a study on impact of macro-economic...

Volume 4, Number 3, July – September’ 2015

ISSN (Print):2279-0896, (Online):2279-090X

PEZZOTTAITE JOURNALS SJIF (2012): 2.844, SJIF (2013): 5.049, SJIF (2014): 5.81

International Journal of Applied Financial Management Perspectives © Pezzottaite Journals. 1891 |P a g e

A STUDY ON IMPACT OF MACRO-ECONOMIC VARIABLES

ON INDIAN STOCK MARKET VOLATILITY

Simran Waraich19 Amanjot Kaur Sodhi20

ABSTRACT

Stock prices and their volatility have now become the widespread features of securities markets. The growing linkages of stock

market indices with inflation, liquidity, growth rate, crude oil prices, exchange rates etc. have given volatility a new

dimension - influence of macroeconomic variables. This research paper revisits the relationship between stock price and some

key macro-economic variables in India for the period 2010-2015 using quarterly time series data.

KEYWORDS

Stock Market, Stock Prices, Volatility, Liquidity, Macroeconomic Variables etc.

INTRODUCTION

In the year 1991, the government of India initiated the process of economic reforms. The deregulation of the Indian economic

system led to a tremendous change in the Indian capital market. Since then the Indian Capital Market has undergone metamorphic

reforms. Indian Capital Market is now being known amongst the most transparent, well-organized and clean markets across the

world. Stock market has gained significant importance from the economy point of view. It is a key driver of the economy‟s

financial development and growth.

The Indian Stock Market has always exhibited dramatic movements. Form 3,739.69 points on 31st March 1999, Bombay Stock

Exchange (BSE) Sensitivity Index (SENSEX) had reached to...level points in March, 2015. At times, the stock prices have

appeared too volatile to be justified by changes in fundamentals. In the recent past, there have been perceptions that volatility in

the market has gone up; Inter and Intra-day volatility. According to a comprehensive analysis undertaken by SEBI - the volatility

has not gone up much in the recent past, as it has been perceived. Indian stock market provides a very high rate of return and

comparatively moderate volatility. Efficiency of Indian market appear to have improved in the past few years owing to contraction

in settlement cycles, introduction of derivative products, improvement in corporate governance practices etc. In addition, the stock

market is in many ways influenced by the domestic and international macroeconomic fundamentals. According to Aggarwal

(1981), “the rising indices in the stock markets cannot be taken to be a leading indicator of the revival of the economy in India and

vice-versa”. On the contrary, Shah and Thomas (1997) supported the idea that stock prices are a minor, which reflect the real

economy. Many more researchers have studied the interaction of share market returns and the macroeconomic variables and all

studies provide different conclusions.

Stock Market Volatility: Many factors like expected corporate earnings, interest rates, monetary flows, political stability and

liquidity within the banking system, drive stock prices. All these factors have the power to roil the stock markets. Merton Miller

(1991) the winner of the 1990 Nobel Prize in economics - writes in his book Financial Innovation and Market Volatility - “By

volatility public seems to mean days when large market movements, particularly down moves, occur. These precipitous market

wide price drops cannot always be traced to a specific news event. Nor should this lack of smoking gun be seen as in any way

anomalous in market for assets like common stock whose value depends on subjective judgment about cash flow and resale prices

in highly uncertain future. The public takes a more deterministic view of stock prices; if the market crashes, there must be a

specific reason.”

Macro-Economic Variables: The characteristics that describe a macro economy are usually referred to as the macroeconomic

variables. Macroeconomics is the study of the economy as a whole. It examines the cyclical moments and trends in economy wide

phenomenon, such as unemployment, inflation, economic growth, money supply, budget deficits and exchange rates etc. These

variables are pertinent to a broad economy at the national level and affect a large population rather than a few select individuals.

These are the key indicators of economic performance and are closely monitored by governments, businesses and consumers.

This research paper tries to explore whether the movement of Bombay Stock Exchange and National Stock Exchange‟s indices is

the result of some selected macroeconomic variables. The study considers macroeconomic variables as Index of Industrial

production (IIP), Wholesale price Index (WPI), Cash Reserve Ratio (CRR), US Dollar Price (USD into INR), Crude Oil Prices,

Gross Domestic Product (GDP) and Bombay Stock Exchange‟s and National Stock Exchange‟s indices in the form of SENSEX

19Assistant Professor, Chandigarh Business School of Administration, Punjab, India, [email protected] 20Assistant Professor, Chandigarh Business School of Administration, Punjab, India, [email protected]

mailto:[email protected]

mailto:[email protected]





and Nifty CNX, NSE by using quarterly data spanning from April, 2010 to March, 2015. In the study ADF test and Regression

analysis have been used to see the effect of macroeconomic variables on BSE and NSE Indices.

REVIEW OF LITERATURE

In the past decades, many academicians, policy makers, practitioners and investors have attempted to foresee the relationship

between stock markets movement and macroeconomic variables. Several empirical studies have been undertaken to examine the

effect of macroeconomic variables on the stock prices.

Mukherjee and Naka (1995) by applying vector error correction model studied the relationship between Japanese Stock Market

on one hand and money supply, inflation, exchange rate and real economic activity and concluded that co integration indeed

existed; Pethe and Karnik (2000) studied the data for a period of 5 years to examine the relationship between stock market

indices and macro-economic variables. The study reveals the weak causality running from IIP to share price indices and the state

of economy had affected stock prices.

Naka, Mukherjee and Tufte (2001) studied the relationship between BSE Sensex and macroeconomic variables, which were IIP,

CPI, money market rates, and money supply from the period 1960 to 1995. The study concluded that five variables were co-

integrated and there exist long-term relationship among these variables by applying VECM. It was also concluded that domestic

inflation was most severe deterrent to stock performance; Ray and Vani (2003) applied VAR model and artificial neural network

to study stock market fluctuations and real economic factors in Indian stock market. A significance influence was found for fiscal

deficit and foreign investment in explaining stock market movement.

Gay, Robert D(2008) investigated the time-series relationship between stock market index prices and the macroeconomic

variables of exchange rate and oil price for Brazil, Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model.

Although no significant relationship was found between respective exchange rate and oil price on the stock market index prices of

either BRIC country. In addition, there was no significant relationship found between present and past stock market returns,

suggesting the markets of Brazil, Russia, India, and China exhibit the weak form of market efficiency.

Ahmed (2008) analyzed the relationships between stock prices and macroeconomic variables vis-a-vis IIP, FDI, Exports, money

supply, exchange rate and interest rate. The study reveals that stock prices in India lead the economic activity except movement of

interest rates. Indian stock market seems to be influenced not only by performance but also by expected potential performances;

Sharma and Mahendru (2010) studied the impact of macroeconomic variables on stock prices. Multiple regression models were

used to study the impact. His study reveals that exchange rate and gold prices affect the stock prices while inflation and foreign

exchange reserves do not influence the stock prices.

Bayezid Ali Mohammad (2011) investigates the impact of changes in selected microeconomic and macroeconomic variables on

stock returns at Dhaka Stock Exchange. A Multivariate Regression Model computed on Standard OLS Formula has been used to

estimate the relationship. It was found that inflation and foreign remittance have negative influence and industrial production

index; market P/Es and monthly percent average growth in market capitalization have positive influence on stock returns;

Tripathi, Parashar & Jaiswal (2014) examined the impact of macroeconomic variables on different sectoral indices. It was

concluded that amongst all macroeconomic variables only Foreign Institutional Investment affects all sectoral indices however

rest of the macroeconomic variables selectively affect different indices in India.

Singh Anamika (2014), the purpose of the study was to examine the monetary policy impact on market volatility ARCH Model

had proven that NIFTY volatility is being influenced whenever monetary policy announced. CRR and SLR are negatively

correlated with market indices while Repo rate and Reverse Repo Rate are positively correlated; Ramanujam and Leela (2014)

analyzed the long-term relationship between CNX NIFTY and macroeconomic variables that is Exchange rate, index of industrial

production and GDP. Results reveals that GDP and exchange rate affect all NIFTY stock prices whereas negative correlation

exist between stock prices and index of industrial production.

RESEARCH METHODOLOGY

With a view to accomplish the pre-determined objective of this research, different set of techniques and tests have been adopted.

ADF test is used to find the stationarity or non-stationarity variables of data series. Inferential statistics technique is used to infer

the results by using different methods like linear regression analysis, which create a mathematical model that can be used to

predict the values of a stock price of Bombay stock exchange indices based upon the values of macroeconomic variables. In other

words, we use the model to predict the value of Y when we know the value of X. Here, we used the sign-f to analysis the overall

significance of the sample regressions and t- test and p-value to check the individual significance of the macroeconomic variables.





OBJECTIVE OF STUDY

Studying the impact of Macroeconomic variables on Indian stock market. BSE SENSEX and NSE-CNX NIFTY have been

considered as representing Indian stock market.

LIMITATIONS OF STUDY

Reliability: This study is based on the analysis of the secondary data that has been collected.

Accuracy: The result & conclusion of this study might not be accurate due to reliability of the secondary data &

limitation on the variables selected & the time span considered.

Time: A time span of only 5 years has been considered for examining the relation between macroeconomic variables

and Indian stock market.

Limited Variables: This study mainly focuses on selected independent variables, which may not completely represent

the macroeconomic variables.

DATA ANALYSIS

Table 1.1 presents the summary of data of all the variables and the indices w.e.f. April 2010 to March 2015 on quarterly basis.

Table-1.1: Summary of the Data Collected

Qu

art

er

Year

BS

E-S

ense

x

(ba

se 1

978

-

79

=1

00

)

CN

X N

ifty

(ba

se 1

995

= 1

00

) WPI

IIP

(b

ase

20

04

-

05

=1

00

)

CRR

(Avg.)

US $

into

INR

Crude Oil

Prices

(Rs. per

barrel)

GDP (Rs in Cr.

at market

price (at

current price)

Q1 Jun-10 17274.31 5178.5 139.2 157 5.96 45.72 3560.47 1762793

Q2 Sep-10 18549.18 5542.82 141.4 159.2 6 46.66 3510.08 1808963

Q3 Dec-10 20101.23 6040.92 144.2 166.7 6 45.32 3830.54 2079416

Q4 Mar-11 18594.01 5574.02 148.5 179 6 45.89 4511.91 2224454

Q1 Jun-11 18668.18 5601.31 152.5 167.9 6 45.28 4922.42 2067324

Q2 Sep-11 17399.57 5229.64 155.1 164.3 6 46.22 4717.38 2078195

Q3 Dec-11 16482.43 4948.89 157.2 168.7 6 51.51 5249.71 2345626

Q4 Mar-12 17203.26 5209.2 159.7 180.1 5.25 51.08 5648 2483801

Q1 Jun-12 16805.44 5098.4 164 167.5 4.75 54.57 5545.32 2315032

Q2 Sep-12 17639.79 5345.67 167.3 165 4.67 55.12 5674.05 2317116

Q3 Dec-12 18919.41 5753.07 168.7 172.2 4.33 54.2 5512.3 2637519

Q4 Mar-13 19495.19 5899.66 170.4 184.1 4.08 54.27 5690.6 2750952

Q1 Jun-13 19304.3 5848.78 172 165.8 4 55.81 5553.43 2546935

Q2 Sep-13 19326.12 5739.053 178.4 168.1 4 62.26 6682.62 2658622

Q3 Dec-13 20701.55 6153.127 180.6 170.9 4 61.92 6489.06 3002683

Q4 Mar-14 21093.49 6276.63 179.6 183.3 4 61.74 6408.32 3146833

Q1 Jun-14 23847.4 7126.91 181.8 173.3 4 59.82 6359.73 2980178

Q2 Sep-14 26230.65 7839.211 185.8 170.3 4 60.59 6083.53 3080059

Q3 Dec-14 27478.13 8226.68 181.2 174.2 4 61.97 4609.25 3166327

Q4 Mar-15 28268.08 8644.087 176.3 189.5 4 62.32 3218.25 3314644

Sources: Authors Compilation

Checking Stationarity

Stationarity as a condition for the time series data was checked individually for all the variables. For the purpose unit root test was

applied with augmented dickey fuller unit root test and the results have been stated in table 1.2. The results indicate the t statistics

and p value of Augmented Dickey Fuller Unit Root Test of the level at which each variable was found stationary.





Table-1.2: ADF Level

Variable Null Hypothesis Variables Found To

Be Stationary At:

Augmented Dickey Fuller

Unit Root Test Statistics

t- statistics P value

BSE-Sensex H0: BSE-Sensex has a unit root 1st difference, none -2.582467 0.0131

CNX Nifty H0: CNX Nifty has a unit root 1st difference, none -2.531118 0.0147

WPI H0: WPI has a unit root 1st difference, trend

and intercept

-4.125576 0.0241

IIP H0: IIP has a unit root 1st difference, none -11.00585 0.0000

CRR H0: CRR has a unit root Level, none -2.270263 0.0259

US $ into INR H0: US $ into INR has a unit root 1st difference, none -3.811202 0.0007

Crude oil Prices H0: Crude oil Prices has a unit root 2nd difference, none -6.215605 0.0000

GDP H0: GDP has a unit root 1st difference, intercept -5.271572 0.0008


Regression Analysis

(A) Effect of various economic indicators on BSE SENSEX

To study the effect of various economic indicators including WPI, IIP, CRR, US $ into INR, Crude oil Prices and GDP on BSE

Sensex multiple regression technique has been applied. The following regression equation was used depending upon the level at

which each variable was found stationary.

D(BSE_SENSEX) C D(WPI) D(IIP) CRR D(US_$ _INR) D(CRUDE_OIL_PRICES,2) D(GDP)

The results shown in table 1.3 as below were obtained:

Table-1.3: Simple Regression between Δ SENSEX and Macroeconomic Variables

Dependent Variable: D(BSE_SENSEX)

Method: Least Squares

Date: 07/23/15 Time: 21:23

Sample (adjusted): 12/01/2010 3/01/2015

Included observations: 18 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 4394.307 1128.420 3.894212 0.0025

CRR -770.0113 243.3252 -3.164537 0.0090

D(CRUDE_OIL_PRICES,2) 0.580357 0.461140 1.258527 0.2343

D(GDP) 0.003969 0.002108 1.883072 0.0864

D(IIP) -76.13359 44.10515 -1.726184 0.1123

D(US_$_INTO_INR) -355.7803 105.1494 -3.383570 0.0061

D(WPI) -33.96646 98.91269 -0.343398 0.7378

R-squared 0.683518 Mean dependent var 539.9389

Adjusted R-squared 0.510891 S.D. dependent var 1157.035

S.E. of regression 809.1874 Akaike info criterion 16.51524

Sum squared resid 7202627. Schwarz criterion 16.86150

Log likelihood -141.6372 Hannan-Quinn criter. 16.56298

F-statistic 3.959514 Durbin-Watson stat 1.642749

Prob(F-statistic) 0.023417


Interpretation

Significance of Independent Variables

To check whether the independent variables WPI, IIP, CRR, US $ into INR, Crude oil Prices and GDP significantly influence the

dependent variable i.e. BSE SENSEX, the null hypothesis taken was:

H0: variables WPI, IIP, CRR, US $ into INR, Crude oil Prices and GDP are insignificantly explaining the variations in BSE

SENSEX





As shown in table 1.3 amongst all variables CRR and US $ into INR have a p value less than .05 which means we cannot accept

the null hypothesis and we can conclude that CRR and US $ into INR have been found to be significantly influencing the

dependent variable i.e. BSE SENSEX. All other variables have a p value >.05 which means we accept the null hypothesis that the

variables WPI, IIP, Crude oil Prices and GDP do not influence the dependent variable i.e. BSE SENSEX

Checking the Model Efficiency

The adjusted R square as given in table 1.3 is .510891 which means 51.08% of change in BSE SENSEX is caused by the

independent variables WPI, IIP, CRR, US $ into INR, Crude oil Prices and GDP taken together and the rest 48.92% change is due

to other variables beyond the preview of the study.

Checking the Model Fitness

H0: Model is not a good fit/ model is insignificantly explaining the dependent variable i.e. BSE SENSEX. The f statistics and p

value as given in table 4 indicates the model fitness. Since p value = 0.023417 which is less than .05 we cannot accept the null

hypothesis which means model is significantly explaining the dependent variable i.e. BSE Sensex.

(B) Effect of various Economic Indicators on CNX NIFTY

Taking CNX NIFTY as dependent variable and various economic indicators including WPI, IIP, CRR, US $ into INR, Crude oil

Prices and GDP as the independent variables the regression analysis was done and following result(as shown in table 1.4) were

obtained

Table-1.4: Simple Regression between Δ SENSEX and Macroeconomic Variables

Dependent Variable: D(CNX_NIFTY)

Method: Least Squares

Date: 07/23/15 Time: 21:32

Sample (adjusted): 12/01/2010 3/01/2015

Included observations: 18 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 1296.664 349.1000 3.714306 0.0034

CRR -216.2761 75.27765 -2.873045 0.0152

D(CRUDE_OIL_PRICES,2) 0.184746 0.142663 1.294982 0.2218

D(GDP) 0.001187 0.000652 1.820575 0.0960

D(IIP) -23.51090 13.64484 -1.723062 0.1128

D(US_$_INTO_INR) -115.2357 32.53012 -3.542430 0.0046

D(WPI) -24.66022 30.60068 -0.805872 0.4374

R-squared 0.691445 Mean dependent var 172.2926

Adjusted R-squared 0.523142 S.D. dependent var 362.5214

S.E. of regression 250.3388 Akaike info criterion 14.16881

Sum squared resid 689364.7 Schwarz criterion 14.51506

Log likelihood -120.5193 Hannan-Quinn criter 14.21655

F-statistic 4.108339 Durbin-Watson stat 1.507475

Prob(F-statistic) 0.020738

Interpretation

CRR and US $ into INR found to be significantly influencing the dependent variable i.e. CNX NIFTY.

Economic indicators including WPI, IIP, Crude oil Prices and GDP have been found to be insignificantly influencing

CNX NIFTY.

52.31% of change in CNX NIFTY is caused by the independent variables WPI, IIP, CRR, US $ into INR, Crude oil

Prices and GDP taken together and the rest 47.79% change is due to other variables beyond the preview of the study.

As p value of F statistics is less than .05 we can conclude that model is a good fit i.e. model is significantly explaining

the dependent variable i.e. CNX NIFTY.

CONCLUSION

The results of this study should not be treated as conclusive. There are other important factors like cost of equity capital, asset

valuation, industry analysis, a firm's management and operational efficiency etc., that account for any changes in the stock prices.

Any investor while making investment decisions must consider all relevant factors and sources of information.





REFERENCES

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Akron Business and Economic review, 7-12.

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Return: A Case on Dhaka Stock Exchange (DSE). Interdisciplinary Journal of Research in Business, 1(5), 08-16.

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India. Pacific Business Review International, 6(12).

14. Quarterly data of cash reserve ratio, exchange rate (US dollar price) was taken from Reserve Bank of India (RBI)

database site. Retrieved from http://dbie.rbi.org.in/

15. Bombay stock exchange indices were taken from Bombay Stock exchange Limited site. Retrieved from

http://www.bseindia.com/

16. National Stock Exchange Indices were taken from the National Stock Exchange Limited site. Retrieved from

http://www.nseindia.com/

17. Quarterly Index of industrial production, wholesale price index, GDP was taken from Central Statistical Office site. Retrieved from http://mospi.nic.in/Mospi_New/site/home.aspx

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http://www.researchgate.net/publication/268435848_Impact_of_Micro_and_Macroeconomic_Variables_on_Eme





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*****

http://www.researchgate.net/publication/263620295_The_Impact_of_Macroeconomic_Variables_on_Stock_Pri

http://www.academia.edu/1368026/Impact_of_Micro_and_Macroeconomic_Variables_on_Emerging_Stock_Market

http://www.researchgate.net/publication/277474665_A_Comprehensive_(Micro_and_Macro)_Determination_of

http://stats.stackexchange.com/questions/64612/how-do-you-interpret-results-from-unit-root-tests-wit

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