Dirasat, Administrative Sciences, Volume 42, No. 2, 2015 The Impact of Capital Expenditure on Working Capital Management: An Empirical Study on Amman Stock Exchange Fayez Salim Haddad* ABSTRACT This study examines the impact of capital expenditures on working capital management on Amman Stock Exchange (ASE) over the time period 2000 through 2012. The study used Shulman and Cox's (1985) Net Liquidity Balance (NLB) and Working Capital Requirement (WCR) as a proxy for working capital measurement. Using panel data, and three regression models: ordinary least squares (OLS), fixed effect, and seemingly unrelated regression (SUR). The results found that capital expenditure has a significant positive impact on NLB, which means that Jordanian industrial companies manage their cash efficiently. This result is in line with previous empirical researches. In contrast to previous researches findings, the results revealed that capital expenditure has a significant positive impact on WCR, which means that Jordanian industrial companies did not manage their working capital requirement efficiently when they tend to invest in fixed assets. These results will help Jordanian industrial companies to review their working capital requirement when investing in fixed assets. Keywords: Working Capital Management, Capital Expenditure, Net Liquidity Balance, Amman Stock Exchange, Jordan. INTRODUCTION The investment decisions in both current and fixed assets are of most important for any firm to achieve its goal of maximizing shareholders wealth. Managing current assets or what is called working capital management concerns the ability of the firm to meet its short-term requirement, as well as, its ability to pay shortterm debt. While, managing fixed assets or what is called capital expenditure decision concerns the ability of the firm to invest in profitable long-term project that generate cash flow to support the continuity of operating activity. The main objective of working capital is to attain an optimal level for each of the working capital variables. Achieving this optimal level will reduce their financing costs or increasing the funds available for investing in fixed assets, and therefore achieving the balance between risk and efficiency. * Faculty of Business, The University of Jordan, Amman. Received on 4/6/2014 and Accepted for Publication on 20/10/2014. * This study has been conducted throughout the sabbatical leave granted to the author from his employer, the University of Jordan. Most empirical research has examined the determinants of working capital, and the relationship between working capital management and profitability. While, few studies examined the impact of capital expenditure on working capital in developed and developing countries. Appuhami (2008) using Ordinary Least Squares (OLS) regression models found that firm's capital expenditure has a significant impact on working capital management. Using the same methodology, Valipour et al., (2012) found insignificant impact of capital expenditure on working capital. Using random and fixed effect methodology to account for stationarity, Celik and Boyacioglu (2013) found a significant impact of capital expenditure on working capital. In addition to the previous two methodologies, this study will use seemingly unrelated regression to solve the autocorrelation problem. Also, this study is expected to better understanding the impact of capital expenditure on working capital management especially in emerging market like Jordan. The remainder of the study is organized as follows: Section 2 describes the objectives of the study. Section 3 explains the literature review. Section 4 presents the methodology of the study; its study sample, operational definition of the study variables, study hypotheses, and - 657-2015 DAR Publishers/University of Jordan. All Rights Reserved.
The Impact of Fayez Salim Haddad study models. Section 5 reports the empirical results, and finally section 6 presents the conclusions. The objectives of the Study Our main goal of this study is to understand the impact of capital expenditure on working capital. Other goals include the impact of capital expenditure on working capital in developing financial market, such as Amman Stock Exchange, and to make managers understands the effect of working capital and capital expenditure decisions. Literature Review Working capital management is defined as the ability of a company to fund the difference between current assets and current liabilities. In other words, working capital includes the process of managing short-term assets (current assets) and short-term liabilities (current liabilities) to ensure that the amount of cash into business is sufficient to sustain the company`s operations in an efficient and effective way (Harris, 2005). Working capital is a significant issue during financial decision making as a part of investment in assets that requires efficient management for working capital to reach the appropriate level and maximize shareholder value (Deloof, 2003 and Afza and Nazir, 2007). Most of the researches of working capital management concentrate in examining the determinants of working capital, and the relationship between working capital and profitability. There are very few studies mentioned below that examined the impact of capital expenditure on working capital. These empirical studies used Shulman and Cox (1985) Net Liquidity Balance (NLB) and Working Capital Requirement (WCR) as a proxy for working capital management. Hawawini et al., (1986) used working capital requirement to measure working capital management when examining the industry influence on working capital decisions. They found that growth in sales effect working capital, this result was confirmed by Hill et al. (2010). Moussawi et al., (2006) found that size and increase in sales growth have an impact on working capital management. Chiou and Cheng (2006) in examining the determinants of working capital found that debt ratio and cash flow from financial activities have a significant impact on working capital management. Palombini and Nakamara (2012) using multiple regression of feasible generalized least squares methodology (FGLS) examined the determinants factors of working capital management in the Brazilian market. They found that debt level, size, and free cash flow have significant affect on working capital management. Appuhami (2008) examined the impact of firm's capital expenditure on working capital management using data from Thailand stock exchange. The Shulman and Cox's (1985) measures for working capital management were used as dependent variable and capital expenditure as independent variable. Also, operating cash flow, sales growth, leverage, and size were used as control variables. The results of ordinary least squares (OLS) regression models show that firm's capital expenditure has a significant impact on working capital management, and the firm's operating cash flow, operating expenditure, and finance expenditure, which was recognized as control variables, has a significant relationship with working capital management. Using the same methodology and the same variables, Valipour et al., (2012) found insignificant impact of capital expenditure on working capital. The results show significant relationship of the control variables finance expenditure, growth sales, and leverage with net liquidity balance. Also, the results show significant relationship of the control variables finance expenditure, growth sales, and market to book (size) with working capital requirement. Using the same variables except the size variable, and random and fixed effect methodology to account for stationarity, Celik and Boyacioglu (2013) found a significant impact of capital expenditure and all control variables except sales growth on working capital management. Methodology Study Sample The study sample includes all industrial public shareholding firms listed on Amman Stock Exchange (ASE) during the period (2000-2012). As with all same studies, we exclude financial institutions because they have different set of financial data. Only firms that have the required annual financial and accounting data to calculate the study main and control variables were included in the sample. Applying this criterion resulted in a final sample encompasses a total of 47 industrial firms. Study Variables Following Appuhami, (2008), Valipour et al., (2012), and Celik and Boyacioglu (2013), this study used the following dependent, independent, and control variables. - 658 -
Dirasat, Administrative Sciences, Volume 42, No. 2, 2015 Dependent Variables This study used net liquidity balance (NLB) and working capital requirement (WCR) as dependent variables to measure working capital management (Shulman and Cox, 1985): NLB = {(Cash + Short-term Investments)-Short Borrowing}/Total Assets. WCR = {(Accounts Receivables + Inventories)-(Payables + Other Payable)}/Total Assets Independent Variable The independent variable is capital expenditure (CE) and is measured using the following formula: Control Variables The following control variables are measured as follows: 1. Operating Expenditure (OE) is the cost of ongoing operations, product or system, and is measured using the following formula: 2. Finance Expenditure (FE) is the cost incurred on debt capital, and is measured using the following formula: 3. Operating Cash flow (OCF) is measured as: 4. Leverage (LEV) is measured as: 5. Market to book value ratio (M/B) is measured as: / 6. Sales growth (SG) is measured as: SG = (Sales t Sales t-1 ) / Sales t-1 Study Hypotheses To test the impact of capital expenditure on working capital management, this study followed the hypotheses developed by Kim, et al (1998), Opler et al., (1999), Wu (2001), and Appuhami (2008). They demonstrated that more growth opportunities and more fluctuations of future cash flow will lead to increase in both capital expenditure, and cash balance and short-term investment of a company. Thus, expected cash flows and growth opportunities have positive correlation with NLB, and capital expenditure is positively correlated with NLB. Also, when a company has growth opportunities, this will increase the holding cash, since it manages working capital efficiently, resulting in less demand on working capital. Therefore, expected capital expenditure is negatively related to WCR. Accordingly, the study alternative hypotheses are as follows: HA1: Capital expenditure is positively related to NLB. HA2: Capital expenditure is negatively related to WCR. Study Models The following two models were used to test the impact of capital expenditure on working capital management: NLB it = ß 0 + ß 1 CE it + ß 2 OE it + ß 3 FE it + ß 4 OCF it + ß 5 Lev it + ß 6 M/B it + ß 7 SG it + εi (1) WCR it = ß 0 + ß 1 CE it + ß 2 OE it + ß 3 FE it + ß 4 OCF it + ß 5 Lev it + ß 6 M/B it + ß 7 SG it + εi (2) Where: NLB it: Net Liquidity Balance for Firm i at time t WCR it : Working Capital Requirement for Firm i at time t CE: Capital Expenditure for Firm i at time t OE: Operating Expenditure for Firm i at time t FE : Financial Expenditure for Firm i at time t OCF: Operating Cash Flow for Firm i at time t Lev: Leverage Ratio for Firm i at time t M/B : Market to Book ratio for Firm i at time t SG : Sales Growth for Firm i at time t Empirical Results Descriptive Statistics Descriptive statistics summary for the dependent and independent variables for the study variables are shown in Table 1. The mean for all the study variables are very close to their median except for NLB/TA, M/B and SG. The median of NLB/TA is 0.0221 slightly below the mean, indicating that most NLB/TA values are around the average of 0.0557, with a distribution skewed to the right. The median of M/B is 1.177 slightly below the mean, indicating that most M/B values are around the average of 1.455, with a distribution skewed to the right. The median of SG is 0.0426 slightly below the mean, indicating that most SG values are around the average of 0.1137, with a distribution highly skewed to the right. - 659 -
The Impact of Fayez Salim Haddad Table (1) Summary Statistics for Main Study Variables 1 N Mean Median Maximum Minimum Standard Deviation Skewness NLB/TA 564 0.0557 0.0221 1.3970-0.7588 0.1651 1.529 WCR/TA 564 0.2414 0.2133 0.8226-0.6360 0.1902-0.035 CE 564 0.9013 0.9744 1.0000 0.1078 0.1540-2.409 OE 564 0.8589 0.8515 4.0550 0.2792 0.3086 4.308 FE 564 0.0294 0.0153 0.6602 0.0000 0.0545 6.148 OCF/TA 564 0.0423 0.0420 0.6147-0.9800 0.1302-0.755 LEV 564 0.3302 0.3016 1.7948 0.0064 0.2191 1.263 M/B 564 1.4555 1.1774 6.5411 0.1794 0.9659 1.793 S.G 564 0.1137 0.0426 6.2968-0.8971 0.5449 5.427 1: Description of study main variables NLB/TA: Net Liquid Balance/ Total Assets, WCR/TA: Working Capital Requirements/ Total Assets, CE: Capital Expenditures, OE: Operating Expenditures, FE: Financial Expenditures, CF/TA: Operating Cash Flows/Total Assets, LEV: The Leverage Ratio, M/B:Market to Book value, S.G: Sales growth. Regression Analysis The panel data analysis used to test the hypotheses, we examined three regression techniques, the first one is ordinary least squares model (OLS) which depends on minimizing the sum of squared residuals, and it based on the assumption that intercept and coefficient are constant over time and cross sections. Table (2) shows summary statistics for OLS regression results. The results show that all independent variables, except for finance expenditure (FE) and sales growth (SG), are significant at 0.01 level for the two models. The adjusted R-squared which explain the variation in the dependent variables are 26.97% for the first model and 10.11% for the second model. The total regression equations are significant as both probabilities are 0.000 with F-statistics equal 30.7 and 10.05 for the two models respectively. In addition, multicollinearity has been examined as shown by the collinearity statistics in table (3), multicollinearity would be a problem if we have tolerance approach zero and VIF (variance inflation factor) approach 10. The table shows good indicators that multicollinearity is not a problem among variables (Belsley et al., 1980 and Myers, 1990). The other problem that could affect the accuracy of our result is the autocorrelation or serial correlation, which happens when there are relations between values separated from each other by a given time lag. Durbin-Watson statistics is used to detect the presence of the autocorrelation; the Durbin-Watson statistics range in value from 0 to 4; a value near 2 indicate non-autocorrelation; a value toward 0 indicates positive autocorrelation and a value toward 4 indicates negative autocorrelation (Zhu et al., 2011). The results in table (2) detect a positive autocorrelation, the value of Durbin Watson for the first model is.65 and.39 for the second model. Table (2) Ordinary Least Squares Regression NLB it = ß 0 + ß 1 CE it + ß 2 OE it + ß 3 FE it + ß 4 OCF it + ß 5 Lev it + ß 6 M/B it + ß 7 SG it + εi (1) WCR it = ß 0 + ß 1 CE it + ß 2 OE it + ß 3 FE it + ß 4 OCF it + ß 5 Lev it + ß 6 M/B it +ß 7 SG it +εi (2) Variables MODEL (1) MODEL (2) Constant CE OE FE 0.0688* (1.7553) 0.1209*** (3.0306) -0.0926*** (-4.1650) 0.0089 (0.0688) 0.09002* (1.7962) 0.3291*** (6.4534) -0.0658** (-2.3146) 0.1579 (0.9589) - 660 -
Dirasat, Administrative Sciences, Volume 42, No. 2, 2015 OCF/TA LEV M/B S.G 0.2154*** (4.4906) -0.2630*** (-8.2570) 0.0248*** (4.0041) -0.0125 (-1.1024) -0.1466** (-2.3911) -0.1592*** (-3.9089) -0.0218*** (-2.7533) -0.0233 (-1.6059) Adjusted R-squared 26.97% 10.11% F-Statistics 30.70*** 10.05*** Durbin Watson.65.39 Description of study main variables: CE: Capital Expenditures, OE: Operating Expenditures, FE: Financial Expenditures, OCF/TA: Operating Cash Flows/Total Assets, LEV: The Leverage Ratio, M/B: Market to Book value, S.G: Sales growth. ***, **, * Coefficient significant at the 0.01, 0.05 and 0.10 levels, respectively. (2-tailed T-test). Table (3) Collinearity Statistics Variables Tolerance VIF CE.938 1.067 OE.746 1.341 FE.718 1.392 OCF/TA.905 1.104 LEV.726 1.377 M/B.925 1.081 SG.925 1.081 Description of study main variables: CE: Capital Expenditures, OE: Operating Expenditures, FE: Financial Expenditures, OCF/TA: Operating Cash Flows/Total Assets, LEV: The Leverage Ratio, M/B: Market to Book value, S.G: Sales growth. The second method is fixed effect model (FEM) which estimates the intercept as a coefficient of dummy variable. The model allows intercept to vary for each cross section and considered the individual effect, fixed effect model suggested to be more fitted for our data set because the continuous change of variables among the twelve year period (Shiu, 2011 and Zhu et al., 2011). However, Hausman test is used to assure which model is more suitable to be used, fixed effect or random effect model. The hypothesis that "there are random effects" is rejected for both models as shown in table (4), therefore fixed effect model is more suitable than random effect model for our data. Table (4) Correlated Random Effects - Hausman Test/ Test cross-section random effects Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob. Cross-section random (NLB) 5.9430 7 0.5464 Cross-section random (WCR) 10.0985 7 0.1831 NLB: net liquidity balance WCR: working capital requirement - 661 -
The Impact of Fayez Salim Haddad Table (5) shows summary statistics for fixed effect regressions results. The results for the first model show that all independent variables, except for finance expenditure (FE) and sales growth (SG), are significant at 0.05 levels, or better. The second model results show that all independent variables, except for finance expenditure (FE) and market to book value (M/B), are significant at 0.05 levels, or better. The adjusted R-squared which explain the variation in the dependent variables are 51% for the first model and 62.66% for the second model. The total regression equations are significant as both probabilities are 0.000 with F-statistics equal 10.16 and 15.76 for the two models respectively. The results in table (5) detect a positive autocorrelation, the value of Durbin Watson for the first model is 1.005 and.95 for the second model. Table (5) Fixed Effect Regression NLB it = ß 0 + ß 1 CE it + ß 2 OE it + ß 3 FE it + ß 4 OCF it + ß 5 Lev it + ß 6 M/B it + ß 7 SG it + εi (1) WCR it = ß 0 + ß 1 CE it + ß 2 OE it + ß 3 FE it + ß 4 OCF it + ß 5 Lev it + ß 6 M/B it +ß 7 SG it +εi (2) Variables MODEL (1) MODEL (2) Constant CE OE FE OCF/TA LEV M/B S.G 0.0585 (0.9013) 0.1247* (1.8176) -0.0925*** (-3.7940) 0.1131 (0.8150) 0.1800*** (4.0234) -0.2114*** (-5.2711) 0.0168** (2.1536) -0.0111 (-1.1273) 0.2054*** (3.1479) 0.2120*** (3.0725) -0.1042*** (-4.2499) 0.1761 (1.2616) -0.0965** (-2.1445) -0.2250*** (-5.5766) 0.0065 (0.8247) -0.0163* (-1.6520) Adjusted R-squared 51% 62.66% F-Statistics 10.16*** 15.76*** Durbin Watson 1.005.95 Description of study main variables: CE: Capital Expenditures, OE: Operating Expenditures, FE: Financial Expenditures, OCF/TA: Operating Cash Flows/Total Assets, LEV: The Leverage Ratio, M/B: Market to Book value, S.G: Sales growth. ***, **, * Coefficient significant at the 0.01, 0.05 and 0.10 levels, respectively. (2- tailed T-test). The third method is seemingly unrelated regression (SUR) model, which considered as a special case of generalized regression model and consists of several regression equations, each equation have the dependent variable and different set of independent variables and each equation estimated separately. Seemingly unrelated regression can solve the autocorrelation problem and thus will give the most accurate result if the autocorrelation exist. Table (6) shows summary statistics for seemingly unrelated regression results. The results show that Durbin-Watson statistics for the first model is 1.84 and 1.99 for the second model, which indicating that the autocorrelation problem has been resolved. The adjusted R-squared which explain the variation in the dependent variables are 19.29% for the first model and 15.2% for the second model. The total regression equations are significant as both probabilities are 0.000-662 -
Dirasat, Administrative Sciences, Volume 42, No. 2, 2015 with F-statistics equal 20.45 and 14.68 for the two models, respectively. The results for the first model show that capital expenditure has a significant positive relationship with net liquidity balance (NLB). The regression coefficient of capital expenditure is 0.1545 and is significant at 0.01 level. This means that NLB is increased by 0.1545 for each one Jordanian Dinar (JD) of capital expenditure. This result means that Jordanian industrial companies managed their cash and cash equivalent efficiently. This result supports the alternative hypothesis that capital expenditure is positively related to NLB. This result is consistent with Appuhami, 2008 using data collected from Thailand stock exchange, and Celik and Boyacioglu, 2013 using data from Istanbul Stock Exchange. The results for the first model also show that all control variables, except for finance expenditure (FE) and sales growth (SG), are significant at 0.01 levels. The results for the second model show that capital expenditure has a significant positive relationship with working capital requirement (WCR). The regression coefficient of capital expenditure is 0.3703 and is significant at 0.01 levels. This means that WCR is increased by 0.3703 for each one Jordanian Dinar (JD) of capital expenditure. This result means that Jordanian industrial companies did not manage their working capital requirement efficiently when they tend to invest in capital expenditure. This result does not support the alternative hypothesis that capital expenditure is negatively related to WCR. The results for the second model also show that all control variables, except for finance expenditure (FE) and market to book value (M/B), are significant at 0.05 level, or better, but with negative coefficients. Table (6) Seemingly Unrelated Regression NLB it = ß 0 + ß 1 CE it + ß 2 OE it + ß 3 FE it + ß 4 OCF it + ß 5 Lev it + ß 6 M/B it + ß 7 SG it + εi (1) WCR it = ß 0 + ß 1 CE it + ß 2 OE it + ß 3 FE it + ß 4 OCF it + ß 5 Lev it + ß 6 M/B it +ß 7 SG it +εi (2) Variables MODEL (1) MODEL (2) CE OE FE OCF/TA LEV M/B S.G 0.1545*** (8.6506) -0.0679*** (-4.6744) -0.0113 (-0.1167) 0.1484*** (6.3533) -0.1533*** (-5.1952) 0.013407*** (2.9581) -0.00765 (-1.4246) 0.3703*** (14.6747) -0.0411** (-2.2604) 0.113857 (0.9159) -0.0989*** (-3.7052) -0.2328*** (-6.826) -0.0004 (-0.063) -0.01117* (-1.831) Adjusted R-squared 19.29% 15.20% F-Statistics 20.45*** 14.68*** Durbin Watson 1.84 1.99 Description of study main variables: CE: Capital Expenditures, OE: Operating Expenditures, FE: Financial Expenditures, OCF/TA: Operating Cash Flows/Total Assets, LEV: The Leverage Ratio, M/B: Market to Book value, S.G: Sales growth. ***, **, * Coefficient significant at the 0.01, 0.05 and 0.10 levels, respectively. (2-tailed T-test). - 663 -
The Impact of Fayez Salim Haddad Conclusions This study examines the impact of capital expenditures on working capital management using Shulman and Cox's (1985) Net Liquidity Balance (NLB) and Working Capital Requirement (WCR) as a proxy for working capital measurement. The panel data analysis used to test the hypotheses, we examined three regression techniques: ordinary least squares (OLS), fixed effect, and seemingly unrelated regression (SUR). The results for the first two techniques detect a positive autocorrelation or serial correlation problem that could affect the accuracy of our result. The results of the third technique (SUR) overcome this problem, and show that capital expenditure has a significant positive impact on NLB, which means that Jordanian industrial companies manage their cash and cash equivalent efficiently. On the other hand, the results show that capital expenditure has a significant positive impact on WCR, which means that Jordanian industrial companies did not manage their working capital requirement efficiently when they tend to invest in capital expenditure. The findings will help these companies to review their working capital requirement when investing in fixed assets. REFERENCES Abuzayed, B. 2012. Working Capital Management and Firms' Performance in Emerging Markets: The Case Of Jordan, International Journal of Managerial Finance, 8(2): 155-179. Afza, T. and Nazir, M. S. 2007. Is it Better to be Aggressive or Conservative in Managing Working Capital, Journal of quality and technology management, 3(2): 11-21. Al-Debi'e, M. M. 2011. Working Capital Management and Profitability: The Case of Industrial Firms in Jordan, European Journal of Economics, Finance and Administrative Sciences, (36): 75-86. Al-Mwalla, M. 2012. The Impact of Working Capital Management Policies on Firm s Profitability and Value: The Case of Jordan, International Research Journal of Finance and Economics, (85): 147-153. Appuhami, B.A.R. 2008. The impact of firms capital expenditure on working capital management: an empirical study across industries in Thailand, International Management Review, 4(1): 11-24. Arshad, Z. and Gondal, M. Y. 2013. Impact of Working Capital Management on Profitability: A Case of The Pakistan Cement Industry, Interdisciplinary Journal of Contemporary Research In Business, 5(2): 384-390. Bellouma M., Omri, A. and Omri, M.A. 2005. The Determinants of Lending Relationship in Tunisian Context, Journal of Emerging Market Finance, 4(2): 135-150. Bellouma, M. 2011. The Impact of Working Capital Management on Profitability: The Case of Small and Medium-Sized Export Companies in Tunisia, Management international/ International Management/ Gestiòn Internacional, 15(3): 71-88. Belsely, D. A., E. Kuh, and R. E. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and Sources of Multicollinearity, New York: Wiley. Celik, I. and Boyacioglu, N. 2013. The Impact of Fixed Assets Expenditure on Working Capital Management: An Application on Manufacturing Enterprises in Istanbul Stock Exchange, Journal of Suleyman Demirel University Institute of Social Sciences, 17: 81-99. Chen, J.J. 2004. Determinants of capital structure of Chineselisted companies, Journal of Business Research, 57: 1341 1351. Chiou, J. and Cheng, L. 2006. The determinants of working capital management, The Journal of American Academy of Business, Cambridge, 10(1): 149-155. Deloof, M. 2003. Does Working Capital Management Affects Profitability Of Belgian Firms? Journal of Business Finance and Accounting, 30(3): 573-87. Deloof, M. and M. Jeger. 1996. Trade Credit, Product Quality, and Intra group Trade: Some European Evidence, Financial Management, 25(3): 945 68. Dong, H. P. and Su, J.T. 2010. The Relationship between Working Capital Management and Profitability: A Vietnam Case, International Research Journal of Finance and Economics, 49(49): 59-67. Filbeck, G. and T. Krueger. 2005. An analysis of working capital management results across industries, Mid-American Journal of Business, 20(2): 11-18. Gill, A., Biger, N. and Mathur, N. 2010. The Relationship between Working Capital Management and Profitability: Evidence From the United States, Business and Economics Journal, P. 1-9. Harris, A. 2005. Working Capital Management: Difficult but Rewarding, Financial Executive, 21(4): 52-53. Hawawini, G., Viallet C. and Vora, A. 1986. Industry Influence on Corporate Working Capital Decisions, Sloan Management Review, 27 (4): 15-24. Hill, M., Wayne, G. and Highfield, G. 2010. Net Operating - 664 -
Dirasat, Administrative Sciences, Volume 42, No. 2, 2015 Working Capital Behavior: A First Look, Financial Management, Summer 2010, P. 783-805. Jose, M. L., Lancaster, C. and Stevens, J. L. 1996. Corporate Returns and Cash Conversion Cycles, Journal of Economics and finance, 20(1): 33-46. Kim, C., Mauer, D. and Sherman, E. 1998. The Determinants of Corporate Lequidity: Theory and Evidence, Journal of Financial and Quantitative Analysis, 33(3): 335-359. Moussawi, R., Laplante, M., Kieschnick, R. and Baranchuk, N. 2006. Corporate working capital management: determinants and consequences, Working paper. http:// finance.baylor.edu/ seminars/ papers/ cwcm_current.pdf. Myers, R, H. 1990. Classical and Modern Regression with Applications, Second Edition, Boston: PWS and Kent Publishing Company, Inc. Nunn, K.P. 1981. The strategic determinants of working capital: a product line perspective, The Journal of Financial Research, 4(3): 207-219. Opler, T., Pinkowitz, L., Stulz, R. and Williamson, R. 1999. The Determinants and Implications of Corporate Cash Holding, Journal of Financial Economics, 52 (1): 3-46. Palombini, N. and Nakamura, W. 2012. The Determinant Factors of Working Capital Management in the Brazilian Market, Revista de Administração de Empresas, 52(1): 55-69. http://www.scielo.br/scielo.php?script=sci_arttext & pid=s0034-75902012000100005 &lng= en&tlng= en. 10.1590 / S0034-75902012000100005. Richards, V. and Laughlin, E. 1980. A Cash Conversion Cycle Approach to Liquidity Analysis, Financial Management, 9(1): 32-38. Sathyamoorthi C.R. and Wally-Dima, L.B. 2008. Working capital management: the case of listed retail domestic companies in Botswana, The Icfaian Journal of Management Research, 7(5): 7-24. Shin, H. and Soenen, L. 1998. Efficiency of Working Capital and Corporate Profitability, Financial Practice and Education, 8(2): 37-45. Shiu, Y.M. 2011. Reinsurance and Capital Structure: Evidence from the United Kingdom Non-Life Insurance Industry, The Journal of Risk and Insurance, 78(2): 475-494. Shulman, J. M. and Cox, R. A. K. 1985. An Integrative Approach to Working Capital Management, Journal of Cash Management, 5 (6): 64-68. Teruel, P. and Solano, P. 2007. Effects of Working Capital Management on SME Profitability, International Journal of Managerial Finance, 3 (2): 164-177. Valipour, H., Moradi, J. and Karimi, K. 2012. The Impact of Capital Expenditure on Working Capital Management: Empirical Evidences from Tehran Stock Exchange, International Research Journal of Finance and Economics, (85): 14-25. Wu, Q. S. 2001. The determinants of working capital management Policy and its Impact on Performance, National Science Council Project, Project No. NSC 89-2416- H- 224-028. Zhu, M., Kui, C. and Fang, Y. 2011. Demand for Corporate Insurance in China: Empirical Analysis with Province-Level Panel Data, The Chinese Economy, 44(6): 15-29. - 665 -
The Impact of Fayez Salim Haddad أثر المصاريف ال أرسمالية على ادارة أرس المال العامل: د ارسة عملية على سوق عمان المالي فايز سليم حداد* ملخص فحصت هذه الد ارسة أثر المصاريف ال أرسمالية على أرس المال العامل في سوق عمان المالي خلال الف ت رة 2000-2012. اعتمدت الد ارسة على مقياس صافي السيولة (NLB) Net Liquidity Balance ومقياس متطلبات أرس المال العامل (WCR) Working Capital Requirement في د ارسة (1985) Cox's Shulman and لقياس ادا رة أرس المال العامل. باستخدام بيانات التحليل المقطعي data) (panel وثلاثة نماذج انحدار هي نموذج المربعات الصغرى ordinary least (OLS) squares ونموذج الا ثر الثابت fixed effect ونموذج صور( SUR ).seemingly unrelated regression بينت النتاي ج وجود علاقة موجبة ومهمة احصاي يا بين المصاريف ال أرسمالية وصافي السيولة مما يعني أن الشركات الصناعية الا ردنية تستطيع ادارة النقدية لديها بكفاءة. هذه النتيجة تتماشى مع نفس النتيجة التي توصلت اليها الد ارسات العملية السابقة. على عكس ما توصلت اليه الد ارسات السابقة, بينت النتاي ج وجود علاقة موجبة ومهمة احصاي يا بين المصاريف ال أرسمالية ومتطلبات أرس المال العامل مما يعني أن الشركات الصناعية الا ردنية لا تستطيع ادا رة متطلبات أرس المال العامل بكفاءة عندما تقوم بالاستثمار في الا صول الثابتة. نتاي ج هذه الد ارسة سوف تساعد الشركات الصناعية الا ردنية في م ارجعة متطلبات أرس المال العامل عندما تقوم بالاستثمار في الا صول الثابتة. الكلمات الدالة: ادا رة أرس المال العامل المصاريف ال أرسمالية صافي السيولة متطلبات أرس المال العامل سوق عمان المالي الا ردن. * كلية الا عمال الجامعة الا ردنية عمان. تاريخ استلام البحث 2014/6/4 وتاريخ قبوله 2014/10/20. - 666 -