors can be used to account for the variation in asset returns.

Generally, IMF (2012) explains that the effectiveness of macroeconomic policy responses in the

LICs is conditioned importantly by features of their financial systems. According to Alenoghena,

Enakali-Osoba and Mesagan (2014), a well-developed capital market is crucial to well-

functioning capital markets, increased economic efficiency, investment and growth through

price discovery, liquidity provision, reduction in transaction costs and risk diversification.

Quixima and Almeida (2014) aver that economic growth also leads to increased demand for

credit that should support the development of the financial sector. This explains why the

relationship between the stock market and economic development is bidirectional in nature.

For instance, Karimo and Ogbonna (2017) contend that the causal relationship between

financial development and economic growth depends on the stage of economic development.

In their view, in the early stages of economic development, the supply-leading view (financial

sector deepening leading to increased economic growth) can stimulate real capital formation

and that this inevitably creates opportunities for savers and investors and causes an increase in

economic growth. The supply-leading view becomes less-important, the authors argue, as

financial and economic development proceed, the demand-pull (a well-developed economy

driving the development of the financial sector) gradually starts to dominate. Another angle to

this perspective is the assertion by Greenwood and Smith (1997) that market formation is

endogenous and that the costs of market formation will typically require that market

development follows some period of real development. What this implies is that financial

market development may not have to wait for foreign capital in order to develop, although,

they acknowledge that economic history is also replete with examples illustrating the

importance of financial markets in facilitating economic growth.

Without equivocation, Ozlen and Ugor (2012) declare that the role of macro-economic variables

in asset pricing theories is accepted to be important and further explained that macro-

economic factors such as inflation rate, exchange rate, interest rate, current account deficit and

unemployment rate as contained in the literature on finance have been shown to be critical in

predicting the variability of stock returns. Ozlen and Ugor, however, pointed out that there is

no standard set of macro-economic variables, despite the clear relationship between stock

market turnover and economic activities.

Historically, efforts have been channeled towards studying the reasons for the variation in stock

prices. Davis and Etheridge (2006) traced the origin of the theory of speculation to Bachelier’s

Brownian motion which they described as arising as a model of the fluctuations in stock prices.

Courtault, Kabanov, Bru, Crepel, Lebon and Marchand (2000) declare the pioneering work of

Bachelier as of exceptional merit; further stating that the theory of Brownian motion, one of

the most important mathematical discoveries of the twentieth century, was initiated and used

for the mathematical modeling of price movements and the evaluation of contingent claims in

financial markets. As a result, the significance of stock price movements and prediction has

attained a historic status and gained currency over time thereby becoming one of the main

features of both mathematical and modern finance theories. In addition to this, Fama (1990)

points out that many authors find that large fractions of annual stock-return variances can be

traced to forecasts of variables such as GNP, industrial production, and investment that are

important determinants of the cash flows to firms. He further explain that measuring the total

return variation explained by a combination of shocks to expected cash flows, time-varying

expected returns, and shocks to expected returns is a logical way to judge the efficiency or

rationality of stock prices. This explains why Adjei, Ossei and Mensah (2016) point out that

various studies have shown that there is a strong and positive relationship between the

financial sector and economic development. For example, Murcia (2014) used exchange rate,

gold reserves, consumer price index, wholesale price index, investments and overseas workers’

remittances in his study of selected Philippines’ stock market indices.

THEORETICAL LITERATURE

In addition to the foregoing, there is a linkage between macroeconomic variables and stock

market returns as several models [arbitrage pricing theory (APT), aggregate demand and

aggregate supply (AD/AS), monetary transmission mechanisms] provide a basis for the long-run

relationship and short-run dynamic interactions among macroeconomic variables and stock

prices (Ibrahim & Aziz, 2003). Similarly, Murcia (2014) succinctly points out that the Arbitrage

Pricing Theory (APT) links macroeconomic indicators and stock market returns. What appears

to be an important crucible on the relationship between macro-economic factors and stock

returns are the theoretical foundations upon which some of the relationships are derived. For

example, Dincergok (2016) while attributing the return on a stock to be determined by future

cash flows and the discount rate and that what affects these variables equally affects returns on

stock prices, points out that the main dis-advantage of this macro-economic factor model is

that there is no theoretical basis for the selection of the macro-economic variables. However,

this was further to the Arbitrage Pricing Theory deployed by Chen, Roll and Ross (1986). In

addition, the opinion of Osisanwo and Atanda (2012) is apt and particularly throws light on

other factors in addition to important macro-economic indicators that affect stock prices such

as seasonal variation of the market, enlightenment of the investing public or general awareness

of the market, political and social crisis, amongst others.

Regarding the relationship between stock performance and economic growth, Olweny and

Kimani (2011) posit that when the stock market mobilizes savings, it simultaneously allocates a

larger portion of the same to firms with relatively high prospects as indicated by their returns

and level of risk. Bastas and Soytas (2014) enthuse that overall, there is not a common view on

the link between macro-economic variables and stock market. Following this line of argument,

Gajdka and Pietraszewski (2016) explain that there is no agreement, however, on the specific

mechanisms underlying the relationships between the real sphere of the economy and the

stock market or on their direction, i.e. whether it is the real economy that influences the capital

market or whether it is the other way round.

Inflation (a major macro-economic variable) according to Uwubanmwen and Eghosa (2015) is

measured by inflation rate, the annualized percentage change in the general price index

(usually the Consumer Price Index) over time. In their effort to clarify the impact of inflation on

the value of investments, Uwubanmwen and Eghosa (2015) state that the essence of

investment is to attain a reasonable return while minimizing risk. To them, minimizing risk and

earning reasonable returns on investment calls for proper attention on the current rate of

inflation otherwise the value of the investment will be eroded overtime. Following the Fisherian

hypothesis, Tripathi and Kumar (2014) point out that there is a positive relationship between

inflation and stock returns whereby nominal stock returns should rise along with inflation

providing investors a hedge against inflation.

There is a causal relationship between stock returns and unemployment rate, hence, Lougani,

Rush and Tave (1990) elaborated on this relationship. They are of the opinion that in a well-

functioning stock market, the industry stock price represents the present value of expected

future industry profits. Continuing, the authors explain that an increase in the dispersion of

stock prices across industries reflects the occurrence of shocks that are expected to have

differential impacts on industrial profits. Farsio and Fazel (2013) while agreeing with the notion

of a causal relationship between unemployment and stock prices, however, advocates that

since both set of variables are endogenous, moreso, when their movements depend on a

variety of exogenous variables, makes it essential to conduct deeper analysis in order to identify

the predominant causes of movements in these variables before assuming any causal

relationships. But the argument of Farmer (2015) on the causal link between the two variables

is straight forward when the author states that given the causal link from the stock market to

unemployment, it should be possible to predict the future history of the unemployment rate

using its own past and the past history of the stock market. Mainstream economic theory

depicts on a strong link between stock market activity and unemployment (Tapa, Tom, Lekoma,

Ebersohn and Phiri, 2016). Gonzalo and Taamouti (2017) on their part inform that

unemployment rate is chosen in their study to represent the real economy because in addition

to its accuracy, it gauges the economy’s growth rate. In addition, they point out that

unemployment rate is one of the important indicators for the Federal Reserve to determine the

health of the economy when setting monetary policy.

Regarding interest rate, Ayopo, Ishola and Olukayode (2016) are of the opinion that the

monetary policy rule specifies the means through which the monetary policy authority controls

economic activities in an economy, the stock market inclusive. According to this hypothesis,

they further averred, interest rate is the key instrument used in determining the direction at

which the economy moves.

In the case of exchange rate, Aydemir and Demirhan (2009) in emphasizing the significance of

exchange rate as a macro-economic variable, posit that the relationship between stock prices

and exchange rates has preoccupied the minds of economists since both play important roles in

influencing the development of a country. Following globalization and increased integration of

international financial markets, exchange rates assume more fundamental importance in

macro-economic analysis. As in all areas, globalization affects the exchange rate and share

prices (Zeren & Mustafa, 2016). From this perspective, Tursoy (2017) aver that changes in local

stock markets, caused by events that occur in other countries or in the international market, as

well as the interaction between exchange rates, merit close attention because the markets are

more open to international investment and the exchange rate regimes are floating. Salisu and

Ndako (2017) on their part, highlight the significance of studies on the link between macro-

economic variables and the stock market in financial literature by pointing out that the search

for evidence in favour of the linkages between exchange rate shocks and real and financial

variables of the economy is a clear motivation for assessing the nexus between exchange rate

and stock market prices. Theoretically, Tursoy (2017), explains that there are two approaches

(traditional and portfolio) as contained in financial literature which explain the connection

between stock prices and exchange rates. The author, however, points out that the direction of

causation differs in the two approaches and that in the traditional approach, exchange rate

changes usually lead to changes in stock prices, and in the portfolio approach, the reverse is the

case.

EMPIRICAL LITERATURE

The empirical test carried out by Kumuda and Simi (2015) on the relationship between

macroeconomic variables (inflation rate, unemployment rate, GDP rate and interest rate) and

stock prices shows that inflation rate and unemployment rate have positive relationship with

stock prices while GDP and interest rate were negatively related with stock prices. In the case of

the United States of America, Sirucek (2012) used a select group of macro-economic variables

to establish the effect, implication, impact and relationship between these variables and the US

indices (S & P 500) and the Dow Jones Industrial Average (DJIA) between 1999 and 2012 and

found that the macroeconomic factors have stronger relationship with the DJIA than the S & P

500. Based on the results obtained, the author concluded that the model used thus confirmed

the economic theory justifying the impact of macro-economic variables on share prices. In the

case of Sri Lanka, Nijam, Ismail and Musthafa (2015) regressed the following macro-economic

variables: Gross Domestic Product, Inflation, Interest Rate, Balance of Payment and Exchange

rate on the All Share Price Index of Colombo stock exchange. The analysis reveals that the

macro-economic variables have significant relationship with the Index. The analysis further

revealed that the stock market index significantly relates to the Gross Domestic Product,

Exchange rate and Interest rate in a positive manner while it has negative relation with inflation

rate. On its part, it was established that the Balance of Payment was found to have insignificant

relationship with the stock price Index. In particular, Gonzalo and Taamouti (2017) found that

only anticipated unemployment rate has a strong impact on stock prices which is against the

general findings in the literature, they added.

Alenoghena (2014) in his study of the relationship between capital market, financial deepening

and economic growth of Nigeria found that stock market capitalization, narrow money

diversification and interest rate significantly impacted the promotion of economic growth of the

country during the period of the study other measures of liquidity were invariant in explaining

the trend of economic growth, although they exhibited very strong coefficients in the process.

Karimo and Ogbonna (2017) adopting the Toda-Yamamoto augumented Granger causality test

and established that the growth-financial deepening nexus in Nigeria follows the supply-leading

hypothesis as against the hypothesis of growth leading financial deepening. On their part,

Alenoghena, Enakali-Osoba and Mesagan (2014) found that financial deepening variables

actually positively impacted on the performance of the Nigerian stock market.

SECTION THREE: RESEARCH METHODS

The model adopted in this study is the intertemporal capital asset pricing model. Low and Wang

(2006) argue that this model is ideal for an empirical link between asset prices and economic

factors. Therefore, this study models the joint behaviour between prices and companies’

fundamental indicators (proxies for macro-economic variables) in Nigeria for the period: 1993 –

2017. The ex post facto research design was adopted and as such, secondary data were used.

The study adopted ordinary least squares method of regression analysis and used Unit Root

Tests (to check the time series properties of the variables prior data estimation) and

Autoregressive Distributed Lag and Diagnostic tests (normality test, serial correlation test and

heteroscedasticity test) to analyze the impact of Macro-economic Indicators on Stock Market

Performance in Nigeria. It also used the Granger Causality Test to establish the direction of

causality of the variables used, viz: All Share Index (ASI), Gross Domestic Product (GDP),

Inflation (INF), Industrial Production Index (IPI), Unemployment Rate (UR), Interest Rate (IR) and

Exchange Rate (ER).

Model Specification

The empirical theory adopted in this study is the Arbitrage Pricing Theory and is specified as

follows:

ASI = ƒ(GDP, INF, IPI, UR, IR and ER) … Equation (1)

Transformed into an econometric model, thus:

LogASI = α0 + β0Log GDP + β1INF. + 21IPI + β3UR + β4IR + β5ER + εt … Equation (2)

Where:

ASI = All Share Index

GDP = Gross Domestic Product growth rate

INF = Inflation

IPI = Industrial Production Index

UR = Unemployment Rate

IR = Interest Rate

ER = Exchange Rate

εt = Error Term

The relationship between the Dependent variable (ASI) and the Explanatory variables – gross

domestic product (GDP), inflation (INF), industrial production index (IPI), unemployment rate

(UR), interest rate (IR) and exchange rate (ER) specifies a simple model in the estimation of the

relationship between stock returns in Nigeria proxied by ASI and GDP, INF, IPI, UR, IR, ER while

α is a constant, β0 - β5 are the slope coefficients that captures the sensitivity of the stock returns

to gross domestic product per capita, inflation, industrial production, unemployment, interest

and exchange rate while εt is the stochastic error term.

TABLE 1: A PRIORI EXPECTATION

Dependent Variable Independent Variable Change in Sign

All Share Index Gross Domestic Product +

All Share Index Inflation -

All Share Index Industrial Production Index +

All Share Index Unemployment Rate +

All Share Index Interest Rate -

All Share Index Exchange Rate -/+

Source: Authors’ Compilation, 2019.

SECTION FOUR: DATA PRESENTATION AND ANALYSIS

Data

The data used in this study (Appendices 1 & 2: DATA SET FOR TREND ANALYSIS AND DATA SET

FOR EMPIRICAL ANALYSIS respectively) were from secondary sources such as Nigerian Stock

Exchange Fact Books (several editions), National Bureau of Statistics and Central Bank of Nigeria

Statistical Bulletins (several Issues). The data are data relating to All Share Index (ASI), Gross

Domestic Product (GDP), Inflation (INF), Industrial Production Index (IPI), Unemployment Rate

(UR), Interest Rate (IR) and Exchange Rate (IR).

Trend Analysis

Market Volume of Securities Traded

Figure 1 shows that the market volume increased through the years to edge up at 193.14billion in

2008 when the market was booming. After that, it experienced a major decline. The volume rose

from 0.47 billion in 1993 to well over 18billion in 2009 but declined sharply thereafter until

2010 – 2017 when it steadied and stood at 100.5billion at the end of 2017.

Market Turnover

As seen in the Figure 2 below, the average values of total turnover during the period of 1993 to

2008 shows an increasing trend just as that of the market volume. The value rose from N0.05

billion in 1993 to a peak of N18.71 billion in the year 2008. However, the Figure shows that the

recent trend from 2010 – 2017 is cyclical is that of an upward trend.

New Issues

In Figure 3 the average values of new issues during the period of 1993 to 2008 shows an

increasing trend. Specifically, the average value which was N3.94 billion in 1993 rose to the

highest point at the end of the year 2008 with a value of N2576.19 billion but declined in 2009 to

N275.24 billion. Thereafter, some fluctuations were noticed in the trend till recent time.

All Share Index on the Nigerian Stock Exchange

In Figure 4, the average values of the all shares index during the period of 1993 to 2008 shows

an increasing trend. The figure depicts that the average value of all share index which was

1229.03 in 1993 rose to 50424.70 at the end of the year 2008 but between 2010 and 2017, this

value shows some cyclicality.

19931996

19992002

20052008

20112014

2017

0.00

50.00

100.00

150.00

200.00

250.00Market Volume

Year

Mar

ket V

olum

e (B

illio

n)

19931996

19992002

20052008

20112014

2017

0.002.004.006.008.00

10.0012.0014.0016.0018.0020.00

Total Turnover

Year

Tota

l Tur

nove

r (N

' Bill

ion)

Figure 1: Market Volume Figure 2: Total Turnover (N' Billion)

19931996

19992002

20052008

20112014

2017

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00New Issues

Year

Val

ue N

ew Is

sues

(N'B

illio

n)

0.00

10000.00

20000.00

30000.00

40000.00

50000.00

60000.00 All Share Index

Year

Inde

x

Figure 3: New Issues Figure 4: All Share Index

Number of Listed Companies

Figure 5 depicts increasing trend between the years 1993 and 2010. Explicitly, the average

number of the listed companies which was 174.00 in 1993 increased to 217.00 at the end of the

year 2010. Thereafter, the number began to decline and was just above 170 at the end of 2017.

The highest number recorded during the period was 217.00 companies that were recorded in

2010. The implication of this is that more companies delisted than those that were newly listed

on the stock exchange.

Number of Listed Securities.

In Figure 6, the average number of listed securities during the period of 1993 to 2017 shows a

declining trend as it fell from 272.00 in 1993 to 247 at the end of year 2017. However, the

highest number (310.00) of listed securities was recorded in 2007 and the trend clearly shows a

declining trend in recent years.

0.00

50.00

100.00

150.00

200.00

250.00Number of Listed Companies

Year

Num

ber

19931996

19992002

20052008

20112014

2017

0.0050.00

100.00150.00200.00250.00300.00350.00

Number of Listed Securities

Year

Num

ber

Figure 5: No of Listed Companies Figure 6: Number of Listed Securities

From the overview, the stock market indices in Nigeria indicated declining trends after the initial

lift in its indices in the boom years of 2004 – 2009. The boom could be attributed to reform of

the financial markets but risky behaviour on the part of market participants and distress

resolution mechanism of the banking sector crisis of that era can be said to have accounted for

this situation. While the expectation is that economic growth can impact the stock market

positively especially when the macro economy witnessed positive growth, the stock market

declined. Can this be attributed to a falling equity culture and/ or that economic growth did not

impact the stock market in Nigeria positively

Descriptive Analysis

The result of the descriptive statistics for variables considered in this study is presented in Table

1. From the table, the average value of all share index (ASI) is 20411.15 with minimum and

maximum values of 1229.03 and 50424.70 respectively. The real gross domestic product growth

rate (GDP) during the period takes its own values between -2.04% and 15.33%, with an average

value of 4.62%. Inflation rate (INF) ranges from 5.38% to 72.84% with a mean value of 18.57%

during the period. The industrial production index (IPI) proxied by manufacturing capacity

utilization has a minimum value of 29.29% and a maximum value of 64.31% with 47.70% as the

average value. The ranges of the values that unemployment (UR) has are 5.10% and 19.70%

with an average value of 11.42%. With respect to interest rate (IR), the minimum and maximum

values of 18.36% and 36.09% respectively are recorded with an average value of 23.56%. The

minimum and maximum values of exchange rate (ER) indicate that the rate of exchange during

the period of this study hovers around N21.89 per USD and N305.79 per USD with an average

value of N120.25 per USD. However, the standard deviation reveals diverse variability in the

series.

Table 1: Summary Statistics

ASI GDP INF IPI UR IR ER Mean  20411.15  4.62  18.57  47.70  11.42  23.56  120.25 Maximum  50424.70  15.33  72.84  64.31  19.70  36.09  305.79 Minimum  1229.03 -2.04  5.38  29.29  5.10  18.36  21.89 Std. Dev.  14306.37  3.99  17.53  11.25  3.74  4.245  71.96 Observations  25  25  25  25  25  25  25Source: Authors’ Computation, 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.Note: ASI represents All Share Index, GDP represents Real Gross Domestic Product Growth Rate, INF represents Inflation Rate, IPI represents Industrial Production Index, UR represents Unemployment Rate, IR represents Interest Rate and ER represents Exchange Rate.

Stationarity Test

The summary of the result of the unit root tests carried out in their level and first difference

forms using Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) approaches are presented

in Table 2. The ADF and PP tests results provide evidence that the null hypothesis that all the

series except gross domestic product growth rate (GDP), unemployment (UR) and interest rates

(IR) have unit roots can be safely accepted at all levels within the 1% and 10% conventional

levels of significance. In other words, the acceptance of null hypothesis indicates that the series

are not stationary at level. Investigating further, the result shows that the series can only be made

stationary by first difference. However, the rejection of the null hypothesis at level within the 1%

to 10% conventional level of significance when gross domestic product growth rate (GDP),

unemployment (UR) and interest rates (IR) series are tested for unit root; strongly indicates that

the series are integrated of order (0). It is, therefore, worth concluding that the series have

different orders of integration, that is, both at I(0) and I(1) and the study proceeds to bounds

testing of ARDL approach to examine the long run relationships among the series.

Table2: Unit Root Test

Variable/t-stat/Critical Value

Augmented Dickey-Fuller Phillips-Perron@Level @1st Diff. Order @Level @1st Diff. Order

ASI

t-Stat -2.176 -4.488***

I(1)

-2.171 -4.692***

I(1)1% -4.394 -4.441 -4.394 -4.4165% -3.612 -3.633 -3.612 -3.62210% -3.243 -3.255 -3.243 -3.249

GDP

t-Stat -2.243 -6.913***

I(1)

-2.096 -9.285***

I(1)1% -4.394 -4.416 -4.394 -4.4165% -3.612 -3.622 -3.612 -3.62210% -3.243 -3.249 -3.243 -3.249

INF

t-Stat -2.033 -4.800***

I(1)

-1.522 -5.277***

I(1)1% -4.394 -4.416 -4.394 -4.4165% -3.612 -3.622 -3.612 -3.62210% -3.243 -3.249 -3.243 -3.249

IPI

t-Stat -1.923 -5.815***

I(1)

-1.959 -5.815***

I(1)1% -4.394 -4.416 -4.394 -4.4165% -3.612 -3.622 -3.612 -3.62210% -3.243 -3.249 -3.243 -3.249

UR

t-Stat -3.431* -4.558***

I(0)

-3.375* -10.081***

I(0)1% -4.394 -4.468 -4.394 -4.4165% -3.612 -3.645 -3.612 -3.62210% 3.243 -3.261 -3.243 -3.249

IRt-Stat

-4.773*** -5.385***

I(0)-4.651*** -20.097***

I(0)1% -4.394 -4.441 -4.394 -4.4165% -3.612 -3.633 -3.612 -3.62210% -3.243 -3.255 -3.243 -3.249

ER

t-Stat -1.608 -3.565**

I(1)

-1.336 -3.565**

I(1)1% -4.416 -4.416 -4.394 -4.4165% -3.622 -3.622 -3.612 -3.62210% -3.249 -3.249 -3.243 -3.249

Source: Authors’ Computation 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.Note: ASI represents All Share Index, GDP represents Real Gross Domestic Product Growth Rate, INF represents Inflation Rate, IPI represents Industrial Production Index, UR represents Unemployment Rate, IR represents Interest Rate and ER represents Exchange Rate.***, **and * represents the level of significance at 1%, 5% and 10% respectively.

Bounds Co-integration Test

Following the unit root test results in the preceding subsection, the study proceeds to ascertain

whether long run relationships exist among the variables and in achieving this, the study

followed the approached developed by Pesaran, Shin and Smith (2001). One of the major

strengths of this approach is that the variables in the cointegrating relationship can be a mixture

of I(0) or I(1). In addition, the approach performs better when it comes to a small sample data.

As in Table 3, the computed F-statistic value of 3.650 is greater than the upper critical bound

value of 3.61, thus indicating the existence of a long-run relationship among the variables.

Alternatively, this suggests the rejection of the null hypothesis of no co-integration at 5% level of

significance. In view of this, the long-run and short-run relationship between the variables is

represented with an error correction model.

Table 3: Bounds Co-Integration Test

Test Statistic Value K

F-statistic 3.667196 6

Critical Value Bounds

Significance I0 Bound I1 Bound

10% 2.12 3.235% 2.45 3.612.5% 2.75 3.991% 3.15 4.43

Source: Authors’ Computation 2018 with underlying data obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.

Short - Run Coefficient

The results from the estimation of the short – run and the long-run models based on the estimated

ARDL (1, 0, 0, 1, 1, 1, 1) using AIC1 are presented in Table 4 below. Focusing on the error

correction term (ECMt-1) of the short – run model, it can be seen that the coefficient has the

1

expected sign and it is statistically significant at 1% level of significance. This further confirms

the existence of a long-run relationship between the dependent variable and the independent

variables.

The result further shows that current value of GDP, INF and ER significantly affect ASI in the

short-run. Specifically, the three variables (GDP, INF and ER) have inverse relationship with

ASI in the short run. On the other hand, the coefficient of IPI, UR, and IR are positive but

statistically insignificant meaning that that all share index (ASI) is not influenced by IPI, UR and IR

significantly during the period of this study in the short – run.

Long Run Coefficient

Long Run Coefficient

The long-run dynamics of the relationship between independent and dependent variables as

displayed in the Table imply that in the long-run; the relationship all share index (ASI) and gross

domestic product growth rate (GDP) remains negative and statistically significant at 5% level. Similarly,

inflation (INF) and interest rate (IR) exhibit negative and significant relationship with all share index

(ASI) in the long – run at 5% level of significance. Conversely, the coefficient industrial production index

(IPI) is positive and statistically significant at 1% level. This indicates that 1.87 percent increase in all

share index (ASI) in the long run is associated with 1 percent increase in industrial production index (IPI).

Finally, it is evident from the F = Stat (Prob.) = 31.90 (0.000) and R2 = 0.967 that the ARDL model

is significant and fit.

Table 4: ARDL Cointegrating Short Run and Long Run Forms

Short Run Coefficients

Variable Coefficient Std. Error t-Statistic Prob.   

D(GDP) -0.059211 0.024133 -2.453585 0.0304D(INF) -0.007033 0.003532 -1.991042 0.0697DLOG(IPI) 0.814029 0.542424 1.500725 0.1593D(UR) 0.005229 0.011808 0.442873 0.6657D(IR) 0.013334 0.019250 0.692668 0.5017D(ER) -0.009220 0.003203 -2.878912 0.0139CointEq(-1) -0.774706 0.138864 -5.578885 0.0001

    Cointeq = LOG(ASI) - (-0.0764*GDP -0.0091*INF + 1.8683*LOG(IPI) +        0.0424*UR -0.0543*IR + 0.0052*ER + 3.4163 )

Long Run Coefficients

Variable Coefficient Std. Error t-Statistic Prob.   

GDP -0.076431 0.026457 -2.888879 0.0136INF -0.009078 0.003708 -2.448536 0.0307LOG(IPI) 1.868325 0.538542 3.469227 0.0046UR 0.042436 0.022820 1.859607 0.0876IR -0.054303 0.022851 -2.376394 0.0350ER 0.005192 0.002102 2.470218 0.0295C 3.416324 2.120445 1.611135 0.1331

Source: Authors’ Computation 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.Note: ASI represents All Share Index, GDP represents Real Gross Domestic Product Growth Rate, INF represents Inflation Rate, IPI represents Industrial Production Index, UR represents Unemployment Rate, IR represents Interest Rate and ER represents Exchange Rate. R2 = 0.967, F = Stat (Prob.) = 31.90 (0.000), Durbin-Watson stat = 1.70

Diagnostic Tests

The Autoregressive Distributed Lag (ARDL) model is validated by conducting normality test,

serial correlation test, and heteroscedasticity test on the residual of the estimated model using

Jarque-Bera for normality test with the null hypothesis of normality, Breusch-Pagan serial

correlation for serial correlation test with the null hypothesis of no serial correlation and

Breusch-Pagan-Godfrey and ARCH effects tests for heteroscedasticity with the null hypothesis

of homoscedastic. Following the tests result presented in Table 5, the model passed all the

diagnostic tests. In other words, the insignificant values of the test results suggest the acceptance

of null hypotheses and indicate that the residual is normally distributed, there is no evidence of

serial correlation and the model is free from heteroscedasticity problem.

Table 5: Diagnostic Test

Normality Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.519339     Prob. F(1,11) 0.4862Obs*R-squared 1.082018     Prob. Chi-Square(1) 0.2982

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.540073     Prob. F(11,12) 0.2345Obs*R-squared 14.04865     Prob. Chi-Square(11) 0.2303Scaled explained SS 1.821402     Prob. Chi-Square(11) 0.9990

Source: Authors’ Computation, 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.

Granger Causality

According to the results in Table 6, it can be seen that there exist a unidirectional short-run

causal relationship among some of the variables. Specifically, at 5% level of significance the

results show that ASI Granger causes IPI (prob. = 0.0214). Also, it shows that ER Granger

causes ASI (prob.=0.0499). The causality between UR and GDP shows that UR Granger causes

GDP (prob. = 0.0256). Furthermore, causality flows from IR to GDP, ER to IPI and ER to IR.

Table 6: Pairwise Granger Causality Tests

 Null Hypothesis: Obs F-Statistic Prob. 

 GDP does not Granger Cause LOG(ASI)  24  0.03064 0.8627 LOG(ASI) does not Granger Cause GDP  0.02798 0.8688

 INF does not Granger Cause LOG(ASI)  24  0.46397 0.5032 LOG(ASI) does not Granger Cause INF  1.49065 0.2356

 LOG(IPI) does not Granger Cause LOG(ASI)  24  2.52328 0.1271 LOG(ASI) does not Granger Cause LOG(IPI)  6.17898 0.0214

 UR does not Granger Cause LOG(ASI)  24  0.74127 0.3990 LOG(ASI) does not Granger Cause UR  2.31218 0.1433

 IR does not Granger Cause LOG(ASI)  24  0.03133 0.8612 LOG(ASI) does not Granger Cause IR  1.66793 0.2106

 ER does not Granger Cause LOG(ASI)  24  4.32972 0.0499 LOG(ASI) does not Granger Cause ER  0.16839 0.6857

 INF does not Granger Cause GDP  24  0.00031 0.9860 GDP does not Granger Cause INF  1.27165 0.2722

 LOG(IPI) does not Granger Cause GDP  24  0.23373 0.6338 GDP does not Granger Cause LOG(IPI)  2.23555 0.1497

 UR does not Granger Cause GDP  24  5.77570 0.0256 GDP does not Granger Cause UR  0.19169 0.6660

 IR does not Granger Cause GDP  24  3.53075 0.0742 GDP does not Granger Cause IR  0.29917 0.5902

 ER does not Granger Cause GDP  24  0.00754 0.9316 GDP does not Granger Cause ER  2.42187 0.1346

 LOG(IPI) does not Granger Cause INF  24  0.01290 0.9107 INF does not Granger Cause LOG(IPI)  2.63533 0.1194

 UR does not Granger Cause INF  24  0.08535 0.7730 INF does not Granger Cause UR  2.64787 0.1186

 IR does not Granger Cause INF  24  2.05268 0.1667 INF does not Granger Cause IR  1.34164 0.2597

 ER does not Granger Cause INF  24  0.25206 0.6209 INF does not Granger Cause ER  0.30566 0.5862

 UR does not Granger Cause LOG(IPI)  24  2.18484 0.1542 LOG(IPI) does not Granger Cause UR  0.29328 0.5938

 IR does not Granger Cause LOG(IPI)  24  2.15124 0.1573 LOG(IPI) does not Granger Cause IR  0.11567 0.7372

 ER does not Granger Cause LOG(IPI)  24  4.96215 0.0370 LOG(IPI) does not Granger Cause ER  2.20846 0.1521

 IR does not Granger Cause UR  24  0.44749 0.5108 UR does not Granger Cause IR  0.07621 0.7852

 ER does not Granger Cause UR  24  2.31853 0.1428 UR does not Granger Cause ER  0.30284 0.5879

 ER does not Granger Cause IR  24  3.39916 0.0794 IR does not Granger Cause ER  0.58103 0.4544

Source: Authors’ Computation 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.

SECTION 5.0: CONCLUSION AND RECOMMENDATIONS

Based on the findings, the study concludes that equilibrium exists among the variables, thus,

economic growth and development were found to have impacted stock market performance in

Nigeria during the period covered by this study. The study, therefore, recommend, amongst

others, that the economic growth and development of Nigeria should be pursued more vigorously

in order to enhance the performance of the stock market. Also, policy makers should strive to put

in place measures that can boost liquidity of the stock market which can improve its performance

over-time. This can be done through the use of monetary policies such as the lowering of the

monetary policy rates by the Monetary Policy Committee of the Central Bank of Nigeria which

can significantly improve liquidity in the equity market. Furthermore, quick passage of the

already amended Investments and Securities Act should be carried out and provisions of the Act

should be implemented to the letter in order to make the market more competitive

internationally. Finally, transaction costs in the stock market should be reduced drastically in

order to improve the volume of transactions in the market.

REFERENCES

Adjei, S.K., Osei, B. & Mensah, I.O. (2016). Financial Deepening and Economic Growth In

Ghana: International Journal of Current Research, 8(1), 25736-25743

Aigbovo, O. & Izekor, A. O. (2015). Stock Market Development and Economic Growth in

Nigeria: An Empirical Assessment. International Journal of Business and Social Science,

6(9), 27 -36.

Alenoghena, R.O. (2014). Capital market, financial deepening and Nigeria’s economic growth:

empirical evidence. Journal of Economic & Financial Studies, 2(4), 43-52.

Alenoghena, R.O. (2014). Capital Market, Financial Deepening and Nigeria’s Economic

Growth: Cointegration and Error Correction Model Approach. Global Journal of

commerce & Management Perspective, 3(3), 18-26.

Alenoghena, R.O., Osoba, C.E, & Mesagan, P.E. (2018). Financial Deepening and Performance

of the Nigerian Capital Market: Empirical Evidence. Global Journal of commerce &

Management perspective, 3(4), 142-151.

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

Rates: Evidence from Turkey. International Research Journal of Finance and

Economics, 1, 23.

Ayopo, B. A., Isola, L. A. & Olukayode, S. R. (2016). Stock Market Response to Economic

Growth and Interest Rate Volatility: Evidence from Nigeria, International Journal of

Economics and Financial Issues, 6/4. 354-360

Bastas, U. & Soytas, U. (2014). Do Stock Returns Trigger Economic Growth? Evidence from

Turkey, Retrieved from https://www.researchgate.net/publication/29054038_DO

STOCK_htm

Chen, N., Roll, R. & Ross, S. A. (1986). Economic Forces and the Stock Market: Testing the

APT and Alternative Asset Pricing Theories. The Journal of Business, 59(3): 383 – 403.

Courtault, J.-M. , Kabanov,Y., Bru, B., Crepel, P., Lebon, I. & Le Marchand, A. (2000). Louis

Bachelier On the centenary of Théorie de la Spéculation. Mathematical Finance, 10 (3):

339-353.

Davis, M. & Etheridge, A. (2006). Louis Bachelier’s Theory of speculation: The origins of

modern finance, Princeton University Press, Princeton and Oxford, 188+xvi.

Dincergok, B. (2016). Stock Return Indices and Macroeconomic Factors: Evidene from Borsa

Istanbul, Journal of Accounting, Finance and Auditing Studies, 2(3), 307-322.

Fama, E. F. (1990). Term-Structure Forecasts of Interest Rates, Inflation and Real Returns.

Journal of Monetary Economics, 25, 59-76.

Farmer, R.E.A. (2015). The Stock Market Crash Really Did Cause the Great Recession, Oxford

Bulletin of Economics and Statistics, 0305-9049

Farsio, F. & Fazel, S. (2013). The Stock Market/Unemployment Relationship in USA, China and

Japan, International Journal of Economics and Finance, 5(3)

Gajdka, J. & Pietraszewski, P. (2016). Economic Growth and Stock Prices: Evidence from

Central and Eastern European Countries, Studia Prawno-Ekonomiczne, t. XCVIII, s. 179-

196

Gonzalo, J. and Taamouti, A. (2017). The Reaction of Stock Market Returns to Unemployment:

Working Papers: Madrid, 126

Greenwood, J. & Smith, B. D. (1997). Financial Markets in Development, and the Development

of Financial Markets, Journal of Economic Dynamics and Control, 21, 145-181

Hassan, A., Babafemi, O. D. & Jakada, A. H. (2016). Financial Market Development and

Economic Growth in Nigeria: Evidence from VECM Approach. International Journal of

Applied Economic Studies, 4(3), 1 – 13.

IMF (2012). Enhancing Financial Sectors Surveillance In Low Income Countries, Financial

Deepening and Macro Stability.

Karimo, T.M. & Ogbonna, O.E. (2017). Financial Deepening and Economic Growth Nexus in

Nigeria: Supply- Leading or Demand-Following?. www.mdpi.com/journal/economies.

Khyareh, M. M. & Oskou, V. (2015). The Effect of Stock Market on Economic Growth: The

Case of Iran. International Journal of Academic Research in Economics and

Management Sciences, 4(4), 52-62.

Kumuda, P. R. & Simi, S. V. (2015). Macroeconomic Variables and Stock Prices – Evidence

from India. International Journal of Current Engineering and Scientific Research

(IJCESR), 2(11), 15-19.

Loungani, P., Rush, M. and Tave, W. (1990), Stock Market Dispersion and Real Economic

Activity – Evidence From Quarterly Data: Working Paper: Chicago (WP-90-15)

Maduka, A. C. & Onwuka, K. O. (2013). Financial Market Structure and Economic Growth:

Evidence from Nigeria Data. Asian Economic & Financial Review, 3(1), 75-98.

Murcia, J. V. B. (2014). Macroeconomic Estimation of Selected Philippine Stock Market

Indices. Southeast Asian Interdisciplinary Research Journal, 2(2), 21-30.

Nijam, H. M., Ismail, S. M. M. & Musthafa, A. M. M. (2015). The Impact of Macro-Economic

Variables on Stock Market Performance: Evidence from Sri Lanka. Journal of Emerging

Trends in Economics & Management Sciences (JETEMS), 6(2), 151-157.

Njogo, B. O. & Ogunlowore, A. J. (2014). Impact of Nigerian Financial Markets on the

Economic Growth. International Journal of Financial Markets, 1(3), 77 – 82.

Odo. S. I., Anoke, C. I., Onyeisi, O. S. & Chukwu, B. C. (2017). Capital Market Indicators and

Economic Growth in Nigeria; An Autoregressive Distributed Lag (ARDL) Model. Asian

Journal of Economics, Business & Accounting, 2(3), 1-16.

Olweny, T. O. & Kimani, D. (2011), Stock Market Performance and Economic Growth:

Empirical Evidence from Kenya using Causality Test Approach, Advances in

Management & Applied Economics, 1(3), 153 – 196.

Osisanwo, B. G. & Atanda, A. A. (2012), Determinants of Stock Market Returns in Nigeria: A

Time Series Analysis, African Journal of Scientific Research, 9(1)

Ozlen, S. & Ugur, E. (2012), Macroeconomic Factors and Stock Returns, International Journal

of Academic Research in Business and Social Sciences, 2(9)

Pagano,M. (1993). Financial market and growth: An overview, European economic review,

37(23) 613-622

Quixina, Y. & Almeida, A. (2014). Financial Development and Economic Growth in a Natural

Resource Based Economy: Evidence from Angola. FEP Working Paper, 1-16.

Salisu, A. & Ndako, U. B. (2017). A New Look at the Stock Price – Exchange Rate Nexus:

Working Paper Series: Ibadan, CWPS 0031

Sirucek, M. (2012). Macroeconomic variables and stock market: US review. International

Journal of Computer Science and Management Studies, 1-10.

Taiwo, J. N., Alaka, A. & Afieroho, E. (2016). Capital Market and Economic Growth in Nigeria.

Account and Financial Management Journal, 1(8), 497 – 525.

Tapa, N., Tom, Z., Lekoma, M., Ebersohn, J. and Phiri, A. (2016). The Unemployment-Stock

Market Relationship in South Africa: Evidence from Symmetric and Asymmetric

cointegration models: Retrieved from https://mpra.ub.uni-muenchen.de/74101/MPRA

Paper No. 74101

Tripathi, V., Kumar, A. (2014), Relationship between Inflation and Stock Returns – Evidence

from BRICS markets using Panel Co-integration Test, International Journal of

Accounting and Financial Reporting, 4(2)

Tursoy, T. (2017), Causality between Stock Prices and Exchange Rates in Turkey: Empirical

Evidence from the ARDL Bounds Test and a Combined Cointegration Approach.

International Journal of Financial Studies, 5(8)

Ume, K.E. Nelson, N. & Onwumere, J.U.J. (2015). Measuring the Dynamics of Financial

Deepening and Economic Growth in Nigeria, 1981-2013: Using Engel-Granger Residual

Based Approach. IOSR Journal of Economics and Finance, 6(1), 82-89.

Uwubanmwen, A. & Eghosa, I. I. (2015). Inflation Rate and Stock Returns: Evidence from the

Nigerian Stock Market, International Journal of Business and Social Science, Vol. 6, No. 11

Yadirichukwu, E. & Chigbu, E. E. (2014). The impact of capital market on economic growth: the

Nigerian Perspective. International Journal of Development and Sustainability, 3(4), 838

-864.

Zeren, F. & Mustafa, K. (2016), Time Varying Causality between Stock Market and Exchange

Rate: Evidence from Turkey, Japan and England, Economic Research,-Ekonomska

Istrazivanja, 29(1), 696-705

APPENDIX 1: DATA SET USED FOR TREND ANALYSIS

Year ASI Market Volume (Billion)

Total Turnover (N' Billion)

Value of New Issues (N'Billion)

No of Listed Companies

No of Listed Securities

1993 1229.03 0.47 0.05 3.94 174.00 272.00

1994 1913.23 0.52 0.06 2.67 177.00 276.00

1995 3815.12 0.40 0.12 7.08 181.00 276.00

1996 5955.14 0.88 0.16 21.45 183.00 276.00

1997 7638.59 1.25 0.22 9.11 182.00 264.00

1998 5961.88 2.10 0.23 17.28 186.00 264.00

1999 5264.19 3.11 0.27 44.44 196.00 269.00

2000 6701.18 5.00 0.37 35.71 196.00 261.00

2001 10185.08 5.16 0.49 44.71 194.00 261.00

2002 11631.87 6.62 0.95 65.32 195.00 258.00

2003 15559.90 13.31 0.88 184.97 200.00 265.00

2004 24738.65 19.21 2.61 227.38 207.00 276.00

2005 22876.72 26.69 3.13 728.66 214.00 287.00

2006 27647.51 36.66 5.74 1489.63 202.00 288.00

2007 48773.31 138.28 16.18 2296.37 212.00 310.00

2008 50424.70 193.14 18.71 2576.19 213.00 299.00

2009 23091.55 102.85 3.77 275.24 216.00 266.00

2010 24775.51 93.34 4.41 2437.24 217.00 264.00

2011 23393.65 89.58 3.30 1810.39 201.00 277.00

2012 23432.62 89.15 3.33 195.36 198.00 285.00

2013 36207.08 106.54 4.58 286.76 198.00 279.00

2014 39409.82 108.47 6.30 271.05 196.00 280.00

2015 30867.20 92.86 4.23 1386.74 190.00 286.00

2016 26624.08 95.83 2.56 1558.17 170.00 247.00

2017 32161.11 100.52 5.87 2271.36 167.00 247.00

APPENDIX 2: DATA SET USED FOR EMPIRICAL ANALYSIS

Year ASI GDP INF IPI UR IR ER1993 1229.03 -2.04 57.17 37.19 7.68 36.09 22.051994 1913.23 -1.82 57.03 30.40 7.59 21.00 21.891995 3815.12 -0.08 72.84 29.29 7.51 20.79 21.891996 5955.14 4.19 29.27 32.46 7.41 20.86 21.891997 7638.59 2.93 8.53 30.40 10.20 23.32 21.891998 5961.88 2.58 10.00 32.40 10.00 21.34 21.891999 5264.19 0.58 6.62 34.60 12.50 27.19 92.692000 6701.18 5.01 6.93 36.10 13.50 21.55 102.112001 10185.08 5.92 18.87 42.70 13.60 21.34 111.942002 11631.87 15.33 12.88 54.90 12.55 30.19 120.972003 15559.90 7.35 14.03 56.50 11.20 22.88 129.362004 24738.65 9.25 15.00 55.70 11.00 20.82 133.502005 22876.72 6.44 17.86 54.80 12.79 19.49 132.152006 27647.51 6.06 8.24 53.30 13.80 18.70 128.652007 48773.31 6.59 5.38 53.38 14.20 18.36 125.832008 50424.70 6.76 11.58 53.84 13.60 18.70 118.572009 23091.55 8.04 11.54 55.14 19.70 22.62 148.882010 24775.51 9.13 13.72 56.22 5.10 22.51 150.302011 23393.65 5.31 10.84 56.22 6.00 22.42 153.862012 23432.62 4.21 12.22 57.61 10.60 23.79 157.502013 36207.08 5.49 8.48 57.90 15.16 24.69 157.312014 39409.82 6.22 8.06 58.23 6.40 25.74 158.552015 30867.20 2.79 9.02 64.31 10.40 26.71 193.282016 26624.08 -1.58 15.70 44.30 14.20 27.29 253.492017 32161.11 0.83 22.38 54.50 18.80 30.68 305.79