Journal of Contemporary Issues in Business Research. About JCIBR

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2 About JCIBR Journal of Contemporary Issues in Business Research (JCIBR) is to promote the understanding of managers and organizations within and across nations. It is an interdisciplinary journal directed towards academics, policy makers and practitioners in business and non-profit organizations. The journal aims to provide a forum to widen and deepen discussion about all areas of business and social sciences as well as being readable, accessible and easy to use. The purpose of JCIBR is to provide a forum for the exchange of ideas between academicians and practitioners and to disseminate the results of empirical and theoretical research, policy issues, and practical applications to as broad an audience as possible. Editorial Board Editorial Board of Journal of Contemporary Issues in Business Research (JCIBR) consists of PhD doctors from all over the world. JCIBR have following editorial board including USA, UAE, Canada, Hong Kong, Jordan, Malaysia, Taiwan, South Korea, SAARC, ASEAN and European countries. Dr. Sergey Ivanov Associate Professor University of District of Columbia, USA Dr. Tsuji Yamada Chair and Professor The State University of New Jersey, USA Dr. Asayehgn Desta Professor of Business Economics Dominican University of California, USA Dr. Pearl Steinbuch Program Director & Professor Mount Ida College, USA Dr. Mbodja Mougouà Department of Finance Wayne State University, USA Dr. Sunil K Sapra Professor California State University, USA Warren R. Braden, Sr Springfield College School of Human Services, USA Dr. James Maxwell Head and Professor University of Northern Iowa, USA Copyright 2012 JCIBR, All Rights Reserved

3 Nodas Katsikas Dept. of Accounting Finance & Economics Oxford Brookes University, UK Dr. Haitham Nobanee Assistant Professor of Finance Abu Dhabi University, UAE Dr. Mirjana Radovic-Markovic Professor Institute of Economic Sciences, Serbia Dr. Tonatiuh Najera Ruiz Associate Dean SolBridge International School of Business Dr. Yuval Cohen Senior Lecturer The Open University, Isreal Dr. Michael Charles Pickett Assistant Professor National University Canada, Canada Dr. Rita Yi Man Li Assistant Professor Hong Kong Shue Yan University Dr. Mahdi Salehi Assistant Professor Ferdowsi University of Mashh, Iran Dr. Ghousia Khatoon Professor and Head HKBK College of Engineering Dr. Vinai K.Singh Professor Punjab University Dr. Rajiv Mahajan Global Institute of Management & Emerging Technologies Dr. V. Mahalakshmi Department of Management Studies Panimalar Engineering College Dr. M. Jaya Department. of Commerce Asan Memorial College of Arts & Science Dr. Abdurrahman Ghaleb Almekhlafi Associate Professor United Arab Emirates University, UAE Dr. Chun Chu Liu Professor Chang Jung Christian University Dr. Ioan Andone Professor University of Iasi, Romania Dr. Zulnaidi Yaacob School of Distance Education Universiti Sains Malaysia Dr. Bestun S.ahmad Lecturer Sahaladdin University Hawler Dr. M. Shabri Abd. Majid Associate Professor Syiah Kuala University Dr. Chau So Ling Assistant Professor Hang Seng Management College Dr. Alim Al Ayub Ahmed Assistant Professor ASA University Bangladesh Dr. P Kiran Sree Principal I/C & Head NBKR Institute of Science &Technology Dr. Purva Kansal Assistant Professor of Marketing Punjab University Dr. Amalendu Bhunia Department of Commerce University of Kalyani Dr. Anju Gupta DAV Centenary College New Delhi Dr. Ramzi Madi Al Al Bayt University Jordon Copyright 2012 JCIBR, All Rights Reserved

4 Dr. Michael F. Shaughnessy Eastern New Mexico University USA Dr. Debra Preston Russ Faculty Advisor University of Alaska Anchorage, USA Dr. Sukumar Senthilkumar Chonbuck National University South Korea Sarfaraz Hashemkhani Zolfani Amirkabir University of Technology Iran Dr. Sudhir K. Samantaray Post Graduate Government College Panjab University Dr. Mohinder C. Dhiman Professor Kurukshetra University Dr. R.Uma Rani Sri Sarada College Eric K. Martin Dept. of Management University of Virginia, USA Dr. Riktesh Srivastava Assistant Professor University City of Sharjah, UAE Hanudin Amin International Islamic University Malaysia Dr. Pankaj Thakur Research Coordinator Indus International University Bathu Dr. D.V. Chandra Shekar Tellakula Jalayya Polisetty Somasundaram College Dr. Shaikh Abdul Hannan Vivekanand College (LPP) Dr. Devinder Pal Singh Vivekanand College (LPP) Satish Mittal Punjabi University Dr. Prafulla Shede Abasaheb Garware College Dr. R. C. Sihag Ex-Dean CCS Haryana Agricultural University Dr. BVH Kameswara Sastry Management Studies Somasundaram College Copyright 2012 JCIBR, All Rights Reserved

5 Research Articles Table of Contents COINTEGRATION AND CAUSAL RELATIONSHIP AMONG CRUDE PRICE, DOMESTIC GOLD PRICE AND FINANCIAL VARIABLES-AN EVIDENCE OF BSE AND NSE. 1 DR. AMALENDU BHUNIA FACTORS INFLUENCING CORPORATE WORKING CAPITAL MANAGEMENT: EVIDENCE FROM AN EMERGING ECONOMY. 11 KAMAL NASER, RANA NUSEIBEH and AHMED AL-HADEYA AN INTERVIEW WITH ROGER KAUFMAN: MEGA THINKING AND PLANNING IN THE BUSINESS/INDUSTRY WORLD MICHAEL F. SHAUGHNESSY Call for as Editorial and Reviewer Board Member Call for Papers Copyright 2012 JCIBR, All Rights Reserved

6 COINTEGRATION AND CAUSAL RELATIONSHIP AMONG CRUDE PRICE, DOMESTIC GOLD PRICE AND FINANCIAL VARIABLES-AN EVIDENCE OF BSE AND NSE DR. AMALENDU BHUNIA Associate Professor, Dept. of Commerce, University of Kalyani, West Bengal, India ABSTRACT The present study investigates the cointegration relationships among crude oil price, domestic gold price and selected financial variables (exchange rates and stock price indices) in India. Increasing crude oil prices will increase the production costs which will affect cash flow and will decrease stock prices. Investors are showing fewer concerns in the stock markets and investing in yellow metals due to increasing trend in gold prices on account of no fear and no future loss. Again, exchange rate fluctuations will affect international trades, thus influence the stock market. This study is based on secondary data obtained from various data sources including BSE database, NSE database and World Gold Council database for the period from January 2, 1991 to October 31, In the course of analysis, ADF unit root test, Johansen cointegration analysis and Granger causality test have been designed. Johansen cointegration test result indicates that there exists a long-term relationship among the selected variables. Granger causality test result shows that there must be either bidirectional or no causality among the variables. Keywords: Crude oil price, gold price, exchange rates, stock price indices, Johansen cointegration test. INTRODUCTION The study of the financial market of a country in relation to macro-economic and financial variables has been the main issue of many researches since LPG. Generally, stock market is influenced by numerous interconnected economic, social, political, and these factors interact with each other in a very intricate approach and stock prices are determined by few macroeconomic variables, for example, the crude oil price, the gold price, the exchange rate and the inflation rate (Abbas Alavi Rad, 2011). Indian stock market is greatly influenced by three critical factors, i.e., international crude oil price, gold price and exchange rates. Crude oil prices are tracked in the Indian economy with lots of interest. Since India imports around 80% of crude oil from the international market, any significant change in price of petroleum makes an impact on inflation numbers which in turn impacts the stock market (Vivek Sharma, 2012). Increasing crude oil prices will increase the production costs which will affect cash flow and will decrease stock prices (Ayhan Kapusuzoglu, 2011). The global economic turmoil is likely to stimulate uncertainty in gold prices, which has already made it a risky asset for investors in India. Gold prices, by and large, rise when attitudes on the economy and the financial markets are bearish or there is uncertainty over future trends. Investment demand will return only when there is some clarity. The domestic gold prices have topped in India for the first time, breaks all time record and the volatile situation in global markets had helped the yellow metal to gain handsomely. However, the coming days will see huge funds moving from gold to sensex and nifty. Many researchers have been done the long-run and short-run relationships among stock price index and gold price in developed and developing countries. Empirical results show that gold price can greatly affect the stock market (Mahmood Yahyazadehfar and Ahmad Babaie, 2012). Again, exchange rate fluctuations will affect international trades, Copyright 2012 JCIBR, All Rights Reserved 1

7 thus influence the stock market. At the same time, exchange rate fluctuations will directly affect the profits; thereby impact the stock prices (Wang et al, 2010). This paper aims to examine the dynamic relationships between crude oil price, domestic gold price and selected financial variables in India. Section two briefly glimpses the review of related literatures across the world. In section three, the materials and methods adopted are presented. The empirical results and inferences are discussed in section four and section five concludes the paper. REVIEW OF LITERATURES Mahmood Yahyazadehfar and Ahmad Babaie (2012) have made a study to examine the impact of macroeconomic variables such as interest rate, house price and gold price on stock price in capital market of Iran based on monthly data from March 2001 to April 2011 using VAR and Johansen-Juselius model. From the study it is clear that most of fluctuation in stock price can be recognized to itself, nevertheless among the selected variables, the house price has main role on stock price fluctuation. Subarna K. Samanta and Ali H. M. Zadeh (2012) examined the co-movements of selected macro-variables (gold price, stock price, real exchange rate and the crude oil price) based on 21 years data using econometric models for the periods from January 1989 to September The study exposes that there is a cointegration relationship between the variables. S. Kaliyamoorthy and S. Parithi (2012) have made a study to examine the relationship between gold price and stock market for the period from June 2009 to June They prove that there is no relationship with the stock market and gold price and stock market is not a ground for rising gold price. Jana Šimáková (2011) has made a study to examine the characteristics co-movement relationship between the oil price levels and gold price levels for the period from 1970 to 2010 using cointegration test and Granger causality test method. He established that there is reality of a long-term relationship between selected variables. Thai-Ha Le and Youngho Chang (2011) made a study on Dynamic Relationships between the Price of Oil, Gold and Financial Variables in Japan: A Bounds Testing Approach and they confirmed that the price of gold and stock, among others, can help form expectations of higher inflation over time. In the short run, only gold price impacts the interest rate in Japan. Gagan Deep Sharma and Mandeep Mahendra (2010) made a study to evaluate the long-term relationship between BSE and Macro-economic variables (exchange rates, foreign exchange reserve, inflation rate and gold price) for the period from January 2008 to January 2009 using multiple regression model. The study reveals that exchange rate and gold price influences the stock prices in India. Wang et al (2010) used daily data and time series method to investigate the impacts of fluctuations and long and short-term relationships in crude oil price, gold price, and exchange rates of the US dollar vs. various currencies on the stock price indices. A considerable number of studies on the relationship between crude oil price, gold price, exchange rates and stock price indices have been undertaken. Only a few studies have examined the relationship between crude oil price, gold price, exchange rates with stock market in general and Indian stock exchanges in particular. Based on very few studies in India, it is found that the impact of crude oil price rise, gold price rise and devaluation of currencies in Indian stock market is unvoiced. The conclusive sum of this retrospective review of relevant literature produced till date on the offered subject reveals wide room for the validity and originates of this work and reflects some decisive evidences that affirm its viability, as may be marked here it. The existence of crude oil price, gold price exchange rates and stock price indices of stock market Copyright 2012 JCIBR, All Rights Reserved 2

8 in India are hardly available. Therefore, the present study aims to examine the changes of exchange rates or increase in daily crude oil price, gold price and its impact on stock price indices in India. MATERIALS AND METHODS Data Source The study is based absolutely on secondary data obtained from various data sources including BSE and NSE database and the world gold council database for the period from January 2, 1991 to October 31, Sample Design The present study considers daily data encompassing the closing stock price indices of both BSE (Sensex) and NSE (Nifty), the daily domestic gold price indices, daily crude oil indices (WCI) and exchange rates. After fitting daily closing indexes with the corresponding gold price, crude oil price and exchange rates, there are 5400 observations. Eviews 7.0 package program has been used for arranging the data and implementation of econometric analyses. Tools Used In the course of analysis of the present study, only econometric tools include Augmented Dickey Fuller (ADF) both at levels and 1st differences and Johansen s system co-integration test have been used. Model Specification Unit root test. A time series is stationary or not or include unit root for which Augmented Dickey- Fuller (ADF-1979) test method has been used in the study. ADF test considered as an appropriate tool to check the stationariaty of time series data (Mehmood & Ahmad, 2012; Mehmood, 2012a; Naz, 2012, and Mehmood, 2012b). The time series is non-stationary if the critical value is lower than the calculated value, subsequently null hypothesis is rejected and series is decided to be stationary. H 0 : Series is stationary H 1 : Series is non-stationary If all the sets of data are found I (1) (non-stationary), and if the regression produces a I (0) error term, the equation is said to be co-integrated. On the other, if there are two variables, x t and y t, which are both non-stationary in levels but stationary in first differences, then x t and y t would become integrated of order one, I(1), and their linear combination should have the form: z t = x t - ay t (1) [Claire G. Gilmore et al, (2009)] However, if there is a I (0) such that z t is also integrated of order zero, I (0), the linear combination of x t and y t is said to be stationary and the selected variables are also to be co-integrated (Engle & Granger, 1987). If two variables are co-integrated, there will be an underlying long-run relationship between them. However, for determining the presence of unit roots, an extension of the Dickey and Fuller (1981) method has been applied. The ADF test uses a regression of the first differences of the series against the series lagged once, and lagged difference terms, with optional constant and time trend terms: Copyright 2012 JCIBR, All Rights Reserved 3

9 y t = a 0 + a 1 t + γy t-1 + Σb i y t-1 + e t (2) In the equation is the first-difference operator, a 0 is an intercept, a 1 t is a linear time trend, e t is an error term, and i is the number of lagged first-differenced terms such that e t is the white noise. The test for a unit root has the null hypothesis that signifies γ = 0. If the coefficient is significantly different from zero, the hypothesis that y t contains a unit root is considered as rejected. If the test on the level series fails to reject, the ADF procedure is then applied to the first-differences of the series. Rejection leads to the conclusion that the series is integrated of order one, I (1). A limitation of the Dickey-Fuller test is its assumption that the errors are statistically independent and have constant variances. Johansen co-integration test. Co-integration tests provide a mean to determine whether a set of endogenous variables share a common long-run stochastic trend. A finding of co-integration indicates interdependence of the endogenous variables, which may be the result of economic linkages between the markets or arbitrage activity between investors. Hypothesis to be examined with Johansen co-integration test to be applied on the study has been presented below: H 0 : There is no co-integration relationship between variables H 1 : There is co-integration relationship between variables The Johansen (1988) approach to testing for co-integration relies on the relationship between the rank of a matrix and its characteristic roots, or eigenvalues. Let X t be a vector of n time series variables, each of which is integrated of order (1), and assume that X t can be modelled by a Vector Auto Regression (VAR): Rewriting the VAR as X t = A 1 x t A p x t-p + ε t (3) x t = ΠX t-1 + ΣΓ X t-i + ε t (4) Where, Π = ΣA i - I, Γ i = - ΣA i. If the coefficient matrix Π has a reduced rank r < k, there exists k x r matrices α and β each with rank r such that Π = αβ and β x t are stationary. The number of co-integrating relations is given by r, and each column of β is a cointegrating vector. There exists three possibilities, according to Johansen s (1995) cointegrated Vector Autoregressive Model: (i) if Π is of full rank, all elements of X become stationary and none of the series has a unit root, (ii) if the rank of Π = 0, there are no combinations which are stationary and there are no co-integrating vectors, (iii) if the rank of Π is r such that 0 < r < k, then the X variables are co-integrated and there exists r co-integrating vectors. Equation (4) can be modified to allow for an intercept and a linear trend. The number of distinct co-integrating vectors can be obtained by determining the significance of the characteristic roots of Π. To identify the number of characteristic roots that are not different from unity we have used two statistics, the trace test and the maximum eigenvalue test: λ trace (r) = -TΣln(1 - λ i ) (5) and Copyright 2012 JCIBR, All Rights Reserved 4

10 λ max (r,r+1) = -Tln(1 - λ r+1 ) (6) Where, λ i = the estimated values of the characteristic roots (eigenvalues) obtained from the estimated Π matrix, r is the number of co-integrating vectors, and T = the number of usable observations. The trace test evaluates the null hypothesis that the number of distinct co-integrating vectors is less than or equal to r against a general alternative hypothesis (the number of distinct co-integrating vectors is more than or equal to r). The maximum eigenvalue test examines the number of co-integrating vectors versus that number plus one. If the variables in X t are not co-integrated, the rank of Π is zero and all the characteristic roots are zero. Since ln (1) = 0, each of the expressions ln (1 - λ i ) will equal zero in that case. Critical values for the test are provided by Johansen and Juselius (1990) and by Osterwald-Lenum (1992). Pairwise Granger causality Tests. We test for the dearth of Granger causality by estimating the following VAR model (Olushina Olawale Awe, 2012): Yt = a 0 + a 1 Y t a p Y t-p + b 1 X t b p X t-p +U t (7) Xt = c 0 + c 1 X t c p X t-p + d 1 Y t d p Y t-p +V t (8) Testing H 0 :b 1 =b 2 = =b p =0 against H 1 : Not H 0 is a test that Xt does not Granger-cause Yt. Similarly, testing H 0 : d 1 = d 2 = = d p =0 against H 1 : Not H 0 is a test that Yt does not Granger cause Xt. In case of Granger causality between the two variables, null hypothesis is rejected if the probability value is less than alpha (0.05). EMPIRICAL RESULTS AND ANALYSIS Unit Root Test Results Johansen cointegration analysis is needed where there are any long-term cointegration relationships between crude oil price, gold price, exchange rates and stock price indices of BSE and NSE. Cointegration analysis is possible if the series are stationary. In order to stationarity analysis, unit root test of Augmented Dickey-Fuller (ADF) is conducted with the levels and first differences of each series on the condition that the null hypothesis is nonstationary, so rejection of the unit root hypothesis supports stationarity. Variables Exchange rates Gold price Sensex Crude price TABLE 1 Results of Augmented Dickey-Fuller Test at level Intercept but no trend Intercept and trend Test Critical Prob. Test Critical statistics value statistics value (5%) (5%) Prob Copyright 2012 JCIBR, All Rights Reserved 5

11 Variables Exchange rates Gold price Sensex Crude price TABLE 2 Results of Augmented Dickey-Fuller Test at 1 st difference Intercept but no trend Intercept and trend Test statistics Critical value (5%) Prob Test statistics Critical value (5%) Prob Table-1 and 2 shows the results of unit root test. It reveals that time series are nonstationary at levels. However, table shows that the gold price and BSE and NSE stock price indices are stationary at 1st difference [1(1)]. Augmented Dickey Fuller unit root analysis test reveals that errors have constant variance and are statistically independent. Therefore, cointegration test can be applied on these variables, as supported in (Hina Shahzadi and M.N. Chohan, 2012). Johansen Cointegration Test Results Consequently, Johansen cointegration test is used to determine whether there is cointegration as well as the number of co-integrating relationships, that is, whether there are any long-term cointegration relationships between crude oil price, gold price, exchange rates and stock price indices of BSE and NSE or not. Two likelihood ratio tests are used, the Trace Test and the Maximum Eigen Value test, to determine the number of co-integrating vectors. The estimation for each series assumes linear deterministic trend unrestricted with intercepts and no trends. A lag of 1 to 4 (in 1 st differences) is used for each series, based on the Swartz Information Criterion (SIC). Table 3 Johansen Cointegration Test Result Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 Critical No. of CE(s) Eigenvalue Statistic Value Prob.** None * At most 1 * At most At most At most Trace test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Copyright 2012 JCIBR, All Rights Reserved 6

12 Table 3 (Cont.) Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 Critical No. of CE(s) Eigenvalue Statistic Value Prob.** None * At most 1 * At most At most At most Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Included observations: 5201 after adjustments Trend assumption: Linear deterministic trend Lags interval (in first differences): 1 to 4 Table-3 demonstrates the johansen cointegration test results. It assures the long-term relationship among the selected variables. The result shows that the series is cointegrated, as both the trace and the maximum eigenvalue tests reject the null hypothesis of no cointegration, suggesting that there are two significant co-integrating vectors in the model. This implies that there are two common stochastic trends, indicating a degree of market integration. Therefore, it may conclude that there exists a stationary, long-run relationship among the variables, as supported in (Saha and Bhunia, 2011). Pairwise Granger causality Tests Results The Granger causality test is a statistical proposition test for determining whether one time series is helpful in forecasting another. The pairwise Granger causality test has been performed in the present study in search of direction of causation among the selected financial variables. Table-4 reveals that no causality exists between (i) gold price and exchange rates, (ii) Nifty and exchange rates, (iii) sensex and exchange rates, (iv) exchange rates and sensex, (v) WCI and exchange rates, (vi) exchange rates and WCI, (vii) Nifty and gold price, (viii) Gold price and sensex, (ix) WCI and gold price, (xi) gold price and WCI, (xii) Nifty and sensex and (xiii) WCI and Nifty. Bidirectional causality exists between (i) Gold_Price and Nifty, (ii) Sensex and Gold Price, (iii) Exchange_Rates and Gold Price, (iv) Sensex and Nifty, (v) Nifty and WCI, (vi) WCI and Sensex and (vii) Sensex and WCI. Copyright 2012 JCIBR, All Rights Reserved 7

13 It is important to note that the pronouncement of causality between the selected variables does not mean that movement in one variable actually causes movements in another variable. To a certain extent, causality basically entails in order of movements in the time series (Olushina Olawale Awe, 2012). Table-4: Pairwise Granger Causality Test Results Null Hypothesis Obs F-Statistic Prob. Decision Type of Causality Gold_Price Exchange_Rates DNR H 0 No causality Exchange_Rates Gold_Price E-05 Reject H 0 Bi-directional causality Nifty Exchange_Rates DNR H 0 No causality Exchange_Rates Nifty DNR H 0 No causality Sensex Exchange_Rates DNR H 0 No causality Exchange_Rates Sensex DNR H 0 No causality Wci Exchange_Rates DNR H 0 No causality Exchange_Rates Wci DNR H 0 No causality Nifty Gold Price DNR H 0 No causality Gold_Price Nifty Reject H 0 Bi-directional causality Sensex Gold Price E-06 Reject H 0 Bi-directional causality Gold_Price Sensex DNR H 0 No causality Wci Gold Price DNR H 0 No causality Gold_Price Wci DNR H 0 No causality Sensex Nifty E-46 Reject H 0 Bi-directional causality Nifty Sensex DNR H 0 No causality Wci Nifty DNR H 0 No causality Nifty WCI E-09 Reject H 0 Bi-directional causality Wci Sensex Reject H 0 Bi-directional causality Sensex WCI E-09 Reject H 0 Bi-directional causality Note: Decision rule: reject H 0 if P-value < 0.05, DNR = Do not reject; = does not Granger cause. Copyright 2012 JCIBR, All Rights Reserved 8

14 CONCLUSIONS This paper aims at exploring the relationship among the crude oil price, gold price and selected financial variables in India. The principal conclusion of the empirical results is that the selected time series exhibit non-stationary and hence provide indication of long-term cointegration relationship. Multivariate cointegration test results indicate that long-term cointegration stable relationships are present under the study period. In a nutshell, selected variables are closely interlinked. The crude oil price is an essential unpredictable variable that operates as a channel during which the exchange rates and stock prices are associated, with the intention that the oil importing countries policy makers should keep an eye on the effects of changes in oil prices levels on their own economies and stock markets (Mohamed Abdelaziz and Georgios Chortareas, 2008). During the period from 1991 to October-2012, stock markets crashed due to Asian financial crisis, global financial crisis and recent European crisis but gold price continues to increase in India because of safe haven financial investment as well as jewellery. World Gold Council report says that India stands today as the world s largest single market for gold consumption. The measure of the relationship among the selected variables on Indian stock price indices used in this study is based on the financial market indicators. There is necessitate to expand this unambiguity including other macro-economic and financial market indicators (such as interest rate, inflation rate, housing price) relevant to the impact in order to arrive at more robust empirical analysis. This could be a possible area for future research in India (Mishra and Mohan, 2012). REFERENCES Awe, O. O. (2012). On Pairwise Granger causality Modelling and Econometric Analysis of Selected Economic Indicators. Interstat statjournals.net/year/2012/articles/ pdf, Gilmore, C. G., McManus, G. M., and Sharma, R. (2009). The Dynamics of Gold Prices, Gold Mining Stock Prices and Stock Market Prices Comovements. Research in Applied Economics, 2 (II). Johansen, S., and Juselius, K. (1990). Maximum Likelihood Estimation and Inferences on Co-integration with Applications to the Demand for Money. Oxford Bulletin of Economics and Statistics, 52, Kaliyamoorthy, S., and Parithi, S. (2012. Relationship of Gold Market and Stock Market: An Analysis. International Journal of Business and Management Tomorrow, 2 (6), 1-6. Kapusuzoglu, A. (2011). Relationships between Oil Price and Stock Market: An Empirical Analysis from Istanbul Stock Exchange (ISE). International Journal of Economics and Finance, 3 (6), Le, Thai-Ha and Chang, Y. (2011). Dynamic Relationships between the Price of Oil, Gold and Financial Variables in Japan: A Bounds Testing Approach. Online at MPRA Paper No Mishra, R. N., and Mohan, G. J. (2012). Gold Prices and Financial Stability in India. RBI working paper series, Department Of Economic And Policy Research, 2, Mehmood, S. (2012a). Dynamics of Exports and Economic Growth at Regional Level: A Study on Pakistan s Exports to SAARC. Journal of Contemporary Issues in Business Research, 1 (1), Copyright 2012 JCIBR, All Rights Reserved 9

15 Mehmood, S. (2012b). Effect of Different Factors on Gross Domestic Product: A Comparative Study of Pakistan and Bangladesh. Academy of Contemporary Research Journal, 1 (1), Mehmood, S., & Ahmad, Z. (2012). Forecasting Pakistan s Exports to SAARC: An Application of Univiriate ARIMA Model. Journal of Contemporary Issues in Business Research, 1 (3), Mohamed, A., Georgios, C., and Andrea, C. (2008). Stock Prices, Exchange Rates, and Oil: Evidence from Middle East Oil-Exporting Countries. ASE working paper, Nahid, K. A. (2010). The European stock market impulse to the U.S. financial crisis, Journal of International Business and Cultural Studies. The European Stock Market working paper, Naz, F. (2012). A UNIVARIATE TIME SERIES MODELLING OF DATES EXPORTS IN PAKISTAN. Journal of Contemporary Issues in Business Research, 1 (2), Newey, W. K., & West, K. C. (1987). A simple positive definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, Osterwald-Lenum, M. (1992). A Note with Quintiles of the Asymptotic Distribution of the Maximum Likelihood Co-integration Rank Test Statistic. Oxford Bulletin of Economics and Statistics, 54, Rad, A. A. (2011). Macroeconomic Variables and Stock Market: Evidence from Iran. International Journal of Economics and Finance Studies, 3 (1), Samanta, S. K., and Zadeh, A. H. M. (2012). Co-Movements of Oil, Gold, the US Dollar, and Stocks. Modern Economy, 3, Shahzadi, H., and Chohan, M. N. (2012). Impact of Gold Prices on Stock Exchange: A Case Study of Pakistan. Working paper series, Karachi Stock Exchange, 10 (2), Sharma, V. (2012). Three critical economic factors that influence the Indian stock market. Money life, Sharma, G. D., and Mahendra, M. (2010). Impact of Macro-economic Variables on Stock Prices in India. Global Journal of Management and Business Research, 10 (7), Šimáková, J. (2011). Analysis of the Relationship between Oil and Gold Prices. Journal of Finance, 51 (1), Wang, M., Wang, C., and Huang, T. (2010). Relationships among Oil Price, Gold Price, Exchange Rate and International Stock Markets. International Research Journal of Finance and Economics, 47, Yahyazadehfar, M., and Babaie, A. (2012). Macroeconomic Variables and Stock Price: New Evidence from Iran. Middle-East Journal of Scientific Research, 11 (4), Copyright 2012 JCIBR, All Rights Reserved 10

16 FACTORS INFLUENCING CORPORATE WORKING CAPITAL MANAGEMENT: EVIDENCE FROM AN EMERGING ECONOMY KAMAL NASER 1, RANA NUSEIBEH 2 and AHMED AL-HADEYA 3 ABSTRACT The main purpose of this study is to explore factors that influence working capital management by non-financial companies listed on Abu Dhabi Securities Exchange. The cash conversion cycle is used to proxy working capital management where short cycle implies effective management. Six factors frequently employed in previous research were used to explain variations in cash conversion cycle. The factors included industry type, sales growth, operating cash flows, return on equity, leverage and size. The latest annual reports for all non-financial companies listed on Abu Dhabi Securities Exchange and operate in the UAE were used to achieve the purpose of the study. The result of the analysis revealed that the effectiveness of working capital management of the companies covered in the study are influenced by sales growth, size and the level of corporate leverage. INTRODUCTION Working capital is a financial measure used to assess corporate liquidity. Reasonable working capital should be available to any firm to ensure that it has sufficient funds to cover its short-term obligations and to pay for future operating expenses. This guarantees the continuity of the firm s operations. In fact, it is possible to see a profitable firm forced out of business due to inability to meet its short- term obligations when they fall due. It is therefore vital for any firm to manage its working capital successfully to ensure continuity. In the last few years, several studies have been undertaken to investigate factors affecting corporate working capital. Most of the studies examined the relationship between corporate working capital and profitability (see for example: Hayajneh and Yassine, 2011; Sabri, 2012; Gill et al., 2010; Dong and Su, 2010; Boisjoly, 2009; Falope and Ajilore, 2009; Mathuva, 2009; SEN and ORUÇ, 2009; Uyar, 2009; Samilogu and Demirgunes, 2008; Teruel and Solano, 2007; Raheman and Nasr, 2007; Lazaridis and Tryfonidis, 2006; Padachi, 2006; Eljelly, 2004; Deloof, 2003; Shin and Soenen, 1998; Jose et al., 1996). Other studies, attempted to establish a relationship between the efficiency of working capital management and a number of corporate attributes. In this respect, Charlton et al. (2002) examined the relationship between the industry type and liquidity of the corporation and working capital management. Jeng-Ren et al. (2006) studied the relationship between working capital management and a number of firm s attributes such as size, industry type, leverage position and growth. Moss and Stine (1993) explored the relationship between firm s working capital management and its size. It is evident that the focus of most of previous studies undertaken to examine factors that influence working capital was on profitability with few studies employing corporate attributes such as size, growth, leverage and industry type to explain level of efficiency of working capital management. In the current study, an attempt will be made to test all these factors. So far, no attempt has been made to examine the relationship between working capital management and firm s attributes of companies listed on Abu Dhabi Securities Exchange (ADX). ADX was established in 15 th November 2000 to trade shares of companies Any views or opinions expressed in this manuscript are those of the authors and do not necessarily reflect the position of their employers, the editor or the publisher 1 Financial and Economic Advisor- Kuwait Fund. 2 Freelance Financial Analyst 3 Economic Advisor- Kuwait Fund Copyright 2012 JCIBR, All Rights Reserved 11

17 operating in the United Arab Emirates (UAE). Hence, ADX is considered to be one of the youngest stock exchanges in the region. At the end of 2011, the number of listed companies on the KSE reached 69. On 6 September 2011, market capitalization of the companies listed on the market reached AED billion (USD $58.20 billion). Examining the relationship between working capital management and profitability of companies listed on ADX is, therefore, important and expected to add a new dimension to the literature, since most of the companies operating in UAE do not face liquidity problems. In previous research, the focus of working capital management was on how to secure enough liquidity to meet forms of short-term obligations. In a small country, like the UAE with large oil revenues, the focus of working capital management will be on how to make use of the surplus of liquidity. The remainder of the paper is organized as follows. The following section will provide a brief explanation to working capital management. Section three reviews recent studies about the relationship between corporate working capital management and firm s attributes. Study methodology is explained in section four. The findings are discussed in section five, with the conclusion offered in the last section. WORKING CAPITAL MANAGEMENT Working capital is the difference between current assets and current liabilities. Working capital management involves managing cash, receivables, inventories and payables. In cash management, the firm ensures that cash is available to meet its running expenses and reduces the cost of cash holding. Receivables management involves adopting proper credit policy to the firm s customers. Proper credit policy is expected to attract customers and boost sales. Although such a policy would have a positive impact on profit and return on capital, it affects the firm s cash flows. Hence, corporate management needs to strike a balance between boosting sales and securing necessary cash flows. Inventories management is achieved by maintaining a certain level of inventory that allows the flow of production and reduces the cost of raw materials. This is expected to minimize ordering costs, lower time lead in production and work in progress, keep finished goods at the lowest level possible and avoid over production and storage costs. All of this would result in an increase in cash flow. On the other hand, firms manage payables though identifying a proper source of financing. Inventories are ideally financed by credit secured from suppliers. In some cases, the firm might need to secure a bank loan to pay for inventories by using overdrafts or by factoring receivables through converting them into cash. Related Studies and Hypotheses Development Different variables have been employed in the literature to explain the efficiency of working capital management. However, most of the studies attempted to establish a relationship between working capital management measured by cash conversion cycle (CCC) and profitability by using various measures. Other studies employed firm s attributes such as size, industry type, operating cash flow, sales growth, and debt ratio. Studies employed any of these variables will be reviewed. Profitability. As mentioned earlier, most of the studies used to explain the level of efficiency of working capital management focused on profitability as an important factor. It is argued that efficient working capital management results in low CCC and this would lead to speedy availability of cash flows and, hence, better profitability. In this respect, Jose et al. (1996) looked at the relationship between profitability and working capital management by using return on assets (ROA) and return on equity (ROE) as proxies of profitability and CCC as a proxy of working capital management. The researchers conducted the analysis according to Copyright 2012 JCIBR, All Rights Reserved 12

18 the industry type of the corporations. They reported a negative relationship between CCC and ROA across all industries. That is to say, high profitability results in low cash conversion cycle. However, a negative relationship was reported between CCC and ROE only in the services and retail/ whole sale industries. The researchers indicated that other factors such as corporate competitive position, capital intensity, production process, product durability and marketing channels might determine the relationship between CCC and ROE. Shin and Soenen (1998) tested the relationship between the firm s efficiency of working capital measured by the net trade cycle and profitability measured by ROA and return on sales (ROS). They found a negative significant relationship between the efficiency of working capital management and profitability. Similarly, Yung-Jang (2002) collected data from Japan and Taiwan and employed CCC as an indicator on working capital management and ROA and ROE as measures of profitability (performance). He concluded that the relationship between CCC and each of ROA and ROE is commonly negative and sensitive to the type of the industry for Japanese and Taiwanese companies included in his study. In the same way, Deloof (2003) examined the relationship between working capital management measured by CCC and profitability measured by gross operating income. He reported negative and significant relationships between the number of days of each of the components of the CCC (receivables, inventories and payable) and profitability. However, a negative but not significant relationship between CCC and profitability was identified in the sample companies. He, therefore, concluded that profitability can be improved by reducing the number of days of collecting receivables and number of days of holding inventories. In a similar line of research, Eljelly (2004) looked at the relationship between working capital management and profitability in a sample of Saudi companies operating in three sectors of the economy (agriculture, manufacturing and services). He used the current ratio and CCC as measures of liquidity and used net operating income plus depreciation divided by net sales as a measure of corporate profitability. He noticed variations in CCC among industries covered in the study. He also noticed that corporate size, represented by net sales and total assets, together with CCC affect the profitability of capital-intensive (agricultural and industrial companies) more than the profitability of labor-intensive (services companies). He concluded that shorter CCC is related to higher profitability. He also observed that the relationship between working capital management and profitability was stable over the period of study. Lazaridis and Tryfonidis (2006) looked into the relationship between working capital management and profitability in a sample of companies listed on Athens Stock Exchange. They related each of the CCC components with corporate profitability measured by gross operating profit [(sales - cost of sales)/ (total assets - financial assets)]. They noticed a negative significant relationship exists between corporate profitability and receivables, payables and CCC days. The relation was also negative with inventories days but insignificant. They explained the negative relation between the holding period for accounts payable and profitability on the grounds that less profitable firms may take advantage of credit period granted by suppliers and wait longer before paying their bills. They also reveal a positive relationship between corporate size measured by sales and profitability and corporate profitability decreases as the financial debt ratio increases. They concluded that managers are likely to improve their profit by efficiently handling their CCC through maintaining receivables, inventories and payables days at an optimal level. Teruel and Solano (2007) also tested the relationship between corporate working capital management measured by number of days receivables, inventories, payables and CCC and profit measured by ROA in a sample of small and medium sized Spanish companies. They observed a negative significant relationship between profitability and each of the working capital management variables. They concluded that managers can create value by Copyright 2012 JCIBR, All Rights Reserved 13

19 speeding up collection of receivables and reducing the days of holding inventories. They also concluded that shortening the cash conversion cycle improves corporate profitability. Raheman and Naser (2007) conducted similar research on a sample of Pakistani companies. The working capital management variables used by Teruel and Solano (2007) were employed and they replaced ROA with net operating profit as a proxy of profitability. They reported a strong negative relationship between variables of the working capital management and corporate profitability and managers can create a positive value for the shareholders by reducing the cash conversion cycle to a minimum level. They also reported a positive relationship between corporate size and its profitability and a significant negative relationship between corporate debt and its profitability. Samilogu and Demirgunes (2008) investigated the effect of working capital management measured by CCC and its components and corporate profitability measured by ROA in a sample of companies listed on Istanbul Stock Exchange. They observed that while receivables period, inventories period, and leverage negatively affect corporate profitability, corporate growth measured by growth in sales positively affect profitability. SEN and ORUÇ (2009) attempted to determine the relationship between the efficiency of corporate working capital management and profitability in a sample of companies listed on Istanbul Stock Exchange. The researcher employed several variables to proxy working capital management: CCC, net liquid balance, current ratio, receivables period and inventory periods. He used return on assets as a measure of profitability. He reported negative significant relationship between all variables used to proxy working capital management and profitability. The result was consistent among all companies included in the study and across sectors. Uyar (2009) considered the relationship between corporate CCC and profitability measured by ROA and ROE in a sample of companies listed on Istanbul Stock Exchange. He found significant negative relation between the two variables. Dong and Su (2010) studied the relationship between working capital management measured through CCC and its components and profitability measured by gross operating profit in a sample of companies listed on Vietnam Stock Exchange. They presented negative significant relationship between the two variables. Gill et al. (2010) examined the relationship between working capital management measured by CCC and profitability measured by gross operating profit in a sample of American companies listed on New York Exchange. They concluded that management can create profit by correctly handling CCC and maintaining receivables at an optimal level. Rimo and Panbunyuen (2010) looked into corporate characteristics that affect working capital management in a sample of companies listed on NASDAQ OMX Stockholm Exchange. They reported that corporate profitability is affected by CCC. On the other hand, Rimo and Panbunyuen (2010) reported positive relationship between CCC and profitability measured by ROA in a sample of companies listed on NASDAQ OMX Stockholm Exchange. The researchers concluded that the surveyed companies have less effective working capital management. Hayajneh and Yassine (2011) investigated the relationship between working capital efficiency and profitability in a sample of 53 manufacturing companies listed on Amman Exchange Market. They reported a negative and significant relationship between profitability and each of average receivable collection period, average conversion inventory period and average payment period, and cash conversion cycle. Al-Mwalla (2012) examined the impact of working capital management policies on the firms profitability and value in a sample of 57 industrial companies on Amman Stocks Market for the period of 2001 to The researcher concluded a conservative investment Copyright 2012 JCIBR, All Rights Reserved 14

20 policy has a positive impact on the sampled companies profitability. It is therefore hypothesized that: Hypothesis 1: Working capital management is related to firm s profitability. Size. Few studies were undertaken to examine the relationship between corporate working capital management and corporate size. It is argued that corporate size influences corporate working capital management since large size companies have more bargaining power over customers (receivables) and suppliers (payables) than small size companies. A large company can negotiate with its supplier credit terms including large quantity discounts and long time payment terms. In addition, large firms are usually well established and have higher inventory turnover than small firms. In this respect, Moss and Stine (1993) studied the relationship between corporate size, measured by net sales and total assets, and CCC in a sample of retail companies. They noticed that large size companies tend to have shorter CCC than small size companies. They also noticed that small size companies have longer receivables, inventories and payable periods than small size companies. This led them to conclude that large size firms have better working capital management than small firms. They advised small size companies to improve their working capital management by handling their receivables and inventories. In a similar line of research, Jose et al. (1996) looked at the relationship between corporate working capital management measured by CCC and its size measured by sales. They reported negative relationship between the two variables in five out seven sectors covered in their study. However, Jeng-Ren et al. (2006) explored the relationship between net liquid balance and corporate size. They observed positive relationship between the two variables. The outcome of their study implied that large size companies tend to produce more net liquid balance than small size companies. Uyar (2010) examined the relationship between working capital management measured by CCC and corporate size in a sample of companies listed on Istanbul Stock Exchange. He observed a negative significant relation between working capital management and corporate size. Rimo and Panbunyuen (2010) explored the relationship between CCC and corporate size measured by total assets. They a reported negative relationship between the two variables. They concluded that working capital management is more effective with the large size companies surveyed in their study. It is therefore hypothesized that: Hypothesis 2: Working capital management is related to firm s size. Industry. Companies operating in different sectors tend to have different capital structures, different operations, different products, different markets, different customers and different credit policies. All of these factors impact corporate working capital management. Therefore, it is fair to say that working capital management is influenced by industry type. This relation has been empirically tested by Jose et al. (1996) who compared the CCC among seven sectors and noticed that construction companies tend to have the highest CCC, while manufacturing companies reported the lowest CCC. Hyun-Han and Soenen (1998) examined the relationship between working capital management and industry type in a sample of companies operating in eight sectors. They found the agricultural companies to have short receivable periods and the oil and gas extraction, and communication companies to have a short inventory periods due to low levels of inventory that they maintain over the time. Lancaster and Stevens (1999) looked into the relationship between earnings and cash flows under different industries. They concluded that industry impacts the relationship between cash flows. Charlton et al. (2002) Copyright 2012 JCIBR, All Rights Reserved 15