Relationship between Macroeconomic Factors and Aggregate Stock Returns in BRICS Stock Markets – A Panel Data Analysis

New Age Business Strategies in Emerging Global Markets, First Impression: 2015, Excel India Publishers, Page: 104-123.

Relationship between Macroeconomic Factors and Aggregate Stock

Returns in BRICS Stock Markets – A Panel Data Analysis

Dr. Vanita Tripathi1 and Arnav Kumar

2

1Associate Professor (Finance) & P.I. (UGC M.R.P.), Department of Commerce,

Delhi School of Economics, University of Delhi, India. 2Research Scholar, Department of Commerce, Delhi School of Economics,

University of Delhi, India.

Email: Email address: [email protected],

[email protected]

Abstract

This paper examines the relationship between select macroeconomic factors (i.e., GDP,

Inflation, Interest Rate, Exchange Rate and Money Supply) and aggregate stock returns in

emerging markets constituting the BRICS block over the period 1995 to 2014 using quarterly

panel data. This relationship is also examined during two sub periods viz., a Pre Crisis period

(1995:Q1 to 2007:Q2) and a Post Crisis Period (2007:Q3 to 2014:Q4). Robust econometric

tests like Panel Granger Causality Test, Pedroni’s Panel Cointegration Test and Panel Auto

Regressive Distributed Lag (ARDL) Model has been used.

We find that primarily in short run there is unidirectional causality running from stock returns

to GDP growth rate, inflation rate, rate of change in exchange rate and money supply. The

results are almost similar in pre and post crisis periods, except that in the pre crisis period,

there is bidirectional causality between stock returns and inflation, while in the post crisis

period it disappears. Long run panel causality results reveals unidirectional causality from

stock returns to GDP growth rate in total and post crisis periods. However in pre crisis period,

there was no long run causal relationship.

Pedroni’s panel cointegration test shows that stock indices are cointegrated with GDP in total

period and with GDP, inflation and money supply in post crisis period. Panel ARDL models

have explanatory power ranging from 28% in total period to 62% in post crisis period. We

find that while current stock returns are negatively linked to rate of change in exchange rate

and money supply; they are positively linked to their own lagged values. In pre crisis period,

rate of change in money supply significantly explains stock returns while in post crisis

period, inflation rate, interest rate and rate of change in exchange rate and money supply

negatively affects BRICS panel stock returns.

These findings, besides augmenting the empirical literature and knowledge domain on the

topic, have significant implications for policy makers, regulators, researchers and investing

community in emerging markets. The regulators need to ensure that financial sector reforms

agenda consciously considers interlinkages between stock markets and real economy. The

investment community can devise investment strategy, using the results of this study to earn

arbitrage profits in emerging stock markets.

Keywords: Aggregate Stock Returns, BRICS Stock Markets, Macroeconomic Factors, Panel

Auto Regressive Distributed Lag (ARDL) Model, Panel Causality, Panel Cointegration.

JEL Classification: B26, C23, C58, E44.

mailto:[email protected]

mailto:[email protected]

Relationship between Macroeconomic Factors and Aggregate Stock Returns in BRICS Stock Markets

– A Panel Data Analysis

Introduction

In the present-day scenario, where there is an increasing integration of the financial markets

and implementation of various stock market reforms, the activities in the stock markets and

their relationships with the macro economy have assumed significant importance. As

mentioned by Galbraith (1955), “the stock market is but a mirror, which provides an image of

the underlying or fundamental economic situation”.

Therefore, in the past two decades an increasing attention is being paid to the relationship

between share returns and the macroeconomic variables by both economists and finance

specialists. Different macroeconomic factors which have been examined for their possible

relationship with stock returns are - GDP growth rate, Inflation rate, Interest rate, Foreign

Exchange Rate and Money Supply. According to Flannery and Protopapadakis (2002) “there

are two direct benefits of identifying the macro variables that influence aggregate equity

returns - it may indicate hedging opportunities for investors and if investors as a group are

averse to fluctuations in these variables, these variables may constitute priced factors”.

Macroeconomic factors influence the stock market performance and particularly stock returns

through their effect on future cash flows and the rate at which they are discounted. The

relationship between stock prices and macroeconomic variables is well illustrated by

theoretical stock valuation models such as Dividend Discount Model (DDM), Free Cash

Flow Valuation, and Residual Income Valuation. According to the models, the current price

of an equity share is approximately equal to the present value of all future cash flows; thus

any economic variable affecting cash flows and required rate of return in turn influences the

share value as well.

GDP growth rate is typically used as a proxy for the level of real economic activity. It is

theoretically shown that the productive capacity of an economy rises during economic

growth, which in turn contributes to the ability of firms to generate cash flows. Hence a

positive relationship between real economy and stock prices exist [Fama (1981), Mukherjee

and Naka (1995)].

In the process of stock valuation, it is important to consider the effects of inflation on stock

returns. In theory, stocks should be inflation neutral, and rising inflation should have no

impact on stock valuations. A negative relationship between inflation and stock prices is

contended in literature because an increase in the rate of inflation is accompanied by both

lower expected earnings growth and higher required real returns. [Fisher (1930), Fama

(1981), Tripathi and Kumar (2015 a & b)].

Interest rates are expected to be negatively related to market returns either through the

inflationary or discount factor effect. An increase in interest rate increases the discount rate

(or minimum required rate of return) and hence reduces share prices [Asprem (1989),

Mukherjee and Naka (1995)].

There is no theoretical consensus on the existence of relationship between stock prices and

exchange rates or on the direction of the relationship. However, in the literature, two

approaches have been asserted to establish a relationship between exchange rate and stock

prices: The goods market model and the portfolio balance model. Goods market model

suggests that changes in exchange rates affect the competitiveness of a firm, which in turn

influence the firm’s earnings or its cost of funds and hence its stock price. Thus, goods

market models represent a positive relationship between stock prices and exchanges rates

with direction of causation running from exchange rates to stock prices. On the other hand,

Portfolio balance model assumes a negative relationship between stock prices and exchange

rates. A rise in domestic stock prices would increase the demand for domestic currency and


cause exchange rate to appreciate. A rising stock market leads to the appreciation of domestic

currency through direct and indirect channels. [Maysami et al. (2004), Mukherjee and Naka

(1995)].

Money supply’s net impact on stock returns is also debatable and can be positive or negative.

An increase in money supply increases liquidity making more money available for

consumption and investments and lowers interest rate in the economy favourably affecting

corporate performance and stock returns. But, they also build up substantial inflationary

pressure in the economy which could negatively impact stock returns. [Chaudhuri and Smiles

(2004), Mukherjee and Naka (1995)].

BRICS is an acronym for a group of five prominent emerging and developing economies of

Brazil, Russia, India, China and South Africa. They have big, fast-growing economies and

now command significant political and economic influence at global level. In 2014, these five

BRICS economies jointly represented about 40% of world’s population and 20% of world’s

nominal GDP.

Our objective in this paper is to examine both the short term and long term dynamic

relationship between aggregate stock returns in BRICS and their major macroeconomic

factors, i.e., GDP, Inflation, Interest Rate, Exchange Rate and Money Supply. We also probe

whether any of the macroeconomic variables are useful in predicting BRICS stock returns.

We also investigate for the presence of any causal (lead-lag) relationship between BRICS

stock returns and major macroeconomic variables in the short and long term. We study this

relationship for these countries collectively using Panel data.

The remaining paper is structured as follows: Section 2 provides review of literature. Section

3 explains the data and methodology. Section 4 elucidates the empirical results. Section 5

provides the conclusions and implications of the study.

Review of Literature

A plethora of studies have examined the relationship between macroeconomic variables and

stock returns in developed markets of US, UK and other European markets. However the

literature on such a relationship in emerging markets has been limited and is growing only

recently especially in the context of India.

Fama (1981, 1990) reported a strong relationship is present between stock returns and

macroeconomic variables, notably, inflation, national output and industrial production. Stock

returns are determined by forecasts of more relevant real variables and negative stock returns-

inflation relations are induced by negative relationships between inflation and real activity.

Chen et al. (1986) were the first to explore a set of economic state variables as systematic

influences on stock market returns and have examined their influence on asset pricing.

Macro-economic variables that systematically affect stock market returns are- spread between

long and short interest rates, expected and unexpected inflation, industrial production, and the

spread between high- and low-grade bonds.

Chang and Pinegar (1989) affirmed that there exists a close relationship between stock

market performance and the domestic economic activity. They also report unidirectional

Granger causality running from large firms' stocks returns to future growth rates in industrial

production at least six months in advance.

Mukherjee and Naka (1995) suggested that cointegration relation existed and positive

relationship was found between the Japanese industrial production and stock return.



Maysami and Koh (2000) reported that changes in Singapore’s stock market levels do form

a cointegrating relationship with changes in price levels, money supply, short- and long-term

interest rates, and exchange rates.

Abugri (2008) found that Interest rates and exchange rates are significant in three out of the

four Latin American markets examined. The performance of money supply and industrial

production is generally weak.

Gay (2008) reported that , though not significant, but the relationship between exchange rates

and stock prices was positive while, the relationship between respective stock market prices

and monthly oil prices was negative but insignificant.

Agarwalla and Tuteja (2008) revealed causality running from economic growth proxied by

industrial production to share price index and not the other way round which shows that stock

markets in India are still demand driven and industry led.

Singh (2010) indicated that IIP is the only variable having bilateral causal relationship with

BSE Sensex. WPI is having unilateral causality with BSE Sensex.

Hsing (2011) finds that South Africa’s stock market index is positively influenced by the

growth rate of real GDP, the ratio of the money supply to GDP and the U.S. stock market

index and negatively affected by the ratio of the government deficit to GDP, the domestic real

interest rate, the nominal effective exchange rate, the domestic inflation rate, and the U.S.

government bond yield.

Dasgupta (2012) found one cointegration vector and long-run relationships between BSE

SENSEX with index of industrial production and call money rate. They further found no

short-run unilateral or bilateral causal relationships between BSE SENSEX with the

macroeconomic variables.

Tripathi and Seth (2014) conveyed a significant correlation among stock market indicators

and macroeconomic factors and identified Inflation, Interest rate and Exchange rate as three

principal factors through Factor analysis. They also reported presence of five co-integrating

relationships between stock market and macro-economic variables.

Tripathi and Kumar (2015 a & b) used granger causality and panel cointegration on BRICS

market to conclude that while inflation rate may be significantly related to stock returns in the

short run, they do not seem to move together in the long run.

Tripathi and Kumar (2015 c) used ARDL model and reported that Stock returns generally

lead rather than follow GDP and Inflation. Also, they find significant negative relationship of

stock returns with Interest Rate, Exchange Rate and Oil Prices and a positive relationship

with money supply.

Overall, it can be said that, the studies have comprehensively analysed the developed markets

and arrived at some common ground. But for developing markets, the consensus is largely

lacking both due to varying results for most macroeconomic variables and paucity of

research.

Data and Methodology

Data

The period of present study is 1995: Q1 to 2014: Q4. Frequency of all data is quarterly. The

data comprises of macroeconomic variables and stock indices values for all BRICS nations.

We have considered five prominent macroeconomic variables, i.e., GDP, Inflation, Interest

Rate, Exchange Rate and Money Supply. The operational definitions, time period of

availability, source and symbol of each macroeconomic variable for each country is provided


in Table 1. Using individual country data, we have constructed a panel data of BRICS stock

index and macroeconomic variables.

Table 1: Data Description (Macroeconomic Variables)

S.

No. Country

Macroeconomic

Variables Operational Definition

Time

Period Source Symbol

1. Brazil GDP Fixed PPP, 2005 Prices

1996: Q1

-2014:

Q3

OECD BGDP

2. Brazil Inflation Consumer Price Index, Base

2010

1995: Q1

-2014:

Q4

OECD BINF

3. Brazil Interest Rate Brazil Selic Target Rate

1999: Q1

-2014:

Q4

Bloomberg BIR

4. Brazil Exchange Rate 1 USD in Brazilian Real(BRL)

1995: Q1

-2014:

Q4

Bloomberg BER

5. Brazil Money Supply Broad Money Supply (M3)

1995: Q1

-2014:

Q4

Central

Bank of

Brazil

BMS

6. Russia GDP Fixed PPP, 2005 Prices

1995: Q1

-2014:

Q3

OECD RGDP

7. Russia Inflation Consumer Price Index, Base

2010

1995: Q1

-2014:

Q4

OECD RINF

8. Russia Interest Rate Russia Refinancing Rate

1995: Q1

-2014:

Q4

Bloomberg RIR

9. Russia Exchange Rate 1 USD in Russian Ruble (RUB)

1995: Q1

-2014:

Q4

Bloomberg RER

10. Russia Money Supply Narrow Money Supply (M1)

2002: Q2

-2014:

Q4

Bloomberg RMS

11. India GDP Fixed PPP, 2005 Prices

1996: Q2

-2014:

Q4

OECD IGDP

12. India Inflation Consumer Price Index, Base

2010

1995: Q1

-2014:

Q4

OECD IINF

13. India Interest Rate Weighted Average Call Money

Rates

1995: Q1

-2014:

Q4

RBI IIR

14. India Exchange Rate 1 USD in Indian Rupees

1995: Q1

-2014:

Q4

RBI IER

15. India Money Supply Broad Money (M3)

1995: Q1

-2014:

Q4

RBI IMS

16. China GDP GDP at current prices

1995: Q1

-2014:

Q3

National

Bureau of

Statistics

CGDP

17. China Inflation Consumer Price Index, Base

2010

1995: Q1

-2014:

Q4

OECD CINF

18. China Interest Rate 1 Year Benchmark Lending 1996: Q2 Bloomberg CIR



Rates -2014:

Q4

19. China Exchange Rate 1 USD in Chinese Yuan (CNY)

1995: Q1

-2014:

Q4

Bloomberg CER

20. China Money Supply Money Supply (M2)

1996: Q1

-2014:

Q4

Bloomberg CMS

21. South

Africa GDP Fixed PPP, 2005 Prices

2002: Q1

-2014:

Q4

OECD SAGDP

22. South

Africa Inflation

Consumer Price Index, Base

2010

2002: Q1

-2014:

Q4

OECD SAINF

23. South

Africa Interest Rate Average Repo Rate

2002: Q1

-2014:

Q4

Bloomberg SAIR

24. South

Africa Exchange Rate 1 USD in South African Rand

2002: Q1

-2014:

Q4

Bloomberg SAER

25. South

Africa Money Supply Money Supply (M2)

2002: Q1

-2014:

Q4

Bloomberg SAMS

26. Panel GDP -

1995: Q1

-2014:

Q4

- PGDP

27. Panel Inflation -

1995: Q1

-2014:

Q4

- PINF

28. Panel Interest Rate -

1995: Q1

-2014:

Q4

- PIR

29. Panel Exchange Rate -

1995: Q1

-2014:

Q4

- PER

30. Panel Money Supply -

1995: Q1

-2014:

Q4

- PMS

The detailed description of stock market variables of each country is given in Table 2.

Table 2: Data Description (Stock Market Variables)

S.No. Country Stock Exchange Stock Index Time Period Source Symbol

1. Brazil Sao Paulo Stock

Exchange Ibovespa

1995: Q1 to

2014: Q4

Yahoo

Finance BINDEX

2. Russia Moscow Stock

Exchange RTSI INDEX

1995: Q3 to

2014: Q4

Yahoo

Finance RINDEX

3. India Bombay Stock

Exchange BSE SENSEX

1995: Q1 to

2014: Q4

Yahoo

Finance IINDEX

4. China Shanghai Stock

Exchange

Shanghai SE

Composite

1995: Q1 to

2014: Q4

Yahoo

Finance CINDEX

5. South

Africa

Johannesburg

Stock Exchange

FTSE-JSE All Share

Index

2002: Q1 to

2014: Q4

Yahoo

Finance SAINDEX

6. Panel

(Index) -

1995: Q1 -

2014: Q4 - PINDEX


Methodology

1. Panel Unit Root Test

If the mean, variance and auto-covariance of a time series data is time invariant, it is said to

be stationary. Following Panel unit root tests have been applied.

a. Levin, Lin, and Chu (LLC) Test

IHS (2013): “Levin, Lin, and Chu test assume that there is a common unit root process so that

is identical across cross-sections. LLC consider the following basic ADF specification:

∆𝑦𝑖𝑡 = 𝛼𝑦𝑖𝑡−1 + ∑ 𝛽𝑖𝑗𝑝𝑖𝑗=1 ∆𝑦𝑖𝑡−𝑗 + 𝑋′𝑖𝑡𝛿 + 𝜖𝑖𝑡 (1)

Where, we assume a common α = ρ - 1, but allow the lag order for the difference terms 𝑝𝑖, to

vary across cross-sections. The null and alternative hypotheses for the tests may be written

as: 𝐻0: 𝛼 = 0 (unit root) and 𝐻1: 𝛼 < 0 (no unit root).” (p. 488).

b. Im, Pesaran and Shin (IPS) Test

IHS (2013): “The Im, Pesaran, and Shim test allow for individual unit root processes so that

𝑝𝑖 may vary across cross-sections. IPS begins by specifying a separate ADF regression for

each cross section: ∆𝑦𝑖𝑡 = 𝛼𝑦𝑖𝑡−1 + ∑ 𝛽𝑖𝑗𝑝𝑖𝑗=1 ∆𝑦𝑖𝑡−𝑗 + 𝑋′𝑖𝑡𝛿 + 𝜖𝑖𝑡 (2)

The null hypothesis may be written as, 𝐻0 ∶ 𝛼𝑖 = 0, 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑖.

While the alternative hypothesis is given by: 𝐻1 ∶ {𝛼𝑖 = 0 𝑓𝑜𝑟 𝑖 = 1, 2, … , 𝑁1 𝛼𝑖 < 0 𝑓𝑜𝑟 𝑖 = 𝑁 + 1, 𝑁 + 2, … , 𝑁

”

(p. 491-492).

c. Fisher-ADF and Fisher-PP Test

IHS (2013): “The Fisher-ADF and PP tests allow for individual unit root processes so that 𝑝𝑖

may vary across cross-sections. The tests are characterized by the combining of individual

unit root tests to derive a panel-specific result. The null and alternate hypotheses are same as

for IPS” (p. 492-493).

2. Panel Stacked Granger Causality Test

IHS (2013): “In general, the bivariate regressions in a panel data context take the form:

𝑦𝑖,𝑡 = 𝛼0,𝑖 + 𝛼1,𝑖𝑦𝑖,𝑡−1 + ⋯ + 𝛼𝑙,𝑖𝑦𝑖,𝑡−𝑙 + 𝛽1,𝑖𝑥𝑖,𝑡−1 + ⋯ + 𝛽𝑙,𝑖𝑥𝑖,𝑡−𝑙 + 𝜖𝑖,𝑡 ……..... (3), and

𝑥𝑖,𝑡 = 𝛼0,𝑖 + 𝛼1,𝑖𝑥𝑖,𝑡−1 + ⋯ + 𝛼𝑙,𝑖𝑥𝑖,𝑡−𝑙 + 𝛽1,𝑖𝑦𝑖,𝑡−1 + ⋯ + 𝛽𝑙,𝑖𝑦𝑖,𝑡−𝑙 + 𝜖𝑖,𝑡 ……… (4).

Where t denotes the time period dimension of the panel, and i denotes the cross-sectional

dimension. This test treats the panel data as one large stacked set of data, and then performs

the Granger Causality test in the standard way, with the exception of not letting data from one

cross-section enter the lagged values of data from the next cross-section.

This method assumes that all coefficients are same across all cross-sections, i.e.:

𝛼0,𝑖 = 𝛼0,𝑗 , 𝛼1,𝑖 = 𝛼1,𝑗 , … , 𝛼𝑙,𝑖 = 𝛼𝑙,𝑗 , ∀ 𝑖, 𝑗 and 𝛽1,𝑖 = 𝛽1,𝑗 , … , 𝛽𝑙,𝑖 = 𝛽𝑙,𝑗 , ∀ 𝑖, 𝑗” (5)

Granger causality test establishes short run causality if we take stationary values. “Causality

tests by the level Vector Auto Regression (VAR) (non-stationary) can complement the result

of the cointegration tests in terms of long-run information” [Worthington & Higgs, 2007]. So,



non-stationary level time series data of variables have been used to determine long run

causality.

Optimal lag length for conducting granger causality test (both short and long run) has been

determined as per the Akaike Information Criterion (AIC) within the VAR framework.

3. Pedroni’s Panel Cointegration Test (Engle-Granger Based)

Pedroni Cointegration test (Engle-Granger Based) has been applied on panel data of stock

index values and macroeconomic variables to determine whether a long term cointegrating or

equilibrium relationship exists between stock return and macroeconomic variables for BRICS

stock markets when taken together as a panel.

The Engle-Granger (1987) cointegration test is based on an examination of the residuals of a

spurious regression performed using I(1) variables.

Pedroni proposes several tests for cointegration that allow for heterogeneous intercepts and

trend coefficients across cross-sections. Consider the following regression:

yit = αi + δit + β1ix1i,t + β2ix2i,t + ⋯ + βMixMi,t + ei,t …………………….. (6)

for t = 1,…….,T; i = 1,……, N; m = 1, ……., M; where y and x are assumed to be integrated

of order one, e.g. I(1). The parameters αi and δi are individual and trend effects which may be

set to zero if desired.

Under the null hypothesis of no cointegration, the residuals ei,t will be I(1). The general

approach is to obtain residuals from Equation 1 and then to test whether residuals are I (1) by

running the auxiliary regression,

ei,t = ρieit−1 + uit ………………………………………………………… (7)

for each cross-section. Pedroni describes various methods of constructing statistics for testing

for null hypothesis of no cointegration (ρi = 1 ). There are two alternative hypotheses: the

homogenous alternative, (ρi = ρ) < 1 for all i (which Pedroni terms the within-dimension test

or panel statistics test), and the heterogeneous alternative, ρi < 1 for all i (also referred to as

the between-dimension or group statistics test).

The Pedroni panel cointegration statistic ℵN,T is constructed from the residuals from Equation

7. A total of eleven statistics with varying degree of properties (size and power for different

N and T) are generated. Pedroni shows that the standardized statistic is asymptotically

normally distributed,

ℵN,T− μ√N

√v → N(0, 1) ………………………………………………………………….. (8)

Where μ and v are Monte Carlo generated adjustment terms.

4. Panel ARDL Model

The Autoregressive Distributed Lag (ARDL) approach was introduced by Pesaran et al.

(1996). ARDL model has been used here for the analysis of both short-run dynamic and long

run relationship between Stock returns and Macroeconomic variables in BRICS markets.

An autoregressive distributed lag model is considered as

ARDL (1, 1) model: yt= μ + α1yt-1 + β0xt + β1xt-1 + ut. ………………………… (9)

Where yt and xt are stationary variables, and ut is a white noise.

Our ARDL model regresses panel stock index variable on their own lagged values; on

stationary (short run) contemporary and lagged values of panel macroeconomic variables and

on non-stationary (long run) values of panel macroeconomic variables.

Thus, while the stationary contemporaneous and lagged values will determine the short run

relationship between macroeconomic variables and stock returns, the non-stationary ones will

establish the long run relationship. Optimal AIC Lags for Panel ARDL model is 5 in Total

and Post Crisis Periods and 4 in Pre Crisis Period.


Empirical Results and Discussion

1. Panel Unit Root Test Results

We applied four tests to check whether our panel data is stationary or not. These tests are

Levin, Lin& Chu Test, Im, Pesaran & Shin Test, ADF fisher test and PP-Fisher Test. The

results are presented in Table 3 and 4 for at level and first differenced series. The results

reveal that all the panel series are non stationary at level in all the three time periods and their

log of first difference is stationary in all the three time periods. This shows that our data

series are I (1) and hence can be used in further analysis without worrying about emergence

of any spurious relationship.

Table 3: Panel Unit Root Tests Results (At Level)

A. Panel Unit Root Test Results (Total Period – At Level)

Panel

Variables

Levin, Lin &

Chu

Im, Pesaran &

Shin

ADF-Fisher PP-Fisher

t-stat. Prob. W-

Stat.

Prob. Chi-

square

Prob. Chi-

square

Prob.

INDEX 0.75 0.77 1.05 0.85 7.79 0.65 6.55 0.77

GDP 2.64 0.99 4.51 0.99 2.63 0.98 18.63 0.05

INF 5.71 0.99 8.79 0.99 0.03 0.99 0.03 0.99

IR -4.93* 0.00 -5.48* 0.00 56.07* 0.00 70.74* 0.00

ER 1.23 0.88 1.91 0.97 3.52 0.97 3.57 0.97

MS 14.79 0.99 15.42 0.99 0.004 0.99 0.001 0.99

*Denotes significant at α = 0.05.

B. Panel Unit Root Tests Results (Pre Crisis Period - At Level)

Panel

Variables

Levin, Lin &

Chu

Im, Pesaran &

Shin


t-stat. Prob. W-

Stat.

Prob. Chi-

square

Prob. Chi-

square

Prob.

INDEX 7.83 0.99 7.47 0.99 0.07 0.99 0.03 0.99

GDP 7.20 0.99 7.07 0.99 12.94 0.23 20.53 0.03

INF 0.60 0.73 2.95 0.99 1.69 0.99 2.86 0.99

IR -4.10* 0.00 -4.03* 0.00 37.97* 0.00 52.16* 0.00

ER 1.25 0.90 2.55 0.99 10.29 0.42 8.80 0.55

MS 15.01 0.99 16.95 0.99 0.00 0.99 0.00 0.99


C. Panel Unit Root Tests Results (Post Crisis Period - At Level)

Panel

Variables

Levin, Lin &

Chu

Im, Pesaran &

Shin


t-stat. Prob. W-

Stat.

Prob. Chi-

square

Prob. Chi-

square

Prob.

INDEX 1.06 0.86 -0.62 0.27 15.17 0.13 24.48* 0.01

GDP 1.26 0.90 0.97 0.84 15.51 0.12 18.05 0.05

INF 1.09 0.86 3.95 0.99 0.88 0.99 1.00 0.99

IR -2.29* 0.01 -2.17* 0.02 20.15* 0.03 8.74 0.56

ER 0.05 0.52 1.99 0.98 4.40 0.93 7.45 0.68

MS 3.17 0.99 6.29 0.99 0.07 0.99 0.01 0.99




Table 4: Panel Unit Root Tests Results (at Log of First Difference)

A. Panel Unit Root Tests Results (Total Period – Log of First Difference)

Panel

Variables

Levin, Lin & Chu Im, Pesaran & Shin ADF-Fisher PP-Fisher

t-stat. Prob. W-Stat. Prob. Chi-

square

Prob. Chi-

square

Prob.

INDEX -11.77* 0.00 -11.05* 0.00 120.19* 0.00 143.11* 0.00

GDP -7.73* 0.00 -11.55* 0.00 84.03* 0.00 114.58* 0.00

INF -9.68* 0.00 11.91* 0.00 121.98* 0.00 120.58* 0.00

IR -2.63* 0.00 -10.24* 0.00 100.07* 0.00 141.86* 0.00

ER -7.21* 0.00 -9.48* 0.00 101.58* 0.00 146.85* 0.00

MS -9.72* 0.00 -11.02* 0.00 121.18* 0.00 140.90* 0.00


B. Panel Unit Root Tests Results (Pre Crisis Period – Log of First Difference)

Panel

Variables

Levin, Lin &

Chu

Im, Pesaran &

Shin


t-stat. Prob. W-

Stat.

Prob. Chi-

square

Prob. Chi-

square

Prob.

INDEX -7.19* 0.00 -7.09* 0.00 71.11* 0.00 102.92* 0.00

GDP -5.02* 0.00 -7.73* 0.00 77.03* 0.00 89.74* 0.00

INF -7.58* 0.00 -8.92* 0.00 91.15* 0.00 76.26* 0.00

IR 0.86* 0.80 -6.36* 0.00 60.27* 0.00 112.87* 0.00

ER -2.64* 0.00 -5.25* 0.00 49.97* 0.00 108.41* 0.00

MS -6.75* 0.00 -7.54* 0.00 77.91* 0.00 142.62* 0.00


C. Panel Unit Root Tests Results (Post Crisis Period – Log of First Difference)

Panel

Variables

Levin, Lin &

Chu

Im, Pesaran &

Shin


t-stat. Prob. W-

Stat.

Prob. Chi-

square

Prob. Chi-

square

Prob.

INDEX -5.85* 0.00 -5.66* 0.00 49.97* 0.00 52.96* 0.00

GDP -3.74* 0.00 -6.11* 0.00 54.87* 0.00 74.73* 0.00

INF -8.81* 0.00 -8.43* 0.00 77.98* 0.00 67.14* 0.00

IR -2.68* 0.00 -3.40* 0.00 28.84* 0.00 42.64* 0.00

ER -2.89* 0.00 -5.28* 0.00 46.92* 0.00 60.91* 0.00

MS -6.04* 0.00 -6.75* 0.00 61.34* 0.00 89.33* 0.00


2. Panel Stacked Granger Causality Results

(a) Short Run Panel Causality Results

The panel data short run Granger Causality results presented in Table 5 show unidirectional

causality from stock return to four macroeconomic factors viz. GDP growth rate, Inflation

rate, changes in exchange rate and money supply in the total period. On the other hand,

interest rate granger causes stock return in total period. In the pre crisis period, there is

bidirectional causality between stock returns and inflation rate and unidirectional causality


from stock return to GDP growth rate and changes in exchange rate.

In the post crisis period, there is bi directional causality between stock returns and interest

rate. We also find unidirectional causality from stock returns to GDP growth rate and changes

in money supply in this period.

Table 5: Short Run Stacked Panel Causality Test Results

Total Period Pre crisis Post Crisis

Null Hypothesis F-

Statistic Prob.

F-

Statistic Prob.

F-

Statistic Prob.

DLOG(PGDP) does not Granger

Cause DLOG(PINDEX) 0.50 0.77 0.61 0.66 0.70 0.60

DLOG(PINDEX) does not Granger

Cause DLOG(PGDP) 11.35* 0.00 7.77* 0.00 22.70* 0.00

DLOG(PINF) does not Granger

Cause DLOG(PINDEX) 1.95 0.09 3.12* 0.02 1.51 0.20


Cause DLOG(PINF) 9.10* 0.00 11.94* 0.00 1.04 0.39

DLOG(PIR) does not Granger Cause

DLOG(PINDEX) 2.50* 0.03 1.78 0.14 4.37* 0.00


Cause DLOG(PIR) 0.56 0.73 1.31 0.27 2.66* 0.04

DLOG(PER) does not Granger



Cause DLOG(PER) 10.00* 0.00 8.13* 0.00 2.03 0.09

DLOG(PMS) does not Granger



Cause DLOG(PMS) 6.09* 0.00 1.34 0.26 6.65* 0.00

Note: * Denotes Significant at 5% Level.

(b) Long Run Panel Causality Results

Long run Stacked Panel causality test results as presented in Table 6 show that in the total

period stock prices granger causes GDP growth rate in the long run. No long run causal

relationship existed in pre crisis period. However post crisis, stock market is granger causing

GDP and Interest rates while there is unidirectional causality from money supply to stock

prices in the long run.

Table 6: Long Run Stacked Panel Causality Test Results

Total Period Pre Crisis Post Crisis

Null Hypothesis F-

Statistic Prob.

F-

Statistic Prob.

F-

Statistic Prob.

PGDP does not Granger Cause

PINDEX 0.31 0.90 0.00 0.95 0.08 0.99

PINDEX does not Granger Cause

PGDP 10.57* 0.00 0.52 0.47 6.29* 0.00

PINF does not Granger Cause

PINDEX 0.43 0.83 0.60 0.44 0.30 0.91




PINF 0.53 0.76 1.70 0.19 0.23 0.95

PIR does not Granger Cause

PINDEX 0.11 0.99 0.05 0.82 1.69 0.14


PIR 0.18 0.97 0.00 0.96 3.63* 0.00

PER does not Granger Cause

PINDEX 0.27 0.93 0.00 0.98 0.49 0.78


PER 0.19 0.96 3.06 0.08 0.05 1.00

PMS does not Granger Cause

PINDEX 0.66 0.65 1.03 0.31 4.39* 0.00


PMS 2.12 0.06 0.58 0.45 1.10 0.37

Note: * Denotes Significant at 5% Level.

3. Pedroni Panel Cointegration Results

The results regarding panel data are provided in Tables 7 to 11. These tables show that there

is cointegrating relationship between stock prices and GDP in total period and post crisis

period. There is cointegrating relationship between stock prices and inflation as well as

between stock prices and money supply in post crisis period. The panel data shows that there

is absolutely no cointegration of stock prices with interest rate and exchange rate.

(I) GDP

Table 7: Pedroni Panel Cointegration Test Results (GDP)

Pedroni

Panel

Statistic

Total Period Pre Crisis Period Post Crisis Period

Simple Weighted Simple Weighted Simple Weighted

Stat. Prob. Stat. Prob. Stat. Prob. Stat. Prob. Stat. Prob. Stat. Prob.

Panel v-

Statistic 4.75* 0.00 4.29* 0.00 -0.28 0.61 -0.29 0.62 3.00* 0.00 2.76* 0.00

Panel rho-

Statistic -2.93* 0.00 -3.23* 0.00 2.05 0.98 0.25 0.60 -1.38 0.08 -2.04* 0.02

Panel PP-

Statistic -1.95* 0.03 -2.17* 0.02 3.28 1.00 0.86 0.80 -1.60 0.05 -2.84* 0.00

Panel ADF-

Statistic -2.12* 0.02 -2.79* 0.00 3.14 1.00 1.29 0.90 -2.36* 0.01 -3.32* 0.00

Group rho-

Statistic -1.78* 0.04 NA NA 0.40 0.66 NA NA -0.77 0.22 NA NA

Group PP-

Statistic -1.68 0.05 NA NA 2.44 0.99 NA NA -2.95* 0.00 NA NA

Group

ADF-

Statistic -2.40* 0.01 NA NA 2.73 1.00 NA NA -3.50* 0.00 NA NA

* Denotes Significant at 5% level.


(II) Inflation

Table 8: Pedroni Panel Cointegration Test Results (Inflation)

Pedroni

Panel

Statistic




Panel v-

Statistic 1.19 0.12 1.90* 0.03 0.60 0.27 0.86 0.20 2.03* 0.02 2.45* 0.01

Panel rho-

Statistic 0.11 0.54 -0.85 0.20 1.54 0.94 1.39 0.92 -1.36 0.09 -1.61 0.05

Panel PP-

Statistic 0.29 0.62 -0.62 0.27 2.01 0.98 2.09 0.98 -1.46 0.07 -2.04* 0.02

Panel ADF-

Statistic -0.18 0.43 -1.41 0.08 2.27 0.99 2.42 0.99 -2.40* 0.01 -2.60* 0.00

Group rho-

Statistic 0.21 0.58 NA NA 1.86 0.97 NA NA -0.57 0.28 NA NA

Group PP-


Group

ADF-



(III) Interest Rate

Table 9: Pedroni Panel Cointegration Test Results (Interest Rate)

Pedroni

Panel

Statistic




Panel v-

Statistic -0.95 0.83 -0.64 0.74 -0.95 0.83 -0.48 0.69 0.95 0.17 0.86 0.20

Panel rho-

Statistic -0.56 0.29 -0.18 0.43 0.95 0.83 2.37 0.99 -0.01 0.50 0.15 0.56

Panel PP-

Statistic -1.24 0.11 -0.41 0.34 1.59 0.94 3.73 1.00 0.14 0.56 0.21 0.58

Panel ADF-

Statistic 0.14 0.56 0.02 0.51 4.40 1.00 4.91 1.00 -0.06 0.48 0.26 0.60

Group rho-

Statistic 1.07 0.86 NA NA 3.31 1.00 NA NA 0.25 0.60 NA NA

Group PP-

Statistic -0.02 0.49 NA NA 5.70 1.00 NA NA -0.28 0.39 NA NA

Group

ADF-





(IV) Exchange Rate

Table 10: Pedroni Panel Cointegration Test Results (Exchange Rate)

Pedroni

Panel

Statistic




Panel v-

Statistic -1.39 0.92 -0.78 0.78 -1.13 0.87 -0.76 0.78 0.88 0.19 1.25 0.11

Panel rho-

Statistic 1.16 0.88 0.21 0.58 3.67 1.00 3.17 1.00 -1.37 0.08 -0.67 0.25

Panel PP-

Statistic 0.58 0.72 -0.11 0.45 6.79 1.00 5.66 1.00 -2.80* 0.00 -1.91* 0.03

Panel ADF-

Statistic 0.40 0.66 -0.40 0.34 5.89 1.00 5.13 1.00 -3.20* 0.00 -2.60* 0.00

Group rho-


Group PP-


Group

ADF-



(V) Money Supply

Table 11: Pedroni Panel Cointegration Test Results (Money Supply)

Pedroni

Panel

Statistic




Panel v-

Statistic 1.44 0.08 3.01* 0.00 0.65 0.26 1.27 0.10 2.24* 0.01 2.68* 0.00

Panel rho-

Statistic 0.33 0.63 -1.20 0.11 0.08 0.53 -0.68 0.25 -1.55 0.06 -1.87* 0.03

Panel PP-

Statistic 0.91 0.82 -0.61 0.27 0.30 0.62 -0.36 0.36 -1.61 0.05 -2.24* 0.01

Panel ADF-

Statistic 0.55 0.71 -1.14 0.13 -0.19 0.43 -1.31 0.10 -2.56* 0.01 -2.93* 0.00

Group rho-


Group PP-


Group

ADF-

Statistic -1.15 0.12 NA NA -0.89 0.19 NA NA -2.76* 0.00 NA NA



4. Panel ARDL Model Results

Finally, we run the Panel ARDL models to see the short and long run contemporary and lead-

lag relationships between stock returns and macroeconomic variables of BRICS as one

collective group. We find that while current stock returns are negatively linked to rate of

change in exchange rate and money supply; they are positively linked to their own lagged

values. In pre crisis period, rate of change in money supply significantly explains stock

returns while in post crisis period, inflation rate, interest rate and rate of change in exchange

rate and money supply negatively affects BRICS panel stock returns (Table 12).

Panel ARDL Models have explanatory power ranging from 28% in total period to 62% in

post crisis periods. Also, while the Total Period & Post Crisis ARDL models are significant

at 5%, the Pre Crisis ARDL model is significant at 10% (Table 13).

Table 12: Panel ARDL Model Results- Coefficients of Model

Variable Total Period Pre Crisis Period Post Crisis Period

C 0.18 -0.27 0.90

DLOG(PINDEX(-1)) 0.20* 0.00 -0.16

DLOG(PINDEX(-2)) -0.04 -0.04 -0.29*

DLOG(PINDEX(-3)) 0.05 -0.02 -0.07

DLOG(PINDEX(-4)) -0.09 0.02 -0.11

DLOG(PINDEX(-5)) 0.00 NA -0.10

DLOG(PGDP) 0.11 1.47 0.19

DLOG(PGDP(-1)) -0.31 0.80 -0.34

DLOG(PGDP(-2)) -0.06 0.83 0.33

DLOG(PGDP(-3)) -0.10 0.76 0.37

DLOG(PGDP(-4)) -0.24 -0.69 0.18

DLOG(PGDP(-5)) 0.22 NA 0.72

DLOG(PINF) -0.68 -0.35 -0.13

DLOG(PINF(-1)) -1.53 0.32 -1.11

DLOG(PINF(-2)) 0.56 1.82 -2.53*

DLOG(PINF(-3)) -0.67 0.96 -0.47

DLOG(PINF(-4)) 0.72 1.02 -0.13

DLOG(PINF(-5)) 0.79 NA 0.64

DLOG(PIR) -0.03 -0.05 0.02

DLOG(PIR(-1)) -0.04 -0.14 0.12

DLOG(PIR(-2)) -0.09 0.03 -0.25*

DLOG(PIR(-3)) 0.07 -0.02 -0.02

DLOG(PIR(-4)) -0.03 -0.13 -0.15

DLOG(PIR(-5)) -0.06 NA -0.08

DLOG(PER) -0.65* -0.37 -0.47*

DLOG(PER(-1)) 0.34* -0.03 0.13

DLOG(PER(-2)) 0.16 0.15 0.22

DLOG(PER(-3)) -0.03 -0.19 0.17

DLOG(PER(-4)) 0.13 -0.04 0.18

DLOG(PER(-5)) 0.19 NA 0.23

DLOG(PMS) 0.00 -0.25 0.18



DLOG(PMS(-1)) -0.34 -0.94 0.38

DLOG(PMS(-2)) 0.81* 0.10 -1.06*

DLOG(PMS(-3)) -0.36 -0.60 -0.51

DLOG(PMS(-4)) -0.75* -1.07* -0.04

DLOG(PMS(-5)) 0.31 NA -0.24

LOG(PGDP) 0.00 -0.05 0.00

LOG(PINF) -0.04 0.03 -0.26*

LOG(PIR) -0.01 0.02 0.01

LOG(PER) 0.00 0.03 0.03

LOG(PMS) 0.00 -0.03 -0.01

* Denotes significant at 5% level. Values are regression coefficients.

Table 13: Panel ARDL Model Summary

Panel ARDL Model F-Stat. Probability R2

Total Period 2.33* 0.00 0.28

Pre Crisis Period 1.46** 0.08 0.32

Post Crisis Period 2.92* 0.00 0.62

* Significant at 5% level.

** Significant at 10% level.

Figure 1-3 presents graphic representation of actual, fitted & residuals of Panel ARDL

Models in the total period, pre crisis period and the post crisis periods respectively.

Figure 1: Panel ARDL Model Graph (Total Period)

-.8

-.6

-.4

-.2

.0

.2

.4

.6

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

Residual Actual Fitted


Figure 2: Panel ARDL Model Graph (Pre Crisis Period)

-.4

-.2

.0

.2

.4

-.4

-.2

.0

.2

.4

.6


Figure 3: Panel ARDL Model Graph (Post Crisis Period)

-.3

-.2

-.1

.0

.1

.2

-.4

-.2

.0

.2

.4

.6




Conclusion and Implications

This paper examines the relationship between select macroeconomic factors (i.e., GDP,

Inflation, Interest Rate, Exchange Rate and Money Supply) and aggregate stock returns in

emerging markets constituting the BRICS block over the period 1995 to 2014 using quarterly

panel data. This relationship is also examined during two sub periods viz., a Pre Crisis period

(1995:Q1 to 2007:Q2) and a Post Crisis Period (2007:Q3 to 2014:Q4). Robust econometric

tests like Panel Granger Causality Test, Pedroni’s Panel Cointegration Test and Panel Auto

Regressive Distributed Lag (ARDL) Model has been used.

We find that primarily in short run there is unidirectional causality running from stock returns

to GDP growth rate, inflation rate, rate of change in exchange rate and money supply. The

results are almost similar in pre and post crisis periods, except that in the pre crisis period,

there is bidirectional causality between stock returns and inflation, while in the post crisis

period it disappears. Long run panel causality results reveals unidirectional causality from

stock returns to GDP growth rate in total and post crisis periods. However in pre crisis period,

there was no long run causal relationship.

Pedroni’s panel cointegration test shows that stock indices are cointegrated with GDP in total

period and with GDP, inflation and money supply in post crisis period. Panel ARDL models

have explanatory power ranging from 28% in total period to 62% in post crisis period. We

find that while current stock returns are negatively linked to rate of change in exchange rate

and money supply; they are positively linked to their own lagged values. In pre crisis period,

rate of change in money supply significantly explains stock returns while in post crisis

period, inflation rate, interest rate and rate of change in exchange rate and money supply

negatively affects BRICS panel stock returns.

Results indicate that Stock Markets already discount the GDP and Inflation data and hence

stock prices tend to lead rather than follow GDP and Inflation. However, Money Supply leads

Stock Prices. The causal, led-lag & Cointegrating relationships have significantly increased

in the Post crisis period indicating the impact of Global Financial Crisis in deepening this

relationship.

These findings, besides augmenting the empirical literature and knowledge domain on the

topic, have significant implications for policy makers, regulators, researchers and investing

community in emerging markets. Policy makers and regulators should watch out for impact

of fluctuations in exchange rate, interest rate, money supply and oil prices on volatility in

their stock markets. The regulators need to ensure that financial sector reforms agenda

consciously considers interlinkages between stock markets and real economy. Investor can

search for presence of exploitable arbitrage opportunities in BRICS markets to earn above

normal returns on the basis of these variables especially GDP and Money Supply.

Acknowledgement: This paper is based on a comprehensive study undertaken under UGC

Major Research Project (M.R.P.) titled “Relationship between Macroeconomic Factors and

aggregate stock returns in Emerging Markets- An Empirical Study of BICS stock Markets” of

which Dr. Vanita Tripathi is the Principal Investigator (P.I.). The authors gratefully

acknowledge the financial support provided by University Grants Commission, New Delhi

for this study.


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Documents

Relationship between Macroeconomic Factors and Aggregate Stock Returns in BRICS Stock Markets – A Panel Data Analysis