An Analytical Study of the Impact of Balance of Payment on
Indian Stock
Market
Prof. Narender Kumar
Head
& Dean, Department of Commerce,
M.D.
University, Rohtak, Haryana
Dr.
Seema Rathee
Assistant Professor
Department
of Commerce,
M.D.
University, Rohtak, Haryana
Abstract
The present paper is an endeavor to
highlight the impacts of current account and capital account on CNX Nifty and BSE
100. The study is purely based on secondary data. The analysis of which was
made through the application of Karl Pearson's coefficient of Correlation and Multiple
Regression. The study found that the current account (CAD) is the most
important predictor of CNX Nifty with R square values of .596 and the impact of
capital account is more important in case of BSE 100 with R square values of
.699. Though, the current account is a significant factor for all outcome
variables yet its impact on CNX Nifty has been greater than other outcomes; and the aggregate impact of all the
predictors jointly showed more impact on CNX Nifty (R2change= 69.5 per
cent)than other outcome variables. It was further indicated through the results
that if all the two selected independent factors remain constant, then also
there are other factors as well which explain CNX Nifty and BSE 100 up to 1649.480 and 2336.893 units.
Keywords: Current Account Deficit, Capital
Account, BSE Sensex, and NSE CNX Nifty.
Introduction:
Many developing countries, including India,
restricted the flow of foreign capital till the early 1990s and depended on
external aid and official development assistance. The financial sector reforms
commenced in the beginning of 1990's, and the implementation of various
measures including a number of structural and institutional changes
in the different segments of the financial markets, particularly
since 1997, brought dramatic changes in the functioning of the financial
sector of the economy (Agrawalla, 2006).
Later, most
of the developing countries opened up their economies by deregulating
capital controls with a view to attracting foreign capital, supplementing
it with domestic capital to stimulate domestic growth and output. Since
then, portfolio flows from foreign institutional investors (FII) have emerged
as a major source of capital for developing market economies (EMEs) such
as Brazil, Russia, India, China and South Africa. Besides, the surge in foreign
portfolio flows since 1990s can be attributed to greater integration among international
financial markets, advancement in information technology and growing
interest in EMEs among FIIs such as private equity funds and hedge funds
so as to achieve international diversification and reduce the risk in their portfolio. Stock exchange serves a vital
function for businesses considering going public. An economy that experiences
sustainable growth is likely to have a very effective stock exchange. While developed
countries fully usurp the benefits of the raising capital through the stock
exchange, developing countries do not have effective stock exchange at the
desired level. Being one of the most important pillars of the country economy,
stock market is carefully observed by governmental bodies, companies and
investors (Nazir et al., 2010). The foreign capital markets integrated rapidly
during post globalisation period but contraction in demand for exports (both
merchandise and services) and the increase in fuel and gold imports resulted
into a recordhigh current account deficit during 2012 in India. The exports
registered a growth from $18.5 billion to $309.7 billion between 199091 and
201112; the average annual growth rate of merchandise exports doubled during
the last two decades, from 9 per cent in 199192 to 19992000 to 20 per cent
during 200001 to 201112; though, exports grew during the last two decades,
they were not in line with the growth in imports (export/GDP increased 11
percentage points between 1990191 and 201112 whereas imports/GDP increased by
18 percentage points over the same period); the increase in imports of oil as a
proportion of GDP doubled during 200405 and 201112; nonoil imports increased
from 14.4 per cent to 18.5 per cent of GDP, specifically the gold has been an
important contributor (increasing from 1.5 per cent to 2.5 per cent of GDP
between 200405 and2011 12).; the import of oil and gold registered a
sharp increment during 201112 with growth rates of 45per cent and 40percent
respectively (relative to 22 per cent and 18per cent in the previous year);
consequently, the merchandise trade balance aggravated significantly over the
last two decades (from 2.9 per cent (ve) of GDP in 199091 to an estimated 10.2
per cent (ve) of GDP in 201112) and the CAD went up to an all time high of
4.8 per cent last year on account of a heavy trade deficit and higher gold
imports. The Government of India acted
on multiple fronts, curbing gold imports, opening currency swap windows to get
fresh dollar flows, and increasing money market rates to reduce speculation,
resulting into CAD comes down to 1.2 per cent of GDP in Q2 and the foreign
exchange reserves were at over US $295 billion as of December, 2013.
Review
of Literature:
Suraksha
and Chikara, Kuldip (2014)
analyzed the impacts of foreign exchange reserves, current account, and capital
account on GDP, Sensex, Nifty and fiscal deficit. The study is purely based on
secondary data.The analysis of the study was made through the application of
Karl Pearson's coefficient of Correlation and Multi Regression OLS model
(Ordinary Least Square). The study found
that the current account (CAD)is the most important predictor of GDP, BSE, NSE
and fiscal defici. Though, the current account is a significant factor for all
outcome variables yet its impact on GDP and fiscal deficit has been greater
than other two outcomes; and the aggregate impact of all the predictors jointly
showed more impact on BSE than other outcome variables.
Karam
pal and Mittal, Ruhee (2008)
Secrutinized
the long‐run relationship between
the Indian capital markets and key macroeconomic variables such as interest
rates, inflation rate, exchange rates and gross domestic savings (GDS) of
Indian economy. – Quarterly time series data spanning
the period from January 1995 to December 2008 has been used. The unit root
test, the co‐integration
test and error correction mechanism (ECM) have been applied to derive the long
run and short‐term
statistical dynamics. The study found that there is co‐integration between
macroeconomic variables and Indian stock indices which is indicative of a long‐run relationship. The ECM
shows that the rate of inflation has a significant impact on both the BSE
Sensex and the S&P CNX Nifty. Interest rates on the other hand, have a
significant impact on S&P CNX Nifty only. However, in case of foreign
exchange rate, significant impact is seen only on BSE Sensex. The changing GDS
is observed as insignificantly associated with both the BSE Sensex and the
S&P CNX Nifty. Study, on the whole, conclusively establishes that the
capital markets indices are dependent on macroeconomic variables even though
the same may not be statistically significant in all the cases.
.
Ozcan,Ahmet
(2012) In his
study, the relationship between macroeconomic variables and Istanbul Stock
Exchange (ISE) industry index is
examined. The selected macroeconomic variables for the
study include interest rates, consumer price index, money supply, exchange
rate, gold prices, oil prices, current account deficit and export volume. The Johansen’s
cointegration test is utilized to determine the impact of selected
macroeconomic variables on ISE industry index. The result of the Johansen’s
cointegration shows that macroeconomic variables exhibit a long run equilibrium
relationship with the ISE industry index.
Apergis
and Eleftherio (2002)
investigated that the relationship among the index of Athens
stock exchange, interest rate and
inflation and concluded that inflation has greater impact on the
performance of the index of Athens
stock exchange than interest rate.
Rapach
(2001) analyzed
the long run relationship between inflation and the stock prices. Using
macroeconomic data from sixteen developed countries, it is concluded that there
is a weak relationship between inflation and stock prices.
Liu ve Shrestha (2008) examined the relationship between a
set of macroeconomic variables and the index of Chinese stock market. By
employing heteroscedastic cointegration, they found that a significant
relationship exists between the index of the Chinese stock market and
macroeconomic variables. They concluded that inflation, exchange rate and
interest rate have a negative relationship with the index of Chinese stock
market.
Olayinka Olufisayo Akinlo, Obafemi Awolowo
University, IleIfe, Nigeria (2011) They have studied
the relationship between foreign
exchange reserves and stock market development
in
Nigeria over the period 19812011. They have used multivariate framework
incorporating an interest rate variable. The study found that a long run relationship
exists among exchange rate reserves, interest rates and stock market
development. Foreign reserves have a positive effect on stock market growth. Bidirectional
causality exists between interest rates and stock market growth. Finally, a
bidirectional relationship exists between interest rates and foreign reserves.
Akmal,
Muhammad Shahbaz (2007) scrutinized
the relationship between stock prices and rate of inflation using ARDL approach
for the period 19712006.The result of the study depicted that stock hedges are
not in favour of inflation in long run as well as in short run and found that
black economy effects long run and short run prices of the stock.
Objective
of the Study: The
main objective of the study is to analyse the impact of current account and
capital account on BSE 100 and CNX Nifty.
Hypotheses
of the Study
The hypotheses are developed on the
basis of literature review and objective of the study. The null hypotheses
framed under the study are stated below:
1. H01 : There is no significant
impact of current account on CNX Nifty
and BSE 100.
2. H02 : There is no significant
impact of capital account on CNX Nifty
and BSE 100.
Research
Methodology
Data
Collection
The present study is purely based on
secondary data covering 14 financial years from 200001 to 201314.The
requisite data related to current account and capital account have been collected from various sources i.e.
Hand Book of Statistics and Bulletin of Reserve Bank of India and
the data of BSE Sensex and CNX Nifty
have been taken from the websites of BSE (www.bseindia) and NSE (www.nseindia)
respectively.
Statistical
Tools & Techniques
In order to analyze the collected
data, the statistical tools such as Karl Pearson's coefficient of Correlation
and Multiple Regression is used. Correlation coefficient is a statistical
measure that
determines the degree to which the
movements of variables are associated. In the present study, the linear
relationship between Independent Variables
current account, and capital account, and dependent variables CNX Nifty, and BSE 100 is established. The multiple regression
analysis
is a technique used to evaluate the
effects of two or more independent variables on a single dependent variable.
Here, an attempt is made to study the impact of Independent
Variables current account, capital
account on dependent variables CNX Nifty and BSE 100.
Analysis
and Interpretation:
A. Regression analysis of Current Account,
Capital Account and CNX Nifty
B.
Impact of flow of Current Account and Capital Account on BSE 100 and CNX Nifty.
Independent
Variables: Current Account, and
Capital Account.
Dependent
Variables: CNX Nifty and BSE 100.
Table:1
Descriptive Statistics


Mean

Std.
Deviation

N

BSE100

4648.7857

2057.17309

14

CAD

1167.5829

1583.82193

14

KA

1964.1386

1515.01335

14

Table: 2a Pearson Correlation Coefficients

CAD

CAPITAL
ACCOUNT

BSE
100

NSE

CAD

1

0.751

0.446

0.772

CAPITAL
ACCOUNT

0.751

1

0.802

0.832

BSE
100

0.446

0.751

1

0.823

NSE

0.772

0.832

0.823

1


Table:
3a
Model Summary^{c}

Model

R

R
Square

Adjusted
R Square

Std.
Error of the Estimate

Change
Statistics

DurbinWatson

R
Square Change

F
Change

df1

df2

Sig.
F Change

1

.446^{a}

.199

.132

1916.33083

.199

2.981

1

12

.110


2

.836^{b}

.699

.644

1227.70025

.500

18.237

1

11

.001

1.413

a.
Predictors: (Constant), CAD

b.
Predictors: (Constant), CAD, KA

c.
Dependent Variable: BSE100


Table:3b
Model Summary^{c}



Model

R

R
Square

Adjusted
R Square

Std.
Error of the Estimate

Change
Statistics

DurbinWatson



R
Square Change

F
Change

df1

df2

Sig.
F Change



1

.772^{a}

.596

.562

1311.57096

.596

17.671

1

12

.001




2

.861^{b}

.742

.695

1094.30722

.146

6.238

1

11

.030

1.703



a.
Predictors: (Constant), CAD



b.
Predictors: (Constant), CAD, KA
C
.Dependent Variable:NSE



Table 3(a) & 3(b) exposed the strength of relationship
between the model and the dependent variables.
The values of R depict the
multiple correlation coefficients between the predictors (independent variables)
and the outcome (dependent variable). When only current account was used as
predictor, a moderate correlation (r=.446) between current account and BSE
100 was observed. The next column gives the value of R2, which tells us a
measure of how much of the variability in the outcome (BSE 100) is accounted
for the predictors (Current Account, and Capital Account). For the first model its value is
.199 {Table 3(a)}, which means that current account accounts for 20 per cent
variation in BSE 100. However, when the other
predictor (Capital Account ) is included as well, the value increases
to .699 or 69.9 percent. Therefore, if current account accounts for 20 per cent
variations, we can say that capital account accounts for an additional 50 per
cent variance in the outcome variable . Table 3(b) exposed the value
of R2 for NSE output and for the current account its value is .596 which
shows that current account accounts
for 59.6 per cent of variations in NSE and when the other predictor capital account is included as
well, the value increases to .742 or 74.2 percent which means that inclusion
of capital account accounts for 14.6 percent of variation in NSE.
Table:4a
ANOVA^{a}

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

10947608.042

1

10947608.042

2.981

.110^{b}

Residual

44067886.315




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