THE EFFECT OF WORKING CAPITAL MANAGEMENT ON

FIRM PERFORMANCE AND EARNINGS VOLATILITY:

AN ANALYSIS OF THAI LISTED FIRMS

BY

MISS PAKARAT SOISRI

AN INDEPENDENT STUDY SUBMITTED IN PARTIAL

FULFILLMENT OF THE REQUIREMENTS FOR

THE DEGREE OF MASTER OF SCIENCE

PROGRAM IN FINANCE (INTERNATIONAL PROGRAM)

FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2014

COPYRIGHT OF THAMMASAT UNIVERSITY

THE EFFECT OF WORKING CAPITAL MANAGEMENT ON

FIRM PERFORMANCE AND EARNINGS VOLATILITY:

AN ANALYSIS OF THAI LISTED FIRMS

BY

MISS PAKARAT SOISRI

AN INDEPENDENT STUDY SUBMITTED IN PARTIAL

FULFILLMENT OF THE REQUIREMENTS FOR

THE DEGREE OF MASTER OF SCIENCE

PROGRAM IN FINANCE (INTERNATIONAL PROGRAM)

FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2014

COPYRIGHT OF THAMMASAT UNIVERSITY

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Independent Study Title THE EFFECT OF WORKING CAPITAL

MANAGEMENT ON FIRM PERFORMANCE

AND EARNINGS VOLATILITY: AN

ANALYSIS OF THAI LISTED FIRMS

Author Miss Pakarat Soisri

Degree Master of Science (Finance)

Major Field/Faculty/University Master of Science Program in Finance

(International Program)

Faculty of Commerce and Accountancy

Thammasat University

Independent Study Advisor Associate Professor Seksak Jumreornvong, Ph.D.

Academic Years 2014

ABSTRACT

This paper aims to analyze the relationship between working capital

management and firm performance, including profitability and firm value, and earnings

volatility served as firm risk of non-financial Thai listed firms for 2004 to 2013. The

proxy for working capital management is the cash conversion cycle and its components.

Moreover, this study also examines the effect of market phases on working capital

management. The results reveal a significantly negative relationship between the cash

conversion cycle and firm performance. Moreover, the study finds a positive

relationship between the cash conversion cycle and firm risk. The results, however, do

indicate a significant relationship between the cash conversion cycle and firm

performance during bear and bull markets in a negative and positive direction,

respectively. Hence, the relationship between the cash conversion cycle and firm

performance is different in each market phase.

Keywords: Working capital management, Cash conversion cycle, Profitability, Firm

value, Firm risk, Earnings volatility, Market phrase, Bull market, Bear market

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ACKNOWLEDGEMENTS

I want to express my gratitude and deep appreciation to Associate Professor

Seksak Jumreornvong, Ph.D., who provided insight and expertise that greatly assisted

this research. Furthermore, I am deeply grateful to Ajarn Chaiyuth Padungsaksawasdi,

Ph.D. for his helpful suggestions and guidance during the independent study defenses.

Additionally, I would also like to thank MIF’s staff and my colleague for their

helpful coordination and suggestions regarding the requirements during this program.

Finally, this project would have been impossible without the support of my

family; Prapassorn Soisri, Pavika Soisri, Chaaim Soisri, Bunma Soisri, and Pisit

Tangcharoenkul, as well as those people who are not mentioned but have greatly

inspired and encouraged me throughout this difficult project.

Miss Pakarat Soisri

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TABLE OF CONTENTS

Page

ABSTRACT (1)

ACKNOWLEDGEMENTS (2)

LIST OF TABLES (5)

LIST OF FIGURES (6)

CHAPTER 1 INTRODUCTION 1

CHAPTER 2 REVIEW OF LITERATURE 3

CHAPTER 3 CONCEPTUAL FRAMEWORK 5

3.1 Cash conversion cycle 5

3.2 Research hypotheses 7

CHAPTER 4 RESEARCH METHODOLOGY AND DATA 9

4.1 Research Methodology 9

4.1.1 The effect of working capital management on firm performance 9

4.1.2 The effect of working capital management on firm risk 12

4.1.3 The effect of market phrase on working capital management 13

4.1.4 Panel data regression 14

4.2 Data 15

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CHAPTER 5 RESULTS AND DISCUSSION 16

5.1 Descriptive Statistics 16

5.2 Empirical Results 18

5.2.1 Overall Sample 18

5.2.2 Market Phases 22

CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS 26

REFERENCES 28

APPENDICES

APPENDIX A 32

APPENDIX B 33

APPENDIX C 36

APPENDIX D 39

BIOGRAPHY 48

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LIST OF TABLES

Tables Page

A.1 Market Phases 32

B.1 Descriptive Statistics – Overall sample 33

B.2 Descriptive Statistics – Bear Market 34

B.3 Descriptive Statistics – Bull Market 35

C.1 Pearson’s Correlation Matrix – Overall sample 36

C.2 Pearson’s Correlation Matrix – Bear Market 37

C.3 Pearson’s Correlation Matrix – Bull Market 38

D.1 The effect of working capital management on profitability 39

D.2 The effect of working capital management on firm value 40

D.3 The effect of working capital management on firm risk 41

D.4 Market Phases: The effect of working capital management on profitability 42

D.5 Market Phases: The effect of working capital management on firm value 44

D.6 Market Phases: The effect of working capital management on firm risk 46

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LIST OF FIGURES

Figures Page

3.1 Cash Conversion Cycle 6

A.1 Historical Annual Market Return 32

1

CHAPTER 1

INTRODUCTION

Corporate finance involves the financial activities in investment and financing

that are directly related to the effective operation of a corporation. The primary goal of

corporate finance is maximizing shareholders’ wealth and providing the proper returns

to all stakeholders. Long-term financial decisions, such as capital structure, dividends

and valuations, give rise to future cash flows and company growth opportunities. That

being said, short-term decisions regarding working capital are also an important

element in successfully driving a business. Working capital management can affect the

company's overall financial health and its ability to use working capital efficiently to

grow and expand the company's business.

An optimal level of working capital is necessary for each firm. This working

capital can be affected by both internal and external factors. The primary factor that

affects working capital is the nature of the business. Each business has different

requirements for working capital. Some firms in trading or industrial businesses, for

example, require heavy inventory investments, while firms in the hotel service business

require less working capital but a greater investment in fixed assets. Moreover, the need

for working capital increases with the firm’s growth and expansion periods. However,

in the case of seasonal fluctuations in sales, the ultimate requirement for working capital

will fluctuate.

To be efficient, working capital management clearly requires company

objectives to be achieved. Consequently, the main objectives of working capital

management include increasing company profitability (Pass and Pike (1984)). The first

aim of this research, thus, is to investigate the potential relationship between working

capital management and firm performance, which includes profitability and firm value.

In this investigation, the data analysis will involve information from companies listed

on the Stock Exchange of Thailand from 2004 through 2013.

Another main goal of working capital management is to ensure that the

organization has sufficient liquidity to meet short-term obligations so as to continue in

business (Pass and Pike (1984)). Therefore, working capital management is directly

2

responsible for the avoidance of firm risk. Previous studies have focused primarily on

the effect of working capital management on firm performance. Only a few studies have

investigated the relationship between working capital management and firm risk.

Hence, this research also explores the relationship between them. To measure the firm

risk, earnings volatility will be used.

The cash conversion cycle is used as a proxy for working capital management,

as it has been widely used to measure the effectiveness of working capital management

(Shin and Soenen (1998), Deloof (2003), Reheman and Nasr (2007), and Takon

(2013)). Moreover, its components, including accounts receivable days, accounts

payable days and inventory days are also served as a proxy for working capital

management. The cash conversion cycle is a measure of liquidity that illustrates how

quickly a company cycles between cash outlays and cash inflows. More specifically, it

is an indication of how well-managed the company is financially.

Moreover, this study seeks to contribute to the scholarly knowledge by

examining the effects of the bull and bear market phases on working capital

management. A bull market refers to a rise in stock prices over a given period in which

investors are optimistic. In addition, the economy tends to be good, unemployment is

low, and people spend more money (Chauvet and Potter (2000)). In a bear market, stock

prices are declining. Moreover, the economy tends to be weak, unemployment

increases, and consumers spend less. Therefore, in different situations, firm investments

and financing activity might not be the same, and this affects working capital

management.

Last, the research questions are designed to determine whether working capital

management have a relationship with firm performance, and firm risk and, if so, the

direction that relationship takes. Another relevant question under investigation is

whether market phases influence working capital management.

The rest of the paper is organized as follow. Chapter 2 would be review of the

literature. Chapter 3 explains the conceptual framework. Data and research

methodology would be described in Chapter 4. Chapter 5 provides the results and

discussions. Finally, Chapter 6 concludes and recommendations the paper.

3

CHAPTER 2

REVIEW OF LITERATURE

Research on working capital management has been a part of corporate finance

for a long time and numerous academic studies on working capital management have

been undertaken. This section will discuss the literature related to working capital

management with an emphasis on firm performance and firm risk.

The existing literature mostly explores the effects of working capital on

performance, both profitability and firm value. The first proxy for measuring working

capital management is the cash conversion cycle (CCC). The cash conversion cycle can

be used to assess how well a company is managing its working capital. Almost all the

empirical studies find a negative relationship between the cash conversion cycle and

firm performance.

More specifically, Jose, Lancaster and Stevens (1996) examine the relationship

between working capital management and return on assets/return on equity of United

States firms using the cash conversion cycle as a measure of working capital

management. The results show a significant negative relationship between the cash

conversion cycle and profitability. Moreover, Shin and Soenen (1998) examine the

relationship between working capital management and value creation for shareholders.

The results show a strong negative relationship between the length of the firm’s cash

conversion cycle and its profitability. Deloof (2003) studies an analysis on the effects

of working capital management and gross operating income in Belgium using the cash

conversion cycle and its components as a proxy for the period from 1992 through 1996.

The results show a negative relationship between gross operating income and the cash

conversion cycle. Moreover, the findings confirm that firms can improve their

profitability by reducing accounts receivable days and inventory days.

Additionally, Lazaridis and Tryfonidis (2006) find statistical significance

between gross operating profit and the cash conversion cycle. Moreover, firms can

create profits by managing the cash conversion cycle and keeping each different

component at an optimum level. In addition, Reheman and Nasr (2007) consider the

impact of working capital management on return on assets using a sample of listed

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companies at the Karachi Stock Exchange (KSE) for 1999 through 2004. They use the

cash conversion cycle and its components as working capital management policies.

They also find a significant negative relationship between working capital management

and firm profitability. More recently, Takon (2013) investigates the impact of return on

assets of Nigerian firms for the period from 2000 through 2009. The results show that

the cash conversion cycle has a significant negative relationship with profitability.

Based on these findings, the study proposes that firms reduce their cash conversion

cycle to increase profitability and create more value for shareholders. Similarly, Elielly

(2004) measures liquidity using the current ratio and the cash conversion cycle on a

sample of companies in Saudi Arabia. The study finds a negative relationship between

a firm’s profitability and its liquidity level.

The implementation of aggressive investment policy and aggressive financing

policy is the one of popular as a measure of working capital management. An

aggressive investment policy is measured as current assets over total assets, whereas an

aggressive financing policy is measured as current liabilities over total assets. A lower

ratio of aggressive investment policy leads to higher working capital aggressiveness.

On the other hand, a higher ratio of aggressive financing policy leads to higher working

capital aggressiveness.

Weinraub and Visscher (1998) discuss the relationship between aggressive

working capital and return on asset. The sample is U.S. firms from 1987 through 1997.

The results show that more aggressive working capital policies are associated with

higher returns, while more conservative working capital policies are associated with

lower returns. Similarly, Nazir and Afza (2009) examine the impact of aggressive

working capital investment and financing policies on the profitability of Pakistani firms

for 1998 through 2005. The results show more working capital aggressiveness leads to

higher profitability. More recently, Alshubiri (2011) investigates the relationship

between working capital practices and firm performance using return on assets, return

on investment, and Tobin’s Q. The sample includes industrial firms listed on the

Amman Stock Exchange for 2004 through 2008. The study shows that firm with more

aggressive working capital policy leads to high profitability.

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CHAPTER 3

CONCEPTUAL FRAMEWORK

This section will discuss the conceptual framework for the study. First, the cash

conversion cycle will be examined and then the research hypotheses will be presented.

3.1 Cash Conversion Cycle

The cash conversion cycle indicates how efficiently firms are managing their

working capital. The cash conversion cycle expresses the length of time, in days,

between when the firm pays cash to purchase its initial inventory and when it receives

cash from the sale of the finished goods. The cash conversion cycle allows an investor

to gauge the company's overall health.

In many cases, firms do business by starting with the purchase of inventory from

suppliers in the form of raw materials or finished goods. Then, firms sell products; the

average time it takes to create and sell inventory is called the inventory days. Firms

often sell products on credit, which results in accounts receivable. The average time

after the company sells the finished goods until it receives cash is called the accounts

receivable days. This process is the company’s operating cycle, which is measured by

summing the accounts receivable days and inventory days (Lawrence (2003)).

Operating cycle = Inventory Days + Accounts Receivable Days

The process of producing and selling a product also includes the purchase of

production inputs (raw materials) on account, which results in accounts payable.

Accounts payable reduce the number of days a firm’s resources are tied up in the

operating cycle. The time it takes to pay the accounts payable, measured in days, is the

accounts payable days. The operating cycle less the accounts payable days is referred

to as the cash conversion cycle. The cash conversion cycle is defined as:

Cash conversion cycle

= Inventory Days + Accounts Receivable Days – Accounts Payable Days

= Operating Cycle – Accounts Payable Days

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Where

Accounts Receivable Days (Average time it takes to receive cash after selling finished

goods)

= 365*(Average Accounts Receivable/Total Sales)

Accounts Payable Days (Average time it takes to pay suppliers)

= 365*(Average Accounts Payable/Cost of Goods Sold)

Inventory Days (Average time it takes to create and sell inventory)

= 365*(Average Inventory/Cost of Goods Sold)

Figure 3.1 Cash Conversion Cycle (Berk and Demarzo (2007))

The firm with a shorter cash conversion cycle can indicate more efficient

working capital management. When the inventory days are low, this means a company

is able to collect cash from revenues quickly. A firm can speed up payments from

customers with lower accounts receivable days; having fewer accounts receivable days

allows for quick collection of receivables that it can use to invest in the short-term. In

other words, offering short credit term may reduce the doubtful accounts and bad debts.

The last thing that can be done is slowing down payments to its suppliers as longer

accounts payable days; in this way, the buyer realizes the benefits from using the entire

credit time and paying on the last possible date. The lower the cash conversion cycle

Accounts Payable Days

Operating Cycle

Inventory Days Accounts Receivable Days

Cash Conversion Cycle

Buy Inventory Pay for Inventory Sell Finished Goods Receive Payment

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is, the better the management effectiveness is. This is because the firm can allow a

business to acquire cash quickly that can be used for additional purchases or debt

repayments, so the business is able to use its working capital in other areas.

On the other hand, when a company takes an extended period of time to collect

outstanding accounts receivable, it has too much inventory on hand or pays its expenses

too quickly, it lengthens the cash conversion cycle. A longer cash conversion cycle

means it takes a longer period of time to generate cash, which can a drag on liquidity.

If there is not enough cash flow to meet the needs of short-term debt obligations, the

firm will face a risk. In the worst case, this will result in an insolvency or bankruptcy

problem. This indicates that the longer the cash conversion cycle is, the lower is the

effectiveness for the working capital management.

In some cases, the cash conversion cycle illustrates a negative number of days;

for instance, if the company receives cash from receivables before paying the suppliers.

This is good for the company, in that it collects receivables before paying the suppliers.

However, if a firm is too quick to pay suppliers, it must reserve enough money and

working capital to keep running the business until it receives the cash from receivables.

Hence, the cash conversion cycle is an important metric for evaluating a company’s

operational efficiency; it is especially useful for comparing close competitors.

3.2 Research Hypotheses

This research focuses on the effect of working capital management on firm

performance and firm risk. It will also consider whether the market phase affects

working capital management. The research questions will be answered through the

following hypotheses.

Hypothesis 1:

1.1 There is a relationship between working capital management and firm profitability.

1.2 There is a relationship between working capital management and firm value.

1.3 There is a relationship between working capital management and firm risk.

Hypothesis 2:

2.1 The relationship between working capital management and firm profitability is

different in the various market phases.

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2.2 The relationship between working capital management and firm value is different

in the various market phases.

2.3 The relationship between working capital management and firm risk is different in

the various market phases.

A company must have a different management strategy for operating in each

market phases, both bear and bull. Moreover, the market phase also affects investor and

firm behavior in investment and financing decisions, as well as working capital

management. Hence, this study expects the relationship between the cash conversion

cycle and firm performance/firm risk to be different in bear and bull markets.

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CHAPTER 4

RESEARCH METHODOLOGY AND DATA

4.1 Research Methodology

To study the effect of working capital management on firm performance and

firm risk, this part is classified into four sections. The first section defines working

capital management and performance. The second section defines working capital

management and firm risk. The next discusses the effect of market phase. Finally, panel

data regression is employed.

4.1.1 Testing the effect of working capital management on firm

performance

To study the effect of working capital management on firm performance, firm

value and profitability serve as the proxy of firm performance. First, a good measure of

a company’s performance is its profitability; profitability measures the efficiency with

which a company turns business activity into profits. The return on assets (ROA) is one

of the most widely used profitability ratios (Jose, Lancaster and Stevens (1996),

Reheman and Nasr (2007), Nazir and Afza (2009) and Alshubiri (2011)). This is

because it is related to both the profit margin and asset turnover and shows the rate of

return for both creditors and investors of the company. It provides an idea as to the

efficiency of management in using assets to generate earnings. ROA can be calculated

by adding back the company’s after-tax interest expense to its net income and then

dividing by its total assets. The company will seek to use operating returns before

incurring the cost of borrowing. To test the hypothesis regarding the relationship

between profitability and working capital management, the following equation is used:

ROAi,t = β0 + β1CCCi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (1)

ROAi,t = β0 + β1ARi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (2)

ROAi,t = β0 + β1APi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (3)

ROAi,t = β0 + β1INVi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (4)

10

Where:

i = 1,2,3,… that denotes each firm.

t = 1, 2,….,10 that denotes period t each fiscal year

Firm value is investigated by using Tobin’s Q as a proxy (Tobin (1969)).

Tobin’s Q has been widely used to measure firm value, which is defined as the ratio of

the market value of debt and equity to the replacement cost of total assets. Furthermore,

asset value is at replacement cost rather than at historical accounting cost, which takes

into account the effects of inflation. The formula for Tobin’s Q is as follows:

Tobin′s Q = Market value of debt and equity

Replacement cost of total assets

However, Tobin’s Q is quite difficult to calculate because the market value of

debt is inconvenient to estimate. Moreover, estimating replacement costs is also

complicated. The existing literature (Thomsen, Pedersen and Kvist (2006), and

Florackis, Kostakis, and Ozkan (2009)) claims to use the book value rather than the

market value of debts or assets. Therefore, this research replaces the market value of

debt and equity with the market value of the company and substitutes the replacement

cost of total assets with the book value of total assets. The new formula is as follows:

Tobin′s Q = Market value of company

Book value of total assets

Note that the market value of the company is calculated by multiplying the

number of shares outstanding by the current market price of the stock of company i.

The concept of this ratio is related to the price-to-book ratio (P/B ratio), which

is also known as the market-to-book ratio (M/B ratio). The reason for use of this ratio

is that book value is a cumulative balance sheet amount, and book value is positive and

more stable than earnings or earnings per share (EPS). Book value has also been used

in the valuation of companies that do not expect to continue as going concerns. Penman

(1996) argues that the M/B ratio reflects the future profitability and growth

opportunities of the firm. If the ratio is above one, meaning that investment in assets

generates earnings that provide a higher value than investment; this will motivate new

investment and greater growth opportunity. This also implies that the stock price is

11

overvalued. Conversely, if the ratio is below one, investment in assets is not attractive

and represents a lower growth opportunity. It also implies that the stock price is

undervalued.

To test the hypothesis on the relationship between firm value and working

capital management, the cash conversion cycle is the proxy for the efficiency of

working capital management. The main regression model follows Deloof (2003) and is

defined as follows:

Qi,t = β0 + β1CCCi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (5)

Qi,t = β0 + β1ARi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (6)

Qi,t = β0 + β1APi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (7)

Qi,t = β0 + β1INVi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (8)

Where:

i = 1,2,3,… that denotes each firm.

t = 1, 2,….,10 that denotes period t each fiscal year

The dependent variable in the regression model is return on assets (ROAi,t) and

Tobin’s Q (Qi,t) defined as the value of company i during fiscal year t. The independent

variable is the cash conversion cycle (CCCi,t) and its components. It includes accounts

payable days (APi,t), account receivable days (ARi,t) and inventory days (INVi,t).

This research also adds control variables for financial data to test their effects

on firm performance, including firm size, sales growth, debt ratio, and current ratio

(Deloof (2003)). Firm size (Sizei,t) is measured by a natural logarithm of total assets for

each year. Sales growth (Growthi,t) is calculated from the current year’s sales minus

the previous year’s sales and then divided by the previous year’s sales. The debt ratio

(DRi,t) is measured by the sum of short-term and long-term debt divided by total assets;

it indicates how much the company relies on debt to finance assets. The current ratio

(CRi,t) is mainly used to provide an idea of the company's ability to pay back its short-

term liabilities with its short-term assets. The higher the current ratio is, the more

capable the company is of paying its obligations. The current ratio is measured by the

proportion of current assets and current liabilities. These control variables show a

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significant relationship between working capital and firm performance in existing

studies (Deloof (2003)).

4.1.2 Testing the effect of working capital management on firm risk

Working capital management entails the process of balancing the needs of short-

term assets and short-term debt. Moreover, it ensures that a company has sufficient cash

flow or liquidity to meet its short-term debt obligations. Therefore, working capital

management is directly concerned with firm risk.

To measure firm risk, earnings volatility serves as the proxy. Earnings volatility

is measured following Minton and Schrand (1999). It is defined as the standard

deviation in a firm’s quarterly earnings before interest, tax, depreciation and

amortization (EBITDA) over the past 12 quarters.

Earnings volatility refers to the degree of stability in a firm’s earnings. Volatile

earnings lead to difficulties ahead. Especially when a firm must borrow funds for

investments, the predicted cash flow to pay debt obligations may not materialize.

Empirically, Minton and Schrand (1999) claim that volatility in earnings increases a

firm’s cost to access external capital and reduces its value. In addition, high earnings

volatility increases the likelihood of a negative earnings surprise. Tureman and Titman

(1988) suggest that smoothing earnings reduces a firm’s perceived probability of

default. Hence, earnings volatility is an appropriate proxy for firm risk.

The model that examines the relationship between working capital management

and firm risk is as follows:

Riski,t = β0 + β1CCCi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (9)

Riski,t = β0 + β1ARi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (10)

Riski,t = β0 + β1APi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (11)

Riski,t = β0 + β1INVi,t+β2DRi,t + β3Sizei,t+β4Growthi,t+β5CRi,t + εi,t (12)

Where:

i = 1,2,3,… that denotes the each firm

t = 1,2,….,10 that denotes period t each fiscal year

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To test working capital management and firm risk, the dependent variable is

earnings volatility (Riski,t). The independent variable is the cash conversion cycle

(CCCi,t), accounts payable days (APi,t), account receivable days (ARi,t) and inventory

days (INVi,t). In addition, other control variables are included in the regression to

capture the effect of firm risk.

4.1.3 Testing the effect of market phases on working capital

management

This section develops the test on the effect of market phases on the regression

model. This study uses a subsample test that is divided into bull and bear market phases.

Suntraruk (2007) classifies bull and bear markets based on an up-and-down market.

This procedure considers each year from 2004 through 2013 independently. Therefore,

each of the 10 years is classified as either a bull market or a bear market based on the

mean annual market return over the entire period. A year in which the annual market

return is greater than the mean annual market return is a bull market, while a bear

market is a year in which the annual market return is lower than the mean annual market

return. (Docking and Koch (2005)).

Before ranking, this research calculates the daily market return from the market

index (SET index) and then an average daily market return for each year. Finally, the

daily market return is converted to an annual market return using the effective annual

rate (EAR). The EAR is the continuously compounded market index return during year

t, which can be measured as follows (Berk and Demarzo (2007)):

EARi = (1 + Ri)period − 1

Where:

EARi is the annual market return in each year i

Ri is the average daily market return in year i

Period is the number of trading days in each year

After that, the mean annual market return over the entire year is computed,

which is equal to 9.92% for classifying market phases into a bull or bear market. Bull

market includes 2007, 2009, 2010, and 2012, whereas bear market includes 2004, 2005,

14

2006, 2008, 2011, and 2013 (see the appendix A for more details). Finally, both the bull

and bear subsample will be used to test the effect of market phases on working capital

management for firm performance and firm risk.

4.1.4 Panel data regression

The data in this research come from several Thai-listed firms and many periods

from 2004 through 2013. Therefore, a panel data regression model that has both a

cross-sectional dimension and a time-series dimension is employed. Generally,

problems in panel data are related to an omitted variable. Fixed effects regression and

random effects regression are the main techniques used for analysis of panel data to

solve for any omitted variable.

The fixed effects model is used when one seeks to control for omitted variables

that differ between cases but are constant over time. Furthermore, omitted variables are

correlated with independent variables. This constant can be removed from the data

through differencing (e.g., by taking a first difference which will remove any time-

invariant components of the model). Some omitted variables will vary between cases

and are also not constant over time, while others may be fixed between cases but vary

over time. The random effects model is more efficient than the fixed effects model

because it uses the changes in the variables over time to estimate the effects of the

independent variables on the dependent variables. However, the random effects

assumption is that the individual specific effects are not correlated with the independent

variables.

To determine whether to apply the fixed effects or the random effects model,

the Hausman test can be used to choose the best regression model (Reyna (2011)).

Hausman Test: Hypothesis

H0 : Omitted variables are not correlated with independent variables.

H1 : Omitted variables are correlated with independent variables.

If the Hausman test rejects the null hypothesis, meaning omitted variables are

correlated with independent variables, the fixed effects model is employed. On the other

hand, if the Hausman test accepts the null hypothesis, omitted variables are not

correlated with independent variables, and the random effects model is more efficient.

15

Based on testing by using the Hausman test, the fixed effects model is

appropriate for running the regression.

4.2 Data

This research examines a sample of companies listed on the Stock Exchange of

Thailand from 2004 through 2013. The data are mainly collected from two sources. The

related financial and accounting data are retrieved from Datastream, whereas the data

about daily stock prices for separate bull and bear markets are collected from the

Setsmart database. Firms in the financial services industry are excluded because

working capital has a different purpose for those firms. Following construction

contracts in Thai accounting standard (TAS) 11, property and construction industry

uses accounting standards that differ from other industries. Therefore, firms in the

property and construction industry are also excluded from the sample.

An unbalanced panel data is used in this study. Firms listed after 2004 are added

to the sample, while firms de-listed remain in the sample until they are de-listed.

Finally, the data are presented on an annual basis because the database provides the

most completely data on an annual basis. However, only the data for calculating

earnings volatility is based on quarterly basis. This research also winsorizes both

independent variables and dependent variables at the 1% and 99% levels to reduce the

effects of outliers.

16

CHAPTER 5

RESULTS AND DISCUSSION

5.1 Descriptive Statistics

Table B.1 represents the summary statistics for the variables in the overall

sample. The data includes information from non-financial companies listed in Thailand

over the period of 2004 to 2013. The property and construction industry is excluded

from the samples. The dependent variables are Tobin’s Q (Q), which is the proxy for

firm value, return on assets (ROA), as the proxy for profitability, and earnings volatility

(Risk), as the proxy for firm risk. The independent variables include working capital

management that is cash conversion cycle and its component; accounts receivable days,

inventory days and accounts payable days. Moreover, this paper also adds control

variables to test for the effect of firm performance and firm risk. These control variables

include firm size (Size), sales growth (Growth), debt ratio (DR) and current ratio (CR).

The table shows the mean, median, standard deviation, minimum amount and

maximum amount of the variables. The average and the median of the firm value on

the overall sample are fairly closed (1.59 and 1.25 respectively). The standard deviation

is 1.29 with a range between 0.17 and 16.94. The profitability has an average and

median of 0.08 and 0.07, respectively. The distribution is moderate dispersed, with a

standard deviation of 0.07%. The lowest observed profitability is -0.36 and the highest

is 0.47.

The firm risk has an average of 0.40 and a median of 0.10. The standard

deviation is 0.94, with a minimum of 0.01 and a maximum of 12.97. For the cash

conversion cycle, there is average of 85.10 and a median of 77.37. The standard

deviation is moderate, at 0.94, indicating dispersed observations with a range between

-99.57 and 348.58. Moreover, the average of accounts receivable days, inventory days

and accounts payable days are 62.04, 71.48 and 47.15, respectively.

For the control variables, the average and median of the firm leverage are 31.02

and 30.43, respectively. The distribution is not high, as the standard deviation is 21.12,

with a minimum of 0.1 and a maximum of 106.00. The firm size has an average of

15.01 and a median of 14.72, with standard deviation of 1.59.

17

The firm has low distribution of firm sizes, resulting from a minimum of 10.40

to a maximum of 21.20. Sales growth has average and median of 0.16 and 0.10,

respectively. The lowest amount of sales growth is -0.84; the highest amount of sales

growth is 7.27. The current ratio has an average of 1.80 and a median of 1.37. The

standard deviation of the current ratio is 1.51; it ranges from 0.01 to 20.18.

Moreover, this paper also develops the test on the effect of the market phases

on the regression model. It uses the subsample test to separate the overall sample into

bull and bear markets. Table B.2 represents the summary statistics for the variables in

a bear market. A bear market is found in 2004, 2005, 2006, 2008 and 2011. Table B.3

represents the summary statistics for the variables in a bull market. A bull market is

found in 2007, 2009, 2010 and 2012.

The average firm value during a bear market is 1.77, while the average firm

value during a bull market is 1.36. Hence, the average firm value during a bull market

is slightly higher than that value during a bear market. The average profitability is

0.08% during a bear market; this value is really closed with of a bull market at 0.07%.

Another important variable is the earnings volatility, which is used as the proxy

for firm risk. It reports that the average of the firm risk during a bear markets is 0.39.

The average of the firm risk during a bull markets is 0.41. Hence, during a bull market,

a firm has a higher firm risk than during a bear market, on average. Additionally, a firm

has an average cash conversion cycle of 90.17 during a bull market. The average cash

conversion cycle during a bear market is 85.73. Moreover, the average of the accounts

receivable days during a bear market and bull market are slightly closed at 61.81 and

62.39, respectively. Finally, the average of inventory days and accounts payable days

during bear and bull markets are quiet closed.

Moreover, the correlation coefficient matrix among the variables used for a

regression is displayed in Table C.1 for the overall sample. The cash conversion shows

a negative correlation with the Tobin’s Q as the firm value. This implies that a lower

cash conversion cycle leads to a higher firm value. Moreover, there is a statistically

significant negative relationship between the cash conversion and the return on assets,

as profitability indicates that a firm with a lower cash conversion cycle experiences

higher profitability. The correlation coefficient between the cash conversion cycle and

18

earnings volatility as firm risk is positive, implying that the lower cash conversion, the

lower firm risk.

Tables C.2 and C.3 illustrate the correlation matrix during bear and bull

markets, respectively. Cash conversion cycle is statistically significantly negatively

correlated with the firm value during a bear market, suggesting that a lower cash

conversion cycle leads to a higher firm value. During a bear market, the correlation

between the cash conversion cycle and firm value, however, is not statistically

significant. Additionally, there is a negative relationship between the cash conversion

and profitability during both bear and bull market, implying that a lower cash

conversion cycle leads to high profitability. Lastly, the cash conversion cycle shows a

positive correlation with earnings volatility as the firm risk in both bear and bull

markets. This indicates that when the cash conversion is lower, firm risk is lower.

5.2 Empirical Results

5.2.1 Overall Sample

5.2.1.1 The effect of working capital management on profitability

Table D.1 presents the results of regression estimated with fixed effects to test

hypothesis 1.1. The table shows a relationship between profitability as ROA and

working capital management. This research applies the cash conversion cycle as a

proxy for working capital management for the first model. Moreover, the second model

applies accounts receivable days as independent variable. Finally, inventory days and

accounts payable days are added as the independent variables for the third and fourth

models, respectively.

The results indicate a significant relationship between the cash conversion cycle

and ROA or profitability at the 1% level. Hence, the model fails to reject the hypothesis.

The cash conversion cycle’s coefficients have a negative relationship with profitability.

This is the same finding as that of Shin and Soenen (1998), Deloof (2003), Reheman

and Nasr (2007), and Takon (2013). The rational reason to support this model is

demonstrated by the lower cash conversion cycle, which shows a more efficient

working capital management that leads to higher profitability. In addition, a higher cash

19

conversion cycle is the less efficient working capital management that leads to lower

profitability.

Moreover, this paper also considers the effect of cash conversion cycles’

components; accounts receivable days, inventory days and accounts payable days. First

of all, the results show a significantly negative relationship between accounts receivable

days and profitability at the 1% level. It indicates that lower accounts receivable days

lead to higher profitability. One possible explanation is having fewer accounts

receivable days allows for quick collection of receivables that can bring it to invest in

short-term and pay back to the creditors. Another explanation, offering short credit term

may reduce the bad debts and directly lead to higher firm profitability.

The next one, surprisingly, the results reveal a significantly negative

relationship between the inventory days and profitability at the 1% level. This can be

interpreted that a lower inventory days lead to lower profitability and a higher inventory

days lead to higher profitability. Because fewer inventory days, which can quickly

generate money from increased sales. It can turns its working capital over more times

per year and that allows it to generate more sales per money invested. Furthermore,

there are the significantly positive relationship between accounts payables days and

profitability. This is reasonable because a longer accounts payable days can delay

payment to suppliers. The buyer realizes benefits from using the whole credit time and

paying on the last possible date. Therefore, it indicates that longer accounts payable

days lead to higher profitability.

Furthermore, this research also adds control variables to capture the effects on

profitability. First, for the debt ratio, which is the proxy for the firm’s leverage, the

results reveal a significantly negative relationship between the debt ratio and

profitability with a 99% level of confidence. Therefore, a high amount of debt used by

the company decreases firm profitability. This research finds there is a significant

relationship between firm size and profitability. With respect to direction, firm size

coefficients have a negative relationship with profitability. Hence, small firms achieve

greater profitability than large firms.

For the relationship between sales growth and profitability, it reveals a

statistically significant positive relationship at the 1% level. This implies that a firm

with high sales growth has more profitability than a firm with low sales growth. Finally,

20

the current ratio is an important factor that should influence firm profitability. However,

this paper finds that the current ratio is not the determinant of profitability, which is

represented by the statistical insignificance of the current ratio coefficients.

5.2.1.2 The effect of working capital management on firm value

The result of the fixed effect specification model regression between working

capital management and firm value is presented in Table D.2. The cash conversion

cycle is the proxy of working capital management. Moreover, the second model applies

accounts receivable days as independent variable. Finally, inventory days and accounts

payable days are added as the independent variables for the third and fourth models,

respectively.

Surprisingly, the results suggest a statistically significant relationship between

the cash conversion cycle and firm value at the 1% level. The direction of the

relationship is negative. This indicates that when the cash conversion cycle is lower,

the firm value grows higher. In contrast, when the cash conversion cycle is higher, the

firm value becomes lower. Moreover, the results are consistent with the results of the

relationship between profitability and working capital management. This is reasonable

because lower cash conversion cycle which shows a more efficient working capital

management that can increase firm value.

Moreover, this paper also examines the effect of cash conversion cycles’

components; accounts receivable days, inventory days and accounts payable days. First

of all, the results show statistically insignificant relationship between both accounts

receivable days and inventory days with firm value. This implies that the accounts

receivable days and inventory days does not influence firm value. However, the results

reveal that there are the significantly positive relationship between accounts payables

days and firm value at 1% level. One possible explanation is that longer accounts

payable days mean extending the length of time to pay the creditors. The buyer realizes

benefits from using the whole credit time and paying on the last possible date. Hence,

it implies that longer accounts payable days lead to higher firm value.

In addition, this research adds control variables to capture the effects on firm

value. First, for the debt ratio, which is the proxy for the firm’s leverage, the results

suggest a significant relationship between the debt ratio and firm value at the 99% level

21

of confidence. With respect to direction, the debt ratio’s coefficients have a positive

relationship with firm value. Therefore, a high amount of company debt increases firm

value in the overall sample. Moreover, this paper reports a significant positive

relationship between firm size and firm value at the 1% level. Hence, small firms have

less firm value than large firms.

For the relationship between sales growth and firm value, the results show that

sales growth is not the determinant of firm value, represented by the statistical

insignificance of the sales growth coefficients. Finally, the current ratio is an important

factor that should affect not only profitability but also firm value. The results amazingly

find statistically insignificant current ratio coefficients with respect to firm value. This

implies that the current ratio does not influence firm value.

5.2.1.3 The effect of working capital management on firm risk

Finally, the results of regression estimated with fixed effects to test the

relationship between firm risk as earnings volatility and working capital management

is shown in Table D.3. This research applies the cash conversion cycle and its

components as a proxy of working capital management.

With respect to the results, working capital management has a statistically

significant positive relationship (at the 1% level) with firm risk. Hence, the model fails

to reject the hypothesis that a relationship exists between working capital management

and firm risk. This can imply that companies with a lower cash conversion cycle have

lower firm risk due to higher efficiency in working capital management and that a

higher cash conversion cycle tends to indicate higher firm risk. One possible

explanation for this is that a longer cash conversion cycle means it takes a longer time

to generate cash. Firms may have insufficient liquidity to meet short-term obligations

as they fall due and thus to continue on in business, so the firm will face higher risk.

Therefore, a higher cash conversion cycle increases firm risk.

However, the results suggest that the accounts receivable days and accounts

payable days do not influence firm risk. While, the results show that inventory days has

a statistically significant positive relationship (at the 1% level) with firm risk. This

implies that a higher inventory days tends to mean higher firm risk and a lower

inventory days tends to mean lower firm risk. One possible explanation is that longer

22

inventory days mean firm cannot quickly generate money from increased sales and lead

to higher firm risk.

Furthermore, this research also adds control variables to capture the effects on

firm risk. First, for the debt ratio, which is the proxy for the firm’s leverage, the results

show a significantly negative relationship between debt ratio and firm risk at the 90%

level of confidence. Therefore, a high amount of company debt decreases firm risk. For

the relationship between firm size and firm risk, the coefficients have a positive sign

and the coefficients of the overall sample reveal a statistically significant positive

relationship at the 1% level. This implies that large firms have more risk than small

firms. Surprisingly, this research finds that sales growth has no influence on firm risk,

represented by the insignificance of the sales growth coefficients. Finally, this paper

finds that the current ratio is not the determinant of firm risk, represented by the

statistically insignificant current ratio coefficients.

5.2.2 Market phases

5.2.2.1 The effect of working capital management on profitability

Table D.4 presents the results of regression estimated with fixed effects to test

hypothesis 2. The table shows a relationship between profitability as ROA and working

capital management during the bear and bull markets. This research applies the cash

conversion cycle and it components; accounts receivable days, inventory days and

accounts payable days as a proxy for working capital management.

During a bear market, the results show a significantly negative relationship

between the cash conversion cycle and profitability at the 1% level. The results of a

bear market are consistent with the overall sample. One possible explanation is

demonstrated by the lower cash conversion cycle, which shows a more efficient

working capital management that leads to higher profitability. In addition, a higher cash

conversion cycle is the less efficient working capital management that leads to lower

profitability.

However, during a bull market, surprisingly, the results reveal a significantly

positive relationship between the cash conversion cycle and profitability at the 1%

level. This can be interpreted that a lower cash conversion cycle leads to lower

profitability and a higher cash conversion cycle leads to higher profitability. With

23

regard to direction, the cash conversion cycle’s coefficients have a different relationship

with profitability during bear and bull markets. The model fails to reject the hypothesis;

the relationship between the cash conversion cycle and firm profitability is different in

each market phase.

The rational reason for this is that during a bull market the economy tends to be

good, unemployment is low, and people are spending more money (Chauvet and Potter

(2000)). A firm also seems more trustworthy to both customers and creditors. Thus,

firms become braver and more willing to take risk in investments. Even though a longer

cash conversion cycle might mean less effective management of working capital, firms

can take this risk by extending the cash conversion cycle. Hence, a longer cash

conversion cycle during a bull period may lead to higher firm profitability. However,

in a bear market, which tends to indicate an economic recession, financial managers

might overreact in managing a firm’s operations and its working capital. Therefore, a

shortened cash conversion cycle is better for a firm as it will increase firm profitability.

Furthermore, this research also adds cash conversion cycle’s components to

capture the effects on profitability. First, for the accounts receivable days, the results

reveal a significantly negative relationship between accounts receivable days and

profitability in both bear and bull markets with a 99% level of confidence. Therefore, a

high day of accounts receivable decreases firm profitability. For the relationship

between accounts payable days/inventory days and profitability, both models (bear and

bull markets) have a statistically significant negative relationship at the 1% level. It can

be interpreted that when inventory days and accounts payable days are higher, the firm

profitability grows higher. Based on the results of both markets, they are not consistence

with the result of the relationship between cash conversion cycle and profitability.

Finally, this research rejects the hypothesis and it indicates that the relationship between

the cash conversion cycle’s components and firm profitability is not different in each

market phase.

24

5.2.2.2 The effect of working capital management on firm value

The result of the fixed effect specification model regression between working

capital management and firm value during the bear and bull markets is presented in

Table D.5. The cash conversion cycle and its components are the proxy of working

capital management.

First of all, this table reports a statistically significant relationship between the

cash conversion cycle and firm value at the 1% level. The direction of the relationship

during a bear market is negative, while the direction of the relationship during a bull

market is positive. This indicates that during a bear market when the cash conversion

cycle is lower, the firm value grows higher. In contrast, during a bull market when the

cash conversion cycle is higher, the firm value becomes lower. Therefore, the model

fails to reject the hypothesis; the relationship between the cash conversion cycle and

firm value is different in each market phase. Moreover, the results of bull and bear

markets are consistent with the results of the relationship between profitability and

working capital management. This is reasonable because during a bull market, a firm

is emboldened to extend the period of its cash conversion cycle due to good economy.

Hence, a longer cash conversion cycle during a bull market leads not only to higher

firm profitability but also to higher firm value. However, during a bear market, a firm

must manage and operate its business more carefully. Therefore, a lower cash

conversion cycle is better for the firm because it can increase firm value.

Moreover, this research also adds cash conversion cycle’s components to

capture the effects on firm value. During a bear market, the result shows a significantly

negative relationship between accounts receivable days and firm value. It can explain

that during a bear market, when accounts receivable days grow high, the firm value will

decrease. Moreover, during a bear market, there are the positive relationship between

accounts payable days and firm value. It implies that longer accounts payable days lead

to higher firm value. Finally, during a bull market, the cash conversion cycle’s

components; accounts receivable days, inventory days and accounts payable days are

an important factor that should affect not only profitability but also firm value. The

results amazingly find statistically insignificant their coefficients with respect to firm

value. This implies that during a bull market they do not influence firm value.

25

5.2.2.3 The effect of working capital management on firm risk

Finally, the results of regression estimated with fixed effects to test the

relationship between firm risk as earnings volatility and working capital management

is shown in Table D.6. This research applies the cash conversion cycle and its

components as a proxy of working capital management. The first fixed effect

specification model presents a bear market. The second model presents a bull market.

With respect to the results, both bear and bull markets, working capital

management has a statistically significant positive relationship (at the 1% level) with

firm risk. Hence, the model fails to reject the hypothesis that a relationship exists

between working capital management and firm risk. This is consistent with the finding

for the overall sample. It can imply that companies with a lower cash conversion cycle

have lower firm risk due to higher efficiency in working capital management and that

a higher cash conversion cycle tends to indicate higher firm risk. One possible

explanation for this is that a longer cash conversion cycle means that firms may have

not enough cash to meet short-term obligations, therefore the firm will confront with

higher firm risk. Based on the results of both markets, this research rejects the

hypothesis; the relationship between the cash conversion cycle and firm risk is not

different in each market phase.

Moreover, in term of a bear market, the results show that inventory days have a

statistically significant positive relationship (at the 1% level) with firm risk. It can be

interpreted that during a bear market, longer inventory days lead to higher firm risk.

Surprisingly, this research finds that during a bear market, accounts receivable days and

accounts payable days have no influence on firm risk, represented by the insignificance

of their coefficients. Finally, in terms of a bull market, the cash conversion cycle’s

components; accounts receivable days, inventory days and accounts payable days do

not influence firm risk, represented by the insignificance of their coefficients.

26

CHAPTER 6

CONCLUSIONS AND RECOMMENDATIONS

Working capital management is an extremely important part of the financial

decision-making of all firms. This research paper has examined the effect of working

capital management on firm performance and firm risk. To accomplish this goal, data

for a sample of listed firms on the Stock Exchange of Thailand, excluding the financial

and property and construction industries, has been collected for the period of 2004

through 2013. The proxy for working capital management is the cash conversion cycle

and its components; accounts receivable days, inventory days and accounts payable

days. A metric that expresses the length of time, in days, shows how quickly a company

cycles between cash outflows and cash inflows. Moreover, this study sheds light on the

impact of market phases – both bull and bear markets– on working capital management.

For firm profitability, the results find that firms in which the cash conversion

cycle is lower exhibit more efficient working capital management, which leads to more

profitability. The results are consistent with prior literature. Moreover, this paper tests

how working capital management and firm value interact with each other. The results

show that they do have such effects, when the cash conversion cycle is lower; firm

value leads to higher. Additionally, this paper finds that companies with lower cash

conversion cycles tend to have lower firm risk due to higher efficiency in working

capital management.

The results between bear and bull markets differ because during a bull market,

which indicates a good economy, financial managers are brave enough to invest in more

risk, such as extending the cash conversion cycle. Therefore, a longer cash conversion

cycle during a bull period leads to higher firm profitability and firm value. However,

during a bear market, surprisingly, the results reveal that a lower cash conversion cycle

leads to lower firm performance and a higher cash conversion cycle leads to higher firm

performance. Because firms must exercise more carefully in working capital

management.

This study offers helpful insight into the likely effects of working capital

management. Especially, in different situations that firm’s making decision for

27

investments and financing activity might be different that will be useful to companies

in managing working capital to maximize shareholder wealth. Moreover, individual

investors, institutional investors, and anyone interested in corporate finance and

working capital management will benefit in choosing good firms for investments.

Future research recommendations include using the other proxy for working

capital management to examine the relationship with other performance measures, such

as the return on equity and other risk. Moreover, future research might separate firms

into other criteria to explore the effects of working capital management on different

criteria.

28

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APPENDICES

32

APPENDIX A

MARKET PHASES

Figure A.1 10-year Historical Annual Market Return

Table A.1 Market Phases

A year in which the market return is greater than the mean market return is a

bull market, while a bear market is a year in which the market return is lower than

the mean market return.

Year Annual Market Return Market Phases

2004 -13.48% Bear

2005 6.83% Bear

2006 -4.75% Bear

2007 26.21% Bull

2008 -47.61% Bear

2009 63.17% Bull

2010 40.57% Bull

2011 -0.72% Bear

2012 35.73% Bull

2013 -6.70% Bear

Mean annual market return 9.92%

33

APPENDIX B

DESCRIPTIVE STATISTICS

Table B.1 Descriptive Statistics – Overall sample

Variable Mean St. Dev. Minimum Median Maximum

ROA 0.0760 0.0646 -0.3624 0.0678 0.4746

Q 1.5929 1.2940 0.1700 1.2500 16.9400

Risk 0.3990 0.9434 0.0027 0.0998 12.9698

CCC 85.0976 70.8925 -99.5734 77.3723 348.5799

AR 62.0442 41.1006 0.2987 56.0180 367.5806

INV 71.4790 60.1265 1.1027 56.9144 418.0177

AP 47.1498 35.0574 0.1713 39.9609 309.1578

DR 31.0177 21.1164 0.1000 30.4300 106.0000

Size 15.0101 1.5853 10.3956 14.7169 21.2037

Growth 0.1569 0.3955 -0.8406 0.1041 7.2652

CR 1.7984 1.5122 0.0100 1.3700 20.1800

Table B.1 represents the summary statistics for the variables in the overall sample.

The sample is non-financial companies listed in Thailand from 2004 through 2013.

The property and construction industry is excluded from the sample. Tobin’s Q (Q)

is defined as firm value measured by market value divided by book value of total

assets. ROA is calculated by net income divided by total assets. Earnings volatility

(Risk) is the standard deviation in a firm’s quarterly income over the past 12 quarters.

The cash conversion cycle (CCC) is measured by subtracting the accounts payable

days (AP) from the sum of accounts receivable days (AR) and inventory days (INV).

Firm size (Size) is measured by a natural logarithm of total assets. Sales growth

(Growth) is calculated from the current year’s sales minus the previous year’s sales

and then divided by the previous year’s sales. Debt ratio (DR) is measured by the

sum of short-term and long-term debt divided by total assets. The current ratio (CR)

is measured by the proportion of current assets to current liabilities.

34

Table B.2 Descriptive Statistics – Bear Market

Table B.2 represents the summary statistics for the variables during a bear market.

Bear market covers the period of 2004, 2005, 2006, 2008 and 2011.The sample is

non-financial companies listed in Thailand. The property and construction industry

is excluded from the sample. Tobin’s Q (Q) is defined as firm value measured by

market value divided by book value of total assets. ROA is calculated by net income

divided by total assets. Earnings volatility (Risk) is the standard deviation in a firm’s

quarterly income over the past 12 quarters. The cash conversion cycle (CCC) is

measured by subtracting the accounts payable days (AP) from the sum of accounts

receivable days (AR) and inventory days (INV). Firm size (Size) is measured by a

natural logarithm of total assets. Sales growth (Growth) is calculated from the current

year’s sales minus the previous year’s sales and then divided by the previous year’s

sales. Debt ratio (DR) is measured by the sum of short-term and long-term debt

divided by total assets. The current ratio (CR) is measured by the proportion of

current assets to current liabilities.

Variable Mean St. Dev. Minimum Median Maximum

ROA 0.0758 0.0661 -0.3624 0.0682 0.4626

Q 1.7571 1.4129 0.2500 1.4150 16.9400

Risk 0.3896 0.9075 0.0027 0.1001 12.9698

CCC 85.7327 71.0191 -99.5734 79.1805 248.5799

AR 61.8136 41.0541 0.7230 55.4466 367.5806

INV 71.5446 59.2946 1.1027 57.3449 418.0177

AP 46.0238 34.2322 0.2543 39.0966 309.1578

DR 31.1012 21.1997 0.1000 30.0700 106.0000

Size 14.9793 1.5754 10.3956 14.6947 21.2037

Growth 0.1719 0.3605 -0.7901 0.1140 7.2652

CR 1.8158 1.5196 0.0100 1.3850 19.4200

35

Table B.3 Descriptive Statistics – Bull Market

This table represents the summary statistics for the variables during a bull market.

Bull market covers the period of 2007, 2009, 2010 and 2012. The sample is non-

financial companies listed in Thailand. The property and construction industry is

excluded from the sample. Tobin’s Q (Q) is defined as firm value measured by market

value divided by book value of total assets. ROA is calculated by net income divided

by total assets. Earnings volatility (Risk) is the standard deviation in a firm’s

quarterly income over the past 12 quarters. The cash conversion cycle (CCC) is

measured by subtracting the accounts payable days (AP) from the sum of accounts

receivable days (AR) and inventory days (INV). Firm size (Size) is measured by a

natural logarithm of total assets. Sales growth (Growth) is calculated from the current

year’s sales minus the previous year’s sales and then divided by the previous year’s

sales. Debt ratio (DR) is measured by the sum of short-term and long-term debt

divided by total assets. The current ratio (CR) is measured by the proportion of

current assets to current liabilities.

Variable Mean St. Dev. Minimum Median Maximum

ROA 0.0764 0.0623 -0.1615 0.0673 0.4746

Q 1.3573 1.0585 0.1700 1.0700 10.3900

Risk 0.4127 0.9937 0.0027 0.0998 12.8957

CCC 90.1686 70.7385 -88.6761 75.5172 323.1805

AR 62.3851 41.1909 0.2987 56.5486 364.5844

INV 71.3827 61.3630 1.1986 56.3348 398.5502

AP 48.8071 36.1943 0.1713 40.8167 270.4617

DR 30.8941 21.0043 0.1000 30.8800 101.6800

Size 15.0563 1.5999 11.3745 14.7520 21.0476

Growth 0.1345 0.4420 -0.8406 0.0866 4.7143

CR 1.7726 1.5016 0.0500 1.3500 20.1800

APPENDIX C

PEARSON’S CORRELATION MATRIX

Table C.1 Pearson’s Correlation Matrix – Overall sample

This table represents the correlation coefficient matrix among the variables used for regression. The sample is non-financial companies

listed in Thailand from 2004 through 2013. The property and construction industry is excluded from the sample. The null hypothesis of

Pearson’s correlation is that the variables do not have a linear relationship in the population represented by the sample. * indicate the

statistical significance level at 5%.

Variable Q ROA Risk CCC AR INV AP DR Size Growth CR

Q 1.0000

ROA -0.0115 1.0000

Risk 0.0313* 0.2948* 1.0000

CCC -0.0164 -0.0296 -0.0047 1.0000

AR -0.0126 -0.0236 -0.0124 0.3883* 1.0000

INV -0.0085 -0.0238 -0.0162 0.7964* 0.0188 1.0000

AP 0.0067 -0.0167 -0.0159 -0.3301* 0.2782* 0.0571* 1.0000

DR 0.0663* -0.0669* 0.0087 0.0164 0.0085 0.0638* 0.0284 1.0000

Size -0.0086 -0.0088 0.0297 -0.0814* -0.1389* 0.0019 -0.0298 0.2820* 1.0000

Growth -0.0034 -0.0004 0.0108 0.0217 -0.0098 0.0245 -0.0084 0.0129 0.0191 1.0000

CR -0.0143 0.0007 -0.0083 0.2091* 0.1181* 0.0911* -0.0761* -0.2375* -0.1362* -0.0010 1.0000

36

Table C.2 Pearson’s Correlation Matrix – Bear Market

This table represents the correlation coefficient matrix among the variables used for regression. The sample is non-financial companies

listed in Thailand during a bear market. Bear market covers the period of 2004, 2005, 2006, 2008 and 2011. The null hypothesis of

Pearson’s correlation is that the variables do not have a linear relationship in the population represented by the sample. * indicate the

statistical significance level at 5%.

Variable Q ROA Risk CCC AR INV AP DR Size Growth CR

Q 1.0000

ROA -0.0898* 1.0000

Risk 0.1520* 0.0321 1.0000

CCC -0.0606* -0.0301 -0.0163 1.0000

AR -0.0260 -0.0154 -0.0416 0.4124* 1.0000

INV -0.0414 -0.0200 -0.0086 0.8063* 0.0094 1.0000

AP 0.0217 0.0007 -0.0237 -0.2104* 0.3469* 0.0957* 1.0000

DR 0.1279* -0.0933* 0.0024 0.0166 0.0351 0.0604* 0.0383 1.0000

Size 0.0031 -0.0211 0.0685* -0.0816* -0.1287* 0.0177 -0.0029 0.2462* 1.0000

Growth -0.0027 -0.0016 0.0176 0.0149 -0.0070 0.0160 -0.0094 0.0149 0.0245 1.0000

CR -0.0319 0.0091 -0.0097 0.2660* 0.0789* 0.1094* -0.1125* -0.2520* -0.1248* -0.0034 1.0000

37

Table C.3 Pearson’s Correlation Matrix – Bull Market

This table represents the correlation coefficient matrix among the variables used for regression. The sample is non-financial companies

listed in Thailand during a bull market. Bull market covers the period of 2007, 2009, 2010 and 2012. The null hypothesis of Pearson’s

correlation is that the variables do not have a linear relationship in the population represented by the sample. * indicate the statistical

significance level at 5%.

Variable Q ROA Risk CCC AR INV AP DR Size Growth CR

Q 1.0000

ROA -0.0898* 1.0000

Risk 0.1520* 0.0321 1.0000

CCC -0.0606* -0.0301 -0.0163 1.0000

AR -0.0260 -0.0154 -0.0416 0.4124* 1.0000

INV -0.0414 -0.0200 -0.0086 0.8063* 0.0094 1.0000

AP 0.0217 0.0007 -0.0237 -0.2104* 0.3469* 0.0957* 1.0000

DR 0.1279* -0.0933* 0.0024 0.0166 0.0351 0.0604* 0.0383 1.0000

Size 0.0031 -0.0211 0.0685* -0.0816* -0.1287* 0.0177 -0.0029 0.2462* 1.0000

Growth -0.0027 -0.0016 0.0176 0.0149 -0.0070 0.0160 -0.0094 0.0149 0.0245 1.0000

CR -0.0319 0.0091 -0.0097 0.2660* 0.0789* 0.1094* -0.1125* -0.2520* -0.1248* -0.0034 1.0000

38

39

APPENDIX D

RESULTS

Table D.1 The effect of working capital management on profitability

This table shows the results for testing the relationship between working capital

management and profitability. The sample is Thai non-financial firms listed from

2004 to 2013 for overall sample. The property and construction industry is excluded

from the sample. The dependent variable is return on assets (ROA). Cash conversion

cycle (CCC) is the independent variables. Moreover, Firm size (Size), sales growth

(growth), debt ratio (DR) and current ratio (CR) are included in this research. ***,

** and * indicate the statistical significance level at 1%, 5% and 10% respectively.

(1) (2) (3) (4)

DR -0.0013*** -0.0013*** -0.0015*** -0.0014***

(-11.19) (-11.74) (-11.56) (-12.11)

Size -0.0121*** -0.0131*** -0.0134*** -0.0148***

(-3.73) (-4.12) (-4.21) (-4.61)

Growth 0.0226*** 0.0229*** 0.0223*** 0.0228***

(7.19) (7.30) -7.1000 (7.24)

CR 0.0009 0.0005 0.0002 -0.0005

(0.70) (0.43) (0.17) (-0.39)

CCC -0.0002***

(-3.74)

AR -0.0002***

(-3.52)

INV -0.0002***

(-4.36)

AP 0.0003***

(3.85)

Constant 0.3032*** 0.3204*** 0.3280*** 0.3482***

(6.29) (6.73) (6.83) (7.17)

Prob > F 0.0000 0.0000 0.0000 0.0000

Overall R2 0.1030 0.1961 0.2011 0.2837

Observations 2022 2045 2029 2035

40

Table D.2 The effect of working capital management on firm value

This table shows the fixed effects regression results for testing the relationship

between working capital management and firm value. The sample is non-financial

companies listed in Thailand over the period of 2004 to 2013 for overall sample. The

property and construction industry is excluded from the sample. The dependent

variable is Tobin’s Q (Q). It is a proxy for firm value. Cash conversion cycle (CCC)

is the independent variables and proxy for working capital management. Moreover,

Firm size (Size), sales growth (Growth), debt ratio (DR) and current ratio (CR) are

included in this research as control variables. ***, ** and * indicate the statistical

significance level at 1%, 5% and 10% respectively.

(5) (6) (7) (8)

DR 0.0131*** 0.0122*** 0.0119*** 0.0121***

(5.06) (4.74) (4.64) (4.73)

Size 0.3865*** 0.3700*** 0.3733*** 0.3831***

(5.08) (4.88) (4.94) (5.01)

Growth 0.0303 0.0371 0.0367 0.0432

(0.40) (0.48) (0.48) (0.56)

CR 0.0149 0.0083 0.0054 0.0168

(0.50) (0.28) (0.18) (0.56)

CCC -0.0023***

(-2.63)

AR -0.0018

(-1.45)

INV -0.0003

(-0.34)

AP 0.0033**

-2.3100

Constant -4.4984*** -4.2890*** -4.4101*** -4.7588***

(-3.92) (-3.75) (-3.86) (-4.10)

Prob > F 0.0000 0.0000 0.0000 0.0000

Overall R2 0.2378 0.2252 0.2263 0.2298

Observations 1821 1836 1825 1829

41

Table D.3 The effect of working capital management on firm risk

This table shows the fixed effects regression results for testing the relationship

between working capital management and firm risk. The sample is non-financial

companies listed in Thailand over the period of 2004 to 2013 for overall sample.

The property and construction industry is excluded from the sample. The

dependent variable is earnings volatility (Risk). It is a proxy for firm risk. Cash

conversion cycle (CCC) is the independent variables and proxy for working capital

management. Moreover, Firm size (Size), sales growth (Growth), debt ratio (DR)

and current ratio (CR) are included in this research as control variables. ***, **

and * indicate the statistical significance level at 1%, 5% and 10% respectively.

(9) (10) (11) (12)

DR -0.0020* -0.0013* -0.0016* -0.0011*

(-1.24) (-0.83) (-1.02) (-0.73)

Size 0.1916*** 0.1976*** 0.1997*** 0.1983***

(4.19) (4.35) (4.39) (4.32)

Growth -0.0029 -0.0059 -0.0020 -0.0083

(-0.06) (-0.12) (-0.04) (-0.17)

CR 0.0011 0.0070 0.0084 0.0083

(0.06) (0.39) (0.47) (0.46)

CCC 0.0014***

(-2.70)

AR 0.0007

(0.97)

INV 0.0015***

-2.4600

AP 0.0001

(0.12)

Constant -2.5542*** -2.5974*** -2.6870*** -2.5739***

(-3.72) (-3.80) (-3.92) (-3.70)

Prob > F 0.0000 0.0000 0.0000 0.0000

Overall R2 0.3355 0.3360 0.3381 0.3390

Observations 1832 1848 1837 1840

Table D.4 Market Phases: The effect of working capital management on profitability

(1) (2) (3) (4)

Bear Bull Bear Bull Bear Bull Bear Bull

DR -0.0013*** -0.0001***

-0.0014*** -0.0011***

-0.0014*** -0.0011***

-0.0014*** -0.0011***

(-8.85) (-4.34)

(-9.31) (-4.69)

(-9.18) (-4.73)

(-9.43) (-5.04)

Size -0.0082** -0.0154**

-0.0100** -0.0178**

-0.0105*** -0.0152**

-0.0108*** -0.0174**

(-1.98) (-2.12)

(-2.44) (-2.48)

(-2.58) (-2.09)

(-2.62) (-2.38)

Growth 0.0301*** 0.0230***

0.0303*** 0.0229***

0.0293*** 0.0239***

0.0299*** 0.0239***

(6.05) (4.10)

(6.10) (4.08)

(5.90) -4.2500

(6.01) (4.25)

CR 0.0033* 0.0021

0.0025 0.0020

0.0026 0.0012

0.0019 0.0009

(1.86) (0.91)

(1.44) (0.87)

(1.51) (0.53)

(1.09) (0.38)

CCC -0.0002*** 0.0022***

This table shows the fixed effects regression results for testing the relationship between working capital management and profitability

in each market phase. Bear market covers the period of 2004, 2005, 2006, 2008, 2011 and 2013. Bull market covers the period of 2007,

2009, 2010 and 2012. The property and construction industry is excluded from the sample. The dependent variable is return on assets

(ROA). It is a proxy for firm profitability. Cash conversion cycle (CCC) is the independent variables and proxy for working capital

management. Moreover, Firm size (Size), sales growth (growth), debt ratio (DR) and current ratio (CR) are included in this research as

control variables. ***, ** and * indicate the statistical significance level at 1%, 5% and 10% respectively.

42

(-2.85) (4.78)

AR

-0.0002** -0.0003***

(-2.22) (-2.86)

INV

-0.0002** -0.0003***

(-2.54) (-2.65)

AP

-0.0001 -0.0003***

(-1.04) (-2.69)

Constant 0.2419*** 0.3450***

0.2687*** 0.3863***

0.2781*** 0.3476***

0.2770*** 0.3814***

(3.91) (3.22) (4.42) (3.54) (4.53) (3.21) (4.43) (3.49)

Prob > F 0.0000 0.0000

0.0000 0.0000

0.0000 0.0000

0.0000 0.0000

Overall R2 0.1293 0.1873

0.1153 0.1799

0.1152 0.1865

0.1053 0.1768

Observations 1206 816 1224 821 1212 817 1216 819

43

Table D.5 Market Phases: The effect of working capital management on firm value

This table shows the fixed effects regression results for testing the relationship between working capital management and firm value in

each market phase. Bear market covers the period of 2004, 2005, 2006, 2008, 2011 and 2013. Bull market covers the period of 2007,

2009, 2010 and 2012. The property and construction industry is excluded from the sample. The dependent variable is Tobin’s Q (Q). It

is a proxy for firm value. Cash conversion cycle (CCC) is the independent variables and proxy for working capital management.

Moreover, Firm size (Size), sales growth (growth), debt ratio (DR) and current ratio (CR) are included in this research as control

variables. ***, ** and * indicate the statistical significance level at 1%, 5% and 10% respectively.

(5) (6) (7) (8)

Bear Bull Bear Bull Bear Bull Bear Bull

DR 0.0120*** 0.0072*

0.0111*** 0.0058

0.0103*** 0.0066*

0.0111*** 0.0067*

(3.20) (1.77)

(2.99) (1.43)

(2.78) (1.65)

(2.97) (1.68)

Size 0.5989*** 0.4194***

0.5848*** 0.4583***

0.5738*** 0.4232***

0.5846*** 0.4615***

(5.74) (3.07)

(5.62) (3.30)

(5.58) (3.10)

(5.61) (3.35)

Growth 0.1601 0.1326

0.1647 0.1419

0.1793 0.1331

0.1561 0.1496

(1.13) (1.03)

(1.17) (1.10)

(1.27) (1.03)

(1.11) (1.16)

CR 0.0045 0.0368

-0.0071 0.0254

-0.0100 0.0273

0.0048 0.0477

(0.11) (0.69)

(-0.17) (0.48)

(-0.24) (0.51)

(0.11) (0.89)

CCC -0.0029** 0.0012

44

(-2.24) (0.94)

AR

-0.0033* 0.0022

(-1.67) (1.24)

INV

-0.0005 -0.0012

(-0.28) (-0.84)

AP

0.0071*** 0.0032

(2.85) (1.64)

Constant -7.4593*** -5.1973**

-7.2431*** -5.9695***

-7.2750*** -5.2386**

-7.7630*** -6.1041***

(-4.76) (-2.52) (-4.65) (-2.82) (-4.68) (-2.54) (-4.90) (-2.91)

Prob > F 0.0000 0.0000

0.0000 0.0000

0.0000 0.0000

0.0000 0.0000

Overall R2 0.2436 0.3275

0.3309 0.3199

0.3297 0.3264

0.3422 0.3225

Observations 1070 751 1081 755 1073 752 1076 753

45

Table D.6 Market Phases: The effect of working capital management on firm risk

This table shows the fixed effects regression results for testing the relationship between working capital management and firm risk in

each market phase. Bear market covers the period of 2004, 2005, 2006, 2008, 2011 and 2013. Bull market covers the period of 2007,

2009, 2010 and 2012. The property and construction industry is excluded from the sample. The dependent variable is firm risk (risk).

It is a proxy for firm risk. Cash conversion cycle (CCC) is the independent variables and proxy for working capital management.

Moreover, Firm size (Size), sales growth (growth), debt ratio (DR) and current ratio (CR) are included in this research as control

variables. ***, ** and * indicate the statistical significance level at 1%, 5% and 10% respectively.

(9) (10) (11) (12)

Bear Bull Bear Bull Bear Bull Bear Bull

DR -0.0010 -0.0029

-0.0001 -0.0022

-0.0006 -0.0025

-0.0001 -0.0020

(-0.48) (-0.85)

(-0.06) (-0.67)

(-0.28) (-0.77)

(-0.04) (-0.61)

Size 0.1970*** 0.1246

0.2069*** 0.1273

0.2904*** 0.1228

0.2063*** 0.1285

(3.49) (1.13)

(3.70) (1.15)

(3.76) (1.12)

(3.65) (1.15)

Growth 0.0087 -0.0428

-0.0059 -0.0338

0.0024 -0.0405

-0.0076 -0.0317

(0.11) (-0.40)

(-0.08) (-0.32)

(0.03) (-0.38)

(-0.10) (-0.30)

CR -0.0035 0.0123

0.0061 0.0159

0.0027 0.0229

0.0047 0.0210

(-0.16) (0.28)

(0.27) (0.37)

(0.12) (0.52)

(0.21) (0.48)

CCC 0.0019*** 0.0013***

(2.61) (1.19)

46

AR

0.0004 0.0006

(0.43) (0.42)

INV

0.0021** 0.0017

(2.45) (1.33)

AP

-0.0001 0.0008

(-0.63) (0.46)

Constant -2.6990*** -1.5109

-2.7600*** -1.5035

-2.9022*** -1.5173

-2.6837*** -1.5351

(-3.19) (-0.91) (-3.29) (-0.90) (-3.46) (-0.92) (-3.13) (-0.91)

Prob > F 0.0000 0.0000

0.0000 0.0000

0.0000 0.0000

0.0000 0.0000

Overall R2 0.2281 0.1359

0.2299 0.3443

0.3310 0.3365

0.3332 0.3449

47

48

BIOGRAPHY

Name Miss Pakarat Soisri

Date of Birth March 6, 1989

Educational Attainment 2011: Bachelor of Business Administration (Finance)

Scholarship 2014: Kasikorn Bank Scholarship

Work Experiences 2012 - 2014: Trader, Proprietary Trading

Trinity Securities Company Limited

2011- 2012 : Credit Analyst, Business Loans for SMEs

Bangkok Bank Public Company Limited