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IMPACT OF MACRO-ECONOMIC FACTORS

ON SECTORAL INDICES – EVIDENCE FROM INDIAN MARKETS Naveen R.S., Alumnus

N. Sivakumar, PhD, Associate Professor

Department of Management and Commerce,

Sri Sathya Sai Institute of Higher Learning, India

Introduction

Stock markets play the important role of channeling funds to appropriate sectors

of the economy. While the returns on securities are closely linked to the performance

of corporates, the performance of the corporate sector is closely linked to several

macro-economic factors. The objective of this paper is to understand the impact of

macro-economic variables on specific sectoral indices of the National stock exchange

(NSE) of India.

The paper is structured as follows: after a very brief introduction to APT, the

paper provides a review of existing research literature. The paper then continues with

the details and results of the study conducted in this paper, regarding the impact of

macro-economic variables on sectoral indices. The paper concludes with important

considerations for efficient investments.

Arbitrage Pricing Theory

The arbitrage pricing theory (APT) recognizes the importance of macro-

economic variables in influencing stock market returns. Developed by Ross in the year

1976, the theory tries to capture the impact of non-market factors that affect the asset

prices. The APT model is given as follows:

E (rj) = rf + bj1RP1 + bj2RP2 + bj3RP3 + bj4RP4 + ... + bjnRPn, (1)

where E(rj) = expected rate of return for the asset; rf = the risk-free rate; bj = the

sensitivity of the asset's return to the particular factor; RP = the risk premium

associated with the particular macro-economic factor.

Berry et al. (1988) give three important characteristics for factors to fit into the

APT model (1):

At the start of every time period the factors should be completely unpredictable

by the markets.

Each factor must be believed to have a pervasive impact on the stock returns.

The relevant factors must affect the asset prices.

The APT accommodates several macro-economic factors and therefore is

considered more practical and gives a better forecasting ability than the CAPM model

(Ross, 1976).

Literature Review

Inspite of the fact that theoretically no study has explained an appropriate way of

choosing the various economic factors to be included in an APT model (Azeez and

Yonoezawa, 2003), the need for studying the impact of macro economic variables on

stock returns has been highlighted by several scholars (Osoro and Willy, 2014;

Habibullah and Baharumshah, 2000; Hondroyiannis and Papapetrou; 2001; Maysami et

al., 2004; Singh, 2010).

175

Table 1.Studies Showing the Impact of Macro Economic Variables

on Stock Returns Sl

No Authors

Region of

Study

Dependent

Variable Macro-economic factors Results

Studies in Indian context

1 Pal and Mittal

(2011) India

BSE Sensex

and Nifty

Interest rates, inflation

rate, exchange rates

and gross domestic savings

Rate of inflation and Gross domestic savings has a significant impact on

both the BSE Sensex and the Nifty.

Interest rates have a significant impact Nifty only. and foreign exchange rate

on BSE Sensex only.

2 Naik and Padhi

(2012) India

BSE

Sensex

Index of industrial

production (IIP), wholesale price index

(WPI), money supply,

treasury bills rates and exchange rates

Stock returns are positively related to

Money supply and IIP, but negatively relate to inflation. Exchange rates and

the short-term interest rate were found

to be insignificant in determining stock returns.

3 Ray and Vani

(2003) India

BSE

Sensex

National output, fiscal

deficit, interest rate, inflation, exchange

rate, money supply,

foreign institutional investment

Interest rate, output, money supply,

inflation rate and the exchange rate have significant impact on the stock

market movement, while the other

variables have very negligible impact on the stock market.

4 Pethe and

Karnik (2000) India

BSE

Sensex IIP

Weak relationship between IIP and

stock returns

Studies in International context

5 Omran and

Pointon (2001) Egypt

Egyptian

stock

market

Inflation rate

Inflation rate had an impact upon the

Egyptian stock market performance in

a general sense

6 Benaković and Posedel (2010)

Croatia

14 stocks of

Croatian

capital market

Inflation, industrial production, interest

rates,

market index and oil prices

Inflation marked a negative risk premium in one period and a positive

one in another period. The other

factors did not show a significant impact.

7 Haque and

Sarwar (2012) Pakistan

Stocks of

Karachi

stock exchange

Gross domestic

product (GDP), inflation rate (CPI),

interest rate of saving

accounts (IR),money supply (M1), budget

deficit (BD) and

exchange rate

Gross domestic product and exchange

rate had a very positive impact while, inflation, money supply, interest rate,

and budget deficit had a nature

negative impact

8 Gan et al.

(2006)

New

Zealand NZSE 40

Inflation rate, interest

rates, money

supply(M1), oil prices

and real GDP

Positive impact of GDP and oil prices

and negative impact of interest rates and money supply on stock returns,

9

Osamwonyi and

Evbayiro-Osagie (2012)

Nigeria

Nigerian capital

market

index

interest rates, inflation rates, exchange rates,

fiscal deficit, GDP and

money supply

Interest rate and money supply had

negative significance on the stock

exchange both for long and short term. Fiscal deficit and inflation had a

positive impact though not significant

in the long run. Exchange rate affected positively in short run but it affected

negatively in the long run.

10 Chen et al.

(1986) USA NYSE

Inflation, T-bill rate,

G-sec rate, Industrial

production, Per capita

consumption and oil

prices

Industrial production, anticipated and unanticipated inflation, yield spread

between the long and short term

government bonds were significantly

related to stock returns. Consumption

and oil prices were not significantly

related.

176

Table 1 gives a sample of studies of impact of macro economic variables on

stock returns.

The above table shows that the impact of macro-economic variables on the broad

market has been extensively studied. Yet the studies on the impact of macro-economic

variables on specific sectors are quite limited. Saeed (2012) examined the impact of

macroeconomic factor variables on stock market returns of nine sectors of Karachi

stock exchange 100 index by using multifactor models within APT frame work. The

variables were money supply, exchange rates, IIP, short term interest rate and oil

prices. The results showed that macro-economic variables had a significant impact on

returns of sectors but their effect on variation on returns was meager. Only short term

interest rates had a significant impact on returns of various sectors, whereas Oil prices

and Exchange Rates had a significant impact only on few sectors like oil and gas and

automobile.

Osoro and Willy (2014) studied the effects of the macroeconomic environment

on the financial performance of firms listed in the manufacturing and allied market

segment of the Nairobi stock exchange. The conclusions of the study indicated that the

interest rate, inflation rate and foreign exchange rates had a significant effect on the

performances of the firms in construction and manufacturing sectors.

Finally, Tripathi et al. (2014) have studied the impact of a few macro-economic

factors like forex rates, crude oil prices, FII investments, current account balance and

forex reserves on a few sectoral indices of the National Stock Exchange in India like

CNX Auto, Bank, Energy, FMCG and IT and found that only FII investments affect all

stock indices.

The above studies point out to the need and utility of studying the impact of a

large set of macro-economic factors with more granularity, understanding the specific

impact on different sectoral indices. This research paper is intended to cover this

research gap, especially in the Indian context.

Methodology

Objective of the research is to study the impact of macro-economic variables on

the returns of sectoral indices of NSE in India.

Hypotheses

The following null hypotheses have been formulated for this study:

Null hypothesis 1: The selected macro-economic variables do not have any

impact on the returns of the sectoral stock indices both on a regular and lagged basis.

Null hypothesis 2: The selected macro-economic variables do not have a

differentiated impact on the returns of the sectoral stock indices both on a regular and

lagged basis.

Sampling procedure

The macroeconomic factors were selected based on the literature review. A

larger set was deliberately chosen as several past studies had limited data sets.

Similarly, all the sectoral indices on National Stock Exchange for which data was

available for a period of 10 years were chosen for the study, leading to seven different

sectoral indices. The macro-economic factors along with the data of sectoral indices

were collected on the basis of their release frequency. Later these variables were

converted into monthly data to standardize them for the statistical analysis.

177

Table 2 gives the macro-economic variables and sectoral indices collected and

analyzed for the study.

Table 2. Macro-Economic Factors and Sectoral Indices Analyzed in the Study Factor/Variable Frequency Source

General Macro- economic factors

Gross domestic product (GDP) Quarterly RBI database

Index of industrial production (IIP) Monthly RBI database

Crude oil prices (CROIL) Daily indexm-undi.com

Net investment of FII's (FII) Monthly capitaline.com

Monetary factors

Consumer Price Index (CPI) Monthly inflation.eu

Wholesale Price Index (WPI) Monthly capitaline.com

Money supply (M3) Monthly RBI database

Interest rate factors

91 day Treasury bill rate (TBILL) Weekly RBI database

Average Gsec rate (GSEC) Monthly investing.com

Average call money rate (CMR) Monthly RBI database

External sector factors

Balance of payments (BOP) Monthly capitaline.com

Balance of trade (BOT) Monthly RBI database

Foreign exchange rate USD-INR (FRX) Daily capitaline.com

Alternative investment factors

Average gold prices (GP) Monthly RBI database

Average silver prices (SP) Monthly RBI database

Sectoral indices

CNX auto index (AUTO) Daily Capitaline.com

CNX energy index (ENERGY) Daily Capitaline.com

CNX finance index (FINANCE) Daily Capitaline.com

CNX FMCG index (FMCG) Daily Capitaline.com

CNX IT index (IT) Daily Capitaline.com

CNX metal index (METAL) Daily Capitaline.com

CNX pharma index (PHARMA) Daily Capitaline.com

CNX PSU bank index (PSU BANK) Daily Capitaline.com

All the data collected for this study were for the period April 2005 to March

2015.

Statistical techniques used in the study

Unit root test: Augmented Dickey-Fuller (ADF) tests were performed on all the

variables used in the study to test for their stationarity. All the data analysed for the

study were converted into their log normal first differences to ensure data stationarity

and remove issues of multi-collinearity of independent variables (Gujarati, 2003).

Regression models:

Multivariate regression model: A multivariate regression was developed as

below to measure the impact of macro-economic factors on and sectoral indices:

Index = a + b1 GDP + b2 IIP + b3 CROIL+ b4 FII + b5 CPI + b6 WPI + b7 M3 +

b8+TBILL+ b9 GSEC + b10 CMR + b11 BOP + b12 BOT + b13 FRX +

b14 GP + b15 SP+ ε, (2)

where Index represents log normal difference of the sectoral index value, GDP

represents log normal difference of gross domestic product, IIP -log normal difference

of index of industrial production, CROIL -log normal difference of crude oil prices, FII

178

-log normal difference of net of FII, CPI -log normal difference of the CPI inflation,

WPI -log normal difference of WPI inflation, M3 -log normal difference of money

supply (M3), TBILL -log normal difference of 91 day Treasury bill rate, GSEC -log

normal difference of Gsec yields, CMR -log normal difference of call money rate, BOP

-log normal difference of balance of payments, BOT -log normal difference of balance

of trade, FRX -log normal difference of Forex rates, GP -log normal difference of

average gold price, SP -log normal difference of average silver price, a is the constant,

b1, b2,……….,b15 are the regression coefficients, and ε is the error term.

The above regression model (2) was used separately to study each sectoral index.

Lagged regression model: A lagged regression model was used to study the

lagged impact of the macro-economic factors on various sectoral indices, as the

announcement of a few macro-economic factors lagged the index values. The

regression model for lagged regression was the same as multivariate regression model

with the macroeconomic factors lagged as per their announcement schedule by the

Reserve Bank of India (RBI, 2016).

Presentation of Results

Unit root test: Table 3 presents the results of the ADF tests performed on the

data variables collected both at levels and log normal differences values. An analysis of

the table shows while the levels values exhibit non-stationarity, the values at log

normal differences are stationary. Therefore log normal differences values were used

for regression analysis.

Table 3. Results of Unit-Root Tests

Name of the variable

Levels values Log normal differences values

ADF test

value P-value

ADF test

value P-value

GDP -0.53 0.88 -3.23 0.01

IIP -2.58 0.45 -6.33 0.01

CROIL -3.47 0 -4.91 0.01

FII -2.85 0.05 -6.92 0.01

CPI -1.65 0.45 -6.33 0.01

WPI -1.2 0.67 -5.95 0.01

M3 1.62 0.99 -6.33 0.01

TBILL -1.84 0.36 -4.09 0.01

GSEC -2.91 0.04 -5.49 0.01

CMR -2.34 0.15 -6.04 0.01

BOP -4.21 0 -6.71 0.01

BOT -1.49 0.53 -6.88 0.01

FRX 0.094 0.96 -3.96 0.01

GP -1.39 0.58 -5.29 0.01

SP -1.44 0.55 -4.86 0.01

AUTO 1.54 0.99 -3.67 0.03

ENERGY -2.33 0.1 -4.58 0.01

FINANCE -0.39 0.9 -4.35 0.01

FMCG 1.35 0.99 -4.66 0.01

IT 0.4 0.98 -4.12 0.01

METAL -2.84 0.05 -4.25 0.01

PHARMA 4.46 1 -4.66 0.01

PSU BANK -1.87 0.34 -4.85 0.01

Note: The shaded cells represent values which are significant at a significance level of 95%.

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Multivariate regression: Table 4 presents the coefficients of the multivariate

regression performed for the various sectoral indices. Based on the variance inflation

factor (VIF) factors presented in the table, it can be noted that there is no multi-

collinearity among the independent factors in the regression (spsstests.com, 2015).

Table 4. Results of Multivariate Regression Factor Auto Energy Finance FMCG IT Metal Pharma PSU Bank

VIF Constant 0.04 0.01 0.05 0.02 0.01 0.04 0.02 0.03

GDP 0.44 0.54 0.56 -0.03 0.19 0.66 -0.24 1.03 2.17

IIP 0.09 0.01 0.37 -0.23 -0.44 0.71 -0.42 0.34 3.37

CROIL 0.19 0.31 -0.11 0.33 0.83 -0.13 0.63 -0.19 3.31

FII 0.00 0.00 -0.01 0.00 0.01 -0.02 0.01 -0.01 2.44

CPI -0.51 -0.81 4.17 -4.65 -11.18 9.76 -8.30 3.78 2.00

WPI -0.09 -0.30 0.76 0.02 -2.46 0.48 -0.75 -0.42 1.78

M3 -1.60 -0.11 -2.67 0.24 -0.45 -3.02 0.30 -1.93 1.12

TBILL -0.25 -0.06 -0.30 0.00 0.06 -0.57 0.07 -0.32 1.39

GSEC 0.12 -0.23 -0.10 -0.21 -0.05 0.19 -0.24 -0.18 2.47

CMR 0.02 0.00 0.00 0.00 0.00 0.03 0.01 0.00 1.32

BOP 0.00 0.00 -0.01 0.00 0.01 -0.02 0.01 -0.01 1.18

BOT 0.01 0.01 0.10 -0.04 -0.13 0.15 -0.08 0.09 1.60

FRX -0.71 -1.35 -1.17 -0.99 -1.08 -0.81 -1.28 -1.46 1.17

GP -0.30 0.17 -0.76 0.38 0.33 -1.11 0.49 -0.66 1.26

SP 0.24 -0.04 0.37 -0.18 -0.09 0.75 -0.22 0.33 1.47

Note: The shaded cells represent values which are significant at a significance level of 95%.

Lagged Regression: Table 5 presents the coefficients of the lagged regression

performed on the various sectoral indices. The VIF values show the lack of multi-

collinearity among independent factors.

Table 5. Results of Lagged Regression Factor Auto Energy Finance FMCG IT Metal Pharma PSU Bank

VIF Constant 0.05 0.03 0.06 0.03 0.04 0.04 0.03 0.04

GDP -0.16 -0.26 -0.13 -0.28 -0.10 0.21 -0.17 -0.09 1.87

IIP -0.14 -0.15 -0.24 -0.01 0.26 -0.28 0.13 -0.16 2.95

CROIL 0.22 0.25 0.04 0.12 0.49 0.32 0.32 -0.09 2.67

FII -0.01 0.00 0.00 0.00 -0.01 -0.01 0.00 -0.01 1.29

CPI -1.35 -2.39 1.42 -2.76 -6.41 1.01 -4.30 2.64 1.30

WPI 1.41 1.12 1.68 0.43 0.23 1.44 0.04 1.55 1.82

M3 -1.38 -0.17 -1.21 -0.40 -2.30 -0.01 -1.03 -0.69 1.11

TBILL -0.18 -0.09 -0.25 -0.05 0.05 -0.39 0.00 -0.31 1.39

GSEC 0.16 0.14 0.02 0.06 0.20 0.30 0.08 -0.04 2.29

CMR 0.02 0.01 0.02 -0.01 -0.03 0.06 -0.02 0.03 1.15

BOP 0.00 0.00 0.01 -0.01 -0.02 0.02 -0.02 0.01 1.16

BOT -0.01 0.00 0.00 0.01 0.06 -0.01 0.04 0.02 1.26

FRX -0.95 -1.46 -2.22 -0.64 -0.43 -2.17 -0.67 -2.61 1.29

GP -0.33 -0.09 -0.46 -0.02 0.03 -0.82 0.11 -0.37 1.22

SP 0.18 0.00 -0.01 0.02 0.05 0.25 0.03 -0.10 1.56

Note: The shaded cells represent values which are significant at a significance level of 95%.

Analysis of Results

Table 6 isolates the statistically significant factors affecting various sectoral

indices for both the types of regression performed and presents them along with the

type of impact.

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Table 6. Statistically Significant Factors Affecting Various Sectoral Indices Type Impact Auto Energy Finance FMCG IT Metal Pharma PSU bank

Multivariate

regression

+ve -- GDP

CROIL

IIP

BOT CROIL CROIL

SP

CROIL GDP BOT IIP

BOT

-ve FRX

TBILL FRX FRX

IIP

FRX CPI

BOT

CPI --

BOT

FRX IIP

FRX

CPI

Lagged

regression

+ve CROIL CROIL -- -- CROIL -- CROIL --

-ve FRX FRX FRX -- -- FRX BOP FRX

An analysis of the above table shows that several factors significantly impact the

sectoral indices. This leads to the non-acceptance of null hypothesis 1. Similarly, the

factors that affect different sectoral indices are not the same always. This leads to the

non-acceptance of null hypothesis 2. Additionally, the following observations can be

made from Table 6:

Crude oil prices (CROIL) has a pervasive positive impact on most of the

sectoral indices on a normal and lagged basis. This shows the reliance of the Indian

economy and almost every sector on crude oil prices.

Foreign exchange rates (FRX) has a pervasive negative impact on most

sectoral indices both on a normal and lagged basis. This shows the integration of the

Indian economy with the global economy and any depreciation of currency has a

negative impact on various sectors.

With regard to the financial sector indices (FINANCE and PSU BANK), it is

useful to note that general economic factors (GDP and IIP) have a positive impact on

them. When the economy grows, it provides for growth of these sectors.

In relation to the commodities sector indices (ENERGY and METAL), it is

logical to note that while crude oil prices positively impacts the energy index, silver

prices have a significant impact on the metal index.

With regard to the manufacturing sector indices (AUTO, FMCG and

PHARMA), apart from the pervasive impact of crude oil and forex rates, there are

many dissimilarities in the factors that impact them. This shows the highly

differentiated nature of the manufacturing sector.

The emerging sector index (IT) is positively impacted by crude oil prices like

other indices, showing its growing integration of this sector into the Indian economy.

Conclusions

This study was performed with the objective of understanding the impact of

macro-economic factors on various sectoral indices in India. The study showed these

factors have a varied impact on the sectoral indices. Based on the discussion in the

previous section the following conclusions can be derived:

It is necessary to understand the type of impact macro-economic factors have

on individual sectoral indices to make efficient investment decisions.

While certain factors like crude oil prices, forex rates and national income have

181

a pervasive impact on several sectors, each sector also has its own unique factors

affecting them. It is necessary to understand these unique factors to make proper

investment decisions.

In the current times, efficient investments need to be data driven (Accenture,

2016). This paper has shown that with appropriate data, sectoral investments can be

made more efficient.

Dedication

The authors humbly dedicate this paper to Bhagawan Sri Sathya Sai Baba, the

Founder Chancellor of Sri Sathya Sai Institute of Higher Learning, Prasanthinilayam,

India.

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IMPACT OF MACRO-ECONOMIC FACTORS

ON SECTORAL INDICES – EVIDENCE FROM INDIAN MARKETS

Naveen R.S.

N.Sivakumar

Sri Sathya Sai Institute of Higher Learning, India

Abstract

The impact of macro-economic factors on stock returns has been long proven

through research studies. This paper extends this idea to understand the impact of

macro-economic factors on sectoral indices of the National stock exchange (NSE) of

India. The study uses data over a 10 year period. Based on regression models, the study

shows that crude oil prices and forex rates have a pervasive significant impact on

sectoral indices. Besides, there are several other macro-economic factors which affect

specific sectoral indices.

Keywords: sectoral indices, macro-economic factors, National stock exchange