The Impact of Stock Market and Banking Sector ...globalbizresearch.org/economics/images/files/77617_15...Saudi Arabia, this study aims to empirically investigate the independent effects

Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)

An Online International (Double-Blind) Refereed Research Journal (ISSN: 2306-367X)

2019 Vol: 8 Issue: 1

2884 www.globalbizresearch.org

The Impact of Stock Market and Banking Sector Development and

Economic Growth in an Oil-Rich Economy:

The Case of Saudi Arabia

Ali Babikir,

Department of Mathematics and Statistics, College of Science,

Taibah University, Saudi Arabia.

E-mail: [email protected]

Ajab Alfreedi,

Department of Mathematics and Statistics, College of Science,

Taibah University, Saudi Arabia.

E-mail: [email protected]

ــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ

Abstract

This paper empirically investigated the separate effects of banking sector and stock market

development on economic growth in Saudi Arabia using data covering the span of 1997Q1 to

2017Q2 employing the Autoregressive Distributed Lag (ARDL) approach to co-integration

analysis. Fixing the potential effects of trade openness and the oil price on Saudi economic

activities, we find that the banking sector development has a negative highly significant effect

on economic growth. In contrast, the impact of stock market development is either positive or

insignificant. The findings suggest that the Saudi Arabian financial sector can be positively

motivated through the allocation and mobilisation of resources. The results, however,

indicate that economic growth is affected and dominated by the crude oil sector in Saudi

Arabia. In general, the results suggest that the relationship between economic growth and

financial development may differ in resource-dominated and oil exporting economies and

countries.

ــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ

Key Words: Economic growth; stock market; bank sector; oil price; Saudi Arabia; ARDL

method.

JEL Classifications: G10; G20; O11; Q32.

mailto:[email protected]

mailto:[email protected]





1. Introduction

Recent years have observed a growing interest among a academia and researchers in

understanding the linkage between financial sector development and economic growth in oil-

exporting countries see among others Nili and Rastad, (2007); Beck (2011); Barajas Chami

and Yousefi (2013); Cevik and Rahmati (2013); Samargandi Fidrmuc and Ghosh (2014),

Eugene (2016) and Lotfali and Golanz (2017). However, most of these studies did not include

stock market development indicators. This is because stock markets represent a small

proportion of financial sector while the banks dominate the activities of financial sectors in

most countries, see, Naceur Cherif and Kandil (2014). Levine (2004) found that stock markets

are one of the factors that promote economic growth through the stock market’s role in

liquidity creation, management and diversification of risk and foreign capital inflow in most

countries. Lin et al., (2009) and Kurronen (2015) notes that the structure of the financial

sector pursues the structure of the economy production. Based on this countries have the

ability to have different levels of stock market development and banking sector development.

So the economy uses different instruments and channels to allocate financial resources.

Levine (1996) states that the impact of the separation process of the banking sector and stock

market development on economic growth may illustrate the effect of the financial sector

development in stimulating economic growth.

Barajas et al., (2013) found that in oil rich countries the banking sector has a minor effect

on the economic growth as the country increases its dependence on oil. On other hand, the

effect of stock market development may be higher in oil-exporting countries than in oil-

importing countries. Kurronen (2015) used a sample of 128 countries over the span of 1995 to

2009 and found that resource dependent economies have smaller banking sectors compared to

market-based economies that are more dependent on financing. Mohammed Aljebrin (2018)

investigates the relationship between non-oil openness and financial development impact on

economic growth in Saudi Arabia, he found a positive relationship between financial

development and economic growth. Mohammed Rehman (2018) examines the linkage

between financial development - through banking sector and stock market - and economic

growth, the study found that there was no long run co-integration between financial

development and economic growth.

Naceur and Ghazouani (2007) investigated the role of the banking sector and stock market

development on economic growth in the region of Middle East and North Africa (MENA)

over the period 1979–2003. They used oil price and other control variables to examine the

impact of the oil sector on the economic activities, as the region dominated by major oil-

exporting countries that involve some OPEC countries member like Saudi Arabia, Kuwait and

Iran. The results of the study showed that stock market development has a negative impact on





economic growth when liquid liabilities is used to represent banking sector development and

positive when banking sector development is examined using domestic credit to private

sector. Generally, the study results show the insignificant effect of banking sector and stock

market development on economic growth in the MENA region. In contrast the oil price has a

significant positive effect on economic growth in the region, which means that economic

growth is driven by the oil sector in the region.

Controlling for the possible impact of trade openness and oil price on economic growth in

Saudi Arabia, this study aims to empirically investigate the independent effects of the banking

sector and stock market development on economic growth in Saudi Arabia employing the

autoregressive distributed lag (ARDL) approach to cointegration analysis.

To the best of our knowledge, a limited number of studies have attempted to examine the

interaction between financial sector development and economic growth in the case of an

economy dominated by natural resources. Nili and Rastad (2007), Beck (2011) and Eugene

(2016), are among the few authors who deal with how the redundancy of oil can impact the

relationship between financial sector development and economic growth, and whether there is

any sign of a natural resource effect in the relation between financial sector development and

economic growth. In the context of Saudi Arabia as one of the oil-rich economies a few

studies investigated the relationship between financial sector development and economic

growth see among other Ibrahim (2013), Samargandi et al. (2014), Alghfais (2016) and Nasir

et al. (2017). In contrast none has investigated the stock market development indicators in the

relationship between financial sector development and economic growth in Saudi Arabia.

This study is therefore an attempt to fill this gap in the literature. The outcome of the study

reaches important results for policy-makers and researchers in Saudi to consider, as well as

for other oil-rich economies that are trying to figure out the separate impact of the stock

market and banking sectors on economic growth.

The remainder of this paper is organized as follows. Section 2 presents the methodology

and the econometric models used in the study. Section 3 describes the data and the

construction of stock market and banking sector indicators used in empirical analysis. Section

4 reports and discusses the empirical results. Finally, section 5 provides the conclusion and

some final remarks.

2. Methodology

2.1 Empirical Methodology

To investigate the links and effects of the banking sector and stock market development on

economic growth in Saudi controlling for the influence of trade openness and crude oil price,

the study employs the long-linear empirical model specified below:

lnRgdp = β0 + β1lnBSD + β2lnSMD + β3lntrdeopen + β4lnOilp + εt





where RGDP is the real GDP, SMD is the stock market indicators that includes (STMindex,

MCgdp,VTgdp and Turn), BSD is the banking sector development indicators (Bnkindex,

CPgdp, M2gdp, and DBAgdp), Oilp is the international crude oil price, trdopen is the trade

openness and εt is the error term.

Following Nwani and Bassey Orie (2016) this study uses the Autoregressive Distributed

Lag (ARDL-Bounds) testing approach to co-integration introduced by Pesaran et al. (2001).

This approach provides some attractive and practical statistical advantages compared to other

co-integration methods. As other co-integration methods need all the variables to be

integrated of the same order, the ARDL test procedure produces valid results even if the order

of the variables is different. In addition the ARDL procedure provides consistent and efficient

results in the cases of small and large sample sizes, for more details see, Pesaran et al.,

(2001). The ARDL model can be specified as:

∆𝑙𝑛𝑅𝑔𝑑𝑝𝑡 = 𝛽0 +∑𝛽1𝑖

𝑛

𝑖=1

∆𝑙𝑛𝑅𝑔𝑑𝑝𝑡−𝑖 +∑𝛽2𝑖

𝑛

𝑖=0

∆𝑙𝑛𝑆𝑀𝐷2𝑡−𝑖 +∑𝛽3𝑖

𝑛

𝑖=0

∆𝑙𝑛𝐵𝑆𝐷2𝑡−𝑖

+∑𝛽4𝑖

𝑛

𝑖=0

∆𝑙𝑛𝑂𝑖𝑙𝑝3𝑡−𝑖 +∑𝛽5𝑖

𝑛

𝑖=0

∆𝑙𝑛𝑡𝑟𝑑𝑜𝑝𝑒𝑛3𝑡−𝑖 + 𝛽6𝑙𝑛𝑅𝑔𝑑𝑝𝑡−1

+ 𝛽7𝑙𝑛𝑆𝑀𝐷1𝑡−1 + 𝛽8𝑙𝑛𝐵𝑆𝐷2𝑡−1 + 𝛽9𝑙𝑛𝑡𝑟𝑑𝑜𝑝𝑒𝑛𝑡−1 + 𝛽10𝑙𝑛𝑂𝑖𝑙𝑝𝑡−1 + 𝜀𝑡

where Δ is the difference operator and εt the white noise error term. The test includes

conducting F-test for joint significance of the coefficients of lagged variables for the purpose

of investigating the presence of a long-run relationship among the variables. The null

hypotheses of no long-run relationship existence among the variables (H0: β7=β8=

β9=β10=0) is investigated. The rejection and acceptance of H0 is based on following

criterions; if the F- value is greater than the upper bound then reject H0 so the variables are

co-integrated, if the F-value is less than the lower bound then accept H0 so the variables are

not co-integrated, the decision is inconclusive if the F-value is between upper and lower

bounds. The estimation of the error correction model of the short-run relationships is as

follows;

∆𝑙𝑛𝑅𝑔𝑑𝑝𝑡 = 𝛽0 +∑𝛽1𝑖

𝑛

𝑖=1

∆𝑙𝑛𝑅𝑔𝑑𝑝𝑡−𝑖 +∑𝛽2𝑖

𝑛

𝑖=0

∆𝑙𝑛𝑆𝑀𝐷2𝑡−𝑖 +∑𝛽3𝑖

𝑛

𝑖=0

∆𝑙𝑛𝐵𝑆𝐷2𝑡−𝑖

+∑𝛽4𝑖

𝑛

𝑖=0

∆𝑙𝑛𝑂𝑖𝑙𝑝3𝑡−𝑖 +∑𝛽5𝑖

𝑛

𝑖=0

∆𝑙𝑛𝑡𝑟𝑑𝑜𝑝𝑒𝑛3𝑡−𝑖 + 𝜆1𝐸𝐶𝑀𝑡−1 + 𝑢1𝑡

A negative and significant ECMt−1 coefficient (λ1) indicates that any short term

disequilibrium between the dependent and independent variables will converge back to the





long-run equilibrium

relationship. This study implements the ARDL bounds cointegration analysis using Eviews 9.

3. Data Description

This study uses quarterly data covering the span of 1997Q1 to 2017Q2 which provides the

longest reliable and available data-set for financial and stock market development in Saudi

Arabia. Economic growth is defined as the real GDP (RGDP). Two control variables are

included to capture other components of the Saudi macroeconomic environment that could

influence the growth of the Saudi economy. The variables include: Global price of Brent

Crude (in US dollars per barrel) and the ratio of total trade (exports plus imports) to GDP

which explains the degree of the Saudi economy openness to trade. Three widely used

measures of financial sector development; Domestic credit to private sector over GDP

(CPGDP), Liquid Liabilities over GDP (M2GDP) and Deposit money bank assets to GDP

(DBAGPD) are employed to capture different characteristics of Saudi’s financial sector

activities. In addition, three stock market development indicators are employed to capture

different components of the development of Saudi’s stock market. The ratio of stock market

capitalisation to GDP (MCgdp) measures the magnitude of the Saudi stock market; value of

trades of domestic stocks over GDP (VTgdp) captures the liquidity of the stock market while

turnover ratio (Turn) captures the effectiveness of the stock market in resource allocation.

Note that none of these indicators could be viewed as the best or overall indicator of

development of the stock market. Due to the high association between the development

indicators (see Table 1), a composite index is constructed from these indicators using

principal component analysis (PCA). Principal component analysis (PCA) has commonly

been employed to address the problem of multicollinearity by reducing a large set of

correlated variables into a smaller set of uncorrelated variables see, Stock and Watson,

(2002), and has been extensively employed in the construction of financial development

indices in recent studies, see for instance, Samargandi et al. (2014). Table 2 shows that the

first principal component accounts for about 88% of the total variation in the three stock

market indicators. STMindex is constructed as a linear combination of the three stock market

indicators with weights given as the first eigenvector.

Three indicators are employed to measure the development of the Saudi banking sector.

The ratio of private credit to GDP (CPgdp) which measures the role of the banking sector in

private sector activities; broad money (M2) over GDP (M2gdp) which explains the capability

of the banking sector to endure sudden demand for withdrawal of deposits by clients, Naceur

et al. (2014), while the ratio of bank assets to GDP (DBAgdp) gauges the size of the banking

sector. Table 1 shows the high correlation between the three indicators of banking sector

development. Using principal component analysis (PCA), the banking sector development





index (Bnkindex) is formulated as a linear combination of the three indicators of banking

sector development (CPgdp, M2gdp and DBAgdp) with weights given as the first

eigenvector, which capturing approximately 99% of the total variation in the three indicators

(see Table 2). Time series data on the variables are obtained from Saudi Arabian Monetary

Agency, Saudi General Authority for Statistics and Economic Data of Federal Reserve Bank

of St. Louis.

Table 1: The correlation matrix

Indicators of Banking Sector Development

CPGDP DBAGDP M2GDP

CPGDP 1.00

DBAGDP 0.99 1.00

M2GDP 0.99 0.99 1.00

Indicators of Stock Market Development

VTGDP TURN MCGDP

VTGDP 1.00

TURN 0.89 1.00

MCGDP 0.85 0.70 1.00

Table 2: Eigenvalues, proportion and eigenvectors of each first principal component

STMindex Bankindex

Eigenvalues 2.6270 2.9683

Proportion 0.8757 0.9933

Eigenvectors (Loadings)

VTgdp 0.602468

Turn 0.569281

MCgdp 0.559421

CPgdp

0.577688

DBAgdp

0.577178

M2gdp

0.577184

3.1 Unit Root Test

The ARDL-bounds test is not a condition for the same order of integration. Stationarity

test are therefore performed first in levels and then in first difference to investigate the

presence of unit roots and the order of integration in all the variables. The results of the

Phillips-Perron (PP) and Augmented Dickey-Fuller (ADF) stationarity tests presented in

Table 3 below show that all the variables are nonstationary in level but are stationary of first

difference at 1% significance, thus, all variables are integrated of order one I(1).





Table 3: ADF and PP unit root tests

Level I(0) First difference I(1)

Variable ADF PP ADF PP

lnRGDP -1.5488 -1.6825 -7.0175*** -7.0175***

lnSMindex -2.0422 -2.0981 -10.1769*** -10.0963***

lnBnkindex -0.7350 -0.0365 -3.5960*** -6.1169***

lnMCgdp -1.6415 -2.0057 -6.3594*** -6.6591***

lnVTgdp -2.0422 -2.0981 -10.1769*** -10.0963***

lnTurn -2.5638 -2.4162 -8.9416*** -10.9394***

lnCPgdp -0.0782 0.0710 -5.9066*** -5.9066***

lnM2gdp 0.2214 0.3419 -6.4214*** -6.4712***

lnDBAgdp -0.3117 -0.5386 -6.8599*** -6.9342***

lnOilp -1.5782 -1.6370 -7.0661*** -6.5304***

lntrdopen -1.5741 -1.4434 -7.5521*** -7.6128***

Notes: All the variables are in the natural log form. ***Level of significance at 1%.

4. Empirical results

4.1 Co-Integration Analysis

Using financial and stock market development indicators and two indices which one

constructed from bank sector development indicators and the other from the stock market

development indicators using PCA, this study tested for co-integration on eight alternative

specifications employing different indicators for stock market and banking sector

development. The results of the co-integration test based on the ARDL-bounds testing

method, applied using Eviews 9 software are showed in Table 4. The results indicate that in

all the specifications, the F-statistic is greater than the upper critical bound at 1% significance

level for all specification except 2 and 4 which are greater than the upper critical bound at 5%

significance level. Thus, this study rejects the null hypothesis of no co-integration. This

indicates that there is a long-run causal relationship among the variables between banking

sector development, stock market development, crude oil price, trade openness and economic

growth in Saudi.

Table 4: ARDL-bounds cointegration test

Functions F-statistic Result 1. F RGDP(RGDP| SMindex, Bnkindex, Oilp, trdopen) ARDL(1, 1,1, 0, 0) 4.9661*** Cointegration 2. FRGDP(RGDP| SMindex, M2gdp, Oilp, trdopen) ARDL(1,0, 1, 0, 1) 3.4963** Cointegration 3. F RGDP(RGDP| MCgdp, Bnkindex, Oilp, trdopen) ARDL(1, 1, 0, 1, 1) 6.3702*** Cointegration 4. F RGDP(RGDP| MCgdp, CPgdp, Oilp, trdopen) ARDL(1, 0, 1, 1, 1) 4.1451** Cointegration 5. F RGDP(RGDP| MCgdp, M2gdp, Oilp, trdopen) ARDL(1, 0, 1, 0, 1) 4.8950*** Cointegration 6. F RGDP(RGDP| VTgdp, DBAgdp, Oilp, trdopen) ARDL(1, 0, 1, 0, 1) 6.6957*** Cointegration





7. F RGDP(RGDP| Turn, M2gdp, Oilp, trdopen) ARDL(1, 1, 1, 0, 1) 4.7220*** Cointegration 8. F RGDP(RGDP| MCgdp, DBAgdp, Oilp, trdopen) ARDL(1, 0, 1, 1, 1) 6.2228*** Cointegration

Critical Value Bounds I0 Bound I1 Bound

1% 3.06 4.15

5% 2.39 3.38

Notes: Restricted intercept and no trend (k = 4). **Level of significance at 5%. ***Level of

significance at 1%. Source of Asymptotic critical value bounds: Pesaran et al. (2001)

4.2 Estimates of Long-Run

The long-run estimated coefficients of the eight ARDL specifications are provided in

Table 5. The results show that in all the eight specifications, the coefficients of long-run

models of the banking sector development are negative and highly significant. This shows

that the banking sector development has a significantly negative long-run effect on economic

growth, indicating that the banking sector development does not stimulate economic growth

in Saudi. Regarding the stock market development, the long-run coefficient is positive but

only specification 4, 6 and 8 are significant, while specifications 1, 2, 3, 5 and 7 are

insignificant. Among the two control variables the empirical results show that in all the

specifications, the oil price coefficient is positive and highly significant at (1% and 5%), this

is evidence that the oil price is the long-run main factor of economic growth in Saudi. On the

other hand, for trade openness all specifications were found to be insignificant except

specification 4 which is significant at 5% level. The long-run coefficients in Table 5 provide a

clear understanding of an economy significantly dominated by activities in the oil sector. A

1% rise in oil price increases the growth of the economy on average by 0.27% in the long-run.

Given that the oil price is significantly determined in the international oil market and not by

internal performance and activities, see among other, Quixina and Almeida (2014) and

Samargandi et al., (2014). Thus, a drop in crude oil price would oppositely affect the

economy performance. Specifically, a 1% decrease in crude oil price would produce a

reduction in the level of economic growth by around 0.27%.

Table 5: Estimates of long-run coefficients

1 2 3 4 5 6 7 8

C 7.3055*** 7.3759*** 7.4423*** 7.4319*** 7.3901*** 7.6013*** 7.3274*** 7.4899***

[23.2439] [62.0377] [30.8845] [41.4521] [62.8892] [62.5559] [62.9200] [53.3894]

lnSMindex

0.0094 0.00683

[0.5471] [0.8378]

lnMCgdp

0.0429 0.1812*** 0.0229

0.0496**

[1.0784] [3.1014] [1.1888]

[2.1234]

lnVTgdp

0.0239***

[2.9722]

lnTurn

0.0230

[1.7289]

lnBnkindex -0.473***

-0.410***





[-3.3492]

[-3.6855]

lnCPgdp

-0.893***

[-4.1182]

lnM2gdp

-0.709***

-0.733***

-0.743***

[-6.5730]

[-6.7974]

[-6.9342]

lnDBAgdp

-0.659***

-0.685***

[-6.7617]

[-5.7896]

lnOilp

0.4756** 0.1403** 0.3389** 0.5504** 0.1324** 0.1802*** 0.1334** 0.2087***

[2.3934] [2.4172] [2.3478] [2.4689] [2.2846] [3.2971] [2.4074] [3.1599]

lntrdopen

0.4177 0.1571 0.2311 0.8107** 0.1608 0.0092 0.1449 -0.0445

[1.1934] [0.8818] [0.7563] [2.1998] [0.9050] [0.0549] [0.8475] [-0.2223]

Note: t-statistics in [ ]. **Level of significance at 5%. ***Level of significance at 1%.

4.3 Estimates of Short-Run

Table 6 presents the estimated coefficients of the short-run error correction. The ECM (−1)

coefficients are negative and significant at a high significance level. These coefficients

indicate that the short-run equilibrium is adjusted in long-run equilibrium. The short-run

coefficient of banking sector development for all the eight specifications is negative and

significant at a high level of 1%. The effect of the negative significance short-run estimates of

banking sector development on economic growth in Saudi, indicates the high degree of

inefficiency in resource allocation and mobilization of the Saudi banking sector. On the other

hand, the short-run stock market effect on the economic growth is positive and significant at

the 5% level for 63% of specifications, namely, (1, 4, 6, 7 and 8), while specifications (2, 3

and 5) realise highly insignificant positive short-run coefficients for half the specification of

the ratio of stock market capitalisation to GDP (MCgdp) as an indicator of stock market

development in Saudi. The results reveal that the oil price short-run coefficient is positive and

significant at the 1% level in all the eight specifications. These results confirm the role of the

oil sector that drives economic activities in Saudi. Unpredictably, the trade openness

coefficient is negative and insignificant for 6 out of 8 specifications.

Table 6: Estimates of short-run error correction

1 2 3 4 5 6 7 8

ECM(-1) -

0.0892**

-

0.294***

-

0.0982** -0.167***

-

0.293**

*

-

0.295***

-

0.297***

-

0.257***

[-2.2352] [-4.4996] [-2.5845] [-3.3811]

[-

4.5304] [-4.1804] [-4.6379] [-3.7452]

ΔlnSMindex 0.0071** 0.00203

[2.2329] [0.4091]

ΔlnMCgdp

0.0042 0.0303**

* 0.0067

0.0127**

[1.1966] [4.7369] [1.2068]

[2.0402]





ΔlnVTgdp

0.0070**

*

[2.6982]

ΔlnTurn

0.01550*

*

[2.5699]

ΔlnBnkindex -

0.317***

-

0.317***

[-17.579]

[-17.897]

ΔlnCPgdp

-0.525***

[-8.3878]

ΔlnM2gdp

-

0.576***

-

0.585**

*

-

0.629***

[-8.6382]

[-

8.7256] [-9.0363]

lnDBAgdp

-

0.604***

-

0.605***

[-9.0429]

[-8.8273]

ΔlnOilp 0.0544** 0.0413**

* 0.1038** 0.4435**

0.0388*

*

0.0532**

*

0.3697**

*

0.0534**

*

[2.4306] [2.6918] [2.3513] [2.7616] [2.5101] [3.6095] [2.6709] [3.5329]

Δlntrdopen 0.0372 -

0.357*** 0.0226 -0.1604 -0.0505 -0.0279 -0.0454 -0.037**

[1.1749] [-4.4261] [0.7294] [-0.2430] [-

0.8832] [-0.5127] [-0.8195] [-2.0335]

Diagnostic tests

Adj R2 0.9941 0.9852 0.9947 0.9857 0.9853 0.9859 0.9866 0.9854

BG 0.1191

(0.9422)

1.0606

(0.3031)

0.5639

(0.7543)

1.5840

(0.4529)

1.8155

(0.1707)

0.9171

(0.4047)

1.4549

(0.2409)

0.9344

(0.3979)

Hetero 0.1691

(0.6809)

0.0239

(0.8771)

0.5448

(0.4604)

0.3157

(0.854)

0.01533

(0.9015)

0.1506

(0.6945)

0.0017

(0.9671)

0.0593

(0.8083)

RESET 0.0468

(0.8295)

0.7885

(0.3777)

0.5650

(0.4549)

1.4400

(0.1580)

1.6917

(0.0893)

1.5301

(0.1307)

1.3819

(0.1621)

0.8882

(0.3776)

Theil Co. 0.0054 0.0088 0.0055 0.0091 0.0089 0.0085 0.0083 0.0086

Notes: Adj. R2 means Adjusted R-squared. BG is the Breusch–Godfrey serial correlation LM test.

Hetero is the ARCH test for heteroscedasticity, RESET is the Ramsey RESET test; Theil Co is Theil

inequality coefficient. t-statistics in [ ], p-values in ( ).

*** Level of significance at 1%, ** Level of significance at 5% and * Level of significance at 10%.

4.4 Diagnostic tests

The diagnostic test results at the bottom of Table 6 for the underlying ARDL models

suggest that all models’ specifications pass the diagnostic test against serial correlation,

functional form misspecification and heteroscedasticity in each of the ARDL models

specified. Table 7 provides the cumulative sum of recursive residuals (CUSUM) for each

specification which is within the critical boundaries for the 5% significance level. These

results confirm that the long-run coefficients and all short-run coefficients in the error

correction model are stable. The overall goodness of fit of the estimated models is very high





ranging from 98% to 99% for all models that are the expected results for ARDL as the models

involve the dependent variable lags. Finally, the table includes the Thiel inequality coefficient

that indicates the goodness of the models for forecasting abilities.

Table 7: cumulative sum of recursive residuals CUSUM

Figure 1: CUSUM for coefficient stability of ECM

specification 1

-30

-20

-10

0

10

20

30

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17

CUSUM 5% Significance


specification 2

-30

-20

-10

0

10

20

30

2000 2002 2004 2006 2008 2010 2012 2014 2016



specification 3

-30

-20

-10

0

10

20

30

2000 2002 2004 2006 2008 2010 2012 2014 2016



specification 4

-30

-20

-10

0

10

20

30

2000 2002 2004 2006 2008 2010 2012 2014 2016



specification 5

-30

-20

-10

0

10

20

30

2000 2002 2004 2006 2008 2010 2012 2014 2016



specification 6

-30

-20

-10

0

10

20

30

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17



specification 7 Figure 8: CUSUM for coefficient stability of ECM

specification 8





-30

-20

-10

0

10

20

30

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17


-30

-20

-10

0

10

20

30

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17


5. Conclusions and Recommendations

This paper contributes to the literature on financial development and growth by

investigating the independent effects of the stock market and banking sector of an oil-rich

economy, Saudi Arabia, which has not been studied in such a context. The empirical study

results, based on the ARDL approach to cointegration analysis over the period 1997Q1 to

2017Q2 indicated that both the banking sector and stock market are not significant drivers of

the Saudi economy. The results reveal that the banking sector development has a negative

significant impact on economic growth, while the stock market has 5 out of 8 specifications

(about 63%) indicating a positive significant effect on the Saudi economic growth and the

remaining 3 specifications have an insignificant impact on growth. On the other hand, the oil

price has a highly significant positive effect on Saudi economic growth.

These results also show the specific nature of resource-rich and oil economies such as

Saudi Arabia. Thus the economies that depend on resources do not need to follow the same

manner of development of manufacturing economies. The economy critically depends on

foreign markets and price, as shown in our analysis of the strong role played by the oil price.

In general, the role of the financial sector development is notable in industrialized and

developed economies. The dominant role of oil in the Saudi economy may be the reason

behind the underdevelopment of banking sector. In fact, the banking sector plays a notable

and important role in agricultural and industrialized economies, where banking sector

provides a good channel for allocation of resources amongst firms, so this role is less

effective when the economy depends on a commodity that has a features that are easy to

market and highly liquid. Thus, the findings of this study shed light on the need to improve

efficiency of resource allocation and mobilisation in the financial sector in Saudi Arabia. Our

results suggest that the growth of the Saudi economy is negatively affected by banking sector

development. Thus, from a policy view, a more develop of Saudi banking system with aiming

to encourage economy growth is needed. If the current running strategy for economic

diversification continues, we can expect that banking sector will contribute and stimulate





economic growth by encouraging economic activities of other sector and reducing economic

dependence on oil in future. In contract, Saudi’s great dependency on the oil indicates that the

economy will be highly affected by international oil prices shocks.

References

Adeniyi, O., Oyinlola, A., Omisakin, O., and Egwaikhide, F. O. 2015, Financial development and

economic growth in Nigeria: Evidence from threshold modelling. Economic Analysis and Policy, 47,

11–21. doi:10.1016/j.eap.2015.06.003.

Barajas, A., Chami, R., and Yousefi S. R. 2016, The finance and growth nexus re-examined: do all

countries benefit equally? Journal of Banking and Financial Economics 1(5), 5–38.

DOI: 10.7172/2353-6845.jbfe.2016.1.1.

Beck, T. 2011, Finance and oil: Is there a resource curse in financial development. Center Discussion

Paper No. 2011- 017. Netherlands: Tilburg University. Retrieved from

https://pure.uvt.nl/portal/fies/1316213/2011-017.pdf.

Ben Naceur, S. B., Cherif, M., and Kandil, M. 2014, What drives the development of the MENA

financial sector? Borsa Istanbul Review, 14, 212–223. doi:10.1016/j. bir.2014.09.002.

Cevik, S., and Rahmati, M. 2013, Searching for the finance growth Nexus in Libya. IMF Working

Paper No. 13/92. Washington, DC: International Monetary Fund. Retrieved from

https://www.imf.org/external/pubs/ft/wp/2013/wp1392.pdf.

Chinazaekpere Nwani and Jacob Bassey Orie. 2016, Economic growth in oil-exporting countries: Do

stock market and banking sector development matter? Evidence from Nigeria, Cogent Economics &

Finance, 4: 1153872. https://doi.org/10.1080/23322039.2016.1153872.

Eugene Iheanacho, 2016, The impact of financial development on economic growth in Nigeria: An

ARDL analysis. Economies, 4, 26; doi:10.3390/economies 4040026.

Kurronen, S. 2015, Financial sector in resource-dependent economies. Emerging Markets Review, 23,

208–229. doi:10.1016/j.ememar.2015.04.010.

Levine, R. 1996, Stock markets: A spur to economic growth. Finance and Development, 33, 7–10.

Levine, R. 2004, Finance and growth: Theory and evidence. NBER Working Paper 10766. Cambridge:

National Bureau for Economic Research. doi:10.3386/w10766.

Lin, J. Y., Sun, X., and Jiang, Y. 2009, Toward a theory of optimal financial structure. World Bank

Policy Research Working Paper 5038. Washington, DC: World Bank. http://dx.doi.org/10.1596/prwp.

Lotfali Agheli and Golanz Hadian. 2017, Financial development, commercial development and

economic growth in selected emerging and the Middle Eastern countries. International journal of

Economics and Financial issues. 7(3). 362-370.

Mohamed Abbas IBRAHIM. 2013, Financial development and economic growth in Saudi

Arabian economy. Applied Econometrics and International Development. 13 (1). 133- 144.

Mohammed Aljebrin. 2018, Non-oil trade openness and financial development impacts on economic

growth in Saudi Arabia. International journal of Economics and Financial issues. 8(5). 251-260.

Mohammed Alghfais 2016, Comparative analysis: the impact of financial sector development on

economic growth in the non-oil sector in Saudi Arabia. SAMA Working Paper, WP/16/5.

Mohammed Ziaur Rehman. 2018, Banking sector development, stock market development and

economic growth evidence from Saudi Arabia. Academy of Accounting and Financial Studies Journal.

22(4).

https://pure.uvt.nl/portal/fies/1316213/2011-017.pdf

https://www.imf.org/external/pubs/ft/wp/2013/wp1392.pdf

https://doi.org/10.1080/23322039.2016.1153872

http://dx.doi.org/10.1596/prwp





Naceur, S. B., and Ghazouani, S. 2007, Stock markets, banks, and economic growth: Empirical

evidence from the MENA region. Research in International Business and Finance, 21, 297–315.

doi:10.1016/j.ribaf.2006.05.002.

Najeeb Muhammad Nasir, Mohammed Ziaur Rehman and Nasir Ali. 2017, Foreign direct investment,

financial development and economic growth evidence from Saudi Arabia. International Journal of

Financial Research, 8 (4), 228 – 239.

Narayan, P. K. 2005, The saving and investment nexus for China: Evidence from cointegration tests.

Applied Economics, 37, 1979–1990. doi:10.1080/00036840500278103.

Nili, M., and Rastad, M. 2007, Addressing the growth failure of the oil economies: The role of

financial development. The Quarterly Review of Economics and Finance, 46, 726–740.

doi:10.1016/j.qref.2006.08.007.

Pesaran, M., Shin, Y., and Smith, R. 2001, Bounds testing approaches to the analysis of level

relationships. Journal of Applied Econometrics, 16, 289–326. doi:10.1002/jae.616

Quixina, Y. and Almeida, A. 2014, Financial development and economic growth in a natural resource

based economy: evidence from Angola. FEP Working Papers No: 542. Available online:

http://www.fep.up. pt/investigacao/workingpapers/wp542.pdf.

Samargandi, N., Fidrmuc, J., and Ghosh, S. 2014, Financial development and economic growth in an

oil-rich economy: The case of Saudi Arabia. Economic Modelling, 43, 267–278.

doi:10.1016/j.econmod.2014.07.042.

Stock, J. H., and Watson, M. W. 2002, Forecasting using principal components from a large number of

predictors. Journal of the American Statistical Association, 97, 1167–1179.

doi:10.1198/016214502388618960.

http://www.fep.up/

Documents

The Impact of Stock Market and Banking Sector ...globalbizresearch.org/economics/images/files/77617_15...Saudi Arabia, this study aims to empirically investigate the independent effects