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96 CHAPTER IV STOCK MARKET DEVELOPMENT AND ECONOMIC GROWTH IN INDIA 4.1 Introduction The main objective behind promoting the development of stock markets in India was to contribute to raising capital and assisting its allocation process in order to strengthen the Indian economy. Consequently, in order to investigate whether the Indian Stock Markets achieves its objective in enhancing the economic growth of the country, this chapter proposes a simple plausible framework for studying some elements of growth that relate to the main aspects of the functions of financial markets. The chapter has four parts. The first part examines the growth trajectory of India with emphasis during the liberalised era. The second part reviews the theoretical literature with regard to stock market and economic growth. The thread of the theoretical argument is that the degree to which financial markets, particularly stock markets, influence real economic growth depends on how effectively they improve capital accumulation, facilitate capital mobilization and increase the productivity of capital investment. The stock market development indicators are dealt in the third part. In the fourth part, an empirical attempt has been made to examine the link between stock market development and economic growth of India. Thus the particular questions that we are trying to answer in this chapter are the following: does the development of the stock market have any influence on India's real economic growth? If it does, have the level of stock market development influenced India's economic growth? 4.2 Growth experience of Indian Economy- A Brief Review Economic Growth in India is often biased by the beliefs in fashion or distortions of perceptions which shape conventional wisdom. The second half of the 20 th century witnessed major swings in perception about economic development in India. The turning

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96

CHAPTER IV

STOCK MARKET DEVELOPMENT AND ECONOMIC

GROWTH IN INDIA

4.1 Introduction

The main objective behind promoting the development of stock markets in India

was to contribute to raising capital and assisting its allocation process in order to

strengthen the Indian economy. Consequently, in order to investigate whether the Indian

Stock Markets achieves its objective in enhancing the economic growth of the country,

this chapter proposes a simple plausible framework for studying some elements of

growth that relate to the main aspects of the functions of financial markets. The chapter

has four parts. The first part examines the growth trajectory of India with emphasis

during the liberalised era. The second part reviews the theoretical literature with regard

to stock market and economic growth. The thread of the theoretical argument is that the

degree to which financial markets, particularly stock markets, influence real economic

growth depends on how effectively they improve capital accumulation, facilitate capital

mobilization and increase the productivity of capital investment. The stock market

development indicators are dealt in the third part. In the fourth part, an empirical attempt

has been made to examine the link between stock market development and economic

growth of India. Thus the particular questions that we are trying to answer in this chapter

are the following: does the development of the stock market have any influence on

India's real economic growth? If it does, have the level of stock market development

influenced India's economic growth?

4.2 Growth experience of Indian Economy- A Brief Review

Economic Growth in India is often biased by the beliefs in fashion or distortions

of perceptions which shape conventional wisdom. The second half of the 20th century

witnessed major swings in perception about economic development in India. The turning

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point in economic growth was approximately around 1950s. During that period, the

objective of India’s development strategy had been to establish a socialistic pattern of

society through economic growth with self-reliance, social justice and alleviation of

poverty. These objectives were to be achieved within a democratic political framework

using the mechanism of a mixed economy where both public and private sectors co-

exist. India initiated planning for national economic development with the establishment

of the Planning Commission.(Kochhar et al 2006).

While the reasons for adopting a centrally directed strategy of development were

understandable against the background of colonial rule, it, however soon became clear

that the actual results of this strategy were far below expectations. Instead of showing

high growth, high public savings and a high degree of self-reliance, India was actually

showing one of the lowest rates of growth in the developing world with a rising public

deficit and a periodic balance of payment crises. Between 1950 and 1990, India’s growth

rate averaged less than 4 per cent per annum. The strategy of industrialization, which

protected domestic industries from foreign competition, was also responsible for high

cost and low growth in the economy.(Kochhar et al 2006, Virmani 2004).

During the late 80’s and early 90’s it was imperative for the country to correct its

clearly faulty developmental process. There have been several reasons put forward for

the failure of the developmental path which necessitated the reforms in 1991. (Ahluwalia

2002) Considering the growth of the country since independence, the turning point or

structural break in economic growth is during the period from 1980-81. The trends in the

GDP( at constant prices) and percapita GDP during the period from 1950’s till 2011 is

shown in Figure 4.1 and Figure 4.2.

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FIGURE:- 4.1

GDP at Factor Cost (At Current Prices- Rs Crore)

Source: Economic Survey, Various Issues.

FIGURE:- 4.2

GDP Percapita (At Constant Prices- 1999 to 2000) in US Dollars

Source: Economic Survey, Various Issues.

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

1950-51 1960-61 1970-71 1980-81 1990-91 2000-01 2007-08 2008-09 2009-10 2010-11

GDP at factor cost current price Rscrore

0

100

200

300

400

500

600

700

800

900

1960-61 1970-71 1980-81 1990-91 2000-01 2007-08 2008-09 2009-10 2010-11

GDP Percapita(Constant Price 1999-00) US dollars

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The above charts reveal that the GDP(at constant prices) was low since 1950’s

and have risen steadily after 1980’s and the trend has continued till 2010-11. Similarly,

GDP percapita (in US Dollars) have increasing steadily since 1990’s.

The annual growth rate of Real GDP at factor cost(at 2004-05 prices) is shown in

Figure 4.3.

FIGURE:-4.3

Annual Growth Rate in GDP at Factor Cost(At 2004-05 prices)

Source: Economic Survey, Various Issues.

The growth of GDP over the period from 1950 to 2011 indicates the overall

improvement of the economy. The rate of growth of GDP vis-à-vis population growth

and inflation rate is indicative of the additional resources being made available in the

country. It also facilitates capital formation as higher FII and FDI is attracted. This, in

turn, is likely to propel further growth and add impetus to the buoyancy of the economy.

A clear picture of the macro-economic environment of the Indian Economy can

be seen by looking at few key indicators for the period from 1950 to 2011.Table 4.1 lists

the trends in a select list of key indicators of Indian economy for the period from 1950 to

2011

0123456789

10

1950-51 1960-61 1970-71 1980-81 1990-91 2000-01 2007-08 2008-09 2009-10 2010-11

Annual Growth rate of GDP at factor cost( 2004-2005 prices)

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TABLE:- 4.1

THE KEY INDICATORS OF THE INDIAN ECONOMY

YEAR

GDP at

factor cost

current

price Rs

crore

GDP at

factor cost

constant

price Rs

crore

Percapita

NNP at

factor cost at

constant

price Rs

crore

Gross

Domestic

Capital

Formation

as a % of

GDP at

current

market price

Gross

Domestic

Savings

as a % of

GDP at

current

market

price

Wholesale

Price

Index

Average

1950-51 9719 224786 5708 8.4 8.6 6.8

1960-61 16512 329825 7121 14 11.2 7.9

1970-71 42981 474135 8091 15.1 14.2 14.3

1980-81 132520 641921 8594 19.9 18.5 36.8

1990-91 515032 1083572 11535 26 22.8 73.7

2000-01 1925017 1864300 16172 24.3 23.7 155.7

2007-08 4582086 3896636 30332 38.1 36.8 116.6

2008-09 5303567 4158676 31754 34.3 32 126

2009-10 6091485 4507637 33843 36.6 38.3 132.8

2010-11 7157412 4885954 35993 35.1 132.8 143.3

Source: Economic Survey, Various Issues.

TABLE:-4.2 ANNUAL GROWTH RATE OF REAL GDP AT FACTOR COST BY INDUSTRY OF ORIGIN

(At 2004-05 prices)

Year Agriculture,

Forestry &

Fishing,

Mining and

Quarrying

Manufacturing,

Construction,

electricity, gas

and water

supply

Trade,

hotels,

transport &

Communic

ation

Financing,

Insurance,

real estate

and

business

services

Public

administrati

on &

defence and

other

services

Gross

Domestic

Product

at factor

cost

1951-52 1.9 4.6 2.6 2.6 3.0 2.3

1961-62 0.3 6.9 6.5 4.3 4.7 3.1

1971-72 -1.7 2.5 2.3 5.2 4.5 1.0

1981-82 5.2 7.4 6.1 8.1 2.1 5.6

1991-92 -1.4 -0.1 2.3 10.8 2.6 1.4

2001-02 5.5 2.7 8.6 7.1 4.1 5.5

2008-09 0.4 4.7 7.5 12 12.5 6.7

2009-10 1.7 8.6 10.3 9.4 12.0 8.4

2010-11 6.8 7.4 11.1 10.4 4.5 8.4

2011-12 1.9 4.5 11.2 9.1 5.9 6.9

Source: Economic Survey, Various Issues.

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TABLE:-4.3

SECTOR-WISE COMPOSITION OF GDP AT FACTOR COST(AT CONSTANT PRICES

(At 2004-05)

Year

Agriculture,

Forestry,

Fishing,

Mining and

Quarrying

Manufacturing,

Construction,

Electricity, Gas

and Water

Supply

Trade, Hotels,

Transport &

Communication

Financing,

Insurance,

Real Estate

& Business

Services

Public

Administration

& Defence and

other Services

GDP at

factor Cost

1950-51 55.03 14.71 11.28 8.55 10.43 100.00

1960-61 50.86 18.31 12.91 7.78 10.14 100.00

1970-71 44.51 21.74 14.49 7.53 11.74 100.00

1980-81 38.70 23.27 16.94 8.23 12.86 100.00

1990-91 33.11 24.22 17.69 11.55 13.44 100.00

2000-01 25.28 24.35 21.63 14.05 14.68 100.00

2007-08 19.28 26.28 25.91 16.12 12.42 100.00

2008-09 18.12 25.77 26.09 16.92 13.09 100.00

2009-10 17.01 25.82 26.56 17.08 13.53 100.00

2010-11 16.75 25.57 27.23 17.40 13.05 100.00

2011-12 15.97 25.00 28.34 17.76 12.93 100.00

Notes : Data for 2009-10 are Provisional Estimates, 2010-11 are Quick Estimates and 2011-12 are Revised

Estimates.

Source: Economic Survey, Various Issues

From the above facts and figures, it is very ev ident that India is

developing into an open-market economy, yet traces of its past autarkic

policies remain. It is clearly evident that the largest contributor to the GDP

is the service sector. Gross Domestic Savings as a percentage of GDP has

risen from 8.6% to 32.3% during the period from 1950 to 2011. Also, Gross

Domestic Capital Formation as a percentage of GDP had increased from

8.4% to 35.1% during the period from 1950 to 2011. Economic liberalization,

including industrial deregulation, privatization of state-owned enterprises,

and reduced controls on foreign trade and investment, began in the early

1990’s and has served to accelerate the country's growth, which has averaged

more than 7% per year since 1997. Also, it is observed from Table 4.3 that

the contribution of Agriculture and allied sector’s to GDP has reduced from

55.05% in 1950-51 to 15.97 % in 2011-12.The contribution of Finance,

Insurance and Real Estate to GDP has increased from 8.55% in 1950 -51 to

17.76% in 2011-12. India's diverse economy encompasses traditional village

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farming, modern agriculture, handicrafts, a wide range of modern industries,

and a multitude of services. Slightly more than half of the work force is in

agriculture, but services are the major source of economic growth,

accounting for more than half of India's output, with only one-third of its

labor force. India has capitalized on its large educated English -speaking

population to become a major exporter of information technology services

and software workers. India’s import bill increased over the years on account

of both crude oil as well non-oil imports. However, the trade deficit has

narrowed because of a rebound in exports. In 2010, the Indian economy

rebounded robustly from the global financial crisis - in large part because of

strong domestic demand - and growth exceeded 8% year-on-year in real

terms. However, India's economic growth in 2011 slowed because of

persistently high inflation and interest rates and little progress on economic

reforms. High international crude prices have exacerbated the government's

fuel subsidy expenditures contributing to a higher fiscal deficit, and a

worsening current account deficit. India's medium-term growth outlook is

positive due to a young population and corresponding low dependency ratio,

healthy savings and investment rates, and increasing integration into the

global economy. India has many long-term challenges that it has not yet fully

addressed, including widespread poverty, inadequate physical and social

infrastructure, limited non-agricultural employment opportunities, scarce

access to quality basic and higher education, and accommodating rural -to-

urban migration.

4.3 Review of literature on causal link between stock market development

and economic growth

There are many studies that emphasise the links between the state of

development of a country's financial sector and the level and rate of economic growth.

The argument essentially is that the functions the financial sector provides are an

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essential catalyst of economic growth. This type of empirical study started with

Goldsmith (1969), and McKinnon (1973), and more recently, Ghani (1992), King and

Levine (1993a, b), Degregorio and Giudotti (1995), Rousseau and Wachtel (1998), Beck

et al., (2000), Levine et al., (2000), Levine (2000) and others. While all these studies

utilize bank measures of financial development, with the exception of a very few recent

empirical works (Atji and Jovanovic (1993), Hargis (1997), and Levine and Zervos

(1996, 1998)), the role of stock markets in the economic development process has been

completely ignored. A part of the problem may stem from the absence of indicators that

can accurately measure the extent of stock market development.

In the present review of literature, the researcher presents firstly the most

important theoretical literature that directly models the role of financial markets in

economic development.

Greenwood and Jovanovic (1990) emphasise in their model both the

informational and risk sharing roles of financial markets in improving capital

mobilisation to the optimal use and hence in increasing growth. They develop a model

with two assets: safe, low-yield technology, and a risky high-yield one, where the return

on the latter is affected by an aggregate and a project specific shock. Financial markets

are able to offer agents a higher return than they invested individually because they

collect information that enables them to decipher the aggregate productivity shock and

they can better diversify project-specific risk due to the large portfolios they hold.

Therefore, financial markets allocate capital more efficiently and the resulting higher

productivity of capital increases growth. It is worth noting that in this model higher

growth stimulates increased participation in financial markets, which leads to the

expansion of financial institutions. Thus, a two-way causality between financial

development and growth emerges in their model.

Greenwald and Stiglitz (1989) propose a theoretical model to examine the impact

of financial market imperfections on the long-term productivity growth of firms. Their

model focuses on failures of firms in selling equity securities, which help firms by

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diversifying the risk of real investment. Particularly, they argue that failures in stock

markets limit the abilities of firms to diversify the risks of their operations and hence

lead to a reduction in the level of such operations as an alternative means of risk

management. They show that since the curtailment of firms' operations will limit the

extent of "on-the-job training" and other learning effects, as well as direct investment in

productivity improvements, the stock market imperfection will adversely affect the rate

of productivity growth.

Levine (1991) constructs an endogenous growth model in which the stock market

emerges to allocate risk, and explores how the markets alter investment incentives in

ways that change steady-state growth rates. He demonstrates that stock markets

accelerate growth by facilitating the ability to trade ownership of firms without

disrupting the productive process occurring within firms and by allowing agents to

diversify portfolios. In the absence of the stock markets, lenders facing liquidity

constraints which would force firms to pay back loans, thus forcing firms to liquidate

(fully or partially) those assets which they own. Since such assets include capital assets,

which embody a firm's technology, this will lower the firm's productivity. He further

explains the effect of tax policies on growth both directly by altering investment

incentives and indirectly by changing the incentives underlying financial contracts.

Levine's model uses the Diamond and Dybvig (1983) structure of preference to create

liquidity risk and also to include productivity shocks that create production risk.

Liquidity risk and productivity risk create incentives for the formation of stock markets.

Productivity risk lowers welfare and discourages agents from investing in firms. The

stock market allows investors to invest in a large number of firms and to diversify away

from idiosyncratic productivity shocks. This raises welfare, the fraction of resources

invested in firms, and the economy's steady-state growth rate. In Levine's model, the

stock market raises the growth rate by increasing the productivity of firms or by

improving the allocation of resources. Thus, the emergence of stock markets to manage

productivity and liquidity risk accelerates growth by attracting resources to socially

productive firms.

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King and Levine (1994) proposed a model in which innovation activities serve as

an engine of growth. A higher rate of successful innovations results in a high growth rate

of productivity. Financial markets appear in two different forms in the model. The first

is where the intermediaries’ acts like venture capital firms. They evaluate, finance and

monitor the risky and costly innovations. The second form is like the stock market. The

present value of the innovation is revealed in the stock market and selling the equity

shares on the market can diversify the risk associated with innovation. Therefore,

according to King and Levine, better development of the financial market can improve

the possibility of successful innovations. They point out that financial institutions play

an active role in evaluating, managing, and funding the entrepreneurial activity that

leads to productivity growth.

Bencivenga and Smith (1991) construct a model that by pooling the economy's

resources eliminates liquidity risk and invests more efficiently. In their model, a bank

enables individuals to pool liquidity risks and can promote higher growth by shifting the

composition of savings towards more capital accumulation and by reducing unnecessary

capital liquidation. Banks channel funds from risk-averse savers to entrepreneurs who

invest in productive capital and hence provide liquidity to the former group by enabling

them to hold bank deposits instead of other liquid and unproductive assets. These funds

are then available for investment in capital accumulation and thus reduce the need for

the self-financing of investment.

The role and impact of stock markets on the economic development process have

not received as much attention as other elements of the financial sector. Historically, the

economists have focused on banks. In the last decade the availability of more

appropriate data has increased the number of empirical researches in this field. Debate

exists, however, over the signs of the effects of stock markets on economic growth:

many theoretical studies suggest that stock market development slows economic growth.

With regard to this debate on the relationships between stock market development and

economic growth, a detailed discussion of the stock market functions is mentioned

above, and how these functions affect economic growth.

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Mohtadi and Agarwal(2004) attempts to evaluate the empirical relationship

between stock market development and economic growth for 21 developing countries

for the period 1977 to 1997 using dynamic panel method. Stock market development is

measured using Market Capitalization Ratio, Total Value of Shares Traded Ratio and

Turnover ratio. The other control variables used are Growth, FDI, Investment and

Secondary School Enrollment. Two alternative models for estimating the long-run

effects of stock market on economic growth are used. The first model is a two stage test

of hypothesis of whether the stock markets effect economic growth and the second

model examines the relationship between Stock market development and economic

growth directly rather than through investment behavior. The empirical relation between

stock market and long run growth is found to be strong even after controlling for lagged

growth, initial level of GDP, FDI and Secondary School Enrollment and Domestic

Investment. The paper suggests that stock market development contributes to economic

growth both directly and indirectly. The results of the study suggests that market size

affects investment which affects growth, market liquidity (Turnover Ratio) has a

positive impact on growth while Values of Shares Traded Ratio is found to be not an

effective measure of stock market liquidity.

Aboudou (2010) examines the impact of stock market development on growth in

West African Monetary Union for the period from 1995 to 2006. Stock Market

Development is measured using Stock Market Size (Market Capitalisation to GDP) and

Liquidity (Volume of shares traded over GDP). Economic Growth is measured using

real per capita GDP. The two controlling variables having impact on economic growth

are FDI and Human Capital (Secondary School enrollment ratio). The two Step

procedure of Engle and Granger approach are adopted. The findings show that both

measures of Stock market development demonstrate the importance of stock market

development to growth. The results show that Liquidity has greater impact on Growth

than size. Also, the two controlling variable, namely FDI and Human Capital are found

to be crucial determinants of economic growth in West African Monetary Union.

Aboudou(2009) examines the causal relationship between stock market

development and economic growth for the West African Monetary Union economy.

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Economic growth is proxied by GDP and stock market capitalization is proxied by

market capitalization and total value traded ratio. By applying the techniques of unit–

root tests and the long–run Granger non causality test proposed by Toda and Yamamoto

(1995), the causal relationships between the real GDP growth rate and two stock market

development proxies are tested. The results are in line with the supply leading

hypothesis in the sense that there is strong causal flow from the stock market

development to economic growth. A unidirectional causal relationship is also observed

between real market capitalization ratio and economic growth.

Odhiambo(2010)examines the dynamic causal relationship between stock market

development and economic growth in South Africa for 1971 to 2007 using ARDL-

Bound Testing Procedure. Stock Market Development was measured by stock market

capitalization ratio, value traded ratio and turnover ratio. The economic growth variable

is measured by real per capita GDP. The results show that causal relationship between

stock market development and economic growth is sensitive to the proxy used for

measuring stock market development. When the stock market capitalisation is used as a

proxy for stock market development, the economic growth is found to Granger-cause

stock market development. However, when the stock market traded value and the stock

market turnover are used, the stock market development seems to Granger-cause

economic growth. Overall, the study finds the causal flow from stock market

development to economic growth to predominate. The findings of this study are

consistent with the conventional supply-leading response in which the financial sector is

expected to precede and induce the real sector development. The results apply

irrespective of whether the causality is estimated in the short-run or in the long-run

Gursoy and Muslumov (1998) examines the causality relationship between stock

markets and economic growth in 20 countries based on time series data from 1981 to

1994 using Sims Test based on Granger causality test. Economic growth indicator used

in this study is real per capita GDP. To develop a stock market development indicator,

the study has created an index comprising of volume and liquidity indicators, namely,

total capitalization/GDP, Volume Transactions/GDP and Volumes of transactions/total

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capitalization. The simple arithmetic average of the relative values of the three indictors

has been computed and this average is used as the stock market development index. The

analysis was based on panel data covering all countries with a time lag of three years

and two years was used to detect the direction of causality. The analysis has shown two-

way causation between stock market development and economic growth. With a 3 year

lag, a feedback phenomenon between stock market development and economic growth

was seen at 5 % level and with a 2 year time lag, causation ran from economic growth

to stock market development at 1 % level. However, time series analysis, for individual

countries does not show conclusive results but suggested a stronger link between stock

market development and economic growth in developing countries.

Suliman and Hala(2011) examines the causal relationship between stock market

development and economic growth for Sudan for the period from 1995 to 2009 using

Granger causality approach. The two indicators for stock market development variables

used in this study are market capitalization ratio and real value traded ratio. Real GDP

growth is used as a proxy for economic development. It is found that causal relationship

between stock market development and economic growth is sensitive to the proxy used

for describing stock market development. When stock market capitalization is used, the

results indicate a bivariate causal relationship between stock market development and

economic growth. When stock market liquidity is used, the results show unidirectional

causal relationship from economic growth to stock market development. Hence, Granger

causality test results suggest that stock market development in Sudan leads to economic

growth for the period under study.

Hossain and Kamal(2010) examines the causal relationship between stock market

development and economic growth in Bangladesh for the period from 1976 to 2008

using Unit Root Test, Cointegration test, Granger causality test and Lagrange Multiplier

test. Economic development is measured by the growth rate of real GDP at a constant

market price and real percapita GDP. Stock Market Development is measured by real

market capitalization ratio. The results suggest a long-run equilibrium relationship

between stock market development and economic growth in Bangladesh and

unidirectional causality from stock market development and economic growth.

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Nikolaos and Antonios(2004)examines empirically the direction of causality

between financial development, economic growth and the degree of openness in Greece

for the period from 1960 to 2000 using multivariate auto regressive VAR model,

Johansen cointegration test and Granger causality test. GDP is used as a proxy for

economic growth, the ratio of money supply(M2) to the level of GDP is used as proxy

for financial development and the actual trade flows, exports plus imports is used as a

proxy for degree of openness. The results of the cointegration analysis suggest the

existence of cointegration between the three variables indicating the presence of

common trend or long-run relationship among these variables. The results of the

causality analysis suggest that there exists a strong bilateral relationship between

financial development and economic growth and between degree of economic growth

and degree of openness.

Pradhan(2011) examines the causality and cointegration relationship between

financial development, economic growth and stock market development in India for the

period from 1994 to 2010 using the unit root test, cointegration test and Error Correction

Model. The findings of the analysis confirm that the time series variables are stationary

at the first differences and there is presence of one cointegrating vectors between

financial development, economic growth and stock market development indicating the

presence of long run equilibrium relationship between financial development, economic

growth and stock market development. The findings suggest that stock market

development is an integral part of economic growth which in turn is associated with the

financial development in the economy.

Arestis, Luintel and Luintel (2005) examines the relationship between economic

growth and stock market development controlling for the effects of commercial banking

sector and stock market volatility for five developed countries viz., Germany, United

States, Japan, United Kingdom and France for the period from 1968 to 1998. Output is

measured by the logarithm of real GDP, stock market development by the logarithm of

stock market capitalisation ratio, banking system by the logarithm of the ratio of

domestic bank credit to nominal GDP and Stock market volatility is measured by an

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eight quarter moving standard deviation of the end of quarter change of stock market

prices. The results suggest that banks and stock markets are promoting economic growth

but the influence of banks is more powerful.

Brasoveanu et al(2008) examines the correlation between capital market

development and economic growth in Romania for the period from 2000 to 2006 using

linear regression function and VAR models. Capital market development is measured by

market capitalization ratio(size Variable), turnover ratio and value traded ratio(liquidity

variable) and eight-quarter moving standard deviation of the end of quarter change of

stock market prices(Volatility ratio). Economic growth is measured by logarithm of real

GDP, GDP growth rate and GDP per capita growth rate. The results suggest that capital

market development is positively correlated with economic growth with feed back

effect, but the strongest link is from economic growth to capital market suggesting that

financial development follows economic growth, economic growth determining

financial institutions to change and develop.

Singh (1997) concentrates in the role of stock markets in the liberalization

process in the developing countries in the 1980's and 1990's. He argues that stock market

development is unlikely to help in achieving quicker industrialization and faster long-

term economic growth in most developing countries. He had cited three reasons for the

same. First, the inherent volatility and arbitrariness of the stock market pricing process

under developing country conditions make a poor guide to efficient investment

allocation. Second, the interactions between the stock and currency markets in the wake

of unfavourable economic shocks may exacerbate macroeconomic instability and reduce

long-term growth. Third, stock market development is likely to undermine the existing

group-banking systems in developing countries, which, despite their many difficulties,

have not been without merit in several countries, not least in the highly successful East

Asian economies.

Levine and Zervos (1996) uses pooled cross-country time series regressions

considering the data on 41 countries over the period 1976-1993. The paper uses an

aggregate index of overall stock market development constructed by Demirguc-Kunt

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and Levine (1996b) which combines information on stock market size, liquidity and

integration with world capital markets. While assessing the relationship between stock

market development and economic growth the paper includes a large number of control

variables namely, the logarithm of initial per capita GDP, the logarithm of initial

secondary school enrollment rate, the number of revolutions and coups, the ratio of

government consumption expenditures to GDP, the inflation rate and the black market

exchange rate premium. Using the instrumental variable method of estimation the study

observes that the stock market development is positively correlated with economic

growth even after controlling for other factors associated with long-run growth.

Nazir et al(2010) examines the relationship between stock market development

and economic growth in Pakistan for the period from 1986 to 2008 using Augmented

Dicky-fuller test. The relationship between stock market and economic growth was

analyzed using two major measures of stock market development, Size(market

capitalization divided by GDP) and liquidity(total value of traded shares divided GDP)

as independent variables along with FDI and HDI of Pakistan. The impact of these

variables is empirically tested on GDP per capita as dependent variable for economic

growth. The results revealed that economic growth can be attained by increasing the size

of the stock markets of a country as well as the market capitalization in an emerging

market like Pakistan

Alajekwu and Achugbu(2012) investigate the role of stock market development

on economic growth of Nigeria using a 15-year time series data from 1994 - 2008. The

method of analysis used Ordinary Least Square (OLS) techniques. The stock market

capitalization ratio was used as a proxy for market size while value traded ratio and

turnover ratio were used as proxy for market liquidity. The results show that market

capitalization and value traded ratios have a very weak negative correlation with

economic growth while turnover ratio has a very strong positive correlation with

economic growth. Also, stock market capitalization has a strong positive correlation

with stock turnover ratio. This result implies that liquidity has propensity to spur

economic growth in Nigeria and that market capitalization influences market liquidity.

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Nieuweburgh, Buelens and Cuyvers(2006) attempt to investigate the role of

finance for economic growth in Belgium during the period from 1830 to 2005 using

Granger Causality test and Cointegration Analysis. Stock market development is

measured using 4 indicators, namely, total market capitalization, total number of listed

shares, international character of BXS and the financial depth (degree of concentration).

Bank Development is measured using saving in commercial banks and bank note

circulation and Economic Growth is measured using annual percentage increase in real

percapita GDP. Stock market development and bank development, independently predict

economic growth. Granger Causality analysis finds that both stock market development

and bank development independently predicts economic growth. The cointegration

analysis finds the strongest evidence for growth promoting role of stock markets and

finds that stock market development was a better forecaster of economic growth than

bank based development.

Caporale, et al(2004) examines the causal linkage between stock market

development, financial development/bank development and economic growth for 7

countries, namely, Argentina, Chile, Greece, Korea, Malaysia, Philippines and Portugal

for the period from 1977 to 1998. The econometric methodology used in the study is

Toda and Yamamoto(1995) approach to test for causality in VARs and emphasizes the

possibility of omitted variable bias. Stock Market development is measured using

market capitalization ratio and value traded ratio. Bank deposit liabilities to nominal

GDP and the ratio of bank claims on the private sector to nominal GDP are used as

proxy for bank development. Economic Development is measured using GDP. The

results show that very little evidence of causality was found between bank development

and economic growth. However, causality between financial development, stock market

development and economic growth has been found in 5 countries out of the seven but

the measure of financial development which produced this result was stock market

development.

Seetanah et al(2010) examines the complex linkages between banking sector

development, stock market development and economic growth in a unified framework

for 27 developing countries for the period from 1991 to 2007 using Panel VAR

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framework. The indicators of Stock Market development used in this study are ratio of

stock market capitalization to GDP, ratio of total value of shares traded to GDP and ratio

of total value if shares traded to market capitalization. Banking sector development is

measured using value of credits by financial intermediaries to private sector divided by

GDP. Growth is measured using real per capita GDP, country’s investment divided by

GDP and secondary school enrollment rate and total of export and import divided by

GDP. The findings show that stock market development is an important ingredient of

growth and stock market development and banking development complement each

other.

Levine and Zervos (1998) investigates whether measures of stock market

liquidity, size, volatility and integration with world capital markets are robustly

correlated with current and future rates of economic growth, capital accumulation,

productivity improvements and saving rates using data on 47 countries from 1976 to

1993. Stock market development indicators used in this study are market capitalization

ratio, turnover ratio and value traded ratio. Integration with world markets is measured

using International Capital Asset Pricing Model and International Arbitrage Pricing

Theory. Banking sector development is measured using the ratio of value of loans made

by commercial banks to private enterprises to GDP. The growth indicators used in the

study are output growth, capital stock growth, productivity growth and savings. The

results suggest a strong and statistically significant relationship between stock market

development and economic growth after controlling for initial income, initial investment

in education, political stability, fiscal policy, openness to trade and macroeconomic

stability. The level of banking development also turns out to be significant in explaining

growth.

Beck and Levine (2004) use panel econometric techniques to assess the

relationship between stock markets, banks and economic growth over the period 1976-

1998 in a panel of 40 countries. They specifically examine whether both measures of

stock market and bank development, have a positive relationship with economic growth

after (i) controlling for simultaneity bias, omitted variable bias and the routine inclusion

of lagged dependent variables in growth regressions (ii) moving to data averaged over

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five-years instead of quarterly or annual data (iii) assessing the robustness of the results

using several variants of the system estimator and (iv) controlling for many other growth

determinants. Their study shows that the turnover ratio and bank credit both enter

significantly and positively in the growth regressions using the two-step estimator. The

one-step estimator, however, indicates that bank credit does not always enter with a p-

value below 0.10. Specifically, bank credit does not enter significantly when either trade

openness or inflation is controlled for. However, even with the one-step estimator the

financial indicators always enter jointly significantly. Using the alternative system

estimator, it is found that both the stock market liquidity and bank development enter the

growth regressions significantly except when controlling for trade openness. In the

regression controlling for trade openness, bank credit enters with a p-value below 0.05

but turnover is insignificant. Even in this regression, however, they enter jointly

significantly.

Kirankabes and Basarir(2012) examines the causality relationship between the

economic growth and stock market development of Turkey and for the period from 1998

to 2010 using Unit Root Test, cointegration test, Granger Causality Test and VAR

model. Economic Growth is measured using GDP and stock market development is

measured using Istanbul Stock Exchange (ISE) 100 index. The findings show that there

is a long-term relationship between economic growth and the ISE 100 Index, and a one-

way causality relationship with the ISE 100 towards Economic Growth.

Arestis, Luintel and Luintel (2005) examines whether financial structure

influences economic growth in six developing countries namely., Greece, India, South

Korea, Philippines, South Africa and Taiwan over a period of 30(minimum) to

39(maximum) years using multivariate vector auto-regression(VAR), time series and

dynamic heterogeneous panel methods . Financial Structure Ratio is defined as the ratio

of market capitalization to bank lending and economic growth is measured using real

Gross Domestic Product and real gross Fixed Investment. Thus higher Financial

Structure Ratio means a system that is more of the market-based variety while a lower

Financial Structure Ratio means more of a bank-based type. Based on a Cobb-Douglas

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production function specification relating output-labour ratio to capital-labour ratio and

financial structure, their time-series results show that for the majority of the sample

countries financial structure significantly explains economic growth. The results from

the dynamic heterogeneous panels also confirm the significance of the financial

structure.

Demetriades and Khaled (1996) examine the causal relationship between

financial development and economic growth from a time-series perspective considering

data from 16 countries over 27 years and demonstrate that the relationship is country-

specific. Financial development has been measured by two ratios viz., ratio of bank

deposit liabilities to nominal GDP and ratio of bank claims on the private sector to

nominal GDP. They find from the Engle-Granger results that at least one of the financial

indicators is cointegrated with real GDP per capita in five countries viz., Honduras,

South Africa, Sri Lanka, Turkey and Venezuela. On the other hand, based on the

Johansen cointegration test, cointegration was determined between at least one indicator

of financial development and real GDP per capita in 13 out of 16 countries. The

evidence seems stronger in the case of the first financial indicator as this indicator is

observed to be cointegrated with real GDP per capita in 13 countries. Countries which

show no evidence of cointegration between financial development and economic growth

according to Johansen results are Pakistan, Spain and Sri Lanka. Causality tests show

that a bi-directional causal relationship exists in six countries viz., Honduras, India,

Korea, Mauritius, Thailand and Venezuela. There are only three countries viz.,

Honduras, Spain and Sri Lanka in which financial indicator causes economic growth.

The study also finds clear evidence of reverse causation in six countries viz., El

Salvador, Greece, Pakistan, Portugal, South Africa, and Turkey, which refutes the

hypothesis that finance is a leading sector in these countries.

Calderon and Liu (2003) examines the direction of causality between financial

development and economic growth using Geweke decomposition test on pooled data of

109 developing and industrialized countries from 1960 to 1994. The study divides the

countries into two sub-samples viz., developing countries and industrial countries, and

uses two measures of financial development viz., the ratio of broad money (M2) to GDP

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and the ratio of credit provided by financial intermediaries to the private sector to GDP.

The study also includes a set of controlling variables namely initial human capital, initial

income level, a measure of government size, black market exchange rate premium and

regional dummies for Latin America, East Asia and Africa. The results show that

financial development generally leads to economic growth, the Granger causality from

financial development to economic growth and the Granger causality from economic

growth to financial development coexist, financial deepening contributes more to the

causal relationships in the developing countries than in the industrial countries, the

longer the sampling interval, the larger the effect of financial development on economic

growth and financial deepening propels economic growth through both a more rapid

capital accumulation and productivity growth, with the latter channel being the

strongest.

Beck and Levine(2004) attempts to analyze the link between stock market

development, bank development and economic growth for 40 countries for the period

1975-1998. Stock market development is measured using turnover ratio, banking sector

development is measured by using ‘bank credit’ which is deposit-taking bank claims on

the private sector divided by GDP. Economic Growth is measured using real per capita

GDP growth rate. The result suggest evidence for robust statistical relationship between

banks, stock markets and economic growth and cross-country growth regressions show

the importance of the overall level of financial development, rather than the

composition of the financial system.

Boubakari and Jin(2010) explores the causality relationship between stock

market development and economic growth for 5 Euronext countries, namely, Belgium,

France, Portugal, Netherlands and United Kingdom) for the period from 1995 to 2008

using Granger causality test. Stock market development is measured using market

capitalization, total trade value, turnover ratio and Economic growth is measured using

GDP in USD and Foreign Direct Investment Causal relations were investigated for each

country. The results of the study suggest a positive links between the stock market and

economic growth for some countries for which the stock market is liquid and highly

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active. However, the causality relationship is rejected for the countries in which the

stock market is small and less liquid

Paramati and Gupta(2011) investigates whether the stock market performance

leads to economic growth or vice versa and examines the short-run and long-run

dynamics of the stock market in India using monthly Index of Industrial Production (IIP)

and GDP data for the period from 1996 to 2009. The study uses Unit root (ADF, PP and

KPSS) tests, Granger Causality test, Engle-Granger Cointegration test and Error

Correction Model. The monthly results of Granger causality test show that there is a

bidirectional relationship between IIP and Stock prices (BSE and NSE) and quarterly

results reveal that there is no relationship between GDP and BSE but in the case of NSE

and GDP there is a unidirectional relationship and that runs from GDP to NSE. The

Engle-Granger residual based cointegration test suggests that there is a long-run

relationship between the stock market performance and economic growth. Similarly, the

results of error correction model reveal that when the long-run equilibrium deviates then

the economic growth adjusts to restore equilibrium by rectifying the disequilibrium. This

study provides evidence in favor of ‘demand following’ hypothesis in the short-run.

Antonios(2010) examines the relationship between financial development and

economic growth for Ireland for the period 1965-2007 using a vector error correction

model (VECM). Financial market development is estimated by the effect of credit

market development and stock market development on economic growth. The objective

of this study was to examine the long-run relationship between these variables applying

the Johansen cointegration analysis taking into account the maximum eigenvalues and

trace statistics tests. The results of the Granger causality tests indicated that economic

growth causes credit market development, while there is a bilateral causal relationship

between stock market development and economic growth. Therefore, it can be inferred

that economic growth has a positive effect on stock market development and credit

market development taking into account the positive effect of industrial production

growth on economic growth for Ireland.

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Acaravci et al(2007)examines the causal relationship between financial

development and economic growth in Turkey for the period from 1986 to 2004 using

unit root tests, cointegration tests, VECM and VAR framework. Economic growth is

proxied using GDP and financial development is proxied using domestic credit provided

by banking sector. The results show one-way causality from financial development to

economic growth in Turkey.

Azarmi et al(2005) examines the empirical relation between stock market

development and economic growth for a period from 1981 to 2001. Growth is proxied

by using real per capita GDP while stock market development is proxied using market

capitalization ratio, total turnover ratio and turnover ratio. The study finds a negative

correlation between stock market development and economic growth for the post-

liberalization period. The results are consistent with the suggestion that the Indian Stock

market is a casino for the sub-period of post liberalization and for the entire ten-year

event study period.

In order to evaluate the relationship between stock market development and

national growth rates, capital accumulations, rates of technological change, and savings

rates, in the two important recent papers, Levine and Zervos (1996,1998) build on Atji

and Jovanovic's study using various measures of stock market development. They argue

that well-developed stock markets may be able to offer different kinds of impetus to

investment and growth from the development of the banking system. In particular, they

show that increased stock market capitalisation measured by the ratio of the stock

market value to GDP, may improve an economy's ability to mobilise capital and

diversify risk. Liquidity is another important indicator of stock market development in

that it may be inversely related to transaction costs, which impede the efficient

functioning of stock markets. Liquidity is measured by total value of shares traded

relative to either GDP or total market capitalisation. The latter is known as the turnover

ratio and may be an indicator of the level of transaction costs. Other stock market

developments indicators in which Levine and Zervos used are the volatility of market

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returns and the ability of markets to diversify risk internationally- the degree of stock

market integration with world markets.

Using data from 47 countries over the period 1976-93 Levine and Zervos run

cross-country regressions and find that stock market liquidity is positively and

significantly correlated with current and future rates of economic growth, capital

accumulation, and productivity growth. They also find after including both stock market

and bank indicators in the same regressions, that both banking development and stock

market liquidity are good predictors of economic growth, capital accumulation and

productivity growth. They conclude that stock markets provide different services from

those provided by banks.

In this chapter we attempt to address the gap in this field by providing an

empirical analysis of the effect of stock market development on economic growth in an

individual country- India- by proposing a simple plausible framework that suggests that

the stock market may influence economic growth.

4.4 Stock Market Development Indicators

As mentioned above, well-functioning stock markets can play an important role

in economic development processes by performing the following functions: aggregate

and mobilise capital, enhance liquidity, provide risk pooling and sharing services,

monitor managers and exert corporate control. It is difficult, however, to construct

accurate measures of these functions. Consequently, this study use indicators to suit the

purpose of the concept of stock market development, by constructing proxies for stock

market development that are most commonly used by academics and practitioners (see

Demirguc-Kunt and Levine, 1996a; Levine and Zervos, 1998a, b; and Beck et al.,

1999a). These indicators are associated with the size, and liquidity of the stock market.

While these indicators may be still imperfect measures of how well a stock market

performs the above functions, these measures or indicators together may provide a more

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accurate picture than if we use only a single indicator. It is useful to provide a brief and

schematic description of such indicators:

(i) Stock Market Size

The study uses Market Capitalisation Ratio (MCR) as indicator to measure the

stock market size. The assumption behind this measure is that overall market size is

positively correlated with the ability to mobilize capital and diversify risk on an

economy-wide basis. The market capitalisation refers to the total value of listed shares

on the stock exchange. Capitalisation of a company is calculated by multiplying the

number of shares outstanding of that company by its share price. To calculate the market

capitalisation, this information is aggregated for all the companies listed in the stock

market. The assumption underlying the use of this variable as an indicator for stock

market development is that the size of the stock market is a measure of the availability

of finance (Rajan and Zingales, 1996; Demirguc-Kunt and Maksimovic, 1998; and

Subrahamanyam and Titman, 1999) and the ability to mobilise capital, diversify the risk

and resources allocation processes. Bekaert and Harvey (1995b, 1997) also argue that

the ratio of equity capitalization to GDP is a useful tool in characterizing the time-series

of market integration. A large market size (market capitalisation relative to economic

activity) suggests that the country is more likely to be integrated into world capital

markets. Furthermore, in an important empirical study, Demirguc-Kunt and Levine

(1996a) find that large stock markets measured by equity capitalisation to GDP are more

liquid, less volatile, more internationally integrated, stronger with regard to information

disclosure laws and international accounting standards, and have unrestricted capital

flows than smaller markets.

Table:- 4.4 and Figure:- 4.4 shows the market capitalisation in BSE and NSE after

1990. It is seen that the market capitalisation has increased over the period of years

indicating that the size of the stock market has been growing over the period of time.

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FIGURE:- 4.4

Trends of market capitalisation in BSE and NSE(Rs Crore)

(ii) Stock Market Liquidity

Liquidity is an important attribute of stock market development because

theoretically more liquid stock markets improve the allocation of capital to their optimal

use, influence investment in the long term and facilitate technological innovation,

thereby enhancing long term growth. Greater liquidity also has a direct impact on the

effectiveness of the governance function of the stock market. First, increased market

activity encourages information acquisition, which in turn increases the information

content of share prices. Second, the effective use of the stock market for corporate

control activities requires that the market be liquid. Takeovers require a liquid capital

market where bidders access a vast amount of capital at short notice. Thus, measures of

market liquidity may reflect the function of the market for corporate control as well.

Therefore, a measure of market liquidity may be a good proxy for information

production as well as the monitoring control function of capital markets. Increased stock

market liquidity can also reduce the cost of equity capital through a reduction in the

expected return that investors require when investing in equity to compensate them for

0

10,00,000

20,00,000

30,00,000

40,00,000

50,00,000

60,00,000

70,00,000

80,00,000

Market Capitalisation BSE Market Capitalisation NSE

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the risks i. e., risk premium (Ahimud and Mendelson, 1986; Ahimud et al., 1997; Henry,

2000a, b).

A comprehensive measure of liquidity would quantify all the costs associated

with trading, including the time costs and the uncertainty of finding a counterpart and

settling the trade. To measure liquidity, we will use two measures: turnover ratio and

total value of shares traded ratio.

The total value of shares traded ratio equals total value of shares traded on the

stock market exchange divided by GDP. The total value traded ratio measures the

organized trading of firm equity as a share of national output and therefore should

positively reflect liquidity on an economy-wide basis. The total value traded ratio

complements the market capitalization ratio: although a market may be large, there may

be little trading. A higher value traded corresponds to greater liquidity in the market and

greater attractiveness for investors. If trading in the market represents the actions of

investors buying and selling to attain their desired position, then trading activity

measures the speed at which new information is incorporated into prices. The ratio of

organized equity trading as a share of GDP positively reflects liquidity on an economy-

wide base. This ratio also complements the market capitalisation ratio since the market

size measured by market capitalisation be large, but relatively inactive as measured by

trading activity.

The second measure of market liquidity is the turnover ratio. This ratio is equal to

the value traded divided by market capitalisation. It measures the size of equity

transaction relative to the size of the stock market. High turnover ratio is often used as

an indicator of low transaction costs. A higher turnover ratio may represent greater

liquidity and market efficiency. Brennan and Subrahmanyam (1996) find that the

number of analysts following a stock is strongly positively related to the liquidity of the

stocks and that low turnover stocks are followed by fewer analysts and thus are slower to

react to information than high turnover stocks. Thus, illiquid stocks react to market

information more slowly than do liquid stocks. However, an excessively high turnover

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ratio may represent inefficiency or excessive speculative trading. The higher turnover

ratio in many Asian markets has been attributed to the speculative trading in those

markets, which may not represent useful economic activity. Bencivenga et al., (1996)

give a model in which excessive liquidity and turnover lower the economic growth rates.

Since this indicator is the ratio of a stock and a flow variable, we apply a similar

deflating procedure as for the market capitalisation indicator.

It is worth noting here that the turnover ratio complements the earlier cited

measure of liquidity, since although markets may be small compared to the size of the

economy (as measured by the value traded as a percentage of GDP) they may be liquid.

Thus, while an absolute measure of liquidity (such as the value traded as a percentage of

GDP) may be indicative of liquidity in the economy as a whole, it may be misleading as

a measure of market liquidity if the size of the economy is very large. A classic example

is Brazil. In this country there is not much equity trading relative to the size of the

economy (which is large), however, it has a higher turnover ratio reflecting a small but

active stock market (Demirguc-Kunt and Levine, 1996a). Consequently, incorporating

market size measures by market capitalisation, total value traded as a percentage of

GDP, and turnover ratio, provides a more comprehensive picture of stock market

development than any single indicator can provide.

Another advantage of using value-traded ratio and turnover ratios is that the main

purpose of this study is to evaluate whether the liquidity services provided by the stock

market are robustly correlated with economic growth. Unlike much of the literature on

liquidity that focuses on evaluating whether a stock's liquidity affects its price and rate

of return we do not want to measure the degree of liquidity. We want to measure the

degree to which the stock market provides liquidity to the Indian economy. The stock

market may be highly liquid with correspondingly high turnover ratios, but it may not be

providing significant liquidity to the economy as a whole. Thus, turnover ratio may not

satisfy our objectives. However, value-traded ratio measures trading relative to the size

of the whole economy. Therefore, the value-traded and trading-volatility ratios may

provide more information about the provision of liquidity than turnover ratios.

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As shown from the above discussion, it is apparent that the value-traded ratio is

more closely associated with this study, because, unlike other liquidity indicators, it

focuses on economy wide bases. However, using the value-traded ratio has a potential

disadvantage. If the market anticipates large corporate profits, stock prices will

invariably rise. This price rise would increase the value of transactions and therefore

raise the value-traded ratio. In this case, this liquidity indication would rise without a

rise in the number of transactions or a fall in the transaction costs (Levine and Zervos,

1998a). This price also affects the market capitalization ratio. To avoid the influence of

the price effect we need to look at the stock market capitalisation and the value-traded

ratio together. If we include both indicators together in the regression and the value

traded remains significantly correlated with growth after controlling for the market

capitalisation ratio, then this implies that the price effect is not dominating the

relationship between the value-traded ratio and growth.

Table 4.4 and Figure 4.5 shows the total value traded in BSE and NSE after

1992. It is seen that the total value traded has increased over the period of years

indicating that liquidity of the Indian stock markets has been increasing over the period

of time.

FIGURE 4.5

Trends of total value added in BSE and NSE

0

5,00,000

10,00,000

15,00,000

20,00,000

25,00,000

30,00,000

35,00,000

40,00,000

45,00,000

Total Value Traded BSE Total Value Traded NSE

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TABLE:-4.4

Market capitalisation, value traded and turnover ratio of BSE and NSE

Year

Market Capitalisation BSE

Market Capitalisation NSE

Total Value Traded BSE

Total Value Traded NSE

Turnover ratio in BSE

Turnover ratio in NSE

1990-91 90,836 NA

1991-92 3,23,363 NA

1992-93 1,88,146 NA 45,696

0.243

1993-94 3,68,071 NA 84,536

0.230

1994-95 4,35,481 3,63,350 67,749 1,805

0.156 0.005

1995-96 5,26,476 4,01,459 50,064 67,287

0.095 0.168

1996-97 4,63,915 4,19,367 1,24,190 2,95,403

0.268 0.704

1997-98 5,60,325 4,81,503 2,07,113 3,70,193

0.370 0.769

1998-99 5,45,361 4,91,175 3,10,750 4,14,474

0.570 0.844

1999-00 9,12,842 10,20,426 6,86,428 8,39,052

0.752 0.822

2000-01 5,71,553 6,57,847 10,00,032 13,39,510

1.750 2.036

2001-02 6,12,224 6,36,861 3,07,292 5,13,167

0.502 0.806

2002-03 5,72,198 5,37,133 3,14,073 6,17,989

0.549 1.151

2003-04 12,01,207 11,20,976 5,03,053 10,99,534

0.419 0.981

2004-05 16,98,428 15,85,585 5,18,715 11,40,072

0.305 0.719

2005-06 30,22,191 28,13,201 8,16,074 15,69,558

0.270 0.558

2006-07 35,45,041 33,67,350 9,56,185 19,45,287

0.270 0.578

2007-08 51,38,014 48,58,122 15,78,857 35,51,038

0.307 0.731

2008-09 30,86,075 28,96,194 11,00,074 27,52,023

0.356 0.950

2009-10 61,65,619 60,09,173 13,78,809 41,38,023

0.224 0.689

2010-11 6,839,084 6,009,173 1,105,027 3,577,410

0.162 0.595

2011-12 6,214,941 6,702,616 667,498 2,810,893

0.107 0.419

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4.5 Methodology and Data Analysis

The present study uses monthly data of BSE for the time span of November 1994

to March 2012. The collected data are converted into logarithmic form. The variables

selected are used for understanding the causal relationship between stock market

development and economic growth. One of the important indicators of economic growth

is the GDP. However, the GDP data are available only annual basis. Hence, Index of

Industrial Production (IIP) is taken as the proxy variable. The selection of IIP as a proxy

variable is supported by various studies pertaining to stock market and economic growth

(Paramati and Gupta 2005, Nair 2008).

For measuring the Market Capitalisation Ratio(MCR) and Value Traded

Ratio(VTR), the GDP figure is needed. However, monthly GDP figure is not available

in India during the analysis period. As IIP is used as a proxy for GDP, MCR and VTR

cannot be estimated and only Turnover ratio (TR) is used as an index of stock market

development.

4.5.1 Empirical model

The present study undertakes a comprehensive set of econometric tests for the

empirical analysis such as; Unit root (ADF, PP and KPSS) tests, Granger Causality test,

Engle-Granger Cointegration method and finally; Error Correction Model (ECM).

The study starts with the conventional unit root tests, to find out the order of

integration. The important unit root tests used here are the Augmented Dickey-Fuller

(Dickey and Fuller, 1979) test, Phillips-Perron (Phillips and Perron, 1988) test and the

KPSS (Kwiatkowski et al. 1992) test. All of these unit root tests are used to test whether

the data contains unit root (non-stationary) or is a stationary process. A series is said to

be stationary if the mean and auto co variances of the series do not depend on the time

factor. Any series that is not stationary then it is said to be non-stationary. A series is

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said to be integrated of order ‘d’ which can be denoted by I (d), means that it has to be

differenced ‘d’ times before it becomes stationary. Otherwise, if a series by itself, let say

stationary at levels, without having to be differenced, then that is said to be I(0). It is

very essential to apply unit root tests for individual series to come up with some idea

that whether the variables are integrated with same order or not. If the order of

integration is same for the entire variables then it is quite possible that study can find out

the long run and short run dynamic behavior of the variables by employing Engle-

Granger cointegration test and error correction model. (Details of the test are given in

Chapter I).

Cointegration exists for variables means despite variables are individually non-

stationary, a linear combination of two or more time series can be stationary and there is

a long-run equilibrium relationship between these variables. If the error term in (1) or (2)

is stationary while the regressors are individually trending, there may be some transitory

correlation between the individual regressors and error term. However, in the long run,

the correlation must be zero because of the fact that the variables must eventually

diverge from stationary ones. Thus the regression on the level of the variables is

meaningful and not spurious.

There are two most widely used cointegration tests namely Engle-Granger (1987)

two model approaches and the Johansen (1998) and Johansen and Juselius (JJ) (1990)

maximum likelihood estimator. Gonzalo (1994) provide empirical evidence to support

the Johansen’s method is superior over other methods (ordinary least squares, nonlinear

least squares, principal components and canonical correlations) for testing the number of

so integrating relationship. Therefore, we employ the maximum likelihood method of

Johansen (1988 and Johansen (1988) and Johansen and Juselius (1990) to test the

cointegration. The JJ test is based on vector autoregressive model. (Details are given in

Chapter I).

The causality between stock market development and economic growth are tested

by using ordinary Granger Causality bi-variate test and Granger test based on Error

Correction Model(ECM) methodology. From the ordinary Granger bi-variate test, we

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will be able to test the existence of short run relationship between variables. However,

the ECM methodology provides the evidence of short run as well as long run dynamic

relationship between variables. (Details are given in Chapter I).

Variance decomposition and impulse response analysis are also done in this

context. Impulse responses trace out the responsiveness of the dependent variables in the

VAR to shocks to each of the variables. So, for each variable from each equation

separately, a unit shock is applied to the error, and the effects upon the VAR system

over time are noted. Variance decompositions offer a slightly different method for

examining VAR system dynamics. They give the proportion of the movements in the

dependent variables that are due to their ‘own’ shocks, versus shocks to the other

variables. A shock to the ith variable will directly affect that variable of course, but it

will also be transmitted to all of the other variables in the system through the dynamic

structure of the VAR. (Details are given in Chapter I).

4.5.2 Analysis and results

(i) Descriptive statistics for BSE index

Table:- 4.5 displays general information about the Index for industrial production

(IIP), Market Capitalisation (MC), Turnover Ratio (TR) and Value Traded (VT) of

BSE. The all stock market indices are positively skewed. It indicates there is more

number of occurance of all these indices. During the selected period of the analysis ie,

since 1991 all selected indices such as market size and liquidity are showing very high

fluctuations and that too at a higher level. The values of Kurtosis show that except MCR,

all indices are showing a leptokurtic shapes. The leptokurtic distribution means that the

concerned distributions are more “peaked” and have “fatter tails; and hence greater

possibility of extreme outcomes, than is the case in the normal distribution. Consistent

with the skewness and kurtosis findings, the Jarque-Bera statistic is highly significant

(P=0.000) thereby rejecting the hypothesis that the series in BSE are normally

distributed.

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Table:-4.5

Stock Market Indices of BSE

BSE

IIP

Market

Capitalisation

(MC)

Value Traded

(VT)

Turnover Ratio

(TR)

Mean 208.9801 23012.41 557.1474 3.586400

Median 184.7000 9273.830 466.3906 2.870490

Maximum 402.9000 72945.70 1990.887 16.52077

Minimum 109.6000 3485.160 20.24000 0.444527

Std. Dev. 71.44913 22542.91 404.1543 2.784921

Skewness 0.594829 0.915977 0.803357 2.132559

Kurtosis 2.184393 2.270862 3.275433 8.487960

Jarque-Bera 18.11771 33.85538 23.14149 420.6903

Probability 0.000116 0.000000 0.000009 0.000000

Sum 43676.84 4809593. 116443.8 749.5576

Sum Sq. Dev. 1061836. 1.06E+11 33974871 1613.203

Observations 209 209 209 209

The following charts represent the trends in IIP with stock market indices of

MCR, TR and VTR in log forms. The trends in movements of MCR and TR are more

close to IIP movements. Compared to other stock market indices, the VTR witnessed

more fluctuations.

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Figure:- 4.6

Trends in IIP and stock market variables of BSE

(ii) Granger causality test results

The Granger causality test shows the short run relation between various entities.

Here the test result shows relationship between economic growths as indicated by the

proxy variable IIP with stock market index Turnover Ratio (TR). Analysis is done on the

log values of the variables. The results of this test are shown in Table:-3. The results

indicates that there is an unidirectional relationship exists between IIP and TR. Turnover

ratio does not Granger cause the economic growth while the economic growth as

represented by IIP does Granger cause the stock market development which is

represented by TR.

-2

0

2

4

6

8

10

12

25 50 75 100 125 150 175 200

LOGIIP LOGMCLOGVT LOGTR

Trends in IIP and stock market variables of BSE

logTR

logMC

logVT

logIIP

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Table:-4.6

Granger Causality Results for Economic growth

Index Vs. selected Stock Market Indices of BSE

Variables Causality F-statistics P-value

Turnover ratio (TR) TR IIP 0.29875 0.7421

IIP TR 3.10637* 0.0469

‘*’ shows the test statistics at 5% level of significance and ‘**’ shows the test statistics at 10% level of significance

(iii) Unit root and cointegration analysis

If there is any causal connection exists between economic growth and stock

market development, the next immediate concern would be to understand the long-run

relationship between these variables. The existence of long run relationship can be

understood through the integration test. If the two variables are co-integrated, then it

could be presumed that these variables have a long run relationship. For the

cointegration analysis, stationarity is first verified with estimates in Table :-4.7 The

initial hypothesis is that the variables contain a unit root. As discussed before, the unit

root test are conducted by using Augmented Dickey-Fuller (ADF) test, Phillips-Perron

(PP) test and the Kwiatkowski, Phillips, Schmidt and Shin (KPSS) test. The tests are

conducted for BSE. The tests are conducted at variable levels and first difference levels.

The results show that the hypothesis of unit root is not rejected at levels for BSE and the

same null hypothesis of unit root is rejected at first difference levels. Hence all variables

are integrated of order 1, I(1) for BSE. The cointegration analysis and long relationship

of economic growth and stock market development are pursued only in the case of BSE.

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Table:- 4.7

Unit root result (log values) of IIP and Turnover ratio of BSE

(iv) The Engle Granger Method

The cointegration and long run relationship between stock market development

and economic growth are evaluated based on the Engle Granger method. According to

this method, suppose we expect that there exists a single long-run relationship between

the two I(1) variables Y and X of the form : Y = 0 + 1X . Two steps involved in this

procedure. As a first step, estimate by OLS the long run relationship using the

cointegrating regression:

Y = 0 + 1X +u ----------------------------(4.1)

^ ^ ^

It is estimated as: Y = 0 + 1X ----------------------------(4.2)

Defining and saving the disequilibrium errors as:

Level Variables

ADF PP KPSS

t-value P-value t-value P-value LM stat Critical

value at

5%

IIP -0.74613 0.8312 -0.71154 0.8403 1.824631 0.463

TR -1.46464 0.5497 -1.7249 0.4172 0.477925 0.463

First Difference variables

ADF PP KPSS ADF PP KPSS

t-value P-value t-value t-value P-value t-value

IIP -2.88064 0.0495 -37.3113 0.0001 0.07763 0.463

TR -14.0392 0 -19.6921 0 0.301596 0.463

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^ ^ ^ ^

u = Y – Y = Y – ( 0 + 1X) -----------------------------(4.3)

If Y and X are cointegrated, u should be stationary I(0). If ‘u’ is stationary, there

exists a long run relationship between these variables and in the long run the market

disequilibrium will be corrected to certain extent by the movement of these variables.

The Engel Granger method is known as residual method for evaluating the co-

integration. Once it is cointegrated, then the Residual based Error Correction model will

be the next step, which is given as below.

From the basic model of Y = 0 + 1X +u, the Error Correction model (ECM)

specification is as follows.

^ ^ ^

Y= 0 + 1X + 2 (Yt-1- 0- 1 Xt-1) +v ------------------------------(4.4)

The interpretation is that Y is purported to change between t-1 and t as a result of change

in X between t-1 and t and in part to correct for any disequilibrium that existed in

previous period. The Cointegrating vector is [ 0 1]. 1 measures the LR relation

between Y and X, 1 measures the SR relation between X and Y and 2 speed

adjustment back to equilibrium.

(v) Relationship between economic growth and stock market development

The relationship between economic growth and stock market development are

assessed based on IIP as the dependent variable (variable for economic growth) and

turnover ratio(TR) as the independent variable (variable for stock market development).

LogIIP = 0 + 1logTR+u ------------------------------(4.5)

The result of the above model is given in Table 4.8.

The result shows that there is a negative relationship between TR and IIP in the

long run and this relationship is significant.

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Table:-4.8

Regression result of turnover ratio on IIP index

Variable Coefficient Std. Error t-Statistic Prob.

LOGTR -0.139306 0.031596 -4.408918 0.0000

C 5.429788 0.039463 137.5901 0.0000

R-squared 0.085845 Akaike info criterion 0.573196

Adjusted R-

squared 0.081429 Schwarz criterion 0.605180

F-statistic 19.43856 Hannan-Quinn criter. 0.586127

Prob(F-

statistic) 0.000017 Durbin-Watson stat 0.047133

To understand whether these two variables are integrated, the residuals terms of

the first equation is regressed to test the stationarity. The result of the stationarity result

of the ‘u’ term is given in the Table:- 4.9..

Table:-4.9

Unit root result if residual term of regression of TR on IIP index

Null Hypothesis: RESIDTRLEVEL has a unit root

Exogenous: Constant

Lag Length: 12 (Automatic based on SIC, MAXLAG=14)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -2.959301 0.0406

Test critical values: 1% level -3.463749

5% level -2.876123

10% level -2.574622

The test result indicates that null hypothesis of existence of unit root in residual

term is rejected. It implies that ‘u’ is stationary, there exists a long run relationship

between these variables and in the long run the market disequilibrium will be corrected

to certain extent by the movement of these variables. The error correction mode is

applied to understand the long run relationship between IIP and TR.

Y= 0 + 1X + 2 (Yt-1- 0- 1 Xt-1) +v ---------------------------(4.6)

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logIIP= 0.006+0.046logTR-0.013(logIIPt-1-5.42+-0.14logTRt-1) -----(4.7)

The above error correction (ECM) shows that in the short run there exists

positive relationship between IIP and TR. It shows that the stock market development

positively affect the economic growth of the nation. However, in the long run, the

relationship is negative. These two variables are cointegrated in the long-run. The

deviation between IIP and TR are corrected in the long-run by a desirable change in TR

by 1.3 percent per period.

TABLE:-4.10

The Result of Error Correction model showing the causal relation

between IIP and Turnover ratio (TR)

Dependent Variable: IIP

Variable Coefficient Coefficient values Std. Error t-Statistic Prob.

Constant of IIP 0 0.005568 0.003795 1.467159 0.1439

TR 1 0.045976 0.016821 2.733187 0.0068

U(-1) 2 -0.012752 0.012023 -1.060585 0.2901

Constant of IIP 0 5.429788 0.039463 137.5901 0.0000

TR 1 -0.139306 0.031596 -4.408918 0.0000

R-squared 0.044593 Akaike info criterion -2.958305

Adjusted R-squared 0.035272 Schwarz criterion -2.910168

F-statistic 4.784127 Hannan-Quinn criter. -2.938841

Prob(F-statistic) 0.009318 Durbin-Watson stat 2.916552

(vi) Vector Autoregression (VAR) model between IIP and TR

Various literature survey results indicate that economic growth and stock market

development are mutually interrelated and these relationships may be time lagged also.

VAR model helps us to decipher the mutual interconnection between variables with time

lag.

The following Table shows the VAR model result between IIP and TR, which

represent economic growth and stock market development respectively. The result is

given in the following Table. It shows that economic growth has a trend always. Current

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values of economic growth are influenced by its lagged values. Compared to IIP, the TR

has more fluctuating trend.

TABLE:-4.11

VAR between IIP and TR

LOGIIP LOGTR

LOGIIP(-1)

0.496784

[ 8.04727]

-0.541057

[-1.93772]

LOGIIP(-2)

0.495842

[ 8.02308]

0.458225

[ 1.63924]

LOGTR(-1)

-0.011572

[-0.75702]

0.745799

[ 10.7867]

LOGTR(-2)

0.011644

[ 0.76630]

0.207177

[ 3.01443]

C

0.046708

[ 0.80239]

0.486249

[ 1.84681]

R-squared 0.979282 0.906019

Adj. R-squared 0.978871 0.904158

Sum sq. resids 0.469876 9.612812

S.E. equation 0.048230 0.218147

F-statistic 2386.945 486.8448

Log likelihood 336.3883 23.98560

Akaike AIC -3.201819 -0.183436

Schwarz SC -3.121319 -0.102935

Mean dependent 5.291224 1.038675

S.D. dependent 0.331803 0.704648

Determinant resid covariance (dof adj.) 0.000109

Determinant resid covariance 0.000104

Log likelihood 361.7235

Akaike information criterion -3.398295

Schwarz criterion -3.237294

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IIP has positive relationship with its lagged values. IIP has negative relationship with

one month lagged values of TR. However, IIP has positive relationship with two month

lagged values of TR. The result obtained is similar to the error correction model

explained earlier.

(vii) Variance Decomposition and Impulse Response Results

The variance decompositions which show the proportion of the movements in the

dependent variables that are due to their ‘own shocks, versus shocks to the other

variable. The result of variance decomposition is given in Table :- 4.12. The variance

decomposition has done for twenty months. The result indicates that the economic

growth behaves exogenously. In the initial period, the variation in changes in economic

growth is caused by the economic growth itself. As time passes, the change in economic

growth is contributed by the selected stock market variables. However, the impact

exerted by the stock market development variables on economic growth is very low.

Only less than 1% of variation in economic growth is attributed by stock market

development even after twenty months. Similar to economic growth, the stock market

development represented by TR behaves exogenously. The contribution of economic

growth in creating variation in TR is very minimum in the Indian stock market.

Table:-4.12

Forecast Variance Decomposition Analysis of Stock Market

Development and Economic Growth

Horizon IIP TR

IIP

1 100.00 0.00

5 99.84 0.16

10 99.86 0.14

15 99.89 0.11

20 99.90 0.10

TR

1 1.30 98.70

5 0.51 99.49

10 0.46 99.54

15 0.79 99.21

20 1.41 98.59

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The impulse response estimates is given in the Table 4.13. It provides normalized

responses for the economic growth (IIP) for a typical shock to and from the economic

growth variable. These responses represent unit shocks measured standard deviations.

As can be seen from the results, the shocks in stock market development variables have

less impact on economic growth variable, IIP. The shock emanated from economic

growth has some impact on the stock market development and the impact decrease over

the time.

TABLE:-4.13

Result of impulse response function between

stock market development and economic growth

Horizon IIP TR IIP TR

To IIP From IIP

1 0.048 0.000 0.000 0.217

5 0.033 -0.001 -0.001 0.152

10 0.031 -0.001 -0.001 0.125

15 0.030 -0.001 -0.001 0.103

20 0.029 0.000 0.000 0.084

The impulse response function is shown graphically as given below. The first set

of figures show the response of IIP and TR due to one unit shock impulse of IIP. It has

fluctuating impact on TR. After initial periods, the effect creates negative impacts which

sustain over a long period. The second set of figures show the impact of changes in TR

on IIP and TR. It shows that impact is negative on IIP and it become steady after the

initial seven time periods.

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Figure 4.7

Impulse response functions diagramatically

4.6 Conclusion

The foregoing analysis made an attempt to explore the relationship between stock

market development and economic growth in the Indian economy during the period

from 1994 to 2012. The study primarily revolved around two major questions: first

whether at all any relationship exists between stock market development and economic

growth and secondly, what could be the nature and direction of the causal relationship, if

any i.e. does development of stock market promote economic growth or vice versa? The

nature of the causal link between these is evaluated by using IIP (Index of Industrial

-.02

.00

.02

.04

.06

2 4 6 8 10 12 14 16 18 20

Response of LOGIIP to LOGIIP

-.02

.00

.02

.04

.06

2 4 6 8 10 12 14 16 18 20

Response of LOGIIP to LOGTR

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14 16 18 20

Response of LOGTR to LOGIIP

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14 16 18 20

Response of LOGTR to LOGTR

Response to Cholesky One S.D. Innovations ± 2 S.E.

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Production)as proxy for economic growth and Turnover ratio (TR) as proxy for stock

market development. The result of the Granger causality test shows that there is

unidirectional relationship between IIP and Turnover Ratio. Turnover ratio does not

Granger causes the economic growth while the economic growth as represented by IIP

does Granger cause the stock market development which is represented by TR. The test

results suggest that stock market development in India leads to economic growth at least

for the period under study, which is in line with the ‘supply leading’ hypotheses. In

addition, to that the causal relationship between stock market development and

economic growth is sensitive to the proxy used for describing the stock market

development.

REFERENCES

1) Aboudou, MamanTachiwou.( (2009), ‘Causality Test Between Stock Market

Development and Economic Growth in Western African Monetary Union,’

Economia Seria Management, Vol 12(2), pp 14-27.

2) Aboudou, MamanTachiwou,(2010), ‘ Stock Market Development and Economic

Growth: The case of West African Monetary Union,’, International Journal of

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