FACTORS AFFECTING THE LEVEL OF CASH HOLDING OF
COMPANIES LISTED IN VIETNAM STOCK MARKET
In Partial Fulfillment of the Requirements of the Degree of
MASTER OF BUSINESS ADMINISTRATION
In FINANCE
By
Mr. Nguyen Nhat Anh
ID: MBA02002
International University - Vietnam National University HCMC
Sep 2014
I
INTERNATIONAL UNIVERSITY
SCHOOL OF BUSINESS
SOCIALIST REPUBLIC OF VIETNAM
Independence - Freedom - Happiness
ASSURANCE QUALIFIED THESIS
Student’s Name:
Nguyen Nhat Anh
Student ID:
MBA02002
Title of Thesis:
Factors affecting the level of cash holding of companies listed in Vietnam
stock market
Advisor: PhD. Duong Nhu Hung
I assure that the content of this thesis has been qualified all requirements for a research paper and
able to participate in the final thesis defense.
Approved by
(Signed)
PhD. Duong Nhu Hung
II
FACTORS AFFECTING THE LEVEL OF CASH HOLDING OF COMPANIES LISTED
IN VIETNAM STOCK MARKET
In Partial Fulfillment of the Requirements of the Degree of
MASTER OF BUSINESS ADMINISTRATION
In Finance
By
Mr. Nguyen Nhat Anh
ID: MBA02002
International University - Vietnam National University HCMC
Sep 2014
Under the guidance and approval of the committee, and approved by all its members, this thesis
has been accepted in partial fulfillment of the requirements for the degree.
Approved:
---------------------------------------------Chairperson
---------------------------------------------Committee member
---------------------------------------------Committee member
--------------------------------------------Committee member
--------------------------------------------Committee member
--------------------------------------------Committee member
III
Acknowledge
First and foremost, I would like to give sincere thanks to my advisor of this thesis, PhD.
Duong Nhu Hung, Lecturer of International University, Vietnam National University, Ho Chi
Minh City (VNU-HCM) for the valuable guidance and advice in such a long way for successful
completion of my thesis within the time frame. He inspired me greatly to work in this thesis with
his initial orientation. His willingness, faith and patience in my abilities always boost my
confidence and motivate me contributed tremendously to this thesis. I highly appreciate when
each time he correct each minor error. Without instructions from PhD. Duong Nhu Hung, I could
neither stick right way to the topic nor find the direction to complete the research.
Secondly, my gratitude goes to all lecturers of the School of Business of International
University, Vietnam National University, Ho Chi Minh City (VNU-HCM), who has made me
familiar, understand and passion on the concepts and knowledge of all courses under MBA
program. Especially, Mr. Lai Tran Thanh Son, Ms. Pham Thi Anh Tho and Mr. Nguyen Hoang
Phu – please receive my thanks for your kind supports when I get stuff with papers.
Finally, I am forever indebted to my parents for their understanding, endless patience and
encouragement when it was most required. I am also grateful to Mr. Ho Huu Tien and Mr. Le
Minh Giac - my classmate in MBA program. My classmates have followed up and supported so
much on quality of thesis. Last but not latest, I want to say thanks to my close friends, Ms. Pham
Mai Tram, Mr. Nguyen Chi Thanh, Ms. Nguyen Phuong Trang, Mr. Nguyen Tran Phuong, and
Ms. La Y Yen for their spirit support. I cannot fulfil this thesis without encourage, and
sometimes criticisms to helps me stand up after failures. Without helps of the particular that
mentioned above, I would face many difficulties while doing this thesis.
IV
Plagiarism Statements
I would like to declare that, apart from the acknowledged references, this thesis either
does not use language, ideas, or other original material from anyone; or has not been previously
submitted to any other educational and research programs or institutions. I fully understand that
any writings in this thesis contradicted to the above statement will automatically lead to the
rejection from the MBA program at the International University - Vietnam National University –
Ho Chi Minh City (VNU-HCM).
V
Copyright Statement
This copy of the thesis has been supplied on condition that anyone who consults it is
understood to recognize that its copyright rests with its author and that no quotation from the
thesis and no information derived from it may be published without the author’s prior consent.
© Nguyen Nhat Anh/ MBA02002/ 2010–2014
VI
Table of content
Acknowledge ........................................................................................................................... IV
Plagiarism Statements ............................................................................................................... V
Copyright Statement ................................................................................................................. VI
Table of content....................................................................................................................... VII
List of tables .............................................................................................................................. X
List of Figures .......................................................................................................................... XI
List of Abbreviations ............................................................................................................... XII
Abstract ................................................................................................................................. XIII
Chapter 1.
Introduction ....................................................................................................... 1
1.1.
Problem Statement .................................................................................................... 1
1.2.
Research Objectives .................................................................................................. 3
1.3.
Research Questions ................................................................................................... 4
1.4.
Research Scope ......................................................................................................... 4
1.6.
Theoretical Framework ............................................................................................. 5
1.7.
Contribution and Implication of Thesis to Business and Society................................ 5
1.8.
Structure of the Thesis .............................................................................................. 6
Chapter 2.
2.1.
Literature Review .............................................................................................. 7
Definitions, Time Value and Reasons for Cash Holding ............................................ 7
2.1.1.
Cash Definitions............................................................................................. 7
2.1.2.
Time Value of Cash........................................................................................ 7
2.1.3.
Reasons for Cash Holding .............................................................................. 8
2.2.
Information Asymmetry Theory and Related Theories .............................................. 9
2.3.
Factors Affecting Cash Holding from Related Theories .......................................... 12
2.3.1.
Size of Business ........................................................................................... 13
2.3.2.
Financial Leverage ....................................................................................... 14
2.3.3.
Liquid Assets Substitutes ............................................................................. 16
2.3.4.
Cash Flow .................................................................................................... 16
VII
2.3.5.
Cash Flow Volatility .................................................................................... 17
2.3.6.
Liability to Bank .......................................................................................... 18
2.3.7.
Dividend Payout ........................................................................................... 20
Chapter 3.
Research Methodology .................................................................................... 24
3.1.
Data Sources ........................................................................................................... 24
3.2.
Data Statistics ......................................................................................................... 26
3.2.1.
Descriptive Statistics .................................................................................... 26
3.2.2.
Univariate Analysis ...................................................................................... 26
3.2.3.
Regression Analysis ..................................................................................... 27
3.2.3.1. Fama-MacBeth Regression Model ............................................................. 28
3.2.3.2. Cross-Sectional Regression Model with Mean Value of Variables ............. 29
3.2.3.3. Pooled Ordinary Least Squares Regression Model with Year Dummy ....... 30
3.2.3.4. Pooled Ordinary Least Squares Regression Model with Year Dummy and
Industry Dummy ....................................................................................................... 31
3.2.3.5. Fixed Effects Regression Model ................................................................. 33
3.2.4.
3.3.
Other Supported Tests for Regression Models .............................................. 34
Determined Dependent and Independent Variables ................................................. 35
3.3.1.
Dependent Variable – Cash .......................................................................... 35
3.3.2.
Independent Variables .................................................................................. 36
3.3.2.1. Size of Business (SIZE) ............................................................................. 36
3.3.2.2. Bank Debt (BANKDEBT) ......................................................................... 36
3.3.2.3. Cash Flow (CFLOW) ................................................................................. 37
3.3.2.4. Cash Flow Violability (CFVOLAT) ........................................................... 37
3.3.2.5. Liquid Assets (LIQ) ................................................................................... 37
3.3.2.6. Dividend (DIVIDEND) .............................................................................. 37
Chapter 4.
4.1.
Data Analysis and Results ................................................................................ 39
Descriptive Statistics............................................................................................... 39
VIII
4.2.
Univariate Analysis................................................................................................. 42
4.3.
Regression Results .................................................................................................. 44
4.3.1.
Fama–MacBeth Regression Model ............................................................... 44
4.3.2.
Ordinary Least Squares Regression with 5 Years Average ............................ 48
4.3.3.
Pooled OLS Regression with Year Dummies ............................................... 50
4.3.4.
Pooled OLS Regression with Year Dummies and Industry Dummies ........... 53
4.3.5.
Fixed Effects Regression Model ................................................................... 56
4.4.
Result from Supporting Tests for Regression Models .............................................. 57
4.4.1.
4.5.
Hausman Test .............................................................................................. 57
Summary of Results ................................................................................................ 59
Chapter 5.
Research Conclusion........................................................................................ 63
5.1.
Conclusion and Implication .................................................................................... 63
5.2.
Limitation of the Study ........................................................................................... 65
5.3.
Future Research ...................................................................................................... 66
References ................................................................................................................................ 68
Appendix.................................................................................................................................. 72
Appendix 1: 98 Listed Companies Chosen ............................................................................ 72
Appendix 2: Secondary Data for Excel and Stata Input ......................................................... 79
IX
List of tables
Table 1: Summary of Previous Studies ..................................................................................... 20
Table 2: List of Industry Dummies ........................................................................................... 31
Table 3: Summary of Formulas for Variables ........................................................................... 38
Table 4: Results from Descriptive Statistics .............................................................................. 39
Table 5: Results from Univariate Analysis ................................................................................ 42
Table 6: Correlations Matrix of Variables from Unvariate Analysis .......................................... 43
Table 7: Results from Fama – MacBeth Regression Model ....................................................... 44
Table 8: VIF Test for Fama-MacBeth Model ............................................................................ 46
Table 9: Results from Ordinary Least Squares Regression with 5 Years Average ..................... 48
Table 10: VIF Test for Cross-Sectional Regression Model........................................................ 49
Table 11: Results from Pooled OLS Regression with Year Dummies ....................................... 50
Table 12: VIF Test for Pooled OLS Model with Year Dummies ............................................... 51
Table 13: Results from Pooled OLS Regression with Year Dummies and Industry Dummies ... 53
Table 14:VIF Test for Pooled OLS with Year and Industry Dummies ....................................... 54
Table 15: Results from Fixed Regression Model ....................................................................... 56
Table 16: Results from Hausman Test ...................................................................................... 57
Table 17: Result Summary of Regression Models ..................................................................... 59
Table 18: Thesis’s Result Summary .......................................................................................... 60
X
List of Figures
Figure 1: Theoretical Framework to Be Applied in This Research .............................................. 5
Figure 2: Selected Regression Models ...................................................................................... 28
Figure 3: Cash Ratio Over Five Years ...................................................................................... 40
Figure 4: % Standard Deviation of Cash Flow over Total Assets .............................................. 41
Figure 5: Graphical Correlation Matrix ..................................................................................... 43
Figure 6: Percentage of Cash Holding in Years Firms Paid Dividend ........................................ 47
Figure 7: Correlations between Cash, Income and Bank Debt ................................................... 47
XI
List of Abbreviations
BANKDEBT: Bank Debt
BVND: Billion Viet Nam Dong (Currency)
CFLOW: Cash Flow
CFVOLAT: Cash Flow Volatility
DF: Degrees of Freedom
EBITDA: Earning Before Interest, Tax, Depreciation and Amortization
EMH: Efficient Markets Hypothesis
EMU: Euro Countries
FV: Future Value of Cash
HNX: Ha Noi Stock Exchange
HOSE: Ho Chi Minh Stock Exchange
LEV: Leverage
LIQ: Liquidity
OLS: Ordinary Least Square Regression
Pooled OLS: Pooled Ordinary Least Square Regression Model
PV: Present Value of Cash
SIZE: Size of Firm
VIF: Variance Inflation Factor
XII
Abstract
Understanding factors affecting the level of cash holding is and has been a challenging
task in finance to reach business objectives. That is the reason why cash management is as much
an integral part of business cycle in any period. Statistical and econometrical models are broadly
used in analysis of specific factors affecting. This paper investigates the factors affecting the
level of cash holding with the combined application of descriptive statistics, univariate analysis
and regression methods such as model Fama–MacBeth, regression using cross-sectional data of
average value, pooled OLS regression with year dummies, pooled OLS regression with year
dummies and industry dummies. This paper also outlines the practical steps which need to be
undertaken to use above methods on companies listed in Vietnam stock market. A framework for
each methodology is drawn up. The emphasis is on analysis trend of factors affecting the level of
cash holding when being put in consideration and explanation with related theories. The main
goal of this study is to present the result of each method and factors affecting the level of cash
holding in order to bring general insights of Vietnam stock market to help individual investors
make decisions.
Keywords: Vietnam stock market, descriptive, univariate, regression, level of cash
holding.
XIII
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XIV
Chapter 1. Introduction
The aim of this chapter is to provide the problem background and then to understand the
purpose behind the research and questions, to which this thesis seeks and answer. It also states
goals that the author will seek in respect to both theoretical and practical contribution.
1.1. Problem Statement
The phrase “cash is king” is an expression commonly referred to finance, although its
meaning might not be as simple as this expression suggests. Until now, there are two opposing
views when looking at the level of cash that a company should hold.
For the first view, cash is normally regarded as "just an asset that a firm need to help it to
function” (Atrill & McLaney, 2004, p.124). In order to support for this statement, Berk and
DeMarzo (2011) gave several reasons for a company to hold cash. Those researches believed
that holding cash was one of the essential elements needed for a firm to grow and prosper. Cash
is also considering as the life-blood of any firm and if so without it, the survival is very unlikely.
Every firm needs cash daily to pay its bills and to pay off its liabilities on time, so that it can
survive. The firm can make use of cash to invest; for strategic purposes such as acquisitions; for
a marketing tool; to solve short-term obligations, to pay daily transactions, for repaying its loans,
paying its employees, even to satisfy bank requirements and so on.
Contrary to above arguments, the optimal level of cash holdings in a company should be
zero, Maness and Zietlow (2004) supported. This relates to the old personal saying of Plautus is
that people have to spend cash to earn cash. Instead of holding on cash, a company could has
either invested in a new project to earn a potential return or distributed it among shareholders to
let them invest elsewhere, also this resulting in a potential return. This was not just a theoretical
perspective as around the new millennium increasingly many companies were striving for
decreased cash balance; financial managers globally advocated the benefits of small cash
1
holdings, Mintz (2000) stated. Holding excess cash might lead to a loss of an opportunity that
could have increased the company’s value. With holding high level of cash, it will decrease
earning yield of business indirectly, or even directly, because it does not maximize profit or
potential opportunity from daily transactions or long-term investments. If the management
capability is weak in cash monitoring, it may lead to liquidity crisis, even to bankruptcy. If so,
what factors will decide this optimal level of cash holding by making costs and benefits on
balance?
Company can hold cash for many reasons, short-term or long-term, finance or nonfinance, strategic or operational objectives. No matter what purpose, cash flow shows us how the
company has performed in managing inflows and outflows of cash and provides a sharper
picture of the company's ability to pay bills and creditors, and to finance the growth. Deep
understanding on factors affecting the level of cash holding first, and then implementing
effective cash management solutions are the most popular approach to improve business
performance by now. For this reason, it is very important for every firm to monitor its cash flow
in order to adequate plan expenditures.
The author’s personal opinion is that cash management is always a hot spot for managers.
With context of high inflation and economic downturn in Vietnam currently, high liquidity of
cash will enhance the confidence of investors. Masan group (1,970 bVND); Techcombank
(3,142 bVND); Dam Phu My (3,748 bVND); PetroGas service (3,621 bVND); Vinamilk
(3,101.4 bVND); FPT (1,900 bVND); Bao Viet insurance (2,700 bVND); Hoa Phat group (1,064
bVND) and Hoang Anh Gia Lai group (2,335 bVND) were highlighted as bright spots for cash
management and creating advantage with outstanding profit in their market segments. Looking
into the financial reports at the end of 2011s, many companies cannot have good financial results
like above companies.
The factors, which affect cash holding, are investigated by many authors in the developed
countries. Kim et al. (1998) and Opler et al. (1999) did use data of American companies.
2
Pinkowitz and Williamson (2001) did use data of American, German and Japan. Ferreira and
Vilela (2014) were with Euro companies (EMU); Ozkan and Ozkan (2004) were with the UK
companies; Custodio et al. (2005) and Bates et al. (2009) were with the US companies; and
Meggison and Wei (2012) were with Chinese companies. These researches had been giving
important resources to other researchers, investors and managers.
As summary of financial reports of companies listed in Vietnam stock market from 2009
to 2012, the ratio of cash and cash equivalence over the total asset is decreased from 2009
(9.9%). This ration is lowest in 2011, with 8.9%. However, at the end of 2012 (9.2%), this ratio
grow up, reach to the ratio of 2010 (9.2%). Beside some basic statistics on non-business
magazines, there are few researches on cash management. The empirical studies are actually lack
for demands of researchers and investors.
In the context of Vietnam, rapid changes in business environment, but there are few
academic researches on evaluating the factors that affect level of cash holding in firm. With this
research, the author research and supplements a close analysis to cash management of companies
listed in Vietnam stock market based on historical data. By combining and simulating
approaches of previous empirical researches, the author also expects to provide the answer for
management issue of what factors create strong influences on their cash levels.
1.2. Research Objectives
This research will concentrate on clarifying factors which affect decision of how much cash was
hold by companies listed in Vietnam stock market. The objectives are:

To identify factors that affect the level of cash holding and then measure on their impact
to cash management of companies listed in Vietnam stock market.

To propose recommendations for the management.
3
1.3. Research Questions
This research will provide a practice of 98 companies listed on Vietnam stock market
resulted from related theories. It will focus on two questions:

What firm factors affect the level of cash holding of companies listed on Vietnam stock
market?

What are the implications for cash management on companies listed on Vietnam stock
market?
1.4. Research Scope
Due to time limitation and data availability, this research focuses on companies listed on
two stock exchanges in Vietnam: Ho Chi Minh and Hanoi Stock Exchange. Most of them are
supervised by State Securities Commission of Vietnam.
The data set is mainly extracted from annual consolidated financial reports, which we
have checked for errors. Due to the size and early stage of the stock market, the sample could
only cover in five years and for ninety-eight companies. The methodology part will clearly show
other criteria for selecting and filtering data set
4
1.6. Theoretical Framework
The author defines framework for this research as below:
Research Objectives
Literature Review
Research Design
Collect Data from HSX and HNX
Data Analysis
Decision and Implication
Conclusion and Recommendation
Figure 1: Theoretical Framework to Be Applied in This Research
1.7. Contribution and Implication of Thesis to Business and Society
This research provides a review of Vietnam stock market based on examining the level of
cash holding and cash management. It suggests an approach allowing local individual investors
to make informed decisions in stock market. A suitable model for analyzing cash holding is
really necessary for any local individual investor to make informed decisions in the local stock
market.
5
1.8. Structure of the Thesis
The research will be in such a way as below. Chapter 1, introduction, will includes the
research problem and the importance of research. It also mentions to research’s scopes,
objectives, questions, structure, and contribution. Chapter 2, literature review, will deal with the
origin, terminology, and reasons why companies want to hold cash. The next part will point out
factors, which affect the level of cash holding predicted by related theories and research’s results
of previous authors, both in foreign countries and Vietnam. Chapter 3, methodology, describes
approaches for statistics. It describes the methods by which the author used to collect primary
and secondary data; as well as how to describes and check variables. Regression methods will be
introduced to show how to analyze dataset; depending on analyzing the advantage and
disadvantage of previous authors and current practice on Vietnam stock market. Chapter 4
discusses results at length and detail. Chapter 5 will be a summary of results, conclusions,
implications and limitation also. The author will focus to answer the main questions mentioned
in the beginning of this research. The author will also initiate several suggestions to make this
research more complete and thorough.
6
Chapter 2. Literature Review
This chapter uses existing concepts, models, formulations and frameworks on the basis of
previous authors and substantially develops own views and insights. It includes two main parts.
First one explains definitions and reasons why companies want to hold cash. The next part
explains factors predicted, which affect level of cash holding, by using financial theories, as well
as presents academic results of previous authors.
2.1. Definitions, Time Value and Reasons for Cash Holding
2.1.1. Cash Definitions
Everyone is actually familiar with the term cash daily, and more generally, the Oxford
online dictionary defines cash as “coins and notes”. According to financial theories, they definite
cash that include cash, short-term securities, and cash equivalences (Opler et al., 1999; Ferreira
& Vilela, 2004; Bates, Bates et al. 2009). Many investors accept as true that cash equivalences
convert into cash in short period.
2.1.2. Time Value of Cash
Time value of cash is one of the most basic principles in finance. “One dollar today is
worth more than a dollar tomorrow” is perhaps one of the most famous quotes in the finance. It
means that if a person should invest one dollar today it will earn interest and will therefore be
worth more the day after. Berk and DeMarzo (2011) postulated that the interest rate would be
dependent on the investment and has to adjust by subtracting the amount of inflation on the
currency. Both investors and companies use the concept of time value of cash in order to
calculate the present as well as the future value (FV) of their operations. For example, they
calculate the present value (PV) of the cost and benefits for a project in order to determine if the
project is profitable or not. Berk and DeMarzo (2011) gave another example to calculate if their
7
current cash level is worth enough to cover their future expenditures. The formula shows the
calculation for PV, which is the present value calculated by the future value FV, the interest i,
and the period n as shown in the formulas. Thus, Maness and Zietlow (2004) postulated that this
leads to the argument that the optimal level of cash that a company should hold is zero, since it is
then considered as an idle resource for the company that do not provide any or little additional
value.
2.1.3. Reasons for Cash Holding
In summary, the four following reasons that companies want to hold cash. The first
reason is for business operating transactions. Businesses do need cash to use for operating
activities business. The demands for cash among different types of firm are also different. For
example, the retail business has available cash in safe box to control operations. In addition,
these businesses need cash to replenish inventory deficits or to pay salaries. Conversely,
Damodaran (2008) confirmed that trades computer software could operate with much smaller
cash balances. The second reason is for reservation motivation. According to Custodio et al.
(2005), a business held cash to finance the activities and investments when no other financial
resources available or too expensive. The firm holds cash to cover unexpected costs and potential
liabilities cannot be determined. For example, Damodaran (2008) confirmed that is cyclical
business to accumulate cash during the economic boom and use that cash in case of recession to
cover deficit from operations. If the firm does not have available liquid assets, the cash flow
shortage can stop investing in the profitable projects. Thus, Ozkan and Ozkan (2004) advised
that keeping cash is to avoid financial cost and depletion. Managers consider this motivation to
hold cash as reservation motivation. The third reason is the agency cost. In public businesses, it
always exist problem between owners and managers. The board will decide whether to return
into stock or to retain in the business. In many businesses, the managers, with available programs
and activities, create funds to help them pursue these programs. Therefore, when the
8
management pays more attention to gain more control over the optimal allocation of resources,
they will accumulation of cash. Damodaran (2008) gave his opinion that it is not for shareholders
but for the purpose of opening wide funds. The management is favorite cash holdings, because
they are afraid of risks. According to Opler et al. (1999), the management holds cash to get
flexibility in pursuing their own goals. The final one is for investing in the future. If the capital
market is efficient, businesses can raise capital to invest in new and potential profitable projects
or other investments that has no transaction costs. However, in reality, businesses must always
face with the limitations of borrowing and the cost to access capital markets. These restrictions
may stem from inside, or can also come from outside the firm, but mostly, from outside the firm.
According to Damodaran (2008), In order to face these challenges, firm must reserve cash
available to cover investment needs in the future. If not, they run into the risk of reject the
project and investment value. The author mainly uses the first two reasons in this study. Two
main theories used to explain factors are the trade-off theory and the pecking order theory.
2.2. Information Asymmetry Theory and Related Theories
In the 1960s, both Fama and Samuelson developed independently the efficient markets
hypothesis (EMH), that market prices fully reflect all available information. In practice,
behavioral economists attributed the imperfections because of cognitive biases such as
representative bias or information bias. Information asymmetry theory is mainly used in this
study. And other related theories are also referred, including agency theory, trade-off theory,
pecking order theory, and cash flow theory. These theories play a central role in corporate
finance and directly associate with level of cash holding in firms.
Asymmetric information occurs when one group of participants has better or more timely
information than other groups. According to Myers and Majluf (1984), asymmetric information
makes raising capital more difficult and answer the reason why existence of asymmetric
information makes external financing costly. The outside parties want to ensure that they
9
purchase securities not priced too high, so they will need a reasonable discount. Because parties
outside the enterprise hold less information than the management, they can evaluate too low
stock valuations based on the information provided by the management. Smith and Stulz (1985)
delivered evidence that is consistent with asymmetric information problems of external
financing. For example, investors discount the value of firms when they attempt to sell risky
securities. As view of Opler et al., (1999) asymmetric information led to situation that outside
capital becomes more expensive. This model predicts increased costs when raising capital
securities, which sold, are sensitive and the problem of information asymmetry information
becomes more important. Growth opportunities are intangible in nature and their value fall
precipitously in financial distress and bankruptcy. This would in turn imply that firms with
greater growth opportunities have greater incentives to avoid financial distress and bankruptcy
and hence hold larger cash and marketable securities.
Agency theory is using in many fields and concerns itself with the problem of ensuring
that someone acting on the behalf of another, an agent representing a principal, serves the
interest of the principal. Agency problem arises where the two parties have different interests and
asymmetric information, such that the principal cannot directly ensure that the agent is always
acting in the principal’s best interests. As opinion of Eisenhardt (1989), the issue at hand is that
the agent might have other goals and interests than the principal and would act to achieve these
at the expense of the principal. The theory assumes that in certain situations, if possible, the
agent would act to serve his or her objectives and the solution in these situations comes from
aligning the interests of the principal and the agent. Harris and Raviv (1978) showed that these
relationships are numerous and cross-links, as such lawyer - clients, employer - employee and
shareholders-managers. Agency problems that might arise in a shareholder-manager situation,
concerns among other things the optimal level of cash holdings. Jensen (1986) worried that
managers have motives for having higher levels of corporate cash holdings than what is optimal
for shareholders.
10
Trade-off theory implies that businesses will set up an objective of cash level by
balancing between marginal cost and marginal benefit of holding cash. The author summarizes
three main benefits from holding cash as below. Firstly, holding cash is to reduce the ability of
financial exhaustion, because the amount of cash held will act as a safety provision for
unexpected losses or limitations from raising capital from outside sources. Secondly, cash
holding allows businesses to pursue optimal investment policy, even whenever businesses
encounter financial constraints. As enterprises face difficulties in raising external capital,
businesses may be forced to abandon the investment project has a positive net present value.
Thirdly, holding cash reduces the cost of raising capital from outside or avoid forced liquidation
of existing assets of the enterprise. The role of cash is considering as a buffer between the source
and use of firm resources. Ferreira and Vilela (2004) proved that traditional marginal cost of
holding cash is the opportunity cost of capital because it reduces the rate of return calculated on
current assets.
Pecking order theory implies that the cost of financing increases with asymmetric
information. Managers know more about their companies’ prospects, risks and value than
outside investors. Pecking order theory of Myers and Majluf (1984) said that companies finance
for investments in the order as follows: the first is retained earnings, then safe debt, followed by
risky debt, and finally equity. The basic purpose of this order is to reduce funding costs due to
problems of asymmetric information and other financial expenses. When having enough
sufficient operating cash flow, it will fund new projects to seek potential returns. Otherwise,
businesses will pay the debt and holding cash. As research of Ferreira and Vilela (2004), when
retained earnings are not sufficient to fund existing projects, they will use the amount of
reservation and, if necessary, will now borrowings.
According to free cash flow theory, Jensen (1986) did define that free cash flow is cash
flow exceeds the amount necessary to fund all projects with positive net present value,
discounted at the cost of capital involved. Free cash flow theory is a subordination of agency cost
11
theory. Interests and motivations of shareholders and management have conflicting issues related
to the optimal size of the firm and the payment of dividends to shareholders. The conflict is
particularly severe in the enterprise with great free cash flow. Jensen (1986) believed that it is
usually the enterprise free cash flow greater than the opportunity profitable investment. Free cash
flow theory is also basing on the theory of asymmetric information and the theory of agency
costs. According to Jensen (1986), the payment to shareholders will reduce the resources under
management's control, thus reducing their strength. Therefore, the management tends to increase
the amount of cash available under their control to achieve power and autonomy in investment
decisions. When cash is available to serve the purpose of investment, management does not need
to raise capital from outside and not provide the external parties information about the
investment projects. It is called the independence in management power. Therefore, management
can make the investment projects negatively impact the welfare of shareholders. In reality, the
shareholder wishes to receive more cash back when having free cash flow.
2.3. Factors Affecting Cash Holding from Related Theories
Based on related theories, the author will present clearly how firms reacted with; and
changes on level of cash holding when having changes of independent factors. Related factors,
which presented in previous academic researches, will be summarized along with above tradeoff, pecking order and/or cash flow theories, if any. Size of business, financial leverage, bank
debt, cash flow, cash flow volatility, liquidity of working capital, and finally dividend payout
yearly are exact main seven factors that previous authors reviewed. Those factors have close
relationship with marginal cost and benefit; with the investment and reservation purpose, and
with asymmetric information and agency cost. People can see the details in below review and
result summary at the end of this chapter.
12
2.3.1. Size of Business
Based on trade-off theory, Faulkender (2002) argued for that if a firm was larger, the
demand for cashing holding is lower. A similar result also presented by Bover and Watson
(2005) that larger firms were likely to have lower demand of cash holding. Actually, as to Kim et
al. (1998), large firms were less likely to face borrowing constraints than small firms, because of
scale economies resulting from a fixed cost component of security issuance costs. Furthermore,
Ferreira and Vilela (2004) protested that small firms with high business risks and strong growth
opportunities tended to hold more cash because it will be more expensive for small firms’ to
raise funds. The transaction fees, which usually accompany with raised funds, were fixed, and
thus, the marginal cost were higher for small firms. Besides that, Ferreira and Vilela (2004)
advocated that larger firms were more likely to be diversified to be in probability of financial
distress so larger firms hold less cash. These arguments proposed a negative relation between the
size of a firm and the demand of cash holding. In the research of Pinkowitz and Williamson
(2001), the determinants of cash holding for the United States, Japan, and Germany were
investigated. A regression for all the three countries showed that there was a negative relation
between firm size and cash holding. Nevertheless, when they tested the three countries
separately, evidence shows that Japan and the US had a negative relation whereas Germany had
a positive relation between firm size and cash holding. Bates et al. (2009) investigated why cash
holding for US industrial firms doubled from the year 1980s to 2000s, and related factors.
Initially, they found evidence that there is a negative relation between firm size and cash holding
in the 1980s and 1990s and they are consistent with models of a transaction demand for cash.
However, in the year 2000s they concluded that there is a positive relation between firm size and
cash holding due to agency problems. One more study, Ozkan and Ozkan (2004) found a
positive but insignificant relation between firm size and cash holding. This positive coefficient
suggested that there might be other factors affecting the way in which size of firms applies
influence on their cash holding decisions. A typical case is that it may be that large firms are
13
more successful in generating cash flows and profit so that they can go back to accumulate more
cash. In addition, large firms have greater growth opportunities and smaller liquid assets besides
cash. In this case, they may decide to hold more cash, Ozkan & Ozkan (2004) argued. However,
these arguments were not strongly supported by evidences.
According to pecking order theory, Ferreira and Vilela (2004) and Opler et al. (1999)
found no evidence to support for a positive relation between firm size and cash holding. They
presumed that large firms had more successful, and have more cash after controlling for
investment. But it is weak confirmation.
Pointing to cash flow theory, Ferreira and Vilela (2004) debated that larger firms are
more likely to have more stakeholders, which will increase the management’s self-determination
of making decisions. In addition, larger firms are not likely to be the target of a takeover, which
requires more resources. Thus, it was expected that managers of large firms have powers that are
more discretionary over the investment and financial policies of the firm, which leads to a
greater amount of cash holding. So there is a positive relation between firm size and cash
holding. However, at the end Ferreira and Vilela (2004) and Opler et al. (1999) did not still find
any evidence to support this positive relation. They are their subjective consider for reference
only.
In Vietnam, Trinh (2012) did use financial data in order to look into the percentage of
holding cash over total assets. The author explained that the market is volatile much, so big firms
did tend to hold more cash.
2.3.2. Financial Leverage
According to trade-off theory, Ferreira & Vilela (2004) recognized that leverage ratio
performs as a proxy for the ability of the firms to issue debt. In this way, the firm has a higher
ability to increase debt so that firm will hold less cash. On the other hand, a firm with less
capability to increase debt holds more cash. Ozkan and Ozkan (2004) observed and saw that
14
firms with high leverage have lower cash holdings in order to lower the cost of cash holding.
This means that leverage has a negative relation on cash holding. In common, it is believed that
leverage increases the probability of bankruptcy due to the stress that amortization plans placed
on the firm treasury management. To reduce the chance of experiencing financial distress, firms
with higher leverage are expected to hold more cash. There was the same result as Ozkan and
Ozkan (2004), however Thomas et al. (2007) mentioned the defensive purposes for cash
holdings when firms could settle up all of their debt duties at the end of the sample period.
Thomas et al. (2007) saw that the considerable average ratio of cash over assets for the U.S.
industrial firms increased rapidly, by 129%, from 1980 to 2004. Because of this increase in the
average cash ratio, firms could settle up all of their debt duties with their cash holdings at the end
of the sample period, so that the average firms had no leverage when leverage is measured by net
debt. This change in cash ratios and net debt was the result of a secular trend, but is more
pronounced for firms that do not pay dividends. The average cash ratio increased over the
sample period because firms change: their cash flow became riskier, they hold fewer inventories
and accounts receivable, and were increasingly R&D intensive. The defensive purposes for cash
holdings appeared to explain the increase in the average cash ratio. However, Ferreira & Vilela
(2004) found no evidence for this positive relation between leverage and cash holding. These
arguments finally suggested that leverage may have an unknown (positive and negative) relation
between leverage and cash holding.
Relying on pecking order theory, Ferreira & Vilela (2004) perceived that debt grows
when investment exceeds retained earnings and falls down when investment is less than retained
earnings. Cash holding fall when investment exceeds retained earnings and grow when
investment is less than retained earnings. That was a reason why Ferreira & Vilela (2004)
suggested that there was a negative relation between leverage and cash holdings. According to
Opler et al. (1999), a firm’s debt reacted to changes in the internal funds of the firm. When the
15
firm’s internal funds increased, its leverage decreased. For more details, most of the time firms
gained internal funds instead of issuing equity because it was expensive due to adverse selection.
Because with internal funds the firm often spent more cash than receiving cash, the firm
decreases cash holdings and raises debt. In short, this relationship between cash holdings, debt,
and internal resources suggested that there is a negative relation between leverage and cash
holdings.
Relating to cash flow theory, Ferreira and Vilela (2004) noticed that firms with low
leverage were less subject to monitoring, allowing for superior managerial discretion. This meant
that firms with less leverage hold more cash which is a negative relation between leverage and
cash holding.
2.3.3. Liquid Assets Substitutes
In balance sheet, firms can have liquid assets substitutes. These assets can be converted
into cash easily in short time and with low costs. These include accounts receivable and
inventories and this is in fact the net working capital minus cash. They are substitutes for cash
and therefore trade-off theory predicted a negative relationship between liquid assets and cash
holding, Bigelli and Sanchez (2010); Ferreira and Vilela (2004); Ozkan and Ozkan (2004)
examined. Furthermore, Opler et al. (1999) found evidence that large firms hold liquid assets
substitutes so that they could be able to keep investing when cash flow is too low and when
outside resources are costly. Those evidences came from analysis on balance sheet of firms
researched. It has a relation with pecking order and cash flow theories.
2.3.4. Cash Flow
Ferreira and Vilela (2004) believed that areal reason for firm to hold cash is cash flows.
Cash flow was recognized after corporate tax profit plus depreciation. If there is large cash flow
means that there is enough liquidity so the firm holds less cash. However, they had found no
16
evidence to support this relation. It is clear to say that short-term and long-term abilities are
balance with large cash flow. According to the trade-off model, cash flow provides a ready
source of liquidity. Kim et al. (1998) alleged that cash flow could be seen as cash substitutes.
Consequently, they expected that there is a negative relation between cash flow and cash
holdings.
When cash flow is high, means that the operating activities are going well and positive
cash flow can buffer for any loss from financing or investing activities. The firm can invest more
in order to grow, so that the firm has to hold more cash, Ferreira & Vilela (2004) confirmed one
more time. Likewise, when there is high cash flow are expected to hold more cash because the
firm prefer internal finance more than external finance, Ozkan & Ozkan (2004) analyzed. It
helps to reduce cost much. Due to pecking order theory, these arguments strongly pointed out
that there were a positive relation between cash flow and cash holding.
In Vietnam, Phuong (2013) did research on special factors that affect cash level of 125
listed firms in Viet Nam from 2009 to 2012. As author’s result, seven factors that affects to level
of cash in those firms are size of business, leverage, bank liability, cash flow, cash flow
violability, and dividend. The pecking order theory is concluded as the most suitable theory to
explain for changes on corporate cash management.
2.3.5. Cash Flow Volatility
According to trade-off theory, cash holding can be very important for a company when it
is suffering because of lower cash flows or worse firm circumstances. Bigelli and Sanchez
(2010); and Ozkan and Ozkan (2004) dealt with that literature therefore expected a positive
relation between volatility of cash flows and cash holding. The greater the volatility of cash flow,
the greater the possibility that the firm will be short of liquid assets. It is easy to see that if the
firm has to pass up some valuable growth opportunities, it will be costly to be short of cash.
Therefore, firms with high cash flow volatility hold more cash in order to avoid the expected
17
costs of liquidity constraints, Ozkan & Ozkan (2004) concluded. Another supporting is that,
Ferreira & Vilela (2004) also found evidence that there is a positive relation between cash flow
volatility and cash holding in the EMU countries. Firms with more cash flow volatility faced a
higher probability of experiencing cash shortages due to unexpected bad cash flows, they
supported. In their paper, Opler et al. (1999) also had found evidence that US firms with more
cash flow volatility hold larger amount of cash. Two remaining theories do not much relate to
cash flow volatility.
Megginson and Wei (2012) studied the relation between state ownership and cash
holdings in China’s share-issue privatized firms from 1993 to 2007. In their research, level of
cash holding had negative relation with size of business, financial leverage, and net non-fixed
assets, but had positive relation with cash flow fluctuation.
In Vietnam, Khanh (2013) did support the opinion that many firms in samples held much
cash, because of economic crisis and fluctuation. Firms held cash to avoid risks, not because of
finding good opportunities. They balance their operations and focus on their strength, not
diversify investment. The result showed that cash flow, cash flow volatility, level of leverage,
and debts were factors affecting to cash level.
2.3.6. Liability to Bank
Based on trade-off theory, Ferreira and Vilela (2004) provided evidence that EMU firms
had a closer relationship with banks. In EMU countries, banks owned a significant proportion of
firm’s stock; they provided more. In addition, according to Krivogorsky, Grudnitski, and Dick
(2009), firms in Continental Europe relied more on bank debt than bonds for their external funds.
It seem that firms that rely on bank loans as major source of financing are less likely to
experience agency and asymmetric information problems associated with other kinds of debt.
Ferreira & Vilela (2004) argued that banks were in a better position to evaluate the firm’s credit
quality and to monitor and control the firm’s financial policies. Pinkowitz and Williamson
18
(2001) used industrial firms from the Germany, U.S, and Japan to explain effects of bank power
on cash holdings. The first notice is that Japanese firms hold a greater percentage of their assets
in cash, which is slightly greater than the U.S. The author recorded that Japanese cash balance
were affected by the monopoly power of banks. During periods of powerful banks, firms’ high
cash holdings are steady with banks extracting charges. The explanation also suggested for this
finding is that banks encourage firms to maintain large cash balances in order to extract charges
from firms or to reduce their monitoring costs. When banks weakened, because of war, Japanese
cash levels became more like U.S. firms. This author went to conclusion that strong Japanese
banks persuade firms to hold large cash balances. This is contrary to widely held beliefs about
the Japanese governance system. According to Ozkan and Ozkan (2004), banks could minimize
the information costs and could get access to information not otherwise publicly available. Banks
could monitor borrower’s private information more effectively than other lenders. When a bank
provide a loan or renew a loan to a firm means that there is positive information about that firm.
So when a firm has bank debt means that it decreases the probability of experiencing financial
distress. In this case, firms with bank debt hold less cash. It seems that it is a secure and very
popular relationship.
According to pecking order theory, Ferreira and Vilela (2004) noted that bank debt was
negative related to cash holding of a firm for precautionary reasons. It is expected that firms that
rely on bank loans as major source of financing are less likely to experience agency and
asymmetric information problems associated with other kinds of debt. This is because banks are
in a better position to evaluate the firm’s credit quality and to monitor and control the firm’s
financial policies. Finally, Ferreira and Vilela (2004) gave a clear opinion that because agency
and asymmetric information problems were a source of significant indirect financing costs,
which may limit the access to capital markets, one would expect that firms with a greater
proportion of bank debt had less cash holdings for precautionary motives.
19
Same evaluation as leverage factor, relying on cash flow theory, Ferreira and Vilela
(2004) concluded that firms with a good relation with banks were more subject to monitoring,
which could decrease superior managerial discretion. This means that firms with bank debt hold
less cash.
In Vietnam, according to Trinh (2012), firms which had more debts and long-term debts,
did hold less cash.
2.3.7. Dividend Payout
In relation with trade-off theory, according to Ferreira and Vilela (2004); Ozkan and
Ozkan (2004); and Pinkowitz and Williamson (2001), there is a negative relation between
dividend payment and cash holdings. Their viewpoints are that firm pays dividends could afford
to hold less cash when they are more capable of raising funds when needed by cutting dividend.
The same opinion, a firm that does not pay dividends had to use the capital markets to raise
funds, Opler et al. (1999) and Ferreira and Vilela (2004) agreed. Therefore, an important job of
top management level is to balance profit and price of common shares. Current volume and price
of common share are the positive input factor dividend paid-out, however profit is the key factor
to decide how much firm gave out.
Hereby is the summary table of above related studies, which provided ideas, concepts,
and theories for this study.
Table 1: Summary of Previous Studies
Author
Kim et al.
Research
Source of Research Result
Year
Data
1998
U.S
Level of cash holding has positive relation with
investment opportunity and cash flow; however it
has negative relation with size of business, financial
leverage, cash conversion cycle and financial crisis
20
possibility.
Opler et al.
1999
America
Group businesses of high growth opportunity, low
risks, or small size hold more cash than other
businesses. Simultaneously, firm which easily got
entrance to capital market holds less cash than
others did.
Pinkowitz
2001
U.S,
Japanese firms hold more cash than others do in
and
German
German and the U.S. The author noted that cash
Williamsion
and Japan
balance of Japanese firms did effected by bank’s
monopoly power.
Ferreira and 2004
EMU
Vilela
Research’s result showed the positive relation
between the level of cash holding and investment
opportunity; and has negative relation with total
assets that not include cash, leverage and size of
business.
Ozkan
and 2004
UK
Ozkan
The author did find evidences of inverse correlation
between ownership structure and level of cash
holding. Level of cash has positive relation with
cash flow from operations and growth opportunity,
but it has negative relation with non-fixed assets and
liabilities to bank.
Custodio, et 2005
al.
U.S
The author found that level of cash holding
increased during economic crisis. Firms hold more
cash or kept cash balance stable while risk-free
interest in short-term went down.
21
Bates et al.
2009
U.S
The author found some main reasons for high level
of cash holding, such as decrease in inventory
(decrease in non-fixed assets); high fluctuation of
cash flow; increase in investment opportunity and
R&D expenditures.
Meggison
2012
China
and Wei
Level of cash holding had negative relation with
size of business, financial leverage, and net nonfixed assets, but had positive relation with firm
profit, growth opportunity, expenditures and cash
flow fluctuation.
Trinh
2012
Vietnam
Vietnamese companies did not concentrate on
opportunity cost. The author explained that in the
testing period, the market is volatile, so big firms
did tend to hold more cash. Firms, which had more
debts and long-term debts, did hold less cash.
Khanh
2013
Vietnam
Cash flow, cash flow volatility, level of leverage,
credit limit, outsider finance, debts, business cycle,
economic risks and investment opportunity were
factors affecting to cash level.
Firms held cash to avoid risks, not because of
finding good opportunities.
Phuong
2013
Vietnam
Seven factors that affects to level of cash in firms
are size of business, leverage, bank liability, cash
flow, cash flow violability, opportunity and
dividend.
22
The pecking order theory is concluded as the most
suitable theory to explain for changes on corporate
cash management.
23
Chapter 3. Research Methodology
This chapter will explain the theoretical and logical thinking of the author regarding the
approach and structure of the thesis
3.1. Data Sources
In this research, the author has used secondary sources of data.
The author has collected data of the selected firms and design own database structure.
The data is collected from official website of Ho Chi Minh (HOSE) and Ha Noi (HNX) stock
exchange and Cafef website (http:// http://cafef.vn/). Those data are considered by the author to
be reliable due to the requirement of publicly companies listed to be independently audited.
Financial statements of selected companies are collected.
Companies listed in both stock exchanges are selected as followings criteria. Firstly,
Companies must be listed in one of two stock exchanges at least five years back from 2014. Five
years are selected because of time limit and long enough to do thesis. Secondly, companies
which operates in field of finance, bank, insurance, stock market, real estate and energy. Thirdly,
construction or energy companies, which operate on trading real estate or hotel, are also
eliminated. The financial condition of those firms has under special control of government and
has different kind of operations. 98 listed companies were finally selected. The main industries
of companies listed are clearly defined in a stated document of government. It is called VSIC
2007. Revenue is the united reason to decide what company is in what industry. Twenty one
industries are showed in Pooled OLS regression analysis below for united presentation purpose
only.
The sample size is reviewed again to ensure that they are enough for thesis analysis.
Since multi-regression is mainly used in this thesis, practical theory of Tabachnick and Fidell,
(1996) guided that minimum sample size could be calculated as formulas 50 + 8*m (in which m:
24
number of independent variables. In this thesis, the author will tend to use 33 variables, so
minimum sample size is 314. In reality, total sample size of thesis is 490, as formulas 98*5 (in
which 5: five years). Therefore, current sample size of thesis can be satisfied.
Data set is gathered from financial reports, and then grouped as tabular table. The
purpose is mainly using for below statistics, such as descriptive statistics, unvariate analysis, and
four multi-regression. As following empirical models of previous researches, the author uses six
variables, including size of firm, leverage, bank debt, cash flow, liquidity, and dividend. The
author combines Stata 12 software to generate complex statistics such as percentile, quartiles, Rsquare, P-value, F test, and regression data. The author also uses Excel 2007 as supporting tool
for Stata 12 to compute and draw out tendency of cash holding over the years. Software has
specific strength and then when the author combines, they will save much time for this thesis.
Other than the created database, a number of other secondary sources have been used.
There are both advantages and disadvantages with having secondary sources according to
Bryman & Bell (2011). Firstly, it has saved both cost and time in the data collection process.
Secondly, the data collected has higher quality than the author would have been able to produce
by myself and finally it allows the author to conduct their study over a number of years in the
past. The disadvantage is that the author might lack familiarity with the data since they has not
collected the data by themselves, might lack understanding on the complexity of the data and do
not has any control over the data (Bryman & Bell, 2011, p.320-321). However, the data collected
was of the nature that the author had previously encountered academically. Given the advantages
and disadvantages, the author has chosen to use the following secondary sources.
The historical database of stock closing prices of the listed firms that has been
investigated is a widely used database when it comes to business research and has a high quality
of data. Stock prices at the end of each companies and each year are collected from Cafef
website (http:// http://cafef.vn/). Since the author is a student at Ho Chi Minh International
University, the author has authorized to buy the access to the university library database and
25
related academic link for relevant literature. Keywords that has been used in searches has ranged
from, cash holdings, cash to corporate finance. In addition to articles, a wide variety of textbooks
has been used in this thesis. These books are mostly related to business research, cash, cash
holding, statistics and corporate finance.
3.2. Data Statistics
This part says clearly about how statistics and models will be used for analyzing dataset
to develop results. They include three main methods. They are descriptive statistics, univariate
analysis, and fours models of regression. Reliability and validity of variables and the united
techniques of the data treatment will be concerned carefully.
3.2.1. Descriptive Statistics
Descriptive statistics will be completed basically by computing quantities from research data.
The author uses percentile and quartiles to measure the tendency of data set and observe the
balance of entire distribution of data points. The author will essentially use the mean and median.
A graphical technique, area chart is used to extend and visualize data set and then help the author
to see the initial tendency of cash holding. The author combines Stata 12 software to generate
percentile and quartiles and Excel 2007 to compute and draw out tendency of cash holding over
the years.
3.2.2. Univariate Analysis
In statistics, the Pearson correlation is a measure of the linear correlation (the
dependence) between two variables X and Y. It was developed by Karl Pearson from a related
idea introduced by Francis Galton in the 1880s. Checking univariate will be done by
comparisons of independent variables on the quartile points, through the observation on
percentages of cash to the total assets. Built quartile for each year in period, will help to explain
why their positions are maybe overlap. The construction and comparison across quartiles are to
26
check whether there is a significant difference statistically between the company which has large
cash balance (the fourth quartile) and small balance (first quartile). T-statistic is to test
hypotheses about statistically significant differences between fourth quartiles and first quartiles,
as Opler et al. (1999) and Ferreira and Vilela (2004) wrote down. To calculate the quartiles and
t-statistics as well as p_value, the author uses Stata 12 software. According to Pearson (1895),
correlation coefficients between variables, go from -1 to 1 and closer to 1 means strong
correlation. A negative value indicates an inverse relationship. In Stata 12, author can use the
command “pwcorr Y X1 X2... Xn, star (0.05) sign” or pairwise correlation tool available. The
confidence level is 95%. Graphical presentation of those correlation is shown by command
“graph matrix Y X, half maxis(ylabel(none) xlabel(none))”.
3.2.3. Regression Analysis
Opler et al. (1999), Ferreira and Vilela (2004) and Pinkowitz and Williamson (2001) had
used regression models in their empirical studies, includes: models of Fama-MacBeth, crosssectional regression using average values, pooled OLS regression with year dummies and finally
pooled OLS regression with both year and industry dummies. We can see selected regression
models in figure 2 below.
27
Figure 2: Selected Regression Models
In the study, the author will use the same four regression models to find the correlation
between the level of cash hold and specific factors of the listed firms in Vietnam stock market. In
each regression model, the null and alternative hypotheses will be set up to test about the linear
correlation coefficient between level of cash holding and factors. The null hypotheses is Ho: β =
0 (the linear correlation coefficient is zero) and alternative hypotheses is H1: β ≠ 0 (the linear
correlation coefficient is different from zero). These tests are two-tailed. P-value is also used to
support final conclusion making. The author will assume that variables cash and factors are
normally distributed. Since each regression has different pros and cons, so that combination of
four regressions will help to explain the relation between variables better.
3.2.3.1.
Fama-MacBeth Regression Model
In a research in 1973, Fama-MacBeth model is the financial model used in the study,
"Risk, profitability and balance: the empirical test" which was performed by two authors, Fama
and MacBeth. On the other studies of factors affect the level of cash holdings, these authors had
also used this approach. Under this method, the cross-sectional regressions will be run for each
year. This approach eliminates the problem of residual correlation sequence that be often found
in time series data regression over time. The Fama-MacBeth model was effective when viewed
every year as an independent cross-sectional data, as Opler et al. (1999) confirmed. The author
will conduct regression over two steps. First step, the author runs cross-sectional data for each
year separately. Study period are five years (from 2009 to 2013), so model will be performed
fifth times. Where the regression model’s variance has changes, the author will use the Robust
option in Stata 12 to fix. Second step, the author will compare the t-statistic as well as p_value
well as correlation coefficient of 5 result to reach conclusion.
Cross-sectional regression for each year is determined as follows:
28
CASHi= β0+ β1SIZEi+ β2LEVi+ β3BANKDEPTi + β4CFLOWi + β5CFVOLATi+ β6LIQi+
β7DIVIDENDi+ ui
Where i = 1, 2, …., N.
And where: CASHi is the amount of cash or cash equivalence hold by firm i. SIZEi is the
natural logarithm cash over total assets of firm i. LEVi is ratio of the total liabilities over total
assets of firm i. BANKDEPTi is the ratio of bank liabilities over total liabilities of firm i.
CFLOWi is the ratio of cash flow over total assets of firm i. CFVOLATi is the ratio of average
standard deviation of cash flow (from 2009 to 2013) over average total assets (from 2009 to
2013) of firm i. LIQi is the ratio of working capital exclude cash & equivalent over total assets of
firm i. DIVIDENDi is the dummy variable of firm i with 1 value if dividend paid and 0 value if
not paid. Ui is the variance of firm i.
3.2.3.2.
Cross-Sectional Regression Model with Mean Value of Variables
In this regression model, each variable of each firm is average value for period 2009-2013. OLS
regression is run in Stata 12.
Cross-sectional regression for each year is determined as follows:
CASHi= β0+ β1SIZEi+ β2LEVi+ β3BANKDEPTi + β4CFLOWi + β5CFVOLATi+ β6LIQi+
β7DIVIDENDi+ ui
Where i = 1, 2, …., N.
And where: CASHi is the amount of cash or cash equivalence hold by firm i. SIZEi is the
natural logarithm cash over total assets of firm i. LEVi is ratio of the total liabilities over total
assets of firm i. BANKDEPTi is the ratio of bank liabilities over total liabilities of firm i.
CFLOWi is the ratio of cash flow over total assets of firm i. CFVOLATi is the ratio of average
standard deviation of cash flow (from 2009 to 2013) over average total assets (from 2009 to
2013) of firm i. LIQi is the ratio of working capital exclude cash & equivalent over total assets of
29
firm i. DIVIDENDi is the dummy variable of firm i with 1 value if dividend paid and 0 value if
not paid. Ui is the variance of firm i.
3.2.3.3.
Pooled Ordinary Least Squares Regression Model with Year Dummy
Pooled OLS is a variant of OLS regression. Dummy variables are added to OLS
regression to see the result over the years. This action also makes samples increased. Ferreira and
Vilela (2004) used in their research with the same purpose. The author uses closing stock prices
at the end of each year for year dummies.
Pooled OLS regression for each year is determined as follows:
CASHit= β0+ β1SIZEit+ β2LEVit+ β3BANKDEPTit + β4CFLOWit + β5CFVOLATit+
β6LIQit+ β7DIVIDENDit+ β82009it + β92010it + β102011it + β112012it+ β122013it + uit
Where i = 1, 2, …., N; t= 1, 2,…., Ti
And where: CASHi is the amount of cash or cash equivalence hold by firm I in year t.
SIZEi is the natural logarithm cash over total assets of firm i in year t. LEVi is ratio of the total
liabilities over total assets of firm i in year t. BANKDEPTi is the ratio of bank liabilities over
total liabilities of firm i in year t. CFLOWi is the ratio of cash flow over total assets of firm i in
year t. CFVOLATi is the ratio of average standard deviation of cash flow (from 2009 to 2013)
over average total assets (from 2009 to 2013) of firm i in year t. LIQi is the ratio of working
capital exclude cash & equivalent over total assets of firm i in year t. DIVIDENDi is the dummy
variable of firm i with 1 value if dividend paid and 0 value if not paid. Uit is the variance of firm
i in year t. β82009it is the dummy variable of year 2009, with 1 is year 2009 and 0 is not year
2009. β92010it is the dummy variable of year 2010, with 1 is year 2010 and 0 is not year 2010.
β102011it is the dummy variable of year 2011, with 1 is year 2011 and 0 is not year 2011.
β112012it is the dummy variable of year 2012, with 1 is year 2012 and 0 is not year 2012.
β122013it is the dummy variable of year 2013, with 1 is year 2013 and 0 is not year 2013.
30
3.2.3.4.
Pooled Ordinary Least Squares Regression Model with Year Dummy
and Industry Dummy
This method is similar to Pooled Ordinary Least Squares Regression (Pooled OLS) with
year dummy. Industry dummy variables are added to OLS regression to see the change of result
if have due to the change of industry. Beside above dummies, 21 industry dummies are in the list
below. Total variables will be 33.
Table 2: List of Industry Dummies
Ord.
Industry
1
Wholesaler Trading
2
Retailer Trading
3
Textiles - Leather
4
Chemicals - Pharmaceutical
5
Service activities related to transportation
6
Other mining and quarrying
7
Exploitation, processing, purchase ore
8
Warehouse
9
Processing Metal products and non-metallic mineral
10
Machinery - Vehicles
11
Furniture and related products
12
Paper Products and Printing
13
Processing Other products (medical equipment, toys, jewelry, ...)
14
Wood products
15
Products from plastic and rubber
16
Electrical equipment - Electronics - Telecommunications
31
17
Food - Beverage - Tobacco
18
Cultivation
19
Waterway Transport
20
Telecommunications
21
Construction
Pooled OLS regression for each year is determined as follows:
CASHit= β0+ β1SIZEit+ β2LEVit+ β3BANKDEPTit + β4CFLOWit + β5CFVOLATit+
β6LIQit+ β7DIVIDENDit+ β82009it +
β92010it +
β102011it + β112012it+ β122013it +
β13Industry1it + β14Industry2it + β15Industry3it + β16Industry4it + β17Industry5it +
β18Industry6it + β19Industry7it + β20Industry8it + β21Industry9it + β22Industry10it +
β23Industry11it + β24Industry12it + β25Industry13it + β26Industry14it + β27Industry15it +
β28Industry16it + β29Industry17it + β30Industry18it + β31Industry19it + β32Industry20it +
β33Industry21it + uit
Where i = 1, 2, …., N; t= 1, 2,…., Ti
And where: CASHi is the amount of cash or cash equivalence hold by firm I in year t.
SIZEi is the natural logarithm cash over total assets of firm i in year t. LEVi is ratio of the total
liabilities over total assets of firm i in year t. BANKDEPTi is the ratio of bank liabilities over
total liabilities of firm i in year t. CFLOWi is the ratio of cash flow over total assets of firm i in
year t. CFVOLATi is the ratio of average standard deviation of cash flow (from 2009 to 2013)
over average total assets (from 2009 to 2013) of firm i in year t. LIQi is the ratio of working
capital exclude cash & equivalent over total assets of firm i in year t. DIVIDENDi is the dummy
variable of firm i with 1 value if dividend paid and 0 value if not paid. Ui is the variance of firm i
in year t. β92009it is the dummy variable of year 2009, with 1 is year 2009 and 0 is not year
2009. β102010it is the dummy variable of year 2010, with 1 is year 2010 and 0 is not year 2010.
32
β112011it is the dummy variable of year 2011, with 1 is year 2011 and 0 is not year 2011.
β122012it is the dummy variable of year 2012, with 1 is year 2012 and 0 is not year 2012.
β132013it is the dummy variable of year 2013, with 1 is year 2013 and 0 is not year 2013.
β14Industry1it is the dummy variable of industry 1. β15Industry2it is the dummy variable of
industry 2. β16Industry3it is the dummy variable of industry 3. β17Industry4it is the dummy
variable of industry 4. β18Industry5it is the dummy variable of industry 5. Β19Industry6it is the
dummy variable of industry 6. β20Industry7it is the dummy variable of industry 7.
β21Industry8it is the dummy variable of industry 8. β22Industry9it is the dummy variable of
industry 9. β23Industry10it is the dummy variable of industry 10. β24Industry11it is the dummy
variable of industry 11. β25Industry12it is the dummy variable of industry 12. β26Industry13it is
the dummy variable of industry 13. βΒ27Industry14it is the dummy variable of industry 14.
β28Industry15it is the dummy variable of industry 15. β29Industry16it is the dummy variable of
industry 16. β30Industry17it is the dummy variable of industry 17. β31Industry18it is the
dummy variable of industry 18. β32Industry19it is the dummy variable of industry 19.
β33Industry20it is the dummy variable of industry 20. β34Industry21it is the dummy variable of
industry 21.
3.2.3.5.
Fixed Effects Regression Model
In panel data analysis, the term fixed effects is used to refer to an estimator for the
coefficients in the regression model. If we assume fixed effects, we impose time independent
effects for each entity that are possibly correlated with the regressors. The panel data of 98 firms
is presented from 2009s to 2013s with independent variables to analyze the impact of variables
that vary over time. First step is to convert time-series data into panel data by using the
command xtset “xtset comp year” then use the stata command “xtreg” to run the fixed effects.
Firms “COMP” represents the entities or panel (i) and “year” represents the time variable (t).
Panel variable refers to “strongly balanced”. It means to the fact the all firms have data for all
33
five years. If, one firm does not have data for one year, then the data is unbalanced. Ideally,
people want to have a balanced dataset but this is not always the case, however you can still run
the fixed effects model.
Fixed effects regression model is determined as follows:
CASHit= β0+ β1SIZEit+ β2LEVit+ β3BANKDEPTit + β4CFLOWit + β5CFVOLATit+
β6LIQit+ β7DIVIDENDit+ αi + uit
Where i = 1, 2, …., N;
t= 1, 2,…., Ti
And where: CASHi is the amount of cash or cash equivalence hold by firm i in year t.
SIZEi is the natural logarithm cash over total assets of firm i in year t. LEVi is ratio of the total
liabilities over total assets of firm i in year t. BANKDEPTi is the ratio of bank liabilities over
total liabilities of firm i in year t. CFLOWi is the ratio of cash flow over total assets of firm i in
year t. CFVOLATi is the ratio of average standard deviation of cash flow (from 2009 to 2013)
over average total assets (from 2009 to 2013) of firm i in year t. LIQi is the ratio of working
capital exclude cash & equivalent over total assets of firm i in year t. DIVIDENDi is the dummy
variable of firm i with 1 value if dividend paid and 0 value if not paid. αi (i=1…n) is the
unknown intercept for each entity. Ui is the variance of firm i in year t.
3.2.4. Other Supported Tests for Regression Models
Each regression model has advantages and disadvantages, however its regression result
cannot show possibilities of high correlation among variables; the heteroscedasticity in a linear
model; or multicollinearity. These tests were designed to give the signal for the author, then in
order to reduce negative effects on regression models above and to support for the conclusion.
As Kumar (1975), multicollinearity (also collinearity) is a statistical phenomenon in
which two or more independent variables in the multiple regression model are highly correlated.
According to Farrar (1967), multicollinearity does not reduce the predictive power or reliability
of the model as a whole, at least within the sample data themselves; it only affects calculations
34
regarding individual predictors. A high degree of multicollinearity can also prevent computer
software packages from performing the matrix inversion required for computing the regression
coefficients, or it may make the results of that inversion inaccurate. An important assumption for
the multiple regression model is that independent variables are not perfectly multicolinear. One
regressor should not be a linear function of another. When multicollinearity is present standand
errors may be inflated. Stock and Watson (2007) said that Stata software will drop one of the
variables to avoid a division by zero in the OLS procedure. The Stata command to check for
multicollinearity is VIF (variance inflation factor), and is used right after running the regression.
A vif > 10 or a 1/vif < 0.10 indicates multicollinearity.
In statistics, the Breusch–Pagan test is used to test for heteroscedasticity in a linear
regression model. It tests whether the estimated variance of the residuals from a regression are
dependent on the values of the independent variables. The null hypothesis is that variances
across entities is zero. This is, no significant difference across units, or no panel effect. The
command in Stata is xttset0 type it right after running the random effects model.
3.3. Determined Dependent and Independent Variables
3.3.1. Dependent Variable – Cash
In order to measure the ratio of cash, the authors used two common measures. The first
measure is the most popular and used, ratio of cash, cash equivalents divided by the book value
of total assets. This measure was first used by Kim at el. (1998), followed by the authors Ozkan
and Ozkan (2004), and Bates et al. (2009). The second is a measure for the natural logarithm of
the ratio of cash, cash equivalents and total assets excluding. It was the first used by Opler et al.
(1999), followed by the authors Pinkowitz and Williamson (2001) and Custodio, et al. (2005). In
this research, the author will use the ratio of cash and cash equivalents divided by the book value
35
of total assets, as to Kim et al.(1998), as the dependent variable to measure the correlation
between the amount of cash holdings and the specific factors of the firm.
3.3.2. Independent Variables
Compiled from the predictions of factors that affect the amount of cash holding from
three financial theories - tradeoff theory, pecking order theory, the theory of free cash flow-and
the empirical studies of the previous authors, Kim et al. (1998); Opler et al. (1999); Pinkowitz
and Williamson (2001); Ferreira and Vilela (2004); Ozkan and Ozkan (2004); Custodio, et al.
(2005); and Bates et al. (2009). The author draws the factors that affect the amount of listed
corporate cash holdings are: firm size, financial leverage, bank debt, cash flow, cash flow
volatility, liquid assets not including cash, the chances of dividend payments. These variables
serve as independent variables to explain the variation amount of the firm holdings.
3.3.2.1.
Size of Business (SIZE)
Firm size is measured by taking the natural logarithm of the total assets of each firm in
the last year of the study period and was used as constants for all years in the studied period
(Opler et al. (1999); Pinkowitz and Williamson (2001); Ferreira and Vilela (2004); Ozkan and
Ozkan (2004); and Custodio, et al. (2005)). When be converted to logarithm, the number and
data become significant figures. Total property is very large, however the logarithm makes it
smaller, more compact, reducing the variance, and making the normally distributed variables. In
this research, firm sizes are calculated by taking the natural logarithm of the total assets over the
firm in 2013 and used this data as constants for the year from 2009 to 2013. Total asset balance
amount is collected on their balance sheet at the end of the financial year.
3.3.2.2.
Bank Debt (BANKDEBT)
As Ferreira and Vilela (2004), and Ozkan and Ozkan (2004), bank debt is calculated as
the ratio of total bank debt divided by total debt. As the same way of calculation, the author uses
36
the ratio of bank debt as the way of previous authors. The balance of total debt, both short-term
loan and long-term loans, will be collected on companies’ balance sheets at the end of the
financial year. Debt is collected from payable notes on the financial statements. The author
excludes firms which have not present or not present clear information of bank debt.
3.3.2.3.
Cash Flow (CFLOW)
Cash flow variables are represented by the ratio of cash flow divided by total assets, as
the way that Ozkan and Ozkan (2004) and Bates et al. (2009) did. Cash flow is calculated by
taking the profit before tax, interest expense and depreciation (EBITDA) then deducting taxes,
interest expense and dividends. The author calculates cash flow ratio as the way previous authors
had done.
3.3.2.4.
Cash Flow Violability (CFVOLAT)
Fluctuations in cash flow variables are calculated by dividing the standard deviation of
cash flow by average total assets. It was way that two authors, Kim et al. (1998) and Ozkan and
Ozkan (2004) used. The author calculates the ratio cash flow volatility by taking the standard
deviation of cash flow in five years (2009 - 2013) divided by average total assets of five years
(2009-2013). Total assets were collected from firms’ balance sheets at the end of the financial
year.
3.3.2.5.
Liquid Assets (LIQ)
Liquid assets variable is represented by the ratio of net current assets minus cash and cash
equivalents divided by total assets. It was way that two authors, Ozkan and Ozkan (2004 ) and
Bates et al. (2009) did use. The author now uses the same way like above. Current assets, current
liabilities, cash and cash equivalents, total assets were collected from their balance sheet at the
end of the financial year.
3.3.2.6.
Dividend (DIVIDEND)
37
Dividend dummy is composed of two sets of value, 0 and 1. If within financial year,
firms have to pay dividends, the dividend variable takes the value 1 and the value 0 if not paid in
dividends. It was the way of Opler et al. (1999), Pinkowitz and Williamson (2001), Ferreira and
Vilela (2004), Ozkan and Ozkan (2004), Custodio, et al. (2005) and Bates et al. (2009). The
author determines value of dividend dummies by seeing dividend paid in the cash flow
statement, items dividends and interest paid to the owners. In case of having dividend payments,
dividend dummy variable takes the value 1. Conversely, variable takes value 0.
Table 3: Summary of Formulas for Variables
Dependent Variable
Calculation Formulas
CASH-Cash ratio
Cash and equivalent / total assets
Independent Variables
SIZE-Size of business
Natural logarithm of total assets
LEV-Financial leverage
Total liabilities / total assets
BANKDEBT-Bank Debts
Bank liabilities / total liabilities
CFLOW-Cash flow
(EBITDA – interest – corporate tax – dividend) / total
assets
CFLOWAT-Cash
violability
LIQ-Liquidity
exclude cash
flow Average standard deviation of cash flow (2009-2013)/
average total assets (2009-2013)
Assets, (Current assets – current liabilities – cash & equivalent) /
total assets
DIVIDEND-dummy dividend If dividend paid, variable value is 1. If not, it is 0.
paid
38
Chapter 4. Data Analysis and Results
The findings present the research result respectively research methodology is presented in
chapter three, including descriptive statistics, univariate analysis, and regression methods. The
first section presents the preliminary analysis of the data, the descriptive statistics, and tendency
of dependent variable, univariate outliers, normality, and examination of relationship between
variables. The second section analyzes the hypotheses and then presents analyses of the proposed
models. Data were analyzed using Stata version 12 and Excel version 2007.
4.1. Descriptive Statistics
Descriptive statistics are very important and meaningful for this thesis allows simpler
interpretation because dataset is large and hard to visualize. In table 05, we can see easily that
average rate of cash holding over total assets of 98 firms over five years is 11.9%, value 89
billion VND (bVND). It is quite a big balance at the end of year. Additionally, mean of leverage
and bank debt ratio show that firms’ borrowing ratio is only 25.2%, however debt from banks is
quite high, 83.9% if any.
Table 4: Results from Descriptive Statistics
variable
mean
p25
p50
p75
sd
CASH
SIZE
LEV
BANKDEBT
CFLOW
CFLOVAT
LIQ
DIVIDEND
.1192199
26.56168
.252598
.8399878
.0854143
.0534755
.1365245
.8163265
.0339252
25.818
.069
.647
.031
.021
-.001
1
.0796032
26.527
.225
1
.071
.038
.1305
1
.1748642
27.3
.417
1
.123
.06
.263
1
.1160025
1.184751
.2108854
1.662004
.1111144
.0632884
.5123252
.3876134
39
Dividend paid-out ratio is 81.6%, even when firms bore losses. The ratio of loss is nearly
7%. Liquidity ratio is in safe range of normal firm, 13.6%. All descriptive statistics show that
firms keep high level of cash at the end of year.
Figure 3: Cash Ratio Over Five Years
In the figure 3, tendency of cash holding decrease slightly from 2009, but it did increase
from 2012. According to a statistics, Rodion (2012) observed that the Vietnam's economic
growth has slowed down to between four and five percent. That is not enough to create jobs for
the rapidly growing population and the rising number of people entering the job market. Hoang
(2012) said that the Vietnam’s leading experts have voiced concern that Vietnam is entering into
a fresh battle to stave off yet another economic downturn, citing the central bank’s latest ceiling
deposit rate reduction. Hoang (2012) also cited statement of Cao Sy Kiem, chairman of the
Association of Small and Medium Enterprises, said small and medium firms were still paying an
annual lending rate of 18% to 19% even though the ceiling deposit rate has been lowered to
12%. The mean of cash holding ratio over 5 years is reasonable if we observe that economic
condition last 5 years. Firms have had tried to keep cash available and stable, but also must
limited cash out flow in operations due to economic downturn.
40
Figure 4: % Standard Deviation of Cash Flow over Total Assets
Figure 04 describes the downward tendency of cash flow. This ratio is calculated by taking
standard deviation of cash flow in 5 years divided total assets of each year. This tendency was
positive with level of cash holding in figure 03. The ratio decreased rapidly from 2009-2011s,
and slowly went down in 2012s and 2013s. A notice was that while level of cash holding went
down, the total assets increase, average 11%. It seems that firms accumulated assets and expand
business even downturn of economy in recent years.
41
4.2. Univariate Analysis
Each quartile has 98 firms. Most of independent variables, which are belonging to first or
fourth quartile, are significant statistics with confidence level 95%.
Table 5: Results from Univariate Analysis
Variable
Obs
CASH
490
SIZE
490
LEV
490
BANKDEBT
490
CFLOW
490
CFLOVAT
490
LIQ
490
DIVIDEND
490
Percentile
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
Centile
.0004672
.0339191
.0796032
.1748773
.9225227
23.244
25.8065
26.527
27.30025
30.761
0
.068
.225
.419
.757
0
.645
1
1
34.804
-.621
.031
.071
.123
1.029
.002
.021
.038
.06
.531
-5.984
-.00225
.1305
.264
8.414
0
1
1
1
1
Binom. Interp.
[95% Conf. Interval]
.0004672
.0294214
.0722549
.1493352
.9225227
23.244
25.64807
26.40561
27.20596
30.761
0
.0384379
.1984122
.3779768
.757
0
.2008758
1
1
34.804
-.621
.025
.063
.1089884
1.029
.002
.0184379
.035
.057
.531
-5.984
-.0335621
.111
.232
8.414
0
1
1
1
1
.0004672*
.039873
.0912877
.1926788
.9225227*
23.244*
25.92701
26.71359
27.41712
30.761*
0*
.0880116
.2483918
.452
.757*
0*
.8050347
1
1
34.804*
-.621*
.037
.079
.13
1.029*
.002*
.023
.0421959
.0665621
.531*
-5.984*
.023
.1491959
.281
8.414*
0*
1
1
1
1*
42
Comparing the average of variables over first and fourth quartile is much significant
when cash ratio changes. So, the author needs to run regression to observe more in later.
However, in this step, the author runs another descriptive statistics to see the relationship
between variables. Pairwise correlations of variables were run with confidence level 95% for
displaying coefficients in the table 06.
Table 6: Correlations Matrix of Variables from Unvariate Analysis
CASH
CASH
SIZE
LEV
BANKDEBT
CFLOW
CFLOVAT
DIVIDEND
SIZE
LEV BANKDEBT
1.0000
-0.1075* 1.0000
-0.3280* 0.3786* 1.0000
-0.1299* 0.0710
0.0415
1.0000
0.3110* -0.0856 -0.2340* -0.0683
0.1469* -0.2188* -0.1312* -0.0775
0.1498* 0.0913* -0.1200* -0.0021
CFLOW
CFLOVAT DIVIDEND
1.0000
0.3609* 1.0000
0.0930* -0.0405
1.0000
According to Tabacknick & Fidell (1996), when correlation coefficient is high, or reach
to 0.8, the multicollinearity may happen, however the coefficients between independent variables
are quite low, fluctuating from -0.328 to 0.3786. It helps to show that multicollinearity is low
possibility to happen in the dataset.
The highest correlation coefficient is 0.3786, which show the strong correlation between
size of firms and leverage. By scanning the table 06, we can see that correlation between cash
holding and other variables as followings:

Cash has positive relation with cash flow, cash flow volatility, and dividend.

Cash has negative relation with size of firm, leverage, and bank debt.
Below graph matrix produces a graphical representation of the correlation matrix by
presenting a series of scatter plots for all variables as below to support for table 06:
Figure 5: Graphical Correlation Matrix
43
CASH
SIZE
LEV
BANKDEBT
CFLOW
CFLOVAT
LIQ
DIVIDEND
4.3. Regression Results
4.3.1. Fama–MacBeth Regression Model
In this regression model, the confidence level is 95%. Table 07 is the result on below.
Table 7: Results from Fama – MacBeth Regression Model
44
Number of obs
490
F( 8, 481)
13.49
Prob > F
0
R-squared
0.2003
Root MSE
0.10459
CASH
Coef.
SIZE
LEV
BANKDEBT
CFLOW
CFLOVAT
LIQ
DIVIDEND
Y2009/10/11/12/13
_cons
Number of obs
490
F( 8, 481)
13.47
Prob > F
0
R-squared
0.2009
Root MSE
0.10456
y2009
Robust
Std. Err. t
0.001507
-0.1537
-0.0069357
0.2627144
0.0210049
-0.0281746
0.0326652
-0.0062815
0.1147423
0.0059038
0.0449128
0.0025485
0.0946552
0.1069747
0.0141362
0.0115613
0.0136272
0.1429969
0.03
-3.42
-2.72
2.78
0.2
-1.99
2.83
-0.46
0.8
Coef.
Number of obs
490
F( 8, 481)
13.46
Prob > F
0
R-squared
0.1999
Root MSE
0.10462
y2010
Robust
Std. Err. t
0.0002374
-0.1544514
-0.0069563
0.2611944
0.0194858
-0.0281493
0.0335371
-0.0091633
0.1127174
0.0057812
0.0444135
0.0025469
0.0923798
0.1074733
0.0141238
0.0116016
0.0109753
0.1394758
0.04
-3.48
-2.73
2.83
0.18
-1.99
2.89
-0.83
0.81
Coef.
Number of obs
490
F( 8, 481)
14.32
Prob > F
0
R-squared
0.1999
Root MSE
0.10462
y2011
Robust
Std. Err. t
0.0003384
-0.1544419
-0.0069034
0.2597771
0.0208069
-0.0280813
0.0327497
0.0008517
0.1086686
0.0057577
0.0440732
0.0025646
0.0926527
0.1068932
0.014188
0.0114906
0.0132549
0.137859
0.06
-3.5
-2.69
2.8
0.19
-1.98
2.85
0.06
0.79
Coef.
Number of obs
F( 8, 481)
Prob > F
R-squared
Root MSE
y2012
Robust
Std. Err. t
0.0003853
-0.1546363
-0.0069145
0.2584282
0.0212768
-0.0279999
0.0327599
-0.0025653
0.1082372
0.0057987
0.044661
0.0025377
0.0930318
0.1066027
0.0142913
0.0115444
0.0119788
0.1391186
0.07
-3.46
-2.72
2.78
0.2
-1.96
2.84
-0.21
0.78
Coef.
490
13.67
0
0.2033
0.1044
y2013
Robust
Std. Err. t
-0.0000627
-0.1530942
-0.0072215
0.2601507
0.022222
-0.0277504
0.0345745
0.0172194
0.1143344
0.0057622
0.0445218
0.0024858
0.092137
0.1074552
0.0142288
0.0115988
0.0117352
0.1383883
-0.01
-3.44
-2.91
2.82
0.21
-1.95
2.98
1.47
0.83
R-squared is from 0.1999 to 0.2009. The low value of R-squared states that only ~20% of
the total variation in level of cash were explained by variation of above factors. The low value of
R-squared indicated that there might be many other important variables that contribute to the
determination of level of cash holding. The result of five regressions show the similar result and
correlation between variables. The null and alternative hypotheses are: Ho: β = 0 (the linear
correlation coefficient is zero) and H1: β ≠ 0 (the linear correlation coefficient is different from
zero). As mentioned in research methodology chapter, the author assumes that variables are
normally distributed. From the alternative hypothesis, people know that this test is two-tailed.
Hence, area in each tail of the t distribution equal 0.025 and degrees of freedom df are 4,408.
From the t distribution table of Merrington (1941), the critical values of t are -1.960 and 1.960.
In the figure run from Stata 12, the value of leverage, bank debt, cash flow, liquidity and
dividend are in the rejection region. So that, the linear correlation coefficient between cash
holding and leverage, bank debt, cash flow, liquidity and dividend are different from zero. While
keeping other factors unchanged, level of cash holding was changed much due to change of two
factors, leverage (negative) and cash flow (positive). In addition, when comparing p-value of
45
each factor with the significance level 0.05, p-value of firm’s size (~0.940) and cash flow
volatility (~0.802) are greater then 0.05. As the result, the author rejects the null hypothesis. So,
level of cash holding has no significant correlation with size of firms and cash flow volatility.
The author uses the Stata command VIF to check for multicollinearity.
Table 8: VIF Test for Fama-MacBeth Model
. vif
Variable
VIF
1/VIF
LEV
CFLOW
SIZE
CFLOVAT
LIQ
DIVIDEND
BANKDEBT
1.28
1.26
1.25
1.24
1.12
1.06
1.01
0.780779
0.793287
0.798763
0.809633
0.890777
0.945986
0.988317
Mean VIF
1.17
The mean vif (1.17) and each vif of variables are quite low, far to multicollinearity (10).
The negative correlation between level of cash holding and liquidity (coefficient =
negative 0.028) is consistent with the trade-off theory. On this theory, firm holds more liquidity,
excluding cash and equivalence; balance of cash will go down. Level of cash holding has
negative correlation with both leverage and bank debt. This result is supported by result of
Ferreira and Vilela (2004). It means that borrow cash was not kept on hand, but was utilized for
business operation. One of the above utilization is that firms did not only pay dividend, but also
reserve cash for other purposes.
46
Figure 6: Percentage of Cash Holding in Years Firms Paid Dividend
The figure 05 indicates that even firms paid dividend, the amount of cash reserved at the
end of years is double amount of dividend paid out. The positive correlation of cash holding and
dividend here is not strong for opinion of using excess cash to pay dividend. However, this is
consistent with results of Opler et al (1999) and Custodio et al. (2005). The author continues to
group dataset and use Excel 2010 to draw graph and show the correlation between cash holding
and bank debt when firms’ income changes. It will help to clarify the correlation between cash
and both leverage and bank debt.
Figure 7: Correlations between Cash, Income and Bank Debt
47
Comparing to period of positive income, when firm was in negative income, the cash
holding decreased much, from 12.5% to 4.4%, and also the bank debt increase from 74.3% to
83.5%. This is consistent with pecking order theory, when retain for earning decreases and cash
holding cannot buffer for business activities, firms react to change and increase bank debt.
However, this is oppose to indication of Kim et al (1998) that the lower profit, the higher cash
holding is. As to Opler et al. (1999), because internal funds the firm often spent more cash than
receiving cash, the firm decreases cash holding and raises debt. Positive relation between cash
holding and leverage is consistent with cash flow theory. As opinion of Ferreira and Vilela
(2004), firms with low leverage were less subject to monitoring, allowing for superior
managerial discretion. This relation between cash holdings, debt, and internal resources
suggested that there is a negative correlation between leverage and cash holdings. In summary,
the correlation is consistent with all three theories. However, with low level of R-square
(0.1999), the model would produce poor predictions, so the author should generate other
regression models to measure how well the regression consistent with the data.
4.3.2. Ordinary Least Squares Regression with 5 Years Average
R-squared of regression result is 0.4182 is quite high when compares with previous
Fama-Macbeth regression model, 0.1999. It means that this model is more consistent with
dataset, compared to Fama-Macbeth regression model. With this R-square, 41.82% changes of
cash mean can be explained by the variation of independent variables. By another way, 59.18%
changes of cash mean cannot be explained. The reason is because of unpredictable factors that
are not concerned in this study.
Table 9: Results from Ordinary Least Squares Regression with 5 Years Average
48
Equation
CASH_MEAN
Obs
Parms
RMSE
"R-sq"
F
P
98
8
.0673208
0.4182
9.241256
0.0000
CASH_MEAN
Coef.
SIZE_MEAN
LEV_MEAN
BANKDEBT_MEAN
CFLOW_MEAN
CFVOLAT_MEAN
LIQ_MEAN
DIVIDEND_MEAN
_cons
.0014847
-.2104057
-.0162702
.3317434
-.1428694
-.0780228
.0287456
.1130525
Std. Err.
.0067465
.042707
.0070669
.1083331
.1312201
.0273962
.0257753
.1733525
t
0.22
-4.93
-2.30
3.06
-1.09
-2.85
1.12
0.65
P>|t|
0.826
0.000
0.024
0.003
0.279
0.005
0.268
0.516
[95% Conf. Interval]
-.0119184
-.2952507
-.0303098
.1165207
-.403561
-.1324503
-.0224615
-.2313426
.0148877
-.1255607
-.0022307
.5469661
.1178223
-.0235954
.0799527
.4574475
The null and alternative hypotheses are: Ho: β = 0 (the linear correlation coefficient is
zero) and H1: β ≠ 0 (the linear correlation coefficient is different from zero). The confidence
level is 95%. Area in each tail of the t distribution equal 0.025 and degrees of freedome df are
782. From the t distribution table of Merrington (1941), the critical values of t are -1.960 and
1.960. In the figure run from Stata 12, the value of leverage, bank debt, cash flow, and liquidity
are in the rejection region. So that, the linear correlation coefficient between cash holding and
leverage, bank debt, cash flow, and liquidity are different from zero. And the linear correlation
coefficient between cash holding and firm’s size; cash flow volatility; and dividend are zero.
With mean value of variables, this regression model shows that leverage, bank debt, cash flow,
liquidity affect level of cash holding. In this model, the linear correlation coefficient between
cash holding and dividend is zero. This result is not consistent with empirical studies of Opler et
al. (1999), Custodio, et al. (2005), and Phuong (2013). It is the difference between this model
and Fama-Macbeth model.
The author uses the Stata command VIF to check for multicollinearity.
Table 10: VIF Test for Cross-Sectional Regression Model
49
. vif
Variable
VIF
1/VIF
LEV_MEAN
CFLOW_MEAN
SIZE_MEAN
CFVOLAT_MEAN
LIQ_MEAN
DIVIDEND_M~N
BANKDEBT_M~N
1.47
1.38
1.33
1.30
1.27
1.18
1.04
0.678273
0.722950
0.753895
0.772199
0.787195
0.847007
0.960249
Mean VIF
1.28
The mean vif (1.28) and each vif of variables are quite low, far to multicollinearity (10).
4.3.3. Pooled OLS Regression with Year Dummies
In this Pooled OLS regression model, the confidence level is 95%. R-squared of
regression result is 0.2039 is not much higher, when comparing with Fama-Macbeth regression
model, 0.1999. It indicates that 05 year dummies added did not affect to R-squared much.
Table 11: Results from Pooled OLS Regression with Year Dummies
50
note: Y2013 omitted because of collinearity
Equation
Obs
Parms
RMSE
"R-sq"
F
P
CASH
490
12
.1046882
0.2039
11.12814
0.0000
CASH
Coef.
SIZE
LEV
BANKDEBT
CFLOW
CFLOVAT
LIQ
DIVIDEND
Y2009
Y2010
Y2011
Y2012
Y2013
_cons
-.00018
-.1530947
-.0072192
.2629966
.0213117
-.0279361
.0346445
-.0188896
-.021254
-.0132005
-.0156736
0
.1344688
Std. Err.
.0044955
.0254807
.0028737
.0484582
.083174
.0098009
.0126547
.0151387
.0151145
.0150821
.0150357
(omitted)
.1178747
t
P>|t|
[95% Conf. Interval]
-0.04
-6.01
-2.51
5.43
0.26
-2.85
2.74
-1.25
-1.41
-0.88
-1.04
0.968
0.000
0.012
0.000
0.798
0.005
0.006
0.213
0.160
0.382
0.298
-.0090134
-.2031628
-.0128658
.1677792
-.1421202
-.0471944
.0097789
-.0486362
-.050953
-.0428358
-.0452177
.0086535
-.1030267
-.0015726
.358214
.1847436
-.0086779
.0595102
.0108569
.008445
.0164348
.0138706
1.14
0.255
-.0971479
.3660855
The null and alternative hypotheses are: Ho: β = 0 (the linear correlation coefficient is zero) and
H1: β ≠ 0 (the linear correlation coefficient is different from zero). Area in each tail of the t
distribution equal 0.025 and degrees of freedome df are 5,878. From the t distribution table of
Merrington (1941), the critical values of t are -1.960 and 1.960. In the figure run from Stata 12,
the value of leverage, bank debt, cash flow, liquidity, and dividend are in the rejection region. So
that, the linear correlation coefficient between cash holding and leverage, bank debt, cash flow,
liquidity and dividend are different from zero. This result is consistent with result from FamaMacbeth model and previous authors Kim et al. (1998), Opler et al (1999), Pinkowitz and
Williamson (2001), Ferreira and Vilela (2004), Ozkan and Ozkan (2004), Custodio et al. (2005),
Bates et al. (2009), Meggison and Wei (2012), and Phuong (2013). Additionally, the result is
also consistent with pecking order theory and cash flow theory. And the linear correlation
coefficient between cash holding and firm’s size; cash flow volatility and closing stock price of
year 2009-2012s are zero.
The author uses the Stata command VIF to check for multicollinearity.
Table 12: VIF Test for Pooled OLS Model with Year Dummies
51
. vif
Variable
VIF
1/VIF
Y2012
Y2013
Y2011
Y2010
CFLOW
LEV
SIZE
CFLOVAT
LIQ
DIVIDEND
BANKDEBT
1.65
1.64
1.63
1.62
1.29
1.29
1.27
1.24
1.12
1.07
1.02
0.605246
0.609964
0.613993
0.618904
0.773058
0.776195
0.790081
0.808840
0.888915
0.931509
0.982531
Mean VIF
1.35
The mean vif (1.35) and each vif of variables are quite low, far to multicollinearity (10).
However, in regression model, dummy variable, year 2013, is omitted because of
multicollinearity. According to Tabacknick & Fidell (1996), there was some evidence of
multicollinearity that could weaken regression analyses. This is a reason for the author to
consider whether we can remove closing price of year 2013 from the regression analysis or not,
due to the high correlation with other variables. Therefore, the author will increase the number of
variables and run one more Pooled OLS regression with both year dummies and industry
dummies to have another regression relation when industry dummies are pooled. It will help to
have more points of view and to sharpen the author’s model on final.
52
4.3.4. Pooled OLS Regression with Year Dummies and Industry Dummies
In this Pooled OLS regression model, confidence level is 95%. R-square (0.2967) is
much higher than Pooled OLS regression with year dummies (0.2039).
Table 13: Results from Pooled OLS Regression with Year Dummies and Industry
Dummies
note: Y2013 omitted because of collinearity
note: INDUSTRY21 omitted because of collinearity
Equation
Obs
Parms
RMSE
"R-sq"
F
P
CASH
490
32
.100523
0.2967
6.232182
0.0000
CASH
Coef.
SIZE
LEV
BANKDEBT
CFLOW
CFLOVAT
LIQ
DIVIDEND
Y2009
Y2010
Y2011
Y2012
Y2013
INDUSTRY1
INDUSTRY2
INDUSTRY3
INDUSTRY4
INDUSTRY5
INDUSTRY6
INDUSTRY7
INDUSTRY8
INDUSTRY9
INDUSTRY10
INDUSTRY11
INDUSTRY12
INDUSTRY13
INDUSTRY14
INDUSTRY15
INDUSTRY16
INDUSTRY17
INDUSTRY18
INDUSTRY19
INDUSTRY20
INDUSTRY21
_cons
-.0014209
-.1288726
-.0070142
.2240204
.0999557
-.0201483
.0353365
-.0190097
-.0210827
-.0141568
-.0165655
0
-.0232596
-.0500787
-.0403213
.0074256
.0575333
-.046778
-.0765262
-.027788
.0434972
.1445922
.0282113
-.0248942
-.0357
.0330441
-.0365133
.0154301
.0272164
.0559656
-.0303139
.0091409
0
.1480997
Std. Err.
.0048278
.0278764
.0028099
.0503157
.0890582
.0096816
.0130011
.0145557
.0145286
.0144932
.0144452
(omitted)
.0406925
.0558791
.046489
.0365271
.0395472
.0454335
.0565479
.0562289
.0405527
.0475263
.0405886
.0377721
.0577662
.0567568
.0395084
.035758
.0347846
.0404333
.0410518
.045362
(omitted)
.1362316
t
P>|t|
[95% Conf. Interval]
-0.29
-4.62
-2.50
4.45
1.12
-2.08
2.72
-1.31
-1.45
-0.98
-1.15
0.769
0.000
0.013
0.000
0.262
0.038
0.007
0.192
0.147
0.329
0.252
-.0109082
-.1836541
-.0125361
.1251422
-.0750576
-.0391742
.0097874
-.0476139
-.0496336
-.0426382
-.0449527
.0080664
-.074091
-.0014922
.3228985
.274969
-.0011224
.0608857
.0095945
.0074683
.0143247
.0118216
-0.57
-0.90
-0.87
0.20
1.45
-1.03
-1.35
-0.49
1.07
3.04
0.70
-0.66
-0.62
0.58
-0.92
0.43
0.78
1.38
-0.74
0.20
0.568
0.371
0.386
0.839
0.146
0.304
0.177
0.621
0.284
0.002
0.487
0.510
0.537
0.561
0.356
0.666
0.434
0.167
0.461
0.840
-.1032268
-.1598899
-.1316795
-.064356
-.0201832
-.1360619
-.1876516
-.1382867
-.0361952
.0511954
-.0515517
-.0991223
-.1492196
-.0784918
-.1141535
-.0548401
-.0411408
-.0234922
-.1109871
-.0800026
.0567076
.0597324
.0510369
.0792072
.1352498
.0425059
.0345993
.0827107
.1231897
.2379889
.1079744
.0493339
.0778196
.1445801
.0411269
.0857002
.0955735
.1354235
.0503592
.0982845
1.09
0.278
-.1196169
.4158162
.
The null and alternative hypotheses are: Ho: β = 0 (the linear correlation coefficient is
zero) and H1: β ≠ 0 (the linear correlation coefficient is different from zero). Area in each tail of
the t distribution equal 0.025 and degrees of freedome df are 15,188. From the t distribution table
53
of Merrington (1941), the critical values of t are -1.960 and 1.960. This result is consistent with
result from Fama-Macbeth model. In table 10, we can see that level of cash holding has positive
relation with cash flow, cash flow volatility, and dividend. And level of cash holding has
negative relation with are size of business, leverage, bank debt, liquidity. This result is consistent
with result of Ozkan and Ozkan (2004); Bates et al (2009) and Meggison and Wei (2012). Year
dummies and industry dummies were not accounted to the result, because of rejecting Ho
hypothesis.
Sizes of firm and cash flow volatility have no relation with cash holding. This result is
opposed to with result of Ozkan and Ozkan (2004). The correlation between cash holding and
size of firm, leverage, bank debt and liquidity shows that this model is consistent with previous
results of Ferreira and Vilela (2004); Custodio et al. (2005). This result is supported by theories
of trade-off and pecking order, but oppose to free cash flow.
Note that dummy variable year 2013 and industry 21 are omitted because of
multicollinearity. Industry21 is a group of construction companies, refer to table 02.
Construction company had specific relation to level of cash holding because it requires large
amount of cash in short period and sometimes it operates on real estate. That is the reason why
the author had paid attention to remove construction companies from the beginning. However,
the risk is still being because of the nature of construction industry.
The author uses the Stata command VIF to check for multicollinearity.
Table 14:VIF Test for Pooled OLS with Year and Industry Dummies
54
. vif
Variable
VIF
1/VIF
INDUSTRY17
INDUSTRY4
INDUSTRY16
INDUSTRY12
INDUSTRY5
INDUSTRY15
INDUSTRY19
INDUSTRY1
INDUSTRY18
INDUSTRY9
INDUSTRY11
INDUSTRY21
INDUSTRY20
INDUSTRY3
INDUSTRY6
INDUSTRY10
INDUSTRY8
INDUSTRY14
INDUSTRY13
INDUSTRY2
LEV
Y2009
Y2010
Y2011
Y2012
SIZE
CFLOVAT
CFLOW
DIVIDEND
LIQ
BANKDEBT
20.84
11.58
10.47
8.81
6.33
6.27
5.34
5.26
5.25
5.23
5.16
3.10
3.08
3.06
3.02
3.02
2.12
2.09
2.04
2.03
1.67
1.64
1.64
1.63
1.62
1.58
1.54
1.51
1.23
1.19
1.06
0.047989
0.086321
0.095511
0.113468
0.158081
0.159574
0.187255
0.190286
0.190314
0.191214
0.193774
0.322592
0.324830
0.327019
0.330887
0.331071
0.470747
0.479009
0.490936
0.493075
0.597936
0.608345
0.610615
0.613598
0.617683
0.631652
0.650467
0.661112
0.813703
0.839914
0.947463
Mean VIF
4.21
The mean vif (4.21) shows the medium possibility of multicollinearity (10) happening for
model. The result also points out that Chemicals – Pharmaceutical (industry4); Electrical
equipment - Electronics – Telecommunications (industry16); and Food - Beverage - Tobacco
(industry17) are highly correlated with other variables, however the author cannot find out
reasons within this test. This issue were not mentioned in previous empirical tests.
55
4.3.5. Fixed Effects Regression Model
Confidence level is 95%. The result is shown on below:
Table 15: Results from Fixed Regression Model
. xtreg CASH SIZE LEV BANKDEBT CFLOW CFLOVAT LIQ DIVIDEND, fe vce(robust)
Fixed-effects (within) regression
Group variable: COMP
Number of obs
Number of groups
=
=
490
98
R-sq:
Obs per group: min =
avg =
max =
5
5.0
5
within = 0.0794
between = 0.0193
overall = 0.0256
corr(u_i, Xb)
F(7,97)
Prob > F
= -0.4899
=
=
2.77
0.0114
(Std. Err. adjusted for 98 clusters in COMP)
Robust
Std. Err.
CASH
Coef.
t
SIZE
LEV
BANKDEBT
CFLOW
CFLOVAT
LIQ
DIVIDEND
_cons
.0344116
-.000475
-.0015692
.1411554
.8079611
-.0046942
.0394675
-.8802116
.0479829
.102256
.0009881
.0990445
.7699556
.0083442
.0163286
1.300362
sigma_u
sigma_e
rho
.09862284
.08568744
.56983871
(fraction of variance due to u_i)
0.72
-0.00
-1.59
1.43
1.05
-0.56
2.42
-0.68
P>|t|
0.475
0.996
0.116
0.157
0.297
0.575
0.018
0.500
[95% Conf. Interval]
-.0608212
-.203425
-.0035302
-.0554206
-.7201876
-.0212551
.0070597
-3.461071
.1296444
.2024749
.0003919
.3377313
2.33611
.0118667
.0718753
1.700648
R-square overall is quite low 0.0256. The null and alternative hypotheses are: Ho: β = 0
(the linear correlation coefficient is zero) and H1: β ≠ 0 (the linear correlation coefficient is
different from zero). Area in each tail of the t distribution equal 0.025 and degrees of freedome
df are 40,018. From the t distribution table of Merrington (1941), the critical values of t are 1.960 and 1.960. This result is much different with result of other models used above. We reject
Ho with correlation cefficient of dividend variable (positive t 2.42). We can see that level of cash
56
holding has only positive relation with dividend. This result is consistent with result of Custodio
et al. (2005).
4.4. Result from Supporting Tests for Regression Models
4.4.1. Hausman Test
Result from Hausman above helps the author choose between fixed effect model or
random effect model. This test is put in the end of analysis part just in order to make structure of
study balanced, however this is done before deciding what model.
Table 16: Results from Hausman Test
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
SIZE
LEV
BANKDEBT
CFLOW
CFLOVAT
LIQ
DIVIDEND
.0344116
-.000475
-.0015692
.1411554
.8079611
-.0046942
.0394675
-.0015901
-.1035369
-.0038318
.2132835
.1519073
-.0153829
.0360273
(b-B)
Difference
.0360016
.1030619
.0022626
-.0721282
.6560538
.0106888
.0034402
sqrt(diag(V_b-V_B))
S.E.
.0260898
.038036
.0009444
.0258646
.2651012
.0025606
.0075381
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
37.76
Prob>chi2 =
0.0000
It basically tests whether the unique errors (ui) are correlated with the regressors, the null
hypothesis is they are not. It means that the null hypothesis Ho is: the coefficient of the panel
model is zero (the preferred model is random effects) and the alternative hypothesis H1 is: the
coefficient of the panel model is different from zero (the alternative is fixed effects). The
confidence level is 95%. With the prob (chi2) value less then 0.05, then we reject Ho. It means
57
that fixed effects model is preferred to use in this study. People can continue with fixed effect
model in part 4.3.5 above.
58
4.5. Summary of Results
Combining above regressions, the author summary onto table 11 as followings:
Table 17: Result Summary of Regression Models
Independent
Variables
SIZE
LEV
BANKDEBT
CFLOW
CFVOLAT
LIQ
DIVIDEND
CONS_
N
R-square
Coef.
P>|t|
Coef.
P>|t|
Pooled OLS Regression with Dummies
Fixed Effects Model
Year and Industry
Year Dummies
Dummies
Coef. P>|t|
Coef. P>|t|
Coef.
P>|t|
0.00048108
-0.15406476
-0.00698628
0.26045296
0.02095928
-0.0280311
0.03325728
0.11173998
0.038
-3.46
-2.754
2.802
0.196
-1.974
2.878
0.802
0.0014847
-0.2104057
-0.0162702
0.3317434
-0.1428694
-0.0780228
0.0287456
0.1130525
0.826
0.000
0.024
0.003
0.279
0.005
0.268
0.516
-0.00018
-0.1530947
-0.0072192
0.2629966
0.0213117
-0.0279361
0.0346445
0.1344688
Fama-MacBeth
Regression Model
5
0.1999
Crossectional
Regression Model
98
0.4182
490
0.2039
0.968
0.000
0.012
0.000
0.798
0.005
0.006
0.255
-0.0014209
-0.1288726
-0.0070142
0.2240204
0.0999557
-0.0201483
0.0353365
0.1480997
490
0.2967
0.769
0.000
0.013
0.000
0.262
0.038
0.007
0.278
0.0344116
-0.000475
-0.001569
0.1411554
0.8079611
-0.004694
0.0394675
-0.880212
0.72
0
-1.59
1.43
1.05
-0.56
2.42
-0.68
490
0.0256
From the synthetical result of four regression methods above, six factor (variables),
which affects to level of cash holding of companies listed in Vietnam stock market, are size of
firm, leverage, bank debt, cash flow, liquidity, and dividend. In general, most of figures show the
united correlations of cash ratio (dependent variable) and other independent variables by four
regression models.
However, there are three variances on result among regression models. Firstly, size of
firm indicates contrary correlation between Pooled OLS regression with dummies and FamaMacBeth regression model. Pooled OLS point out positive relation between cash holding and
size of firm. Moreover, high value of p-value indicated that this linear correlation coefficient is
zero. This leads to insignificance impact between them. The result is not consistent with pecking
order theory, cash flow theory, and results of authors Pinkowitz and Williamson (2001); and
Ozkan and Ozkan (2004), and it is not as expectation from the beginning of the author. Secondly,
cross-sectional regression model implies that cash holding has positive relation with cash flow
volatility, however the results are not consistent among regression models. Finally, positive
correlation between dividend and cash flow is consistent with the result of authors Opler et al.
59
(1999) and Custodio et al. (2005), however it is not as expectation from the beginning of the
author. Moreover, with the confidence level 95%, cross-sectional regression model indicates
linear correlation coefficient between cash holding and dividend is zero. Cross-sectional
regression model implies that there is no significance impact between cash holding and dividend.
Factors, which have positive relation with cash holding, are leverage, bank debt, cash
flow volatility, and liquidity. Correlation between cash holding and leverage is consistent with
pecking order theory and most result of authors Kim et al. (1998); Opler et al. (1999);
Pinkowitz and Williamson (2001); Ferreira and Vilela (2004); Ozkan and Ozkan (2004);
Custodio et al. (2005); Bates et al. (2009); Meggison and Wei (2012); and Phuong (2013).
Correlation between cash holding and bank debt is consistent with result of authors Opler et al.
(1999); Pinkowitz and Williamson (2001); Ferreira and Vilela (2004); Ozkan and Ozkan (2004);
Custodio et al. (2005); Bates et al. (2009); Meggison and Wei (2012) ; and Phuong (2013).
Correlation between cash holding and liquidity is consistent with trade off theory and most result
of authors Opler et al. (1999); Pinkowitz and Williamson (2001); Ferreira and Vilela (2004);
Ozkan and Ozkan (2004); Custodio et al. (2005); Bates et al. (2009); Meggison and Wei (2012);
and Phuong (2013).
Note that dummy variable year 2013 and industry 21 are omitted in regression result
because of multicollinearity. Construction firms, in their nature of industry, could be related to
one another in sample size.
For easy view, table 12 shows results of what factors and related theories affect level of
cash holding from the beginning:
Table 18: Thesis’s Result Summary
Independent Expect Result
Variables
by
from
Empirical Studies
In
relation
to
related theory
author analysis
60
SIZE
(+)
(N/A)
(+):
Pinkowitz
and
Williamson (+):Pecking order
No
(2001); Ozkan and Ozkan (2004), theory and Cash
significance Trinh (2012), and Phuong (2013)
flow theory
impact
LEV
(-)
(-)
(-): Kim et al. (1998), Opler et al (-):Pecking order
(1999), Pinkowitz and Williamson theory and Cash
(2001), Ferreira and Vilela (2004),
flow theory
Ozkan and Ozkan (2004), Custodio
et al. (2005), Bates et al. (2009),
Meggison and Wei (2012), Trinh
(2012), and Phuong (2013)
BANKDEBT (-)
(-)
(-): Ferreira and Vilela (2004),
Ozkan
and
Ozkan (2004),
and
Phuong (2013)
CFLOW
(+)
(+)
(+): Opler et al. (1999), Pinkowitz (+):Pecking order
and Williamson (2001), Ferreira and theory
Vilela (2004), Ozkan and Ozkan
(2004), Custodio et al. (2005), Bates
et
al. (2009), and Meggison and Wei
(2012), and Phuong (2013).
LIQ
(-)
(-)
(-): Opler et al.(1999), Pinkowitz and (-):Trade
off
Williamson (2001), Ferreira and theory
Vilela (2004), Ozkan and Ozkan
(2004), Bates et al. (2009), Meggison
61
and Wei (2012), and Phuong (2013)
DIVIDEND
(-)
(+)
(+): Opler et al. (1999), Custodio, et
No
al. (2005), and Phuong (2013)
significance
impact
in
Crosssectional
Regression
Model.
Note that cash flow and cash low volatility are grouped into cash flow.
62
Chapter 5. Research Conclusion
5.1. Conclusion and Implication
This thesis is expected to supplement evidences for factors that affect level of cash
holding, based on theories and empirical researches. 98 samples are companies listed in two
stock exchanges, Ho Chi Minh and Ha Noi, in period from 2009 to 2013. In summary, the result
supports for empirical evidences as well as related theories. Below is summary, which answer
for two thesis questions:
For the first question, in figure above, five factors (variables), which affects to level of
cash holding of companies listed in Vietnam stock market, are leverage, bank debt, cash flow,
liquidity, and dividend. Factors, which have negative relation with cash holding, are leverage,
bank debt, cash flow volatility, and liquidity.
For the second question, three differences in result imply some situations and
recommendation for firms listed in Vietnam stock market. Regression models provide contrary
results about size of firm and cash flow volatility in relation to level of cash holding. It implies
three issues. Firstly, each firm reacted differently to the changes of Vietnam stock market.
Secondly, stock market is highly volatile. Thirdly, operation and investment activities of firms
are not effective, and are possible to bear loss. The remaining difference is in relation between
dividend payout and level of cash holding. At the beginning, the author expected that this was
negative correlation, but actually it was positive correlation. In years, which firms paid out
dividend, they kept a lot of cash. The figure 04 shows that the amount of cash reserved at the end
of years is double amount of dividend paid out. The direct correlation of cash holding and
dividend here is not strong for opinion of using excess cash to pay dividend. However, this is
consistent with results of Opler et al (1999) and Custodio et al. (2005). Another observation is
that, firms held low level of bank debt and liquidity. It means that firms try to pay their bank debt
63
to avoid interest expenses and also found long-term debt to finance for operations. Firms try to
balance between short-term and long-term operation.
The consideration between costs and benefits of holding cash is necessary to find the
optimized level of cash holding. If holding cash on operations brings effectiveness for business,
firm needs to accumulate level of cash for potential projects. Conversely, if there is no integrated
strategy for cash management, firm will face to many difficulties. From the result of this study in
the summary, the author proposes three main recommendations as below:

Firm needs to reduce accumulating cash for field which is not their main industries. With
this manner, firm will avoid wasting its cash on ineffective projects. In period 20092013s, many firms invested in projects which are not their industries such as real esta or
stock market…etc. Instead of earning profit, the return on investment was negative. With
the cash accumulated, firm should concentrate on their industry, their strength to
maximize business operation and profit.

Firm needs to pay more attention to cash flow volatility in order to have best holistic
level of cash holding. As the study’s supposition, if cash flow volatility increases, firm
will hold more cash.
However, the regression with mean value shows negative
correlation, it means that firms sometimes do not pay attention to cash flow volatility in
last 5 years. Ferreira and Vilela (2004) showed that firms which have high volatility in
cash flow, will be possible to face the cash shortage. So that, firms need to focus on cash
flow volatility to avoid business risks and also get business stable.

Firms should have a holistic cash management to accumulate cash in business. The
management should include current account, deposit account, receivables management,
payables management, liquidity management and technology management. The
integrated receivables solutions help to convert firm’s receivables to cash faster and gain
more control over liquidity to take business to new opportunity of profitability. In figure
64
03 in descriptive statistic, normally firms hold cash around 11% over the total assets. If
firm does not have good management on cash, firm could bear costs of holding cash,
such as interest and opportunity cost. The author proposes three approaches to manage
cash as follow. Firstly, firm needs to carefully select strategic partners for external
finances. This will help business to decrease risk of liquidity and shortage on cash.
Moreovers, firms could have a low rate of interest payment. Then, firm can use its cash
for operations or investment instead of limiting it. A notice is that strategic partner
selection is the key for business’s stability. Secondly, firm needs to build and develop
models for forcasting fluctuation of cash holding. So that, firm can enhance its flexibility
on business’s crisis and then increase business’s value. These models can built
professionals internally, or consultant for external partners. Thirdly, firm should find and
select investment channels with low costs. This is good for business in unstable economy.
In practice, high yield projects will have high costs of investment. Firms should ultilize
cash by increasing cash cycle faster such as overnight rate. In case of having surplus
cash, firm could pay dividend or buyback shares. This will enhance investor’s
confidence.
In short, firms should have a backup plan for bad situations and carefully considerate a
cash policy, in which company can have sales growth and maximized profitability. Since there
are limited researches in cash holding in Vietnam, the empirical analysis of this research expect
to provide a new understanding for management of cash.
5.2. Limitation of the Study
This research was carried out by an individual and research’s issues were quite deep and
new in Vietnam, therefore, the research was foreseen that will undergo some difficulties.
Firstly, with the limitation of time and capabilities of collecting data, the author selects
98 companies listed in Vietnam stock market, instead of expanding for non-listed ones. The
65
listed companies can provide audited financial statements, which are supervised by Stock
Exchange Committee. Those financial data are available for the author. However, because of
many fluctuations on listed companies, only 98 companies are consistent with predefined
criteria. Some companies are not listed in examined period 2009-2013.
Secondly, the author has chosen to compare and evaluate companies listed on the
exchanges with the exception of those operating within the finance and real estate sectors since
these sectors are differently and more heavily regulated in regard to the investigated variables
than other sectors. Companies were also excluded on the basis of outlier values as it would also
distort the result due to a large impact of a few observations.
Finally, many micro-economic and macro-economic factors, which are not used in this
research, can be relevant and input to research’s theories. It is limited by the author’s working
experience and academic knowledge on those fields. Those macro-economic factors are
Revenue, salary, right of investor, the level of investor protection, asymmetric information,
equity turnover, R&D expenses, expenditures, and merge & acquisition. Those micro-economic
factors are economic crisis, inflation, unemployment ratio, business environment, taxation, and
supply of capital market.
5.3. Future Research
In future work, it would be appropriate to focus on following aspects:

Expand sample size and studied period of companies listed in Vietnam stock market.

Study and compare with other countries to provide the difference in cash management.

Differentiating between long term and short term debts.

Construction firms should be carefully considered to be in sample size or to be dropped
from the regression equation.
66

Micro-economic and macro-economic factors should be included. Those macroeconomic factors are Revenue, salary, right of investor, the level of investor protection,
asymmetric information, equity turnover, R&D expenses, expenditures, and merge &
acquisition. Those micro-economic factors are economic crisis, inflation, unemployment
ratio, business environment, taxation, and supply of capital market.
67
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71
Appendix
Appendix 1: 98 Listed Companies Chosen
Final
Listed
List
Stock
in Company name
website
Exchange
AAM
HOSE
Công ty cổ phần Thủy sản Mekong
http://www.mekongfish.vn
/vn/index.aspx
ABT
HOSE
Công ty cổ phần Xuất nhập khẩu thủy www.aquatexbentre.com
sản Bến Tre
ACL
HOSE
Công ty cổ phần Xuất nhập khẩu Thủy www.clfish.com
sản Cửu Long An Giang
AGF
HOSE
Công ty cổ phần Xuất nhập khẩu thủy www.agifish.com.vn
sản An Giang
AMV
HASTC
Công ty cổ phần Sản xuất kinh doanh www.amvibiotech.com
dược và Trang thiết bị y tế Việt Mỹ
BBC
HOSE
Công ty Cổ phần Bibica
BBS
HASTC
Công ty cổ phần VICEM Bao bì Bút www.baobibutson.com.vn
www.bibica.com.vn
Sơn
BHS
HOSE
Công ty Cổ phần Đường Biên Hòa
www.bhs.vn
BKC
HASTC
Công ty cổ phần Kho ng sản Bắc Kạn
www.backanco.com
BLF
HASTC
Công ty cổ phần Thủy sản Bạc Liêu
www.baclieufis.vn/index.p
hp
BMC
HOSE
Công ty cổ phần Kho ng sản Bình Định
http://www.bimico.vn
BPC
HASTC
Công ty cổ phần Vicem Bao bì Bỉm sơn
www.baobibimson.vn
72
BTH
HASTC
Công ty cổ phần Chế tạo biến thế và Vật www.ctbt.vn
liệu điện Hà Nội
BXH
HASTC
Công ty cổ phần Vicem Bao bì Hải www.hcpc.vn
Phòng
CAN
HASTC
Công ty Cổ phần Đồ hộp Hạ Long
CAP
HASTC
Công ty cổ phần Lâm nông sản thực www.yfatuf.com.vn
www.canfoco.com.vn
phẩm Yên B i
CLC
HOSE
Công ty Cổ phần C t Lợi
CSM
HOSE
Công ty cổ phần Công nghiệp Cao su www.casumina.com.vn
www.catloi.com.vn
Miền Nam
CTB
HASTC
Công ty cổ phần Chế tạo Bơm Hải www.hpmc.com.vn
Dương
DBT
HASTC
Công ty cổ phần Dược phẩm Bến Tre
www.bepharco.com
DCL
HOSE
Công ty cổ phần Dược phẩm Cửu Long
www.pharimexco.com.vn
DHC
HOSE
Công ty cổ phần Đông Hải Bến Tre
www.dohacobentre.com
DHG
HOSE
Công ty cổ phần Dược Hậu Giang
www.dhgpharma.com.vn
DHT
HASTC
Công ty cổ phần Dược phẩm Hà Tây
www.hataphar.com.vn
DMC
HOSE
Công ty cổ phần Xuất nhập khẩu Y tế www.domesco.com
Domesco
DQC
HOSE
Công ty cổ phần Bóng đèn Điện Quang
www.dienquang.com
DZM
HASTC
Công ty cổ phần Chế tạo m y Dzĩ An
www.vietgen.com
FMC
HOSE
Công ty Cổ phần Thực phẩm Sao Ta
www.fimexvn.com
GDT
HOSE
Công ty cổ phần Chế biến Gỗ Đức www.dtwoodvn.com
Thành
GLT
HASTC
Công ty cổ phần Kỹ thuật điện toàn cầu
www.glt.com.vn
73
GTA
HOSE
Công ty Cổ phần chế biến gỗ Thuận An
HAD
HASTC
Công ty cổ phần Bia Hà Nội - Hải www.hadubeco.com.vn
www.tac.com.vn
Dương
HAI
HOSE
Công ty Cổ phần Nông dược H.A.I
http://www.congtyhai.com
/vn
HHC
HASTC
Công ty cổ phần B nh kẹo Hải Hà
www.haihaco.com.vn
HRC
HOSE
Công Ty Cổ Phần Cao su Hòa Bình
www.horuco.com.vn
HSI
HOSE
Công ty cổ phần Vật tư Tổng hợp và www.hsi.com.vn
Phân bón Hóa sinh
HTV
HOSE
Công ty cổ phần vận tải Hà Tiên
http://www.vantaihatien.co
m.vn
HVT
HASTC
Công ty cổ phần Hóa chất Việt Trì
www.viettrichem.com.vn
KDC
HOSE
Công ty cổ phần Kinh Đô
www.kinhdofood.com
KMR
HOSE
Công ty cổ phần Mirae
www.miraejsc.com
LAF
HOSE
Công ty Cổ phần Chế biến hàng xuất www.lafooco.vn
khẩu Long An
LIX
HOSE
Công ty cổ phần Bột giặt Lix
MCP
HOSE
Công Ty Cổ Phần In và Bao bì Mỹ www.mychau.com.vn
www.lixco.com
Châu
MHL
HASTC
Công ty cổ phần Minh Hữu Liên
www.minghuulien.com
MKV
HASTC
Công ty cổ phần Dược thú y Cai Lậy
www.cailayvet.com.vn
MMC
HASTC
Công ty cổ phần Kho ng sản Mangan
www.mitraco.com.vn
NGC
HASTC
Công ty cổ phần Chế biến thủy sản Xuất www.ngoprexco.com.vn
khẩu Ngô Quyền
NPS
HASTC
Công ty cổ phần May Phú Thịnh - Nhà www.phuthinhnb.com
74
Bè
NSC
HOSE
Công ty Cổ phần Giống cây trồng Trung www.vinaseed.com.vn
Ương
PAC
HOSE
Công ty Cổ phần Pin Ắc quy miền Nam
PMC
HASTC
Công ty cổ phần Dược phẩm dược liệu www.pharmedic.com.vn
www.pinaco.com
Pharmedic
POT
HASTC
Công ty cổ phần Thiết bị Bưu Điện
PTC
HOSE
Công ty cổ phần Đầu tư và Xây dựng www.pticjsc.com
www.postef.com
Bưu điện
PTM
HASTC
Công ty cổ phần Sản xuất, Thương mại www.ptm.vn
và Dịch vụ ôtô PTM
RAL
HOSE
Công ty cổ phần Bóng đèn Phích nước www.rangdongvn.com
Rạng Đông
SAF
HASTC
Công ty cổ phần Lương thực Thực phẩm www.safocofood.com
SAFOCO
SAV
HOSE
Công ty cổ phần Hợp t c kinh tế và www.savimex.com
Xuất nhập khẩu SAVIMEX
SDN
HASTC
Công ty cổ phần Sơn Đồng Nai
www.dongnaipaint.vn
SFI
HOSE
Công ty cổ phần Đại lý Vận tải SAFI
www.safi.com.vn
SFN
HASTC
Công Ty Cổ Phần Dệt lưới Sài Gòn
www.sfn.vn
SGC
HASTC
Công ty cổ phần Xuất nhập khẩu Sa 0
Giang
SGT
HOSE
Công ty cổ phần Công nghệ Viễn thông www.saigontel.com
Sài Gòn
SJ1
HASTC
Công ty cổ phần Thủy sản số 1
http://www.seajoco.vn
75
SPP
HASTC
Công ty cổ phần Bao bì Nhựa Sài Gòn
www.saplastic.com.vn
SRC
HOSE
Công ty cổ phần Cao su Sao vàng
www.src.com.vn
SRF
HOSE
Công ty cổ phần Kỹ Nghệ Lạnh
www.searefico.com
SSC
HOSE
Công ty cổ phần Giống cây trồng miền www.ssc.com.vn
Nam
SSM
HASTC
Công ty cổ phần Chế tạo kết cấu thép www.ssm.com.vn
VNECO.SSM
STP
HASTC
Công ty Cổ phần công nghiệp thương www.stp.com.vn
mại Sông Đà
SVI
HOSE
Công ty cổ phần Bao bì Biên Hòa
TAC
HOSE
Công ty cổ phần Dầu thực vật Tường www.tuongan.com.vn
www.sovi.com.vn
An
TCL
HOSE
Công ty cổ phần Đại lý giao nhận Vận www.tancanglogistics.com
tải xếp dỡ Tân Cảng
.vn
http://www.thbeco.vn
THB
HASTC
Công ty cổ phần Bia Thanh Hóa
TJC
HASTC
Công ty cổ phần Dịch vụ Vận tải và www.transco.com.vn
Thương mại
TMS
HOSE
Công ty cổ phần Kho vận Giao nhận www.transimexsaigon.co
Ngoại thương TP.HCM
m
TNC
HOSE
Công ty cổ phần Cao su Thống Nhất
www.trcbrvt.com
TPC
HOSE
Công ty cổ phần Nhựa Tân Đại Hưng
www.tandaihungplastic.co
m
TRA
HOSE
Công ty cổ phần Traphaco
www.traphaco.com.vn
TS4
HOSE
Công ty cổ phần Thủy sản số 4
http://seafoodno4.com
TSC
HOSE
Công ty cổ phần Vật tư kỹ thuật nông www.tsccantho.com.vn
76
nghiệp Cần Thơ
TTF
HOSE
Công ty cổ phần Tập đoàn Kỹ nghệ gỗ www.truongthanh.com
Trường Thành
TYA
HOSE
Công ty Cổ phần Dây và C p điện Taya 0
Việt Nam
UNI
HASTC
Công ty cổ phần Viễn Liên
www.vienlien.com.vn
VBH
HASTC
Công ty cổ phần Điện tử Bình Hoà
www.viettronicsbinhhoa.com
VDL
HASTC
Công ty cổ phần Thực phẩm Lâm Đồng
www.dalatwine.com.vn
VFG
HOSE
Công ty cổ phần Khử trùng Việt Nam
www.vfc.com.vn
VGP
HASTC
Công ty Cổ phần Cảng rau quả
www.vegeport.com
VGS
HASTC
Công ty cổ phần Ống thép Việt Đức VG www.vgpipe.com.vn
PIPE
VIS
HOSE
Công ty Cổ phần Thép Việt Ý
VNA
HOSE
Công
ty
cổ
phần
vận
www.vis.com.vn
tải
biển http://www.vinaship.com.
VINASHIP
vn
www.vinamilk.com.vn
VNM
HOSE
Công ty Cổ phần Sữa Việt Nam
VNT
HASTC
Công ty cổ phần Giao nhận Vận tải www.vinatranshn.com.vn
Ngoại thương
VPK
HOSE
Công ty Cổ phần bao bì dầu thực vật
0
VSC
HOSE
Công ty cổ phần Container Việt Nam
www.viconship.com
VST
HOSE
Công ty cổ phần Vận tải và Thuê tàu www.vitranschart.com.vn
biển Việt Nam
VTB
HOSE
Công ty Cổ phần Viettronics Tân Bình
www.vtb.com.vn
VTC
HASTC
Công ty cổ phần viễn thông VTC
www.vtctelecom.com.vn
77
VTL
HASTC
Công ty Cổ phần Vang Thăng Long
www.vangthanglong.com.
vn
78
Appendix 2: Secondary Data for Excel and Stata Input
Year
Firm Listed
Cash and
cash
equivalents
Bank debt
Dividends
paid
Cash flow
Outstandin
g shares at
year end
Total assets
Net
working
capital
2009
AAM
128,443
7,703
17,821
39,287
11.34
363,935
100,30
2010
AAM
76,635
6,160
28,350
25,002
11.34
331,336
140,21
4
2011
AAM
49,648
4,620
36,935
35,290
11.34
329,977
164,30
2012
AAM
42,872
-
30,574
(8,497)
10.26
285,761
154,00
2013
AAM
54,687
114,293
4,320
12,109
9.94
302,072
139,64
3
2009
ABT
90,331
44,225
2
97,085
11.34
537,004
188,54
2010
ABT
152,350
61,644
45,616
54,947
13.61
601,925
107,05
2011
ABT
106,047
26,968
95,873
10,894
13.61
478,109
171,52
2012
ABT
111,665
131,629
82,627
(158)
11.02
516,133
189,15
2013
ABT
104,933
229,212
51,670
28,532
11.30
688,269
217,25
2009
ACL
63,657
347,880
13,500
39,984
9.00
613,944
(16,91)
2010
ACL
42,358
429,467
35,500
37,306
18.40
726,085
5,137
2011
ACL
31,541
399,029
38,500
94,569
18.40
793,378
64,330
2012
ACL
9,991
439,567
36,799
(5,613)
18.40
831,753
50,871
2013
ACL
8,575
389,150
8,280
18,883
18.40
718,957
23,150
2009
AGF
18,812
478,495
12,859
43,399
12.86
1,209,944
66,870
2010
AGF
47,609
580,072
25,719
56,125
12.86
1,354,627
(1,927)
2011
AGF
66,099
823,941
25,559
75,663
12.86
1,716,936
92,244
79
2012
AGF
53,830
2013
AGF
235,690
650,300
12,779
60,930
12.86
1,564,982
188,84
12,779
48,647
12.86
2,250,909
166,97
1,059,0
19
2009
AMV
1,856
-
314
1,571
2.10
23,563
7,113
2010
AMV
3,057
2,200
2,149
713
2.10
30,005
5,764
2011
AMV
2,514
5,415
1,219
(2,718)
2.12
28,102
3,107
2012
AMV
1,784
2,577
838
(131)
2.12
23,993
2,667
2013
AMV
2,182
2,013
1,077
214
2.12
27,043
2,748
2009
BBC
204,756
17,659
-
77,525
15.42
736,809
(22,12)
2010
BBC
89,081
4,533
-
77,115
15.42
758,841
60,601
2011
BBC
60,321
-
15,438
61,780
15.42
786,198
152,18
2012
BBC
49,471
-
18,603
48,711
15.42
768,378
143,61
2013
BBC
151,707
-
27,728
57,927
15.42
808,294
76,948
2009
BBS
8,551
30,890
3,450
14,171
3.00
107,619
17,326
2010
BBS
6,569
34,085
1,800
9,736
4.00
122,985
39,373
2011
BBS
5,089
37,101
4,800
9,465
4.00
149,449
44,844
2012
BBS
8,184
58,995
4,800
10,384
4.00
178,442
103,07
2013
BBS
21,597
81,651
4,800
8,468
4.00
217,256
87,054
2009
BHS
86,126
380,173
18,454
125,378
18.53
884,740
111,52
2010
BHS
58,759
339,316
62,595
117,197
18.53
1,015,192
155,95
(54,031
2011
BHS
178,778
466,051
62,674
136,758
30.00
1,281,737
)
1,102,9
2012
BHS
98,524
(71,918
94,135
78,744
31.50
2,107,835
29
2013
BHS
238,292
885,330
)
62,988
(2,002)
62.99
2,193,791
(190,47
80
8)
2009
BKC
4,342
6,190
-
13,021
6.03
145,598
24,238
2010
BKC
22,051
-
4,828
10,981
6.03
142,439
4,831
2011
BKC
2,030
450
8,144
4,462
6.03
134,400
7,236
2012
BKC
759
8,432
-
6.03
141,169
(12,093
(14,227
)
)
(13,727
2013
BKC
2,924
8,421
-
(20,859
6.03
116,928
)
)
(60,321
2009
BLF
10,078
230,454
-
15,682
5.00
338,283
)
(59,045
2010
BLF
19,207
260,235
-
17,224
5.00
375,916
)
(71,899
2011
BLF
18,617
280,777
-
21,928
5.00
423,349
)
(35,729
2012
BLF
6,025
252,497
-
17,419
5.00
410,409
)
(42,575
2013
BLF
21,035
268,827
-
3,738
5.00
479,689
)
2009
BMC
20,285
-
14,862
14,920
8.26
151,936
20,425
2010
BMC
40,051
-
18,165
82,343
8.26
169,597
16,270
2011
BMC
42,507
-
27,072
13,648
8.26
257,555
90,164
2012
BMC
15,218
-
61,920
46,063
12.39
271,042
70,995
2013
BMC
41,429
-
60,538
42,641
12.39
272,812
52,466
2009
BPC
5,115
-
-
22,175
3.80
100,622
36,172
2010
BPC
12,851
18,000
5,700
11,521
3.80
124,636
22,390
81
2011
BPC
9,071
18,445
5,169
11,018
3.80
146,700
32,699
2012
BPC
14,593
40,000
4,742
12,051
3.80
174,184
29,920
2013
BPC
6,072
46,528
3,802
13,357
3.80
182,524
44,006
2009
BTH
5,566
-
3,960
3,227
3.00
58,527
12,927
2010
BTH
16,923
-
3,600
1,648
3.17
78,490
8,336
2011
BTH
12,618
-
1,557
3,436
3.50
64,603
16,611
2012
BTH
8,195
-
1,358
48,271
3.50
48,104
6,270
2013
BTH
9,077
-
-
48,372
3.50
46,989
3,698
2009
BXH
3,419
11,342
2,364
12,036
3.00
71,827
22,811
2010
BXH
12,876
28,513
7,290
2,363
3.00
97,894
13,351
2011
BXH
5,080
27,951
1,200
9,021
3.01
111,774
28,037
928,69
2012
BXH
5,945
22,536
3,012
5,993
3.01
110,379
3
2013
BXH
3,367
32,877
2,401
7,484
3.01
117,205
30,586
2009
CAN
11,425
41,088
4,000
15,037
5.00
165,255
39,185
2010
CAN
11,203
38,754
4,000
17,737
5.00
196,890
36,193
2011
CAN
6,673
59,677
7,500
32,888
5.00
255,207
47,979
2012
CAN
8,717
27,659
10,000
10,836
5.00
228,394
29,135
2013
CAN
22,228
11,226
7,500
13,861
5.00
179,336
27,220
(17,141
2009
CAP
5,260
35,038
2,442
13,318
1.10
66,770
)
(17,458
2010
CAP
8,892
34,029
-
21,248
1.10
78,958
)
2011
CAP
9,123
19,922
-
27,111
1.10
85,849
(4,960)
2012
CAP
29,690
30,983
12,176
18,466
1.10
112,703
(33,528
82
)
2013
CAP
13,137
10,675
5,950
20,976
1.10
101,642
(3,882)
2009
CLC
37,913
207,072
15,717
59,769
13.10
469,667
88,493
2010
CLC
67,484
316,111
32,767
35,239
13.10
602,846
83,961
2011
CLC
85,677
276,760
23,587
64,958
13.10
626,168
55,327
2012
CLC
18,226
281,944
32,760
38,685
13.10
580,066
133,08
8
186,37
2013
CLC
22,649
163,543
26,208
70,987
13.10
577,267
0
213,64
2009
CSM
46,047
474,176
29,150
265,629
12.65
1,162,357
1
310,59
2010
CSM
63,239
334,672
172,498
43,496
33.96
1,181,287
3
265,92
2011
CSM
42,504
747,617
84,500
21,610
42.25
1,522,885
9
518,45
2012
CSM
30,416
582,477
62,679
344,098
48.14
1,847,051
3
1,256,9
2013
CSM
35,295
586,31
175,529
370,242
61.49
2,920,797
09
9
2009
CTB
2,734
-
3,943
6,262
1.71
61,445
7,265
2010
CTB
4,828
2,803
2,568
9,019
1.71
71,805
6,953
2011
CTB
11,478
7,767
2,571
9,696
2.25
12,442
17,705
2012
CTB
15,958
19,389
10,205
3,500
2.76
163,084
(16,395
)
2013
CTB
21,772
28,423
-
15,851
2.76
216,920
6,721
83
2009
DBT
6,165
94,881
5,949
5,843
3.00
268,792
38,184
2010
DBT
10,277
78,727
6,999
8,556
3.00
256,341
34,694
2011
DBT
8,948
108,986
6,678
9,618
3.00
292,490
32,721
2012
DBT
16,906
99,163
6,243
11,078
3.00
342,350
16,736
2013
DBT
14,244
92,234
5,844
9,269
3.00
276,375
22,741
2009
DCL
26,013
251,651
8,097
72,328
9.72
640,720
135,26
3
2010
DCL
24,063
330,322
14,370
2011
DCL
28,140
612,232
19,507
21,320
9.91
792,156
9.91
841,771
(27,619
53,103
(18,093
)
)
2012
DCL
7,772
395,767
24,344
18,606
10.06
665,899
30,662
2013
DCL
16,470
241,185
-
53,947
10.06
611,669
50,698
2009
DHC
9,870
189,157
-
22,643
15.00
400,440
18,029
2010
DHC
13,138
268,449
13,000
23,584
15.00
547,687
1,780
2011
DHC
3,818
212,303
13,500
7,466
15.00
435,520
(138,84
9)
(40,671
2012
DHC
2,712
186,210
-
17,622
15.00
435,520
)
2013
DHC
5,168
101,825
-
46,605
15.00
399,179
2009
DHG
584,129
73,980
30,018
362,101
26.66
1,521,973
(3,479)
177,46
6
327,95
2010
DHG
642,519
12,802
66,880
357,918
26.91
1,819,735
9
479,58
2011
DHG
467,084
21,116
261,400
211,959
65.17
1,995,707
3
84
445,15
2012
DHG
718,975
19,485
131,268
426,793
65.38
2,378,265
2
589,03
2013
DHG
613,287
127,031
229,640
434,655
65.37
3,080,620
0
2009
DHT
21,833
119,324
7,669
21,760
4.12
244,763
38,545
2010
DHT
20,127
121,834
-
25,683
4.12
265,864
17,696
2011
DHT
22,973
147,306
-
29,099
4.48
304,093
47,574
2012
DHT
21,165
113,985
12,549
12,997
6.28
293,031
65,103
2013
DHT
42,202
103,350
-
42,274
6.28
324,780
53,380
2009
DMC
22,822
82,361
27,034
67,383
17.56
709,977
169,12
0
174,90
2010
DMC
41,393
137,068
17,504
73,523
17.56
776,809
8
172,98
2011
DMC
62,495
129,349
38,508
73,149
17.81
836,267
8
258,69
2012
DMC
4,122
77,420
65,895
53,129
17.81
848,948
9
317,25
2013
DMC
3,570
108,761
65,895
53,129
17.81
868,232
2
499,83
2009
DQC
46,446
701,342
9,026
21,798
18.80
1,603,269
0
594,58
2010
DQC
53,731
625,107
18,797
50,277
24.42
1,733,224
8
557,48
2011
DQC
184,927
499,285
30,257
29,512
24.42
1,832,039
9
85
538,26
2012
DQC
201,163
437,223
43,342
17,658
24.42
1,716,046
6
273,32
2013
DQC
432,689
481,145
42,078
101,552
24.42
1,668,580
6
2009
DZM
14,134
37,923
3,969
17,554
2.48
183,621
38,250
2010
DZM
6,918
48,146
10
8,568
3.11
193,789
16,103
2011
DZM
16,273
49,631
3,419
8,912
3.45
248,132
24,150
2012
DZM
25,759
43,514
-
(7,906)
5.40
211,817
21,522
2013
DZM
6,438
39,994
2,698
(1,157)
5.40
173,316
34,415
2009
FMC
282,929
452,874
9,115
19,439
8.00
623,407
(248,04
6)
2010
FMC
40,590
305,850
8,637
32,356
8.00
501,973
2011
FMC
134,547
570,933
16,612
26,479
8.00
778,049
3,913
(88,764
)
2012
FMC
12,738
252,111
18,057
5,636
8.00
458,960
2013
FMC
151,574
405,571
5,056
48,565
13.00
741,771
14,951
(51,617
)
2009
GDT
36,426
41,338
20,736
14,528
10.37
195,960
(6,342)
2010
GDT
46,390
22,427
10,372
27,946
10.37
192,721
6,725
2011
GDT
39,611
11,635
20,745
26,606
10.37
206,248
37,989
2012
GDT
34,976
53,733
31,110
11,776
10.37
257,096
43,463
2013
GDT
3,499
37,205
18,666
23,400
10.37
263,767
97,810
2009
GLT
3,776
6,046
16,858
6,231
9.18
202,228
46,767
2010
GLT
10,539
27,661
15,577
20,130
9.18
191,440
53,889
2011
GLT
14,401
5,967
29,489
3,627
9.18
176,820
33,671
86
2012
GLT
63,351
2,677
31,952
2013
GLT
25,107
36,514
55,618
2,107
9.18
215,463
67,059
9.18
168,122
85,591
(46,633
)
2009
GTA
11,065
-
5,881
14,616
10.40
218,816
60,668
2010
GTA
8,031
-
7,091
16,441
10.40
199,377
75,449
2011
GTA
30,643
-
9,020
13,763
10.40
216,886
51,489
2012
GTA
38,031
-
8,324
15,582
10.40
213,797
51,158
2013
GTA
122,564
80,000
8,884
12,103
10.40
285,577
(30,501
)
(11,108
2009
HAD
20,602
-
1,600
58,316
4.00
119,725
)
2010
HAD
42,756
-
10,000
57,081
4.00
122,338
14,298
2011
HAD
63,627
-
13,953
38,525
4.00
136,265
15,617
2012
HAD
79,518
-
7,796
37,341
4.00
145,048
8,057
2013
HAD
78,528
-
9,918
30,057
4.00
155,332
18,209
2009
HAI
32,087
144,617
-
50,205
14.50
553,395
220,40
7
230,38
2010
HAI
30,107
51,750
29,578
51,742
14.50
506,998
4
255,30
2011
HAI
17,179
157,817
43,482
10,485
15.80
589,689
6
243,67
2012
HAI
21,140
163,739
34,800
9,916
17.40
654,678
3
247,15
2013
HAI
11,931
222,498
51,388
103
17.40
687,631
9
87
2009
HHC
19,698
-
4,992
31,193
5.48
192,350
26,440
2010
HHC
28,400
-
4,023
29,155
5.48
224,397
34,987
2011
HHC
45,088
-
9,023
25,232
8.21
288,333
22,396
2012
HHC
80,654
-
13,532
23,947
8.21
300,326
240
2013
HHC
58,999
-
18,608
18,899
8.21
315,210
28,926
2009
HRC
54,991
8,112
32,340
66,845
17.26
436,749
(13,559
)
2010
HRC
25,045
8,112
36,068
67,022
17.26
502,114
7,424
2011
HRC
36,779
35,192
69,031
64,435
17.26
620,452
12,105
2012
HRC
79,644
107,927
41,780
51,795
17.26
656,835
(13,162
)
(143,98
2013
HRC
69,644
161,596
35,905
36,003
17.26
699,624
0)
2009
HSI
13,620
397,825
-
22,961
10.00
759,579
35,211
2010
HSI
11,419
214,466
10,731
16,626
10.00
607,463
57,062
2011
HSI
19,340
475,942
14,292
13,654
10.00
829,209
22,277
2012
HSI
8,657
518,706
12,726
(4,682)
10.00
826,421
(13,876
)
(65,753
2013
HSI
3,957
395,584
-
(93,850
10.00
581,803
)
)
113,93
2009
HTV
39,886
-
11,299
18,959
10.08
218,451
4
144,70
2010
HTV
40,608
-
5,661
15,929
10.08
247,345
2
2011
HTV
10,793
-
9,917
19,382
10.08
256,019
159,74
88
9
137,22
2012
HTV
8,440
-
9,618
40,570
10.08
296,723
9
141,64
2013
HTV
15,438
26,277
19,236
20,999
10.08
350,075
9
2009
HVT
1,377
143,224
-
31,974
2.49
219,075
2010
HVT
2,834
109,383
-
33,639
2.49
221,575
29,069
(30,377
)
(23,550
2011
HVT
1,155
101,725
6,511
30,208
7.23
217,615
)
(19,057
2012
HVT
1,155
101,725
-
36,718
7.23
217,615
)
(23,550
2013
HVT
1,155
101,725
-
36,718
7.23
217,615
)
(90,240
2009
KDC
984,611
526,747
134,947
469,155
79.55
4,247,601
)
610,92
2010
KDC
672,316
474,343
122,784
545,124
119.52
5,031,920
3
(192,35
2011
KDC
967,330
276,562
-
480,057
119.52
5,809,421
7)
106,86
2012
KDC
829,459
151,089
3,546
569,032
159.92
5,514,704
3
(244,14
2013
KDC
1,958,065 569,825
1,189
721,657
167.63
6,378,246
3)
2009
KMR
10,687
119,497
-
31,414
27.30
520,006
58,284
89
145,06
2010
KMR
14,133
119,493
-
43,595
32.45
613,348
5
204,37
2011
KMR
3,348
119,338
-
11,329
32.45
623,594
4
159,71
2012
KMR
9,117
110,937
-
1,396
33.52
597,453
1
2013
KMR
20,352
101,959
-
40,636
34.40
599,898
74,734
2009
LAF
7,282
85,496
-
26,446
13.39
215,358
39,186
2010
LAF
124,518
6,715
15,191
73,710
13.39
354,368
53,121
2011
LAF
52,031
452,487
15,586
535
13.39
723,638
92,061
2012
LAF
7,731
136,407
-
14.73
235,746
(146,38
(1,410,
4)
639)
2013
LAF
28,649
50,899
-
38,989
14.73
188,794
3,872
2009
LIX
94,475
4,554
3,600
105,137
9.00
296,048
58,243
2010
LIX
52,280
65,270
27,000
52,200
9.00
350,116
138,88
3
2011
LIX
85,063
104,856
45,000
25,715
9.00
406,739
57,767
2012
LIX
84,284
42,261
24,297
44,474
10.80
457,315
64,731
2013
LIX
73,947
44,905
16,200
62,181
21.60
531,654
115,91
0
2009
MCP
25,918
54,095
10,184
15,734
8.20
201,979
54,666
2010
MCP
9,321
58,101
10,728
21,291
8.20
228,091
64,176
2011
MCP
12,900
83,323
13,612
33,296
10.34
305,053
72,047
2012
MCP
5,622
59,065
22,440
13,640
10.34
275,906
69,552
2013
MCP
11,867
87,784
16,415
24,628
10.34
321,038
42,026
90
2009
MHL
11,827
17,495
650
7,856
7.68
57,402
4,662
2010
MHL
39,256
15,430
5,600
(685)
3.98
98,364
(2,716)
2011
MHL
13,093
5,695
2,783
912
3.98
59,852
18,667
2012
MHL
3,631
19,500
-
3,612
3.98
134,607
5,892
2013
MHL
7,494
70,803
-
6,817
3.98
180,680
(1,721)
2009
MKV
1,087
4,380
-
2,039
1.00
20,913
7,437
2010
MKV
2,932
5,986
1,200
992
1.06
23,655
6,465
2011
MKV
1,243
5,028
936
(396)
3.00
19,468
3,696
2012
MKV
2,669
5,066
-
(1,713)
3.00
18,420
(864)
(20,599
2013
MKV
1,953
33,366
-
1,232
3.00
67,995
)
2009
MMC
954
-
2,344
4,362
1.20
37,716
7,959
2010
MMC
4,474
-
-
7,050
3.16
51,522
28,414
2011
MMC
310
4,902
3,792
(4,543)
3.16
53,064
27,109
2012
MMC
541
8,463
-
(1,934)
3.16
50,760
23,818
2013
MMC
66
6,003
-
(4,203)
3.16
41,822
17,438
2009
NGC
1,066
37,408
1,000
3,958
1.20
58,426
17,585
2010
NGC
815
60,581
1,800
2,322
1.20
88,006
8,374
(15,329
2011
NGC
664
76,605
960
4,486
1.20
110,441
)
(22,498
2012
NGC
846
69,788
1,440
3,342
1.20
101,679
)
(29,049
2013
NGC
2,179
72,078
840
4,680
1.20
107,345
)
2009
NPS
24,280
26,966
-
6,102
1.06
106,997
(18,574
91
)
2010
NPS
4,322
7,831
2,295
3,758
2.17
81,358
2011
NPS
9,004
2,608
3,372
5,035
2.17
85,274
(4,250)
(12,451
)
2012
NPS
2,241
3,901
4,329
4,661
2.17
80,065
2013
NPS
3,884
7,804
4,340
343
2.17
80,930
(5,059)
(10,081
)
2009
NSC
72,741
628
4,982
35,745
10.03
246,942
55,749
2010
NSC
17,038
50,315
8,014
41,460
10.03
302,986
58,348
2011
NSC
19,007
76,486
33,011
35,503
10.03
362,548
164,68
4
2012
NSC
121,295
187
28,894
55,589
10.03
393,154
2013
NSC
112,238
-
30,104
75,925
10.03
439,523
58,488
122,60
5
220,86
2009
PAC
55,636
147,038
35,128
131,528
20.54
669,892
7
2010
PAC
197,242
394,509
43,395
110,278
22.55
1,103,439
23,759
2011
PAC
51,478
371,345
10,156
121,880
26.99
1,187,096
78,801
2012
PAC
83,913
292,690
69,731
46,932
26.63
1,145,491
28,511
2013
PAC
199,972
479,746
53,251
66,158
26.63
1,430,558
(119,13
1)
2009
PMC
12,725
-
7,931
27,713
6.46
104,995
39,936
2010
PMC
25,392
-
9,052
30,607
6.46
124,371
42,312
2011
PMC
28,232
-
12,784
33,909
6.46
151,661
52,732
2012
PMC
47,676
-
22,787
29,265
6.46
168,708
46,012
92
2013
PMC
66,987
-
10,527
52,522
6.46
208,928
2009
POT
81,789
86,282
11,692
14,351
19.43
467,727
58,614
123,09
2
114,84
2010
POT
90,944
128,338
14,643
14,508
19.43
511,894
7
176,33
2011
POT
20,546
159,648
15,410
2,989
19.43
644,605
7
175,13
2012
POT
61,605
76,606
5,792
11,396
19.43
634,411
8
2013
POT
56,819
91,098
7,721
11,365
19.43
640,781
24,838
2009
PTC
13,336
44,078
8,868
(349)
10.00
435,378
90,708
2010
PTC
36,012
41,175
1
8,584
10.00
438,620
63,677
2011
PTC
7,961
38,051
-
8,127
10.00
433,678
88,474
2012
PTC
7,972
30,986
-
10.00
360,175
73,712
(43,604
)
2013
PTC
12,715
643
-
7,478
10.00
332,218
89,854
2009
PTM
445
6,547
-
(369)
2.30
21,422
3,788
2010
PTM
1,840
298
-
348
2.30
20,006
8,566
2011
PTM
16,791
-
-
1,097
2.30
43,730
9,208
2012
PTM
18,791
-
-
1,097
2.30
43,730
7,208
2013
PTM
20,791
-
-
1,097
2.30
43,730
5,208
2009
RAL
129,159
438,217
23,000
44,610
11.50
1,049,313
(7,528)
2010
RAL
39,664
547,352
23,000
56,340
11.50
1,170,010
106,16
2
2011
RAL
161,857
663,641
31,050
76,132
11.50
1,399,951
37,039
93
2012
RAL
244,045
679,459
34,500
140,000
11.50
1,531,241
2013
RAL
503,689
785,436
34,500
127,845
11.50
1,772,437
10,727
(180,10
4)
2009
SAF
31,513
-
5,953
14,085
2.71
92,101
612
2010
SAF
19,941
-
2,776
18,677
4.38
105,959
20,413
2011
SAF
27,849
-
-
27,611
4.55
127,976
27,740
2012
SAF
16,004
-
11,364
14,660
4.55
126,483
42,796
2013
SAF
27,822
-
15,683
16,688
4.55
134,213
37,934
2009
SAV
127,168
127,157
19,068
3,106
9.96
585,547
105,65
2
153,62
2010
SAV
93,850
137,855
509
16,712
9.96
536,541
8
181,75
2011
SAV
72,040
154,501
4,819
11,367
9.96
631,632
8
168,40
2012
SAV
39,625
114,746
6,686
10,107
9.96
682,250
8
158,31
2013
SAV
29,013
155,474
7,074
5,620
9.96
653,948
6
2009
SDN
5,285
7,908
945
4,946
1.52
40,550
5,683
2010
SDN
3,621
6,646
2,817
3,543
1.52
40,177
7,521
2011
SDN
4,192
7,379
3,036
3,053
1.52
44,396
7,227
2012
SDN
1,731
1,875
3,492
5,246
1.52
42,003
12,490
2013
SDN
10,633
11,313
3,796
4,446
1.52
53,505
5,180
2009
SFI
88,588
-
5,424
33,981
8.29
326,360
(79,745
)
94
(103,23
2010
SFI
128,180
-
5,862
32,827
8.29
397,670
1)
(153,01
2011
SFI
182,385
-
12,435
23,810
8.29
444,383
3)
(143,65
2012
SFI
168,123
2,045
12,435
37,622
8.70
491,417
4)
(102,53
2013
SFI
152,342
949
12,794
25,020
8.70
517,824
5)
2009
SFN
2,112
4,364
5,032
11,127
3.00
57,519
23,806
2010
SFN
8,633
10,405
3,790
5,865
3.00
61,525
16,721
2011
SFN
2,500
23,745
4,073
9,780
3.00
71,076
15,198
2012
SFN
3,867
22,063
4,583
6,956
3.00
66,057
14,634
2013
SFN
1,531
18,014
4,625
10,365
3.00
70,557
15,746
2009
SGC
13,946
18,898
7,148
17,575
7.15
112,644
59,180
2010
SGC
8,695
10,000
5,957
9,263
7.15
117,012
33,889
2011
SGC
9,413
20,750
7,149
46,056
7.15
142,282
57,829
2012
SGC
10,128
-
21,443
1,897
7.15
127,726
39,859
2013
SGC
14,215
-
17,869
11,050
7.15
130,189
37,723
2009
SGT
449,394
518,737
-
85,503
67.27
2,018,043
(80,648
)
114,34
2010
SGT
6,436
999,365
6
36,216
67.27
2,334,870
2
1,067,9
2011
SGT
54,437
(131,97
-
51
2012
SGT
10,750
771,822
(672,00
67.27
1,463,032
4)
-
(256,54
6)
67.27
1,902,710
(547,45
95
8)
9)
(332,38
2013
SGT
13,381
744,764
-
3,751
67.27
1,813,085
8)
2009
SJ1
44,386
5,485
2,685
9,588
3.85
106,427
4,769
2010
SJ1
5,657
33,525
7,383
4,858
3.85
154,830
8,725
2011
SJ1
6,298
2,823
3,500
14,554
3.85
111,291
6,011
2012
SJ1
1,098
65,948
3,833
13,150
3.85
190,213
(18,509
)
(59,325
2013
SJ1
1,916
104,093
3,834
10,050
3.85
234,498
)
2009
SPP
4,532
154,416
2,328
15,405
3.50
319,544
68,482
2010
SPP
2,791
269,454
6,000
31,302
3.50
453,572
62,793
2011
SPP
2,896
325,693
8,808
27,276
3.50
589,181
109,82
6
2012
SPP
1,348
397,910
6,152
23,987
3.50
659,152
94,070
2013
SPP
1,314
416,068
5,980
30,004
3.50
718,522
93,228
2009
SRC
41,960
153,175
9,359
148,534
10.80
581,816
5,676
2010
SRC
15,252
56,091
12,756
49,337
10.80
570,723
48,504
2011
SRC
27,681
213,042
24,393
23,450
10.80
660,513
29,525
2012
SRC
29,877
129,791
8,105
80,291
10.80
534,620
87,523
2013
SRC
42,218
115,846
20,168
67,008
10.80
525,288
141,37
1
200,42
2009
SRF
57,099
85,803
5,775
38,095
8.02
680,457
8
2010
SRF
170,747
69,222
32,606
19,369
8.02
591,040
40,755
96
2011
SRF
168,654
75,222
29,262
33,009
8.13
718,478
59,628
2012
SRF
129,861
118,941
39,228
269,162
8.13
703,502
90,677
2013
SRF
246,711
109,426
14,878
357,368
16.25
729,106
55,377
2009
SSC
87,824
24,700
10,869
44,059
9.94
232,893
30,517
2010
SSC
62,417
4,055
14,918
39,179
14.99
260,110
87,133
2011
SSC
70,262
2,018
25,050
36,746
14.99
315,581
85,962
156,82
2012
SSC
43,084
13,047
29,585
42,515
14.99
424,017
3
172,01
2013
SSC
25,952
32,251
14,946
66,347
14.99
468,093
9
2009
SSM
29,274
28,686
2,888
36,265
5.50
180,921
24,026
2010
SSM
65,557
55,242
11,063
3,346
5.50
150,936
11,220
2011
SSM
4,753
43,193
8,000
(1)
5.50
186,514
51,834
2012
SSM
21,416
45,143
6,925
(620)
5.50
146,377
33,997
2013
SSM
35,459
58,879
4,171
7,944
5.50
165,302
24,190
2009
STP
4,323
-
10,500
9,445
3.50
87,573
45,389
2010
STP
35,224
-
7,000
22,833
7.00
177,202
65,287
2011
STP
12,632
-
-
18,061
7.00
182,117
64,984
2012
STP
17,743
4,184
9,961
4,444
7.00
183,530
63,448
2013
STP
7,596
10,438
-
11,458
6.64
190,514
89,378
2009
SVI
25,450
-
3,873
28,596
3.90
182,012
6,495
2010
SVI
21,702
-
11,765
36,139
3.90
290,500
3,476
2011
SVI
87,365
-
11,627
59,234
5.92
434,995
(9,555)
(33,653
2012
SVI
43,695
5,000
22,237
64,419
8.92
549,662
)
97
2013
SVI
73,768
-
8,895
111,982
10.70
610,872
2009
TAC
136,448
110,278
37,960
17,725
18.98
651,956
13,929
(34,821
)
2010
TAC
122,473
223,860
26,564
89,169
18.98
944,175
39,264
2011
TAC
103,698
305,790
37,946
15,941
18.98
1,031,008
18,386
2012
TAC
200,553
175,641
30,404
63,687
18.98
1,001,871
(59,489
)
(207,71
2013
TAC
406,466
298,052
30,363
70,225
18.98
1,222,589
6)
2009
TCL
94,100
25,502
-
115,856
17.00
447,332
35,452
2010
TCL
89,122
62,167
41,381
99,287
18.70
612,167
34,017
2011
TCL
75,285
237,834
41,037
115,173
18.70
903,262
56,059
2012
TCL
90,081
233,978
59,668
95,584
20.94
806,858
7,204
2013
TCL
110,719
172,880
19,410
127,440
20.94
795,131
62,855
2009
THB
20,564
76,578
12,868
35,537
11.42
351,698
(72,254
)
(59,244
2010
THB
25,940
42,954
13,542
54,261
11.42
325,422
)
(35,546
2011
THB
23,006
34,581
17,214
43,242
11.42
304,321
)
(18,730
2012
THB
42,139
-
19,422
38,771
11.42
301,348
)
(33,469
2013
THB
89,141
-
19,624
40,442
11.42
343,299
)
2009
TJC
11,306
164,752
1,094
16,451
3.00
251,923
(35,586
98
)
(20,678
2010
TJC
10,656
127,350
1,806
21,946
6.00
248,724
)
(31,668
2011
TJC
21,055
123,843
3,000
15,010
6.00
248,923
)
(20,862
2012
TJC
2,688
104,949
-
9,274
6.00
227,802
)
(84,535
2013
TJC
14,496
75,328
-
18,470
6.00
190,025
)
2009
TMS
53,270
77,191
10,107
47,106
10.10
459,226
36,379
2010
TMS
46,474
59,679
4
73,606
16.52
601,335
42,824
2011
TMS
56,486
76,998
16,512
63,711
18.28
648,299
(4,313)
2012
TMS
87,794
98,986
23,974
66,180
23.07
755,678
(40,112
)
(89,315
2013
TMS
90,512
126,082
21,595
107,653
23.07
836,511
)
2009
TNC
24,216
-
5,847
33,038
19.25
268,526
82,917
2010
TNC
71,656
-
19,266
41,702
19.25
308,375
58,627
2011
TNC
140,096
-
28,843
51,861
19.25
343,609
35,684
2012
TNC
197,672
-
38,453
41,017
19.25
380,977
6,815
2013
TNC
139,520
-
38,453
3,094
19.25
348,066
47,564
2009
TPC
27,494
12,100
-
67,547
20.55
366,347
204,90
0
205,36
2010
TPC
45,253
5,680
4
46,783
24.43
374,506
4
99
(103,24
2011
TPC
374,470
602,339
30,842
17,960
24.43
991,359
6)
239,67
2012
TPC
111,581
235,645
42,983
(3,067)
24.43
613,666
8
321,35
2013
TPC
21,014
291,073
29,777
2,263
24.43
659,919
8
192,71
2009
TRA
5,434
39,651
16,517
43,147
10.20
406,152
0
233,92
2010
TRA
29,068
63,321
217
74,098
12.34
578,868
6
208,60
2011
TRA
43,241
75,188
24,540
76,799
12.34
838,443
4
158,52
2012
TRA
104,329
155,867
48,973
99,281
12.34
968,484
9
184,88
2013
TRA
256,585
82,361
27,078
166,271
12.34
1,087,715
6
2009
TS4
13,207
179,923
7,772
21,372
8.47
366,746
74,255
2010
TS4
2,479
239,255
12,830
25,448
11.50
549,277
71,629
(34,513
2011
TS4
5,138
350,870
20,459
22,955
11.50
707,228
)
(42,862
2012
TS4
1,785
505,514
6,996
26,746
11.50
915,690
)
(54,706
2013
TS4
461
495,455
6,552
26,066
11.50
986,537
)
2009
TSC
76,881
650,138
9,975
27,050
8.31
930,258
(653,44
100
1)
(66,539
2010
TSC
96,753
565,791
16,626
12,688
8.21
830,881
)
(40,894
2011
TSC
138,452
652,681
8,105
35,168
8.01
963,159
)
(59,129
2012
TSC
14,685
-
8,709
(44,206
8.31
550,123
)
)
(20,050
2013
TSC
28,915
-
4
10,681
8.31
331,988
)
1,035,7
2009
TTF
6,930
183,64
21,549
14,089
18.97
2,176,303
85
9
1,613,9
2010
2011
2012
2013
TTF
TTF
TTF
TTF
17,157
217,23
23,424
48,762
31.24
2,634,824
91
5
1,921,4
115,01
24,463
-
48,509
31.24
3,337,028
44
9
1,841,3
214,43
6,318
-
33,636
33.95
3,297,738
92
3
1,981,7
302,70
65,513
-
31,354
51.01
3,518,037
11
4
(133,89
2009
TYA
123,482
365,195
-
37,135
27.90
611,203
3)
(158,04
2010
TYA
188,700
357,305
-
52,885
27.90
785,069
7)
(49,385
2011
TYA
125,051
312,152
-
57,707
27.90
671,370
)
101
2012
TYA
127,947
197,876
-
58,125
27.90
633,818
4,063
2013
TYA
157,514
113,016
-
53,173
27.90
637,197
24,035
2009
UNI
2,354
-
3
18,266
6.76
122,090
91,400
2010
UNI
52,540
-
4,672
8,288
9.25
173,634
99,244
2011
UNI
8,454
-
4,676
9.48
170,325
(11,814
130,79
)
2
2012
UNI
3,907
-
8
13,094
9.47
184,275
63,713
2013
UNI
2,354
-
9,357
(8,485)
13.74
179,342
41,200
2009
VBH
5,554
-
870
3,712
2.90
45,244
5,643
2010
VBH
2,332
-
1,740
4,227
2.90
42,730
21,273
2011
VBH
958
-
2,900
2,655
2.90
36,559
27,443
2012
VBH
11,719
-
2,900
899
2.90
36,421
16,942
2013
VBH
8,971
-
2,030
(578)
2.90
35,257
18,988
2009
VDL
42,235
-
3,444
12,939
2.15
93,906
17,201
2010
VDL
51,080
3,819
2,519
19,631
2.15
119,220
10,760
2011
VDL
23,160
10,466
6,829
12,849
3.13
124,355
47,439
2012
VDL
49,762
9,254
6,599
18,562
3.13
139,517
30,522
2013
VDL
44,241
-
3,730
22,946
6.26
141,381
53,848
2009
VFG
64,420
74,224
17,872
89,978
8.13
639,992
84,112
2010
VFG
77,177
100,148
16,247
61,995
9.76
714,974
99,081
193,97
2011
VFG
40,460
34,737
14,622
86,813
12.68
931,061
5
141,82
2012
VFG
87,903
217,164
38,308
60,194
12.77
1,110,479
2
2013
VFG
142,037
76,834
32,709
56,546
13.20
1,108,128
91,674
102
120,47
2009
VGP
7,622
13,967
8,001
18,069
6.20
171,263
6
146,58
2010
VGP
3,475
25,008
7,241
22,553
8.21
207,598
6
139,02
2011
VGP
5,498
57,201
24,081
567
8.21
242,432
0
2012
VGP
59,352
38,184
15,823
4,757
8.21
208,590
86,255
2013
VGP
10,538
121,112
12,658
7,418
8.21
299,483
33,123
2009
VGS
364,894
524,085
7,307
57,691
13.46
1,128,991
(73,021
)
117,30
2010
VGS
59,382
498,201
22,522
25,513
37.20
1,102,118
0
2011
VGS
25,102
419,330
14,436
7,470
36.00
1,133,689
2012
VGS
31,503
512,042
-
34,636
36.00
1,272,009
42,575
(27,186
)
2013
VGS
19,608
485,864
3,954
33,166
36.00
1,075,956
16,749
2009
VIS
180,125
870,215
40,460
218,332
15.00
1,497,518
(8,646)
2010
VIS
226,782
533,232
-
146,859
30.00
1,657,855
37,516
2011
VIS
400,130
704,197
58,757
4,111
30.00
1,318,453
(96,605
)
1,996,0
2012
2013
VIS
VIS
457,724
(607,89
29,993
27,062
41.27
2,809,733
80
3)
1,789,0
(501,66
347,230
-
94,359
49.22
2,568,778
81
2009
VNA
77,549
576,767
2)
29,417
39,574
20.00
1,073,170
(253,05
103
2)
(202,37
2010
VNA
20,082
664,983
97
152,992
20.00
1,179,413
4)
(250,06
2011
VNA
24,001
641,416
42,186
106,659
20.00
1,392,014
2)
(225,65
2012
VNA
8,446
142,680
132
7,889
20.00
1,311,008
1)
(255,65
2013
VNA
4,537
171,974
9
(8,436)
20.00
1,204,826
7)
2,614,1
2009
VNM
426,135
25,737
351,281
2,908,1
35.13
8,482,036
56
1,765,20
2010
VNM
613,472
2012
2013
2009
VNM
VNM
VNM
VNT
3,156,515
1,252,120
2,140,4
567,960
-
10,773,032
23
18
3,891,3
3,364,6
741,428
55.61
15,582,672
44
31
2,222,99
4,131,9
5,713,5
4
13
00
5,307,0
3,167,23
4,153,3
5,316,8
61
5
05
-
8,056
16,103
-
83.40
2,745,645
48,473
2,661,3
35.31
0
2011
53
83.40
19,697,868
22,875,414
87
5.47
177,405
(7,076)
(15,299
2010
VNT
66,635
-
10,301
12,914
5.47
212,429
)
(32,290
2011
VNT
81,543
-
7,405
18,599
5.47
254,254
)
2012
VNT
85,438
-
18,552
12,531
5.47
263,167
(30,150
104
)
(52,692
2013
VNT
110,643
-
10,075
21,585
5.47
264,465
)
(32,707
2009
VPK
3,932
66,253
-
18,218
8.00
149,756
)
(22,423
2010
VPK
6,985
43,163
-
19,739
8.00
155,464
)
2011
VPK
10,847
31,516
-
34,554
8.00
155,768
4,471
2012
VPK
39,239
19,816
11,997
45,553
8.00
186,976
18,833
2013
VPK
54,832
86,689
15,967
41,150
8.00
216,721
18,599
2009
VSC
58,985
58,883
32,581
156,843
12.03
647,348
110,29
6
177,32
2010
VSC
88,632
24,781
35,895
187,823
12.03
811,576
7
322,07
2011
VSC
59,052
846
70,769
161,788
23.89
856,939
2
233,18
2012
VSC
46,856
40,381
95,079
177,977
24.04
1,054,559
5
(27,636
2013
VSC
281,725
8,399
89,557
214,941
28.81
1,132,563
)
1,625,5
2009
2010
VST
VST
64,285
(424,51
27,006
158,792
40.00
2,789,420
70
4)
1,862,5
(376,66
32,485
124
339,342
43.17
3,325,870
93
2011
VST
176,584
1,668,3
7)
71,451
95,686
59.00
3,255,640
(621,60
105
38
2)
2,084,7
2012
VST
44,281
(58,560
2,520
64
(534,90
59.00
3,019,255
)
1)
2,029,5
2013
VST
53,859
(350,55
-
2,754
59.00
2,761,616
99
3)
2009
VTB
20,303
54,626
-
31,848
11.98
316,737
2010
VTB
37,423
28,673
9,063
20,969
11.98
293,614
71,409
100,50
7
2011
VTB
76,480
23,901
16,713
4,630
11.98
278,599
2012
VTB
20,686
14,131
22,288
(5,560)
11.98
265,584
58,449
113,52
9
130,55
2013
VTB
7,515
24,140
7,657
12,154
11.98
268,507
9
2009
VTC
14,384
11,098
-
8,740
4.05
120,377
21,195
2010
VTC
3,158
6,783
-
4,702
4.05
95,186
32,204
2011
VTC
3,169
8,188
-
(3,870)
4.05
93,778
25,508
2012
VTC
2,199
2,346
-
3,856
4.05
84,853
24,941
2013
VTC
10,540
1,612
-
4,141
4.05
86,360
18,368
2009
VTL
19,756
29,338
1,852
6,939
1.80
104,624
(4,299)
2010
VTL
6,928
50,393
1,979
5,199
1.80
105,409
9,590
2011
VTL
5,470
42,650
1,979
3,173
1.80
92,297
13,257
2012
VTL
6,378
39,228
-
1,043
1.80
121,887
5,472
2013
VTL
7,868
61,306
-
4,569
1.80
102,201
5,060
106