Southern Taiwan University

Department of Business Administration

(IMBA) Program

Master’s Thesis

THE RELATIONSHIP BETWEEN

WORKING CAPITAL MANAGEMENT

AND PROFITABILITY: A VIETNAM

CASE Graduate student: Huynh Phuong Dong

研究生: 桜橄

Advisor: Assistant Professor Jyh-Tay Su 指導教授: 証扐炮

May, 2010

i

Acknowledgements

I am deeply grateful for the encouragement and support throughout the

preparation of this thesis. I would like to express my deepest acknowledgement to my

supervisor, professor Jyh- Tay Su from the Southern Taiwan University, for his valuable

advice and recommendations.

I acknowledge Dr. Tran Phuoc Tru and Mr Nguyen Ba The from Da Nang

university of economics for their support with statistical techniques and data analysis.

I would like to thank my friends in Southern Taiwan University who have

supported me about material as well spirit in order to finish this thesis.

Finally, to my parents and my wife, I wish to extend my loving thanks for their

encouragement. My greatest debt of gratitude is to my wife, Mrs. Le Anh Phe, who was

patiently supporting me during my study in Taiwan. This thesis could not have been

written without her daily encouragement.

ii

Abstract

The working capital management plays an important role for success or failure

of firm in business because of its effect on firm’s profitability as well on liquidity. The

thesis is based on secondary data collected from listed firms in Vietnam Stock Exchange

(VSE) for the period of 2006-2008 with an attempt to investigate the relationship

existing between profitability, the cash conversion cycle and its components for listed

firms in VSE. The finding shows that there is a strong negative relationship between

profitability, measured through gross operating profit, and the cash conversion cycle.

This means that as the cash conversion cycle increases, it will lead to declining of

profitability of firm. Therefore, the managers can create a positive value for the

shareholders by handling the adequate cash conversion cycle and keeping each

different component to an optimum level.

Keywords: Working Capital Management, Gross operating profitability,

Vietnam Stock Exchange.

iii

Table of contents

Acknowledgements ............................................................................................................ i 

Abstract ............................................................................................................................. ii 

Table of contents .............................................................................................................. iii 

List of tables ..................................................................................................................... v 

List of figures ................................................................................................................... vi 

CHAPTER ONE  INTRODUCTION ............................................................................... 1 

1.1 Background ............................................................................................................. 1 

1.2 Aims of research ..................................................................................................... 3 

1.3 Research structure ................................................................................................... 4 

CHAPTER TWO  LITERATURE REVIEW ................................................................... 5 

2.1 Theory about working capital management and profitability ................................. 5 

2.2 Related literature review ......................................................................................... 9 

CHAPTER THREE  RESEARCH METHODOLOGY ................................................. 21 

3.1 Research design .................................................................................................... 21 

3.2 Research method ................................................................................................... 21 

3.3 Variable definitions and model development ....................................................... 22 

3.3.1 Variable measurement ................................................................................... 22 

3.3.1.1 Dependent variable ................................................................................. 22 

3.3.1.2 Independent variable ............................................................................... 24 

3.3.1.3 Control variable ...................................................................................... 26 

3.3.2 Hypotheses ..................................................................................................... 26 

3.3.3 Model development ....................................................................................... 28 

3.3.3.1 Model classification ................................................................................ 28 

3.3.3.2 Model development for this research ...................................................... 29 

3.4 Data collection methods........................................................................................ 30 

3.5 Data transformation .............................................................................................. 31 

3.6 Data analysis methods .......................................................................................... 32 

3.6.1 Descriptive statistics ...................................................................................... 32 

iv

3.6.2 Correlation analysis ....................................................................................... 32 

3.6.3 Multiple Regression analysis ......................................................................... 32 

CHAPTER FOUR  RESEARCH RESULTS .................................................................. 33 

4.1 Descriptive statistics ............................................................................................. 33 

4.2 Correlation analysis .............................................................................................. 35 

4.3 Multiple regression analysis ................................................................................. 37 

CHAPTER FIVE  CONCLUSIONS AND IMPLICATIONS ........................................ 48 

5.1 Conclusions ........................................................................................................... 48 

5.2 Implications .......................................................................................................... 50 

5.3 Limitations of research ......................................................................................... 52 

References ....................................................................................................................... 53 

Appendix ......................................................................................................................... 57 

Appendix 1: Model 1 .................................................................................................. 57 

Appendix 2: Model 2 .................................................................................................. 57 

Appendix 3: Model 3 .................................................................................................. 58 

Appendix 4: Model 4 .................................................................................................. 58 

Appendix 5: Test normal for model 1 ......................................................................... 59 

Appendix 6: Test normal for model 2 ......................................................................... 60 

Appendix 7: Test normal for Model 3 ........................................................................ 62 

Appendix 8: Test normal for model 4 ......................................................................... 63 

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List of tables Table 4.1: Descriptive statistics………………………………………………………... 33

Table 4.2: Correlation matrix………………………………………………………….. 35

Table 4.3: Variables entered/removeb for model 1…………………………………… 37

Table 4.4: Model summaryb for model 1…………………...………………………….. 37

Table 4.5: ANOVA result for model 1… …………….…….………………………... 38

Table 4.6: Result of regression model for mode 1… …….………………..………... 38

Table 4.7: Variables entered/removeb for model 2….…………………….……….…… 39

Table 4.8: Model summaryb for model 2……………..…………………….………….. 39

Table 4.9: ANOVA result for model 2………..…………..…………….…………….. 39

Table 4.10: Result of regression model for mode 2……………………………….…… 40

Table 4.11: Variables entered/removeb for model 3…..………..……………………… 40

Table 4.12: Model summaryb for model 3…………………..…………………………... 41

Table 4.13: ANOVA result for model 3………...……………………………………..... 41

Table 4.14: Result of regression model for mode 3..……………………………….…… 41

Table 4.15: Variables entered/removeb for model 4…..………..……….……………….. 42

Table 4.16: Model summaryb for model 4…….…………….…………………………... 42

Table 4.17: ANOVA result for model 4……………....……….………………………... 42

Table 4.18: Result of regression model for mode 4……………………………….…… 43

Table 5.1: Hypothesis test results………………...……………………………………. 50

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List of figures Figure 1.1: Research structure………………………………………………… 4

Figure 3.1: The Cash conversion cycle………………………………………. 25

Figure 3.2: Analytical model………………………………………………… 30

Figure 5.1: The relationship between working capital management and

profitability……………………………………………………….. 49

1

CHAPTER ONE INTRODUCTION

1.1 Background Assets in commercial firm consist of two kinds: fixed assets and current assets.

Fixed assets include land, building, plant, furniture, etc. Investment in these assets

represents that of part of firm’s capital, which is permanently blocked on a permanent or

fixed basis and is also called fixed capital that generates productive capacity. The form

of these assets does not change, in the normal course. In the contrast, current assets

consist of raw materials, work-in-progress, finished goods, bills receivable, cash, bank

balance, etc. These assets are bought for the purpose of production and sales, like raw

material into semimanufactured products, semimanufactured products into finished

products, finished products into debtors and debtors turned over cash or bills receivable.

The fixed assets are used in increasing production of an organization and the

current assets are utilized in using the fixed assets for day to day working. Therefore,

the current assets, called working capital, may be regarded as the lifeblood of a business

enterprise. It refers to that part of the firm’s capital, which is required for financing

short-term.

The management of this working capital is known as working capital

management. The basis objective of working capital management is to manage firm’s

current assets and current liabilities, in such a way, that working capital are maintained,

at a satisfactory level. The working capital should be neither more nor less, but just

adequate.

Working capital management plays an important role in a firm’s profitability and

risk as well as its value (Smith, 1980). There are a lot of reasons for the importance of

working capital management. For a typical manufacturing firm, the current assets

account for over half of its total assets. For a distribution company, they account for

even more. Excessive levels of current assets can easily result in a firm’s realizing a

substandard return on investment. However, Van Horne and Wachowicz (2004) point

out that excessive level of current assets may have a negative effect of a firm’s

profitability, whereas a low level of current assets may lead to lowers of liquidity and

stock-outs, resulting in difficulties in maintaining smooth operations.

Efficient management of working capital plays an important role of overall

corporate strategy in order to create shareholder value. Working capital is regarded as

2

the result of the time lag between the expenditure for the purchase of raw material and

the collection for the sale of the finished goods. The way of working capital

management can have a significant impact on both the liquidity and profitability of the

company (Shin and Soenen, 1998). The main purpose of any firm is maximum the

profit. But, maintaining liquidity of the firm also is an important objective. The problem

is that increasing profits at the cost of liquidity can bring serious problems to the firm.

Thus, strategy of firm must be a balance between these two objectives of the firms.

Because the importance of profit and liquidity are the same so, one objective should not

be at cost of the other. If we ignore about profit, we cannot survive for a longer period.

Conversely, if we do not care about liquidity, we may face the problem of insolvency.

For these reasons working capital management should be given proper consideration

and will ultimately affect the profitability of the firm.

Working capital management involves planning and controlling current assets

and current liabilities in a manner that eliminates the risk of inability to meet due short

term obligations on the one hand and avoid excessive investment in these assets on the

other hand ( Eljelly, 2004). Lamberson (1995) showed that working capital management

has become one of the most important issues in organization, where many financial

managers are finding it difficult to identify the important drivers of working capital and

the optimum level of working capital. As a result, companies can minimize risk and

improve their overall performance if they can understand the role and determinants of

working capital. A firm may choose an aggressive working capital management policy

with a low level of current assets as percentage of total assets, or it may also be used for

the financing decisions of the firm in the form of high level of current liabilities as

percentage of total liabilities (Afza and Nazir, 2009). Keeping an optimal balance

among each of the working capital components is the main objective of working capital

management. Business success heavily depends on the ability of the financial managers

to effectively manage receivables, inventory, and payables (Filbeck and Krueger, 2005).

Firms can decrease their financing costs and raise the funds available for expansion

projects by minimizing the amount of investment tied up in current assets. Lamberson

(1995) indicated that most of the financial managers’ time and efforts are consumed in

identifying the non-optimal levels of current assets and liabilities and bringing them to

optimal levels. An optimal level of working capital is a balance between risk and

efficiency. It asks continuous monitoring to maintain the optimum level of various

3

components of working capital, such as cash, receivables, inventory and payables (Afza

and Nazir, 2009). A popular measure of working capital management is the cash

conversion cycle, which is defined as the sum of days of sales outstanding (average

collection period) and days of sales in inventory less days of payables outstanding

(Keown et al, 2003). The longer this time lag, the larger the investment in working

capital. A longer cash conversion cycle might increase profitability because it leads to

higher sales. However, corporate profitability might also decrease with the cash

conversion cycle, if the costs of higher investment in working capital is higher and rises

faster than the benefits of holding more inventories and granting more inventories and

trade credit to customers (Deloof, 2003).

Lastly, working capital management plays an important role in managerial

enterprise, it may impact to success or failure of firm in business because working

capital management affect the profitability of the firm. The thesis is expected to

contribute to better understanding of relationship between working capital management

and profitability in order to help managers taking a lot of solutions to create value for

their shareholders, especially in emerging markets like Vietnam.

1.2 Aims of research This research is focusing on working capital management and its effects on

profitability for a sample of Vietnamese firms. The main objectives are:

• To investigate a relationship between working capital management and

profitability over a period of 3 years for 130 Vietnamese firms listed on Vietnam

Stock Exchange.

• To find out the effects of different components of working capital management

on profitability.

• To find out the relationship between profitability and size of Vietnamese firms.

• To find out the relationship between debt ratio used by the Vietnamese firms and

its profitability.

• To draw conclusion about relationship of working capital management and

profitability of Vietnamese firms.

4

1.3 Research structure This thesis is structured into 5 chapters. Chapter 1 introduces the research

including introduction, aims of research, structure of the study. Chapter 2 introduces

about literature review including theory about working capital management and

profitability, literature review about the relationship between working capital

management and profitability. Chapter 3 discusses methodology utilized in the research.

Chapter 4 analyses the data collected and presents the findings of the research. Chapter

5 points out conclusions and the implications of the research findings.

Figure 1.1 Research structure

INTRODUCTION

LITERATURE REVIEW

RESEARCH METHODOLOGY

RESEARCH RESULTS

CONCLUSIONS AND IMPLICATIONS

5

CHAPTER TWO LITERATURE REVIEW

2.1 Theory about working capital management and profitability According to Van Horne and Wachowicz (2004), working capital management

is the administration of current assets in the name of cash, marketable securities,

receivables, and inventories. Osisioma(1997) described working capital management as

the regulation, adjustment, and control of the balance of current assets and current

liabilities of a firm such that maturing obligations are met, and the fixed assets are

properly serviced.

Smith (1980) showed that the working capital management plays an important

role in a firm’s profitability and risk as well as its value. Efficient management of

working capital is very essential in the overall corporate strategy in order to create

shareholder value. Firms try to maintain an optimum level of working capital that

maximizes value for shareholders (Deloof, 2003)

According to Harris (2005), working capital management is a simple and

straightforward concept of ensuring the ability of the organization to fund the difference

between short-term assets and short-term liabilities. However, a ‘total approach’ should

be followed which covers all the activities of the company relating to vendors,

customers and products (Hall, 2002). In practice, working capital management has

become one of the most important issues in organizations, where many financial

managers are finding it difficult to identify the important drivers of working capital and

the optimum level of working capital (Lamberson, 1995). Consequently, companies can

reduce risk and improve their overall performance if they can understand the role and

determinants of working capital. A firm may apply an aggressive working capital

management policy with a low level of current assets as percentage of total assets, or it

may also be used for the financing decisions of the firm in the form of high level of

current liabilities as percentage of total liabilities (Afza and Nazir, 2009). Excessive

levels of current assets may have a negative effect on a firm’s profitability, whereas a

low level of current assets may lead to lower levels of liquidity and stock outs, resulting

in difficulties in maintaining smooth operations (Van Horne and Wachowicz, 2004).

The main objective of working capital management is to maintain an optimal balance

among each of the working capital components (Afza and Nazir, 2009). In the light of

6

view, Filbeck and Krueger (2005) stressed business success heavily depends on the

ability of the financial managers to effectively manage receivables, inventory, and

payables. Firms can reduce their financing costs and/or increase the funds available for

expansion projects by minimizing the amount of investment tied up in current assets

(Afza and Nazir, 2009). Most of the financial managers’ time and efforts are consumed

in identifying the non-optimal levels of current assets and liabilities and bringing them

to optimal levels (Lamberson, 1995). An optimal level of working capital is the one in

which a balance is achieved between risk and efficiency. It requires continuous

monitoring to maintain the optimum level of various components of working capital,

such as cash, receivables, inventory and payables (Afza and Nazir, 2009).

Efficient working capital management is an integral component of the overall

corporate strategy to create shareholder value. Working capital is the result of the time

lag between the expenditure for the purchase of raw materials and the collection for the

sale of the finished products. The continuing flow of cash from suppliers to inventory to

accounts receivable and back into cash is usually referred to as the cash conversion

cycle. Smith (1980) firstly signaled the importance of the trade-offs between the dual

goals of working capital management, i.e. liquidity and profitability. In other words,

decisions that tend to maximize profitability tend not to maximize the chances of

adequate liquidity. Conversely, focusing almost entirely on liquidity will tend to reduce

the potential profitability of the company.

A few key performance ratios of a working capital management system are the

working capital ratio, inventory turnover and the collection ratio. Ratio analysis will

lead management to identify areas of focus such as inventory management, cash

management, accounts receivable and payable management.

The component of working capital management includes following contents:

Number of days accounts receivable

Number of days accounts receivable is a key figure which measures the average

amount of time that a company holds its accounts receivable. It is calculated by the

following equation:

Number of days accounts receivable = Accounts receivable / sales * 365

Number of days inventories

Number of days inventories is a key figure which measures the average amount

of time that a company holds its inventory. It is calculated by the following equation:

7

Number of days inventories = Inventory / Costs of goods sold * 365

Number of days accounts payable

Number of days accounts payable is a key figure which measures the average

amount of time that a company holds its accounts payable. It is calculated by the

following equation:

Number of days accounts payable = Accounts payable / Costs of goods sold * 365

Cash conversion cycle

The cash conversion cycle is defined as the sum of days of sales outstanding

(average collection period) and days of sales in inventory less days of payables

outstanding (Keown et al, 2003). The cash conversion cycle concept also matches the

principle of cash management well, i.e. to collect cash as quickly as possible, to

postpone cash outflow as long as possible, and to put available cash to the best use

when we have it. The length of a firm cash conversion cycle depends on the number of

day’s credit it gets from its suppliers, the length of the production process and the

number of days finished products remain in inventory before they are sold, and finally,

the average collection period from the company customers. The length of the cash

conversion cycle is instrumental in determining the degree to which the firm must rely

on external financing. Accounts payable are a form of spontaneous credit generated

through the normal production process of the firm. This spontaneous credit has no

explicit financing charge (interest payments) as long as no penalty is charged for late

payment. Rising sales often present a firm with a serious financing problem because the

firm must finance its operations. The cash conversion cycle is also closely related to the

issue of firm valuation. The shorter the cash conversion cycle, the higher the present

value of net cash flows generated by the assets and thus, the higher the value of the firm.

Likewise, the shorter the cash conversion cycle, that is, the fewer the number of days

cash is tied up in working capital not offset by “free” financing in the form of deferred

payments, the more liquid the condition of the firm. The cash conversion cycle is a

measure for the efficiency of working capital management as it indicates how quickly

current assets are converted into cash.

Cash conversion cycle = number of days accounts receivable + number of days

inventories – number of days accounts payable

8

Importance of profitability

McMahon (1995) indicated that one goal of financial management is to

maximize the owner’s wealth. Thus, profitability is one of the most important objectives

of financial management as well in determining the success or failure of a business. At

the establishment stage, a business may not be profitable because of investment and

expenses for establishing the business. When the business becomes mature, profits have

to be produced. Thomas and Evanson (2006) indicated that the goal of a business is not

only for the generation of sales, but also for the generation of profits. Profit is especially

important because it is necessary for the survival of a business. Davidson and Dutia

(1991) stressed low profitability contributes to under-capitalization problems because it

leads to fewer dollars as retained earnings and therefore to a reliance on external capital.

Defining and measuring profitability

In general, accounting profits are calculated by revenues minus costs. However,

the problem of profitability measured by accounting is that they ignore risk (Nguyen,

2001). In the economic sense, a firm is profitable only if its profitability is greater than

investors can achieve independently in the capital market. Ross et al. (2005) took some

methods to measure profitability including return on sales, return on assets, and return

on equity.

• Profit margins are computed by dividing net income by revenue and thus express

profits as a percentage of total operating revenue.

• Return on assets is the ratio of net income to average total assets, and measures

managerial performance.

• Return on equity is defined as net income divided by average stockholders’

equity, and shows profit available for stockholders.

Cohen (2005) indicated a lot of different ratios to measure profitability of the

business. They consisted of asset-earning power, return on the owner’s equity, net profit

on sales, and return on investment.

• Asset-earning power is determined by the ratio of earnings before interest and

tax to total assets. It indicates how much operating profit each dollar of total

assets earns.

• Return on the owner’s equity is computed by dividing net income by average

equity, and shows return that the business received in exchange for investment.

9

• Net profit on sales is determined by the ratio between net income and net sales,

and measures the difference between what the business takes in and what it

spends in the process of doing business.

• Return on investment is simply computed by dividing net profit by total

investment. This measure is very useful for measuring profitability.

Factors influencing profitability

Based on the profitability measures presented by Cohen (2005), it can see that

the main factors influencing profitability include revenues, cost and capital. Generally,

revenue is determined or influenced by marketing, sales management and new product

development, whereas cost and capital are mainly affected the financial management

practices.

Burns (1985) found that there were many different economic factors that could

affect profitability. Lev (1983) also showed that variability of profit measures over time

is affected by type of product, degree of competition, degree of capital intensity as well

firm size. Kirchhoff and Kirchhoff (1987) implemented a research in order to examine

family contributions to productivity and profitability in small businesses. The findings

of the research showed that family members are more productive than other employees.

However, in their study family member’s productivity did not increase profitability.

Results showed the opposite, as paid family labor increases, profitability decreases. As

family member participation increases, wage and salary expense increase as a

percentage of revenue, thereby causing profit as a percentage of sales to decline.

2.2 Related literature review Working capital is essential for day-to-day operations of a business, and thus it

is the life-blood of any business. Working capital management is about the management

of current assets and current liabilities in such a way that a satisfactory level of working

capital, which maximizes the profits of the firm, is maintained. Inadequacy of working

capital may lead the firm to insolvency, whereas excessive working capital implies idle

funds which earn no profits. Therefore, efficient management of working capital is an

integral part of the overall corporate strategy to improve corporate profitability. But in

reality, controversy persists on the issue whether the working capital of a firm affects its

profitability or not. Following empirical studies are good literature for establishing the

relationship between working capital management and profitability.

10

By carefully monitoring both the timing and magnitude of cash flows, managers

can minimize loan draws or create cash for investment purposes and therefore lessen net

interest expenses. The cash conversion cycle, by reflecting the net time interval between

actual cash expenditures for the purchase of productive resources and the ultimate

collection of receipts from product sales, provides a valid alternative for measuring

corporate liquidity. The length of the cash conversion cycle is instrumental in

determining the degree to which a firm must rely on external financing. The cash

conversion cycle is closely related to issue of firm valuation. Soenen (1993)

implemented an empirical to examine the relationship between cash conversion cycle

and corporate profitability for 20 different industries during the period 1970-1989. The

research shows that there is negative relationship between a firm’s net trade cycle and

its profitability measured by the total return on total assets. The results demonstrate that

shorter net trade cycles are most commonly associated with higher profitability while

the reverse is also true. However, the relationship is not found to be very strong.

Analysis at the specific industry level indicates that the inverse association between the

net trade cycle and the profitability of firm is very different, depending on the type of

industry. The results show that, in most firms in these industries, managing the

corporate cash cycle efficiently has a direct impact on corporate profitability. This study

also shows that it is of interest for any individual firm to calculate the net trade cycle

and to make a comparison with other firms in the same industry. This calculation would

give the firm an indication of the efficiency of its working capital management relative

to others in the same industry. Moreover, a firm could verify whether changes in

operating and financial management practices with regard to the management of the

different components of working capital had a measurable impact on a profitability of

firm.

The cash conversion cycle measures the time between cash outlays for resources

and cash receipts from product sales. The cash conversion cycle is dynamic in the sense

that it combines both balance sheet and income statement data to create a measure with

a time dimension. A conservative approach to liquidity management results in a higher

cash conversion cycle by increasing the inventory period and the accounts receivables

period while reducing the accounts payables period. Management of the firm’s cash

conversion cycle involves tradeoffs between liquidity and profitability. If the days in

inventory are reduced too far, the firm loses sales from customer requiring credit. If the

11

firm increases the days in payable too much, discounts for early payments and

flexibility for future are both lost. Jose et al., (1996) made an empirical research to

investigate the relationship between profitability measures and management of ongoing

liquidity needs. The sample for research consisted of 2,718 firms for the twenty-year

period from 1974 to 1993. The data are taken from the annual Compustat tapes. The

study used correlation, nonparametric and multiple regression procedures on order to

evaluate the cross-sectional relationships between profitability measures and the cash

conversion cycle. The findings of research shows that more aggressive liquidity

management (lower cash conversion cycle) is associated with higher profitability for

several industries, including natural resources, manufacturing, service, retail and

professional services. For these industries, there is a statistically significant inverse

relationship between cash conversion cycle and profitability and this relationship is not

driven by size. The cross-sectional relationship between cash conversion cycle and

profitability is not significant statistic in the construction industry. The paper also

indicated that there is a statistically significant negative relationship between cash

conversion cycle and profitability disappears when size differences in natural resources

firms are controlled. Finally, a statistically significant negative relationship between

cash conversion cycle and profitability is found only when size differences are

controlled in the financial service industry.

Efficient working capital management plays an important role of corporate

strategy to create shareholder value. Working capital is the result of the time lag

between the expenditure for the purchase of raw materials and the collection for the sale

of the finished products. The continuing flow of cash from suppliers to inventory to

accounts receivable and back into cash is usually referred to as the cash conversion

cycle (Shin and Soenen, 1998). Working capital management can have a significant

impact on the liquidity as well profitability of the company. Smith (1980) first signaled

the importance of the trade-offs between the dual goals of working capital management,

i.e. liquidity and profitability. In other words, decisions that tend to maximize

profitability tend not to maximize the chances of adequate liquidity. Conversely,

focusing almost entirely on liquidity will tend to reduce the potential profitability of the

company. Shin and Soenen (1998) had implemented an empirical research about

efficiency of working capital management and corporate profitability. A sample of

58,985 firm years covering the period 1975-1994 is used in order to investigate the

12

relationship between firm’s efficiency of working capital management and its

profitability. Net trade cycle, calculated by (inventory + accounts receivable – accounts

payable)*365/sale, is used as an independent variable in the regression model.

Profitability, measured by operating income plus depreciation related to total assets

(IA), is used as a dependent variable. Current ratio, sales growth, debt ratio are used as

control variables. Pearson correlation and regression analysis are utilized for examining

this relationship. The results provide strong evidence of a negative association between

the net trade cycle and corporate profitability. Profitability is also significantly

negatively related to current ratio. In other words, liquidity and profitability are clearly

inversely related. The paper claim that reducing the net trade cycle as a way to enhance

the efficiency of working capital management pays off in terms of increased operating

income and higher risk adjusted return for the shareholders. Therefore, reducing the

firm’s net trade cycle is one possible way for the firm to create additional shareholders

value. In addition, the negative coefficient of debt ratio difference implies that an

increase in leverage is associated with a decline in profitability even though using

operating profit before interest as a measure of profitability.

Deloof (2003) investigated the relationship between working capital

management and corporate profitability for a sample of 1,009 large Belgian non-

financial firms for the 1992-1996 periods. The result from analysis showed that there

was a negative relationship between profitability that was measured by gross operating

income and cash conversion cycle as well number of days accounts receivable and

inventories. He suggested that managers can increase corporate profitability by reducing

the number of days accounts receivable and inventories. Less profitable firms waited

longer to pay their bills.

Padachi (2006) had made an attempt to examine the trends in working capital

management and its impact on firms’ performance. This study is based on a sample of

58 Mauritian small manufacturing companies. The data has been collected from the

financial statements of the sample firms having a legal entity and have filed their annual

return to the Registrar of Companies. The primary purpose of this research is to

investigate the impact of working capital management on corporate profitability of

Mauritian small manufacturing firms. The paper used return on total assets in order to

measure firm’s profitability as a dependent variable. The reason for choosing this

variable is the small and medium – size enterprises is characterized by a low fixed assets

13

base and relied to a large extent on accounts payable to fund its gross working capital.

Therefore, a comprehensive measure of profitability is best captured by computing the

return on total assets. Some firms have significant fixed financial assets and were thus

excluded from the calculation of return on total assets. Number of days accounts

receivable, number of days accounts payable, number of days inventories and cash

conversion cycle are used as independent variables. The control variable in the

regression model consist of total debt to total asset, natural logarithm of sales, current

assets to total assets, current liabilities to total assets and sales to current assets. Result

from analysis shows that there is a negative relationship between profitability and

number of days accounts receivable as well number of days accounts payable. In

addition, there is also a negative relationship between cash conversion cycle and

profitability. This means that mangers can increase profitability by shortening their

working capital cycle.

Garcia-Teruel and Martínez – Solano (2007) had implemented an empirical

research to provide evidence about the effects of working capital management on the

profitability of a sample of small and medium-sized (SEM) Spanish firms. The sample

for this study is 8,872 SMEs covering the period 1996-2002. The objective of this

research is to provide evidence about the effects of working capital management on

profitability. Return of assets was used to measure profitability of firms as a dependent

variable. The component of working capital management as number of days accounts

receivable, number of days inventories, number of days accounts payable were used as

independent variables. In order to analyze the effect of working capital management on

profitability of firms, they used correlation and regression analysis. From result of

analysis, they indicated out that the negative relation between profitability and the

number of days accounts receivable and number of days inventories could be explained

if less profitable firms incentivize their customers by granting them long payment

deadlines, or if firms with falling sales and consequently declining profits found their

stock levels rising. Likewise, the negative relation found between profitability and

number of days accounts payable could be a consequence of firms with more problems,

and hence lower profits, delaying their payments. However, they could not confirm that

the number of days accounts payable affected an SEM’s return on assets, as this relation

did not have significance when they controlled for possible endogeneity problems.

Moreover, the study also showed that the relationship between profitability and cash

14

conversion cycle was a negative relationship. Therefore, they suggested that managers

could create value by reducing their cash conversion cycle to a minimum, as far as that

was reasonable.

Working capital management is a very important component of corporate

finance because it directly affects the liquidity and profitability of the company. With

target of contributing towards a very important aspect of financial management known

as working capital management, Raheman and Nasr (2007) have implemented an

empirical research to investigate the relationship between working capital management

and profitability of Pakistani firms. They utilized the secondary data collected from

financial statement of listed firms in Karachi Stock Exchange (KSE) for a period 6 years

from 1999-2004. Net operating profitability was used as a variable while average

collection period, inventory turnover in days, average payment period and cash

conversion cycle were used as independent variables. Current ratio, natural logarithm of

sales, debt ratio and ratio of financial assets to total assets were used as control

variables. They used correlation model in order to measure the degree of association

between different variables under consideration. Regression analysis consisted of two

parts with pooled ordinary least squares and generalized least squares (cross section

weights) used to estimate the causal relationships between profitability variable,

liquidity and other chosen variables. From result of research, they found a significant

negative relationship between net operating profitability and the average collection

period, inventory turnover in days, average payment period and cash conversion cycle.

They suggested that managers can create value for their shareholders by reducing the

number of days accounts receivable and inventories to a reasonable minimum.

Kaushik (2008) had made an empirical study to examine the influence of

working capital on the profitability and evaluate the relationship between working

capital and profitability. The sample chosen for this research was 25 companies in the

India pharmaceutical industry during the period 1996-97 to 2007-08. Many statistical

tools and techniques had been used for analyzing these data. The ratios relating to

working capital management which had been used in this study are: current ratio,

inventory turnover ratio and debtor’s turnover ratio. The profitability measures that had

been selected for this study were: profit before interest and tax margin; and return on

capital employed. The degree of relationship between working capital management and

profitability had been assessed through correlation coefficients between the selected

15

measures of working capital management and profitability taking into account their

magnitudes (i.e., by Pearson’s simple correlation coefficient), ranking of their

magnitudes (i.e. by Spearman’s rank correlation coefficient), and the nature of their

associated changes (i.e. by Kendall’s correlation coefficient). Multiple correlation and

multiple regression techniques had been applied in order to recognize influence of the

selected measures relating to indicators of working capital management on the

profitability. In order to examine whether the computed values of correlation

coefficients and partial regression coefficients were statistically significant or not, t-test

had been used. The multiple correlation coefficients had been tested by F-test. The

findings of research show that liquidity management, inventory management and credit

management had positive contribution towards improvement of the corporate

profitability.

Singh and Pandey (2008) made an attempt to investigate the working capital

components and the impact of working capital management on profitability of Hindalco

industries limited. The research was based on secondary data collection from annual

reports of Hindalco for the study period 1990 to 2007. The ratio analysis, percentage

method and coefficient of correlation had been used to analyze the data. The research

also used the regression analysis in order to examine influence of working capital

management to profitability. The findings from this study show that the contribution of

long term source in working capital was below 30% in all the study period. It had also

been found that during the study period, except 1994, 1998, 2001, 2004 and 2007, the

working capital of Hindalco had registered an increasing trend. In the regression model,

they used current ratio, working capital ratio, inventory turnover ratio, receivables

turnover ratio and working capital to total assets as independent variables. While the

profit before tax to total assets ratios was used as a dependent variable. The regression

results of the research indicated that current ratio, liquid ratio, receivables turnover ratio

and working capital to total assets ratio had statistically significant impact on the

profitability of Hindalco industries limited.

Current assets are important components of total assets and need to be carefully

managed. Smith (1980) claimed that the working capital management played an

important role in a firm’s profitability and risk as well as its value. Efficient

management of working capital is very essential in the overall corporate strategy in

creating shareholder value. Deloof (2003) indicated that firms try to maintain an

16

optimum level of working capital that maximizes that value. The optimum level of

working capital is examined, to a large extent, by the methods adopted by the

management. The managers need to maintain optimum level of various component of

working capital, such as cash, receivables, inventory and payables. Nazir and Afza

(2009) had implemented an empirical research to examine the factors that determine the

working capital requirement of the firms. A sample of 132 manufacturing firms from 14

industrial groups that were listed on Karachi Stock Exchange between the periods 2004-

2007 were taken. In this study, the working capital requirement is used as a dependent

variable while various financial and economical factors, such as operating cycle of the

firm, level of economic activity, leverage, growth of the firm, operating cash flows, firm

size, industry, return on assets and Tobin’s q were used as the determining factors of

working capital management. The findings of research show that working capital

requirement, as a dependent variable, was influenced by various economic and financial

variables related to firm. Industry effect was found significantly influencing the working

capital management practices of non-financial firms operating in different sectors. This

research indicated that there was a positive relationship between operating cycle, was

used to measure the working capital management efficiency of firms, and working

capital requirement. This means that the higher the days of operating cycle, the more

working capital would be required by the firm as operative necessity. Tobin’s q is

positively affecting the requirements of working capital of the firms, indicating that

efficient management of working capital was associated with the stock market

performance of the Karachi Stock Exchange. Moreover, the findings also showed that

leverage of firm, was measured by debt to total assets ratio, was strongly and negatively

related to the working capital requirement of a firm, indicating that companies with an

increasing debt to total assets ratio showed lower working capital requirements. Level of

economic activity was not found to have any significant effect on working capital

management practices of firms in Pakistan. This was consistent with research of

Lamberson (1995) who proved that the response of the firms to change their working

capital requirements with changes in economic conditions was not significant. Lastly,

the research indicated that there was not statistically significant relationship between the

working capital requirement and size of the firm and sales growth.

Corporate finance basically deals with three decisions: capital structure

decisions, capital budgeting decisions, and working capital management decisions.

17

Among these, working capital management is a very important component of corporate

finance since it affects the profitability and liquidity of a firm (Appuhami, 2008). It

regards to current assets and current liabilities. There are many reasons for importance

of working capital management that are taken care by financial managers. For one thing,

a typical manufacturing firm’s current assets account for over half of its total assets. For

a distribution firm, they account for even more. The maintenance of excessive levels of

currents can easily result in a substandard return on a firm’s investment. However,

according to Van Horne and Wachowicz (2004) firm with inadequate levels of current

assets may incur shortages and have difficulties in smoothly maintaining day to day

operating. Basically, the necessary components of an organization’s working capital

depend on the type of business and industry. Cash, debtors, receivables, inventory,

marketable securities, and redeemable futures can be recognized as the component of

organization’s working capital. However, the question is to recognize the factors that

determine the adequacy of working capital based on growth, size, operating cash flow,

etc. The inability to understand the determining factors and measurement of adequate

amounts of working capital will lead an organization to bankruptcy. Departing from

these reasons, Appuhami (2008) had made an attempt to investigate the impact of firm’s

capital expenditure on their working capital management. The research was based on

financial data; the main source of data was financial statements, such as income

statements, balance sheets, and cash flow statements of listed companies in the stock

exchange in Thailand for the period from 2000 to 2005. Working capital requirement

and net liquidity balance were utilized as dependent variables. Capital expenditure,

operating expenditure and financial expenditure were used as independent variables.

Control variables were utilized in the model including: firm’s operating cash flow,

growth of the firm, leverage that measured by total long-term debt capital and divided

by equity. The findings of research showed that firm’s capital expenditure had a

significant impact on working capital management. In addition, the research also found

that the firm’s operating cash flow, which was recognized as a control variable, had a

significant relationship with working capital management.

Working capital management relates to the source and application of short-term

capital. When working capital is improperly managed, allocating more than enough of it

will render management non-efficient and reduce the benefits of short-term investment.

On the other hand, if working capital is too low, the firm may miss profitable

18

investment opportunities or suffer short-term liquidity crises, leading to degradation of

firm credit, as it cannot respond effectively to temporary capital requirements. Narender

et al., (2008) made a research to investigate the determinants of working capital

management cement industry in India for a period of 10 years. The research used net

liquid balance and working capital requirement to assess working capital management,

analyzing the influence of firm characteristics, external business factors and industry

effect. The findings of research indicated that size of the firm affected the company’s

working capital management both in the case of net liquid balance and working capital

ratio. In the case of working capital ratio, the research found that growth of the firm,

operating cash flow, and industry trends in terms of business significantly influence the

liquidity management in case of cement industry in India.

Efficiency in working capital management is so vital for especially production-

firms whose assets are mostly composed of current assets, as it directly affects liquidity

and profitability of any firm (Raheman and Nasr, 2007). According to Kargar and

Bluementhal (1994) bankruptcy may also be likely for firms that put inaccurate working

capital management procedures into practice, even though their profitability is

constantly positive. Thus, it must be avoided to regress from optimal working capital

level by bringing the aim of profit maximization in the foreground, or just in direct

contradiction, to focus only on liquidity and consequently pass over profitability. While

excessive levels of working capital can easily result in a substandard return on assets,

inconsiderable amount of it may incur shortages and difficulties in maintaining day to

day operations. Samiloglu and Demirgunes (2008) made an empirical research to

analyze the effect of working capital management on firm profitability. The sample for

analysis is listed manufacturing firms in Istanbul Stock Exchange (ISE) for the period of

1998-2007. The research used return on assets as a dependent variable. While accounts

receivable, inventory period, cash conversion cycle were used as independent variables.

Firm size, firm growth, leverage and fixed financial assets were used as control

variables. Result from research shows that, for the mentioned sample and period,

accounts receivables period, inventory period and leverage significantly and negatively

affect profitability of Turkish manufacturing firms, while firm growth significantly and

positively. However, the research also indicated that cash conversion cycle, size, and

fixed financial assets have no statistically significant effect on firm profitability. The

research suggested that firm profitability can be increased by shortening accounts

19

receivables and inventory periods. In addition, leverage is another variable affecting

firm profitability negatively. The only variable in the regression model that has

significantly positive effect on profitability is firm growth.

Moss and Stine (1993) stressed that a useful way of assessing the liquidity of

firms is with the cash conversion cycle. It measures the time lag between cash payments

for purchase of inventories and collection of receivables from customers. Traditional

measures of liquidity such as the current ratio and quick ratio are useful liquidity

indicators of firms; they focus on static balance sheet values. The cash conversion cycle

is a dynamic measure of ongoing liquidity management, since it combines both balance

sheet and income statement data to create a measure with a time dimension (Jose et al.,

1996). Uyar (2009) had made an empirical research about the relationship of cash

conversion cycle with firm size and profitability for corporations listed on the Istanbul

Stock Exchange. The findings of research show that there is a significant negative

correlation between the length of cash conversion cycle and the firm size, in terms of

both net sales and total assets. Hence, the research concluded that smaller firms have

longer cash conversion cycle. This finding is parallel to the finding of the study

conducted by Moss and Stine (1993). Since longer cash conversion cycles are associated

with smaller firms, this offers a strong incentive for these firms to better manage their

cash conversion cycle (Moss and Stine 1993). Moreover, finding of the study also

shows that there is a significant negative correlation between the length of cash

conversion cycle and profitability. The reason for this problem is keeping inventory for

a long time, being slow in collecting receivables, and paying debts quickly.

Current assets are considered as one of the important components of total assets

of a firm. A firm may be able to reduce the investment in fixed assets by renting or

leasing plant and machinery, whereas the same policy cannot be followed for the

components of working capital. The high level of current assets may cut down the risk

of liquidity associated with the opportunity cost of funds that may have been invested in

long-term assets. Although, the impact of working capital policies on profitability is

highly important, only a few empirical studies have been carried out to examine this

relationship. Departing from this reason, Afza and Nazir (2009) made an attempt in

order to investigate the potential relationship of aggressive policies with the accounting

and market measures of profitability of Pakistani firms. They used a sample of 204

Pakistani firms divided into 16 industrial groups by Karachi Stock Exchange for the

20

period 1998-2005. The impact of aggressive working capital investment and the

financing policies had been examined using panel data regression models between

working capital policies and profitability. The findings of research indicated that there

was a negative relationship between the profitability that measures of firms and degree

of aggressiveness of working capital investment and financing policies. From those

findings, they suggested that managers can create value if they adopted a conservative

approach towards working capital investment and working capital financing policies. In

addition, the paper also found that investors gave weight to the stocks of those firms that

adopted an aggressive approach to managing their short-term liabilities.

21

CHAPTER THREE RESEARCH METHODOLOGY

3.1 Research design Zikmund (1997) stressed that descriptive research seeks to determine the answers

to who, what, when, where and how questions. The essential difference between

descriptive and causal studies lies in their objectives (Emory, 1985). If the research is

concerned with finding out who, what, where, when, or how much, then the study is

descriptive. If it is concerned with learning why, that is, how one variable affects

another, it is causal (Nguyen, 2001). Major research question of this thesis is trying to

answer for how are the relationships between working capital management and

profitability of listed companies on Vietnam Stock Exchange (VSE).

This thesis is seeking to explain how working capital management affects

profitability. Therefore, this thesis is concerned with learning “why”, that is, how

“working capital management” variables affect the “profitability variable”. This concern

required a causal design to identify the cause-and-effect relationships between working

capital management and profitability of listed companies. Thus, causal research is

adequate in order to implement in this thesis.

3.2 Research method Based on the methods of data collection, Emory (1985) classified research into

two types: observation and surveys. However, Zikmund (1997) expands this

classification into four basic types: surveys, experiments, and observation and

secondary data studies.

• Survey is a research technique in which information is gathered from a

sample of people by use of a questionnaire (Zikmund, 1997).

• Experiment holds the greatest potential for establishing cause-and-effect

relationships. The use of experimentation allows investigation of changes

in one variable while manipulating other variables under controlled

conditions (Zikmund, 1997).

• Observation allows the researcher to monitor and record information

about subjects without questioning them (Emory, 1985).

22

• Secondary data are data gathered and recorded by someone else prior to

the current needs of the researcher (Zikmund, 1997). Secondary data can

be used for such things as forecasting sales by constructing models based

on past sales figures and through extrapolation. The advantage of

secondary data is the savings in time and costs of acquiring information

that are generated (Cavana et al., 2001). However, secondary data as the

sole source of information have the drawbacks of becoming obsolete and

not meeting the specific needs of the particular or setting.

In terms of research technique, this thesis uses secondary data because the main

purpose of this thesis is to investigate the relationship between working capital

management and profitability. Thus, this research uses the audited financial statement of

listed companies on Vietnam Stock Exchange (VSE) to investigate this relationship.

3.3 Variable definitions and model development

3.3.1 Variable measurement 

Variables had to be defined and measured clearly before developing the

hypotheses to test these associations. Pedhazur and Schmelkin (1991) defined a variable

as any attribute or property in which organisms vary. Dependent and independent

variables are two kinds of variables involve in developing a causal model and testing the

hypotheses of association. The dependent variable is the variable of primary interest to

the researcher. The research’s goal is to understand and describe the dependent variable,

or to explain its variability or predict it (Cavana et al., 2001). An independent variable is

one that influences the dependent variable in either positive or negative (Cavana et al.,

2001)

Following is a more detail consideration of the dependent and independent

variables, which are defined and utilized in this thesis.

3.3.1.1 Dependent variable

This thesis examines the impact of working capital management on profitability.

Generally, profitability is viewed as a dependent variable. However, profitability is an

abstract concept and a latent variable, it cannot be measured directly (Nguyen, 2001) In

order to overcome this obstacle, researchers often use indicated variables to indirectly

measure profitability. Nguyen (2001) had reviewed variables that the previous

23

researchers used to measure profitability, such as Burns (1985) measured profitability

using three indicated variables: return on total assets, return on net assets and return on

equity. Hutchinson et al., (2006) used two indicated variables: return on sales and return

on equity to measure profitability, while Cohen (2005) suggested four variables: asset

earning power, return on equity, net profit on sales and return on investment. Generally,

depending upon their own purpose, researchers in the literature review used different

indicated variables to measure profitability. However, Nguyen (2001) concluded that

there were three variables: return on sales (ROS), return on assets (ROA) and return on

equity (ROE) were the most popularly used by the researchers and authors such as Ross

et al. (2005), Burns (1985) to measure profitability.

The empirical research about the relationship between working capital

management and profitability, the previous researches used the following variables to

measure profitability.

• Raheman and Nasr (2007) used net operating profitability (NOP) to measure

profitability of firm. It is defined as operating income plus depreciation, and

divided by total assets minus financial assets.

• Samiloglu and Demirgunes (2008) used return on assets (ROA) to measure

profitability when investigating the effect of working capital management on

firm profitability.

• Singh and Pandey (2008) used the profit before tax to total assets ratios to

measure profitability when investigating impact of working capital management

on the profitability of Hindalco industries limited.

• Deloof (2003) used gross operating income, which is defined as sales minus

cash of goods sold, and divided by total assets minus financial assets, in order to

measure profitability when examining the effect of working capital management

on profitability of Belgian firms.

• Lazaridis and Tryfonidis (2006) used gross operating profit to measure

profitability. It is calculated by subtracting cost of goods sold from total sales

and divided the result with total assets minus financial assets.

This thesis uses gross operating profit as a dependent variable. The reason for

using this variable instead of earnings before interest tax depreciation amortization or

profits before or after taxes is because this research wants to associate operating

‘success’ or ‘failure’ with an operating ratio and relate this variable with other operating

24

variables (i.e. cash conversion cycle). Moreover, the research wants to exclude the

participation of any financial activity from operational activity that might affect overall

profitability. Thus, financial assets are subtracted from total assets. This is consistent

with the research of Deloof (2003) and Lazaridis and Tryfonidis (2006).

3.3.1.2 Independent variable

The research investigates the relationship between working capital management

and profitability, therefore the components of working capital management is used as

independent variables. The independent variables used in this research are consistent

with the research of Deloof (2003) and Lazaridis and Tryfonidis (2006).

• Number of days accounts receivable

Number of days accounts receivable represents the average number of days that

the firm takes to collect payments from its customers. The higher the value, the

higher its investment in accounts receivable (Garcia-Teruel and Martínez - Solano,

2007).

• Number of days accounts payable

Number of days accounts payable reflects the average time it takes firms to pay

their suppliers. The higher the value, the longer firms take to settle their payment

commitments to their suppliers (.Garcia-Teruel and Martínez - Solano, 2007).

Sales - Cost of goods sold

Total assets – Financial assets

Gross operating profitability =

Number of days account

payable =

Average accounts payable

Cost of goods sold

x 365

Average account receivable

Sales

x 365 Number of days account

receivable =

25

• Number of days inventories

This variable reflects the average number of days of stock held by the firm.

Longer storage times represent a greater investment in inventory for a particular level of

operations (Garcia-Teruel and Martínez - Solano, 2007)

• Cash conversion cycle

The cash conversion cycle measures the time between cash outlays for resources

and cash receipts from product sales. The cash conversion cycle is dynamic in the sense

that it combines both balance sheet and income statement data to create a measure with

a time dimension (Jose et al., 1996). The cash conversion cycle, called cash gap, is the

sum of days of sales outstanding (average collection period) and days of sales in

inventory less days of payables outstanding (Keown et al, 2003).

Figure 3.1 The cash conversion cycle

Number of days

inventories =

Average inventory

Cost of goods sold x 365

The cash conversion cycle Inventory

purchase Inventory

sold

Time

Number of days

inventories Number of days accounts

recevable

Cash conversion cycle Number of days

accounts

payable Cash receive

Operating cycle

26

Cash conversion cycle is likely to be negative as well as positive. A positive

result indicates the number of days a company must borrow or tie up capital while

awaiting payment from a customer. A negative result indicates the number of days a

company has received cash from sales before it must pay its suppliers (Hutchinson et

al., 2007)

3.3.1.3 Control variable

The research investigates the relationship between working capital management

and profitability. So, the main purpose of this research is to examine how working

capital management affects profitability. The research will investigate the effect of

partly of working capital management on profitability in condition the other variables is

constant. The following control variables are used in this research:

• The natural logarithm of sale: that is used to measured size of firm ( used by

Deloof, 2003 and Lazaridis and Tryfonidis, 2006).

• Debt ratio: that is used as proxy for leverage, calculated by dividing total debt by

total assets (used by Deloof , 2003 and Lazaridis and Tryfonidis,2006).

• Ratio of financial assets to total assets: that is calculated by financial assets

dividing by total assets (used by Deloof, 2003 and Lazaridis and Tryfonidis,

2006).

 3.3.2 Hypotheses   

A hypothesis is a proposition that is empirically testable. It is an empirical

statement concerned with the relationship among variables (Zikmund, 1997). This thesis

makes a set of testable hypotheses that based on previous researches about this

relationship between working capital management and profitability.

Hypothesis 1:

There is a negative relationship between number of days accounts receivable and

profitability that is measured by gross operating profit.

Cash conversion

cycle =

Number of days

accounts

receivable

+ Number of

days

inventories

- Number of

days

accounts

27

Deloof (2003) indicated that there was a negative relationship between number

of days accounts receivable and profitability when investigating this relationship

between working capital management and profitability of 1,637 firms for period 1991-

1996. Similarly, Lazaridis and Tryfonidis (2006) and Raheman and Nasr (2007) also

showed that the relationship between number of days accounts receivable and

profitability was negative.

Hypothesis 2:

There is a positive relationship between number of days accounts payable and

profitability.

Deloof (2003) and Raheman and Nasr (2007) indicated that there was a negative

relationship between number of days accounts payable and profitability. However,

Lazaridis and Tryfonidis (2006) showed that there was a positive relationship between

them.

Hypothesis 3:

There is a negative relationship between number of days inventories and

profitability.

Deloof (2003), Raheman and Nasr (2007) and Lazaridis and Tryfonidis (2006)

indicated that there was a negative relationship between number of days inventories and

profitability.

Hypothesis 4:

There is a negative relationship between cash conversion cycle and profitability.

Traditional approach to interaction between cash conversion cycle and

profitability posits that relatively long cash conversion periods tend to decrease

profitability. Deloof (2003), Raheman and Nasr (2007) and Lazaridis and Tryfonidis

(2006) indicated that there was a negative relationship between cash conversion cycle

and profitability.

Hypothesis 5:

There may exist a positive relationship between debt ratio and profitability.

Deloof(2003), Raheman and Nasr (2007) and Lazaridis and Tryfonidis (2006) indicated

that there was a negative relationship between debt ratio and profitability.

Hypothesis 6:

There may exist a positive relationship between size of firms and profitability.

28

Deloof (2003), Rahemanand Nasr (2007) and Lazaridis and Tryfonidis (2006)

indicated that there was a positive relationship between natural logarithm of sales, used

to measure size of firm, and profitability.

Hypothesis 7: There may exist negative relationship between ratio of financial assets to

total assets and profitability.

Deloof (2003) and Raheman and Nasr (2007) indicated that there was a negative

relationship between financial assets to total assets ratio and profitability. However,

Lazaridis and Tryfonidis (2006) showed that there was a positive relationship between

them.

3.3.3 Model development     

3.3.3.1 Model classification

Davis (2000) defined a model as any highly formalized representation of a

theoretical system, usually designated through the use of symbols. He emphasized the

importance of models to decision-makers as follows: models are extremely importance

to decisions-makers because they form the basis for the development of decision

support system.

There are a lot of ways in order to categorize models. According to Davis

(2000), all useful classification schemes have three elements in common: level of

aggregation, time dimension, and degree of uncertainty in the process being modeled.

Based on the basic forms of decision models, Davis (2000) classified models

into two types: verbal and mathematical models. Each can be used to transform a

complex real-world process into a more manageable representation of that process. The

verbal model has broad appeal in that it is more easily understood by decision makers

but it is quite difficult to implement, since many implied variables and relationships that

affect the objective are omitted (Davis, 2000).

The generalized mathematical model form can symbolically be represented as

follows:

OI = f (Ai, B j) where

OI = outcome information or objective from the model to be used by the decision maker

or the dependent variable

Ai= Controllable independent variables in the process being modeled

29

Bj = uncontrollable independent variables influencing the process being modeled, or the

environment variables

f= functional relationship between the outcome information variable (the dependent

variable) and the independent variables Ai and Bj (Davis, 2000).

3.3.3.2 Model development for this research

Based on the generalized mathematical model form as indicated by Davis (2000)

and the variables defined in subsection 3.2.1, the model of the relationship between

working capital management and profitability is developed as follows:

 

Where:

GROSSPR it : Gross operating profitability of firm i at time t; i= 1, 2, 3……, 130 firms

β 0: The intercept of equation

β i: Coefficients of Xit variables

Xit: The different independent variables for working capital management of firm i at

time t

t: Time = 1,2,3 years

ε: The error term

Specifically, when converting the above general least squares model into our specified

variables it becomes:

GROSSPRit = β 0 + β1 (WCMit)+ β2(DRit) + β3(LOSit) + β4(FATAit) + ε

Where

WCM: Working capital management, it consists of :

- Number of days accounts receivable (AR)

- Number of days accounts payable (AP)

- Number of days inventories (INV)

- Cash conversion cycle (CCC)

GROSSPRit it = β 0 + + ε βiXit

30

Figure 3.2: Analytical model

LOS: Logarithm of sales

FATA: Financial assets to total assets ratio

3.4 Data collection methods This thesis investigates the relationship between working capital management

and profitability in Vietnam Stock Exchange (VSE), so data used for this thesis is

secondary data. According to Zikmund (1997), secondary data is defined as data

gathered and recorded by someone else prior to the current needs of the researchers.

Secondary data are usually historical, already assembled, and do not require access to

respondents or subjects. This method has been popularly used by previous researchers

in examining the relationship between working capital management and profitability of

firms.

Efficient of working capital

management

‐ Number of days accounts

receivable

‐ Number of days accounts

payable

‐ Number of days inventories

‐ Cash conversion cycle

Profitability of

firms

Debt ratio

Ratio of financial

assets to total assets

ratio

Logarithm of sales

Analytical model for the study

31

The data is collected from audited financial statement of listed companies. In

Vietnam, these financial statements can be obtained from following organizations:

• HOCHIMINH STOCK EXCHANGE ( www.hsx.vn)

• HANOI STOCK EXCHANGE ( www.hnx.vn)

• STATE SECURITIES COMMISSION OF VIETNAM (www.ssc.gov.vn)

The reason the research choose this market because there have not been any

research about this relationship in Vietnam Stock Exchange (VSE). For the purpose of

research, firms in financial sector, banking and finance, insurance, leasing, business

service, renting, and other service are excluded from the sample. The most recent period

for this investigating is 2006-2008. Some of the firms are not included in the sample due

to lack information for the certain period. The sample is based on audited financial

statements of 130 firms that listed in VSE. With 130 firms for period of 2006-2008, it

has 390 observations totally.

3.5 Data transformation This section examines aspects of data transformation including purpose and

methods of data transformation. Zikmund (1997) defined data transformation as the

process of changing data’s original form to a format that is more suitable to perform a

data analysis that will achieve research objectives. Hence, the purpose of data

transformation is to create a more suitable format for data analysis.

This research uses ratios such as: gross operating profitability as a dependent

variable; number of days accounts receivable, number of days accounts payable, number

of days inventories as independent variables and debt ratio, natural logarithm of sales,

ratio of financial assets to total assets as control variables. The ratios are not available in

audited financial statement of listed companies. Hence, the process of deriving these

ratios required a transformation of raw data into more suitable data for analysis.

Computer package (Excel) will help this data transformation easily and quickly.

32

3.6 Data analysis methods

3.6.1 Descriptive statistics 

Descriptive analysis refers to the transformation of the raw data into a form that

will make them easy to understand and interpret. Describing responses or observations

is typically the first form of analysis (Zikmund, 1997).

In this thesis, descriptive statistic is used to calculate averages, frequency

distribution, and percentage distribution used as a form of summarizing data. It is used

to describe characteristics of variables in the sample.

3.6.2 Correlation analysis 

This thesis investigates the relationship between working capital management

and profitability of firms. Therefore, Pearson’s correlation analysis is used to see the

relationship between profitability, used as independent variable, and the component of

working capital management used as independent variables. If efficient working capital

management increases profitability, one should expect a negative relationship between

the measures of working capital management and profitability variable. The results of

correlation coefficients are presented by standard form of reporting correlation results –

correlation matrix.

3.6.3 Multiple Regression analysis 

Multiple regression analysis is an analysis of association that simultaneously

investigates the effect of two or more independent variables on a single, interval-scaled

or ratio-scaled dependent variable (Zikmund, 1997).

Departing from shortcoming of Pearson’s correlation is that they do not allow

identifying causes from consequences. While, the main objective of this research is to

investigates the simultaneous effect of several independent variables (AR, AP, INV,

CCC…) on a dependent variable (GROSSPR). Multiple regressions are appropriate to

be selected in this study.

33

CHAPTER FOUR RESEARCH RESULTS

4.1 Descriptive statistics Descriptive analysis shows the average and standard deviation of the different

variables in the study. It also presents the minimum and maximum values of the

variables which help in getting a total picture about maximum and minimum values a

variable can get.

Table 4.1 Descriptive Statistics

n Minimum Maximum Mean Std. Deviation AR 390 1.92 313.36 51.91 43.62AP 390 .04 313.91 45.40 43.29INV 390 .77 315.21 89.46 64.51CCC 389 -121.70 410.65 96.21 81.75LOS 390 23.22 30.67 26.61 1.345DR 390 .04 .92 .44 .22FATA 390 .00 .84 .12 .15GROSSPR 390 -.43 3.86 .35 .41

130 Vietnam non- financial firms, 2006-2008

• Number of days accounts receivable (AR)= Average of accounts receivable /

Sales* 365 • Number of days accounts payable (AP)= Average of accounts payable / Cost of

goods sold *365 • Number of days inventory (INV) = Average of inventory / Cost of goods sold *

365 • Cash conversion cycle (CCC) = AR+ INV- AP • Natural logarithm of sales (LOS) = ln(sale) • Debt ratio (DR)= Total debt/ Total assets • Ratio of financial assets to total assets (FATA) = Financial assets/ Total assets • Gross operating profitability (GROSSPR) = ( Sales – Cost of goods sold)/ (Total

assets – Financial assets)

Table 1 gives descriptive statistics for 130 Vietnam non-financial firms for a

period of three years from 2006 to 2008 and for a total 390 firm -year observations.

Looking at this table, we can see that the average value of gross operating profitability

34

is 35.2% of total assets, and standard deviation is 40.7%. This figure means that the

value of profitability can deviate from mean to both sides by 40.7%. The maximum and

minimum values of gross operating profitability are 3.86 and -0.43, respectively.

Information from descriptive statistics also indicates that the mean of cash conversion

cycle that used as a proxy to measure the efficiency of working capital management is

96 days and standard deviation is 82 days. The average of number of days accounts

receivable is 52 days with standard deviation 44 days. Minimum time taken by a

company in order to collect cash from customers is nearly 2 days while the maximum

time for this goal is 313 days. The average time of paying to suppliers is 45 days and the

standard deviation is 43 days. Maximum time taken for firm to pay for their suppliers is

314 days while minimum time taken for this purpose is nearly 1day. Moreover, it takes

an average 89 days in order to sell inventory with standard deviation of 65 days.

Maximum time taken by a firm is 315 days, while minimum time to convert inventory

into sales is 1 day.

Natural logarithm of sales that measure the size of the firm is used as a control

variable. Figure from Table 1 indicates that the mean of logarithm of sales is 26.61 and

standard deviation is 1.35. The maximum value of logarithm of sales for a firm in a year

is 30.67 while the minimum value is 23.22.

Debt ratio is used as a proxy for leverage to check the relationship between debt

financing and the profitability. It is also used as a control variable. The result of

descriptive statistics indicates that the average of debt ratio is 44% with standard

deviation of 22%. The maximum debt ratio financing used by a firm is 92% which is

unusual because of debt nearly asset. However, it is also possible if the equity of the

firm is nearly zero. While the minimum of debt ratio is 4%, this means that there is a

company that uses a little debt in its operation.

Lastly, the ratio of financial assets to total assets is used to check the relationship

between the ratio of financial assets to the total assets of Vietnam firms and

profitability. It is also utilized as a control variable. The mean value for this ratio is 12%

with a standard deviation of 15%. The maximum value of financial assets to total assets

is 84% and the minimum value for this purpose is nearly 0%.

35

4.2 Correlation analysis Correlation analysis is used to identify the association between profitability as a

dependent variable and other related variables. In this case, the thesis investigate the

relationship between profitability of firm that is measured by gross operating

profitability and component of working capital management as: number of days

accounts receivable, number of days accounts payable, number of days inventories, cash

conversion cycle as well other control variables.

Table 4.2 Correlation matrix

AR AP INV CCC LOS DR FATA GROSSPR

AR

Sig.(2-tailed)

N

1.000 .408** .285** .549** -.279** -.035 -.043 -.223**

.000 .000 .000 .000 .487 .399 .000

390 390 390 390 390 390 390 390

AP

Sig.(2-tailed)

N

.408** 1.000 .235** -.116* -.138** .157** -.079 .195**

.000 .000 .022 .006 .002 .118 .000

390 390 390 390 390 390 390 390

INV

Sig.(2-tailed)

N

.285** .235** 1.000 .819** -.198** .043 -.119* -.202**

.000 .000 .000 .000 .401 .019 .000

390 390 390 390 390 390 390 390

CCC

Sig.(2-tailed)

N

.549** -.116* .819** 1.000 -.236** -.065 -.077 -.383**

.000 .022 .000 .000 .200 .129 .000

389 389 389 390 389 389 389 389

LOS

Sig.(2-tailed)

N

-.279** -.138** -.198** -.236** 1.000 -.040 -.016 .172**

.000 .006 .000 .000 .435 .746 .001

390 390 390 390 390 390 390 390

DR

Sig.(2-tailed)

N

-.035 .157** .043 -.065 -.040 1.000 -.271** .231**

.487 .002 .401 .200 .435 .000 .000

390 390 390 390 390 390 390 390

FATA

Sig.(2-tailed)

-.043 -.079 -.119* -.077 -.016 -.271** 1.000 .075

.399 .118 .019 .129 .746 .000 .140

36

N 390 390 390 390 390 390 390 390

GROSSPR

Sig.(2-tailed)

N

-.223** .195** -.202** -.383** .172** .231** .075 1.000

.000 .000 .000 .000 .001 .000 .140

390 390 390 390 390 390 390 390

** Correlation is significant at the 0.01level (2-tailed) *Correlation is significant at the 0.05 level (2-tailed) The first, we have started our analysis of correlation results between the number

of days accounts receivable (AR) and gross operating profitability. The result of

correlation analysis shows a negative coefficient – 0.223, with p value of 0.000. It

shows that there is a high significant at α = 1%. This means that if number of days

accounts receivable increase, it will make operating profitability decrease. Correlation

result between number of days inventories (INV) and the gross operating profitability

also indicate the same type of result. The correlation coefficient is – 0.202 and p value is

0.000. It also shows a high significant at α = 1%. It explains for reason why when the

firm takes more time in selling inventory, it will adversely affect its profitability. On the

other hand, correlation result between number of days accounts payable (AP) and gross

operating profitability is a positive. The correlation coefficient is 0.195 and p value is

0.000. It shows highly significant at α = 1%. This means that the more profitable firms

wait longer to pay their bills. The cash conversion cycle that is used as a comprehensive

measure of working capital management also has a negative correlation with gross

operating profitability with coefficient -0.383 and p value is 0.000. It also shows highly

significant at α = 1%. This demonstrates that paying suppliers longer and collecting

payments from customers earlier, and keeping products in stock less time, are all

associated with an increase in the firm’s profitability.

Result from analysis also shows a positive relationship between natural

logarithm of sales, used to measure the size of firm, and the gross operating

profitability. Its coefficient correlation is 0.172 with p value 0.001. It shows highly

significant at α = 1%. This shows that as size of the firm increases, it will increase its

profitability and vice versa.

To sum, result from analyzing of correlation indicates that there is a negative

relationship between cash conversion cycle, number of days accounts receivable,

number of days inventories with the profitability of firms are consistent with the

research of Deloof (2003) and Raheman and Nasr (2007). However, in their study, he

37

indicated a negative relationship between number of days accounts payable and

profitability. Contrast, this thesis shows a positive relationship between number of days

accounts payable and profitability. This analysis suits with the results of Lazaridis and

Tryfonidis (2006)

4.3 Multiple regression analysis A shortcoming of Pearson’s correlations is that they do not allow identifying

causes from consequences. Therefore, regression analysis is used to investigate the

impact of working capital management on corporate profitability. The determinants of

corporate profitability are estimated with pooled ordinary least squares (OLS). This

method was used by Shin and Soenon (1998), Raheman and Nasr (2007). In panel data

(pooled) OLS regression, time series and cross sectional observations are combined and

examined. In other words, several cross-sectional units are observed over a period of

time in a panel data setting.

Model 1

GROSSPRit = β0 + β1 (ARit) + β2 (DRit) + β3 (LOSit) + β4 (FATAit) + ε 4.1

In model 1, we use operating gross profitability (GROSSPR) as a dependent

variable. Number of days accounts receivable (AR) is used as an independent variable.

While, the control variables are used in this model including: debt ratio (DR), natural

logarithm of sales (LOS) and ratio of financial assets to total assets (FATA)

Table 4.3 Variables entered/removeb

Model 1 Variables Entered Variables Removed Method

1 FATA, LOS, DR, ARa Enter

a. All requested variable entered

b. Dependent variable: GROSSPR

Table 4.4 Model summaryb

Model R R Square Adjusted R2 Std. Error of

the Estimate

Durbin-

Watson

1 0.367a .135 .126 .38027 1.863

a. Predictors: (Constant), FATA, LOS, DR, AR

b. Dependent Variable: GROSSPR

38

Table 4.5 ANOVA result

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 8.658 4 2.165 14.969 .000a

Residual 55.673 385 .145

Total 64.331 389

a. Predictors: (Constant) , FATA, LOS, DR, AR

b. Dependent Variable: GROSSPR

Table 4.6 Result of regression model

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

Collinearity

Statistics

B Std.Error Beta Tolerance VIF

1 (Constant) -.948 .412 -2.301 .022

AR -.002 .000 -.169 -3.418 .001 .916 1.091

LOS .042 .015 .138 2.796 .005 .918 1.089

DR .504 .093 .269 5.450 .000 .921 1.086

FATA .392 .136 .143 2.892 .004 .922 1.085

a. Dependent Variable: GROSSPR

Model 1 is estimated with fixed effects and includes number of days accounts

receivable as a measure of accounts receivable policy. The result of this regression

indicates that the coefficient of accounts receivable is negative with -.169 and p-value is

0.001. It shows highly significant at α = 0.01. This implies that the increase or decrease

in accounts receivable will significantly affect profitability of firm. Specifically, with

coefficient of – .169 indicates that when the other variables in the regression model is

no change, if number of days accounts receivable increase 1 day, it will lead to average

of gross operating profitability decrease .169 respectively.

Debt ratio is used as a proxy for leverage, from analysis of regression shows that

there is a positive relationship with dependent variable. The coefficient is 0.269 and has

significant at α = 0.01. This means that if there is an increase in debt ratio it will lead to

increase in profitability of firm. The result also indicates that there is a positive

relationship gross operating profitability and natural logarithm of sales as well ratio of

financial assets to total assets. The coefficients are 0.138 and 0.143 respectively. Both of

39

them are significant at α = 0.01. It implies that the size of firm has effect on profitability

of firm. The larger size leads to more profitable.

The adjusted R2, also called the coefficient of multiple determinations, is the

percentage of the variance in the dependent explained uniquely or jointly by the

independent variables and is 12.6%. The F statistic is used to test significant of R. From

result of SPSS, we see that the model is fit with F-statistics 14.969 and p-value is 0.000.

It shows highly significant at α = 0.01. So concludes that at least one of the AR, DR,

LOS, and FATA is related to GROSSPR.

Model 2

GROSSPRit = β0 + β1 (APit) + β2 (DRit) + β3 (LOSit) + β4 (FATAit) + ε 4.2

In model 2, there are the dependent variable gross operating profitability and the

same independent variables as the equation 4.1. The only difference is number of days

accounts receivable variable replaced by number of days accounts payable variable

Table 4.7 Variables entered/removeb

Model 1 Variables Entered Variables Removed Method

1 FATA, LOS, DR, APa Enter

a. All requested variables entered.

b. Dependent variable: GROSSPR

Table 4.8 Model summaryb

Model R R Square Adjusted R2 Std. Error of

the Estimate

Durbin-

Watson

1 0.381a .145 .137 .37787 1.791

a. Predictors: (Constant), FATA, LOS, DR, AP

b. Dependent Variable: GROSSPR

Table 4.9 ANOVA result

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 9.358 4 2.339 16.384 .000a

Residual 54.973 385 .143

Total 64.331 389

a. Predictors: (Constant) , FATA, LOS, DR, AP

b. Dependent Variable: GROSSPR

40

Table 4.10 Result of regression model

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

Collinearity

Statistics

B Std.Error Beta Tolerance VIF

1 (Constant) -1.703 .391 -4.354 .000

AP .002 .000 .197 4.090 .000 .956 1.046

LOS .064 .014 .212 4.462 .000 .979 1.021

DR .473 .093 .252 5.105 .000 .907 1.102

FATA .446 .135 .162 3.313 .001 .924 1.082

a. Dependent Variable: GROSSPR

Looking at coefficients in table 4.10, we see that there is a positive relationship

between number of days accounts payable and profitability of firm. The coefficient is

0.197 and p value is 0.000. It shows highly significant at α = 0.01. It implies that the

increase or decrease in the number of days accounts payable significantly affects to

profitability of the firm. The positive relationship between the average payment period

and profitability indicates that the more profitable firms wait longer to pay their bills.

The same with result of model 1, logarithm of sales, debt ratio and financial

assets to total assets ratio are the positive relationship with profitability. All of them are

highly significant at α = 0.01.

The adjusted R2 is 13,7% and we see that the model is fit with F-statistics 16.384

and p-value is 0.000. It shows highly significant at α = 0.01. So concludes that at least

one of the AP, DR, LOS, and FATA is related to GROSSPR.

Model 3

GROSSPRit = β0 + β1 (INVit) + β2 (DRit) + β3 (LOSit) + β4 (FATAit) + ε 4.3

The third model is using the number of days inventories as an independent

variable as substitute of average payment period. The other variables are same as they

have been in first and second model.

Table 4.11 Variables entered/removeb

Model 1 Variables Entered Variables Removed Method

1 FATA, LOS, DR, INVa Enter

a. All requested variables entered.

b. Dependent variable: GROSSPR.

41

Table 4.12 Model summaryb

Model R R Square Adjusted R2 Std. Error of

the Estimate

Durbin-

Watson

1 0.367a .135 .126 .38021 1.829

a. Predictors: (Constant), FATA, LOS, DR, AP

b. Dependent Variable: GROSSPR

Table 4.13 ANOVA result

Model Sum of

Squares

Df Mean

Square

F Sig.

1 Regression 8.676 4 2.169 15.004 .000a

Residual 55.655 385 .145

Total 64.331 389

a. Predictors: (Constant) , FATA, LOS, DR, INV

b. Dependent Variable: GROSSPR

Table 4.14 Result of regression model

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

Collinearity

Statistics

B Std.Error Beta Tolerance VIF

1 (Constant) -1.057 .402 -2.627 .009

INV -.001 .000 -.167 -3.436 .001 .946 1.057

LOS .046 .015 .153 3.150 .002 .957 1.045

DR .525 .092 .280 5.688 .000 .925 1.081

FATA .366 .136 .133 2.688 .007 .913 1.096

a. Dependent Variable: GROSSPR

Looking at table 4.14, we can see that there is a negative relationship between inventory

turnover in days and profitability. The coefficient of this relationship is -.167 and

significant at α = 0.01. This means that if the inventory takes more time to sell, it will

adversely affect profitability. The figure of – .167 shows that if other variables in the

regression model are no change, number of days inventories increase one unit, it will

make average gross operating profitability decrease 0.167 unit respectively.

42

Result from this model also shows that the relationship between natural

logarithm of sales, debt ratio, ratio of financial assets to total assets and profitability are

positive with coefficient of 0.153, 0.280, and 0.133 respectively. The most of them are

highly significant at α = 0.01. The adjusted R2 is 12.6%. The coefficient of F statistic is

15.004 and has significant at α = 0.01. So concludes that at least one of the INV, DR,

LOS, and FATA is related to GROSSPR.

Model 4

GROSSPRit = β0 + β1 (CCCit) + β2 (DRit) + β3 (LOSit) + β4 (FATAit) + ε 4.4

In fourth model, cash conversion cycle is used as an independent variable

instead of number of days accounts receivable, number of days accounts payable,

number of days inventories. The other variables are kept the same as they have been in

the first, second and third models.

Table 4.15 Variables entered/removeb

Model 1 Variables Entered Variables Removed Method

1 FATA, LOS, DR, CCCa Enter

a. All requested variable entered

b. Dependent variable: GROSSPR

Table 4.16 Model summaryb

Model R R Square Adjusted R2 Std. Error of

the Estimate

Durbin-

Watson

1 0.460a .211 .203 .36345 1.906

a. Predictors: (Constant), FATA, LOS,DR, AP

b. Dependent Variable: GROSSPR

Table 4.17:

ANOVA result

Model Sum of

Squares

Df Mean

Square

F Sig.

1 Regression 13.592 4 3.398 25.724 .000a

Residual 50.724 385 .132

43

Total 64.316 389

a. Predictors: (Constant) , FATA, LOS, DR, INV

b. Dependent Variable: GROSSPR

Table 4.18 Result of regression model

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

Collinearity

Statistics

B Std.Error Beta Tolerance VIF

1 (Constant) -.571 .390 -1.464 .144

CCC -.002 .000 -.333 -7.081 .000 .928 1.078

LOS .032 .014 .104 2.231 .026 .939 1.065

DR .461 .089 .246 5.196 .000 .916 1.092

FATA .321 .130 .117 2.468 .014 .916 1.092

b. Dependent Variable: GROSSPR

The cash conversion cycle is used popularly to measure efficiency of working

capital management. From result of regression running indicates that there is a negative

relationship between cash conversion cycle and gross operating profitability. The

coefficient is -0.333 with p-value 0.000. It is highly significant at α = 0.01. This implies

that the increase or decrease in the cash conversion cycle significantly affects

profitability of the firm. The adjusted R2 is 20.3%. The coefficient of F statistic is

25.724 and has significant at α = 0.01. So concludes that at least one of the CCC, DR,

LOS, and FATA is related to GROSSPR.

All regression models are tested for multicollinearity. The variance inflation

factor (VIF) or the tolerances of the explanatory variables is used to detect whether one

predictor has a strong linear association with the remaining predictors. VIF measures

how much the variance of an estimated regression coefficient increases if your predictor

are correlation (multicollinearity). The largest VIF among all predictors is often used as

an indicator of serve muticollinearity. All predictors in the regression models had a

variance inflation factor (VIF) ranged between 1and1.1, except of debt ratio in model 2

with value at 1.102, which totally indicates that there is absence of multicollinearity

between the predictors in the regression models.

In addition, all regression models are tested for autocorrelation that is defined as

the cross-correlation of a signal with itself. Informally, it is the similarity between

44

observations as a function of the time separation between them. It is a mathematical tool

for finding repeating patterns, such as the presence of a periodic signal which has been

buried under noise, or identifying the missing fundamental frequency in a signal implied

by its harmonic frequencies. It is often used in signal processing for analyzing functions

or series of values, such as time domain signals.

This research uses Durbin-Watson test in order to test first-order autocorrelation

in the errors. In the most cases, when autocorrelation exits, there is a first-order

autocorrelation. The Durbin-Watson test statistic tests the null hypothesis that the

residuals from an ordinary least squares regression are not autocorrelated against the

alternative that the residuals follow an AR1 process. The Durbin-Watson statistic ranges

in value from 0 to 4. A value near 2 indicates non-autocorrelation; a value toward 0

indicates positive autocorrelation; a value toward 4 indicates negative autocorrelation.

From Durbin-Watson table, we see that du= 1.715 and 4- du= 2.285. This means that if

Durbin – Watson coefficient is ranged from 1.715 to 2.285, there will not be

autocorrelation in the errors. Results from SPSS indicate that all the Durbin-Watson

coefficient in four regression models are range from 1.791 to 1.906. Hence, we can

conclude that there is not autocorrelation in the errors of all regression models.

All the regression models are also tested for homogeneity of errors. This

research uses the Breush-Pagan-Godfrey to test heteroskedasticity that is a hypothesis

test of whether the pattern of the residuals is consistent across the range of predicted

values.

The null hypothesis for this test is (H0): The variance of the residuals is the same

for all values of independent variables.

The alternative hypothesis for this test is (H1): The variance of the residuals is

different for some values of the independent variables.

So, if the result of the test is failure to reject H0, we can conclude that the assumption of

homogeneity of errors is satisfied. Model 1 Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-s tatis tic 1.402692 Prob. F (4,385) 0.2324 Obs*R-squared 5.601993 Prob. Chi-Square(4) 0.2309 Scaled explained SS 82.11388 Prob. Chi-Square(4) 0.0000

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 03/19/10 Time: 14:02

45

Sample: 1 390 Included observations: 390

Variable Coefficient Std. Error t-Statistic Prob.

C -1.279502 0.848012 -1.508825 0.1322 AR -0.000710 0.000950 -0.747092 0.4555

LOS 0.052095 0.030782 1.692360 0.0914 DR 0.182908 0.190309 0.961111 0.3371

FATA -0.067017 0.279098 -0.240122 0.8104

R-squared 0.014364 Mean dependent var 0.142750 Adjusted R-squared 0.004124 S.D. dependent var 0.783953 S.E. of regression 0.782335 Akaike info criterion 2.359671 Sum squared resid 235.6387 Schwarz criterion 2.410519 Log likelihood -455.1358 Hannan-Quinn criter. 2.379827 F-s tatis tic 1.402692 Durbin-Watson stat 2.005696 Prob(F-s tatis tic) 0.232383

Model 2 Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-s tatis tic 1.693144 Prob. F (4,385) 0.1508 Obs*R-squared 16.98086 Prob. Chi-Square(4) 0.0019 Scaled explained SS 217.7705 Prob. Chi-Square(4) 0.0000

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 03/19/10 Time: 14:07 Sample: 1 390 Included observations: 390

Variable Coefficient Std. Error t-Statistic Prob.

C -1.685417 0.736648 -2.287954 0.0227 AP 0.003034 0.000853 3.557986 0.0004

LOS 0.061171 0.027115 2.255998 0.0246 DR 0.134542 0.174428 0.771332 0.4410

FATA 0.012646 0.253632 0.049859 0.9603

R-squared 0.043541 Mean dependent var 0.140957 Adjusted R-squared 0.033603 S.D. dependent var 0.724072 S.E. of regression 0.711803 Akaike info criterion 2.170705 Sum squared resid 195.0652 Schwarz criterion 2.221553 Log likelihood -418.2875 Hannan-Quinn criter. 2.190862 F-s tatis tic 1.693144 Durbin-Watson stat 1.988087 Prob(F-s tatis tic) 0.150781

46

Model 3 Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-s tatis tic 2.046530 Prob. F (4,385) 0.0872 Obs*R-squared 8.119786 Prob. Chi-Square(4) 0.0873 Scaled explained SS 115.1004 Prob. Chi-Square(4) 0.0000

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 03/19/10 Time: 14:08 Sample: 1 390 Included observations: 390

Variable Coefficient Std. Error t-Statistic Prob.

C -1.216748 0.811115 -1.500094 0.1344 INV -0.001016 0.000619 -1.640028 0.1018 LOS 0.051801 0.029538 1.753694 0.0803 DR 0.184654 0.186093 0.992267 0.3217

FATA -0.081438 0.274876 -0.296272 0.7672

R-squared 0.020820 Mean dependent var 0.142706 Adjusted R-squared 0.010647 S.D. dependent var 0.770699 S.E. of regression 0.766585 Akaike info criterion 2.318995 Sum squared resid 226.2462 Schwarz criterion 2.369843 Log likelihood -447.2040 Hannan-Quinn criter. 2.339152 F-s tatis tic 2.046530 Durbin-Watson stat 2.006636 Prob(F-s tatis tic) 0.087235

Model 4 Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-s tatis tic 1.285478 Prob. F (4,384) 0.2751 Obs*R-squared 15.80456 Prob. Chi-Square(4) 0.0033 Scaled explained SS 209.3972 Prob. Chi-Square(4) 0.0000

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 03/19/10 Time: 14:10 Sample: 1 390 Included observations: 389

Variable Coefficient Std. Error t-Statistic Prob.

C -0.566294 0.719295 -0.787290 0.4316 CCC -0.001424 0.000432 -3.294189 0.0011 LOS 0.029587 0.026130 1.132312 0.2582 DR 0.136023 0.163796 0.830441 0.4068

FATA -0.116332 0.240125 -0.484464 0.6283

R-squared 0.040629 Mean dependent var 0.130397 Adjusted R-squared 0.030635 S.D. dependent var 0.680852 S.E. of regression 0.670342 Akaike info criterion 2.050713 Sum squared resid 172.5537 Schwarz criterion 2.101659 Log likelihood -393.8637 Hannan-Quinn criter. 2.070910 F-s tatis tic 1.285478 Durbin-Watson stat 2.015620 Prob(F-s tatis tic) 0.275062

47

The result of running Breusch-Pagan-Godfrey for four regression models

indicates that the null hypothesis H0 is not rejected. Hence, the assumption of

homogeneity of error is satisfied for four models.

48

CHAPTER FIVE CONCLUSIONS AND IMPLICATIONS

5.1 Conclusions This thesis supports for existing literatures such as Shin and Soenen (1998),

Deloof(2003), Raheman and Nars (2007) who have found a strong negative relationship

between the measures of working capital management including the number of days

accounts receivable, number of days inventories and cash conversion cycle with

corporate profitability. Moreover, this thesis also adds to findings of Lazaridis and

Tryfonidis (2006) who claimed that there was a positive relationship between number of

days accounts payable and gross operating profitability.

The negative relationship between corporate profitability that measured by gross

operating profitability and cash conversion cycle that used as measuring efficient

working capital management shows that cash conversion cycle is longer, profitability is

smaller. This study suggests that managers can create value for their shareholders by

reducing the cash conversion cycle to a reasonable range.

Result from empirical analysis of relationship between working capital

management and profitability on Vietnam Stock Exchange also indicates that there is a

negative relationship between number of days accounts receivable and gross

profitability. This aspect, which is consistent with the results found by Deloof (2003),

underlines the importance of working capital management for firms. Lengthening the

deadlines for clients to make their payments, although it may improve profitability

because greater payment facilities may raise sales, also negatively affects profitability.

Thus a more restrictive credit policy giving customers less time to make their payments

improves performance (Garcia-Teruel and Martínez - Solano, 2007).

The findings of this empirical also indicate that number of days inventories has a

negative effect on gross operating profit. This means that the firm’s profitability can

also be improved by reducing the number of days inventories. The negative relationship

between profitability and number of days inventories as well number of days accounts

receivable can be explained if less profitable firms incentivize their customers by

granting them longer payment deadlines, or if with falling sales and consequently

declining profits find their stock levels rising. Besides, this thesis also shows the firms

will have more profitability if firms wait longer to pay their bills.

49

In addition, the control variables are utilized in the regression model also have

the relationship with profitability. Debt ratio, used as a proxy for leverage, natural

logarithm of sales and ratio of financial assets to total assets have a positive relationship

with gross operating profit.

Figure 5.1 The relationship between working capital management and

profitability

Regarding to hypotheses, this research takes following conclusions:

THE RELATIONSHIP BETWEEN WORKING CAPITAL MANAGEMENT AND

PROFITABILITY

Negative

Positive

Number of

days

accounts

receivable

Number of

days

accounts

payable

Number of

days

inventories

Cash

conversion

cycle

Gross

operating

profitability

Debt ratio

Logarithm

of sales

Ratio of

financial

assets to

total assets

Negative

Negative

Positive

Positive

Positive

50

Regarding to hypotheses, this research takes following conclusions:

Table 5.1 Hypothesis test results

Hypothesis 1 There is a negative relationship between number of days accounts receivable

and profitability that is measured by gross operating profit

Hypothesis 2 There is a positive relationship between number of days account payable and

profitability

Hypothesis 3 There is a negative relationship between number of days inventory and

profitability

Hypothesis 4 There is a negative relationship between cash conversion cycle and

profitability.

Hypothesis 5 There may exist a positive relationship between debt ratio and profitability.

Hypothesis 6 There may exist a positive relationship between size of firms and

profitability.

Hypothesis 7 There may exist a positive relationship between financial assets to total

assets ratio and profitability.

5.2 Implications The findings of this empirical research indicate that efficiency of working capital

management has an effect on profitability of firm. Therefore, managers want to create

value for their shareholders, they have to seek several solutions to improve their firm’s

profitability. This thesis as well others previous empirical research have demonstrated

that efficiency of working capital management has the significant relationship with

profitability. This means that in order to improve profitability of firm, a partly must

improve efficiency of working capital management. The findings of research from

regression indicate that cash conversion cycle has the largest negative effect on

profitability. It implies that an increase in the cash conversion cycle by 1 day is

associated with a decline in average gross operating profit by 33.3%. So, the managers

want to increase gross operating profit, they need to seek some of the solutions in order

to decrease cash conversion cycle which represents the number of days between the date

the firm must start to pay cash to its suppliers and the date it begins to receive cash from

its customers (Bodie and Merton, 2000).

51

From the equation of CCC above, it is seen that a firm can reduce its cash

conversion cycle by:

• Reducing the amount of time that goods are held in inventory.

In order to reduce number of days inventories, the managers improve control

process or by having suppliers deliver raw materials exactly when they are needed in the

production process.

• Collecting accounts receivable more quickly.

There are a lot of methods to speed up the collection process. Based on their

condition of operating business, the managers choose suitable solution as offering

discounts to customers who pay faster, and charging interest on accounts that are

overdue.

• Paying its bills more slowly:

In order to increase number of days accounts payable, the managers of listed

firms need to construct an effective cash management that is not only in the part of

speedy collection of its cash and receivables but also it should concentrate to slowing

their disbursement of cash to the customers or suppliers. Slowing disbursement of cash

is not the meaning of delaying the payment or avoiding the payment. Slowing

disbursement of cash is possible with the help of the following methods:

• Avoiding the early payment of cash

The firm should pay its payable only on the last day of the payment. If the firm

avoids early payment of cash, the firm can retain the cash with it and that can be used

for other purpose.

• Centralized disbursement system

Decentralizing collection system will provide the speedy cash collections.

Therefore, centralizing disbursement of cash system takes time for collection from our

accounts as well as we can pay on the date.

Cash conversion

cycle =

Number of days

accounts

receivable

+ Number of

days

inventories

- Number of days

accounts

payable

52

5.3 Limitations of research Although, findings of this empirical research is very important for mangers in

order to seek a lot of solutions to improve their profitability with hope that more and

more created value for their stockholders. However, this research also has the following

limitations:

• The period of time for this study is shortly in compare with some of the previous

studies about the relationship between working capital management and

profitability like Deloof, 2003, Shin and Soenen, 1998.

• Moreover, the study only refers to internal factors but not consider external

factors as industry dummy, level of economic activity (Lazaridis and Tryfonidis,

2006). Future research could further explore in order to expand the contributions

for this field.

\

53

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57

Appendix

Appendix 1: Model 1

Collinearity Diagnosticsa

Model

Dimensi

on Eigenvalue Condition Index

Variance Proportions

(Constant) AR LOS DR FATA

1 1 3.914 1.000 .00 .02 .00 .01 .02

2 .617 2.519 .00 .04 .00 .03 .76

3 .361 3.290 .00 .77 .00 .09 .01

4 .107 6.040 .00 .07 .00 .86 .20

5 .001 58.781 1.00 .10 .99 .01 .01

a. Dependent Variable: GROSSPR

Residuals S tatis ticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value -.2872 .7883 .3516 .14919 390

Residual -.54957 3.56313 .00000 .37831 390

Std. Predicted Value -4.282 2.927 .000 1.000 390

Std. Residual -1.445 9.370 .000 .995 390

a. Dependent Variable: GROSSPR

Appendix 2: Model 2

Collinearity Diagnosticsa

Model

Dimensi

on Eigenvalue Condition Index

Variance Proportions

(Constant) LOS DR FATA AP

1 1 3.875 1.000 .00 .00 .01 .02 .02

2 .645 2.451 .00 .00 .02 .69 .10

3 .365 3.257 .00 .00 .06 .08 .85

4 .114 5.837 .00 .00 .90 .21 .00

5 .001 56.222 1.00 1.00 .01 .00 .02

a. Dependent Variable: GROSSPR

58

Residuals S tatis ticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value -.0966 .9845 .3516 .15510 390

Residual -.55008 3.39857 .00000 .37592 390

Std. Predicted Value -2.890 4.080 .000 1.000 390

Std. Residual -1.456 8.994 .000 .995 390

a. Dependent Variable: GROSSPR

Appendix 3: Model 3

Collinearity Diagnosticsa

Model

Dimensi

on Eigenvalue Condition Index

Variance Proportions

(Constant) LOS DR FATA INV

1 1 3.976 1.000 .00 .00 .01 .02 .02

2 .632 2.508 .00 .00 .02 .73 .04

3 .284 3.743 .00 .00 .14 .02 .80

4 .107 6.098 .00 .01 .81 .22 .09

5 .001 57.909 1.00 .99 .01 .01 .06

a. Dependent Variable: GROSSPR

Residuals S tatis ticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value -.0780 .8276 .3516 .14934 390

Residual -.52869 3.56403 .00000 .37825 390

Std. Predicted Value -2.877 3.188 .000 1.000 390

Std. Residual -1.391 9.374 .000 .995 390

a. Dependent Variable: GROSSPR

Appendix 4: Model 4

Collinearity Diagnosticsa

Model

Dimensi

on Eigenvalue Condition Index

Variance Proportions

(Constant) LOS DR FATA CCC

1 1 3.902 1.000 .00 .00 .01 .02 .02

59

2 .624 2.502 .00 .00 .02 .74 .06

3 .369 3.251 .00 .00 .11 .02 .74

4 .105 6.110 .00 .01 .85 .21 .11

5 .001 58.041 1.00 .99 .02 .01 .08

a. Dependent Variable: GROSSPR

Residuals S tatis ticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value -.2348 .9754 .3519 .18717 389

Residual -.53743 3.30470 .00000 .36157 389

Std. Predicted Value -3.135 3.331 .000 1.000 389

Std. Residual -1.479 9.093 .000 .995 389

a. Dependent Variable: GROSSPR

Appendix 5: Test normal for model 1

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

s1 390 100.0% 0 .0% 390 100.0%

Descriptives

Statistic Std. Error

s1 Mean .8420 .01612

95% Confidence Interval for

Mean

Lower Bound .8103

Upper Bound .8737

5% Trimmed Mean .8441

Median .8317

Variance .101

Std. Deviation .31830

Minimum .04

Maximum 1.56

60

Range 1.52

Interquartile Range .44

Skewness -.072 .124

Kurtosis -.430 .247

T ests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

s1 .032 390 .200* .994 390 .111

a. Lilliefors Significance Correction

*. This is a lower bound of the true significance.

Appendix 6: Test normal for model 2

Case Process ing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

61

Case Process ing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

ResiualAP 390 100.0% 0 .0% 390 100.0%

Descriptives

Statistic Std. Error

ResiualAP Mean .8406 .01602

95% Confidence Interval for

Mean

Lower Bound .8091

Upper Bound .8721

5% Trimmed Mean .8430

Median .8317

Variance .100

Std. Deviation .31630

Minimum .04

Maximum 1.54

Range 1.50

Interquartile Range .44

Skewness -.090 .124

Kurtosis -.435 .247

T ests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

ResiualAP .034 390 .200* .993 390 .081

a. Lilliefors Significance Correction

*. This is a lower bound of the true significance.

62

Appendix 7: Test normal for Model 3

Case Process ing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

ResidualINV 390 100.0% 0 .0% 390 100.0%

Descriptives

Statistic Std. Error

ResidualINV Mean .8294 .01596

95% Confidence Interval for

Mean

Lower Bound .7980

Upper Bound .8608

5% Trimmed Mean .8311

Median .8285

Variance .099

Std. Deviation .31509

Minimum .04

Maximum 1.54

Range 1.50

Interquartile Range .42

Skewness -.102 .124

Kurtosis -.426 .247

T ests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

ResidualINV .047 390 .038 .993 390 .069

a. Lilliefors Significance Correction

63

Appendix 8: Test normal for model 4

Case Process ing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

ResidualCCC 390 100.0% 0 .0% 390 100.0%

Descriptives

Statistic Std. Error

ResidualCCC Mean .8444 .01583

95% Confidence Interval

for Mean

Lower Bound .8133

Upper Bound .8756

5% Trimmed Mean .8468

Median .8394

Variance .098

Std. Deviation .31259

Minimum .04

Maximum 1.54

Range 1.50

Interquartile Range .42

Skewness -.100 .124

Kurtosis -.404 .247

T ests of Normality

64

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

ResidualCCC .042 390 .097 .994 390 .097

a. Lilliefors Significance Correction