Research studies

  WORKING CAPITAL MANAGEMENT AND ITS EMPIRICAL STUDY ON AMMAN STOCK EXCHANGE LISTED COMPANIES

 

Prepared by the researcher

Dr. rida mansur shita, Assistant Professor in Faculty of Economics and Political Science, Department of Finance, University of Tripoil, Libya.

Dr. Solaiman Musbah Albandag, Lecturer at the Higher Institute of Science and Technology, Tarhuna-Libya

Democratic Arabic Center

International Journal of Economic Studies : Twenty-first Issue – May 2022

A Periodical International Journal published by the “Democratic Arab Center” Germany – Berlin

Nationales ISSN-Zentrum für Deutschland
ISSN  2569-7366
International Journal of Economic Studies

Abstract

This study aimed at examining the result of executing capital management on industry’s economic performance. The survey sample contain of 46 industry filed on Amman Stock Exchange. The study’s duration was from 2014 to 2018. Necessary financial data belong to the companies involved were collected and analyzed accordingly. The results revealed that working capital management are important task in the firms’ performance. It was proven that working capital is the back bone of any organization’s growth and profitability.

In addition, this study sought to determine the effect on profitability of manufacturing firms in Jordan.  This study employed panel data methodology.  The data for the study was derived mainly from secondary data sources including Capital Markets Authority Library and Amman Securities Exchange Library. The results from regression analysis indicated that only 26% of variations on financial performance of manufacturing firms could be attributed to working capital management and the remaining portion being influenced by other factors.

  1. INTRODUCTION

Effective working of capital management includes designing and managing the present assets and the present responsibility in the approach that prevents the risk inability to meet the term liabilities on the one hand, to prevent the immoderate investment on to the assets on the other. The capital management is crucial for economical decisions in the industry area. Better management of working capital raises the industry value while working capital management is fundamental to the industry because it measures time investments and increases the expectations of a firm (Abbasali & Emamgholipourarchi, 2012).

There are countless numbers of research and studies conducted in this context. However, we prefer to cite tangible examples from Jordanian companies in order to shed light on WCM so as to better understand the phenomenon, and show the its importance for companies’ survival and growth. We have chosen The Arab Bank, and the Arab Potash Company, since no studies have ever been conducted about them.The Arab Bank has developed from being a tiny bank in Palestine and Jordan to becoming an international one (Abdulraheem, Yahaya, Isiaka, & Aliu, 2011).

The other example that shows clearly the WCM’s significant role on organizations’ performance, liquidity and profitability is the Arab Potash Company. By reviewing the company’s records we can see that in late 1970s and early 1980s the company was at the verge of bankruptcy. Its market share value was less than two dinar per share at the time. However, when a new management took over, the operations in the company have changed drastically. The new management implemented a four step plan to save the company (Afza & Sajid Nazir, 2007; Joshua & Peter, 2010):

  • Efficient working capital management, by which reflected positively on the company’s performance and eventually its profitability.
  • Empowering employees.
  • Differentiation by which the company gained competitive advantage over the nearby Israeli Potash company.
  • Cost cutting.

By adopting a working capital management effectively the Arab Potash Company is a very profitable firm nowadays. Its stocks attract investors strongly, whereas the market value is about forty dinar per share. This example shows the impact of WCM on organizations’ performance and profitability without the need to mine organizations’ data bases looking for supportive statistics to show WCM role in firms’ survival(Olayinka, 2012).

2. LITERATURE SURVEY

In his study, Kumaraswamy (2016)aims to explore the impact of working capital on the strong performance of the cement industry in the GCC countries for the period 2008-2014. Four hypotheses related to working capital components have been investigated using linear regression models. The study identified a positive relationship between the period of transfer of stocks and the average duration of repayment with profitability, and the negative relationship between the average collection period and the profitability of the company. The results of the regression model indicate the average collection period and the stock conversion period to be the most important factors that followed the average repayment period.

In their study,Shivakumar and Thimmaiah (2016)aim to give a conceptual vision in the management of working capital and assess its impact on the liquidity and profitability of the Indian coal company. Liquidity and profitability trade-off has become an important aspect for all organizations. The researchers also aimed at testing the liquidity and profitability of the company’s position. To achieve this correlation target and spearman rank method was used. The bonding method refers to the weak link and negative correlation between liquidity and profitability. The mutual test has also been applied to test liquidity performance. It has been noted that the company’s liquidity position improved during the study period.

Sharaf and Haddad (2015)investigated  the relationship  between  Working Capital Management and Profitability for Industrial Companies Listed in Amman Stock Exchange. The results of regression analysis show a significant negative relationship between cash conversion cycle and profitability.Nguyen, Tran, and Nguyen (2016)examined the effect that working capital management has on firms’ profitability by using the data from listed companies on Vietnamese Stock Exchange. The result implies that there is no correlation between Working Capital Management and firms’ profitability.

Malik and Bukhari (2014)selected ROE as an indicator of profitability and five-years’ data of 38 KSE listed companies from Cement, Chemical and Engineering sectors to study working capital management.Qazi, Shah, Abbas, and Nadeem (2011) limited their research work by taking 20 oil & gas  companies listed on KSE from 2004 to 2009 and net income for measuringprofitability.Asad (2012) studied working capital management by gathering data of 30 textile listed firms as a sample and EPS as symbolizing profitability.

Madhavi (2014)makes an empirical study of the co-relation between liquidity position and profitabilityof the paper mills in Andhra Pradesh. It has been observed that inefficient working capital management makes a negative impact on profitability and liquidity position of the paper mills.

Akoto, Dadson, and Angmor (2013)carried out a thorough study of the relationship between working capital management policies and profitability of  the  thirteen  listed  manufacturing  firms  in  Ghana. At the end of the study, a significantly negative relationship between profitability and accounts receivable days is found to exist. Profitability is significantly positively influenced by the firm’s cash conversion cycle (CCC), current assets ratio and current asset turnover. It is also suggested that managers can create value for the shareholders by creating incentives to reduce their accounts receivable to 30 days.

Turan, Bamal, Vashist, and Turan (2013)attempt to examine the relationship between working capital management and profitability by making an inter sector comparison of two manufacturing industries i.e. chemical industries and pharmaceutical industries. Fifty companies from each sectorbased on market  capitalization and listed on BSE and 500 indices were selected for the research for the period from 2002 to 2011. At the end of the analysis it was concluded that in spite of a similar nature  of both industries inthe manufacturing sector, working capital management variables affect profitability indices more strongly in the chemical industry than in the pharmaceutical industry.

Samson, Mary, and Yemisi (2012)hope to empirically investigate the impact of working capital management on the profitability of a sample of 30 SME’s of Nigeria during 2009. In conclusion, the writer  points  out that managers can create value by reducing their firm’s number of accounts receivable day’s and inventories. At the same time the firm’s profitability could also be improved by reducing the cash conversion cycle.

3. research methodology

The present study is an attempt to analyse working capital management of the listed companies for the period of 2010-2014. The approach adopted is basically analytical and interpretative in nature Before deciding about the broad approach or methods of analysis for the present study, literature on research methodology and research studies related to the working capital management were reviewed (as mentioned in chapter II).

On the basis of the review and the objectives of this study, it was decided to employ quantitative descriptive methods of analysis and investigation of the empirical data on the working capital management in the companies for the period of 2014-2018 as stated earlier. The one of the most usefull case of appling this methodology was the impact of working capital management on company’s profitabilityin (Vuković, Andrić, & Jakšić, 2017).

The analytical and descriptive approach is followed because of the fact that it seeks to analyse and interpret 50 the working capital management over a period of time.

Study Hypotheses is to achieve the study objectives, six hypotheses will be used. These hypotheses are expressed using the null form as follows:

H01: There is no significant relationship between working capital management components and gross operating profit.

H02: There is no significant relationship between working capital management components and return on assets.

H03: There is no significant relationship between working capital management components and return on equity.

H04: There is no significant relationship between cash conversion cycle and gross operating profit.

 H05: There is no significant relationship between cash conversion cycle and return on assets.

H06: There is no significant relationship between cash conversion cycle return on equity.

Study Models:

The following six specifications that will be used to examine the                  relationship between working capital management and profitability:

GOPit= ß0 + ß1ln (RCPit)+ ß2ln (ICPit)+ ß3ln

(PDPit)+ ß4(SIZEit)+ ß5(SGit)+ ß6(LEVit)+εi

Model Specification (1)

ROA it = ß0 + ß1ln (RCPit)+ ß2ln (ICPit)+ ß3ln

(PDPit)+ ß4(SIZEit)+ ß5(SGit)+ ß6(LEVit)+εi

Model Specification (2)

ROEit= ß0 + ß1ln (RCPit)+ ß2ln (ICPit)+ ß3ln

(PDPit)+ ß4(SIZEit)+ ß5(SGit)+ ß6(LEVit)+εi

Model Specification (3)

GOPit= ß0 + ß1ln (CCCit)+ ß2(SIZEit) +

ß3 (SGit) +ß4(LEVit)+εi

 Model Specification (4)

 ROA it = ß0 + ß1ln (CCCit)+ ß2(SIZEit) +

ß3 (SGit) +ß4(LEVit)+εi

 Model Specification (5)

ROEit= ß0 + ß1ln (CCCit)+

ß2(SIZEit) + ß3 (SGit) +ß4(LEVit)+εi

Model Specification (6)

Where as:

GOP = Gross Operating Profit;

ROA = Return on Assets;

ROE = Return on Equity;

ß0 = The regression constant term;

ßi = (i = 1, 2,3,…) are the parameter slope;

RCP = Receivables Collection Period;

ICP = Inventories Conversion Period;

PDP = Payables Deferral Period;

CCC = Cash Conversion Cycle;

SIZE = The Size of the company;

SG = Sales growth;

LEV = The Leverage Ratio; εi = The variable random error whose expected value is zero; (it): i = 1,…,N, refers to the number of companies, t = 1,…, T, refers to the numbers of years.

Fig. 1. Framework of the study

The Empirical Model

Through the primary model it is possible to derive the study’s measuring model in order to test the study’s hypotheses as follow:   As to measure company’s performance while relying on  ROE & ROA, thus we will have two models express the dependant variable to measure company’s performance in each hypothesis.

H1 An increase in working capital, leads to increase in its financial performance

H2- The increase in company’s financial leverage leads to increase in its financial performance

H3- The increase in company’s size leads to increases in its financial performance.

Where;

Fp is firm performance measured as

 1-ROA, return on assets.          2-R0E Return on Equity ,        Ntc, net trade cycle

Icp, inventory conversion period,         App, account payment period , Acp, account collection period,   Siz, firm size,   Lav, financial leverage

4. RESULTS

Empirical results

 Descriptive Statistics

 Descriptive statistical summary of the study variables are shown in Table 1. The mean for all the study variables are very close to their median except for RCP, ICP, CCC, and SG. The median of the RCP is 78 days below the mean of 107 days, with a distribution highly skewed to the right. The median of ICP is 141 days slightly below the mean, indicating that most ICP values are around the average of 176 days, with a distribution skewed to the right. The median of CCC is 161 days below the mean of 215 days, with a distribution skewed to the right. The average sales growth ratio is 11.34% with a standard deviation of 53.38% and median equal to 4.16%, with a distribution highly skewed to the right.

Correlations Analysis

Table 2 results show that the correlation coefficients of working capital management and its empirical study (GOP, ROA, and ROE), and all independent and control variables are significant, and as predicted by prior empirical researches. The results also show a positive significant relationship between the independent variables cash conversion cycle and inventories conversion period with a correlation coefficient of 73.5%, and a positive significant relationship between cash conversion cycle and receivables collection period with a correlation coefficient of 56.3%.

Table 3- The change in return on assets during the study period

X-axis (Years) Y-axis (Assets)
2014 1.93
2015 0.92
2016 2.93
2017 4.95
2018 1.85

In the above table, the X-axis shows the years while the Y-axis shows the assets. In year 2014 the assets reached 1.93 and in year 2015 they reached 0.92, which means the decrease of the asset value from 2015 to 2014. The asset value was 2.93 in 2016, while the asset value in 2017 was 4.95. The asset value was 1.85 in 2018. The overall lowest asset value was in the year of 2015, and the highest asset value was in 2017.

Table 4-The change in Return on Equity during the study period

X-axis(Years) Y-axis(Return On Asset)
2014 0.2
2015 1.45
2016 1.75
2017 4.33
2018 0.2

The above table shows the X-axis, indicating the Return on Asset, and the Y-axis, showing the Years. In year 2014 the return on asset value is 0.2, while in 2015 it increases to 1.45. The asset value reached 1.75 and 4.33 in 2016 and 2017 respectively. The asset value decreased in 2018 to 0.2. Thus, from the above table it can be noticed that the return on asset had the upward trend from 2014 to 2017, and then after 2017 this value decreased.

Table 5: The correlation matrix between the variables of the study

X – Mx Y – My (X – Mx)2 (Y – My)2 (X-Mx)(Y-My)
-2.000

-1.000

0.000

1.000

2.000

Mx: 012.000

-0.586

-1.596

0.414

2.434

-0.666

My: 2.516

4.000

1.000

0.000

1.000

4.000

Sum:10.000

0.343

2.547

0.171

5.924

0.444

Sum:9.430

1.172

1.596

0.000

2.434

-1.332

Sum: 3.870

Table 6- The change in inventory conversion period during the study period

X-Axis (Years) Y-Axis (Return On Asset in Days)
2014 105
2015 125
2016 142
2017 160
2018 187

The table above shows the X-axis representing Years and the Y-axis representing the Return on Asset in Days. As it can be seen, the return on asset increases from year 2014 to year 2018, The correlation matrix between the variables has been calculated.

Table 7- The correlation matrix between the variables of the study

X – Mx Y – My (X – Mx)2 (Y – My)2 (X-Mx)(Y-My)
-2.000

-1.000

0.000

1.000

2.000

Mx: 2012.000

-1.386

-0.136

0.164

2.744

-1.386

My: 1.586

4.000

1.000

0.000

1.000

4.000

Sum: 10.000

1.921

0.018

0.027

7.530

1.921

Sum: 11.417

2.772

0.136

0.000

2.744

-2.772

Sum: 2.880

Table-8The correlation matrix between the variables

X – Mx Y – My (X – Mx)2 (Y – My)2 (X-Mx)(Y-My)
-2.000

-1.000

0.000

1.000

2.000

Mx: 2012.000

-38.800

-18.800

-1.800

16.200

43.200

My: 143.800

4.000

1.000

0.000

1.000

4.000

Sum: 10.000

1505.440

353.440

3.240

262.440

1866.240

Sum: 3990.800

77.600

18.800

0.000

16.200

86.400

Sum: 199.000

Higher average group period of the technical industry at the time study period is similar to the previous periods, where we note the stability of collection period average of factors (2016 – 2017).  This value rises again to the same rates as in previous years, despite the rise in the average collection period of the index. However, we find that the rate of the profitability on property and return on impartiality grew at an increasing rate and in an abnormal correlation, unlike in previous studies, which showed an inverse relationship between profitability on property and return on impartiality. These may be due to the difference between this study and the previous studies.

Table 9 –The change in Account collection period during the study period

X-axis  (Years) Y-axis (Average Collection Period Days)
2014 72
2015 77
2016 85
2017 82
2018 97

On the above table, the X-axis represents years and the Y-axis Average Collection Period. In the years from 2014 to 2016 the average collection period increases, while after 2016 it decreases, only to increase again in 2018,The correlation matrix between the variables has been shown.

Table 10-The correlation matrix between the variables of the study

X – Mx Y – My (X – Mx)2 (Y – My)2 (X-Mx)(Y-My)
-2.000

-1.000

0.000

1.000

2.000

Mx: 2012.000

-10.600

-5.600

2.400

-0.600

14.400

My: 82.600

4.000

1.000

0.000

1.000

4.000

Sum: 10.000

112.360

31.360

5.760

0.360

207.360

Sum: 357.200

21.200

5.600

0.000

-0.600

28.800

Sum: 55.000

Table 11- The change in Account Payment Period during the study period

X-axis (Years) Y-axis (Account Payment Period)
2014 87
2015 102
2016 102
2017 117
2018 123

The X-axis gives the value for each year, while the Y-axis represents Account Payment Period. From 2014 to 2018 the observed value increases, but during 2015 to 2016 the value is the same, which means that there is no account payment period increase during the years of 2015 and 2016

The average repayment period was on the rise during the study period. The average rate for industrial companies in 2014 was approximately 87 days. This taple continued to rise to reach the average level of 2018 (approximately 123 days), which is lower than the average rate if compared to the average period and the average collection period, which kept increasing steadily. This is interpreted as a sign that the Jordanian industrial companies did not have long periods of time to pay off their debts. Therefore, there may be some defect in the company’s working capital policy if the company is exposed to short-term policies,exposing them to the inability to receive creditors’ claims in a timely manner.

Table 12 –The change in Net trade cycle during the study period

X-Axis (Years) Y-Axis(Average Collection Period)
2014 87
2015 98
2016 125
2017 117
2018 152

In this table the lowest average collection period is 87 and it is recorded in the year 2014, while the highest average collection period was recorded in 2018.

The results for the length of the monetary period of the Jordanian industrial companies showed a serious situation. Actually, the results showed that these companies suffer from periods of time in which they are unable to meet their short-term liabilities. The increase continued from 87 days in 2014 to 152 days in 2018. The industries relied on their own funds and borrowed money to cover their obligations. This situation may expose them in the future to financial difficulties in the payment of short-term obligations and weakness in their working capital management.

Table 13- The correlation matrix between the variables of the study are shown in the following table

X-Mx Y-My (X-Mx)2 (Y-My)2 (X-Mx)(Y-My)
-2.000

-1.000

0.000

1.000

2.000

Mx:2012.000

-19.200

-4.200

-4.200

10.800

16.800

My:106.200

4.000

1.000

0.000

1.000

4.000

Sum:10.000

368.640

17.640

17.640

116.640

282.240

Sum: 802.800

38.400

4.200

0.000

10.800

33.600

Sum: 87.000

Table 14- Comparison between the size of the debt and the return

X-axis (Year) Y-axis
Average of ROE Average of ROA Average of size
2014 30.82 2.01 0.2
2015 31.96 4.62 4.62
2016 33.36 3.13 1.64
2017 33.81 1.05 1.05
2018 33.16 1.93 0.26

In the table 14, ROA and average size. The Average of ROE assets value is 30.82, 31.96, 33.36, 33.81, 33.16 in the following years respectfully: 2014, 2015, 2016, 2017, 2018. The highest average of ROE of 33.81 is recorded in 2017, while the lowest value is 30.82, recorded in the year of 2014. The Average of ROA highest value is 4.62 in the year of 2015, and the lowest value is 1.05 in the year of 2017. The highest average of size value is 4.62 in the year of 2015, and lowest value is 0.2 in the year of 2014.

Table 15: correlation between the variable measuring the company’s performance through ROA and the variable measuring ROE.

ROA ROE ICP ACP APP NTC SIZ LAV
ROA 1 0.885249 -0.15039 -0.24149 -0.32047 -0.05632 0.315258 -0.21243
ROE 1 -0.12235 -0.2652 -0.30523 -0.0552 0.232634 -0.26737
ICP 1 0.242323 0.645534 0.659388 -0.18884 -0.16667
ACP 1 0.335013 0.624844 -0.26638 -0.02151
APP 1 0.084601 -0.06413 0.026345
NTC 1 -0.31137 -0.20243
SIZ 1 0.092982
LAV 1

Table 15 shows a strong correlation between the variable measuring the company’s performance through ROA and the variable measuring ROE, whereby the correlation coefficient between them is 88% showing a positive relationship between them. As the researchers agree, the two indicators are an important tool to measure the performance of the company.

5. CONCLUSION

5.1. Conclusion

In this paper several approaches were used to study the association between WCM administration and industry execution. Tobin Q and gross working benefit were measured as important factors in firm productivity and productivity of the organizations with other autonomous factors for 46 chosen organizations recorded on the ASE (Amman Stock Exchange) in Jordan for the period between 2014 and 2018. Based on the the results of the research, it can be concluded that there is a very strong association between the WCM administration and organization execution. The outcomes demonstrate that accumulation time of record receivables and money change cycle are inversely related with the association’s gains. This implies that by shortening gathering period and money transformation cycle the firms can benefit. Otherwise, the connection between other working capital administration segments and company’s productivity is not significant.

5.2. Limitations of the study

Members: 46 modern organizations, Place: Amman stock Exchange-Amman Jordan, Subject: The influence of WCM administration on firms’ execution

The selection of a part of the Jordanian economy makes us unfit to generalize the results of this study to other parts of this economy. Along these lines, additional research should be carried out to incorporate more divisions and more organizations.

5.3. Findings

Because of the present economical critical period, the organizations have been given advice to thoroughly explore their resources. Although the previous study paper concentrates on long-term economical period and investment, organization liquidity proved to be of large significance during the financial critical period. To corresponding to tradeoff concept, the organization has to maintain equality between liquid and return value. The liquidity is a pre-condition to ensure that organizations are capable of reaching their short-term responsibility and their cash flow can be insured against the return value. The significance of the cash flow is shown through continuous economical achievement. The organization may be capable of decreasing the investments in fixed assets by hiring plant and machinery, whereas, similar sachems cannot be used for the parts of WCM. The large level of current assets may decrease the risk of liquidity related with specific value of funds which have been invested as long-term assets.

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Shivakumar, & Thimmaiah, N. B. (2016). Working Capital Management-It’s Impact on Liquidity and Profitability-aStudy of Coal India Ltd. International Journal of Research-Granthaalayah, 4(12), 178-187.

Turan, M. S., Bamal, S., Vashist, B., & Turan, N. (2013). Relationship between Working Capital Management and Profitability: A Comparison of Chemical and Pharmaceutical Industries. Journal of Accounting and Finance, 27(1).

Vuković, B., Andrić, M., & Jakšić, D. (2017). The impact of working capital management on company’s profitability: Empirical evidence from Serbia (Vol. 13).

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