Shounak Bandyopadhyay* & SoumyaSaha** · Inflation and Exchange Rate Impact on Stock Returns of...

SIT Journal of Management Vol: 5; Issue: 1, pp-78-117

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Bandyopadhyay & Saha

Inflation and Exchange Rate Impact on Stock Returns of Banking Companies

of India after the Crisis period of 2008-2009

Shounak Bandyopadhyay* & SoumyaSaha**

Abstract

The reasons for fluctuation of banking stock returns are manifold. This study looks at two specific

factors – Exchange rate and Inflation. Rich body empirical literature also proved the relationship

between inflation, exchange rate with stock returns both at industry level and firm level globally. In

the Indian context, very few studies have been done in this regard. The purpose of this study is to

find whether the banking industry as well as banking firms is exposed to exchange rate risks and

inflation. The study also investigate the casual relationship between inflation and Bank stock returns,

and exchange rate with bank stock returns both industry as well as firm level. The period of study is

1st January 2005 to 31st December 2014. To find the association between the said variables we use

correlation analysis. Augmented - Dickey Fuller test and Philip-Perron test has been used to find the

stationarity. The casual relation among the said is determined by using Granger causality test.

Regression analysis has been done to the find the impact of inflation and exchange rate on stock

returns. This study will help the investors, speculators, arbitrageurs for their investment decisions

and also managers of the company to make strategic decisions.

Keywords: Bankex; Banks; Exposure; Exchange Rate; Inflation; Granger Causality.

*Corresponding Author: SoumyaSaha

*Shounak Bandyopadhyay ; Student; M.Com; St. Xavier’s College (Autonomous), Kolkata

**SoumyaSaha; Assistant Professor; Post-Graduate Department of Commerce; St. Xavier’s

College (Autonomous), Kolkata; email: [email protected]; M: +91-9903935396.


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Introduction

Banks are a major part of any economic system. They provide a strong base to Indian

economy too. Even in share markets, the performance of bank shares is of great

importance. This is justified by the proof that in both BSE and NSE we have separate index

for Banking Sector Shares. But for our study we have taken only BSE Bankex and individually

the 12 companies of the BSE Bankex. Thus, the performance of share market, the rise and

the fall of market is greatly affected by the performance of Banking Sector Shares and this

paper revolves around some of those factors, their understanding and an empirical and

technical analysis of the same.

Internal or External conditions both are involved in measuring the sensitivity of returns of

stocks. Industrial Production, money Supply , Foreign exchange Rate, Interest rate, gold

prices, GDP and oil prices in the world economy are involved in external conditions whereas

dividend policy, earning per share etc. are the contributors of internal factors. This paper

studies the impact of Macro (External) factors on BSE Bankex. Macroeconomic indicators

are already exhibiting signs of slight improvement after a sustained period of deterioration

as Rupee is appreciatingagainst dollar, WPI inflation was 00 in November 2014, interest

rates and gold prices are decreasing with the RBI bringing down its repo rate recently and

industrial production has started to rise marginally.

The study which measures the impact of two specific macroeconomic forces on various

sectors of stock exchange index is rare in the literature so this study provides a new way in

the extending line of literature.

Review of Literature

Few Selected reviews on the particular area of study are as follows:

International Research Papers:

A study by SadiaSaeed and Noreen Akhter on “Impact of macroeconomic factors on

banking index in Pakistan”, analyzed the macroeconomic factors on the banking index of the

Karachi stock exchange. The macroeconomic variables selected were exchange rate,

industrial production, money supply, oil price and short term loan. The study found that oil


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price has impact on banking index, economic variables like money supply, exchange rate;

short term loan and industrial production rate had negative effects on the banking index.

The article entitled “Banking stock returns and their relationship to interest rate and

exchange rate: Australian evidence”, by John Simpson, investigated the dynamic interaction

and long run relationship between Australian bank stock returns and key macroeconomic

variables and monetary policy variables like interest rate and exchange rate. The study

found there was no evidence of Australian bank stock returns from co-integrated relation

with short run and long run exchange rate and interest rate during the study period.

Australian monetary authorities have placed a strong belief on the health and performance

of the banking sector and the financial sector as their setting of monetary and exchange rate

policy.

Gan, Lee, Yong and Zhang (2006)examined the relationship between stock prices and

macroeconomic variables for New Zealand. The variables are long-run and short-run interest

rate, inflation rate, exchange rate, GDP, money supply and domestic retail oil price. Their

findings suggest that there exist a long term relationship between stock prices and selected

variables in New Zealand. However, the Granger causality test suggests that New Zealand

stock exchange is not a good indicator for macroeconomic variables in New Zealand.

A research paper entitled “Interest rate risk and bank common stock returns from the Greek

banking sector” by Konstantinos Drakes, examined the effects of changes in the long run

interest rate on bank stock returns in the Athens stock exchange. The sensitivity of stock

returns was tested by interest rate, which allows the time varying conditional volatility. The

study found that the consistent provides evidence for significant sensitivity of bank stock

returns to interest rate co-movements.

Maghayereh (2003) investigated the long run relationship between the Jordanian stock

prices and selected macroeconomic variables using co-integration analysis and monthly

time series data from January 1987 to December 2000. This study treasures that

macroeconomic variables as exports, foreign reserves, interest rates, inflation, and


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industrial production are reflected in stock prices in the Jordanian capital market. The study

concludes that macroeconomic variables are significant in predicting changes in stock prices.

Erdogan and Ozlale (2005) investigated the influence of varying macroeconomic variables

on stock return of Turkey and found that industrial production and exchange rates were

positively related with the stock return. On the other hand, Circulation in Money (M1) had

no any significant impact on stock return.

Abdalla and Murinde (1997) examined interactions between stock prices and exchange

rates for four emerging markets (India, Korea, Pakistan and Philippines) using granger

causality and co-integration techniques, their study reveals a unidirectional causality

running from exchange rate to stock prices.

Pan et al. (1999) used daily market data to study the causal relationship between stock

prices and exchange rates in China and found that the exchange rates Granger-cause stock

prices with less significant causal relations from stock prices to exchange rate.

Agus and Carl (2004) investigated the statistical relationship between stock prices and

exchange rates using granger causality and Johansen co-integration test in four SEAN

countries (Indonesia, Philippines, Singapore and Thailand). The study found that the

relationship between stock prices and exchange rates is characterized by a feedback system.

The co-integration test found that all the stock prices and exchange rates in the four

countries are co-integrated and the causality runs from exchange rate to stock prices.

Ajayi and Mougoue (1996) investigate the short-and long-run relationship between stock

prices and exchange rates in eight advanced economies, the results on short-run effects in

the U.S. and U.K. markets. Their findings show that an increase in stock prices causes the

currency to depreciate for both countries.

Indian Research Papers:

Luthra and Mahajan (2012) studied the impact of macroeconomic factors on BSE Bankex.

Macroeconomic variables involved GDP growth rate, inflation, gold prices and exchange


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rate. The results conclude that inflation, exchange rate and GDP growth rate affect the

Bankex positively. However Gold Prices affect BSE Bankex negatively but none of these

variables have a significant impact on the stock prices of banks.

A research paper by Pooja Singh (2014)on “An empirical relationship between Indian Stock

Market indices and macroeconomic indicators” tends to convey the relationship between

macroeconomic variables and Indian stock market. The explanatory variables are Index of

Industrial Production, Wholesale Price Index, Money Supply, Interest Rates, Trade Deficit,

Foreign Institutional Investment, Exchange rate, Crude Oil Price and Gold Price. Her

conclusion was that Indian stock market has significant influence of gold prices, inflation,

money supply, exchange rates and foreign institutional investments. The gold has adverse

effect on Indian Stock market that shows the increasing interest of investors in the precious

metal. There is a negative impact exchange rate on stock market. The money supply has

positive impact on the stock market that reveals that lager money in circulation has

favorable impact on stock market during the period of study. The inflow of foreign capital is

value addition to the market as it has significant impact over stock market.

In “Relationship between interest rate changes and banking stock returns in upmarket and

down market situation” by RenuGhosh, (2014)the effects of changes in the interest rate on

common stock returns of banks included in BANKEX in India is studied. The final conclusion

is that a weak relationship is found between Bank stock returns and interest rate changes in

India. With respect to relationship between Bank stock returns and Market returns there

exist a positive and significant relationship. The impact of market returns on the individual

bank stock returns, equally weighted portfolio returns and Bankex returns is higher than the

impact of interest rate changes.

BaranidharanSubburayan, Dr.VanithaSrinivasanin “The Effects of Macroeconomic Variables

on CNX Bankex Returns: Evidence from Indian Stock Market”investigated the long run

interaction between economic variables and CNX bank returns. The key active economic

variables namely exchange rate, interest rate and inflation rate were considered for

analysis. The study reveals from the analysis, selected sample macroeconomic variables


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were influencing the CNX Bankex index. Co-integration test exhibits imply the bank stocks

returns have fixed long run equilibrium relation with selected sample macroeconomic.

Bankex index and Interest rate has causal unidirectional relation with Exchange rate.

Naik and Padhi (2012) studies association between the Indian stock market index (BSE

Sensex) and various macroeconomic variables as industrial production index, wholesale

price index, money supply, treasury bills rates and exchange rates from the time period

1994 to 2011. The analysis reveals that macroeconomic variables and the stock market

index are co-integrated and, hence, a long-run equilibrium relationship exists between

them. This study perceived that the stock prices are positively relate to the money supply

and industrial production but negatively relate to inflation. The exchange rate as well as

short-term interest rate is found to be insignificant in determining stock prices. There is

bidirectional causation exists between industrial production and stock prices but

unidirectional causation from money supply to stock price, stock price to inflation and

interest rates to stock prices is established.

Ray (2013) examined the relationship between macroeconomic variables and stock prices.

The Industrial production presents a measure of overall economic activity in a country and

moves stock prices through its influence on expected future cash flows. Thus, it is expected

that an increase in industrial production index is positively related to stock price. The causal

relationship between industrial production and stock price in India is covered for a period,

1990-91 to 2010-11. The findings specified that there exist no significant causal relationship

between industrial production and share price in India. The result of regression, of course,

suggests that there may have been positive relation between stock price and real industrial

production. The increase in production of industry can enhance stock price and vice versa.

Sireesha (2013) examined the impact of macroeconomic factors upon the movements of

the Indian stock market index Nifty, gold and silver prices through linear regression

technique. Gold returns, Silver returns are selected for the analysis as they are important

now a days and are studied along with the stock returns. The performance of internal

variables shows the interdependence between these variable with returns on stock, gold


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and silver. Stock return is significantly influenced by GDP and inflation while gold return is

significantly influenced by money supply. External variables show significant impact on

dependent variables.

Research Gap:

In the Indian context, most of the studies conducted have been on Stock Market or BSE as a

whole. Very few studies have been conducted on a sectorial basis and specially focusing the

Bankex.All the studies have been confined only to the market level. The effects of variables

are not assessed on the companies, thus leaving out a vital piece of information. It is highly

anticipated that the Inflation Rate has a negative impact on the Indian bank stock market,

but in fact does it have any negative impact or does it provoke the bank stock market

because of grow up of the economy.

The purpose of this study is to investigate the casual relationship between inflation and

Bank stock returns, and exchange rate with bank stock returns Pre Post and during the crisis

period to get a detailed idea. The study is not just confined to the industry (Bankex), the

study also analyses the effect of the chosen variables on a firm level as well.

Objectives of the Study:

1. To examine the impact of exchange rate on Bankex and selected banks under

Bankex.

2. To examine the impact of inflation on Bankex and selected banks under Bankex.

3. To study the causal relationship between exchange rate and Bankex and banks under

Bankex.

4. To study the causal relationship between inflation and Bankex and banks under

Bankex.

Methodology of the study:

a) Sample Selection:


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The study mainly focuses on the WPI inflation index as a proxy for inflation rate and rupee-

dollar Exchange rates. Monthly data of both these variables were collected from the RBI

reference rate archive and the database for Indian economy.

b) Period of Study:

The period of study has been chosen as 1st January 2005 to 31st December 2014. The study

period has been sub divided bases on the price movements in BSE SENSEX into three sub-

periods namely;

i) Pre-crisis period: 1st January 2005 – 31st December 2007

ii) Crisis Period: 1st January 2008 – 31st July 2009

iii) Post Crisis period: 1st August 2009 – 31st December 2014.

c) Research tools:

The daily returns for the individual series are calculated based on the logged difference

as below:

Rit = [Ln (Pit) – Ln (Pit-1)] ………………..Equation…..(1)

Initially, correlation coefficients have been computed of the log values of the return

series of individual stock return of different companies with INR/USD and Crude Oil. It

has been observed that there exist correlations amongst different variables during the

sample period. However, such results do not necessarily imply, true existence of such

dependency as in many cases they may yield spurious results. Accordingly, further

investigation is necessary to establish the inferences drawn from the correlation results.

Descriptive Statistics of the Raw Return, especiallyMean, Standard Deviation has been

calculated.

1. Stationarity Test:


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Regressing non-stationary variables on each other leads to potentially misleading

inferences about the estimated parameters and the degree of association.

Therefore, before beginning of any testing, the order of integration of price series

must be determined. To identify whether the series are I(1), the augmented Dickey-

Fuller (ADF) test (Dickey and Fuller, 1979) has been employed, which involves

estimating the equation.

𝑦𝑡 = 𝛼 + 𝛽𝑡 + 𝛽𝑦𝑡−1 + 𝜃𝑗∆𝑦𝑡−𝑗 + 𝜀𝑡

𝑘

𝑗=1

……………… Equation…..(2)

Where is a constant, the coefficient on a time trend and K the lag order of the

autoregressive processes.

The null hypothesis of the Augmented Dickey-Fuller t-test is

H0:θ = 0 (i.e. the data needs to be differenced to make it stationary)

Versus the alternative hypothesis of

H1: θ < 0 (i.e. the data is stationary and doesn’t need to be differenced)

The Phillips–Perron test is a unit root test. That is, it is used in time series analysis to

test the null hypothesis that a time series is integrated of order 1. It builds on

the Dickey–Fuller test

Null hypothesis in ,

……………equation (3)…

Where is the first difference operator.

Like the augmented Dickey–Fuller test, the Phillips–Perron test addresses the issue

that the process generating data for might have a higher order of autocorrelation

than is admitted in the test equation—making endogenous.

Whilst the augmented Dickey–Fuller test addresses this issue by introducing lags

of as regressors in the test equation, the Phillips–Perron test makes a non-

parametric correction to the t-test statistic.


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1. Regression Model:The study applied the following regression model to find out the

impact of both Exchange Rate and Inflation movement on Stock Return of individual

firm.

𝑅𝑖𝑡 = 𝛽𝑜𝑖 + 𝛽𝐸𝑅 𝑖𝑅𝐸𝑅 + 𝛽𝑖𝑛𝑓 𝑖𝑅𝑖𝑛𝑓 𝑡 + 𝛽𝑚𝑘𝑡 𝑖𝑅𝑚𝑘𝑡 𝑡 + 𝜀𝑖𝑡

…………………..Equation…..(4)

where, i=1,...,13 and t=1,...,120, where the coefficients βmi , βER i and βinf i represent a

measure of sensitivity of Stock Return, i, to Market Risk, Exchange Risk and Inflation risk; εit

is the disturbance term. The introduction of Market Returns, Rmkt, as a third independent

variable, explicitly controls market movements, thereby reducing any correlation between

disturbances. The potential problem of multicollinearity may arise in estimating such a three

factor model from the possibility that the market, Exchange Rate and inflation factors are

correlated. In order to control this problem,Return of Market Portfolio is regressed on the

changes in the Exchange Rate Return and Inflation Return as shown by Equation (5),

𝑅𝑚𝑘𝑡 = 𝛾0 + 𝛾1𝑅𝐸𝑅 𝑡 + 𝛾2𝑅𝑖𝑛𝑓 𝑡 + 𝜀𝑡

…………………..Equation…..(5)

Then, the component of the Market Portfolio Return that is orthogonal to the changes in

the Exchange Rate Return and Inflation Return is obtained by calculating

𝐹𝑚𝑘𝑡 = 𝑅𝑚𝑘𝑡 − ( 𝛾0 + 𝛾1𝑅𝐸𝑅 𝑡 + 𝛾2𝑅𝑖𝑛𝑓 𝑡)

…………………..Equation…..(6)

Finally, firms’ Inflation and Exchange Rate exposure is estimated by regressing firms’ Stock

Market Returns on the changes in the Inflation Rate and Exchange Rate and orthogonal

component of the Market Portfolio, as illustrated by Equation

𝑅𝑖𝑡 = 𝛽0𝑖 + 𝛽𝐸𝑅 𝑖𝑅𝐸𝑅 𝑡 + 𝛽𝑖𝑛𝑓 𝑖𝑅𝑖𝑛𝑓 𝑡 + 𝛽𝑚𝑖𝐹𝑚𝑘𝑡 + 𝜀𝑖𝑡


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…………………..Equation…..(7)

Where R it is the Stock Return of firm i, Fmktis the estimated orthogonal component of the

Market Portfolio (BSE SENSEX), and R ER tis the percentage change in the Exchange Rate

index (INR/USD) over the same period and Rinf t is the percentage change in the Inflation

over the same period. The value obtained for β ER i for the different firms can be

interpreted as the level of exposure to Foreign Exchange Rates, since it indicates the

sensitivity that a stock shows towards these fluctuations. Similarly, the value obtained for β

inf i for the different firms can be interpreted as the level of exposure to Inflation rates,

since it indicates the sensitivity that a stock shows towards these fluctuations. The above

regression model is used to examine the levels of exposure to Foreign Exchange Rate

changes and inflation rate changes that should be reflected in the statistical significance of

the coefficient β ER i and β inf i (two-tailed test) and the direction of such exposure, which is

indicated by the sign that accompanies the coefficient. A positive coefficient (β ER i) means

that Stock Return increases when the Indian Rupee is depreciated against USD. Similarly, a

positive coefficient (β inf i) means that Stock Return increases when the inflation is

increased and vice versa.

2. Granger causality Test

Granger causality can identify whether two variables move one after the other or

contemporaneously. When they move contemporaneously, one provides no information for

characterizing the other. In simple words, the basic premise of the granger’s is that future

cannot cause the present or the past. In our analysis we use the test to find out whether the

exchange rate return series and stock return series data, precede each other or

contemporaneous. The testing is done, based on the following equation.

…………………..Equation…..(8)


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Where yt and xt are two time series and k is the lag length chosen according to the

suitability. If βi is zero, with lag length (i=1, 2,….k), xt fails to granger cause yt. However, the

Granger causality test is applicable to the stationary series only. Testing non-stationary

series with respect to another non-stationary series can generate misleading results, wholly

spurious for drawing inferences.

Empirical analysis and results:

1. Results of the unit root test:

The unit root test is applied to test the stationarity of the data. There exist several test to

test the presence of unit root in the series among them, the most commonly used in the

literature is the Augmented Dickey-Fuller (ADF) test and Phillip Perron test to analyse

stationarity in the time series. The application of unit root test is initial step before

proceeding to the Granger’s causality test. Following are the results of the test:

All the selected variables were non-stationary at price level but they are stationary at 1st

difference or return.

Table 1: Pre Crisis period: Null Hypothesis: Stationarity Test

Variables ADF Lag PP Bandwidth

Bankex -6.06385* 0 -6.6281* 9

Axis Bank -5.75176* 0 -6.2054* 8

Bank of India -3.95426* 1 -7.23435* 10

Bank of Baroda -6.17414* 0 -6.25848* 9

Canara Bank -4.5984* 0 -4.436* 9

Federal Bank -6.51382* 0 -7.57136* 8

HDFC Bank -6.26095* 0 -7.12* 12

ICICI Bank -7.07775* 0 -7.32149* 5

IndusInd Bank -4.23765* 0 -4.08524* 13

Kotak Bank -6.142* 0 -6.14194* 4


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PNB -6.08065* 0 -7.75568* 15

SBI -5.26536* 0 -5.29976* 7

YES Bank -5.26536* 0 -4.69569* 7

*Significant at 5% level

At the price level we find that all the data of share prices of the different companies are

non-stationary. Only in the return series the data are found to be stationary and significant

at 5% level which has been shown in table 1.

Table 2: Crisis Period: Stationarity Test

Variables ADF Lag PP Bandwidth

Bankex -3.37679* 0 -3.32306* 4

Axis Bank -3.43995* 0 -3.42045* 3

Bank of India -5.25873* 0 -5.85739* 7

Bank of Baroda -3.47216* 0 -3.41958* 5

Canara Bank -3.63811* 0 -3.61132* 4

Federal Bank -3.5342* 1 -3.44476* 8

HDFC Bank -3.31543* 0 -3.22161* 9

ICICI Bank -3.17631* 0 -3.13732* 5

IndusInd Bank -3.07038* 0 -3.05794* 2

Kotak Bank -3.35771* 0 -3.28663* 6

PNB -3.64478* 0 -3.59889* 4

SBI -4.22776* 0 -4.23449* 2

YES Bank -2.85434** 0 -2.77389** 3

*Significant at 5% level, **Significant at 10%level.

Table 2 shows the results of ADF test and the PP test for crisis period. The twelve selected

banks stock price is found to be non-stationary as the null hypothesis is accepted at 5 per

cent level of significance for both the tests. But the stock returns series of all these twelve

banks are found to be stationary as the null hypothesis is rejected at 5% level of significance


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for both the tests. Yes bank return from stock was foundto stationary at 10% level of

significance.


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Table 3: Post crisis period: Stationarity Test

Variables ADF Lag lent PP Bandwidth

Bankex -7.678363* 0 -7.746529* 5

Axis Bank -8.077296* 0 -8.344861* 6

Bank of Baroda -7.285793* 0 -7.253365* 3

Bank of India -7.960556* 0 -8.029899* 4

Canara Bank -6.770507* 0 -6.688906* 4

Federal Bank -8.092419* 0 -8.150751* 4

HDFC Bank -7.985942* 0 -7.985943* 1

ICICI Bank -4.254569* 0 -3.554272* 2

IndusInd Bank -7.208212* 1 -7.786377* 11

Kotak Bank -8.369662* 0 -8.37374* 1

PNB -4.672352* 0 -4.672352* 0

SBI -8.095897* 0 -8.095897* 0

YES Bank -7.052061* 0 -7.072245* 10

*Significant at 5% level.

Table 3 shows the results of ADF test and the PP test for post-crisis period. The twelve

selected banks stock price is found to be non-stationary as the null hypothesis is rejected at

5 per cent level of significance for both the test. But the stock returns series of all these

twelve banks are found to be stationary as the null hypothesis is rejected at 5% level of

significance for both the tests.

2. Correlation Analysis:

Table 4: Correlation between Bankex, exchange rate and inflation

Variable Pre- Crisis Crisis Post- Crisis

Bankex

Exchange rate

Inflation Exchange rate

Inflation Exchange rate

Inflation

-0.419368 -0.270699 - 0.308732 -0.230831 -0.699770 0.138696


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Table 4 gives the correlation between Bankex, exchange rate and inflation for three

different periods. There is a statistically significant negative correlation between the

industry (Bankex) and the variables (Exchange rate, Inflation), i.e. if Bankex goes up

exchange rate and inflation goes down and vice versa except in case of the relationship

between Bankex and inflation in the post crisis period which is positive. This means that if

the inflation rate increases the Bankex movement is upward rising and if inflation rate is

decreasing the Bankex also shows a downward trend.

Table 5: Pre-Crisis period:Correlation between Banks, exchange rate and inflation

Variables Exchange Rate Inflation

Axis -0.465223 -0.3349

Bank of India -0.449336 -0.0407

Bank of Baroda -0.003012 -0.1861

Canara Bank -0.443582 -0.1371

Federal Bank -0.328799 -0.4051

HDFC Bank -0.553558 -0.1469

ICICI Bank -0.408343 -0.0346

IndusInd Bank -0.212668 -0.2737

Kotak Bank -0.259976 0.19327

PNB -0.207201 -0.0388

SBI -0.315344 -0.2529

Yes Bank -0.33814 -0.4192

Table 5 shows the results of correlation among the companies listed in Bankex and the

variables exchange rate and inflation in the pre-crisis period. The results show a statistically

significant negative correlation between the companies and the variables (Exchange rate,

Inflation), i.e. if company return goes up exchange rate and inflation goes down and if

exchange rate and inflation goes up the company return falls.


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Table 6:Crisis period: Correlation between Banks, exchange rate and inflation


Axis -0.30554 -0.22546

Bank of india -0.006546 -0.11905

Bank of Baroda -0.141499 -0.2855

Canara Bank -0.45765 -0.1814

Federal Bank -0.352823 -0.31347

HDFC Bnak -0.34425 -0.28401

ICICI Bank -0.394559 -0.17239

IndusInd Bank -0.307203 -0.21048

Kotak Bank -0.29016 -0.24665

PNB -0.184498 -0.27442

SBI -0.080017 -0.19467

Yes Bank -0.288472 -0.19057


variables exchange rate and inflation in the crisis period. The results show a statistically

significant negative correlation between the companies and the variables (Exchange rate,

Inflation), i.e. if company return goes up exchange rate and inflation goes down and if

exchange rate and inflation goes up the company return falls.


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Table 7: Post Crisis period: Correlation between Banks, exchange rate and inflation

Variables Exchange rate Inflation

Axis -0.366663 0.146675

Bank of india -0.554823 0.154169

Bank of Baroda -0.583572 0.157891

Canara Bank -0.569033 0.281551

Federal Bank -0.191376 0.196974

HDFC Bnak -0.103353 0.067268

ICICI Bank -0.341957 0.178914

IndusInd Bank -0.519509 -0.05992

Kotak Bank -0.04461 -0.00529

PNB -0.352015 0.213284

SBI -0.249802 0.215123

Yes Bank -0.658487 0.146298


variables exchange rate and inflation in the post-crisis period. The results show a statistically

significant negative correlation between the companies and Exchange rate i.e. if company

return goes up exchange rate and inflation goes down and if exchange rate and inflation

goes up the company return falls. Whereas the correlation between company returns and

inflation is positive, i.e. if company returns increases inflation also increases except in case

of IndusInd Bank and Kotak Mahindra Bank.


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3. Descriptive Statistics:

Table 8: Pre crisis period: Descriptive Statistics of Bankex

Variables Mean Std. Dev.

PREBANKEX 0.031137 0.080685

Table 9: Pre crisis period: Descriptive Statistics of Banks, Exchange Rate and Inflation


PREAXIS 0.045913 0.094794

PREBANKOFINDIA 0.037718 0.127206

PREBARODA 0.02306 0.109629


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Table 8 and table 9 reports the descriptive Statistics for the monthly returns of Bankex and

the selected banks respectively for the period of 1st January 2005 to 31st December 2007 i.e.

pre-crisis period. The mean return was highest for Axis bank at 0.045913 it has a deviation

of 0.094794 which indicates that although it has highest growth in this period it has very low

risk. The maximum deviation is for Kotak Mahindra Bank during the sample period with a

mean of 0.042143 meaning although Kotak Mahindra Bank had high risk it had high growth

also.

Table 10: Crisis Period:Descriptive Statistics of Bankex


CRISIS BANKEX -0.015745 0.171925

Table 11: Crisis Period:Descriptive Statistics of Banks, Exchange Rate and Inflation

PRECANARA 0.012385 0.109365

PREFEDERAL 0.019818 0.093701

PREHDFC 0.033416 0.078419

PREICICI 0.033366 0.098299

PREINDUSIND 0.020727 0.151625

PREKOTAK 0.042143 0.171893

PREPNB 0.013734 0.101741

PRESBI 0.035843 0.098654

PREYES 0.045173 0.077669

PREEXCHNGE -0.002586 0.015128

PREINFLATION -0.003993 0.136308


CRISISAXIS -0.00276 0.207058


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Table 10 and table 11, reports the descriptive Statistics for the monthly returns of the

Bankex and selected banks respectively for the period of 1st January 2008 to 31st July 2009

i.e. the crisis period. The mean rate of return was highest for PNB at 0.002055 and a

deviation of 0.163928. This is an example of low growth and low risk, whereas the all the

companies had negative growth. The maximum deviation is for Yes Bank at 0.256264 during

the sample period with a mean return of -0.023404 this goes with the trend during the crisis

period of negative growth and high deviation.

Table 12: Post Crisis Period: Descriptive Statistics of Bankex


PostBankex 0.014309 0.079847

Table 13:Post Crisis Period:Descriptive Statistics of Banks, Exchange Rate and Inflation

CRISISBANKOFINDIA -0.005426 0.183866

CRISISBARODA -0.002774 0.176669

CRISISCANARA -0.00795 0.143436

CRISISFEDERAL -0.017657 0.189927

CRISISHDFC -0.007455 0.136878

CRISISICICI -0.025508 0.210998

CRISISINDUSIND -0.020422 0.225529

CRISISKOTAK -0.036013 0.255737

CRISISPNB 0.002055 0.163928

CRISISSBI -0.014094 0.179914

CRISISYESBANK -0.023404 0.256264

CRISISEXCHNG 0.010192 0.057801

CRISISINFLATION -0.079777 0.274147


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POSTAXIS -0.00928 0.226703

POSTBANKOFINDIA -0.001366 0.134018

POSTBARODA 0.014019 0.096752

POSTCANARA 0.006959 0.130576

POSTFEDERAL -0.00708 0.185681

POSTHDFC -0.006991 0.214935

POSTICICI -0.011778 0.223632

POSTINDUSIND 0.034236 0.095712

POSTKOTAK 0.010129 0.099451

POSTPNB -0.017666 0.224538

POSTSBI -0.027089 0.2817

POSTYES 0.024263 0.127129

POSTEXCHNG 0.004248 0.028885

POSTINFLTN 0.021061 0.363211

Table 12 and table 13, reports the descriptive Statistics for the monthly returns of the

Bankex and selected banks respectively for the period of 1st August 2009 to 31st December

2014 i.e. the post crisis period. The mean rate of return was highest for IndusInd Bank at

0.034236 with a deviation of 0.095712 which shows low growth and low risk when

compared to other companies who have either low or negative growth with high risk. The

maximum deviation is for SBI at 0.2817 with a mean of -0.027089 which indicates negative

growth and high risk.

4. Regression analysis:

Table 14: Regression Analysis of Bankex for three different periods


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Period Exchange rate Inflation

Bankex

pre -2.153986* -0.14512*

crisis -0.897783* -0.13887*

post -1.916664* 0.016938

*significant at 5% level

Table 14 infers that during the sample period Bankex is exposed to variation in Exchange

and Inflation rates. The coefficients are negative and statistically significant. This implies

that there is a negative effect on the Bankex returns if the rupee depreciates against US

dollar or the inflation rate rises. Only during the post crisis period inflation shows no

negative impact on Bankex, but the positive return is not statistically significant.

Table 15: Pre-Crisis: Regression Analysis of Banks


Axis Bank -2.451739* -0.258539

Bank of India -2.976269* -0.205515*

Bank of Baroda -0.198134 -0.01529

Canara Bank -2.622114* -0.160388

Federal Bank -1.651239* -0.278168*

HDFC Bank -2.572272* -0.067572

ICICI Bank -2.467884* -0.088632

IndusInd Bank -1.874138 -0.2355

Kotak Bank -2.62146 0.053073

Punjab National Bank -1.055668 -0.140559

State Bank of India -1.644271 -0.261896*

Yes Bank -1.335145 -0.251114*


Based on table 15 out of the 12 companies taken in the pre-crisis period 6 companies along

with Bankex are exposed to rupee dollar exchange rate and 5 companies are exposed to


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variation in inflation rates. The regression coefficients between stock prices and exchange

rate are negative and statistically significant implies that depreciation in rupee against dollar

has a negative impact of stock prices. The regression coefficients between stock prices and

inflation are negative and statistically significant except that of Kotak bank suggests that

increase of inflation has a negative impact on stock prices.

Table 16: Crisis: Regression Analysis of Banks


Axis Bank -1.0704 -0.163258

Bank of India -1.0704 -0.163258

Bank of Baroda -0.405705 -0.181324

Canara Bank -1.122742* -0.087537

Federal Bank -1.128345* -0.209759*

HDFC Bank -0.795044* -0.136582*

ICICI Bank -1.422073* -0.123345

IndusInd Bank -1.174206* -0.16544

Kotak Bank -1.251015* -0.221871*

Punjab National Bank -0.499494* -0.160809

State Bank of India -0.230417* -0.126243

Yes Bank -1.253855* -0.169905

*Significant at 5% level

Table 16 indicates that during the crisis period 7 companies along with the Bankex is

exposed to rupee dollar exchange rate and 3 companies is affected by inflation variation.

The regression coefficients between stock prices and exchange rate are negative and

statistically significant implies that depreciation in rupee against dollar has a negative

impact of stock prices. The regression coefficients between stock prices and inflation are

negative and statistically significant suggests that increase of inflation has a negative impact

on stock prices.


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Table 17: Post Crisis: Regression Analysis of Banks


Axis Bank -2.887774* 0.002784

Bank of India -2.562367* 0.020677

Bank of Baroda -1.938169* 0.014625

Canara Bank -2.528289* 0.054136

Federal Bank -1.213568 0.01773

HDFC Bank -0.741163 0.025901

ICICI Bank -2.36397* 0.014917

IndusInd Bank -1.771633* -0.037394

Kotak Bank -0.167228 -0.005951

Punjab National Bank -2.444066* 0.025404

State Bank of India -2.416907* 0.019815

Yes Bank -2.911097* 0.001427


According to table 17 during the post-crisis period 9 companies is exposed to variation in

exchange rate and no companies with the Bankex is exposed to inflation variation. The

regression coefficients between stock prices and exchange rate are negative and statistically

significant implies that depreciation in rupee against dollar has a negative impact of stock

prices. Inflation variation has a positive impact on stock prices of all companies except that

of IndusInd Bank.


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5. Granger Causality Test.

Table 18: Pre-Crisis: Granger Causality Test

Null Hypothesis: F-Statistic Prob.

PREBANKEX does not Granger Cause PREEXCHNGE 0.28614 0.7533

PREEXCHNGE does not Granger Cause PREBANKEX 0.592 0.5598

PREAXIS does not Granger Cause PREEXCHNGE 0.14062 0.8694

PREEXCHNGE does not Granger Cause PREAXIS 1.35677 0.2734

PREBANKOFINDIA does not Granger Cause PREEXCHNGE 0.11179 0.8946

PREEXCHNGE does not Granger Cause PREBANKOFINDIA 1.11409 0.3419

PREBARODA does not Granger Cause PREEXCHNGE 0.02114 0.9791

PREEXCHNGE does not Granger Cause PREBARODA 0.10199 0.9034

PRECANARA does not Granger Cause PREEXCHNGE 0.94318 0.401

PREEXCHNGE does not Granger Cause PRECANARA 0.18713 0.8303

PREFEDERAL does not Granger Cause PREEXCHNGE 0.09142 0.9129

PREEXCHNGE does not Granger Cause PREFEDERAL 0.8074 0.4558

PREHDFC does not Granger Cause PREEXCHNGE 1.06362 0.3583

PREEXCHNGE does not Granger Cause PREHDFC 0.02668 0.9737

PREICICI does not Granger Cause PREEXCHNGE 0.39121 0.6798

PREEXCHNGE does not Granger Cause PREICICI 1.73747 0.1938

PREINDUSIND does not Granger Cause PREEXCHNGE 0.36054 0.7004

PREEXCHNGE does not Granger Cause PREINDUSIND 1.0101 0.3766

PREKOTAK does not Granger Cause PREEXCHNGE 0.72988 0.4906

PREEXCHNGE does not Granger Cause PREKOTAK 0.28847 0.7515

PREPNB does not Granger Cause PREEXCHNGE 0.7529 0.48

PREEXCHNGE does not Granger Cause PREPNB 0.09645 0.9083

PRESBI does not Granger Cause PREEXCHNGE 0.6107 0.5498

PREEXCHNGE does not Granger Cause PRESBI 1.52842 0.2338


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PREYES does not Granger Cause PREEXCHNGE 0.74067 0.4883

PREEXCHNGE does not Granger Cause PREYES 2.99152 0.0709


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PREBANKEX does not Granger Cause PREINFLATION 0.71427 0.498

PREINFLATION does not Granger Cause PREBANKEX 2.30866 0.1174

PREAXIS does not Granger Cause PREINFLATION 0.45886 0.6365

PREINFLATION does not Granger Cause PREAXIS 1.40118 0.2625

PREBANKOFINDIA does not Granger Cause PREINFLATION 0.49313 0.6157

PREINFLATION does not Granger Cause PREBANKOFINDIA 0.96833 0.3917

PREBARODA does not Granger Cause PREINFLATION 0.33516 0.718

PREINFLATION does not Granger Cause PREBARODA 0.27566 0.761

PRECANARA does not Granger Cause PREINFLATION 1.85038 0.1753

PREINFLATION does not Granger Cause PRECANARA 0.6686 0.5201

PREFEDERAL does not Granger Cause PREINFLATION 0.45732 0.6375

PREINFLATION does not Granger Cause PREFEDERAL 0.51372 0.6036

PREHDFC does not Granger Cause PREINFLATION 0.62112 0.5443

PREINFLATION does not Granger Cause PREHDFC 2.77853 0.0787

PREICICI does not Granger Cause PREINFLATION 0.35671 0.703

PREINFLATION does not Granger Cause PREICICI 2.79856 0.0774

PREINDUSIND does not Granger Cause PREINFLATION 0.1203 0.8871


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PREINFLATION does not Granger Cause PREINDUSIND 0.30313 0.7408

PREKOTAK does not Granger Cause PREINFLATION 0.23616 0.7912

PREINFLATION does not Granger Cause PREKOTAK 0.18252 0.8341


PREPNB does not Granger Cause PREINFLATION 0.46499 0.6327

PREINFLATION does not Granger Cause PREPNB 0.26305 0.7705

PRESBI does not Granger Cause PREINFLATION 1.28015 0.2932

PREINFLATION does not Granger Cause PRESBI 0.90142 0.4171

PREYES does not Granger Cause PREINFLATION 0.17403 0.8414

PREINFLATION does not Granger Cause PREYES 0.25563 0.7767

*rejected at 5% level.

Table 18 shows that the variables have no significant impact on each other and there is no

lead lag effect on the variables i.e. they move contemporaneously.


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Table 19: Crisis Period: Granger Causality Test


CRISISBANKEX does not Granger Cause CRISISEXCHNG 5.08698 0.0251*

CRISISEXCHNG does not Granger Cause CRISISBANKEX 0.23348 0.7953

MRKTRSDL does not Granger Cause CRISISEXCHNG 3.21316 0.0763

CRISISEXCHNG does not Granger Cause MRKTRSDL 0.45574 0.6445

CRISISAXIS does not Granger Cause CRISISEXCHNG 3.10715 0.0818

CRISISEXCHNG does not Granger Cause CRISISAXIS 0.18834 0.8307

CRISISBANKOFINDIA does not Granger Cause CRISISEXCHNG 3.07436 0.0836

CRISISEXCHNG does not Granger Cause CRISISBANKOFINDIA 1.15794 0.3469

CRISISBARODA does not Granger Cause CRISISEXCHNG 6.93447 0.01*

CRISISEXCHNG does not Granger Cause CRISISBARODA 1.11999 0.3581

CRISISCANARA does not Granger Cause CRISISEXCHNG 4.60261 0.0328

CRISISEXCHNG does not Granger Cause CRISISCANARA 0.4696 0.6363

CRISISFEDERAL does not Granger Cause CRISISEXCHNG 3.56232 0.061

CRISISEXCHNG does not Granger Cause CRISISFEDERAL 1.64259 0.2341

CRISISHDFC does not Granger Cause CRISISEXCHNG 7.31125 0.0084*

CRISISEXCHNG does not Granger Cause CRISISHDFC 0.06018 0.9419

CRISISICICI does not Granger Cause CRISISEXCHNG 3.07807 0.0834

CRISISEXCHNG does not Granger Cause CRISISICICI 0.0342 0.9665

CRISISINDUSIND does not Granger Cause CRISISEXCHNG 4.95012 0.0271*

CRISISEXCHNG does not Granger Cause CRISISINDUSIND 0.25243 0.7809

CRISISKOTAK does not Granger Cause CRISISEXCHNG 4.51051 0.0346*

CRISISEXCHNG does not Granger Cause CRISISKOTAK 0.48553 0.627

CRISISPNB does not Granger Cause CRISISEXCHNG 5.87809 0.0166*

CRISISEXCHNG does not Granger Cause CRISISPNB 0.23675 0.7928

CRISISSBI does not Granger Cause CRISISEXCHNG 5.58266 0.0193*


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CRISISEXCHNG does not Granger Cause CRISISSBI 1.01083 0.3929

CRISISYESBANK does not Granger Cause CRISISEXCHNG 4.84854 0.0286*

CRISISEXCHNG does not Granger Cause CRISISYESBANK 0.99154 0.3995

CRISISBANKEX does not Granger Cause CRISISINFLATION 0.35145 0.7107

CRISISINFLATION does not Granger Cause CRISISBANKEX 2.2414 0.1489


CRISISAXIS does not Granger Cause CRISISINFLATION 0.7414 0.4971

CRISISINFLATION does not Granger Cause CRISISAXIS 1.25219 0.3207

CRISISBANKOFINDIA does not Granger Cause CRISISINFLATION 0.10479 0.9013

CRISISINFLATION does not Granger Cause CRISISBANKOFINDIA 1.802 0.2069

CRISISBARODA does not Granger Cause CRISISINFLATION 0.75796 0.4898

CRISISINFLATION does not Granger Cause CRISISBARODA 2.01423 0.1761

CRISISCANARA does not Granger Cause CRISISINFLATION 0.21342 0.8108

CRISISINFLATION does not Granger Cause CRISISCANARA 2.55471 0.119

CRISISFEDERAL does not Granger Cause CRISISINFLATION 0.83914 0.4559

CRISISINFLATION does not Granger Cause CRISISFEDERAL 2.46508 0.1268

CRISISHDFC does not Granger Cause CRISISINFLATION 0.06804 0.9346

CRISISINFLATION does not Granger Cause CRISISHDFC 1.71061 0.222

CRISISICICI does not Granger Cause CRISISINFLATION 0.92428 0.4233

CRISISINFLATION does not Granger Cause CRISISICICI 2.36077 0.1366

CRISISINDUSIND does not Granger Cause CRISISINFLATION 0.22555 0.8014

CRISISINFLATION does not Granger Cause CRISISINDUSIND 2.39481 0.1333

CRISISKOTAK does not Granger Cause CRISISINFLATION 0.07588 0.9274

CRISISINFLATION does not Granger Cause CRISISKOTAK 1.86852 0.1966

CRISISPNB does not Granger Cause CRISISINFLATION 0.56507 0.5827

CRISISINFLATION does not Granger Cause CRISISPNB 1.47826 0.2667

CRISISSBI does not Granger Cause CRISISINFLATION 0.16679 0.8483

CRISISINFLATION does not Granger Cause CRISISSBI 2.42667 0.1303


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CRISISYESBANK does not Granger Cause CRISISINFLATION 1.06902 0.3739

CRISISINFLATION does not Granger Cause CRISISYESBANK 1.71724 0.2209

*rejected at 5% level

In table 19, we see that in the crisis period all the Companies and Bankex have a

unidirectional relationship with exchange rate except ICICI Bank, Axis Bank and Bank of

India. This means that the Foreign Exchange dealings of these Banks have an impact on the

Exchange rate. Here all the firms take a lead as exchange rate lags. But the firms and

Inflation rate moves contemporaneously throughout the crisis period.


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Table 5.3: Post-Crisis period: Granger Causality Test


POSTBANKEX does not Granger Cause POSTEXCHNG 1.54946 0.221

POSTEXCHNG does not Granger Cause POSTBANKEX 0.26494 0.7682

POSTAXIS does not Granger Cause POSTEXCHNG 0.04917 0.9521

POSTEXCHNG does not Granger Cause POSTAXIS 0.3447 0.7099

POSTBANKOFINDIA does not Granger Cause POSTEXCHNG 0.12574 0.8821

POSTEXCHNG does not Granger Cause POSTBANKOFINDIA 0.43039 0.6523

POSTBARODA does not Granger Cause POSTEXCHNG 0.03852 0.9622

POSTEXCHNG does not Granger Cause POSTBARODA 0.66397 0.5187

POSTCANARA does not Granger Cause POSTEXCHNG 0.28499 0.7531

POSTEXCHNG does not Granger Cause POSTCANARA 0.50402 0.6067

POSTFEDERAL does not Granger Cause POSTEXCHNG 0.30901 0.7354

POSTEXCHNG does not Granger Cause POSTFEDERAL 3.14847 0.0503

POSTHDFC does not Granger Cause POSTEXCHNG 1.92311 0.1554

POSTEXCHNG does not Granger Cause POSTHDFC 0.11917 0.8879

POSTICICI does not Granger Cause POSTEXCHNG 1.49283 0.2332

POSTEXCHNG does not Granger Cause POSTICICI 1.28936 0.2832

POSTINDUSIND does not Granger Cause POSTEXCHNG 2.07966 0.1341

POSTEXCHNG does not Granger Cause POSTINDUSIND 1.66979 0.1972

POSTKOTAK does not Granger Cause POSTEXCHNG 0.2755 0.7602

POSTEXCHNG does not Granger Cause POSTKOTAK 0.12676 0.8812

POSTPNB does not Granger Cause POSTEXCHNG 0.48865 0.616

POSTEXCHNG does not Granger Cause POSTPNB 1.43108 0.2474

POSTSBI does not Granger Cause POSTEXCHNG 1.02344 0.3658

POSTEXCHNG does not Granger Cause POSTSBI 0.26855 0.7654

POSTYES does not Granger Cause POSTEXCHNG 1.53631 0.2238

POSTEXCHNG does not Granger Cause POSTYES 0.20462 0.8155


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POSTBANKEX does not Granger Cause POSTINFLTN 0.24035 0.7871

POSTINFLTN does not Granger Cause POSTBANKEX 1.85214 0.1662


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Null Hypothesis F-Statistic Prob.

POSTAXIS does not Granger Cause POSTINFLTN 3.29818 0.0441*

POSTINFLTN does not Granger Cause POSTAXIS 0.00046 0.9995

POSTBANKOFINDIA does not Granger Cause POSTINFLTN 0.99214 0.3771

POSTINFLTN does not Granger Cause POSTBANKOFINDIA 1.6066 0.2095

POSTBARODA does not Granger Cause POSTINFLTN 0.11377 0.8927

POSTINFLTN does not Granger Cause POSTBARODA 0.00381 0.9962

POSTCANARA does not Granger Cause POSTINFLTN 0.22867 0.7963

POSTINFLTN does not Granger Cause POSTCANARA 0.12805 0.8801

POSTFEDERAL does not Granger Cause POSTINFLTN 1.55029 0.221

POSTINFLTN does not Granger Cause POSTFEDERAL 0.20723 0.8134

POSTHDFC does not Granger Cause POSTINFLTN 0.32181 0.7261

POSTINFLTN does not Granger Cause POSTHDFC 0.12223 0.8852

POSTICICI does not Granger Cause POSTINFLTN 0.27355 0.7617

POSTINFLTN does not Granger Cause POSTICICI 2.14199 0.1268

POSTINDUSIND does not Granger Cause POSTINFLTN 0.34976 0.7064

POSTINFLTN does not Granger Cause POSTINDUSIND 0.09771 0.9071

POSTKOTAK does not Granger Cause POSTINFLTN 0.14961 0.8614


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POSTINFLTN does not Granger Cause POSTKOTAK 0.48877 0.6159


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Null Hypothesis Obs. F-Statistic Prob.

POSTPNB does not Granger Cause POSTINFLTN 62 0.10041 0.9046

POSTINFLTN does not Granger Cause POSTPNB 0.17741 0.8379

POSTSBI does not Granger Cause POSTINFLTN 62 0.98081 0.3812

POSTINFLTN does not Granger Cause POSTSBI 1.43316 0.247

POSTYES does not Granger Cause POSTINFLTN 62 0.39418 0.6761

POSTINFLTN does not Granger Cause POSTYES 0.50964 0.6034

*rejection at 5% level

In table 5.3 shows that variables has no significant effect on one another and hence no lead

lag effect exists except there is a unidirectional relationship among the stock prices of Axis

bank and inflation rate in which Axis Bank has a lead and inflation rate lags.

Discussions and Conclusion:

The present study has been initiated to explore the impact of Exchange rate and Inflation

rate on the stock prices of Bankex and the twelve companies listed under Bankex during the

study period from 1st January 2005 to 31st December 2014.

The study reveals that Exchange rate has a negative and statistically significant impact on

the Bankex and stock price of Axis Bank, Bank of India, Canara Bank, HDFC Bank, Federal

Bank and ICICI Bank during the pre-crisis period i.e. 50% of the samples taken under

consideration. This implies that as the Exchange rate increases, there will be a decline in the

stock prices of these companies. Although there is a negative impact on the other six

companies namely Bank of Baroda, IndusInd Bank, Punjab National Bank, State Bank of

India, Yes bank but they are statistically not significant.

Inflation on the other hand has a negative impact which is statistically significant on the

stocks of 33 % i.e. 4 out of 12 the banking companies.

The Granger Causality test shows that the variables move contemporaneously with each

other. In other words there is no lead lag relationship among the variables.


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The study of the samples during the crisis period reveals that 9 companies out of 12 i.e. 75%

of the companies and Bankex is exposed to exchange rate fluctuation. This means that the

depreciation of rupee will lead to the fall of the stock prices of these 9 companies. The

companies which are not significantly affected are Axis Bank, Bank of India, and Bank of

Baroda. During the same period Inflation significantly negatively affected just 3 out of the 12

companies i.e. just 25% of the sample.If the results of the Granger causality test are

interpreted it shows that there exists a lead lag relationship among exchange rate and the

firms. The exchange rate is affected by the movement of stocks of the company, which may

indicate that the exchange rate is affected by the workings of the bank during this period.

But inflation moves contemporaneously with the companies i.e. it shows no lead lag effect.

The analysis of the post crisis period sample shows that the exchange rate has an exposure

on 75% of the banking companies. This exposure is negative in nature. The companies which

are not statistically significantly impacted are Federal Bank, HDFC Bank and Kotak Bank.

Inflation had no statistically significant on any of the companies or the Bankex.The granger

causality test during the post crisis period reveals that none of the variables have a lead lag

effect amongst each other.

The findings of this study have some important implications. Exchange rate has significant

information to forecast Bankex and banking company performance. This is probably due to

the fact that all the Banking companies listed in Bankex engage in foreign exchange. This

gives the speculators and arbitrageurs the opportunity to estimate future stock prices and

take profitable decisions in their favour. This also raises the question that whether the

banking company managers are careful enough when dealing with dealing with foreign

exchange. As for inflation, it has a weak influence on most companies. Therefore, the study

shows that estimating the stock price from this variable is a weak estimate. And this also

might indicate that the banks are able to manage themselves well when it comes under

inflationary pressure.

Other researchers can develop this study by adding other variables such as Interest rate,

Repo rate, money supply etc. that is not included in this study and the effects of this


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variables on stock prices individually or collectively (Bankex) and how the new variables

affect each other. Also if we compare this study with a different crisis period and take into

consideration the same variables and the results are similar, it is quite possible a future

crisis period may be identified, keeping in mind the behaviour and movement of the

variables. But further studies are to be conducted on this issue.

References:

Abdalla, I., & Murinde, V. (1997). Exchange Rate and Stock Price Interaction in Emerging Financial

Markets: Evidence on India, Korea, Pakistan and the phillipines. Applied Financial Economics , 7, 25-

35.

Bittencourt, M. F. (2006, December). Inflation and Finance: Evidence from Brazil.

Boyd, J., & Champ, B. (2003, December). Inflation and Financial Market Performance.

Boyd, J., Levine, R., & Smith, B. D. (2000, February). The Impact of Inflation on Financial Sector

Performance.

Burman, C., & Saha, S. (2014). Exchange Rate and crude oil Exposure on Automobile and Oil Sector

Companies after the Crisis Period of 200-2009.

Cetorelli, N. (2006). Discussion of : Inflation and Financial Performance: What Have Le learned in The

last Ten Years.

Erdogan, E., & Ozlale, U. ErdEffects of Macroeconomic Dynamics on Stock Return: The Case of the

Turkish Stock Exchange Market”. Journal of Economic Corporation , 26 (2), 69-90.

Gan, C. L. (2006). Macroeconomic Variables and Stock Market interactions: New Zeland Evidence.

Investment Management and Financial Innovation , 3 (4), 89-101.

Ghosh, R. (2013, October). Relationship Between Interest Rate Changes and Banking Stock returns in

Up-Market and Down-Market Situation. Asian Journal of Multidisciplinary Studies , 128-146.

Giovannoni, A., & Jorion, P. (1989). The time Variation of Risk and Return in the foreign Exchange

Market and Stock Market. Journal of Finance , 44 (2), 229-543.

Hondroyiannis, G., & Papapetroue, E. (2005). Financial markets and economic growth in Greece,

1986-1999. Journal of International Financial Markets Institutions & Money , 173-188.

Kumar, A. (2011). An Emperical Analysis of Causal Relationship between Stock Market and

Macroeconomic Variables in India. International Journal of Computer Science and Management

Studies , 11 (1), 8-14.


ISSN: 2278-9111

IF: 1.232

42 | P a g e


Maghayereh, & Aktham. (2003). Causal Relations among Stock Prices and Macroeconomic Variables

in the Small Open Economy of Jordan. Economics & Administration , 17 (2), 3-12.

Naceur, S. B., & Ghazouani, S. (2004). Does Inflation Impact on Financial Sector Performance in

MENA Region.

Naceur, S. B., & Ghazouni, S. (2004, November). Does inflation impact on financial sector.

Naik, Kumar, Pramod, & Puja. (2012). The Impact of Macroeconomic Fundmentals on Stock Prices

Revisited: Evidence from Indian Data. Eurasian Journal of Business and Economics , 5 (10), 25-44.

NasseR, O. A., & Jackson, D. (2004). The Impact of Inflation on Financial sector Performance in Latin.

Pan, M., Folk, R., & Liu, Y. (2001). Dynamic Linkages Exchange Rates and Stock Price: Evidence from

pacific Rim Countries.

Prasanna, V. S., & Gopakumar, K. (2008). Inflation and Economic Growth in India - An Empirical

Analysis.

Ray, S. (2012). Testing Granger Causal Relationship between Macroeconomic Variables and Stock

Price.

Reddy, D. V. (2012). IMPACT OF INFLATION AND GDP ON STOCK MARKET RETURNS IN INDIA.

International journal of Advanced Research in Management and social Sciences , 1 (6), 120-136.

Rozeff, S., & Kinney, R. (1976). Capital Market Seasonality: The case of Stock Return. Journal of

Financial Economics , 3, 379-402.

Shanmugam, K. R., & Misra, B. S. (2008). Stock Returns-Inflation Relation in India. October.

Sharma, G. D., & Mahaendru, M. (2010). Impact of Macro Economic Variables on Stock Prices in

India. Global Journal of Management and Business Research , 10 (7), 19-26.

Shiblee, L. (2009, December). The Impact of Inflation, GDP, Unemployment, and Money Supply on

Stock Prices.

Singh, P. (2014). An Empirical Relationship Between Selected Indian Stock Market Indices and

Macroeconomic Indicators. International Journal of Research in Business Management , 2 (9), 81-92.

Soni, P. (2014). INFLATION: A SAVIOR OR CHALLENGE TO INDIAN FINANCIAL MARKET. International

Journal of Research & Development in Technology and Management Science –Kailash , 21 (2).

Web-reference:

BSE. (2015, January). Retrieved from bseindia:

http://www.bseindia.com/markets/equity/EQReports/StockPrcHistori.aspx?expandable=7&flag=0#

markets_eq


ISSN: 2278-9111

IF: 1.232

43 | P a g e


Database on Indian Economy. (n.d.). Retrieved from Reserve Bank of India:

http://dbie.rbi.org.in/DBIE/dbie.rbi?site=home

Reference Rate Archive. (n.d.). Retrieved from Reserve Bank of India:

http://www.rbi.org.in/scripts/ReferenceRateArchive.aspx

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