5.1 FACTORS AFFECTING STOCK MARKET...

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CHAPTER – 5 DATA ANALYSIS – II 5.1 FACTORS AFFECTING STOCK MARKET VOLATILITY 5.1.1 Overview The factors which result in volatility can be classified into two types micro and macro. Micro factors are specific, like dividend decisions, major expansion plans, and receiving of big contracts, earning per share, company size and book value per share have significant impact upon the value of the stock of that company. Macro level factors affect the whole economic structure of the economy, and thereby, the behaviour of the stock market. The impact of these factors clearly gets reflected in the stock market in terms of volatility. These factors include tax system, interest rate, inflation rate, agriculture and industrial production, bank, GDP, Government expenditure, foreign institutional investment, the exchange rate, union budget, imports growth rate, current account deficit, money supply and foreign currency reserves. There are number of factors which result in either rise or fall of stock prices or in other words lead to volatility. But present study is confined to the following factors. (i) Foreign Institutional Investment (ii) Union Budget (iii) Inflation (iv) Interest Rate (v) Corporate Fundamentals 5.1.2 FOREIGN INSTITUTIONAL INVESTMENT 5.1.2.1 Overview Foreign investment refers to investments made by the residents of a country in the financial assets and production processes of another country (Surumpudi, 2006). FIIs are the entities which are established or incorporated outside India and invest in India (Upadhyay, 2006). After the opening up of the borders for capital movement, these investments have grown in leaps and bounds. The effect of foreign investment, however, varies from country to country. It can affect the factor productivity of the recipient country and can also affect the balance of payments. In developing countries like India there has been a great need for foreign capital, not only to increase the productivity of labour but also because foreign capital helps to build up the foreign exchange reserves

Transcript of 5.1 FACTORS AFFECTING STOCK MARKET...

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CHAPTER – 5

DATA ANALYSIS – II 5.1 FACTORS AFFECTING STOCK MARKET VOLATILITY 5.1.1 Overview

The factors which result in volatility can be classified into two types micro and

macro. Micro factors are specific, like dividend decisions, major expansion plans, and

receiving of big contracts, earning per share, company size and book value per share

have significant impact upon the value of the stock of that company. Macro level factors

affect the whole economic structure of the economy, and thereby, the behaviour of the

stock market. The impact of these factors clearly gets reflected in the stock market in

terms of volatility. These factors include tax system, interest rate, inflation rate,

agriculture and industrial production, bank, GDP, Government expenditure, foreign

institutional investment, the exchange rate, union budget, imports growth rate, current

account deficit, money supply and foreign currency reserves. There are number of factors

which result in either rise or fall of stock prices or in other words lead to volatility. But

present study is confined to the following factors.

(i) Foreign Institutional Investment

(ii) Union Budget

(iii) Inflation

(iv) Interest Rate

(v) Corporate Fundamentals

5.1.2 FOREIGN INSTITUTIONAL INVESTMENT

5.1.2.1 Overview

Foreign investment refers to investments made by the residents of a country in

the financial assets and production processes of another country (Surumpudi, 2006). FIIs

are the entities which are established or incorporated outside India and invest in India

(Upadhyay, 2006). After the opening up of the borders for capital movement, these

investments have grown in leaps and bounds. The effect of foreign investment, however,

varies from country to country. It can affect the factor productivity of the recipient

country and can also affect the balance of payments. In developing countries like India

there has been a great need for foreign capital, not only to increase the productivity of

labour but also because foreign capital helps to build up the foreign exchange reserves

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needed to meet trade deficits. Foreign investment provides a channel through which

developing countries can gain access to foreign capital. Such an investment can be of

two types: Foreign direct investment (FDI), and Foreign institutional investment (FII).

Foreign direct investment involves in direct production activities and is also of a medium

to long-term nature. But foreign institutional investment is a short-term investment,

mostly in the financial markets. FII, given its short-term nature, can have bidirectional

causation with the returns of other domestic financial markets such as money markets,

stock markets, and foreign exchange markets (Ray, 2009).

India has taken various measures to attract foreign investment since the beginning

of reforms in 1991. As a result, India succeeded in attracting a foreign institution

investment of around US $121559 million up to 2010-2011. It is evident that such a huge

foreign institutional investment can definitely play a significant role in the development

of a country. India is in the process of liberalizing its capital account, and this has a great

impact on foreign investment and particularly on FII, which affects short-term stability in

the financial markets.

FIIs have emerged as important players in the Indian equity market in the recent

past. Huge foreign capital flows affect not only wide range of economic variables such as

exchange rates, interest rates, foreign exchange reserves, domestic monetary conditions

but also savings and investments, and thus causing a snowballing effect on the Indian

stock market as well. There is a widespread belief that institutional investors particularly

the Foreign Institutional Investors (FIIs) play a major role in the movements of the

leading Indian stock indices (Bodla and Garg, 2007).

FIIs have both positive as well as negative aspects so far as the stock market is

concerned. On the positive side, FIIs investment can bring higher inflow of foreign

reserve which can help in improving the balance of payment situation of country; it can

provide liquidity in the system which can reduce the cost of borrowing, and hence

promote investment activities; and it can also improve the functioning of stock market as

it is believed to invest after a thorough analysis of stock valuation (Panda, 2005). On the

flipside, FIIs can cause heavy fluctuations in the stock market with slight market

disruption; speculators can artificially increase and decrease for short-term gains and

with the slightest hint they can pull back from stock market causing huge volatility in the

stock market, and in this process some make good profit by booking when the index is

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high and some lose money that are not able to sell. Investment by foreign institutions

may not be that dangerous as high stock price indicates the gains of the stock holders.

Sudden withdrawal of investment is dangerous for the people who are holding the stocks

in anticipation of further increase. Hence, it is important for the investor to know when to

enter and when to exit (Sujit, 2010). In the last decade, capital flows attracted more

interest by policy-makers, central banks, international institutions and academia, mainly

because flow volume has grown at a phenomenal rate since the beginning of the 1990s.

5.1.2.2 Impact of Capital Inflows on Stock Market Volatility: Theory and Empirical

Evidence

There is a popular belief that institutional traders destabilize stock prices. Due to

their specific investment behaviour, institutions are supposed to move stock prices away

from fundamentals and thereby induce stock returns autocorrelation and increase returns

volatility. Herding, Positive feedback trading and Contagion are the main arguments put

forward for the destabilizing impact on stock prices induced by institutional investors

(Mazumdar, 2004).

Therefore, the first channel through which capital inflows cause volatility in the

stock market is that of ‘Positive feedback trading’. Positive feedback trading or ‘trend

behaviour’ refers to tendency among fund managers to buy ‘winner’ stocks and selling

‘loser’ stocks. It describes the strategy of rushing in when the markets are booming and

rushing out when the markets are on the decline. Batra (2003) finds strong evidence that

FIIs have been positive feedback investors at the aggregate level on daily basis. Investors

can be positive feedback traders for rational reasons or because of behavioural biases.

Investors with such strategies are often viewed to be destabilizing because their sales

lead the market to fall further and their purchases increase prices further. Besides

contributing to the volatility of stock returns, it is argued that such trading leads to

destabilizing capital flows. This is because equity investors rush into countries whose

stock markets are booming and flee from countries whose stock markets are falling (Bohl

and Brzeszczynski, 2005).

The second channel through which capital inflow causes volatility in the stock

market is herding mentality. Herding behaviour is defined as the tendency of fund

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managers and investors to follow other fund managers trading behavior, ignoring their

own information. Batra (2003) found that in India foreign investors have a tendency to

herd on the equity market.

The third channel through which capital inflow causes stock market volatility is

through contagion. Contagion is then best defined as a significant increase in cross

linkages after a shock to an individual country (or a group of countries). Fund managers

often use contagion strategies, i.e., they sell assets in one country when a crisis hits

another country in the same region. The contagion channel is an extension of the herd

behaviour of fund managers. While herding attracts funds to one region of the world,

contagion precipitates exit of fund manager from the same region in times of crisis

(Dornbusch et al., 2000).

5.1.2.3 Trends in FII Investment

Foreign institutional investment (FII) is one of the main channels of foreign

investment in India. Foreign institutional investors (FIIs) were permitted to invest in

Indian securities market in 1992 to widen and broaden the Indian capital market. The

data showing FII investment trends in India during the period 1992-93 to 2010-2011 is

presented in the following table.

Table 5.1 reveals that cumulative net investment by FIIs which was only US $ 4

mn. in 1992-93 increased to US $ 121559 by 2010-11. Since then, the net investment by

FIIs in India has been positive every year except in 1998-99 and 2008-09 (Graph5.1). A

major factor which led to continuous outflow of funds during the middle and end of the

year 1998 was the worsening outlook on the emerging markets. Credit worthiness of

almost all the South-east Asian nations was severely damaged by the crisis which started

in July 1997. As a result, the FIIs were facing heavy redemption pressures from the

Emerging Markets Funds. The stock markets in all these countries fell continuously from

March 1998 to September 1998.

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Table 5.1: Trends in FII Investment in India

Year Gross

Purchase

(Rs. Crore)

Gross

Sales

(Rs. Crore)

Net

Investment

(Rs. Crore)

Net

Investment

(US $ mn.)

Cumulative

Investment

(US $ mn.)

1992-93 18 4 13 4 4

1993-94 5593 467 5127 1634 1638

1994-95 7631 2835 4796 1528 3167

1995-96 9694 2752 6942 2036 5202

1996-97 15554 6980 8575 2432 7635

1997-98 18695 12737 5958 1650 9285

1998-99 16116 17699 -1584 -386 8898

1999-00 56857 46735 10122 2474 11372

2000-01 74051 64118 9933 2160 13531

2001-02 50071 411308 8763 1839 15371

2002-03 47062 44372 2689 566 15936

2003-04 144855 99091 45764 10005 25942

2004-05 216951 171071 45880 10352 36293

2005-06 346976 305509 41467 9363 45657

2006-07 520506 489665 30841 6821 52477

2007-08 948010 881839 66179 16442 68919

2008-09 614576 660386 -45811 -9837 59082

2009-10 846438 703780 142658 30252 89333

2010-11 992599 846161 146438 32226 121559

Source: Handbook of Statistics of Indian Securities Market, 2011, SEBI.

The integration of the Indian Capital Markets with the international markets thus

spilled over to Indian markets as well. But the low volatility of the Indian market as

compared to emerging market economies and an efficient market structure were some of

the factors responsible for continued confidence of FIIs in Indian Capital market. During

the last few years there has been phenomenal increase in portfolio investment by FIIs in

the Indian market (Graph 5.1).

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Graph 5.1: Trends in Foreign Institutional Investment

-2000

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

1992-93

1993-94

1994-95

1995-96

1996-97

1997-98

1998-99

1999-00

2000-01

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

Year

0

10000

20000

30000

40000

50000

60000

70000

80000

Net Investment(US $ mn. Cumulative Investment(US $ mn.

Source: Compiled from Table 5.1

5.1.2.4 Analysis and Discussion

The causal relationship between the net FII investment and stock market

volatility, i.e., BSE Sensex and NSE Nifty has been analyzed by applying the Granger

Causality Test. For any time-series data analysis, all data series should be stationary. To

study the stationarity of data series, the Augmented Dicker-Fuller (ADF) test has been

conducted. The ADF test statistics for the FII investments is presented in Table 1. The

results show that ADF statistic is -4.408207 for FII. The ADF value for FII is lesser than

critical values leading to inference of rejection of hypothesis with unit root at 1%, 5%

and 10% level of significance. This proves that the time series data of net FII

investments are stationary.

Table 5.2: ADF Test Statistics for FII Investment

t-Statistic Probability

Augmented Dickey-Fuller test statistic -4.408207 0.0004

Test critical value 1% level -3.465014

5% level -2.876677

10% level -2.574917

Source: Calculated from the data of FII given in Appendix 1.

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The ADF test statistics for the stock market volatility, i.e., BSE Sensex and NSE

Nifty are presented in Tables 5.3 and 5.4 respectively. The results show that ADF

statistic is - 4.588579 for volatility of BSE Sensex return and -5.136850 for volatility of

NSE Nifty return. Both the ADF values for volatility of stock market return of BSE

Sensex and NSE Nifty are lesser than critical values leading to inference of rejection of

hypothesis with unit root at 1%, 5% and 10% level of significance. This proves that the

time series data of stock market volatility BSE Sensex and NSE Nifty is stationary.

Table 5.3: ADF Test Statistics for Stock Market Volatility (BSE Sensex)

t-Statistic Probability

Augmented Dickey-Fuller test statistic -4.588579 0.0002

Test critical value 1% level -3.465014

5% level -2.876677

10% level -2.574917

Source: Calculated from the data of stock market volatility (BSE Sensex) given in

Appendix 1.

Table 5.4: ADF Test Statistics for Stock Market Volatility (NSE Nifty)

t-Statistic Probability

Augmented Dickey-Fuller test statistic -5.136850 0.0000

Test critical value 1% level -3.465014

5% level -2.876677

10% level -2.574917

Source: Calculated from the data of stock market volatility (NSE Nifty) given in

Appendix 1.

As the test confirmed that FII investment and stock market volatility i.e., BSE

Sensex and NSE Nifty are stationary in nature, the direction of causal relationship

between the stationary data series has been conducted by applying the Granger-Causality

test. The results of the Granger-Causality test are presented in Table 5.5.

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Table 5.5: Granger-Causality Test Statistics

Null Hypothesis Observation F-statistics Probability

FII does not Granger Cause

Volatility(BSE Sensex) 190 1.38943 0.25180

Volatility (BSE Sensex) does not

Granger Cause FII 190 0.50388 0.60501

FII does not Granger Cause

Volatility(NSE Nifty) 190 1.18699 0.30745

Volatility (NSE Nifty) does not

Granger Cause FII 190 0.35368 0.70257

Source: Compiled from the data given in Appendix 1 using Eviews 5.

Results from Granger Causality suggest independence between FII and stock

market volatility, i.e., BSE Sensex and NSE Nifty. It can be clearly observed that the

probability for the first and second hypothesis is 0.25180 and 0.60501 respectively,

which is greater than critical value 0.05. Hence, null hypothesis is accepted, i.e., it proves

that foreign institutional investments do not cause stock market volatility (BSE Sensex)

and volatility in BSE Sensex does not cause foreign institutional investments. Similar

results appear for the third and fourth hypothesis thus, null hypothesis is accepted in

these cases also.

5.1.2.5 Results

All the series have been observed to be stationary since the calculated ADF

statistics are lesser than critical values. Though theoretically there seems to be a long run

relationship between stock market volatility and FII equity flows in India, yet the

relationship is not statistically significant. As far as causality relationship is concerned, it

suggests independence between FII and stock market volatility i.e., BSE Sensex and

NSE Nifty. Thus, it can be concluded that FII equity flows have neither increased nor

dampened volatility of stock market returns in India. There could be several reasons for

such insignificant relationship. One of the reasons could be that FIIs are allowed to

perform only delivery based trading and are not allowed to indulge in short selling. This

considerably restricts trading activity. Secondly, FIIs had to register with an Indian

broker to carry out their transactions (Mazumdar, 2004). They were required to pay

higher brokerage for trading as compared to domestic institutional investors. This made

their transactions more expensive than those of domestic institutions. Thirdly, FIIs have

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expert knowledge, and therefore, can accurately make predictions about emerging

markets performance in advance, and hence, pro-active with regard to invest or withdraw

decision (Dhillon and Kaur, 2007). Despite the significant share that is allowed to

foreign institutional investments in India, there is no evidence of any strong statistical

relationship between FII equity flows and volatility of stock market returns in India.

5.1.3 UNION BUDGET

5.1.3.1 Overview The global financial crisis spared not even the strongest economies of the world.

In such a state of uncertainty, the fiscal measures taken by the government were meant to

revamp the faith of the investors in the financial system of the economy. The stock

markets are the barometer of fundamental strength of an economy as the development of

stock exchanges is based upon the potential future of the economy. Hence, any kind of

fiscal measures may have a great impact on the rational behaviour of the stock market. In

India, the contents of budget play a major role in developing the perception of investors

for the future prospect of the industrial development (Sharma and Mehta, 2010).

In Wikipedia, the budget is an annual financial statement containing the

estimated receipts and expenditure of the Government of India, which has to be laid

before Parliament in respect of every financial year, which runs from 1st April to 31st

March under article 112 of the constitution. A budget is a powerful tool in the hands of

the government to control the economic resources of the country. It contains proposals

regarding changes in direct and indirect taxes, industrial policy, trade policy, exchange

rate policy and financial sector reforms which may have favourable or adverse impact on

stock market (Gupta and Kundu, 2006). A budget is classified into revenue budget and

capital budget which is based on the nature of expenses. Overall, through budgets the

government is able to implement the fiscal policy. The finance bill actually determines

the rates of both the direct and indirect taxes affecting the individuals as well as all forms

of commercial enterprises. Thus, budget is one macroeconomic phenomenon, which

influences each and every company (Verma and Agarwal, 2005).

The Union Budget is perhaps the most watched event in economic policy making

in India. The core fiscal issues – taxation, expenditures, the fiscal deficit – are obviously

important for macro-economics. In addition, governments have often chosen to use the

Budget speech as a mechanism for announcing important new policy initiatives, and for

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outlining some plans for economic policy in the coming months (Thomas and Shah,

2002).

The market participants expect many changes from the budget and revise their

expectations following the announcement of the same. Market starts reacting well in

advance due to the anticipated changes in policies. Budget proposals may prove better

than or fall short of expectations. They may include unexpected changes. Investors revise

stock prices in response to surprises or shocks and non-fulfilment of their expectations.

Union Budget and the stock market, both share a close relationship. Every time the

Finance Minister walks inside the Parliament, everyone at Dalal Street is glued to TV

sets to listen to what he says. And the index pointer dances to the tune. Stock Market

tends to be greatly influenced by the budget. The Stock Market response reflects the

quality of budget. If the government gives subsidy or tax rebate or if there are some

policy changes then the respective sectors get influenced positively or negatively which

affects the overall stock market (Patra, 2012).

5.1.3.2 Analysis and Discussion

Tables 5.6 and 5.7 depict the daily average returns and budget day returns

presented by Sensex and Nifty respectively during the previous and next 3, 15 and 30

days around the budget day.

Budget Day Effect on Sensex and Nifty: The first set of paired t-tests measure the on-

day (Z) influence of budget on Sensex and Nifty when compared to the previous 3, 15

and 30 days. A cursory glance at both the tables highlights that in most of the cases

budget day returns (ignoring sign) are more than the returns during the previous 30, 15

and 3 trading days. Therefore, when the budget day returns (Z) compared with the long-

term pre-budget return in the case of Sensex , it shows that budget day returns exceed in

all years, i.e., (19 out of 19 budgets),compared to medium-term (19 out of 19 budgets)

and short-term (13 out of 19 budgets); and in the case of Nifty it shows

Table 5.6: Daily Average Returns in Sensex

Year

X1(Last

30 days)

X2(Last

15 days)

X3(Last

3 days)

Z(Budget

day)

Y1(Next

3 days)

Y2(Next

15 days)

Y3(Next

30 days)

1996 0.52 0.56 0.18 -0.72 -0.89 -0.51 0.09

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(interim)

1996 -0.09 -0.09 1.06 1.17 -2.25 -0.64 -0.27

1997 -0.21 0.05 0.68 6.33 2.57 0.14 -0.01

1998 -0.37 -0.66 -1.66 -1.19 -0.89 -1.12 -0.31

1999 -0.08 0.04 -0.51 4.99 1.93 0.67 -0.07

2000 0.16 0.32 0.58 -5.25 -0.42 -0.42 -0.17

2001 0.02 -0.48 -1.54 4.26 -2.01 -0.89 -0.89

2002 0.34 0.52 0.93 -3.94 0.73 -0.05 -0.10

2003 -0.08 0.04 -0.45 0.19 -0.59 -0.30 -0.30

2004

(interim) 0.17 -0.47 -1.70 -1.32 0.97 0.13 -0.05

2004 -0.08 0.23 0.58 -2.29 0.38 0.37 0.19

2005 0.25 -0.05 -0.10 2.17 0.35 -0.06 -0.13

2006 0.31 0.36 0.19 0.85 0.72 0.30 0.36

2007 -0.04 -0.49 -1.32 -4.09 -1.38 0.004 0.11

2008 -0.33 0.11 0.33 -1.39 -2.03 -0.59 -0.21

2009

(interim) -0.01 0.59 -0.04 -3.48 -0.73 0.35 -0.96

2009 0.24 -0.14 0.95 -6.01 0.60 0.22 -0.69

2010 -0.23 -0.10 0.04 1.07 0.39 0.24 1.08

2011 -0.27 -0.11 -1.10 0.69 0.06 0.33 1.22

Source: Calculated from the data taken from BSE website for the said period. Note: All returns are in percentage.

that budget day returns exceed (18 out of 19 budgets) when compared with the long-

term pre-budget returns, when the budget day returns (Z) compared with the medium-

term pre-budgets return, it shows that budget day returns exceed (18 out of 19 budgets)

and when the budget day returns (Z) compared with the short-term pre-budget returns, it

shows that budget day returns exceed (14 out of 19 budgets).

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Table 5.7: Daily Average Returns in Nifty

Year

X1(Last

30 days)

X2(Last

15 days)

X3(Last

3 days)

Z(Budget

day)

Y1(Next

3 days)

Y2(Next

15 days)

Y3(Next

30 days)

1996

(interim) 0.53 0.57 0.08 -0.01 -0.94 -0.48 0.06

1996 -0.03 -0.05 0.78 0.57 -1.92 -0.58 -0.32

1997 -0.16 0.17 0.95 0.59 3.74 0.41 0.12

1998 -0.42 -0.59 -1.27 -0.89 -0.86 -1.20 -0.30

1999 -0.07 0.03 -0.30 4.17 1.98 0.52 0.04

2000 0.20 0.34 0.51 -4.01 0.02 -0.42 -0.29

2001 0.02 -0.46 -1.50 4.22 -2.03 -0.86 -0.86

2002 0.29 0.44 0.73 -4.05 1.05 0.01 -0.02

2003 -0.1 0.04 -0.54 0.99 -0.72 -0.32 -0.37

2004

(interim) 0.14 -0.56 -1.70 -2.28 1.20 0.19 -0.04

2004 -0.07 0.31 0.63 -3.15 0.46 0.43 0.19

2005 0.25 -0.05 0.04 2.03 0.40 -0.02 -0.13

2006 0.24 0.24 0.18 0.24 0.78 0.35 0.36

2007 -0.04 -0.53 -1.23 -3.89 -1.54 0.03 0.15

2008 -0.37 0.19 0.54 -1.17 -1.99 -0.52 -0.17

2009

(interim) -0.01 0.55 0.16 -3.45 - 0.7 -0.56 0.40

2009 0.14 -0.02 1.02 -6.02 -0.69 0.62 0.17

2010 -0.25 0.02 0.02 1.28 1.05 0.37 0.23

2011 -0.27 -0.11 -1.03 0.56 1.26 0.10 0.34

Source: Calculated from the data taken from NSE website for the said period. Note: All returns are in percentage.

The observations made in Tables 5.6 and 5.7 have been further statistically tested

by Paired t- test; and the results so obtained have been presented in Tables 5.8 and 5.9.

The budgets are found to take the markets by surprise in all the cases. In all the tests, the

actual values are found to exceed the table values leading to acceptance of alternative

hypothesis.

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T-values from Paired T-test

Table 5.8: Effect of Budget Day Returns on Sensex

X1 and Z X2 and Z X3 and Z

Actual Value (5%) -5.35¬ -5.25¬ -4.29¬

Table Value (5%) -1.76

-1.76

-1.76

Source: Calculated from the data given in Table 5.6. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

Table 5.9: Effect of Budget Day Returns on Nifty

X1 and Z X2 and Z X3 and Z

Actual Value (5%) -4.98¬ -4.99¬ -4.04¬

Table Value (5%) -1.76

-1.76

-1.76

Source: Calculated from the data given in Table 5.7. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

T- values from Paired t-test

Table 5.10: Impact of Budgets on Sensex

Period Short-term Period Medium-term Period Long-term Period

X1 and

Y1

X2 and

Y1

X3 and

Y1

X1 and

Y2

X2 and

Y2

X3 and

Y2

X1 and

Y3

X2 and

Y3

X3 and

Y3

Actual

Value

(5%)

-5.83¬ -5.01¬ -2.41¬ -2.76¬ -1.78* 2.30 -0.51 0.89 4.05

Table

Value

(5%)

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

Source: Calculated from the data given in Table 5.6. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

The second set of tests provided in Tables 5.10 and 5.11 highlights that budgets

have maximum impact in the short-term (alternative hypotheses have been accepted in

all three cases). In medium-term, the alternative hypothesis has been accepted in two out

of three cases and in long-term, no alternative hypothesis has been accepted.

Table 5.11: Impact of Budgets on Nifty

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Period Short-term Period Medium-term Period Long-term Period X1 and

Y1 X2 and Y1

X3 and Y1

X1 and Y2

X2 and Y2

X3 and Y2

X1 and Y3

X2 and Y3

X3 and Y3

Actual Value (5%)

-5.17¬ -4.62¬ -2.78¬ -3.15¬ -1.87* 2.23 -0.79 0.58 3.94

Table Value (5%)

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

Source: Calculated from the data provided in Table 5.6. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

In total, the actual values exceed the tabular values in five cases (3+2+0) out of

nine at the left tail. This proves that budgets, when taken together have the maximum

impact in the short term post-budget period, with some impact extending into the

medium-term and no significant impact at all on long-term average returns.

Table 5.12: Variance of Returns in Sensex

Year X1(Last 30 days)

X2(Last 15 days)

X3(Last 3 days)

Y1(Next 3 days)

Y2(Next 15 days)

Y3(Next 30 days

1996 (interim) 0.036 0.037 0.009 0.036 0.018 0.016 1996 0.019 0.022 0.016 0.026 0.023 0.024 1997 0.033 0.016 0.010 0.069 0.041 0.060 1998 0.032 0.030 0.007 0.019 0.108 0.083 1999 0.020 0.013 0.007 0.067 0.031 0.070 2000 0.038 0.056 0.113 0.118 0.050 0.081 2001 0.020 0.019 0.026 0.059 0.095 0.069 2002 0.011 0.010 0.024 0.049 0.021 0.013 2003 0.006 0.007 0.007 0.002 0.014 0.016 2004 (interim) 0.032 0.048 0.001 0.023 0.022 0.023 2004 0.024 0.014 0.013 0.024 0.008 0.009 2005 0.009 0.004 0.000 0.015 0.006 0.011 2006 0.009 0.008 0.004 0.012 0.010 0.014 2007 0.015 0.017 0.022 0.077 0.045 0.039 2008 0.095 0.050 0.002 0.105 0.108 0.072 2009 (interim) 0.071 0.044 0.029 0.031 0.031 0.058 2009 0.032 0.028 0.007 0.038 0.041 0.043 2010 0.014 0.013 0.001 0.013 0.005 0.006 2011 0.017 0.021 0.031 0.037 0.019 0.015

Source: Calculated from the data taken from BSE website for the said period.

Table 5.13: Variance of Returns in Nifty

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Year

X1 (Last

30 days)

X2 (Last

15 days)

X3 (Last

3 days)

Y1 (Next

3 days)

Y2 (Next

15 days)

Y3 (Next

30 days

1996

(interim) 0.045 0.055 0.005 0.049 0.019 0.017

1996 0.013 0.015 0.046 0.015 0.019 0.017

1997 0.027 0.018 0.017 0.288 0.095 0.087

1998 0.017 0.022 0.0001 0.055 0.108 0.078

1999 0.031 0.023 0.012 0.067 0.027 0.031

2000 0.027 0.029 0.028 0.093 0.054 0.076

2001 0.017 0.016 0.010 0.051 0.101 0.069

2002 0.011 0.008 0.013 0.032 0.016 0.012

2003 0.008 0.008 0.007 0.002 0.013 0.018

2004

(interim) 0.037 0.054 0.004 0.040 0.026 0.025

2004 0.028 0.017 0.019 0.030 0.011 0.012

2005 0.010 0.004 0.0004 0.017 0.006 0.011

2006 0.008 0.006 0.003 0.007 0.010 0.014

2007 0.016 0.018 0.017 0.090 0.048 0.042

2008 0.121 0.055 0.005 0.105 0.098 0.064

2009

(interim) 0.060 0.039 0.024 0.033 0.029 0.049

2009 0.039 0.037 0.006 0.041 0.042 0.039

2010 0.014 0.011 0.001 0.012 0.006 0.006

2011 0.018 0.022 0.042 0.037 0.019 0.014

Source: Calculated from the data taken from NSE website for the said period.

Tables 5.14 and 5.15 present the F-test values for the tests that compare the

variance among the returns in Sensex (given in Table 5.12) and Nifty (given in Table

5.13) respectively during the short-term, medium-term, and long-term periods after the

budget with one another. In the case of Sensex, barring the year 2002, in no other year

the actual value exceeded the tabular value. However, in Nifty, there was no case where

the actual value exceeded the tabular value. This signifies that volatility does not

generally increase in post-budget situation as the time progresses.

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Table 5.14: F-test Results Comparing Variance among the Returns (Post-budget)

with one another (BSE Sensex)

Year

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Y1 and Y2 df = 14/2 Y2 and Y3 df= 29/14 Y3 and Y1 df= 29/2

1996

(interim) 2.00 3.74 1.13 2.03 2.25 3.33

1996 1.13 3.74 1.04 2.03 1.08 3.33

1997 1.68 3.74 1.46 2.31 1.15 3.33

1998 5.68 19.43 1.30 2.03 4.37 19.46

1999 2.16 3.74 2.26 2.31 1.05 19.46

2000 2.36 3.74 1.62 2.31 1.46 3.33

2001 1.61 19.43 1.38 2.03 1.17 19.46

2002 2.33 3.74 1.62 2.03 3.77¬ 3.33

2003 7.00 19.43 1.14 2.31 8.00 19.46

2004

(interim) 1.05 3.74 1.05 2.31 1.00 3.33

2004 3.00 3.74 1.13 2.31 2.67 3.33

2005 2.50 3.74 1.83 2.31 1.36 3.33

2006 1.20 3.74 1.40 2.31 1.17 19.46

2007 1.71 3.74 1.15 2.03 1.97 3.33

2008 1.03 19.43 1.50 2.03 1.46 3.33

2009

(interim) 1.00 3.74 1.88 2.31 1.87 19.46

2009 1.08 19.43 1.05 2.31 1.13 19.46

2010 2.60 3.74 1.20 2.31 2.17 3.33

2011 1.95 3.74 1.27 2.03 2.47 3.33

Source: Calculated from the data provided in Table 5.12. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

Table 5.15: F-test Results Comparing Variance among the Returns (Post-budget)

with one another (NSE Nifty)

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Year

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Y1 and Y2 df = 14/2 Y2 and Y3 df= 29/14 Y3 and Y1 df= 29/2

1996

(interim) 2.58 3.74 1.12 2.03 2.88 3.33

1996 1.27 19.43 1.12 2.03 1.13 19.46

1997 3.03 3.74 1.09 2.03 3.31 3.33

1998 1.96 19.43 1.38 2.03 1.42 19.46

1999 2.48 3.74 1.15 2.31 2.16 3.33

2000 1.72 3.74 1.41 2.31 1.22 3.33

2001 1.98 19.43 1.46 2.03 1.35 19.46

2002 2.00 3.74 1.33 2.03 2.67 3.33

2003 6.50 19.43 1.38 2.31 9.00 19.46

2004

(interim) 1.54 3.74 1.04 2.03 1.60 3.33

2004 2.73 3.74 1.09 2.31 2.50 3.33

2005 2.83 3.74 1.83 2.31 1.55 3.33

2006 1.43 19.43 1.40 2.31 2.00 19.46

2007 1.88 3.74 1.14 2.03 2.14 3.33

2008 1.07 3.74 1.53 2.03 1.64 3.33

2009

(interim) 1.14 3.74 1.69 2.31 1.48 19.46

2009 1.02 19.43 1.08 2.03 1.05 3.33

2010 2.00 3.74 1.00 2.03 2.00 3.33

2011 1.95 3.74 1.36 2.03 2.64 3.33

Source: Calculated from the data provided in Table 5.13.

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Table 5.16: F-test Results Comparing Variance among the Returns during Post-

budget Periods with Long-term Pre-Budget Period (BSE Sensex)

Year

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

X1 and Y1 df=29/2 X1 and Y2 df=29/14 X1and Y3 df=29/29

1996

(interim) 1.00 3.33 2.00 2.31 2.25¬ 1.85

1996 1.37 3.33 1.21 2.03 1.26 1.84

1997 2.09 3.33 1.24 2.03 1.81 1.84

1998 1.68 3.33 3.38¬ 2.03 2.59¬ 1.84

1999 3.35 3.33 1.55 2.31 3.50¬ 1.84

2000 3.11 3.33 1.32 2.03 2.13¬ 1.84

2001 2.95 3.33 4.75¬ 2.03 3.45¬ 1.84

2002 4.45¬ 3.33 1.91 2.03 1.18 1.84

2003 3.00 19.46 2.33¬ 2.03 2.67¬ 1.84

2004

(interim) 1.39 3.33 1.45 2.31 1.39 1.85

2004 1.00 3.33 3.00¬ 2.31 2.67¬ 1.85

2005 1.67 3.33 1.5 2.03 1.22 1.84

2006 1.34 19.46 1.11 2.03 1.56 1.84

2007 5.13¬ 3.33 3.00¬ 2.03 2.60¬ 1.84

2008 1.11 19.46 1.14 2.31 1.32 1.85

2009

(interim) 2.29 19.46 2.29 2.31 1.22 1.85

2009 1.19 3.33 1.28 2.03 1.34 1.84

2010 1.08 19.46 2.80* 2.31 2.33 1.85

2011 2.18 3.33 1.12 2.03 1.14 1.85

Source: Calculated from the data provided in Table 5.12.

Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

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Table 5.17: F-test Results Comparing Variance among the Returns during Post-

budget Periods with Long-term Pre-Budget Period (NSE Nifty)

Actual

Value

Table Value

(5%)

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

( 5%)

Year X1 and Y1 df=29/2 X1 and Y2 df=29/14 X1 and Y3 df=29/29

1996

(interim) 1.09 3.33 2.37* 2.31 2.65* 1.85

1996 1.15 3.33 1.46 2.03 1.31 1.84

1997 10.67* 3.33 3.52* 2.03 3.22* 1.84

1998 3.23 3.33 6.35* 2.03 4.59* 1.84

1999 2.16 3.33 1.15 2.31 1.00 1.84

2000 3.44 3.33 2.00 2.03 2.81* 1.84

2001 3.00 3.33 5.94* 2.03 4.06* 1.84

2002 2.90 3.33 1.45 2.03 1.09 1.84

2003 4.00 19.46 1.63 2.03 2.25* 1.84

2004

(interim) 1.08 3.33 1.42 2.31 1.48 1.85

2004 1.07 3.33 2.55* 2.31 2.33* 1.85

2005 1.70 3.33 1.67 2.03 1.10 1.84

2006 1.14 19.46 1.25 2.03 1.75 1.84

2007 5.63* 3.33 3.00* 2.03 2.63* 1.84

2008 1.15 19.46 1.23 2.31 1.89* 1.85

2009

(interim) 1.81 19.46 2.07 2.31 1.22 1.85

2009 1.05 3.33 1.07 2.03 1.00 1.84

2010 1.17 19.46 2.33 2.31 2.34 1.85

2011 2.06 3.33 1.06 2.03 1.29 1.85

Source: Calculated from the data provided in Table 5.13. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

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Tables 5.16 and 5.17 highlights the specifically F-test values for the tests that

compare the variance of returns in Sensex and Nifty during short-term, medium-term,

and long-term post-budget periods with that of long-term pre-budget period. In the case

of Sensex, maximum number of significant cases (in 9 out of 19 budgets) appeared

during the long-term period as compared to medium-term (in 6 out of 19 budgets) and

short-term (in 2 out of 19 budgets) periods. However, in the case of Nifty, maximum

number of significant cases (in 10 out of 19 budgets) emerged during the long-term

period as compared to medium-term (in 7 out of 19 budgets) and short-term (in 2 out of

19 budgets) periods. It indicates that the long-term period after the budget tends to be

more volatile than the medium-term and short-term periods as compared to similar long-

term period before the budget.

The test values, however, do not prove whether the market index will rise or fall

in the post-budget period because there has been an increase in the index values after the

presentation of budgets.

If one travels back in time to re-live the budget day moves made since the year

1996, the steep cut was witnessed in the year 2009 when the Sensex and Nifty tumbled

over 6% (as shown in Table 5.6 and Table 5.7). Silence on major policy reforms,

increase in MAT and soaring deficits were some of the irritants that led to the decline. In

2000, once again, the Sensex tumbled over 5 per cent and Nifty over 4%. The budget

caused disappointment among the investors as it did not live up to the `hype' created

ahead of its announcement. The increase in the tax on dividend outgo for companies and

subjecting export earnings to a 20 per cent tax per annum over the next five years were

seen as unfavourable by the market and also, the Budget failed to address macro-

economic issues such as fiscal deficit, Government spending and public sector

disinvestment.

In 2002, the stock markets fell by 3 per cent with a lukewarm budget. In 2007,

the stock market crashed by 4 per cent. This was the biggest fall on a Budget day in the

past few years. The fall was due to market unfriendly Union budget. It increased

dividend distribution tax from 12.5 per cent to 15 per cent, followed by an increase in

excise duty on cement prices, and an extension of minimum alternate tax (MAT) for the

IT sector.

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On the other hand, there have been occasions when budget had helped the market

to move up in jiffy. In 1997, 1999, 2001 and 2005, popular measures taken by the

finance minister like relaxations in taxes helped the Sensex and Nifty to climb sharply.

Hence, if an investor wants to make any gain from budget swings, he will have to

predict the budget announcements that may cause a rise or fall in post- budget share

prices.

5.1.3.3 Results

The impact of various budgets on the stock market over a long period of 17 years

has been studied taking into account the returns and volatility in Sensex and Nifty. The

hypothesis tests at various levels of significance have provided some interesting results

not only from the of an investors’ and regulators point of view but also from the

government viewpoint.

With regard to returns, an investor has a chance to earn big profits by making

investments during the short-term and medium-term periods around the budget (up to15

trading days). However, there may be a risk of abnormal losses if his expectations are not

met from the budget. This is also true in the case of trading on the budget day. As one

moves away from the budget day (up to 30 trading days), the paired t-tests do not show

any significant change in average returns. Hence, budgets are seen to have effect only up

to 15 trading days from the budget day so far as return is concerned.

Volatility, on the other hand, does not generally increase in post-budget situation

as the time period increases. But the long-term period after the budget tends to be more

volatile than the medium-term and short-term periods as compared to similar long-term

period before the budget. In the case of Sensex, only 11% cases (2 out of 19 budgets)

post budget volatility during short term, in 32% cases (6 out of 19 budgets) post-budget

volatility during medium-term and in 47% cases (9 out of 19 budgets) post budget

volatility during long-term tends to increase in relation to the volatility during long-term

before the budget. However, in the case of Nifty, only 10% cases (2 out of 19 budgets)

post-budget volatility during short-term, in 37% cases (7 out of 19 budgets) post-budget

volatility during medium-term and in 53% cases (10 out of 19 budgets) post-budget

volatility during long-term tends to increase in relation to the volatility during long- term

before the budget. Hence, when volatility and return are considered together, the budget

has greater impact on return than volatility in short-term period, while in long-term

period the impact is reversed.

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5.1.4 INFLATION

5.1.4.1 Overview

For a layman it means a rise in prices of commodities of daily use and

subsistence. However, the economists consider it as persistent rise in the general price

level over time. Here, two points are worth noticing; persistent rise and general price

level. One-time rise in prices cannot be termed as inflation; it should continue for some

time to be termed as inflation. Also, unless referring to specific index, inflation usually

rises in the basket of commodities (Rao et al., 2008). Inflation can also be described as a

decline in the real value of money - a loss of purchasing power. It is mostly referred to as

‘too much expenditure chasing too little goods’. It is also referred to as ‘a necessary evil’

for any economy. It is said of inflation that like an elephant, it is easy to spot but

somewhat tricky to measure (Agarwal and Barua, 1999). However, when the general

price level rises, each unit of currency buys fewer goods and services. Thus, inflation

reflects erosion in the purchasing power of money.

In India, the Consumer Price Index (CPI) and the Wholesale Price Index are the two

most important indices. While the CPI pertains to a set of items that a consumer

consumes, the WPI is a basket particular to the wholesale market. Also, CPI measures

changes in prices of goods at retail outlets, while the WPI measures changes in wholesale

prices.

Inflation is measured as the percentage rate of change of a price index over a

particular period of time (Rao and Bhole, 1990). Thus, if the inflation for a particular

week is, say 10%, it means the index is 10% higher than it was in the same week during

the previous year.

Inflation can be attributed to any of the following reasons:

• Inflation via Higher Demand: It is the case of more money chasing few goods. This

means that the demand for the goods is high which leads to higher prices of these goods.

The high demand is usually caused due to an increase in consumer and government

spending. In a developing country like India, inflation via higher demand is usually a

favourable situation for the economy because it is instrumental in continuing the growth

process; also, it can be better managed by the policy-makers.

• Inflation via Lower Supply: Every now and then we hear that poor monsoons have

led to lower harvests of foodgrains, pulses, vegetables, etc. Thus, lower supply in the

economy leads to higher prices. Even if the demand is the same the prices may continue

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to rise due to lower supply. This form of inflation is particularly detrimental for a

growing economy like India and it is relatively difficult to manage.

There are different types of inflations like Creeping Inflation, Galloping Inflation,

Hyperinflation, Stagflation, Deflation (Agarwal and Barua, 1999).

Creeping Inflation: This is also known as mild inflation or moderate inflation. This type

of inflation occurs when the price level persistently rises over a period of time at a mild

rate. When the rate of inflation is less than 10 per cent annually, or it is a single digit

inflation rate, it is considered to be a moderate inflation.

Galloping Inflation: If mild inflation is not checked and if it is uncontrollable, it may

assume the character of galloping inflation. Inflation in the double or triple digit range of

20, 100 or 200 per cent a year is called galloping inflation. Many Latin American

countries such as Argentina, Brazil etc. had inflation rates of 50 to 700 per cent per year

in the 1970s and 1980s.

Hyperinflation: It is a stage of very high rate of inflation. While economies seem to

survive under galloping inflation, a third and deadly strain takes hold when the cancer of

hyperinflation strikes. Nothing good can be said about a market economy in which prices

are rising a million or even a trillion per cent per year. Hyperinflation occurs when the

prices go out of control and the monetary authorities are unable to impose any check on

it. Germany had witnessed this type of inflation in 1920s.

Stagflation: It is an economic situation in which inflation and economic stagnation or

recession occur simultaneously and remain unchecked for a period of time. Stagflation

was witnessed by developed countries in 1970s, when world oil prices rose dramatically.

Deflation: Deflation is the reverse of inflation. It refers to a sustained decline in the price

level of goods and services. It occurs when the annual inflation rate falls below zero per

cent (a negative inflation rate), resulting in an increase in the real value of money. Japan

suffered from deflation for almost a decade in 1990s.

Very low inflation rates are not good especially for the growing economies

because it can happen due to

(a) Lower Demand, and/or

(b) Oversupply.

Both these situations are detrimental to the growth process of the economy, and hence,

not desirable (Henry, 2002).

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While seeking for a wage hike from our bosses we usually quote high inflation

rates as one of the reasons. But when there is a dip in inflation rates we don’t go running

for wage cuts!! Generally, it is not so easy to reduce the wage rates in an economy. So,

the company usually resorts to reduction in work force. On a macro level, it leads to

higher unemployment in the economy. At this point, the RBI takes action by lowering

interest rate to boost domestic investment but this also leads to lower net inflows. Thus,

zero or very low inflation is not conducive for the growth process (Tribedy, 1991).

The preferred solution, therefore, is a middle path. Moderate inflation rate of 4%-

6% is usually considered not bad for a developing economy like India. Moderate

inflation is necessary to act as a lubricant for the wheels of economy. It signifies healthy

demand in the economy and also avoids RBI intervention which affects consumer and

investment spending (Knif et al.,2003).

How does inflation affect stock market?

Generally, stock markets and inflation are believed to be inversely related. For

businesses, higher inflation means that raw materials become expensive which leads to

higher costs of production. The companies usually pass on a fraction of the price rise to

its final consumers; however invariably they have to absorb a fraction themselves. Thus,

it adversely affects the earnings of the companies. Lower earnings by companies can

make them less attractive for investors, and hence, the stock prices may fall (Cohn and

Lessard, 1981).

Also, during a high inflation period investors look for better rate of return on their

investments in order to maintain or improve the purchasing power of their income. The

investors will find better returns only when the P/E ratios are relatively lower. Thus, in

high inflationary periods the P/E ratio should be ideally lower and similarly, at lower

inflation rates P/E ratios would be higher. Summers (1981) analyzed that increased

inflation raises the expected return on alternative assets such as real physical assets (e.g.

owner-occupied housing). Investors make changes in their portfolios by shifting out of

equity holdings and investing the funds released in the process in other assets. Share

prices decline in response to these portfolio adjustments by investors. Rise in inflation

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leads to decrease in after tax profits and hence share prices which are present discounted

values (PDVs) of future after tax earnings of firms - decline (Feldstein,1980).

Unexpected increases in inflation may provoke government or central bank reaction in

the form of changes in fiscal or monetary policy or both. For example, government may

impose price controls or change in tariff rates which might adversely affect the

profitability of firms. The central bank may resort to open market operations to contain

the expansion of money supply pushing up the interest rates in the process. The rise in

interest rates may increase the interest cost of working capital in the short run and

adversely affect the cash flow in the long run as firms resort to cuts in interest sensitive

capital expenditures. This expectation of government or central bank’s reaction may be

the reason for the response of stock prices which is referred to as information effect

(Jaffe and Mandelker, 1976).

5.1.4.2 Analysis and Discussion

The causal relationship between inflation and stock market volatility, i.e., BSE

Sensex and NSE Nifty has been analyzed by applying the Granger Causality Test. For

any time-series data analysis, all data series should be stationary. Augmented Dicker

Fuller (ADF) test has been conducted to study the stationarity of data series. The ADF

test statistics for the inflation is presented in Table 5.18. The results show that ADF

statistic is -4.956150 for inflation. The ADF value for inflation is lesser than critical

values leading to inference of rejection of hypothesis with unit root at 1%, 5% and 10%

levels of significance. This proves that the time series data of inflation is stationary.

Table 5.18: ADF Test Statistics for Inflation

t-Statistic Probability

Augmented Dickey-Fuller test statistic -4.956150 0.0000

Test critical value 1% level -3.465014

5% level -2.876677

10% level -2.574917

Source: Calculated from the data of Inflation given in Appendix 2.

The ADF test statistics for the stock market volatility, i.e., BSE Sensex and NSE

Nifty are presented in Tables 5.19 and 5.20 respectively. The results show that ADF

statistic is - 4.588579 for volatility of BSE Sensex return and -5.136850 for volatility of

NSE Nifty return. Both the ADF values for volatility of stock market return of BSE

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Sensex and NSE Nifty are lesser than critical values leading to inference of rejection of

hypothesis with unit root at 1%, 5% and 10% levels of significance. This proves that the

time series data of volatility of stock market in the case of both BSE Sensex and NSE

Nifty is stationary.

Table 5.19: ADF Test Statistics for Stock Market Volatility (BSE Sensex)

t-Statistic Probability

Augmented Dickey-Fuller test statistic -4.588579 0.0002

Test critical value 1% level -3.465014

5% level -2.876677

10% level -2.574917

Source: Calculated from the data of stock market volatility (BSE Sensex) given in

Appendix 2.

Table 5.20: ADF Test Statistics for Stock Market Volatility (NSE Nifty)

t-Statistic Probability

Augmented Dickey-Fuller test statistic -5.136850 0.0000

Test critical value 1% level -3.465014

5% level -2.876677

10% level -2.574917

Source: Calculated from the data of stock market volatility (NSE Nifty) given in Appendix 2.

As the test confirmed that inflation and stock market volatility of BSE Sensex

and NSE Nifty are stationary in nature, the direction of causal relationship between the

stationary data series has been conducted by applying the Granger-Causality test. The

results of the Granger-Causality test are presented in Table 5.21.

The Granger-Causality results suggest independence between inflation and stock

market volatility, i.e., BSE Sensex and NSE Nifty. It can be clearly observed that for the

first and second hypotheses, the probability values are 0.91553 and 0.12785 respectively,

which are greater than critical value 0.05, hence, null hypothesis is accepted.

Table 5.21: Granger-Causality Test Statistics

Null Hypothesis Observation F-statistics Probability Inflation does not Granger Cause Volatility(BSE Sensex) 190 0.08830 0.91553

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Volatility (BSE Sensex) does not Granger Cause Inflation 190 2.07997 0.12785

Inflation does not Granger Cause Volatility(NSE Nifty) 190 0.01326 0.98683 Volatility (NSE Nifty) does not Granger Cause Inflation 190 1.72908 0.18030

Source: Compiled from the data given in Appendix 2 using Eviews 5.

It proves that inflation does not cause stock market volatility (BSE Sensex) and

volatility in BSE Sensex does not cause inflation. A similar position exists in the case of

third and fourth hypotheses and hence, null hypothesis is accepted in these cases also.

5.1.4.3 Results

All the series have been found to be stationary since the calculated ADF statistics

are less than critical values. Though theoretically, there seems to be a long run

relationship between stock market volatility and inflation, yet the relationship is not

statistically significant. As far as causality relationship is concerned, it suggests

independence between inflation and stock market volatility i.e., BSE Sensex and NSE

Nifty. Thus, it can be concluded that inflation neither increases nor dampens volatility of

stock market returns in India. There could be several reasons for such insignificant

relationship. However, it could be due to net inflows brought by the FIIs and the FDIs.

Thus, in a growing economy like India, even at double digit inflation rates

investors might find it worthwhile investing in the stock market. So, while investing in

stock market, an investor needs to consider the trend of macro-economic indicator like

inflation which affects the whole economy, and hence, the stock market.

5.1.5 INTEREST RATE

5.1.5.1 Overview

The stock market has become an important indicator of the performance of the

Indian economy over the years. With this, the working of the stock market has become a

vital and facilitating subject for academics, investment professionals, and monetary

policy-makers. The stock market works with the sentiments of participants, which

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depend on several factors, making it a very sensitive segment of the economy.

Globalisation and financial sector reforms have added to the sensitivity by increasing

determinants of the stock market movement manifold. Among the several other

important determinants, interest rate is the one which has a relationship with the stock

market and it needs to be carefully studied.

Interest rate, in simple words, means the cost of borrowing funds. It is the

payment a borrower makes to the lender for the facility of using his money. Generally,

interest rate is considered as the cost of capital, means the price paid for the use of

money for a period of time. From the point of view of a borrower, interest rate is the cost

of borrowing money (borrowing rate). From a lender’s point of view, interest rate is the

fee charged for lending money (lending rate) (Amadeo, 2012).

The interest rate in India is not market determined but controlled by the RBI through its

monetary policy. Primarily, there are two main reasons for the fluctuation in interest rate:

• Government alters interest rate to affect the investments: When an economy goes

into recession it does not come back on its own. The US government during the

recent downturn of 2008-09 lowered its interest rate to around 1-2% in order to

propel private investments and for consumers to borrow and spend more.

• Inflation- Interest Rate Linkage: When the inflation starts rising, the central bank

or the RBI responds by increasing the interest rate to reduce money supply in the

economy.

If price is the heart of a commodity market, the rate of interest, which is the price

for the use of money, is the heart of the money and capital markets. Even as in respect of

any commodity there is no single price - there being a complex of prices for the different

varieties of the commodity - there is no single rate of interest. But this is not to say that

the various rates are not related to one another; in fact, they are generally integrated into

a harmonious pattern.

Broadly speaking, the different rates of interest reflect three factors, namely, the

element of risk, the period of the loan and marketability or liquidity of the security.

Greater the probability and magnitude of risk, higher would be the rate. Likewise, longer

the period of the loan, the higher the rate, not so much because of the risk of default as

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that of depreciation in the capital value, on account of a possible rise in the rate of

interest during the period of the loan (Tessaromatis, 1989). Greater the marketability,

lower would be the rate of interest. Marketability is also influenced by statutory

provisions and practices with regard to the transferability of assets and the investment

pattern of institutional investor in particular. If the long-term rate is steadily rising, for

instance, the short-term rate should also rise since there would be tendency for the

borrowers to switch from long to short-term loans, and for lenders to shift funds into the

long-term market. The short-term rates proper are those which rule in the money market,

namely, inter-bank call rate, the Treasury bill rate, the commercial bill rate , the deposit

and lending rates of commercial and co-operative banks and finally the bank rate, which

is the lending rate of the central bank. The long-term rates or the capital market rates

refer to the Government and corporate bond rates, yield on ordinary and preference share

etc. (Apergis and Eleftheriou, 2002).

Bank Rate is the rate at which central bank of the country allows finance to

commercial banks. It is a tool, which central bank uses for short-term purposes. Any

upward revision in Bank Rate by central bank is an indication that banks should also

increase deposit rates as well as Base Rate / Benchmark Prime Lending Rate. Thus, any

revision in the Bank Rate indicates that it is likely that interest rates on our deposits are

likely to either go up or go down, and it can also indicate an increase or decrease in our

EMI.

In theory, the relationship between interest rates and stock prices is negative. This

is due to the cash flow discounting model according to which, present values of stocks

are calculated by discounting the future cash flows at a discount rate. If the discount rate

increases, then present values of stocks decline and vice versa. This discount rate is a risk

adjusted required rate of return and equal to the level of interest rates in the economy.

Therefore, an increase in interest rates lowers present values of stocks directly. Even a

relatively small rise in interest rates can have a major effect on present values if it is

spread out over several years (Durre and Giot, 2005). In addition, rising interest rates

reduce cash flows by reducing the profitability of firms. Due to these two reasons,

present values of stocks decline and so do current stock prices. The inverse holds true as

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well. Apart from the above theoretical reasons, there are some other reasons also which

account for the negative relationship between interest rates and stock prices. First,

interest rates are risk free returns on bonds and as interest rates on bonds rise, bonds

become more attractive and stocks less attractive. Consequently, there is a change in the

asset allocation in favour of bonds rather than stocks. This moves funds from the stock

market to the bond market, which invariably increases the demand for bonds and reduces

the demand for stocks. As a result, the prices of stocks fall. The opposite is true when

interest rates fall and funds are shifted from the bond market to the stock market (Shiller,

1987).

Second, corporate profitability is hit because of increase in interest rates. Interest

rates affect profits in two ways. Firstly, almost all companies borrow money to finance

capital equipment and inventory, so the cost of money, that is, the interest rate they pay,

is of great importance. Secondly a substantial number of sales are in turn financed by

borrowing. The level of interest rates, therefore, has a great deal of influence on the

ability and willingness of customers to make additional purchases. One of the most

outstanding examples is the automobile industry, in which both producers and consumers

are highly financed. The capital-intensive utility and transportation industries are also

large borrowers, as are all the highly leveraged construction and housing industries

(Flannery and James, 1984).

Perhaps the most important effect of interest rate changes on equity prices comes

from the fact that tight monetary policy associated with rising rates adversely affects

business conditions, whereas falling rates stimulate the economy. Given time, most

businesses can adjust to higher rates, but when they change quickly and unexpectedly

most have to curtail expansion plans, cut inventories, and so on. This has a debilitating

effect on the economy, and therefore, corporate profits. Higher rates and smaller profits

mean lower price/earnings multiples, and therefore, lower stock prices. When the

authorities become concerned about the economy, they lower short-term rates and a

reverse effect takes hold (Bordo et al., 2008).

Third, margin debt is money loaned by brokers for which securities are pledged

as collateral. Normally, this money is used for the acquisition of equities, but sometimes

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margin debt is used for purchases of consumer items, such as automobiles. The effect of

rising interest rates on both forms of margin debt is similar, in that rising rates increase

the carrying cost. There is, therefore, reluctance on the part of investors to take on

additional debt as its cost rises. When the service charges become excessive, stocks are

liquidated and the debt is paid off. Rising interest rates have the effect of increasing the

supply of stock put up for sale with consequent downward pressure on prices (Binder

and Merges, 2000). The link between interest rate and stock market is evident from the

following diagram also.

The diagram reflects that there are two sides of the economy (a) The Business

Side, and (b) The Consumer Side. A continuous rise in interest rate affects the stock

price from both the investment and consumption side. Combining the effects from both

the sides, it spells a gloomy situation for the economy. The overall future revenue growth

of the companies would be adversely affected which would lead to negative sentiments

in the market leading to deflated stock prices. Also, at higher interest rate, people tend to

invest more in fixed deposits and bonds as it would be offering higher returns at very low

risk. Hence, it moves funds out of stock market affecting the stock prices adversely (Ray

and Vani, 2007).

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Impact of Interest Rate on Different Sectors

• In the short term: The immediate impact of rise in interest rate is on companies

with high debt in their balance sheet .The interest payment made by them rises

which reduces their EPS. Thus there would be negative sentiments for such stock;

resulting into depleted stock price.

• Over a longer term, high interest rates would have more sector specific impact.

The impact of high interest rate can be witnessed the most in real estate,

automobile and all the capital intensive industries. Hence, any investment made

in these sectors during a situation when the interest rates are high can put an

investor into a big loss.

• Banking sector is likely to benefit most due to high interest rates. The Net Interest

Margin (difference between the interest earned by the banks on the money they

lend and the interest they paid to the depositors) of the banks is likely to increase

leading to growth in profits & the stock prices.

• The sectors like pharmaceuticals, FMCG, IT, etc. are least affected by the interest

rates; and investment can be made in these sectors without much risk.

If interest rate continues to rise for a longer duration, it causes negative impact on

the economy leading to recession.

The effects of interest rates change on a stock’s intrinsic value are more complex than

outlined earlier because of the existence of other economic variables that interact with

interest rates in determining a stock’s value. In addition, if the inflation rate is quite high

and real interest rates do not exist, then the investors are unlikely to move their funds

from the stock market to the bond market in response to an increase in interest rate

(Chakradhara, 2008). Hence, the negative relationship between interest rates and stock

prices is not necessarily true. The relationship can also be positive due to the following

reasons. First, if interest rates increase in response to the economy growing too rapidly

then corporate earnings should also be growing rapidly and so should stock prices.

Second, higher interest rates suggest higher anticipated inflation. This leads to a likely

increase in corporate pricing power because of which higher growth rates of earnings per

share are witnessed by firms. Therefore, when the discount factor is increased in the

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stock valuation formula, the earnings per share are affected and increased. This implies

that lower stock prices are not necessarily warranted (Durre and Giot, 2005). Third, a

positive relationship can be explained in terms of a changing risk premium. For example,

a drop in interest rates could be the result of increased risk or/and precautionary saving

as investors move away from risky assets such as stocks towards less risky assets like

bonds (Barsky, 1989). However, it is important to note that although the negative

relationship between interest rates and stock prices is not automatic or perfect, in the

long run, it is unavoidable. The above discussion reveals that the relationship between

interest rates and stock prices can either be positive or negative. Therefore, the study tries

to examine the nature of relationship between interest rates and stock prices in the Indian

context.

5.1.5.2 Analysis and Discussion

Tables 5.22 and 5.23 depict the daily average returns and announcement of

interest rate day returns given by Sensex and Nifty during the previous and next 3, 15

and 30 days around the announcement of interest rate day.

On Day Effect of Announcement of Interest Rate on Sensex and Nifty: The first set

of paired t-tests measure the on-day (Z) influence of announcement of interest rate on

Sensex and Nifty when compared to the previous 3, 15 and 30 days. A cursory glance at

Tables 5.22 and 5.23 highlights that in most of the cases announcement of interest rate

day returns (ignoring sign) are more than the returns during the previous 30, 15 and 3

trading days. Therefore, when the announcement of interest rate day returns (Z)

compared with the long- term pre-announcement of interest rate return in the case of

Sensex, it shows that announcement of interest rate day returns exceed in all years ( i.e.,

15 out of 15 ), compared to medium-term (12 out of 15) and short-term (11 out of 15),

while in the case of Nifty it shows that when the announcement of interest rate day

returns (Z) compared with the long-term pre-announcement of interest rate return, it

shows that announcement of interest rate day returns exceed (14 out of 15 ),compared to

medium-term (12 out of 15) and short-term (12 out of 15) that budget day returns

exceed.

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Table 5.22: Daily Average Returns in Sensex

Year

X1 (Last

30 days)

X2 (Last

15 days)

X3 (Last

3 days)

Z (Int.

rate)

Y1 (Next

3 days)

Y2 (Next

15 days)

Y3(Next

30 days)

15 April, 97 0.15 -0.26 -0.48 1.60 1.37 0.16 0.14

25 June, 97 0.36 0.47 0.25 -0.63 1.30 0.14 0.33

21 October, 97 0.10 0.38 0.98 -0.92 -1.32 -0.95 -0.57

16 January, 98 -0.19 -0.46 -0.29 0.49 0.35 -0.13 0.31

18 March, 98 0.56 0.65 0.35 0.89 1.19 0.49 0.27

2 April, 98 0.54 0.29 0.52 0.58 1.64 0.15 -0.08

29 April, 98 0.28 0.15 -0.08 -2.80 1.45 -0.11 -0.77

1 March, 99 -0.08 0.04 -0.51 8.60 0.73 0.29 -0.08

1 April, 00 -0.45 -0.65 -1.02 1.03 -1.25 -0.59 -0.62

21 July, 00 -0.02 -0.30 -1.23 -2.48 -2.10 -0.42 0.12

16 February, 01 0.30 0.17 0.24 -2.46 -0.22 -0.93 -0.65

1 March, 01 0.14 0.10 0.99 0.58 -1.80 -1.08 -0.85

22 October, 01 -0.20 0.70 0.27 -0.50 -0.20 0.17 0.45

29 October, 02 -0.33 -0.22 -1.32 1.78 0.75 0.48 0.47

29 April, 03 -0.24 -0.47 -0.12 0.46 0.28 0.24 0.39

Source: Calculated from the data taken from BSE website for the said period. Note: All returns are in percentage.

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Table 5.23: Daily Average Returns in Nifty

Year

X1 (Last

30 Days)

X2(Last

15 Days)

X3(Last

3 Days)

Z (Int.

Rate)

Y1 (Next

3 Days)

Y2( Next

15 Days)

Y3 (Next

30 Days)

15 April, 97 0.12 -0.32 -0.89 1.55 1.44 0.25 0.12

25 June, 97 0.38 0.49 -0.13 0.57 0.86 0.12 0.30

21 October, 97 0.24 0.61 1.08 -1.12 -1.10 -0.78 -0.50

16 January, 98 -0.14 -0.42 -0.21 0.82 0.48 -0.15 0.21

18 March, 98 0.39 0.47 0.01 1.83 0.62 0.61 0.24

2 April, 98 0.49 0.38 0.28 0.63 0.86 0.04 -0.05

29 April, 98 0.21 -0.15 -1.01 0.37 1.05 -0.14 -0.57

1 March, 99 0.09 0.46 0.81 3.46 1.25 0.30 -0.16

1 April, 00 -0.38 -0.58 -0.86 0.41 -1.83 -0.44 -0.57

21 July, 00 -0.09 -0.31 -1.27 -1.91 -1.94 -0.43 0.07

16 February, 01 0.31 0.24 0.34 -2.53 -0.27 -0.95 -0.65

1 March, 01 0.24 0.34 -0.77 0.49 -1.70 -1.04 -0.80

22 October, 01 -0.20 0.62 0.18 -0.03 0.23 0.20 0.43

29 October, 02 -0.27 -0.18 -1.23 1.53 0.51 0.49 0.46

29 April, 03 -0.25 -0.55 -0.17 0.30 0.47 0.25 0.38

Source: Calculated from the data taken from NSE website for the said period. Note: All returns are in percentage.

T-values from Paired t-test

Table 5.24: Effect of Announcement of Interest Rate Day Returns on Sensex

X1 and Z X2 and Z X3 and Z

Actual Value (5%) -2.65¬ -2.41¬ -2.10¬

Table Value (5%) -1.76

-1.76

-1.76

Source: Calculated from the data based on Table 5.22. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

The above inferences (Tables 5.22 and 5.23) have been further statistically tested

by Paired t- test (Tables 5.24 and 5.25).

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Table 5.25: Effect of Announcement of Interest Rate Day Returns on Nifty

X1 and Z X2 and Z X3 and Z

Actual Value (5%) -3.54¬ -2.92¬ -2.24¬

Table Value (5%) -1.76 -1.76 -1.76

Source: Calculated from the data based on Table 5.23. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

The announcements of interest rate are found to take the markets by surprise in

all cases. In all the tests, the actual values are found to exceed the table values leading to

acceptance of alternative hypothesis.

T-values from Paired t-test

Table 5.26: Impact of Announcement of Interest Rate on Sensex

Period Short-term Period Medium-term Period Long-term Period

X1 and

Y1

X2 and

Y1

X3 and

Y1

X1 and

Y2

X2 and

Y2

X3 and

Y2

X1 and

Y3

X2 and

Y3

X3 and

Y3

Actual Value (5%)

-4.91¬ -4.00¬ -3.45¬ -1.79* -.617 1.57 -1.88* -.607 1.39

Table Value (5%)

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

Source: Calculated from the data based on Table 5.22. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

Table 5.27: Impact of Announcement of Interest Rate on Nifty

Period Short-term Period Medium-term Period Long-term Period

X1 and

Y1

X2 and

Y1

X3 and

Y1

X1 and

Y2

X2 and

Y2

X3 and

Y2

X1 and

Y3

X2 and

Y3

X3 and

Y3

Actual

Value

(5%)

-4.65¬ -3.72¬ -3.12¬ -1.88* -0.05 1.65 -1.84* -0.56 2.06

Table

Value

(5%)

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

-1.76

Source: Calculated from the data based on Table 5.23. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

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Table 5.24 provides the second set of tests, which proves that announcement of

interest rate has maximum impact in the short-term period (alternative hypotheses have

been accepted in all the three cases). However, in medium-term and long-term periods,

the alternative hypotheses have been accepted in one out of three cases. In total, the

actual values exceed the tabular values in five cases (3+1+1) out of nine at the left tail.

This proves that announcement of interest rate, when taken together, has maximum

impact in the short-term post- announcement of interest rate period, with some impact

extending into the medium-term and long- term average returns.

Table 5.28: Variance of Returns in Sensex

Year

X1(Last

30 Days)

X2(Last

15 Days)

X3(Last 3

Days)

Y1(Next

3 Days)

Y2(Next

15 Days)

Y3(Next

30 Days

15 April, 97 0.072 0.080 0.018 0.019 0.011 0.008

25 June, 97 0.005 0.006 0.002 0.020 0.017 0.015

21 October, 97 0.012 0.009 0.001 0.002 0.012 0.022

16 January, 98 0.023 0.025 0.014 0.048 0.029 0.028

18 March, 98 0.023 0.025 0.02 0.030 0.020 0.025

2 April, 98 0.020 0.016 0.016 0.009 0.028 0.034

29 April, 98 0.021 0.028 0.008 0.023 0.037 0.044

1 March, 99 0.020 0.013 0.007 0.047 0.290 0.052

1 April, 00 0.053 0.025 0.001 0.288 0.152 0.125

21 July, 00 0.022 0.018 0.014 0.255 0.057 0.036

16 February, 01 0.016 0.020 0.018 0.009 0.053 0.080

1 March, 01 0.026 0.082 0.995 0.076 0.094 0.071

22 October, 01 0.064 0.027 0.042 0.023 0.015 0.017

29 October, 02 0.005 0.006 0.000 0.013 0.006 0.008

29 April, 03 0.016 0.015 0.002 0.001 0.004 0.006

Source: Calculated from the data taken from BSE website for the said period.

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Table 5.29: Variance of Returns in Nifty

Year

X1(Last

30 Days)

X2(Last

15 Days)

X3(Last

3 Days)

Y1(Next 3

Days)

Y2(Next

15 Days)

Y3(Next

30 Days

15 April, 97 0.087 0.080 0.018 0.018 0.016 0.011

25 June, 97 0.007 0.008 0.001 0.009 0.021 0.018

21 October, 97 0.012 0.011 0.002 0.004 0.090 0.060

16 January, 98 0.023 0.025 0.003 0.017 0.019 0.023

18 March, 98 0.019 0.029 0.011 0.003 0.016 0.024

2 April, 98 0.023 0.018 0.053 0.005 0.029 0.027

29 April, 98 0.024 0.029 0.002 0.010 0.026 0.029

1 March, 99 0.037 0.030 0.087 0.050 0.020 0.029

1 April, 00 0.040 0.035 0.002 0.218 0.148 0.107

21 July, 00 0.015 0.013 0.003 0.169 0.040 0.027

16 February, 01 0.013 0.017 0.015 0.080 0.050 0.069

1 March, 01 0.016 0.015 0.09 0.080 0.101 0.710

22 October, 01 0.054 0.018 0.023 0.017 0.013 0.015

29 October, 02 0.005 0.006 0.001 0.006 0.004 0.009

29 April, 03 0.018 0.021 0.004 0.001 0.004 0.006

Source: Calculated from the data taken from NSE website for the said period.

Tables 5.30 and 5.31 shows F-test values for the tests that compare the variance

among the returns in Sensex (given in Table 5.28) and Nifty (given in Table 5.29)

respectively during short-term, medium-term, and long-term periods after the

announcement of interest rate with one another. In case of Sensex and Nifty, except in

two years, in no other year the actual value exceed the tabular value .This signifies that

volatility does not generally increase in post-announcement of interest rate situation as

time period increases.

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Table 5.30: F-test Results Comparing Variance among the Returns (Post-

announcement of Interest Rate) (BSE)

Year

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Y1 and Y2 df = 14/2 Y2 and Y3 df= 29/14 Y3 and Y1 df= 29/2

15 April, 97 1.73 3.74 1.38 2.03 2.38 3.33

25 June, 97 1.18 3.74 1.13 2.03 1.33 3.33

21 October, 97 6.00 19.43 1.83 2.31 11.00 19.46

16 January, 98 1.66 3.74 1.04 2.03 1.71 3.33

18 March, 98 1.50 3.74 1.25 2.31 1.20 3.33

2 April, 98 3.11 19.43 1.21 2.31 3.78 19.46

29 April, 98 1.61 19.43 1.19 2.31 1.91 19.46

1 March, 99 6.17 19.43 5.58* 2.03 1.11 19.46

1 April, 00 1.89 3.74 1.22 2.03 2.30 3.33

21 July, 00 4.48* 3.74 1.58 2.03 7.08* 3.33

16 February, 01 5.89 19.43 1.51 2.31 8.89 19.46

1 March, 01 1.24 19.43 1.32 2.03 1.07 3.33

22 October, 01 1.53 3.74 1.13 2.31 1.35 3.33

29 October, 02 2.17 3.74 1.33 2.31 1.63 3.33

29 April, 03 4.00 19.43 1.50 2.31 6.00 19.46

Source: Calculated from the data based on Table 5.28. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

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Table 5.31: F-test Results Comparing Variance among the Returns (Post-

announcement of Interest Rate) (NSE)

Year

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value (5%)

Y1 and Y2 df = 14/2 Y2 and Y3 df= 29/14 Y3 and Y1 df= 29/2

15 April, 97 1.13 3.74 1.45 2.03 1.64 3.33

25 June, 97 2.33 19.43 1.17 2.03 2.00 19.46

21 October, 97 22.5* 19.43 1.50 2.03 15.00 19.46

16 January, 98 1.12 19.43 1.21 2.31 1.35 19.46

18 March, 98 5.33 19.43 1.5 2.31 8.00 19.46

2 April, 98 5.8 19.43 1.07 2.03 5.40 19.46

29 April, 98 2.6 19.43 1.12 2.31 2.90 19.46

1 March, 99 2.5 3.74 1.45 2.31 1.72 3.33

1 April, 00 1.47 3.74 1.38 2.03 2.04 3.33

21 July, 00 4.23* 3.74 1.48 2.03 6.23* 3.33

16 February, 01 1.60 3.74 1.38 2.31 1.16 3.33

1 March, 01 1.26 19.43 1.42 2.03 1.13 3.33

22 October, 01 1.31 3.74 1.15 2.31 1.13 3.33

29 October, 02 1.50 3.74 2.25 2.31 1.50 19.46

29 April, 03 4.00 19.43 1.50 2.31 6.00 19.46

Source: Calculated from the data based on Table 5.29. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

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Table 5.32: F-test Results Comparing Variance among the Returns during Post-

announcement of Interest Rate Periods with Long-term Pre-

announcement of Interest Rate Period (BSE)

Year

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

X1 and Y1 df=29/2 X1 and Y2 df=29/14 X1and Y3 df=29/29

15 April, 97 3.79 19.46 6.55* 2.31 9.00* 1.85

25 June, 97 4.00* 3.33 3.40* 2.03 3.00* 1.84

21 October, 97 6.00 19.46 1.00 2.31 1.83 1.84

16 January, 98 3.39* 3.33 1.26 2.03 1.22 1.84

18 March, 98 1.30 3.33 1.15 2.31 1.09 1.84

2 April, 98 2.22 19.46 1.40 2.03 1.70 1.84

29 April, 98 1.09 3.33 1.76 2.03 2.09* 1.84

1 March, 99 2.35 3.33 14.5* 2.03 14.5* 1.84

1 April, 00 5.43* 3.33 2.87* 2.03 2.36* 1.84

21 July, 00 11.59* 3.33 2.00* 2.03 1.64 1.84

16 February, 01 1.78 19.46 3.31* 2.03 5.00* 1.84

1 March, 01 2.92 3.33 3.61* 2.03 2.73* 1.84

22 October, 01 2.78 19.46 4.27* 2.31 3.76* 1.85

29 October, 02 2.60 3.33 1.20 2.03 1.60 1.84

29 April, 03 16.00 19.46 4.00* 2.31 2.67* 1.85

Source: Calculated from the data based on Table 5.28. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

Tables 5.32 and 5.33 depict specifically F-test values for the tests that compare

the variance of returns in Sensex and Nifty during short-term, medium-term and long-

term post- announcement of interest rate periods with that of long-term pre-

announcement of interest rate period. In the case of both Sensex and Nifty, there have

been 9 out of 15 significant cases in the long-term period, whereas the medium-term and

short-term periods are represented by 8 out of 15 and 4 out of 15 such cases respectively.

It indicates that the long-term period and medium-term period after the announcement of

interest rate tend to be more volatile than the short-term period when compared to similar

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long-term period before the announcement of interest rate.

Hence, if an investor wants to have any gain from the swing arising after the

announcement of interest rate, he needs to predict whether such an announcement will

cause a rise or fall in share prices.

Table 5.33: F-test Results Comparing Variance among the Returns during Post-

announcement of Interest Rate Periods with Long-term Pre-

announcement of Interest Rate Period (NSE)

Year

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

Actual

Value

Table

Value

(5%)

X1 and Y1 df=29/2 X1 and Y2 df=29/14 X1and Y3 df=29/29

15 April, 97 4.83 19.46 5.44* 2.31 7.90* 1.85

25 June, 97 1.28 3.33 3.00* 2.03 2.57* 1.84

21 October, 97 3.00 19.46 7.50* 2.03 5.00* 1.84

16 January, 98 1.35 19.46 1.21 2.31 1.00 1.85

18 March, 98 6.33 19.46 1.19 2.31 1.26 1.84

2 April, 98 4.60 19.46 1.26 2.03 1.17 1.84

29 April, 98 2.40 19.46 1.08 2.03 1.21 1.84

1 March, 99 1.35 3.33 1.85 2.31 1.28 1.85

1 April, 00 5.45* 3.33 3.70* 2.03 2.68* 1.84

21 July, 00 11.27* 3.33 2.67* 2.03 1.89* 1.84

16 February, 01 6.15* 3.33 3.85* 2.03 5.31* 1.84

1 March, 01 5.00* 3.33 0.16 2.03 4.43* 1.84

22 October, 01 3.18 19.46 4.15* 2.03 3.60* 1.84

29 October, 02 1.20 3.33 1.25 2.31 1.80 1.84

29 April, 03 18.00 19.46 4.50* 2.31 3.00* 1.85

Source: Calculated from the data based on Table 5.29. Note: ¬ Signifies that Null Hypothesis (Ho) is rejected.

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5.1.5.3 Results

An attempt has been made to measure the impact of 15 announcements made

about the interest rate over a period of 17 years on the stock market considering the

returns and volatility in Sensex and Nifty. The hypothesis tests conducted at various

significance levels have provided some interesting results from the investors, the

regulators and the Government point of view.

With regard to returns, an investor has a chance to earn greater profits by

investing during the short-term and medium-term periods (up to15 trading days).

However, he also faces the risk of abnormal losses, if his expectations from such

announcements do not come true. This is also true in the case of trading on the

announcement day. As one moves away from the announcement day (up to 30 trading

days), the paired t-tests do not show any significant change in average returns. Hence,

such announcements have an effect only up to 15 trading days from the budget day so far

as returns are concerned.

Volatility, on the other hand, does not generally increase in a post-announcement

situation as the time period increases. But the long-term period after the announcement

tends to be more volatile than the medium-term and the short-term periods when

compared to similar long-term period before the announcement. In the case of both

Sensex and Nifty, only 26% ( 4 out of 15) post-interest rate announcements caused

volatility during the short-term period, 53% (8 out of 15) post-interest rate

announcements caused volatility during the medium-term period, while and 60% ( 9 out

of 15) post-interest rate announcements caused volatility during the long-term period in

comparison to volatility during the long-term period before such announcements.

Hence, when volatility and return are considered together, it is found that an

announcement of interest rate has a greater impact on return than volatility in short-term

period, but in long-term period such an announcement of interest rate has greater impact

on the volatility than return.

5.1.6 CORPORATE FUNDAMENTALS

5.1.6.1 Overview

Share price is the most important indicator readily available to the investors for

their decision to invest in a particular share. The market price of shares tends to fluctuate

every moment. It may boost up to new heights or even may drop down tremendously. No

one can exactly predict what could be the next price of the share. For predicting share

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prices there are different approaches. Fundamental approach predicts share price on the

basis of financial, environmental and managerial factors, whereas technical approach

takes the help of past trends in predicting future share price. Generally, the price depends

on the demand and supply of the shares. If there are more buyers than the sellers, the

prices of the shares are very likely to rise and vice versa. This is called the demand-

supply mechanism of the share price determination. Another vital factor is the

information regarding any company, which falls under the wide concept of Efficient

Market Hypothesis. If there is any good news regarding the company, the share prices of

that company are likely to boost and viceversa. Understanding of the effect of various

fundamental variables on share price is very much helpful to various parties such as

investors, management, government, etc., as it will help them in taking various important

decisions. Though investors are aware of these facts, but what drives the demand of the

shares? What kind of information causes the rise or fall in share prices? Which are the

factors that contribute to the determination of equity prices? In other words what are the

determinants of equity prices? Various factors undertaken for the study are:

MARKET PRICE (MP):

The market price of the share is mainly determined by the forces of demand and

supply of a particular security in the market (Malhotra, 1987; Piatroski, 2004; Zahir and

Khanna, 1982). The market price reflects the collective wisdom and knowledge of the

market. Daily price fluctuations arise because of changes in the buying and selling

pressure. Due to these fluctuations it becomes difficult to decide as to which market price

should be regressed as a measure of dependent variable. However, the present study

takes into consideration the arithmetic means of high and low market price of share

during the financial year of a firm. Mathematically, it is calculated as:

MP =

Where, PH is the highest market price, PL is the lowest market price during the year

which relates to the t’ period.

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EARNING PER SHARE (EPS)

Since equityholders are residual claimants to the earnings of the corporation, the

net profits to be taken for computation of EPS are usually arrived at after subtracting the

preference dividends. The significance of this ratio flow from the fact that higher the

earnings per share, the more is the scope for a higher rate of dividend and also of retained

earnings, to build up the inner strength of the company. Therefore, a higher EPS would

increase the market price and viceversa.

EPS =

DIVIDEND PER SHARE (DPS)

Dividend per share is the actual amount of dividend (gross) declared per share.

The net profit after tax belongs to shareholders. But the income, which they really

receive, is the amount of earnings distributed and paid as cash dividends. Therefore, a

large number of present and potential investors are more interested in the dividend per

share, rather than the earning per share. The amount of dividend paid to the shareholders

depends upon the dividend policy pursued by a company. The stable dividend policy

helps in resolving uncertainty from the minds of the investors and also plays an

important role in creating a healthy investment climate. The dividend rate of a company

has a significant influence on the market price of a share.

Dividend Per Share =

BOOK VALUE (BV)

It is also known as net asset value per share because it measures the amount of

assets, which the corporation has on behalf of each equity share. BV shows the

investment per share made in the business by the shareholders. A high book value

usually indicates that the company has a good record of past performance, i.e., high

reserves, therefore, high market price (Grewal,1986).

Book value per share =

PRICE EARNING RATIO (P/E )

P/E ratio expresses the relationship between the market price of a company’s

share and its earnings per share. It indicates the extent to which the earnings of each

share are covered by its price. The ratio helps an investor to make an approximate

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calculation of the time required to recover his investment in a company share. The price-

earning ratio has a positive relationship with market price.

P/E =

DIVIDEND YIELD

This is the return earned by an equity shareholder by way of dividends. Dividend

yield is computed as:

Dividend yield = *100 5.1.6.2 ANALYSIS AND DISCUSSION

The multivariate regression analysis has been carried out with Market Price (MP)

as dependent variable and Earning per Share (EPS), Dividend Per share (DPS), Book

Value (BV), Price-Earning Ratio (P/E) and Dividend Yield (DY) as independent

variables. The multivariate analysis technique was selected because it is the most

appropriate tool for evaluating the individual and combined effect of a set of independent

variables on dependent variable.

5.1.6.2.1 ANALYSIS OF HDFC BANK

The test for significance of overall multiple regression model was made through

F-test. This test explains whether there is a linear regression relationship between the

dependent variable and any of the independent variables. The results of the test as shown

in Table 5.34 reveal that in both the cases, i.e., BSE and NSE the F-values are significant

for being less than the significance level of 0.05. Hence, null hypothesis that there is no

relationship between the market price and independent variables in the case of both BSE

(Hoq) and NSE (Hor) stands rejected. As the time series analysis of economic variables

hold true under the assumption of no autocorrelation, Durbin-Watson test has been used.

This assumption is likely to be met if the Durbin-Watson statistic is close to 2. The

computed values of Durbin-Watson statistics are 1.78 and 1.75 in the case of BSE and

NSE respectively. Hence, no autocorrelation exists.

The coefficient of multiple correlation, R, indicates the relationship between

predictors and dependent variable. The value of R in both the cases is 0.985 which shows

a stronger relationship. Similarly, R2, the coefficient of multiple determination, measures

the percentage of the variation in the dependent variable which is explained by variations

in the independent variables taken together. For regression equation, R2 is 0.970 in both

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the cases, indicating that 97.0 per cent of the variation in market price is explained by the

variations in Earning Price share and Price/Earning ratio.

Table 5.34: Regression Model Summary and ANOVA of HDFC BANK

Model R R2 F- change Sig. F-change Durbin-Watson

BSE(Sensex) 0.985 0.970 70.221 0.000 1.78

NSE(Nifty) 0.985 0.970 72.210 0.000 1.75

Source: Compiled from the data given in Appendix 6 using SPSS 16.

Regression Equation:

For BSE Sensex

Market Price (MP) = b0 + b1 Earning Per Share (EPS) +b4 Price/Earning Ratio (P/E)

= -296.345+27.38EPS+10.12P/E

For S&P CNX Nifty

Market Price (MP) = b0 + b1 Earning Per Share (EPS) +b4 Price/Earning Ratio (P/E)

= -294.45 + 27.35EPS+10.09P/E

The b-values denote about the relationship between dependent variable and each

predictor. If the value is positive it explains that there is a positive relationship between

the predictor and the outcome, whereas a negative coefficient represents a negative

relationship. In both the regression equations two of the predictors have positive b-values

indicating positive relationships. Hence, in both the cases market price increases with an

increase in Earning Per share as well as Price Earning Ratio. The b-values also explain

what degree each predictor affects the outcome if the effects of all other predictors are

held constant.

Table 5.35: Coefficients of HDFC Bank (BSE Sensex)

Model b-value t-value Sig.

(Constant) -296.345 -2.588 0.023

EPS 27.376 20.440 0.000

P/E 10.117 2.570 0.023

Source: Compiled from the data given in Appendix 6 using SPSS 16.

Earning Per Share (EPS) (b = 27.37): This value indicates that as EPS increases by

Re.1, MP increases by Rs.27.37. This interpretation stands true only if the effect of P/E

is held constant.

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Price/Earning Ratio (P/E) (b =10.11): This value indicates that as P/E increases by Re.

1, MP increases by Rs.10.11. This interpretation is true only if the effect of EPS is held

constant.

Table 5.36: Coefficients of HDFC Bank (S&P CNX Nifty)

Model b-value t-value Sig.

(Constant) -294.450 -2.588 0.023

EPS 27.351 20.418 0.000

P/E 10.090 2.563 0.024

Source: Compiled from the data given in Appendix 6 using SPSS 16.

Earning Per Share (EPS) (b = 27.35): This value indicates that as EPS increases by

Re.1, MP increases by Rs.27.35. This interpretation stands true only if the effect of P/E

is held constant.

Price/Earning Ratio (P/E) (b =10.09): This value indicates that as P/E increases by Re.

1, MP increase by Rs. 10.09. This interpretation is true only if the effect of EPS is held

constant.

In simple regression, a significant value of t indicates that the slope of the

regression line is significantly different from horizontal, but in multiple regression, it is

not so easy to visualize what the value explains. Well, it is easiest to conceptualize the t-

tests as measures to find whether the predictor is making a significant contribution to the

model. Therefore, if the t-test associated with a b-value is significant (if the value in

column labelled significant is less than the significance level of 0.05) then predictor is

making a significant contribution to the model. The smaller the value of significant and

the larger the value of t, the greater the contribution of the predictor. For both the

models, the EPS and P/E are the significant predictors of market price as their values

stand below the significance level of 0.05. The magnitude of t-statistics reflects that EPS

has greater impact than P/E in both the cases.

5.1.6.2.2 ANALYSIS OF STATE BANK OF INDIA

The test for significance of overall multiple regression model was made through

F-test. The results of the test as shown in Table 5.37 reveal that in both the cases, the F-

values are significant for being less than the significance level of 0.05. Hence, null

hypothesis that there is no relationship between the market price and independent

variables in the case of both BSE (Hoq) and NSE (Hor) stands rejected. The computed

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values of Durbin-Watson statistics are 1.77 and 1.19 in the case of BSE and NSE

respectively. Hence, no autocorrelation exists.

The values of ‘R’ are 0.983 (BSE) and 0.977 (NSE) which reflect a stronger

relationship. For regression equation, R2 values are 0.966 (BSE) and 0.954 (NSE)

indicating that 96.6 per cent and 95.4 per cent respectively of the variation in market

price is explained by the variations in Earning Per Share, Book Value, and

Price/Earning Ratio.

Table 5.37: Regression Model Summary and ANOVA of State Bank of India

Model R R2 F-change Sig. F-change Durbin -Watson

BSE (Sensex) 0.983 0.966 58.185 0.000 1.77

NSE (Nifty) 0.977 0.954 48.592 0.000 1.19

Source: Compiled from the data given in Appendix 6 using SPSS 16.

Regression Equation:

For BSE Sensex

Market Price (MP) = b0 + b1Earning Per Share (EPS) +b3 Book Value (BV) +b4

Price/Earning Ratio (P/E)

= -744.74- 2.21EPS+1.64BV +36.54P/E

For S&P CNX Nifty

Market Price (MP) = b0 + b1Earning Per Share (EPS) +b3 Book Value (BV) +b4

Price/Earning Ratio (P/E)

= -931.92- 10.60 EPS+1.84BV +97.49P/E

The b-values denote about the relationship between dependent variable and each

predictor. In both the regression equations two of the predictors have positive b-values

indicating positive relationships and one has a negative b-value showing a negative

relationship. Hence market price increases with an increase in Price Earning Ratio as

well as Book value. However, a decrease in Earning Per Share causes an increase in

market price. The b-values also explain what degree each predictor affects the outcome if

the effects of all other predictors are held constant.

Earning Per Share (EPS) (b =-2.21) : This value indicates that as EPS decreases by Re.

1, MP increases by Rs. 2.21 .This interpretation stands true only if the effects of BV and

P/E are held constant.

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Table 5.38 Coefficients of State Bank of India (BSE Sensex)

Model b-value t-value Sig.

(Constant) -744.743 -7.477 0.000

EPS -2.213 -.359 0.000

BV 1.643 10.215 0.000

P/E 36.538 11.756 0.000

Source: Compiled from the data given in Appendix 7 using SPSS 16.

Book Value (BV) (b =1.64): This value indicates that as BV increases by Re. 1, MP

increases by 1.64. This interpretation stands true only if the effects of EPS and P/E are

held constant.

Price/Earning Ratio (P/E) (b = 36.53): This value indicates that as P/E increases by Rs.

1, MP increases by Rs.36.53. This interpretation is true only if the effects of EPS and BV

are held constant.

Table 5.39 Coefficients of State Bank of India (S&P CNX Nifty)

Model b-value t-value Sig.

(Constant) -931.923 -7.823 0.000

EPS -10.602 -8.575 0.000

BV 1.843 11.220 0.000

P/E 97.494 68.121 0.000

Source: Compiled from the data given in Appendix 7 using SPSS 16.

Earning Per Share (EPS) (b = -10.60) : This value indicates that as EPS decreases by

Re. 1, MP increases by Rs. 10.60 .This interpretation stands true only if the effects of BV

and P/E are held constant.

Book Value (BV) (b =1.84): This value indicates that as BV increases by Re. 1, MP

increase by 1.84. This interpretation stands true only if the effects of EPS and P/E are

held constant.

Price/Earning Ratio (P/E) (b = 97.49): This value indicates that as P/E increases by Re.

1, MP increase by Rs. 97.49. This interpretation stands true only if the effects of EPS and

BV are held constant.

The t-test measures whether the predictor is making a significant contribution to

the model. Therefore, if the t-test associated with a b-value is significant (if the value in

column labelled significant is less than the significance level of 0.05) then predictor is

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making a significant contribution to the model. The smaller the value of significant and

the larger the value of t, the greater the contribution of the predictor. For both the

models, the EPS, BV, and P/E are the significant predictors of market price as their

values stand below values the significance level of 0.05. The magnitude of t-statistics

reflects that P/E has greater impact than BV and EPS in both the cases.

5.1.6.2.3 ANALYSIS OF TATA MOTORS

The test for significance of overall multiple regression model was made through

F-test. The results of the test as exhibited in Table 5.40 reveal that in both the cases the

F-values are significant for being less than the significance level of 0.05. Hence, null

hypothesis that there is no relationship between the market price and independent

variables in the case of both BSE (Hoq) and NSE (Hor) stands rejected. The computed

values of Durbin-Watson statistics are 1.57 and 1.92 in the case of BSE and NSE

respectively. Hence, no autocorrelation exists.

The values of ‘R’ are 0.947 (BSE) and 0.982 (NSE) which prove a stronger

relationship. For regression equation, R2 values are 0.897 (BSE) and 0.964 (NSE),

indicating that 89.7 per cent and 96.4 per cent respectively of the variation in market

price is explained by variations in Dividend Per Share, Book Value and Dividend Yield.

Table 5.40: Regression Model Summary and ANOVA of Tata Motors

Model R R2 F-change Sig. F-change Durbin -Watson

BSE (Sensex) 0.947 0.897 14.253 0.001 1.575

NSE (Nifty) 0.982 0.964 38.950 0.000 1.925

Source: Compiled from the data given in Appendix 8 using SPSS 16.

Regression Equation:

For BSE Sensex

Market Price (MP) = b0 + b2Dividend Per Share (DPS) +b3 Book Value (BV) +b5

Dividend Yield (DY)

= 133.52 + 50.76DPS +1.26BV - 127.8DY

For S&P CNX Nifty

Market Price (MP) = b0 + b2Dividend Per Share (DPS) +b3 Book Value (BV) +b5

Dividend Yield (DY)

= 169.81 + 58.12 DPS +.97 BV – 145.46 DY

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The b-values denote about the relationship between dependent variable and each

predictor. In both the regression equations two of the predictors have positive b-values

indicating positive relationships and one has a negative b-value showing a negative

relationship. Hence, with the increase in Dividend Per Share, market price increases as

other variables like Book value and Dividend Yield remain constant. Similarly, with

increase in Book Value and decrease in Dividend Yield individually (if other variables

remain constant), market price increases. The b-values also explain what degree each

predictor affects the outcome if the effects of all other predictors are held constant.

Table 5.41: Coefficients of Tata Motors (BSE Sensex)

Model b-value t-value Sig.

(Constant) 133.101 1.793 0.103

DPS 50.779 6.458 0.000

BV 1.2550 2.611 0.026

DY -127.851 -6.517 0.000

Source: Compiled from the data given in Appendix 8 using SPSS 16

Dividend Per Share (DPS) (b = 50.77) : This value indicates that as DPS increases by

Re. 1, MP increases by Rs. 50.77 .This interpretation stands true only if the effects of BV

and DY are held constant.

Book Value (BV) (b=1.25): This value indicates that as BV increases by Re. 1, MP

increases by Rs. 1.25. This interpretation stands true only if the effects of DPS and DY

are held constant.

Dividend Yield (DY) (b = -127.85): This value indicates that as DY increases by Re. 1,

MP decreases by Rs. 127.85. This interpretation stands true only if the effects of DPS

and BV are held constant.

Table 5.42: Coefficients of Tata Motors (S&P CNX Nifty)

Model b-value t-value Sig.

(Constant) 169.813 1.763 0.112

DPS 58.121 14.853 0.000

BV 0.974 1.981 0.079

DY -145.468 -7.123 0.000

Source: Compiled from the data given in Appendix 8 using SPSS 16

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Dividend Per Share (DPS) (b = 58.12) : This value explains that as DPS increases by

Re. 1, MP increases by Rs. 58.12 .This interpretation stands true only if the effects of BV

and DY are held constant.

Book Value (BV) (b =0.97): This value depicts that as BV increases by Re. 1, MP

increases by 97 paise only. This interpretation stands true only if the effects of DPS and

DY are held constant.

Dividend Yield (DY) (b= -145.46): This value reflects that as DY increases by Re. 1,

MP decreases by Rs. 145.46. This interpretation stands true only if the effects of DPS

and BV are held constant.

The t-test measures whether the predictor is making a significant contribution to

the model. Therefore, if the t-test associated with a b-value is significant (if the value in

column labelled significant is less than the significance level of 0.05) then predictor is

making a significant contribution to the model. Smaller the value of significant and

larger the value of t, the greater the contribution of the predictor. For both the models,

the DPS, BV, and DY are the all significant predictors of market price as their values

appear below 0.05. The magnitude of t-statistics reflects that DPS has deeper impact than

DY and BV in both the cases.

5.1.6.2.4 ANALYSIS OF MAHINDRA AND MAHINDRA

The test for significance of overall multiple regression model was made through

F-test. The results of the test as shown in Table 5.43 describe that in both the cases, i.e.,

BSE and NSE the F-values are significant for being less than the significance level of

0.05. Hence, null hypothesis that there is no relationship between the market price and

independent variables in the case of both BSE (Hoq) and NSE (Hor) stands rejected. The

computed values of Durbin-Watson test are 1.28 and 1.46 in the case of BSE and NSE

respectively. Hence, no autocorrelation exists.

The values of ‘R’ are 0.991 (BSE) and 0.959 (NSE) which show a stronger

relationship. For regression equation, R2 values are 0.983(BSE) and 0.920 (NSE),

indicating that 98.3 per cent and 92.0 per cent respectively of the variation in market

price is explained by variations in Earning Price Share, Price/Earning Ratio and

Dividend Yield.

Table 5.43: Regression Model Summary and ANOVA of Mahindra and Mahindra

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Model R R2 F change Sig. F-change Durbin –Watson

BSE (Sensex) 0.991 0.983 90.428 0.000 1.289

NSE (Nifty) 0.959 0.920 25.008 0.000 1.460

Source: Compiled from the data given in Appendix 9 using SPSS 16.

Regression Equation is:

For BSE Sensex

Market Price (MP) = b0 + b1 Earning Per Share (EPS) +b4 Price/Earning Ratio (P/E) +b5

Dividend Yield (DY)

= -379.35 + 13.99 EPS + 28.03P/E + 3.94DY

For S&P CNX Nifty

Market Price (MP) = b0 + b1 Earning Per Share (EPS) +b4 Price/Earning Ratio (P/E) +b5

Dividend Yield (DY)

= -266.43 + 15.12 EPS + 19.29 P/E + 5.65DY

The b-values denote about the relationship between dependent variable and each

predictor. In both the regression equations three of the predictors have positive b-values

indicating positive relationships. Hence, with the increase in Earning Per Share market

price increases as other variables like Price Earning Ratio and Dividend Yield remain

constant. Similarly, with increase in P/E and DY individually (if other variables remain

constant), market price increases. The b-values also explain what degree each predictor

affects the outcome if the effects of all other predictors are held constant.

Table 5.44: Coefficients of Mahindra and Mahindra (BSE Sensex)

Model b-value t-value Sig.

(Constant) -379.352 -11.149 0.000

EPS 13.986 18.351 0.000

P/E 28.026 11.959 0.000

DY 3.943 11.921 0.000

Source: Compiled from the data given in Appendix 9 using SPSS 16.

Earning Per Share (EPS) (b = 13.98): This value reflects that as EPS increases by Re.1,

MP increases by Rs 13.98. This interpretation stands true only if the effects of P/E and

DY are held constant.

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Price/Earning Ratio (b = 28.02): This value indicates that as P/E increases by Re. 1,

MP increases by Rs. 28.02. This interpretation stands true only if the effects of EPS and

DY are held constant.

Dividend Yield (DY) (b = 3.94): This value indicates that as DY increases by Re. 1, MP

increases by Rs. 3.94. This interpretation stands true only if the effects of EPS and P/E

are held constant.

Table 5.45: Coefficients of Mahindra and Mahindra (S&P CNX Nifty)

Model b-value t-value Sig.

(Constant) -266.432 -4.089 0.001

EPS 15.116 9.444 0.000

P/E 19.290 4.445 0.001

DY 5.652 4.421 0.000

Source: Compiled from the data given in Appendix 9 using SPSS 16

Earning Per Share (EPS) (b = 15.11): This value explains that as EPS increases by

Re.1, MP increases by Rs. 15.11. This interpretation stands true only if the effects of P/E

and DY are held constant.

Price/Earning Ratio (b =19.29): This value means that as P/E increases by Re. 1, MP

increases by Rs. 19.29. This interpretation stands true only if the effects of EPS and DY

are held constant.

Dividend Yield (DY) (b = 5.65): This value indicates that as DY increases by Re. 1, MP

increases by Rs. 5.65. This interpretation stands true only if the effects of EPS and P/E

are held constant.

The t-test measures whether the predictor is making a significant contribution to

the model. Therefore, if the t-test associated with a b-value is significant (if the value in

column labelled significant is less than the significance level of 0.05) then predictor is

making a significant contribution to the model. The smaller the value of significant and

the larger the value of t, the greater the contribution of the predictor. For both the

models, the EPS, P/E, and DY are the significant predictors of market price as their

values appear less than the significance level of 0.05. The magnitude of t-statistics

reflects that EPS has greater impact than P/E and DY in both the cases.

5.1.6.2.5 ANALYSIS OF HERO MOTORS

The test for significance of overall multiple regression model was made through

F-test. The results of the test as exhibited in Table 5.46 reveal in both the cases the F-

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values are significant for being less than the significance level of 0.05. Hence, null

hypothesis that there is no relationship between the market price and independent

variables in the case of both BSE (Hoq) and NSE (Hor) stands rejected. The computed

values of Durbin-Watson test are 1.78 and 1.45 in the case of BSE and NSE respectively.

Hence, no autocorrelation exists.

The values of ‘R’ is 0.997(BSE) and 0.996(NSE) which prove a stronger

relationship. For regression equation, R2 values are 0.994 (BSE) and 0.996 (NSE) ,

indicating that 99.4 per cent and 99.6 per cent respectively of the variation in market

price is explained by variations in Earning Price Share, Price/Earning Ratio, Dividend

Yield.

Table 5.46: Regression Model Summary and ANOVA of Hero Motors

Model R R2 F-change Sig. F-change Durbin -Watson

BSE (Sensex) 0.997 0.994 198.68 0.000 1.788

NSE (Nifty) 0.996 0.992 124.028 0.000 1.450

Source: Compiled from the data given in Appendix 10 using SPSS 16.

Regression Equation:

For BSE Sensex

Market Price (MP) = b0 + b1 Earning Per Share (EPS) +b4 Price/Earning Ratio (P/E) +b5

Dividend Yield (DY)

= -406.13 +17.24 EPS + 25.77P/E -18.15 DY

For S&P CNX Nifty

Market Price (MP) = b0 + b1 Earning Per Share (EPS) +b4 Price/Earning Ratio (P/E) +b5

Dividend Yield (DY)

= -497.43 +17.79 EPS + 29.15P/E -11.62DY

The b-values denote about the relationship between dependent variable and each

predictor. In both the regression equations two of the predictors have positive b-values

indicating positive relationships and one has a negative b-value showing a negative

relationship. Hence, market price increases with an increase in Earning Per Share as well

as Price Earning Ratio. However, a decrease in Dividend Yield causes an increase in

market price. The b-values also explain what degree each predictor affects the outcome if

the effects of all other predictors are held constant.

Table 5.47: Coefficients of Hero Motors (BSE Sensex)

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Model b-value t-value Sig.

(Constant) -406.132 -7.822 0.000

EPS 17.241 38.273 0.000

P/E 25.768 9.736 0.000

DY -18.152 -2.795 0.016

Source: Compiled from the data given in Appendix 10 using SPSS 16.

Earning Per Share (EPS) (b = 17.24): This value indicates that as EPS increases by

Re.1, MP increases by Rs. 17.24. This interpretation stands true only if the effects of P/E

and DY are held constant.

Price/Earning Ratio (b = 25.76): This value indicates that as P/E increases by Re. 1,

MP increases by Rs. 25.76. This interpretation comes true only if the effects of EPS and

DY are held constant.

Dividend Yield (DY) (b = -18.15): This value explains that as DY increases by Re. 1,

MP decreases by Rs. 18.15. This interpretation appears true only if the effects of EPS

and P/E are held constant.

Table 5.48: Coefficients of Hero Motors (S&P CNX Nifty)

Model b-value t-value Sig.

(Constant) -497.427 -10.835 0.000

EPS 17.787 27.760 0.000

P/E 29.155 11.556 0.000

DY -11.619 -2.374 0.035

Source: Compiled from the data given in Appendix 10 using SPSS 16.

Earning Per Share (EPS) (b = 17.78): This value denotes that as EPS increases by Re.1,

MP increases by Rs 17.78. This interpretation comes true only if the effects of P/E and

DY are held constant.

Price/Earning Ratio (b = 29.15): This value reflects that as P/E increases by Re. 1, MP

increases by Rs. 29.15. This interpretation stands true only if the effects of EPS and DY

are held constant.

Dividend Yield (DY) (b = -11.61): This value indicates that as DY increases by Re. 1,

MP decreases by Rs. 11.61. This interpretation comes true only if the effects of EPS and

P/E are held constant.

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The t-test measures whether the predictor is making a significant contribution to

the model. Therefore, if the t-test associated with a b-value is significant (if the value in

column labelled significant is less than the significance level of 0.05) then predictor is

making a significant contribution to the model. The smaller the value of significant and

the larger the value of t, the greater the contribution of the predictor. For both the model,

the EPS, P/E, and DY are the significant predictors of market price as their values appear

less than the significance level of 0.05. The magnitude of t-statistics reflects that EPS has

greater impact than P/E and DY in both the cases.

5.1.6.3 Results

The results of the test reveal that in all the selected companies (HDFC Bank,

State Bank of India, Tata Motors, Mahindra and Mahindra, and Hero Motors) and in both

the cases (i.e. BSE and NSE) the F-values are significant for being less than the

significance level of 0.05. Hence, the null hypothesis that there is no relationship

between the market price and independent variables in the case of both BSE (Hoq) and

NSE (Hor) stands rejected. The computed values of Durbin-Watson test in the case of all

the selected companies are less than 2 which reflect that no autocorrelation exists.

The values of R and R2 are greater than 0.90 in all companies under study as well

as in BSE and NSE which establish a stronger relationship between Market Price and

independent variables while R2 indicates that 90.0 per cent of the variation in market

price is explained by variations in independent variables respectively.

The regression analysis further provides that in all the companies under study as

well as in BSE and NSE, the variables such as EPS, P/E, and DY have contributed most

in determining share prices. It has also been found that EPS, and P/E are the most

important determinants which have influenced the share price in banking sector, while in

the automobile sector DY, P/E and EPS are the most significant variables.

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5.2 RELATIONSHIP OF VOLATILITY AND STOCK PRICE

RETURNS

5.2.1 Overview

The relationship between the return on an asset and its variance (or volatility) as a

proxy for risk has been an important topic in financial research. The theoretical asset

pricing models (e.g., Sharpe, 1964; Linter, 1965; Mossin, 1966; Merton, 1973, 1980)

typically link the return (or the price change) of an asset to its own return variance, or to

the covariance between its return and the return on the market portfolio. However, it has

been controversial whether such a relationship is positive or negative. As summarized by

Ballie and DeGennaro (1990)in their research work, most asset-pricing models (e.g.,

Sharpe, 1964; Linter, 1965; Mossin, 1966; Merton, 1973) postulate a positive

relationship between a stock portfolio’s expected returns and volatility. On the other

hand, there is also a long tradition in finance that models stock return volatility as

negatively correlated with stock returns (Black, 1976; Cox and Ross, 1976; Bekaert and

Wu, 2000; Whitelaw, 2000). For example, Bekaert and Wu (2000) claimed that it

appears that volatility in equity markets is asymmetric; returns and conditional volatility

are negatively correlated.

Standard GARCH models assume that positive and negative error terms have a

symmetric effect on the volatility. In other words, good and bad news have the same

effect on the volatility in this model. In practice, this assumption is frequently violated,

in particular by stock returns, in that the volatility increases more ‘after bad news’ than

‘after good news’. This so called Leverage Effect appeared firstly in Black (1976), who

noted that:

“a drop in the value of the firm will cause a negative return on its stock, and will usually

increase the leverage of the stock. [...] That rise in the debt-equity ratio will surely mean

a rise in the volatility of the stock''.

A very simple but plausible explanation for the leverage effect is that negative

returns imply a larger proportion of debt through a reduced market value of the firm,

which leads to a higher volatility. The risk, i.e., the volatility reacts first to larger changes

of the market value, nevertheless it is empirically shown that there is a high volatility

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after smaller changes. On the other hand, Black said nothing about the effect of positive

returns on the volatility. Although the positive returns cause smaller increases, they do

cause an increase in the volatility. From an empirical point of view the volatility reacts

asymmetrically to the sign of the shocks, and therefore. a number of parameterized

extensions of the standard GARCH model, i.e., EGARCH, QGARCH, TGARCH have

been suggested recently.

EGARCH model has several advantages over the pure GARCH specification.

First, since the log (σ2t ) is modelled, then even if the parameters are negative, σ2

t will be

positive. There is thus no need to artificially impose non-negativity constraints on the

model parameters. Second, asymmetries are allowed for under the EGARCH

formulation, if the relationship between volatility and returns is negative, γ, will be

negative (Brooks, 2008).

EGARCH Model

The EGARCH model was proposed by Nelson (1991). The main purpose of this

model is to explain the asymmetrical response of the market under the positive and

negative shocks. The specification for the conditional variance is:

−+++=

−− πσσ

γσβωσ 2)()( 21

1

21

121

2

t

t

t

ttt

uauInIn

The left-hand side is the log of the conditional variance. This implies that the

leverage effect is exponential, rather than quadratic, and that forecasts of the conditional

variance are guaranteed to be non-negative. The presence of leverage effect can be tested

by the hypothesis that γ < 0. The impact is asymmetric if γ≠0.

5.2.2 Analysis and Discussion

Fitting EGARCH Model

The most popular member of the GARCH class of models, i.e., EGARCH model

has been used to examine the relationship between stock returns (BSE Sensex and NSE

Nifty) and volatility. EViews 5 software has been used examine the relationship. The

results are reported in Table 5.49 and 5.50.

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Table 5.49: Coefficient of EGARCH Model (BSE Sensex)

Coefficient Std. Error z-Statistic Prob.

C 0.060662 0.019763 3.069469 0.0021

Variance Equation

C(2) -0.155475 0.009579 -16.23074 0.0000

C(3) 0.247142 0.013459 18.36192 0.0000

C(4) -0.077313 0.007431 -10.40423 0.0000

C(5) 0.958650 0.004080 234.9542 0.0000

R2 -0.000140 Mean dependent variance 0.040432

Adjusted R2 -0.001150 S.D. dependent variance 1.711505

S.E. of regression 1.712489 Akaike info criterion 3.662615

Sum squared residual 11613.16 Schwarz criterion 3.670541

Log likelihood -7256.134 Durbin-Watson statistic 1.860334

Source: Compiled from the data taken from NSE website for selected period using

Eviews 5.

The following conditional variance equations for BSE Sensex and NSE Nifty get while

running EGARCH process.

For BSE Sensex

−+++=

−− πσσ

γσβωσ 2)()( 21

1

21

121

2

t

t

t

ttt

uauInIn

Where, Constant C(2), i.e. ω = - 0.155

GARCH coefficient C(5), i.e. α = 0.958

ARCH coefficient C(3), i.e. β = 0.247

Leverage coefficient C(4), i.e. γ = -0.077

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Table 5.50: Coefficient of EGARCH Model (NSE Nifty)

Coefficient Std. Error z-Statistic Prob.

C 0.061056 0.020487 2.980304 0.0029

Variance Equation

C(2) -0.144746 0.007440 -19.45437 0.0000

C(3) 0.239162 0.009918 24.11423 0.0000

C(4) -0.083336 0.007381 -11.29087 0.0000

C(5) 0.955600 0.003803 251.2848 0.0000

R-squared -0.000143 Mean dependent variance 0.040753

Adjusted R-squared -0.001146 S.D. dependent variance 1.700851

S.E. of regression 1.701826 Akaike info criterion 3.668464

Sum squared residual 11550.09 Schwarz criterion 3.676343

Log likelihood -7319.088 Durbin-Watson statistic 1.874163

Source: Compiled from the data taken from NSE website for selected period using Eviews 5.

For NSE Nifty

−+++=

−− πσσ

γσβωσ 2)()( 21

1

21

121

2

t

t

t

ttt

uauInIn

Where, Constant C(2), i.e. ω = - 0.144

GARCH coefficient C(5), i.e. α = 0.955

ARCH coefficient C(3), i.e. β = 0.239

Leverage coefficient C(4), i.e. γ = -0.083

The presence of leverage effects can be tested as follows:

If γ = 0, then a positive surprise has the same effect on volatility as a negative surprise of

the same magnitude.

If -1< γ <0, a positive surprise increases volatility less than a negative surprise.

If γ < -1, a positive surprise actually reduces volatility while a negative surprise increases

volatility.

The impact is asymmetric, if γ≠0.

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5.2.3 Results

It has been found that all the coefficients are significant. In the case of both BSE

Sensex and NSE Nifty, ARCH and GARCH coefficients are positive and leverage term

is negative, i.e. BSE Sensex (-0.077) and NSE Nifty (-0.083) and statistically different

from zero indicating the existence of the leverage effect for the stock market returns

during the period under study. Therefore return is negatively correlated with volatility.

This implies that returns tend to be more volatile in response to bad news and less

volatile in response to good news i.e. evidence of asymmetry in stock price behaviour.

The observations of the present study just document as evidence of the findings

developed by Schwert (1989), French et al. (1987), Christie (1982) and Black (1976).

They also found out that returns are negatively correlated with volatility. The main

reason of negative correlation between stock and volatility is leverage effect, i.e., when

stock price declines, the value of equity relative to corporate debt also reduces. Thus, it

leads to increase the risk of holding stocks.

5.3 FORECASTING STOCK MARKET VOLATILITY USING

GARCH MODEL 5.3.1 Overview

The study of volatility is always a serious concern for analysts and researchers

because high degree of volatility can affect the smooth functioning of any stock market.

It may also adversely affect the economic growth and business environment (Srivastava,

2008). An increase in stock market volatility can be interpreted as an increase in assets

classes i.e. debt. This shift can result an increase in cost of capital to firms and hence

new firms might abide this effect as investors will turn to purchase of stock in superior,

well-known firms. Whereas there is a general agreement on what constitutes stock

market volatility, and to a smaller extent, on how to quantify it, there is far less

conventionality on the reasons of changes in stock market volatility. A number of

researchers investigated the causes of volatility in the arrival of new, unexpected

information that affect expected returns on a stock. Thus, changes in market volatility

would just reproduce changes in the domestic or global economic environment. Others

maintain that volatility is caused largely by changes in trading volume, practices or

trends, which in turn are resolute by factors such as changes in macroeconomic policies,

shifts in investor's risk appetite and growing uncertainty (Srinivasan et al., 2010).

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Detection of volatility trends would provide insight for designing investment

strategies and for portfolio management. Investors seeking to avoid risk, for example,

may choose to adjust their portfolios by reducing their commitments to assets whose

volatilities are predicted to increase or by using more sophisticated dynamic

diversification approaches to hedge predicted volatility increases. Accurate forecasts of

stock market volatility may improve the performance of option pricing models. In order

to value an option precisely, it is important to accurately forecast the future standard

deviation of returns over the remaining life of the option. This would be useful for

holders and writers of options on the underlying assets. Moreover, the stock market

volatility forecast is an important input for dynamic portfolio insurance strategies

(Srinivasan and Ibrahim, 2010).

As such, financial market volatility has wider ramifications for the entire

economy. The Black Monday on May 17, 2004 had virtually shaken the entire Indian

economy, and regulators, self-regulators, market players as well as government

functionaries were on tenterhooks. The Ketan Parekh fiasco, Harshad Mehta Scam, etc.

are the cases that concern even a common man in the country. Similarly, the stock

market crash of 1987, mini crash of 1989, Southeast Asian currency turmoil, etc. are the

events that have demonstrated the impact of volatility not only on the domestic market

but also the world market abroad. Therefore, volatility forecasting is an issue that has lot

of practical significance in the present day environment wherein events taking place in

one part of the world may affect other parts within seconds (Srivastava and Jain, 2006).

Hence, there is no gainsaying with the statement that volatility estimation is an

essential part in most finance decisions be it asset allocation, derivative pricing or risk

management. However, the question as to what model should be used to calculate

volatility, there is no unique answer as different volatility models are proposed in

literature and are being used by practitioners and these varying models lead to different

volatility estimates.

Financial market volatility is predictable; a prediction of high volatility is really

just a high variance - a prediction that the potential size of a price move is great. Thus,

even perfect predictability of variance does not mean perfect predictability of the size of

market moves or of their direction. Volatility forecasting is a little like predicting

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whether it will rain: You can be correct in predicting the probability of rain, but still have

no rain (Engle, 1993).

The technical term given to volatility clustering behaviour is autoregressive

conditional heteroskedasticity (ARCH). Conditional Heteroscedasticity (ARCH) became

a very popular method in the modelling of stock market volatility. As compared to

traditional time series models, ARCH models allowed the conditional variances to

change during time as functions of precedent errors. First approach was to improve the

univariate ARCH model with a different requirement of the variance function. One

development was introduced by Bollerslev (1986) where the Generalized Autoregressive

Conditional Heteroscedasticity (GARCH) method was presented. Then after, the

integrated GARCH (IGARCH) (Engle et al. 1994) and the exponential GARCH

(EGARCH) (Nelson, 1991) were significant one wherever re-specification of variance

equation was considered. Nevertheless, the extent of empirical research on stock return

volatility in emerging markets like India was not plentiful. While Roy and Karmakar

(1995) focused on the measurement of the average level of sample standard deviation to

investigate whether volatility has gone up; Goyal (1995) used conditional volatility

estimates, as recommended by Schwert (1989), to spot the trend in volatility. He also

analyzed the impact of carry forward system on the intensity of volatility.

ARCH/GARCH models have been worn by Pattanaik and Chatterjee (2000) to model the

volatility in Indian financial market.

5.3.2 Analysis and Discussion

A descriptive investigation of the plot of daily returns on Sensex and Nifty

(Figures 5.1 and 5.2) reveals that returns continuously fluctuated around the mean value

that was close to zero. The return measures were both in positive and negative area.

More fluctuations be tending to cluster together and were separated by periods of relative

calm. This was in agreement with Fama's (1965) observation of "volatility clustering"

(Volatility Clustering means that large changes in time series tend to be followed by

large changes and small changes by small changes.).From the time series graph of the

returns for both the markets, it is analyzed that high volatilities are followed by high

volatilities and low volatilities are followed by low volatilities. That means time series

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have important time varying variances. Additionally, it is appropriate to put conditional

variance into the function to clarify the impact of risk on the returns. Hence, GARCH

model is the excellent tool for the study.

Figure 5.1: Volatility Clustering of Daily Returns of BSE Sensex

Source: Based on the data taken from BSE website for selected period.

Figure 5.2: Volatility Clustering of Daily Returns of S&P CNX Nifty

Source: Based on the data taken from NSE website for selected period.

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Table 5.51: Descriptive Statistics of Daily Returns

Sensex S&P CNX Nifty

Observation Period April 1996-March 2011 April 1996-March 2011

Number of

Observations 3699 3747

Mean 0.000471 0.000472

Median 0.001128 0.001162

Maximum 0.159900 0.163343

Minimum -0.118092 -0.130539

Std. Dev. 0.017296 0.017168

Skewness -0.103023 -0.184996

Kurtosis 8.102657 9.297716

Jarque-Bera 4018.426 (2-tailed p =0.00) 6211.823(2-tailed p =0.00)

Q(1) 19.720 (2-tailed p =0.00) 12.42 (2-tailed p =0.00)

Q2(1) 122.89 (2-tailed p =0.00) 150.61 (2-tailed p =0.00)

ARCH LM

statistics(at lag = 1) 127.95 158.40

Source: Calculated from the data taken from BSE and NSE website for selected period. Notes:

(a) Skewness is a measure of asymmetry of the distribution of the series around its

mean.

(b) Kurtosis measures the peakedness or flatness of the distribution of the series.

(c) Jarque-Bera is a test statistic for testing whether the series is normally distributed.

(d) Q(K) is the Ljung Box statistic identifying the presence of first order

autocorrelation in the returns. Under the null hypothesis of no autocorrelation, it

is distributed as chi-square (K).

(e) Q2(K) is the Ljung Box statistic identifying the presence of first order

autocorrelation in the squared returns. Under the null hypothesis of no

autocorrelation, it is distributed as chi-square (K).

(f) ARCH LM statistic is the Lagrange Multiplier test statistic for the presence of

ARCH .Under the null hypothesis of no heteroskedasticity, it is distributed as a

chi-square (K). Critical value at 1 per cent level of significance is 6.63 at 1 degree

of freedom. Values for other higher lag are also significant.

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Descriptive statistics for both Sensex and Nifty returns are summarized in Table

5.51. For Sensex and Nifty, the skewness statistic for daily returns is found to be

different from zero indicating that the return distribution is not symmetric. Furthermore,

the relatively large excess kurtosis suggests that the underlying data is leptokurtic or

heavily tailed and sharply peaked about the mean when compared with the normal

distribution. The Jarque-Bera statistic calculated to test the null hypothesis of normality

rejects the normality assumption. The results confirm the well-known fact that daily

stock returns are not normally distributed but are leptokurtic and skewed.

Unit Root Tests

Stationarity of the return series were tested by conducting Dickey-Fuller test. The

calculated p-values of ADF for both markets were less than the significance level of 0.05

which leads to an inference that the data of time series under study is stationary. The

results of test confirm that both the return series are stationary. Table 5.52 and 5.53

present the results of these tests.

Table 5.52: ADF Test Statistics for Daily Returns (Sensex)

t-Statistic Probability

Augmented Dickey-Fuller test statistic -56.49521 0.0001

Test critical value 1% level -3.465014

5% level -2.876677

10% level -2.574917

Source: Calculated from the data taken from BSE website for selected period.

Table 5.53: ADF Test Statistics for Daily Returns (Nifty)

t-Statistic Probability

Augmented Dickey-Fuller test statistic -57.75137 0.0001

Test critical value 1% level -3.465014

5% level -2.876677

10% level -2.574917

Source: Calculated from the data taken from NSE website for selected period.

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Application of Box-Jenkins Methodology

Volatility clustering implies a strong autocorrelation in squared returns; therefore,

a simple method for detecting volatility clustering is to calculate the first-order

autocorrelation coefficient in squared returns. To test this hypothesis, one can use the

modified Box-Pierce (Ljung-Box- Pierce or simply Ljung-Box ) statistic (Q), developed

by Ljung and Box, which is defined as Q = n (n + 2) Σ r2 k / (n-k), where n = sample size

and k = lag length and Σ r2 k = autocorrelation function (Ljung and Box, 1978). In an

application, if the computed Q exceeds the critical Q value from the Chi-square table at

the chosen level of significance, one can reject the null hypothesis. The values of Q2(1)

test statistic (reported in Table 5.51), reject the null hypothesis which confirmed the

presence of first-order autocorrelation in the series. The existence of a leptokurtic

distribution, presence of volatility clustering suggested an ARCH or GARCH process,

which was confirmed by computing the value of Lagrange Multiplier (LM) which rejects

the null hypothesis. To sum up, the analysis indicates that daily return series of the index

are not normal and exhibit ‘ARCH effect’.

Fitting GARCH (1, 1) Model

The most popular member of the ARCH class of models, i.e. GARCH (p,q)

model has been used to model volatility of Sensex and Nifty returns. EViews 5 software

for model estimation has been used. The results from the model estimated are reported in

Table 5.54 and 5.45.

The following estimated conditional variance equations for Sensex and Nifty are

obtained while running GARCH (1,1) process.

For Sensex

ht = ω +α1 ε t-1 2 + β1h t-1

ht = 6.5E-06+.120 ε t-1 2 + 0.863 h t-1

For Nifty

ht = ω +α1 ε t-1 2 + β1h t-1

ht = 7.55E-06+.130 ε t-1 2 + 0.851 h t-1

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Table 5.54: Coefficients of GARCH Model (Sensex)

Coefficient Std. Error z-Statistic Prob.

C 0.001148 0.000208 5.509233 0.0000

RET(-1) 0.087753 0.017837 4.919702 0.0000

Variance Equation

C 6.56E-06 8.29E-07 7.909030 0.0000

RESID(-1)^2 0.120143 0.007744 15.51357 0.0000

GARCH(-1) 0.862982 0.007805 110.5744 0.0000

R2 0.003396 Mean dependent variance 0.000471

Adjusted R2 0.002317 S.D. dependent variance 0.017298

S.E. of regression 0.017278 Akaike info criterion -5.520149

Sum squared residual 1.102212 Schwarz criterion -5.511743

Log likelihood 10209.00 F-statistic 3.145587

Durbin-Watson statistic 2.018649 Probability (F-statistic) 0.013613

Source: Compiled from the data taken from BSE website for selected period using Eviews 5.

Table 5.45: Coefficients of GARCH Model (Nifty) Coefficient Std. Error z-Statistic Prob.

C 0.001092 0.000220 4.962655 0.0000

RET(-1) 0.083970 0.017566 4.780146 0.0000

Variance Equation

C 7.55E-06 7.73E-07 9.767712 0.0000

RESID(-1)^2 0.130346 0.006860 19.00021 0.0000

GARCH(-1) 0.851039 0.006154 138.2827 0.0000

R2 0.001139 Mean dependent variance 0.000470

Adjusted R2 0.000071 S.D. dependent variance 0.017169

S.E. of regression 0.017169 Akaike info criterion -5.524704

Sum squared residual 1.102431 Schwarz criterion -5.516389

Log likelihood 10350.01 F-statistic 1.066612

Durbin-Watson statistic 2.042994 Probability (F-statistic) 0.371309

Source: Compiled from the data taken from NSE website for selected period using Eviews 5.

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5.3.3 Results

For time series analysis, it is desirable to have stationary series. Stationarity of

the series can be found by summation of α1+ β1 and the value of summation should be

less than one. As for the stationarity of the variance process, it was observed that α1+ β1

is 0.983 for Sensex. (value of α1 is + 0.120 and that of β1 is +0.863, reported in Table

5.54) and for S&P CNX Nifty it was observed that α1+ β1 is 0.981 (value of α1 is + 0.130

and that of β1 is +0.851, reported in Table 5.55) . Hence, stationarity condition (α1+ β1 <

1) is satisfied in both markets. However, the sum was rather close to one which indicated

a long persistence of shocks in volatility. It implies a ‘long memory’. A large value of

GARCH lag coefficients β1 (+ 0.863 for Sensex and +0.851 for Nifty) indicates that

shocks to conditional variance take a long time to die out, so the volatility is ‘persistent’.

Low value of error coefficient α1 i.e., (+ 0.120 for Sensex and +0.130 for Nifty) suggests

that market surprises induce relatively small revisions in future volatility.

The present study has attempted to devise a volatility forecast model for the BSE

Sensex and S&P CNX Nifty, and concluded the GARCH(1,1) specification fits the

Sensex return and Nifty return time series quite well.

Forecasts of Market Volatility

Once the model has been fitted to market return series, (i.e. Sensex and Nifty, it

can be used to forecast volatility. The model has used to forecasts volatility for one-day-

ahead.

For Sensex ht = ω +α1 ε t-1 2 + β1h t-1

ht = 6.56 E-06+.120 ε t-1 2 + 0.863h t-1

h t+1 = 6.56E-06+.120 (-0.00608)2 + 0.863 (.000163)

=.00008118

For Nifty ht = ω +α1 ε t-1 2 + β1h t-1

ht = 7.55E-06+.130 ε t-1 2 + 0.851 h t-1

h t+1 = 7.55E-06+.130 (-0.00609)2 + 0.851 (.000158)

=.00008663

Where, h t+1 is the one-day-ahead volatility forecast. The values of ε t-1 2 and ht-1 are

calculated by using EViews 5. The value of one-day-ahead volatility is .00008118 for

Sensex and .00008663 for Nifty. Hence, one-day-ahead volatility can be forecast from

constructed model.

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