The impact of economic risk factors on industry stock returns - an empirical investigation in the uae stock market

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

The Impact of Economic Risk Factors on Industry

Stock Returns: An Empirical Investigation in the

UAE Stock Market

Maryam Ali Rashed Alyammahi*, Siti Norbaya Yahaya, Nusaibah Mansor

Universiti Teknikal Malaysia Melaka

Received: 13/07/2019

Accepted: 29/08/2019

Published: 03/09/2019

Abstract

This paper employs a multifactor pricing model to investigate the pricing of several local sources of risk factors and whether these

factors explain the variation in the stock returns for the U. A. E. industries, and if so, to what extent. We examine returns of nine

industries: banking, consumer staples, industrial, insurance, investment & financial service, real estates, service, telecommunication,

and transportation for which data is available. Our results show that local sources of risk factors are very important in explaining the

variations in the monthly excess return for the U. A. E. industries. Also, the local sources of risk factors and industry stock returns are

found to be related. Local market excess return has major influence on industry returns for all industries investigated.

Keywords: Macroeconomic Factors, Returns, Local Risk, Multifactor Model

objective of this study is to identify and examine the extent to

Introduction

which key macroeconomic factors are reflected in the

performance of stock returns in different industries.

Specifically, two major research questions are posed. First,

whether and to what extent do returns in industries respond to

changes in macroeconomic risk factors? Second, is the impact

on industry stock returns similar across industries?

The findings of this study are valuable for several reasons.

First, the findings shed light on the effect of macroeconomic

risk factors on industry returns. Second, the findings help

investors and practitioners improve their understanding of the

influence of risk on returns of different industries. Such

information can help investors make informed decisions with

respect to investment decisions e.g., allocating, timing, and

diversifying portfolios. To the knowledge of the authors, such

a study does not exist for the stock market in the United Arab

Emirates (U.A. E).

This paper employs a multifactor pricing model to

investigate industry stock returns. For our purpose, several

local macroeconomic risk factors are constructed: exchange

rate, export of goods, imports of goods, industrial production,

inflation, money supply (M1), money supply (M2), oil prices,

and composite market price. We examine returns of nine

different U. A. E. industries (for which data is available):

banking, consumer staples, industrial, insurance, investment &

Pricing of financial assets has intrigued researchers in

finance for years. Early analysis was influenced by the

dominant models within the single factor Capital Asset Pricing

Model (CAPM) developed by Sharp (1964), Lintner, Black,

Jensen, and Scholes, and Fama and McBeth (3, 8, 13). These

models apply CAPM, which considers the market index to be

the only relevant factor in measuring an asset’s systematic risk.

Prognostication based around CAPM retains certain strengths,

however many empirical studies on the very same also fail to

provide evidence in favor of a clear relationship between

return and market beta Fama and Fench found evidence of

significant effects on asset returns originating from a set of

microeconomic and company specific factors such as size and

book-to-market ratio along with the market portfolio using

their three-factor model (9).

Roll (1977), Roll and Ross (1980) and Chen, Roll and Ross,

the founders of the Arbitrage Pricing Theory (APT), offered

an alternative model to CAPM (4, 19, 20, 21). They

hypothesized that macroeconomic factors are the only relevant

factors that impact asset returns, but results from measuring

these effects on return vary according to the set of

macroeconomic factors observed. This variation in outcomes

provides a motivation for further research by examining

diverse stock markets under various time frames. The

Corresponding author: Maryam Ali Rashed Alyammahi,

Universiti

Teknikal

Malaysia

Melaka.

E-mail:

Mariam.alyammahi84@gmail.com.

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

financial service, real estates, service, telecommunication, and

transportation.

fundamentals and the Vietnamese stock prices was found.

Finally, the results show that the influence of the US real sector

is stronger than that of the money market.

Literature Review

The relationship between risk factors and asset returns has

Donatas Pilinkus investigated the relationship between

several macroeconomic factors and the main Baltic stock

market indices as an attempt to present a model of the impact

of macroeconomic factors on stock market index, and to

determine what macroeconomic factors that have impact on

stock market index in the short and long runs (6). The data used

are monthly and extend from the January of 2000 to the

December of 2008. The Baltic States included in the study are

Lithuania, Latvia, and Estonia. They discovered different

relationships between macroeconomic factors and stock

market indices in each market with varying impact.

been a subject of debate in the literature. Most of the studies

employ different models to investigate the effect of different

sets of local and global risk factors on the returns of either

individual or portfolios of stocks regardless of industry type.

Christopher Gan, Minsoo Lee, Hua Hwa Au Yong, and

Jun Zhang employed cointegration tests such as the Johansen

Maximum Likelihood and Granger-causality tests to

investigate the relationships between the New Zealand Stock

Index and a set of macroeconomic variables from January

1990 to January 2003 (5). Specifically, they tried to determine

Using Nigeria stock market data from 1985 to 2009,

Anthony Olugbenga Adaramola investigate the impact of

macroeconomic indicators on stock prices in Nigeria. a panel

model was used to examine the impact of macroeconomic

variables on stock prices of the selected firms in Nigeria (1).

A set of macroeconomic variables was used for the analysis.

The macroeconomic variables used are: money supply, interest

rate, exchange rate, inflation rate, oil price,and gross domestic

product. The study revealed that macro-economic variables

have varying significant impact on stock prices of individual

firms in Nigeria. Except for inflation rate and money supply,

all the other macroeconomic variables have significant

impacts on stock prices in Nigeria. Finally, the study

concluded with empirical evidences that changes in

macroeconomic variables can be used to predict changes of

stock prices in Nigeria.

Using the vector autoregression Model, M. N. Khan, N.

Tantisantiwong, S. G. M. Fifield, and D. M. Power investigate

whether economic variables have explanatory power for share

returns in four South Asian stock markets, Namely,

Bangladeshi, Indian, Pakistani, and Sri Lankan (16). The data

covers the period 1998–2012, the study examines the influence

of a selection of local, regional and global economic variables

in explaining equity returns. The South Asian markets

examined are found to be not efficient. Both local and regional

factors can directly and indirectly explain Bangladeshi,

Pakistani and Sri Lankan stock returns while the lagged returns

of the Pakistani stock market and world economic activity can

explain Indian stock returns.

A cross section data that cover six different countries was

used by Jordan French to test five macroeconomic variables

that have been both theorized to affect stock returns and been

proven to do so in past empirical research (11). The study

different analytical methodologies to test the relationships

such as principle component regression, cross section

regression, and factor analysis. The economic variables chosen

are risk premium, industrial production, term structure,

expected inflation, and unexpected inflation. Some economic

variables were found to have an impact on the stock returns

while others are not in the countries studied. For example, risk

premium and industrial production were significant over the

sample, but term structure, expected inflation, and unexpected

inflation were not significant in explaining domestic market

returns. Furthermore, principal component regressions

outperformed cross-sectional ones, with factor analysis as the

least statistically significant model. Not surprisingly, the

whether the New Zealand Stock Index is a leading indicator

for macroeconomic variables. Using innovation accounting

analyses, the paper also investigates the short run dynamic

linkages between NZSE40 and macroeconomic variables. The

authors found that the NZSE40 is consistently determined by

the interest rate, money supply and real GDP. Weak evidences

that the New Zealand Stock Index is a leading indicator for

changes in macroeconomic variables were found.

Orawan Ratanapakorn and Subhash C. Sharma used the

Granger causality test to investigate the long-term and short-

term relationships between the US stock price index (S&P 500)

and several macroeconomic variables over the period 1975:1–

1999:4. Interestingly, the authors observe that the stock prices

negatively relate to the long-term interest rate. On the other

hand, a positive relationship between stock prices and money

supply, industrial production, inflation, the exchange rate and

the short-term interest rate was found. Stock prices were found

to be impacted by the macroeconomic variables in the long-

run but not the short-run according to the Granger causality

sense (17).

Serkan Yilmaz Kandir used annual data from July 1997 to

June 2005 to study the relationship between macroeconomic

factors and stock returns in Turkey (22).

A set of

Macroeconomic variables that is consistent with financial

theory and economic intuition was chosen. The economic

variables are the growth rate of industrial production index,

change in consumer price index, growth rate of narrowly

defined money supply, change in exchange rate, interest rate,

growth rate of international crude oil price and return on the

MSCI World Equity Index. The authors designed a multiple

regression model to test the relationship. Except for the

inflation, all of the portfolio returns seem to be affected by the

exchange rate, interest rate and world market return, while

inflation rate is significant for only three of the twelve

portfolios. No relationships between the stock returns and

industrial production, money supply and oil prices were found.

Khaled Hussainey and Le Khanh Ngoc used monthly time

series data from Vietnamese stock market and covered the

period from January 2001 to April 2008 to investigate the

effects of macroeconomic variables such as the interest rate

and the industrial production on Vietnamese stock prices (12).

The authors also examined how US macroeconomic variables

affect Vietnamese stock prices. The study found significant

relationships among the domestic production sector, money

markets, and stock prices in Viet Nam. Surprisingly, a

significant relationship between the US macroeconomic

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

arbitrage pricing theory was also found to be a less robust

pricing tool than the capital asset pricing model.

real estates, service, telecommunication, and transportation.

Industry stock returns, Rit, are calculated for each industry

index, as:

Methodology and Data Analysis

.1 Methodology

R_i_t

R  ln[

]

(2)

According to Arbitrage Pricing Theory, asset returns are

sensitive to unexpected change in several macroeconomic

factors. Under this assumption, some research used

unexpected components of macroeconomic factors. This

method requires a measure to represent the unanticipated

component of the macroeconomic factors in the actual time

series. We use the ARIMA model for that purpose. The

expected values as represented by ARIMA are subtracted from

the actual values to calculate the unexpected component of the

macroeconomic factors. To examine the effects risk factors

have on the returns of the nine different industries being

investigated, we employ a multifactor pricing model for the U.

A. E data. Eq. (1) provides the framework for implementing

that relationship. It models industry stock returns as a function

of K-macroeconomic risk factors.

^Rit1

where, Rit, Rit-1 are the index values of industry I at time t

and t-1 respectively, in local currency. We choose the broadest

index available to provide a long-term series that shows the

overall trend of stocks in the U. A. E. The industry stock

returns (Rit) are in excess of the local short-term interest rate

in the U. A. E. We used the U. S. A. 3-month Treasury Bill

(ITUSA3D) as a proxy for the short-term interest rate in the U.

A. E. the U. S. A. 3-month Treasury Bill serve as the risk-free

(Rf) rate and is used to measure excess returns for each

industry.

3.2.3 Local Macroeconomic Risk Factors

Table 1 presents a list of factors used in different studies

within APT framework. The choice of macroeconomic factors

was dictated by each factor’s theoretical relevance to asset

pricing, regardless of the location of the market. Additionally,

r_i_t _i



_i_jF  _i_t

(1)



j1

data availability on monthly frequency was also

and,

consideration. Each factor starts in May 2003. Based on the

above approach, we selected a set of macroeconomic factors

that explain the variation on the industry stock returns. These

factors are exchange rate, export of goods, imports of goods,

industrial production, inflation, money supply (M1), money

supply (M2), oil prices, and composite market price.

Foreign Exchange Rate: Foreign exchange rate is

measured as the change from month t-1 to month t in the

natural logarithm of foreign currency exchanges of the U. A.

E. The following equation is used

it = Rit - Rft

where,

= the excess return

it = the return for industry i at time t

ft = risk free interest rate

= the constant term

ij = the betas of the rit on the k risk factors

jt = the risk factors where j = 1….k

it = the error term representing the non-systematic excess







ꢀꢁ_ꢂ

퐹푋 = 푙푛[ _ꢀ_ꢁ_ꢂ₋₁]

(3)

return relative to risk factors

푡

The k risk factors chosen in this study include exchange

rate, export of goods, imports of goods, industrial production,

inflation, money supply (M1), money supply (M2), oil prices,

and composite market price.

The series was obtained from Global Financial Data (GFD)

for the period 5/2003 thru 5/ 2018.

Export of Goods: Export of goods is measured as the

change from month t-1 to month t in the natural logarithm of

export of goods of the U. A. E. The following equation is used:

.2 Data: Description and Sources

.2.1 Definition of data sets and the sample period

The data is divided into two sets. The first set includes

industry stock returns of the U. A. E. stock market on a

monthly basis. The second set consists of monthly

macroeconomic factors. All monthly data is from May 2003

thru May 2018.

^E^Gt

EG  ln[

]

(4)

EG_t_₁

The series was obtained from GFD for the period 5/2003

thru 5/ 2018.

Imports of Goods: Import of goods is measured as the

change from month t-1 to month t in the natural logarithm of

import of goods of the U. A. E. The equation (5) is used.

.2.2 Industry Stock Returns

The industry indices chosen in this study come from the

Securities and Commodities Authorities (SCA). We examine

stock returns of nine different industries for which data is

available in the U. A. E. The industries are banking, consumer

staples, industrial, insurance, investment & financial service,

Table 1: Macroeconomic factors utilized in previous studies

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

Macroeconomic Factors

Industrial Production

Previous studies that utilized specific factors

Chen, Roll, and Ross, Hamao, Wasserfallen, Asprem, Poon and Taylor, McQueen Roley, Orawan

Ratanapakorn and Subhash C. Sharma, Serkan Yilmaz Kandir, Mahdy, Khaled Hussainey and Le

Khanh Ngoc , Jordan French (2,4,10,11,12,14,15,16,17,18,22,23)

Chen, Roll, and Ross, Hamao, Wasserfallen, Asprem, Poon and Taylor, Drehman and Manning,

Orawan Ratanapakorn and Subhash C. Sharma, Serkan Yilmaz Kandir, Mahdy, Anthony Olugbenga

Adaramola, Jordan French (1,7,10,11,14,17,18,22,23)

Inflation

Foreign exchange rate

Asprem, Drehman and manning, and Mahdy, Anthony Olugbenga Adaramola (1,2,4,7,14)

Wasserfallen, Asperm, McQueen and Roley, Christopher Gan, Minsoo Lee, Hua Hwa Au Yong,

and Jun Zhang, Orawan Ratanapakorn and Subhash C. Sharma, Serkan Yilmaz Kandir, Anthony

Olugbenga Adaramola (1,5,15,17,22,23)

Money supply

Oil prices

Chen, Roll, and Ross, Drehman and Manning, Serkan Yilmaz Kandir, Mahdy, Anthony Olugbenga

Adaramola (1,4,7,14,22)

Wasserfallen, Asprem , Christopher Gan, Minsoo Lee, Hua Hwa Au Yong, and Jun Zhang, Orawan

Ratanapakorn and Subhash C. Sharma, Serkan Yilmaz Kandir, Khaled Hussainey and Le Khanh

Ngoc , Anthony Olugbenga Adaramola (1,2,5,12,16,17,22,23)

Interest Rate

Term Structure

Risk Premium

Chen, Roll, and Ross, Poon and Taylor, and Mahdy, Jordan French (4,11,14,18,

Chen, Roll, and Ross, Poon and Taylor, Jordan French (4,11,18)

Unemployment

Employment

Wasserfallen, McQueen and Roley (15,23)

Asprem (2)

Export Prices

Asprem (2)

Consumption

Chen, Roll, and Ross, Asprem (2,4)

McQueen and Roley (15)

Merchandise trade deficit

Gross domestic product

Wages

Drehman and Manning, Anthony Olugbenga Adaramola (1,7)

Wasserfallen (23)

Real investment

Capital formation

Wasserfallen (23)

Asprem (2)

monthly consumer price index of the U. A. E. for period t and

computed using the following equation:

IG_t

IG  ln[

]

(5)

IG_t_₁

푃_푡

퐼_푡= 푙푛[

]

(7)

The series was obtained from GFD for the period 5/2003

thru 5/ 2018.

^푃푡ꢃꢄ

where, P

and Pt-1 are prices at time t and t-1. The consumer

Industrial Production: Monthly growth rate in industrial

production is calculated from the monthly industrial

production index. The industrial production growth rate is the

first difference in the logarithm of the monthly industrial

production index of the U.A. E. If IP denotes the industrial

production rate in month t, then the monthly growth rate is

price index was obtained from GFD. The inflation series is for

the period 5/2003 thru 5/ 2018.

Money Stock (M1)

Money stock (M1) is measured as the change from month

t-1 to month t in the natural logarithm of money stock (M1) of

the U. A. E. The following equation is used

IP_t

^M¹t

IP  ln[

]

M1  ln[

]

(8)

IP_t_₁

M1_t_₁

(

The series was obtained from GFD for the period 5/2003

thru 5/ 2018.

The series was obtained from GFD for the period 5/2003

thru 5/ 2018.

Inflation Rate: The realized inflation rate for period t(It) is

defined as the first difference in the natural logarithm of the

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

Money Stock (M2): Money stock (M2) is measured as the

change from month t-1 to month t in the natural logarithm of

money stock (M2) of the U. A. E. The following equation is

used

4.1 Industry Returns and Risk Factors

After choosing the best ARIMA model for each

macroeconomic risk factor we subtracted the fitted values from

the actual values to form the unexpected components of the

series. The new variables thus created are unexpected

measures for exchange rate, export of goods, imports of goods,

industrial production, inflation, money supply (M1), money

supply (M2), oil prices and local Market Index. After deriving

new measurements for local macroeconomic risk factors, their

influences on the stock price indices for all nine industries were

estimated and tested. OLS was applied to estimate Equation 1

over the sample period.

M 2_t

M 2  ln[

]

(9)

M 2_t_₁

The series was obtained from GFD for the period 5/2003

thru 5/ 2018.

Table 2 presents descriptive statistics for the U. A. E

market index and macroeconomic risk factors for the period

Oil Prices (OG): Oil prices are included as a systematic

risk factor influencing equity markets. Chen et al use the

producer price index / crude petroleum as an approximation of

oil prices in the U.S markets (4), and Hamao uses the Arabian

Light Spot prices in Japanese equity markets. We use Dubai

Arab Light Crude Oil index (10). The oil price growth factor

5/2003 thru 5/ 2018. Table 2 shows that the unexpected

inflation bears the highest risk, while the unexpected exchange

rate bears the lowest level of risk as approximated by standard

deviation. Skewness statistics show that unexpected import of

goods, unexpected inflation, unexpected stock supply (M1),

unexpected stock supply (M2) and unexpected oil prices are

positively skewed with the highest positive skewness found in

unexpected money stock (2) and the lowest in unexpected oil

prices. The negatively skewed factors are unexpected exchange

rates, unexpected export of goods, unexpected industrial

production, and the local market index. The highest negative

skewness found in the unexpected exchange rates and the

lowest in local market index. The Jarque-Bera statistics

indicate that every unexpected macroeconomic risk factor

exhibits significant departure from normality; therefore, the

null hypothesis of normal distribution for those

macroeconomic series is rejected at the 1% level of

significance. The abnormality can be attributed to the

existence of large numbers both positive and negative within

the sample period. The ADF statistics show that all of the

unexpected macroeconomic risk factors are stationary (series

are all I (0)). The null hypothesis of a unit root is rejected at the

(

OG) is constructed as the realized monthly first difference in

the logarithm of Dubai Arab Light Crude Oil index using the

equation below:

^O^Gt

OG  ln[

]

(

10)

OG_t_₁

where Oil t, Oil t-1 are oil prices at time t and t-1

respectively. The series was obtained from GFD for the period

5/2003 thru 5/ 2018.

Market Index: Asset pricing models usually accommodate

a role for a market portfolio to measure risk and to capture all

the information available to the market, not captured by the

non-equity economic factors. The return on the market

portfolio is defined as the monthly first difference in the

logarithm of the national equity market portfolio using the

following equation;

1% level.

Table 3 presents the correlation matrix for the U.A.E

^푅^푚푡

monthly market index and unexpected macroeconomic risk

factors. As reported, a mild correlation exists among the

unexpected macroeconomic factors in the U.A. E The highest

correlation is found between unexpected money stock (M1)

and unexpected money stock (M2). The results also suggest

that the factors do not have any meaningful indication of

multicollinearity. Table 4 reports reactions of industrial stock

returns to several local macroeconomic risk factors for the

U.A.E over the period of 5/2003 thru 5/2018. The results show

that the market index in the U.A. E (MKT) has a significant

positive effect in every relevant industry. Moreover, the most

sensitive industry to the market index is the real estate industry

with a market beta coefficient of 1.3154 and t=41.274, while

the consumer staples industry is considered the least sensitive

to the market index with a market beta coefficient of 0.2172

and t=3.6537, both at the 1% level of significance. With regard

to the macroeconomic risk factors, some important

relationships have been found. The investment & financial

service industry has been found to be the most sensitive

industry to changes in macroeconomic factors during the

sample period.

푅푚 = 푙푛[

]

(11)

푡

푅푚_푡_ꢃ_ꢄ

Rm , and Rmt-1 are the return values of the market at time t

and t-1, respectively, in local currency. We use the most

common but readily available stock return index. The series

for the U.A. E. stock market portfolio is obtained from SCA.

The series covers the period from 5/2003 thru 5/ 2018.

The market return portfolios (Rm

short-term interest rates. The latter is used as a proxy for the

risk-free rate (Rf ) to measure excess returns for the market

portfolio. The short-term interest rate refers to the 3-month

Treasury Bill. We used the U. S. A. 3-month Treasury Bill

ITUSA3D) as a proxy for the short-term interest rate in the U.

) are in excess of local

(

A. E.

Empirical Results

This section of the paper presents the effects of risk factors

on local industry returns in the U. A. E by estimating equation

one using the OLS (Ordinary-Least Squares) regression method.

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

Table 2: Summary Statistics for the U. A. E Market Index and Macroeconomic Factors

May 2003 to May 2018)

(

UFX

UEG

UIG

UIP

UM1

UM2

UOG

MKT

Mean

Median

4.02E-08

2.32E-06

0.000383

-0.001508

0.000129

-8.628826

104.0981

-2.29E-05

-0.002744

0.077976

-0.140907

0.022885

-1.069123

11.08208

2.86E-05

-3.95E-05

0.014765

-0.007507

0.002989

0.850106

6.160759

1.91E-05

4.24E-05

0.005636

-0.005016

0.001628

-0.214331

4.192162

-0.000519

-0.041448

1.655444

-1.109310

0.333008

0.610976

7.306685

-3.55E-05

-0.003481

0.465429

-0.075183

0.037987

10.24494

126.1750

-2.46E-05

-0.003136

0.463609

-0.051503

0.036255

11.63312

149.4470

-8.43E-06

0.002604

0.141301

-0.115256

0.036358

0.020193

4.664792

-0.009014

-0.006794

0.122602

-0.175460

0.044720

-0.319267

4.242781

Maximum

Minimum

Std. Dev.

Skewness

Kurtosis

Jarque-

9328.13

527.1035

0.000000

97.14510

0.000000

12.10439

0.002353

151.1403

0.000000

117588.9

0.000000

165826.6

0.000000

20.91427

0.000029

14.72306

0.000635

Bera

Probability

0.000000

ADF Test

at the

Level I (0)

3.56456*

13.29053*

13.25501*

13.20142*

12.84219*

13.64670*

13.15461*

13.39204*

5.703556*

Sum

Sum Sq.

Dev.

Observatio

7.28E-06

-0.004150

0.094268

0.005173

0.001608

0.003453

0.000477

-0.093867

19.96095

-0.006427

0.259741

-0.004458

0.236602

-0.001526

0.237936

-1.631553

0.359972

.01E-06

181

Note: The ADF Test is an augmented Dickey- Fuller Unit Root Test. The ADF test is a stationary test. The critical values for ADF test are –2.5677, -

.8632, and –3.4359 for significant levels of 10%, 5%, and 1% respectively. *, **, *** Denote significance at 10%, 5%, and 1% level, respectively

Table 3: Correlation Matrix for the U. A. E Market Index and Macroeconomic Factors

(May 2003 to May 2018)

Correlation

UFX

UEG

UIG

UFX

UEG

UIG

UIP

UM1

UM2

UOG

MKT

1.000000

-0.069146

-0.023122

0.245501

-0.189518

0.055098

0.055158

-0.029647

-0.026323

1.000000

-0.049245

0.185461

0.241234

0.085188

0.027636

-0.013231

0.247767

1.000000

0.150903

0.012987

-0.029051

-0.010193

-0.046796

0.017858

UIP

1.000000

0.023538

0.084669

0.074144

0.024693

0.196961

1.000000

0.061127

0.051541

0.076491

0.072108

UM1

UM2

UOG

MKT

1.000000

0.964780

-0.001764

0.042479

1.000000

0.013168

0.010449

1.000000

0.091247

1.000000

Note: local macroeconomic risk factors for UAE are unexpected measures for exchange rate (UFX), export of goods (UEG), imports of goods

UIG), industrial production (UIP), inflation (UI), money stock, M1 (UM1), money stuck, M2 (UM2), oil prices (UOG) and local Market Index (MK

(

The unexpected foreign exchange (UFX), the unexpected

factor with a coefficient value of -0.7652 and t= -2.8125 at the

5% level of significance. R for the nine estimated regressions

import of goods (UIG), the unexpected money stock (M1), and

the unexpected oil prices are not found to have any significant

association with any industry in the U.A. E. However, the same

cannot be said about the other economic factors. Unexpected

export of goods (UEG) has a negative effect on the banking

industry (-0.1082) (t= -2.6680) at the 10% level of significance

while has a positive effect on investment & financial service

industry (0.1559) (2.6656) at the 10% level of significance.

Both Unexpected inflation (UI) and the unexpected industrial

production (UIP) are found to have significant negative effects

on the investment & financial service industry (-0.0129) (t=-

in the U.A. E. are reasonable, which implies that most

variations in the industry returns are explained by the local

market index in addition to the local macroeconomic risk

factors. DW is very close to 2 therefore the serial correlation

problem is ignored. For the purpose of completeness, figure 1

shows movements of the monthly returns of the U. A. E.

industry indices over the period of May 2003 thru May 2018,

while figure 2 shows movement of the U.A. E. monthly

macroeconomic risk factors over the same period.

Generally, the regression results in the U.A. E. as reported

in table 4, show the powerful effect of the local market index

on each of the nine industries with some reasonable effects

.6137), (-2.6266) (t=-2.5812) for the same sample period. In

the case of the unexpected money stock (UM2), the consumer

staples industry was the only industry to be affected by that

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

regarding the local macroeconomic factors such as UEG, UIP,

UI and UM2.

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

Figure 1: Measuring Movement of the Monthly Returns of the U.A. E. industries indices (May 2003 to May 2018)

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

Figure 2: Movement of the U. A. E. Monthly Unexpected Macroeconomic Factors (May 2003 to May 2018)

Table 4: Industrial Stock Returns Reactions to Macroeconomic Risk Factors for the U. A. E.

May 2005 to May 2018)

(

R²

MKT

Industry

Banking

Consta

UFX

UEG

UIG

UIP

UM1

UM2

UOG

MKT

R .

adj

9.3837

0.8271

(1.189

0.0028

(0.882

0.0624

(0.582

0.0689

0.6158

)

0.0180

(0.622

0.8106

(33.20

8) ***

0.869

1.9256

0.866

0.0022

(1.0992

)

0.1082

0.6967

(1.963

(

.5835

)

(2.6680

)

Consumer

Staples

2.5246

(0.1219

)

-0.1520

1.2545)

0.2885

0.3346

)

0.0009

(0.000

0.0046

0.3565

(1.368

0.1122

1.5987

)

0.2172

(3.653

7) ***

0.197

1.4735

0.046

0.0104

0.7652

2.8125

) **

(

)

.0737

***

0.5800

)

Industrials

Insurance

25.555

0.0955

(0.5172

)

0.5998

(0.456

2.2371

(0.870

0.0028

(0.231

0.6248

(1.573

0.7200

1.7367

)

0.0316

0.3650

(4.028

1) ***

0.099

2.2899

0.107

.0107

(0.8099

)

(

.7537

)

0.2957

)

0.1167

(0.755

36.548

(0.8021

)

0.0944

1.4267

(0.752

4.2018

1.1323

)

0.0047

0.1161

(0.202

0.1255

0.2097

)

0.5037

(3.849

1) ***

0.049

2.0593

1.9239

0.078

.0007

(0.3540

)

(

.1389

)

0.2709

)

0.0129

Investment &

Financial

Service

0.0015

(0.988

-9.5824

0.7431)

0.1559

(2.6656

) *

0.1114

(0.207

0.1932

(1.190

0.0718

(1.643

1.1450

(30.91

7) ***

0.857

0.847

2.6266

0.1417

2.5812

2.6137

) *

0.8368

)

Real Estate

Service

0.0042

-4.8953

0.4411)

0.0800

(0.0800

)

0.8777

(1.899

0.0503

(0.055

0.0021

(0.512

0.0040

0.0158

0(.108

1.3154

(41.27

4) ***

0.914

1.8804

0.914

(

3.088

0.0260

0.6916

)

0.1182

1.2607

)

5) ***

0.0290

)

10.689

(0.3863

)

0.1570

(0.9692

)

1.1903

1.0332

)

1.6880

(0.749

0.0118

1.1155

)

0.2212

1.0460

(13.16

1) ***

0.516

1.7307

0.521

0.0031

0.2701

(

0.7756

)

0.9290

)

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 4, Pages: 737-746

Telecommunicat

ion

1.8860

(0.1003

)

0.0022

(0.0203

)

0.7134

(0.466

0.0039

(0.548

0.3429

0.2734

(1.107

0.5368

(9.946

5) ***

0.365

1.8823

0.380

0.0034

0.0663

1.4497

)

0.0040

0.0636

)

0.0476

(

.4929

)

0.0847

)

0.4317

(0.613

-10.103

0.5978)

-0.1112

1.1243)

0.0703

(0.051

0.0056

(0.870

0.1502

0.0558

(0.251

0.6500

(13.39

1) ***

0.509

2.0225

0.511

Transportation

0.0046

(

2.2075

0.7061

0.8314

)

Note: independent variables are unexpected measures for exchange rate (UFX), export of goods (UEG), imports of goods (UIG), industrial production

UIP), inflation (UI), money stock, M1 (UM1), money stuck, M2 (UM2), oil prices (UOG) and local Market Index (MKT). T- Values (in parenthesis).

(

N is the number of observations for each local industry. DW is Durbin-Watson statistic. *, **, *** Denote significance at the 10%, 5%, 1% level

respectively. R is the coefficient of determination adjusted for degrees of freedom.

0. Hamao Yasushi. An Empirical Examination of Arbitrage Pricing

Theory: Using Japanese Data. Japan and the World Economy.

Discussions and Conclusions

This paper examines the domestic sources of risk as an

988;1: 45-61.

explanation of the variation in the industries’stock returnsand

if so to what extent. The results show that local risk factors

have reasonable explanatory power in accounting for the

differences in industry excess returns on a monthly basis. The

factors explained between 04% and 91% of the variations for

the U. A. E. market over the sample period. We compared the

illustrativepowers of the local market, where the excess return

is the only explanatory factor, with that of the multifactor

model. Results suggest that market excess return is the most

importantexplanatoryfactor among domesticriskfactors. Any

variation in the market excess return directly affects industry

stock returns. Adding macroeconomic factors increases the

explanatory power of the models. Investment & financial

service, consumer staples, and banking are among the most

sensitive industries in the U.A. E. In terms of allocating,

timing, and diversifying investment portfolios from a policy

perspective, the significant relationship between risk factors

and industry stock returns is critical information for investors

and practitioners seeking to better understanding how and to

what extent risk factors effect returns by industry.

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factors: empirical evidence from Canadian stock market.

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