the determinants of cash holdings ... - universiteit gent

THE DETERMINANTS OF CASH

HOLDINGS: EVIDENCE FROM

BELGIAN FIRMS

Aantal woorden/ Word count: 18.113

Simon Blondelle Studentennummer/ Student number: 01304385

Promotor/ Supervisor: Prof. dr. Klaas Mulier

Masterproef voorgedragen tot het bekomen van de graad van:

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Engineering

Academiejaar/ Academic year: 2017 – 2018

PERMISSION

Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd en/of

gereproduceerd worden, mits bronvermelding.

I declare that the content of this Master’s Dissertation may be consulted and/or reproduced,

provided that the source is referenced.

Naam student/name student: Simon Blondelle

Handtekening/signature:

I

Foreword

First of all, I would like to express my gratitude to my promotor Professor Klaas Mulier

for reading this thesis and for giving me useful and insightful feedback. His guidance and

support helped me a lot throughout the process of writing this thesis. I would also like to

thank assistant Ilia Samarin for answering the questions I had about the Stata programme.

In the second place, I would like to thank my family for giving me the opportunity to study

Business Engineering and for letting me go on Erasmus. They gave me the chance to further

develop myself. Without them, this master’s thesis would not have been possible. Finally, I

would like to thank my friends and girlfriend for making these last 5 years an amazing and

unforgettable period.

II

Table of contents

Foreword ............................................................................................................................................................................................................. I

Table of contents ............................................................................................................................................................................................ II

Abbreviations ................................................................................................................................................................................................. IV

Tables and figures .......................................................................................................................................................................................... V

Introduction ...................................................................................................................................................................................................... 1

1. Motives of cash holdings ..................................................................................................................................................................... 4

1.1 Transaction motive ........................................................................................................................................................................... 4

1.2 Precautionary motive ....................................................................................................................................................................... 4

1.3 Speculative motive ............................................................................................................................................................................ 5

2. Three main theories of cash holdings ........................................................................................................................................ 5

2.1 Static trade-off theory ...................................................................................................................................................................... 5

2.2 Financial hierarchy theory ............................................................................................................................................................. 7

2.3 Free cash flow theory ....................................................................................................................................................................... 8

3. Hypotheses ................................................................................................................................................................................................. 9

3.1 Hypothesis 1: Cash holdings are negatively related to the firm’s size ....................................................................... 9

3.2 Hypothesis 2: Cash holdings are negatively related to the relationship with the financial institutions .. 10

3.3 Hypothesis 3: Cash holdings are negatively related to net working capital ......................................................... 12

3.4 Hypothesis 4: Cash holdings are negatively related to dividend payments ......................................................... 13

3.5 Hypothesis 5: Cash holdings are negatively related to the leverage ratio of a firm .......................................... 15

3.6 Hypothesis 6: Cash holdings are negatively related to the debt maturity ............................................................ 16

3.7 Hypothesis 7: Cash holdings are positively related to the cash flows ..................................................................... 16

3.8 Hypothesis 8: Cash holdings are positively related to the cash flow variability ................................................ 17

3.9 Hypothesis 9: Cash holdings are positively related to growth opportunities ..................................................... 19

3.10 Summary of the expected relationships ............................................................................................................................. 21

4. Measurements ....................................................................................................................................................................................... 22

4.1 Sample selection .............................................................................................................................................................................. 22

4.2 Dependent variable ........................................................................................................................................................................ 23

4.3 Independent variables .................................................................................................................................................................. 24

4.3.1 Firm’s size ................................................................................................................................................................................. 24

4.3.2 Relationship with the financial institutions .............................................................................................................. 24

III

4.3.3 Net working capital .............................................................................................................................................................. 24

4.3.4 Dividend payments ............................................................................................................................................................... 25

4.3.5 Leverage ratio ......................................................................................................................................................................... 25

4.3.6 Debt maturity .......................................................................................................................................................................... 25

4.3.7 Cash flow ................................................................................................................................................................................... 26

4.3.8 Cash flow variability ............................................................................................................................................................. 28

4.3.9 Growth opportunities .......................................................................................................................................................... 28

4.4 Summary of the measurements ................................................................................................................................................ 30

5. Empirical research .............................................................................................................................................................................. 31

5.1 Methodology ..................................................................................................................................................................................... 31

5.2 Descriptive statistics ..................................................................................................................................................................... 34

5.3 Gauss Markov assumptions ........................................................................................................................................................ 37

5.4 Results .................................................................................................................................................................................................. 40

5.5 Impact of the crisis ......................................................................................................................................................................... 49

6. Conclusions ............................................................................................................................................................................................. 53

7. Limitations ............................................................................................................................................................................................... 55

Bibliography .................................................................................................................................................................................................. V

Attachments .................................................................................................................................................................................................. IX

Attachment 1: Normality of the residuals .................................................................................................................................... IX

Attachment 2: Multicollinearity ........................................................................................................................................................ IX

Attachment 3: Heteroscedasticity ...................................................................................................................................................... X

Attachment 4: Autocorrelation ...................................................................................................................................................... XIII

Attachment 5: Specification errors ............................................................................................................................................... XIV

Attachment 6: Hausman test ............................................................................................................................................................ XVI

Attachment 7: Robustness check growth opportunities ................................................................................................... XVII

IV

Abbreviations

CFLOW Cash flow

CFLOWVAR Cash flow variability

COV Covariance

DEBTMAT Debt maturity

DIV Dividend payments

FE Fixed effects

GM Gauss-Markov

GROWTHOPP Growth opportunities

LEV Leverage

LM Lagrange Multiplier

NBB National Bank of Belgium

NWC Net working capital

OLS Ordinary least squares

PV Present value

RE Random effects

REL Relationship with the financial institutions

SIZE Size of the firm

SME Small and medium-sized enterprises

VIF Variance inflation factor

V

Tables and figures

Figure 1: Static trade-off theory of capital structure ...................................................................................................................... 6

Figure 2: Optimal holdings of liquid assets ......................................................................................................................................... 7

Table 1: Summary of the expected relationships .......................................................................................................................... 21

Table 2: Calculation of the cash flows ................................................................................................................................................. 27

Table 3: Summary of the measurements .......................................................................................................................................... 30

Figure 3: OLS optimisation error term ............................................................................................................................................... 31

Table 4: Descriptive statistics ................................................................................................................................................................ 34

Table 5: Dividend payments ................................................................................................................................................................... 35

Figure 4: Average level of cash holdings over time ...................................................................................................................... 36

Table 6: VIF factors ..................................................................................................................................................................................... 38

Table 7: Correlation matrix ..................................................................................................................................................................... 38

Table 8: Regression results ..................................................................................................................................................................... 40

Table 9: Fixed effects regression high-growth firms versus low-growth firms .............................................................. 46

Table 10: Summary of the results ........................................................................................................................................................ 48

Table 11: OLS regression economic relevance ............................................................................................................................... 49

Table 12: Fixed effects regression economic relevance ............................................................................................................. 49

Table 13: Random effects regression economic relevance ....................................................................................................... 49

Table 14: Impact crisis on level of cash holdings .......................................................................................................................... 51

Table 15: Interaction effect of relationship with financial institutions and crisis .......................................................... 52

Figure 5: Scatter plot heteroscedasticity ............................................................................................................................................. X

Table 16: The White heteroscedasticity test ...................................................................................................................................XII

Table 17: The Breusch-Godfrey Serial Correlation LM test .................................................................................................... XIII

Table 18: The Lagrange multiplier test .............................................................................................................................................. XV

Table 19: Hausman test .......................................................................................................................................................................... XVI

Table 20: Sales growth 2 ....................................................................................................................................................................... XVII

Table 21: Capital expenditures ......................................................................................................................................................... XVIII

Table 22: Research and development costs ................................................................................................................................... XIV

Table 23: Dummy research and development costs .................................................................................................................... XX

1

Introduction

The aim of this master’s thesis is to investigate the determinants of the cash holdings in

Belgian firms with full annual accounts during the period 2006 until 2015. This paper

contributes to the literature as there is limited empirical proof about the determinants of

cash holdings in Belgian firms. The coverage of literature about these firms is rather scarce.

The majority of the research on the determinants of cash holdings is conducted in other

countries and the conclusions may differ across countries. Ozkan & Ozkan (2004) for

example focused on UK companies whereas Opler, Stulz, Pinkowitz, & Williamson (1999)

focused their research on publicly traded US firms and Garcia-Teruel & Martinez-Solano

(2008) on Spanish SME. The results in these papers may not be representative as the

financial systems and the types of firms may differ across countries. For example, Belgian

firms rely more on bank debt than American firms which possibly results in different

conclusions about reliance on bank debt for these countries.

In a firm, management should maximize the wealth of the shareholders. Therefore, firms

must ensure that the level of cash holdings is at a level where the benefits of holding cash

equal the costs of holding cash (Opler et al., 1999). In a world with perfect capital markets,

there would be no benefits related with holding cash. This is based on the theory of

Modigliani & Millar (1958) that argues that when there is an absence of taxes, an absence of

bankruptcy costs, no asymmetric information, no arbitrage opportunities and the existence

of an efficient market, the value of a firm is not affected by the amount of debt, equity or cash

that it is holding. However, financial frictions, information asymmetries and transactions

costs affect the optimal level of cash holdings within a firm. This induces that investments in

cash holdings are very important for firms (Dittmar, Mahrt-Smith, & Servaes, 2003). It can

for example impact the dividend policy or the possibilities to invest.

The principal-agency theory explains why the level of cash holdings is not optimal for

some firms in general. Managers may want to increase the level of cash holdings to

strengthen their discretion in the firm (Opler et al., 1999). According to Dittmar et al. (2003),

there is more cash in companies where there are agency problems. Agency problems in firms

are conflicts between the management and the shareholders of the firms which may have

different interests. The paper of Ditmar et al. (2003) also showed that in countries where

shareholders are not properly protected, companies are holding twice as much cash than in

countries where they are well-protected. A level of cash that is too high can be a danger

because managers might take suboptimal decisions which might affect the value of the

2

company. Corporate governance can for example be a solution to this problem. Corporate

governance is “the system of rules, practices and processes by which a firm is directed and

controlled” (“Corporate governance,” n.d.). The level of cash holdings can be controlled by

one of these policies. Note however that corporate governance policies can be very different

across firms.

Not only excessive cash holdings can be a problem, also holding too little cash can be

disadvantageous for a firm since it can increase its dependency on external financing

resulting in high costs (Garcia-Teruel & Martinez-Solano, 2008).

This thesis focuses on multiple determinants that can be found in the financial

statements of the sample firms. These determinants include a firm’s growth opportunities,

the size of the firm, the relationship with the financial institutions, net working capital,

dividend payments, leverage ratio, debt maturity, cash flow and cash flow variability. There

are also other factors that might influence the level of cash holdings within a firm but these

are not further investigated in this thesis. The aim is to find out whether these variables have

an impact on the level of cash holdings (positive or negative) and if these variables are

economically relevant.

The main benefits of investing in cash holdings are that firms do not have to raise

external funds, they avoid paying high interest costs for these funds and they can save in

terms of transaction costs (Opler et al., 1999). Transaction costs can for example include fees

for investment bankers, fees for lawyers, commission fees for other intermediaries or

informational costs. These costs can be very high and are the reason why firms want to have

sufficient internal liquidity without relying too much on external financing. Cash is a low-

cost financing alternative for firms so they try to minimize the costs of external financing in

the imperfect capital markets by holding more cash (Ozkan & Ozkan, 2004).

The main disadvantage of holding cash is the lower return on the liquid assets compared

to other investment assets or projects. Although the return on the liquid assets is lower, a

good cash management is essential for a firm. Therefore, a good follow-up of the account

receivables, inventories and account payables is needed. A good cash policy is in a firm’s own

interest. Poor cash management is one of the main reasons for business failure. It is known

that more firms fail because of a lack of liquidity rather than of a lack of profitability. A

company may have a lot of revenue but will get nowhere without a good cash management

policy. Cash holdings are therefore an important financial measure to evaluate the health of

a firm. Cash is for example needed to pay employees, to pay suppliers, to invest in property

3

and equipment, to invest in new technology, to expand to new markets, to pay training for

their employees, to do mergers and acquisitions or to pay interest to its creditors. If no cash

is available, firms cannot meet their obligations, they cannot invest in the expansion of the

firm or pay back their creditors. Under these circumstances, the existence of the company is

in danger.

Many studies have used three theoretical theories to describe why firms hold cash: the

static trade-off theory, the free cash flow theory and the pecking order theory. The static

trade-off theory of Myers (1984) argues that there is an optimal cash level that can be

reached by balancing the marginal costs and benefits. The pecking order theory of Myers

(1984) considers cash as a buffer between retained earnings and investment needs. Finally,

the free cash flow theory of Jensen (1986) argues that managers hold extra cash to improve

their discretion in the company. The literature overview goes into further detail of these

three theories.

The first part of this paper describes the main theories and motives of holding cash

giving an explanation about why firms are holding cash and what is the reasoning behind it.

In the second part, nine hypotheses are formulated based on the existing literature. Each

hypothesis relates one of the relevant firm characteristics to the cash holdings in a firm. In

the third part, information about the sample and the measurement of each variable is

described. Next, the fourth part deals with the empirical research. Here, the methodology,

the descriptive statistics, the Gauss-Markov assumptions, the regression results and a small

extra research related to the crisis are explained. The last part consists of the main

conclusions and limitations of this research.

4

1. Motives of cash holdings

The publication of John Maynard Keynes (1936): The General Theory of Employment,

Interest & Money perhaps gives the best explanation about why companies hold cash. In this

work, Keynes writes about the importance of cash and outlines three main motives for

holding cash: the transaction motive, the precautionary motive and the speculative motive.

1.1 Transaction motive

Keynes (1936, p.177) describes the transaction motives as follows: “The need for cash

for the current transactions of personal and business exchanges.” This is the cash that is held

for day-to-day operations and to make routine payments. In a business, there is always a

bridge between the payment of wages, raw materials and other costs and the income they

get from selling their products.

The transaction motive of cash refers to the bridge made by holding cash due to the

different frequencies of income and expenditures. Businesses sometimes do not receive cash

as frequently as they have to make payments. For example, if their clients delay their

payments and cash is needed immediately, sufficient cash must be available to close this gap.

1.2 Precautionary motive

The precautionary motive refers to the safety reasons of holding cash. Keynes (1936,

p.177-178) describes the precautionary motive as follows: “The desire for security as to the

future equivalent of a certain proportion of total resources.” This is the cash that is needed

in case of unexpected problems that result in unexpected costs. It is a buffer against

uncertainties. The cash a company holds is used for unexpected expenses that are not

covered by any risk instruments such as an insurance, collateral or available credit lines

(Whalen, 1966).

The amount of cash on the balance sheet related to the precautionary motive depends

on the optimism and pessimism of the firm about unexpected expenses, access to credit and

the possibility of conversion of illiquid assets into cash. If businesses have easy access to

credit, the precautionary motive of holding cash will almost be absent. Both the transaction

motive and the precautionary motive are determined by the generated income and the level

of business activity.

1.3 Speculative motive

Keynes (1936, p.178) describes the speculative motive as follows: “It is the object of

securing profit from knowing better than the market what the future will bring forth.”

5

Interest rates, bond prices and share prices can go up or down. As a result, companies hold

cash to avoid missing the benefit of having bargain opportunities that may arise soon. These

opportunities can be a fall in prices of the raw materials, a change of government policies, a

higher interest rate on funds or an attractive investment opportunity that arises. These are

opportunities that fall out of the normal activities of the business.

2. Three main theories of cash holdings

There are different theories explaining which factors determine the need to hold cash.

To get a better insight in the existing literature, three theories are hereafter explained. These

three theories include:

1) Static trade-off theory by Myers (1984)

2) Pecking order theory by Myers (1984)

3) Free cash flow theory by Jensen (1986)

2.1 Static trade-off theory

The static trade-off theory of Myers (1984) argues that the optimal leverage ratio of a

firm is determined by comparing the advantages and disadvantages of new debt financing.

Firms must compare the tax benefit of the interest costs of debt with the costs of financial

difficulties and bankruptcy costs. The corporate tax favours debt relative to equity because

of the tax benefits of interests on debt financing.

Figure 1 (Myers, 1984) explains the static trade-off theory. It shows that every enterprise

moves to its optimal debt ratio by replacing equity with debt as long as the tax benefit of

interest costs exceeds the bankruptcy costs and the loss of value of the assets. A company

will increase its debt until the value of the company is maximized. A company can also move

towards its optimal debt ratio by doing the inverse and replacing debt by equity. This is

beneficial in case the costs of bankruptcy and the loss of value of the assets exceed the tax

benefit on the interest of debt. The expected bankruptcy costs of a firm rise when its

profitability decreases and when its earnings become more volatile which causes these firms

to expect having a lower level of debt and a higher level of equity (Fama & French, 2002).

The more debt, the riskier it is for the lender, the higher the cost to finance the debt.

6

A company can reduce the cost of bankruptcy and asset losses by holding more internal

cash. Campbell, Hilscher, & Szilagyi (2008) show that firms with lower cash holdings are

more likely to go bankrupt. Larger cash reserves reduce the likelihood that a company

cannot afford its payments and repay its debt. Therefore, holding larger cash reserves

reduces the risk of financial problems. However, holding large cash reserves is not always

advantageous. A firm needs to make a trade-off between the benefits and disadvantages of

cash holdings.

Keynes (1936) argues that there are two main benefits of holding cash. The first benefit

is that there are lower transaction costs from not having to liquidate assets when cash is

needed for payments. Keynes refers to this as the transaction cost motive. These costs were

first considered as brokerage costs in the paper of Millar & Orr (1966). They assumed that

the decision-maker had two types of assets: saving deposits (or bonds on which the investor

earns interest) and a non-interest-bearing cash balance. If the decision-maker wanted to

transfer money between these two accounts, he needed to pay a constant brokerage cost.

The second benefit is that cash holdings are a valuable buffer against unexpected

circumstances. Keynes (1936) refers to this as the precautionary motive of holding cash. The

trade-off model of Miller & Orr (1966) describes the trade-off between the costs of running

out of cash and the loss of interest, i.e. low return on liquid assets. The lower return on liquid

assets is due to the existence of better investment opportunities that yield a higher return.

Figure 1: Static trade-off theory of capital structure

Source: Myers, (1984)

7

This can lead to conflicts when managers and shareholders have other opinions about the

costs and benefits of holding cash (Opler et al., 1999). For example, managers want to keep

more cash in order to invest in projects that benefit their own interest but this can possibly

harm the shareholders’ wealth. This is also known as the agency problem.

Figure 2: Optimal holdings of liquid assets

Source: Opler et al, (1999)

Figure 2 (Opler et al., 1999) illustrated the marginal cost of liquid assets shortage and

the marginal cost of liquid assets. The intersection of these two gives the optimal level of

liquid assets. The marginal cost of liquid asset shortage is a downward sloping line. A higher

shortage of cash results in a higher cost because firms have to raise funds in external

markets, they have to decrease interesting investment opportunities and/or they have to cut

dividends. The line of the marginal cost of liquid assets is horizontal because Keynes (1936)

argues that there is no reason to think that this marginal cost increases with the amount of

liquid assets held. The cost of holding liquid assets is the opportunity cost because of the

expected lower return on liquid assets.

2.2 The financial hierarchy model

This theory argues that firms do not target an optimal level of cash but that companies

who are performing well and who are more profitable, hold more cash because they use it

as a financial slack. The financial hierarchy model states that companies prefer to finance

their investments with retained earnings first, then with debt and least prefer equity. This

order is due to the level of asymmetric information belonging to each means of financing,

which is the largest for of equity. Asymmetric information arises when the insiders of the

8

company, e.g. the managers, have more knowledge than the outsiders of the company, e.g.

the stakeholders and the creditors. Most of the time, the managers tend to have more

information about the investment opportunities and the profit perspectives of a company

than their investors. In this theory of Myers (1984), cash is used as a buffer between the

retained earnings and the investment needs of the company. If the retained earnings of a

firm do not suffice to finance its investment opportunities, they use their accumulated cash.

If the accumulated cash is also insufficient, debt will be issued. Only after the issuing of debt,

equity is raised if the amount of debt that can be issued is not enough. According to the

pecking order theory, this also explains the negative relationship between debt and cash

holdings because when the investment needs exceed the retained earnings, companies will

use the cash holdings and when that does not suffice, debt will be issued (Myers & Majluf,

1984).

2.3 The free cash flow theory

The free cash flow theory of Jensen (1986) is based on the agency costs that result from

the separation of ownership and control in a firm. This separation of ownership and control

can give rise to conflicts within this firm. These conflicts can be between the managers and

the shareholders or between the shareholders and the creditors. Jensen & Meckling (1976)

define the agency relationship as a contract under which one or more people (the principals)

engage another person, the agent, to perform some service on their behalf which involves

delegating some decision-making authority to the agent. Jensen & Meckling (1976) define

the agency costs as a sum of

1) The monitoring costs for the principal: these are the costs for the principal to

control and monitor the agent

2) The bonding cost for the agent: these are the costs for the agent to convince

the principal he will not make decisions that will harm the shareholders’ value

3) Residual costs that arise from executing suboptimal investments despite the

monitoring and bonding costs.

The agency problem is mostly present in companies where there is a high free cash flow.

Jensen (1986, p.323) describes free cash flow as follows: “Free cash flow is cash flow in

excess of that required to fund all projects that have positive net present values when

discounted at the relevant cost of capital.” Managers can use these resources in a later phase

9

to invest in projects in which they personally benefit even though they have to act in the best

interest of the shareholders. Richardson (2006) found out that in companies with high free

cash flows, there is a higher chance of over-investing.

The free cash flow theory argues that managers want to hold as much cash as possible

because of the number of assets that they have under their control. In that case, the managers

have a discretionary power over the investment process. This power can be disadvantageous

for the shareholders because it allows managers to finance projects with a negative net

present value harming the shareholders’ wealth. The advantage for the manager is that he

avoids the control and monitoring of the capital markets as opposed to the case of raising

external debt (Opler et al., (1999)). A possibility to reduce this agency conflict is to pay out

dividends to the shareholders. The dividend payments would lead to a better control and

monitoring of the investment opportunities because they have to go to the capital market.

Another solution is to reduce the free cash flow by issuing debt which limits the level of cash

available to the management and reduces the inefficiencies inside the firm. However, there

are also bankruptcy costs associate with having too much debt so firms have to take this into

account as well. The expected bankruptcy costs decrease with the leverage ratio so a firm

has to be aware of this.

As mentioned in the paper of Jensen (1984), managers have the incentive to let the firm

grow beyond its optimal size. This is often caused by compensation that is positively related

to growth in sales (Murphy, 1985).

3. Hypotheses

This part describes nine firm characteristics and its expected relationship with the cash

holdings in a firm. These relationships are formulated based on the existing literature. The

firm characteristics that are described include: size of the firm, the relationship with the

financial institutions, net working capital, dividend payments, leverage, debt maturity, cash

flow, cash flow variability and growth opportunities.

3.1 Hypothesis 1: Cash holdings are negatively related to the firm’s size

Size is one of the firms’ characteristics that could affect the cash holding significantly. If

companies want to issue debt they have to pay some fixed fees to obtain a loan. It is argued

that these fees incurred in obtaining loans are fixed regardless the size of the loan. As these

fixed costs are not proportional to the amount of the loan, smaller firms face a higher cost of

10

financing because the marginal cost is higher. The model of Millar & Orr (1966) suggests that

economies of scale exist in cash management. This means that raising funds is relatively

more expensive for smaller firms than for larger firms encouraging small firms to hold more

cash (Ferreira & Vilela, 2004).

Large firms also tend to have less asymmetric information, a greater access to the capital

markets, less constraints for borrowing in the capital markets and lower costs of external

financing so bigger companies are expected to get financing in an easier and cheaper way

(Fazzari & Petersen, 1993; Kim, Maurer, & Sherman, 1998; Mikkelson & Partch, 2003; Ozkan

& Ozkan, 2004). Chung, McInish, Wood, & Whyhowski (1995) argue that larger firms release

more financial information because they are followed by more analysts and are subject to

closer scrutiny.

It is also known that larger firms are more likely to be diversified and therefore have a

lower probability of being in a situation of financial distress and a situation of bankruptcy

(Rajan & Zingales, 1995; Titman & Wessels, 1988). Financial distress is “a condition where a

company cannot meet, or has difficulties of paying off, its financial obligations to its

creditors” (“Financial distress,” n.d.). For example, in periods of financial distress larger and

more diversified companies have the opportunity to sell their non-core assets is an easier

way (Lang, Poulsen, & Stulz, 1995). This also depends on the specificity of the assets. If there

is a high degree of specificity of the assets, a company is more likely to have higher cash

holdings because specific assets are more difficult to sell (Drobetz & Grüninger, 2007). Based

on empirical research and existing literature, the first hypothesis therefore is:

H1: There is a negative relationship between the size of a firm and cash holdings.

3.2 Hypothesis 2: Cash holdings are negatively related to the

relationship with the financial institutions

By establishing good and stable relationships with the banking institutions, asymmetric

information can be reduced. In that case, the company will disclose valuable information to

the banking agents allowing these agents to monitor the company closely (García‐Teruel &

Martínez‐Solano, 2008). This can result in a lower cost of external financing because the

banks are aware of the financial situation of the firms. This reduces the uncertainty for the

banking agents. Petersen & Rajan (1994) investigated the benefits of lending relationships.

They found out that the interest rate of a company decreases with the duration of the

relationship with their bank due to the information exchange between the borrower and the

11

lender. As a result, firms can keep cash at a lower level because they can more easily access

funds from the institutions. In the paper of Luo & Hachiya (2005), this is referred to as the

active monitoring view. Luo & Hachiya (2005) state that banks like to be informed about the

financial situation of firms and that they want to ensure that the managers of the firms are

taking efficient business decisions. The theoretical study of Brealey, Leland, & Pyle (1977)

also shows that a good relationship with the institution can improve the conditions of

lending. According to Ferreira & Vilela (2004), bank debt is negatively related to cash

holdings due to the precautionary motive. Banks are in a good position to evaluate a firm’s

financial policies and to evaluate a firm’s credit quality. The existence of agency costs and

asymmetric information leads to the increase of financing costs and the limitation to the

financial markets. Banks however have a major advantage since they have access to

information that is not publicly available. If a bank has positive information about a firm and

if they have a tight relationship they will more easily grant a loan compared to unknown

firms. The asymmetric information that is present in the imperfect capital market is

mitigated by the monitoring function of the bank. Firms who are granted loans by banks will

also have easier access to the external capital market since that means that banks have got

positive information about this company. It reveals positive news about the borrowing firms’

creditworthiness. Therefore, these firms will have an easier access to bank debt and will hold

less cash (Ozkan & Ozkan, 2004). When the bank is a big source of financing for a firm, banks

obtain a profound knowledge about their financials and their strategic planning. The

research of Ferreira & Vilela (2004) also found out that European firms have a closer

relationship with banks because they own a significant portion in a firm’s stock.

Other research shows that the power of banks has a significant positive influence on the

cash balance of the company. Pinkowitz & Williams (2001) did research in Japan about bank

power. They found out that corporate cash holdings decline if the power of banks weakens

over time. Their empirical results also show that Japanese firms hold more cash than U.S and

German firms and that the level of cash holdings is higher when there is a good relationship

with the bank. This results from the fact that the monopoly power of Japanese banks

determines the cash on the balance sheet. The main bank is the monitor and the

disciplinarian of the firm. These main banks do not have their own monitor which can lead

to the increase of their wealth despite the wealth of the firms. Japan has, just like Germany,

a bank-centred financial system. This means that the principal of the capitalization rests on

12

banks. The good relationship causes a reduction in the agency costs because the relation of

the bank and its monitoring function decrease the asymmetric information. You should

expect that this relationship would result in lower cash holdings. But they found out that in

Japan, the industrial companies are holding higher levels of cash. This is the result of the non-

monitoring of the main banks there. Banks in Japan are supporting to hold large cash

balances. Thereby, banks want to reduce the monitoring costs and want to extract rent from

these firms. By having large monopoly power, banks can induce a very low interest rate on

deposit. These two arguments lead to Japanese banks encouraging firms to hold more cash.

In the existing literature, only in Japan a positive relationship is expected with cash

holdings because of the large monopoly power. This monopoly power is a lot smaller in

European banks because of the existence of a supervisor who is not allowing mergers and

acquisitions that could lead to a monopoly and could lessen competition in the public

interest. Because of the arguments supporting the negative relationship and the absence of

monopoly power in Belgium, the second hypothesis is:

H2: There is a negative relationship between the relationship with the financial institutions and

cash holdings.

3.3 Hypothesis 3: Cash holdings are negatively related to net working

capital

Other liquid assets besides the cash and the cash equivalents are considered as

substitutes of cash. The presence of these liquid assets can affect the companies’ level of cash

holdings. A negative relationship is expected between cash holdings and non-cash liquid

assets. Companies with more non-cash liquid substitutes will hold less cash because the

costs of converting these non-cash liquid assets are lower relatively to the costs of

conversion of the other assets.

The net working capital is calculated as the difference between the current assets and

the current liabilities. Note however that in this case, cash and cash equivalents are excluded

from the current assets. Current assets consist of cash, cash equivalents, accounts receivable,

inventories, marketable securities, prepaid expenses and other liquid assets that can easily

be converted into cash. Except for cash and cash equivalents, these are all non-cash liquid

assets. Current liabilities consist of short-term debt, accounts payable, accrued liabilities and

other debt. Net working capital and cash are seen as substitutes. Since net working capital

can be a source of internal funds, it can be considered as a substitute for cash holdings.

13

Non-cash liquid assets can be easily converted to cash at a relatively low cost compared

to the illiquid assets. For example, the account receivables can be cashed out through

factoring (Bigelli & Sánchez-Vidal, 2012). This can be done by selling and transferring them

to a third party who will collect the revenues instead of you. Of course, you can only sell the

accounts receivable at a certain discount. This discount mainly depends on the maturity of

the accounts receivable. Excess inventories can for example also be sold by offering

discounts for these items. Besides that, the collection period of the accounts receivable can

also be shortened. As a result, cash is available earlier. Of course, this collection period

depends on the industry you are working in. A good measure for this is the accounts

receivable turnover rate:

=𝑛𝑒𝑡 𝑐𝑟𝑒𝑑𝑖𝑡 𝑠𝑎𝑙𝑒𝑠

𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑎𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑟𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒

A high value for this ratio means that collection of accounts receivable is efficient. It

measures how many times a firm collects its accounts receivable.

Another measure for the availability of cash that is based on the net working capital is

the cash conversion cycle.

= 𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑝𝑒𝑟𝑖𝑜𝑑 + 𝑎𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑟𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑝𝑒𝑟𝑖𝑜𝑑

– 𝑎𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑝𝑎𝑦𝑎𝑏𝑙𝑒 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑝𝑒𝑟𝑖𝑜𝑑

The longer this cycle, the longer it takes to generate cash from the short-term assets.

Opler et al. (1999) used the net working capital minus the amount of cash as a measure

for the liquid asset substitutes. The trade-off model states that these liquid assets are

considered as substitutes for cash. In a period with cash shortage, these substitutes can

easily be converted to cash with little or no transaction costs. The third hypothesis therefore

is:

H3: There is a negative relationship between net working capital and cash holdings.

3.4 Hypothesis 4: Cash holdings are positively related to dividend

payments

Companies that pay out dividends on a regularly basis can easily raise funds by reducing

or cutting the payments of these dividends. They can do this at a relatively low cost

compared to companies that do not pay out dividends and have to go to the capital markets

to raise funds (Opler et al., 1999; Ferreira & Vilela, 2004). Dividend payments are therefore

14

seen as an inexpensive source of cash and an easy way to free up cash in case of a liquidity

shortage. Drobetz & Grüninger (2007) also argue that firms who pay out dividends are better

monitored and can raise funds at a lower cost in the external market resulting in a reduction

of their cash buffer. The payment of dividends can also be more attractive for potential

investors leading to an increase of the amount of the money raised when issuing securities

compared to companies that do not pay any dividends. This results in the following

hypothesis: firms who pay out dividends hold less cash than firms that do not pay out

dividends because they are capable of cutting these dividends if necessary and have easier

access to the external market.

Ofcourse, firms will always hold a certain level of cash in the company. Although

companies that pay out dividends have a good access to the financial markets, there is always

a risk of a stock market crash like in 2008. When companies only rely on external funding

and do not hold some buffers, a lending crisis like in 2008 can have some deteriorating

effects on the company’s performance.

Nonetheless, in the literature there also exist a lot of theories that contradict the negative

relationship between the dividend payments and the cash holdings. Ozkan & Ozkan (2004)

argue that it is possible that firms who pay out dividends hold more cash because they want

to avoid being short of cash and no longer being able to pay their dividends on the one hand

and they want to smooth the payment of dividends on the other hand. This is explained by

the precautionary motive which argues that there has to be sufficient cash to fulfil all

payments even when there is an economic downfall (Bigelli & Sánchez-Vidal, 2012). They

also argue that companies who pay out dividends are reluctant to cut their dividend

payments because there are some risks associated with doing so. A change in dividend policy

possibly results in some costs such as a decline in the share price of the firm as a consequence

of the investors’ reactions to the announcement. To avoid having to change their dividend

policy, companies want to hold more cash.

Despite the evidence in favour of a negative relationship between cash holdings and

dividend payments, this thesis expects this relationship to be positive based on following

arguments: the reluctance of the management of a company to cut dividends and the effort

of a firm to smooth dividend payments. The fourth hypothesis therefore is:

H4: There is a positive relationship between dividend payments and cash holdings.

15

3.5 Hypothesis 5: Cash holdings are negatively related to the leverage

ratio of a firm

This hypothesis is explained by the three main theories described earlier: the static

trade-off theory, the pecking order theory and the free cash flow theory.

The static trade-off theory compares the advantages and disadvantages of holding cash.

Because of the precautionary motive, firms hold more cash when there is more debt because

a higher leverage ratio increases the probability of bankruptcy as a result of the rigid

amortizing plans (Ferreira & Vilela, 2004). This theory is also confirmed by Han & Qiu (2007)

who argue that firms with high leverage levels need to save more cash to meet the future

debt payments. Faulkender (2002) also argues that firms with a higher leverage ratio tend

to hold more cash for precautionary purposes. To reduce the possibility of financial distress,

these companies would like to hold more cash. However, a high leverage ratio can also

indicate that a company is able to easily raise external funds and is more subject to

controlling and monitoring by the issuers of this debt. In this case, this results in a lower level

of cash that is kept by companies (Drobetz & Grüninger, 2007). This hypothesis is related to

the hypothesis in Section 3.2 that says that cash holdings are negatively related to the

relation with the financial institutions. If there is a close relationship between a firm and a

bank, banks are in good position to ascertain the credit quality and to control financial

policies. This leads to a decrease in agency problems resulting in a lower financing cost and

an easier access to the external financing markets (Ferreira & Vilela, 2004).

The pecking order theory says that the level of cash holdings decreases when the

investments needs are exceeding the retained earnings and increases when the investment

needs are less than the retained earnings. The pecking order theory also states that the debt

level grows when the investment needs exceed the retained earnings and the available cash

and decreases when investment needs are less than the retained earnings and the available

cash. This theory leads to the hypothesis that there is a negative relationship between the

leverage ratio and the cash holdings. Cash holdings follow an inverse pattern compared to

debt (Ferreira & Vilela, 2004).

The free cash flow theory of Jensen (1986) says that managers favour a low leverage

ratio which means that they are less controlled and monitored by providers of debt allowing

them to more easily undertake projects they want to execute. The free cash flow theory

therefore indicates that there is a negative relationship between the leverage ratio and the

cash holdings. The trade-off theory is ambiguous about this relationship and the pecking

16

order theory and the free cash flow theory suggest that there exists a negative relationship

between the leverage ratio of a firm and its cash holdings. As a result, the fifth hypothesis is:

H5: There is a negative relationship between the leverage ratio of a firm and cash holdings.

3.6 Hypothesis 6: Cash holdings are negatively related to the debt

maturity

The distribution of debt into a long-term part and a short-term part affects the decision

of holding liquid assets (Ferreira & Vilela, 2004). The use of short-term debt leads to the

renewal and negotiation of credit terms on a regular basis. Consequently, a risk of

refinancing exists if these terms cannot be met, which can lead to financial distress (Ferreira

& Vilela, 2004). Risk of refinancing is the risk that a borrower cannot borrow additional

funds to repay its existing debt (“Refinancing risk,” n.d.). This leads to a period of financial

distress where the company has difficulties to pay off its debt and obligations to its creditors.

If a firm is unable to refinance, it needs to sell off some important assets at a lower price

to pay off its debt. A large level of cash holdings can avoid this situation because it avoids

selling off key assets of the company (Harford, Klasa & Maxwell, 2014). This would prevent

an inefficient liquidation. If a company is refinanced, this is often at a higher interest rate. By

holding more cash, the company can afford these interest payments and still has enough cash

for other investment opportunities. This leads to the hypothesis that using more short-term

debt results in a higher level of cash holdings to avoid a situation of potential financial

distress. Higher cash holdings could also increase the likelihood of lenders offering short-

term loans because of the confidence they have in the repayment of the debt and the increase

of creditworthiness for refinancing (Harford et al., 2014). Besides that, in periods where the

market conditions are tighter (supply credit crisis) and when it is more difficult to refinance,

companies are holding even more cash. It is expected that more short-term debt results in

more cash holdings and more long-term debt results in less cash holdings. The sixth

hypothesis therefore is:

H6: There is a negative relationship between the debt maturity and cash holdings.

3.7 Hypothesis 7: Cash holdings are positively related to the cash flows

There are multiple opinions about the relationship between the cash flow of a company

and its cash holdings.

17

The free cash flow theory of Jensen (1986) for example suggests that managers like to

build up the level of cash holdings from the cash flow to have control over a greater amount

of assets. According to the hierarchy theory of Myers (1984), the presence of asymmetric

information leads to a hierarchy in the use of funding sources. Firms have a preference for

internal financing over external financing leading to the hypothesis that firms with a large

cash flow will keep higher cash levels in order to use it in a later phase to finance interesting

investment opportunities. This encourages them to retain excess cash. Opler et al. (1999)

and Ozkan & Ozkan (2004) confirmed this relationship for US and British firms and Ferreira

& Vilela (2004) confirmed this relationship for countries of the European Monetary Union.

Račić & Stanišić (2017) found that Serbian firms with high cash flows have higher cash

holdings because of the preference for internal funding.

Nonetheless, Kim et al. (1998) argue that firms see cash flow as a substitute of cash and

as an additional and immediate source of liquidity to meet the operating expenditures, to

make investments and to pay off liabilities. In this paper, a firm that expects to have a higher

level of cash flow is therefore expected to hold less cash.

However, based on the existing literature, the following hypothesis is formulated:

H7: There is a positive relationship between the cash flows and cash holdings.

3.8 Hypothesis 8: Cash holdings are positively related to the cash flow

variability

Firms with more volatile cash flows face a higher probability of cash shortages in case of

unexpected cash deterioration or unexpected expenditures. As a result, cash flow

uncertainty is assumed to be positively related with cash holdings (Ferreira & Vilela, 2004;

Opler et. Al, 1999; Ozkan & Ozkan, 2004). Firms also tend to hold more cash to alleviate the

expected costs of liquidity constraints (Kim et al., 1998). In former cases, the precautionary

motive is the incentive to hold cash. The paper of Minton & Schrand (1999) also shows that

when a firm has a higher cash flow volatility, they rather delay investment opportunities. It

is however important to keep a certain level of cash to not loose investment opportunities.

Han & Qiu (2007) made a distinction between constrained and unconstrained

companies. They found that for constrained companies, higher cash flow volatility leads to

an increase of the cash holdings. A company is financially unconstrained if they can raise as

much funds as they desire, i.e. have unrestricted access to the external capital markets. A

constrained company on the contrary, cannot execute all its projects with a positive net

18

value. They sometimes have to sacrifice some valuable investment projects because their

investment expenditures depend on the availability of their internal funds rather than on the

existence of projects with a positive net present value (Fazzari, Hubbard, Petersen, Blinder,

& Poterba, 1988). Constrained firms choose their optimal cash policy by balancing the

profitability of current and future investment projects (Almeida, Campello, & Weisbach,

2004).

To make a distinction between financially constrained and financially unconstrained

firms Almeida et al. (2004) use following criteria:

- Pay-out ratio: this is the ratio of the total distributions to a firm’s shareholders

(dividends and stock repurchases) divided by its net operating income after tax.

Fazzari et al. (1998) argue that financially constrained firms have a lower pay-out

ratio than financially unconstrained firms.

- Asset size: small firms are considered to be less-known and more vulnerable to

capital market imperfections and are therefore to be financially constrained.

- Firm’s bond rating: firms that don’t have a bond rating are considered as financially

constrained firms.

- Firm’s commercial paper ratings: firms who never had their paper issue rated are

considered as financially constrained firms.

Because the sample in this thesis largely consists of non-listed Belgian companies, the

last two criteria are less useful to determine whether a company is financially constrained

or financially unconstrained. The pay-out ratio and the asset size however can give a good

indication. Most of the Belgian companies are not listed so the majority is considered as small

and medium sized companies. As a result, it is expected that most of these firms are

financially constrained. The results in the paper of Almeida et al. (2004) are rather

remarkable. Constrained firms on average hold 15% of their total assets in cash or cash

equivalents whereas unconstrained firms only hold 8 to 9% of their assets in cash or cash

equivalents. The estimates of their model show that constrained firms save out 5 or 6 cents

of each additional dollar of cash flow while the unconstrained firms save nothing.

The cash flow sensitivity of cash is also considered to determine the relationship

between the cash flow variability and cash holdings. The cash flow sensitivity of cash is a

firm’s propensity to save cash out of their cash inflows. Financially unconstrained firms are

expected to not systematically save cash out of their cash flows. In a crisis or recession for

example, financially constrained firms are expected to save out even more cash whereas

19

unconstrained firms are expected to not change their cash policy. Liquidity is therefore

irrelevant for unconstrained firms.

In conclusion, it is expected that for financially constrained firms the level of cash and

cash equivalents increases when the cash flow variability is higher. The eighth hypothesis

therefore is:

H8: There is a positive relationship between cash flow variability and cash holdings.

3.9 Hypothesis 9: Cash holdings are positively related to growth

opportunities

The importance of growth opportunities in determining the level of cash holdings has

been described in several empirical studies by Ozkan & Ozkan (2004), Opler et al. (1999)

and Myers & Majluf (1984). Firms with valuable growth opportunities are likely to demand

more funds in the future to realise these opportunities. For most firms, it is not possible to

finance all of their investments opportunities with their operating income so they need to

access the capital markets (D’Mello, Krishnaswami & Larkin, 2008). Companies however

prefer not to rely too much on these markets because of the high cost of debt and equity.

They therefore prefer to keep a certain level of cash to reduce their dependency on the

capital markets and to avoid losing valuable investment opportunities (Han & Qiu, 2007).

The opportunity cost of incurring a cash shortage is also higher for companies with large

investment opportunities (Drobetz & Grüninger, 2007). The pecking order theory is also

consistent with this hypothesis since it states that firms prefer to invest with their own cash

holdings rather than to rely on debt or equity to finance their investment opportunities.

Companies with large investment opportunities are also characterised by a higher

degree of information asymmetry resulting in a higher cost of external financing because

investors do not have all the information about these opportunities (Myers & Majluf, 1984).

Research of Harris & Raviv (1990) says that firms with large opportunities also incur a

cost of financial distress because their value does not depend on their tangible assets or

specific cash flows but on their growth opportunities. A company under financial distress

can incur costs related to this situation, e.g. more expensive financing.

Based on previous findings, the ninth hypothesis is formulated as follows:

H9: There is a positive relationship between growth opportunities and cash holdings.

20

Because there exist multiple measures for growth opportunities in literature and to give

a good understanding, four options that can be used as a proxy for growth opportunities are

briefly explained hereafter.

The first measure is the market-to-book ratio of a company’s equity. The book value is

measured by looking at the accounting value of the equity carried on the balance sheet. The

market value of a company is determined by the market capitalization in the stock market.

The market value of a firm is obtained by multiplying the total number of its outstanding

shares and the current market price of one share. A company’s market value is a good

indication of the investors perception of the business prospects of a firm. The market value

is influenced by the business cycle of a firm which is the economic activity (rise or fall) the

firm experiences over a period of time. A higher market-to-book ratio reflects greater future

gains. A share is considered to be undervalued by the market if its market value is below its

book value meaning that the share is trading at a discount. If the market value of a share is

above its book value, the share is considered to be overvalued.

The market-to-book value of a firm’s assets, also known as Tobin’s Q, is the ratio of the

estimated market value of the assets and the book value of the assets. The book value of the

assets does not include growth options as such but the market value does (Barclay & Smith,

1995). The market value of the firm’s assets is estimated as the book value of the assets

minus the book value of the equity plus the market value of the equity. It is expected that the

market value of a firm is higher than its book value if the firm has positive growth options

(Opler et al., 1999). Saddour (2006) used Tobin’s Q as a proxy for growth opportunities and

found a positive relationship between Tobin’s Q and the level of cash holdings.

Because of the limited number of Belgium companies listed on the stock market, the

market-to-book ratio and Tobin’s Q are not further considered as a proxy for growth

opportunities. These measures are of no further use for this paper since this would strongly

reduce the number of companies that can be used for the econometric model.

Another possibility to measure growth opportunities is the ratio capital expenditures

to total sales. Capital expenditures are funds that are used companies to acquire or upgrade

physical assets. These assets for example include property, industrial buildings and

equipment (“Capital expenditure,” n.d.). Kallapur & Trombley (1999) also found this to be a

very good measure for growth. According to Kallapur & Trombley (1999), the capital

investment activity measured by the ratio of the capital expenditures to total assets is

positively related with the realized growth.

21

Another possibility is to look at the research and development costs to total assets

ratio. Companies that are investing a lot in research and development are looking for

opportunities that may be executed in the future. If there exist some interesting investment

opportunities, the company wants to make sure that they have enough cash to fund these

investments without relying on the external market. Therefore, a positive relationship is

expected between the research and development costs and the growth opportunities.

Sales growth is also a good proxy to measure the growth opportunities. Dittmar (2004)

and Pinkowitz & Williamson (2004) argue that the sales growth over the last years is a

measure of immediate investment opportunities. They state that this is a good measure for

growing companies since a rise in sales is expected to enhance the current assets like

inventory and receivable influencing the assets growth of the company.

3.10 Summary of expected relationships

Table 1: Summary of the expected relationships

Variable Expected relationship with cash holdings

Growth opportunities Positive

Firm’s size Negative

Relationship with financial institutions Negative

Net working capital Negative

Dividend payment Positive

Leverage ratio Negative

Debt maturity Negative

Cash flow Positive

Cash flow variability Positive

22

4. Measurements

4.1 Sample selection

In this paper, the required data for testing the hypotheses are derived from the Belfirst

database from bureau Van Dijk. This database gives access to all Belgian companies with the

obligation to deposit their annual reports to the NBB (National Bank of Belgium) and other

economic entities like self-employed and non-profit organisations. The database also gives

access to the major Luxembourg companies. The Belfirst database contains information

about 2 million companies in Belgium and Luxemburg:

- 565,000 companies with annual accounts (both active or inactive status)

- 314,000 companies without the obligation to deposit the annual accounts

- 435,000 one-man businesses without the obligation to draw up the annual accounts

- 875,000 secondary branches

In a first step, the firms from Luxemburg are removed from the dataset. In a second step,

some Belgian firms are removed. The available data are highly detailed since all Belgian

companies are obliged to report their financial statements annually to the National Bank of

Belgium. The data consist of ten years of repeated measures for data of (un)listed Belgian

firms between 2006 and 2015. Only data until 2015 were available in the database. Besides

that, only firms with full annual reports are selected for this thesis because some information

that is needed for this research cannot be obtained from the annual reports of small firms.

Only ‘large’ firms need to hand in full annual reports. A firm is obliged to hand in full annual

reports when two of the next conditions are exceeded:

- Average workforce is 50

- Yearly turnover is €9,000,000

- Total assets are €4,500,000

Source: National Bank of Belgium

The hypothesis of cash flow variability requires data from three consecutive years to

calculate the standard deviation. Therefore, data from 2004 and 2005 are also collected. For

companies for which no data for three consecutive years were available, the standard

deviation of the cash flows could not be calculated. The data of that year for that specific firm

are excluded. Financial institutions are also excluded from the data set because banks and

insurers are subject to specific rules and regulations (Guizani, 2017). These are the firms of

section K of the NACE-BEL codes: financial activities and insurances. Section K consists of

three subsections:

23

- Subsection 64: financial services, excluding insurances and pension funds

- Subsection 65: insurance, reinsurances and pension funds, excluding mandatory

social insurances

- Subsection 66: support activities for insurances and pension funds

Utilities are also excluded from the sample because they can be subject to supervision

(Opler et. al, 1999). Their level of cash holdings may be regulated by the government.

Therefore, two subsections of section D and E are excluded:

- Subsection 35: production and distribution of electricity, gas, steam and chilled air

- Subsection 36: winning, treatment and distribution of water

Next, public services and public administration are also removed from the dataset

resulting in the exclusion of subsection 84: public administration and defence; mandatory

social insurances. Firms who have missing values for one of the variables are removed from

the dataset because the regressions can only be executed for firms that have data on all the

variables. As a result, a total number of 25 735 firms are eligible to test the hypotheses.

The data collected in this master’s thesis are panel data. Panel data have a cross-sectional

dimension and a time-series dimension. They have a cross-sectional dimension because the

variables of the single firms are collected at a single point in time. They also have a time-

series dimension because for most firms, data is collected over a period from 2004 to 2015.

One of the disadvantages of using panel data is the existence of heteroscedasticity and

autocorrelation. Therefore, some statistical tests are executed to detect heteroscedasticity

and autocorrelation.

4.2 Dependent variable

The cash holdings are measured by the sum of the cash and the cash equivalents. This

sum is then divided by the total assets of the firm.

Formula:

𝐶𝑎𝑠ℎ ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑖,𝑡 = 𝑐𝑎𝑠ℎ𝑖,𝑡 + 𝑐𝑎𝑠ℎ 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡𝑠𝑖,𝑡

𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠𝑖,𝑡

𝐶𝑎𝑠ℎ ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑖,𝑡 =(50 53⁄ )𝑖,𝑡 + (54 ∕ 58)𝑖;𝑡

(20 58⁄ )𝑖,𝑡

24

4.3 Independent variables

4.3.1 Firm’s size

The size of the firm is defined as the logarithm of the book value of the total assets of the

firm. The natural logarithm is used to reduce the spread of the balance sheet values of the

observations in the database resulting in smaller differences between the size of the firms.

In the financial statements, the book value of the total assets of the company is reported by

item 20/58.

4.3.2 Relationship with the financial institutions

Bank debt is defined as the ratio of the total bank debt to the total debt of the firm. There

exist bank credits in the long-term and in the short-term. In the financial statements, the

short-term bank credit is reported by item 173. The long-term bank credit is reported by

item 430/8. The total debt of the firm is reported by the items 17 (long-term debt) and 42/48

(short-term debt).

Formula:

𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝 𝑤𝑖𝑡ℎ 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠𝑖,𝑡 =𝑏𝑎𝑛𝑘 𝑑𝑒𝑏𝑡𝑖,𝑡

𝑡𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡𝑖,𝑡

𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝 𝑤𝑖𝑡ℎ 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠𝑖,𝑡 =173𝑖,𝑡 + (430 8⁄ )𝑖,𝑡

17𝑖,𝑡 + (42 48⁄ )𝑖,𝑡

4.3.3 Net working capital

Companies can also have other liquid assets that can easily be converted into cash at a

low cost. This variable can be measured based on the net working capital formula reduced

by the amount of cash and cash equivalents. Net working capital is defined as the difference

between the limited current assets and the short-term debt capital. This formula contains

cash and cash equivalents so cash and cash equivalents need be subtracted from it. This sum

is then divided by the total assets of the firm.

Formula:

𝑁𝑒𝑡 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑖,𝑡

25

=𝑙𝑖𝑚𝑖𝑡𝑒𝑑 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑎𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 − 𝑠ℎ𝑜𝑟𝑡 − 𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑖,𝑡 − 𝑐𝑎𝑠ℎ 𝑎𝑛𝑑 𝑐𝑎𝑠ℎ 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡𝑠𝑖,𝑡

𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠 𝑖,𝑡

𝑁𝑒𝑡 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑖,𝑡

= ((29 58⁄ )𝑖,𝑡 − 29𝑖,𝑡 − (42 48)⁄

𝑖,𝑡− (492 3)⁄

𝑖,𝑡 − (50 53⁄ )𝑖,𝑡 − (54 58)⁄

𝑖,𝑡)

(20 58⁄ )𝑖,𝑡

4.3.4 Dividend payments

A distinction is made between companies that pay out dividends and do not pay out

dividends. To estimate the effect of the dividend payments a binary dummy variable is

created. If the variable has a value of 0, this means that the company has not paid dividends

to its shareholders. If the variable has a value of 1, this means that the company has paid

dividends to its shareholders regardless the size of the dividend. To estimate the value of the

dummy variable, the following items are summed: return of capital (694), directors and

managers (695) and employees (696). If the sum of these four items is greater than zero, the

binary dummy variable is set equal to 1 in our regression model.

4.3.5 Leverage ratio

The total leverage is defined by the ratio of the total debt and the total assets of the firm.

This variable looks at how much capital comes from debt. The total debt is the sum of the

short-term debt (maturity < 1 year) and the long-term debt (maturity > 1 year).

Formula:

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 =(𝑠ℎ𝑜𝑟𝑡 − 𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡𝑖,𝑡 + 𝑙𝑜𝑛𝑔 − 𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡𝑖,𝑡 )


𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 = ((42 48⁄ )𝑖,𝑡 + 17𝑖,𝑡)

(20 58⁄ )𝑖,𝑡

4.3.6 Debt maturity

The debt maturity is defined as the ratio of long-term debt and total debt.

Formula:

26

𝐷𝑒𝑏𝑡 𝑚𝑎𝑡𝑢𝑟𝑖𝑡𝑦𝑖,𝑡 =𝑙𝑜𝑛𝑔 − 𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡𝑖,𝑡

𝑡𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡𝑖,𝑡

𝐷𝑒𝑏𝑡 𝑚𝑎𝑡𝑢𝑟𝑖𝑡𝑦𝑖,𝑡 = 17𝑖,𝑡

(42 48⁄ )𝑖,𝑡 + 17𝑖,𝑡

4.3.7 Cash flow

The classical approach to calculate the cash flow includes the profit after deduction of all

costs, including the financial cost of debt, and is therefore called the cash flow of equity after

payment of debt (financial cost of debt) but before compensation of the equity (earnings

payable). In this thesis however, this classical calculation of the cash flow is not used but the

cash flow is measured as the operating cash flow after tax instead. This formula is based

upon the formula in the book of Ooghe, Vander Bauwhede & Wymeersch (2012). The cash

flow ratio is defined as the ratio of the operating cash flow after tax and the total assets of

the firm.

27

Formula:

Table 2: Calculation of the cash flows

Items in the financial statements

Profit (loss) for the year after tax 9904𝑖,𝑡

+ Depreciation and amortization of

fixed assets

630𝑖,𝑡 + 6501𝑖,𝑡 − 760𝑖,𝑡 – 761𝑖,𝑡

+ 660𝑖,𝑡 + 661𝑖,𝑡

+ Impairment of the current assets (631 ∕ 4)𝑖,𝑡 + 651𝑖,𝑡

+ Provisions and deferred taxes for

the year

(635 ∕ 7)𝑖,𝑡 + 6560𝑖,𝑡 − 6561𝑖,𝑡 − 762𝑖,𝑡

+ 662𝑖,𝑡 − 780𝑖,𝑡 + 680𝑖,𝑡

+ Losses on disposal of fixed assets 663𝑖,𝑡

- Capital grants charged to the

income statements

9125𝑖,𝑡

- (Non-cash costs – non-cash

returns)

630𝑖,𝑡 + 6501𝑖,𝑡 − 760𝑖,𝑡 − 761𝑖,𝑡

+ 660𝑖,𝑡 + 661𝑖,𝑡 + 631 4⁄𝑖,𝑡 + 651𝑖,𝑡

+(635 7)⁄𝑖,𝑡

+ 6560𝑖,𝑡 − 6561𝑖,𝑡 − 762𝑖,𝑡

+ 662𝑖,𝑡 − 780𝑖,𝑡 + 680𝑖,𝑡 + 663𝑖,𝑡 − 9125𝑖,𝑡

= Cash flow of equity after tax 9904𝑖,𝑡 + 630𝑖,𝑡 + 6501𝑖,𝑡 − 760𝑖,𝑡

− 761𝑖,𝑡 + 660𝑖,𝑡 + 661𝑖,𝑡 + (631 4)⁄𝑖,𝑡

+ 651𝑖,𝑡 + 635 7⁄𝑖,𝑡 + 6560𝑖,𝑡 − 6561𝑖,𝑡

−762𝑖,𝑡 + 662𝑖,𝑡 − 780𝑖,𝑡 + 680𝑖,𝑡 + 663𝑖,𝑡

−9125𝑖,𝑡

Financial costs of loan capital 650𝑖,𝑡 + 653𝑖,𝑡 − 9126𝑖,𝑡

- Depreciation of the expenses on

the issuing of loans and disagio

6501𝑖,𝑡

- Financial cash costs of debt 650𝑖,𝑡 + 653𝑖,𝑡 − 9126𝑖,𝑡 − 6501𝑖,𝑡

= Operating cashflow after tax 9904𝑖,𝑡 + 630𝑖,𝑡 + 6501𝑖,𝑡 − 760𝑖,𝑡 − 761𝑖,𝑡

+ 660𝑖,𝑡 + 661𝑖,𝑡 + (631 4)⁄𝑖,𝑡

+ 651𝑖,𝑡

+ (635 7)⁄𝑖,𝑡

+ 6560𝑖,𝑡 − 6561𝑖,𝑡 − 762𝑖,𝑡

+ 662𝑖,𝑡 − 780𝑖,𝑡 + 680𝑖,𝑡 + 663𝑖,𝑡 − 9125𝑖,𝑡

+ 650𝑖,𝑡 + 653𝑖,𝑡 − 9126𝑖,𝑡 − 6501𝑖,𝑡

28

4.3.8 Cash flow variability

The standard deviation of the cash flows is measured by using the cash flows of the last

three years. Bates, Kahle, & Stulz (2009) also used the standard deviation of the cash flows

as a measure for the cash flow risk. The formula of the standard deviation is as follows:

Formula:

𝜎 = √(1

𝑁) ∑(𝑥𝑖 − 𝜇)²

𝑁

𝑖=1

4.3.9 Growth opportunities

It is not easy to find a good proxy for the variable growth opportunities since they are

not directly reflected in the financial statements of a company so a good estimate is needed

for this variable. For listed firms, most studies use Tobin’s Q which is equal to the market-to-

book value. This seems to be the best proxy for growth opportunities since it is used in most

research. However, most of the firms in this dataset are non-listed firms so it is not possible

to calculate or obtain the market value of these firms. Therefore, an alternative measure is

needed. In this thesis, three alternatives are used to estimate the growth opportunities. As

already mentioned in the first hypothesis (cash holdings are positively related to growth

opportunities), these three alternatives are sales growth, capital expenditures and research

and development costs. The sales growth measure is used in the analysis of the regression

results. The other measures are then used to check the robustness of the results afterwards.

To measure the sales growth, the following formula is used:

Formula:

𝑠𝑎𝑙𝑒𝑠 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡 = 𝑠𝑎𝑙𝑒𝑠𝑖,𝑡 − 𝑠𝑎𝑙𝑒𝑠𝑖,𝑡−1

𝑠𝑎𝑙𝑒𝑠𝑖,𝑡−1

𝑠𝑎𝑙𝑒𝑠 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡 =𝑐70𝑖,𝑡 − 𝑐70𝑖,𝑡−1

𝑐70𝑖,𝑡−1

An alternative sales growth measure is also possible:

Formula:

29

𝑠𝑎𝑙𝑒𝑠 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡 = 𝑠𝑎𝑙𝑒𝑠𝑖,𝑡 − 𝑠𝑎𝑙𝑒𝑠𝑖,𝑡−1

0,5𝑠𝑎𝑙𝑒𝑠𝑖,𝑡 + 0,5𝑠𝑎𝑙𝑒𝑠𝑖,𝑡−1

𝑠𝑎𝑙𝑒𝑠 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡 = 𝑐70𝑖,𝑡 − 𝑐70𝑖,𝑡−1

0,5𝑐70𝑖,𝑡 + 0,5𝑐70𝑖,𝑡−1

An alternative measure is the research and development to sales ratio. The costs of

research and development can be found in the notes of the financial statements in part 6.2.1.

To measure the investment in research and development the items 8021 and 8071 are used.

This total amount is then divided by the number of sales.

Formula:

𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑎𝑛𝑑 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡𝑖,𝑡 =𝑐8021𝑖,𝑡 + 𝑐8071𝑖,𝑡

𝑐70𝑖,𝑡

Another possibility is to test whether the investment in research in development has an

impact on the cash holdings without looking at the amount firms are investing in research

and development. In this situation, a dummy variable is created that equals 0 if the sum of

the two items (8021 and 8071) is zero and equals 1 if the sum of these two items is larger

than zero.

The last alternative are the capital expenditures. The following formula is used to

measure the capital expenditures:

Formula:

𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠𝑖,𝑡 = 𝑡𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝑎𝑠𝑠𝑒𝑡𝑠𝑖,𝑡− 𝑡𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝑎𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 +𝑑𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛𝑠 𝑖,𝑡


𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠𝑖,𝑡 =(22 27⁄ )𝑖,𝑡 − (22 27⁄ )𝑖,𝑡−1 + 630𝑖,𝑡

(20 ∕ 58)𝑖,𝑡

30

4.4 Summary of the measurements

Table 3: Summary of the measurements

Growth

opportunities

- Sales growth: 2 formulas

- 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠

𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠

- 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑠 𝑖𝑛 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑎𝑛𝑑 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡

𝑠𝑎𝑙𝑒𝑠

- Dummy variable research and development

Size Logarithm of the total assets

Relationship

with financial

institutions

𝐵𝑎𝑛𝑘 𝑑𝑒𝑏𝑡

𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡

Net working

capital

𝐿𝑖𝑚𝑖𝑡𝑒𝑑 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑎𝑠𝑠𝑒𝑡𝑠 − 𝑆ℎ𝑜𝑟𝑡 𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 − 𝐶𝑎𝑠ℎ 𝑎𝑛 𝑐𝑎𝑠ℎ 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡𝑠

𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠

Dividend

payments

0 if the firm does not pay out dividends, 1 if the firm does pay out dividends

Leverage 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡


Debt

maturity

𝐿𝑜𝑛𝑔 − 𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡

𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡

Cash flow 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑎𝑓𝑡𝑒𝑟 𝑡𝑎𝑥


Cash flow

variability

Standard deviation of the cash flows

Cash

holdings

𝐶𝑎𝑠ℎ 𝑎𝑛𝑑 𝑐𝑎𝑠ℎ 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡𝑠


31

5. Empirical research

5.1 Methodology

To test the hypotheses, the ordinary least squares (OLS) method is one of the methods

that are used. This method is a statistical regression analysis that tries to find the ‘line of best

fit’ for a certain dataset. It is a statistical technique for the analysis and modelling of linear

relationships. The method aims to create a line that minimizes the sum of the squared errors

by the result of the associated equation. The square is determined by the distance between

a certain data point and the regression line. In figure 3, this is the sum of the vertical squared

distances between each data point and the corresponding point on

the regression lines.

The smaller the difference between the data points and the estimated regression model, the

better the regression model fits the data. The R-squared of the model gives a measure of how

close the data is to the regression line. R-squared is the percentage of how much the

variability in the cash holdings is determined by the variability in the explanatory variables.

R-squared is a percentage between 0 and 100. 0 means that the econometric model does not

explain any variability in the cash holdings. 100 means that econometric model explains all

the variability in the cash holdings. The higher the R-squared, the better the model fits the

data.

By using the OLS method, the following regression model is used:

𝐶𝐴𝑆𝐻𝐻𝑂𝐿𝐷𝑖,𝑡 = 𝛽0 + 𝛽1 ∗ 𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽2 ∗ 𝑅𝐸𝐿𝑖,𝑡 + 𝛽3 ∗ 𝑁𝑊𝐶𝑖,𝑡 + 𝛽4 ∗ 𝐷𝐼𝑉𝑖,𝑡 + 𝛽5 ∗

𝐿𝐸𝑉𝑖,𝑡 + 𝛽6 ∗ 𝐷𝐸𝐵𝑇𝑀𝐴𝑇𝑖,𝑡 + 𝛽7 ∗ 𝐶𝐹𝐿𝑂𝑊𝑖,𝑡 + 𝛽8 ∗ 𝐶𝐹𝐿𝑂𝑊𝑉𝐴𝑅𝑖,𝑡 + 𝛽9 ∗

𝐺𝑅𝑂𝑊𝑇𝐻𝑂𝑃𝑃𝑖,𝑡 + 𝜇𝑖,𝑡

(1)

Figure 3: OLS optimization error term

Source: "Why You Need to Check Your Residual Plots for Regression Analysis," 2012)

32

with 𝐶𝐴𝑆𝐻𝐻𝑂𝐿𝐷𝑖,𝑡 = cash holdings of firm 𝑖 in year 𝑡, 𝑆𝐼𝑍𝐸𝑖,𝑡 = size of firm i in year t, 𝑅𝐸𝐿𝑖,𝑡

= relationship with the financial institutions of firm i in year t , 𝑁𝑊𝐶𝑖,𝑡 = net working capital

of firm i in year t, 𝐷𝐼𝑉𝑖,𝑡 = dividend payments of firm i in year t, 𝐿𝐸𝑉𝑖,𝑡 = leverage of firm i in

year t, 𝐷𝐸𝐵𝑇𝑀𝐴𝑇𝑖,𝑡 = debt maturity of firm i in year t, 𝐶𝐹𝐿𝑂𝑊𝑖,𝑡 = cash flow of firm i in year

t, 𝐶𝐹𝐿𝑂𝑊𝑉𝐴𝑅 𝑖,𝑡= cash flow variability of firm i in year t, 𝐺𝑅𝑂𝑊𝑇𝐻𝑂𝑃𝑃𝑖,𝑡 = growth

opportunities of firm i in year t, 𝜇𝑖𝑡 is the error term in this equation. This variable is created

because the model does not fully represent the actual relationship between the independent

and the explanatory variables.

In this paper, there are three ways of assessing the determinants of cash holdings. They

include the pooled OLS regression, the panel model of fixed effects and the panel model of

random effects. Equation (1) reflects the pooled OLS regression.

Pooled OLS regressions are multiple linear regressions applied to panel data. If the non-

observable individual effects do not play an important role the pooled OLS regression is the

appropriate way of evaluating the determinants of the cash holdings. The pooled OLS treats

all observations as one cross-section.

In this paper, the model includes nine variables that might influence the cash holdings

but most likely there are also other factors (observable and non-observable) that influence

the amount of cash. Therefore, the panel model of fixed effects and random effects are also

tested. If there is no correlation between the non-observable effects of a firm and the nine

determinants, then the random effects (RE) model is the best way to evaluate (Guizani,

2017). If there is a correlation between the non-observable effects and the determinants, the

fixed effects (FE) model is the best way to evaluate. The fixed effects model (within

estimator) uses the technique of demeaning. For every variable, their average over time is

deducted from their individual observations. The fixed effects model excludes variables that

do not change over time. For this thesis, it is assumed that the FE model is the most realistic

model. The other two models are added to serve as a robustness check. Considering the non-

observable individual firm effects, the following equation is used:

𝐶𝐴𝑆𝐻𝐻𝑂𝐿𝐷𝑖,𝑡 = 𝛽0 + 𝛽1 ∗ 𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽2 ∗ 𝑅𝐸𝐿𝑖,𝑡 + 𝛽3 ∗ 𝑁𝑊𝐶𝑖,𝑡 + 𝛽4 ∗ 𝐷𝐼𝑉𝑖,𝑡 + 𝛽5 ∗

𝐿𝐸𝑉𝑖,𝑡 + 𝛽6 ∗ 𝐷𝐸𝐵𝑇𝑀𝐴𝑇𝑖,𝑡 + 𝛽7 ∗ 𝐶𝐹𝐿𝑂𝑊𝑖,𝑡 + 𝛽8 ∗ 𝐶𝐹𝐿𝑂𝑊𝑉𝐴𝑅𝑖,𝑡 + 𝛽9 ∗

𝐺𝑅𝑂𝑊𝑇𝐻𝑂𝑃𝑃𝑖,𝑡 + 𝜑𝑖

+ 𝜔𝑡 + 𝜇𝑖,𝑡

(2)

33

where 𝜑𝑖 represents the firms’ non-observable individual effects in the regression model and

𝜔𝑡 represents the year fixed effects in the regression model.

𝜑𝑖 allows the regression model to control for variables that are constant over time but

differ across the different firms in the data set as well as characteristics of the industry in

which they are operating. These are unobservable factors such as preferences, management

ideas, corporate governance, risk and return preferences, management styles and

organizational structures. By using this model, the time invariant differences between the

firms are excluded. Each firm in the dataset is identified by a unique identification number.

In the fixed effects model, it is assumed that:

𝐶𝑜𝑣(𝜑𝑖, 𝑋𝑖,𝑡) ≠ 0

In the random effects model, it is assumed that

𝐶𝑜𝑣(𝜑𝑖, 𝑋𝑖,𝑡) = 0

The Hausman test is executed to differentiate between the random effects model and fixed

effects model. The initial hypothesis of the Hausman test is that the individual-firm effects

are modelled by a random effects model. This means that there is no correlation between the

non-observable individual effects and the explanatory variables. The alternative hypothesis

is that the individual-firm effects are modelled by a fixed effects model. If the p-value is small,

the initial hypothesis can be rejected which means that the individual effects are modelled

by the fixed effects model.

As already mentioned, 𝜔𝑡 represents the year fixed effects in the regression model. These

are annual dummies that are included to control for some year-specific effects that may

affect the cash holdings of the firms. These dummy variables change over time but are equal

in each of the time periods. Dummy variables are added from 2007 until 2015 because of the

dummy variable trap.

34

5.2 Descriptive statistics

Table 4: Descriptive statistics

VARIABLES (1) (2) (3) (4) (5) (6) (7)

VARIABLES N mean sd min max var median

Cashhold 154,075 0.143 0.196 0 0.932 0.0383 0.0592

Size 154,075 15.74 1.646 11.03 19.69 2.709 15.73

Rel 154,075 0.151 0.238 0 0.879 0.0568 0

Nwc 154,075 0.0182 0.379 -1.741 0.867 0.144 0.0254

Div 154,075 0.237 0.425 0 1 0.181 0

Lev 154,075 0.612 0.421 0.00171 2.925 0.177 0.597

Debtmat 154,075 0.179 0.276 0 0.963 0.0760 0.00580

Cflow 154,075 0.0825 0.129 -0.482 0.456 0.0168 0.0752

Cflowvar 154,075 0.0651 0.106 0.00159 0.694 0.0112 0.0314

Growthopp 154,075 0.0699 0.549 -0.999 4.240 0.301 0.0206

Table 4 presents the descriptive statistics for the variables used in the analysis. It shows

that the mean cash holdings level is 14,3% which means that cash holdings are a big part of

a firm’s total assets.

The next variable is size. This variable is represented by the logarithm of the total assets.

On average, the logarithm of the total assets is 15.74 which means that on average firms have

€5 126 840 of total assets on their balance sheet. The smallest company in this sample has

€61 698 of total assets on their balance sheet and the biggest company in this sample has

€355 842 936 of total assets on their balance sheet. The sample thus covers a broad range

of sizes.

The relationship with the financial institutions is represented by the ratio of total bank

debt (short-term and long-term) to total assets. On average, 15,1% of the total assets of the

35

firms is bank debt. However, there are also companies who do not have any bank debt on

their balance sheet and only rely on equity or other types of funding.

The net working capital ratio is represented by the ratio of net working capital (less cash

and cash equivalents) to total assets. The average is 1,82%. This is a rather small number

due to the exclusion of cash and cash equivalents out of the net working capital since cash

and cash equivalents are the biggest part of the net working capital. On average, if the cash

and cash equivalent are excluded, the amount of limited current assets is higher than the

amount of short-term liabilities. However, the standard deviation is very big so this is not

the case for all the firms in this database. Some firms do have a good liquidity management

and have a ratio of 80% and higher. Some firms do have a ratio of less than -100% which

indicates that they have a negative equity number meaning that these firms have more debt

than assets.

The average and other descriptive statistics about the dividend payments (dummy

variable) do not provide a lot of information. Only 24% of the yearly observations report

dividend payments meaning that the majority of the firms in this sample do not pay out

dividends to their shareholders. The distribution is shown in table 5.

Table 5: Dividend payments

Number of observations Frequency

Paying dividends 36 521 23,70%

Not paying dividends 117 554 76,30%

Total 154 075 100%

The debt level is represented by the leverage ratio. On average, 61,2% of the total assets

is debt. A common rule of thumb is that a good solvability ratio is between 60% and 70% so

the average percentage gives a positive sign for the Belgian firms. However, this dataset also

contains firms that are highly leveraged. For the maximum value, debt for example equals

2.925 times the total assets. These types of companies cannot exist for a long time since they

have negative equity.

When examining the debt maturity of the Belgian firms, most of the firms rely on short-

term debt. On average 17,9% of the total debt consists out of long-term debt which is a small

36

part and suggests that most firms rely on short-term funding (less than one year) instead of

long-term funding.

For growth opportunities, the first sales growth measure was used in the sample as this

gave the highest R-squared for all the regressions. The regressions with the other growth

opportunities were also executed resulting in the same results and conclusions. The average

sales growth over one year for a firm equals 7% in this sample. However, there are also firms

that have very high growth rates. The maximum growth rate in this sample equals 424%

meaning that some firms quadrupled their sales from last year. Some firms almost lost all

their sales which is indicated by the minimum percentage of -99.9%.

Figure 4: Average level of cash holdings over time

Figure 4 shows the evolution of the level of cash holdings from 2006 until 2015. In 2006,

the average level of cash holdings was around 13%. Between 2008 and 2010, during the

years of the economic and financial crisis, there was an increase of the average ratio of cash

holdings indicating that firms tend to hold more cash during periods of financial recession.

Between 2010 and 2013, there was a small decrease of the average level of cash holdings.

This is also tested later in this paper. After 2013, the level of cash holdings increased again

to 15%.

11,50%

12,00%

12,50%

13,00%

13,50%

14,00%

14,50%

15,00%

15,50%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Cas

h h

old

ings

Year

Average level of cash holdings over time

37

5.3 Gauss-Markov assumptions

In the previous part, the pooled OLS method was used to test the hypotheses. However,

this method is only a valuable method if the Gauss-Markov assumptions are met. It is

important to check and control if these assumptions are met before drawing conclusions

about the relationships.

The first assumption says that the model must be linear in the parameters. Secondly, the

x-values are fixed over repetitive samples (fixed regressor model). Thirdly, there should be

variation in the values of the independent variables. Another assumption states that the

number of observations must be greater than the parameters that are estimated. These four

assumptions are all met for data sample.

Furthermore, there can be no outliers. These are values that are very large in relation to

the rest of the observations in the sample. One must avoid that these outliers are dominating

the regression results. To avoid outliers in the variables, the technique of winsorizing is used.

This technique limits extreme values to reduce the effect of the outliers on the regression. In

this paper, all data below the second percentile is set equal to the lowest value in the second

percentile. All data above the 98th percentile, is set equal to the highest value in the 98th

percentile. The choice of the 96% winsorizing is due the existence of outliers in the highest

and lowest two percentiles. All the data outside the confidence interval of the other 96% of

the data are replaced by the largest number or the smallest number of this interval.

There must be no multicollinearity between the variables in the regression. By

examining the VIF factors (table 6) and the correlation table (table 7), it can be seen that

there is no multicollinearity problem because none of the variables have a VIF factor greater

than 10 (common rule of thumb).

38

Table 6: VIF factors

Table 7: Correlation matrix

In the presence of homoscedasticity, the variance of the error terms is constant. If this

condition is not met, there is heteroscedasticity in the econometric model. To test this, a

graphical analysis and the White heteroscedasticity test are executed. Both tests give an

indication of heteroscedasticity for this sample. The two tests can be found in attachment 3

of the appendix.

Autocorrelation means that there is a systematic pattern in the error terms. This usually

occurs for time series data and panel data. To test this, the Breusch-Godrey LM test is

conducted for which the results can be found in attachment 4 of the appendix. Results show

that for this sample data, autocorrelation is present.

Because of the existence of heteroscedasticity and autocorrelation, the OLS estimation is

adjusted by using the vce(cluster) command in Stata. This command gives the correct

adjusted standard errors. This standard error correction is performed for the three

regression models.

The GM assumptions require the model to be correctly specified. If this assumption is

not met, there are some specification errors that can give autocorrelation and

39

heteroscedasticity problems as a result. These specification errors can be avoided by adding

and removing variables until the correct model is specified. However, since the purpose of

this paper is to test the influence of the nine determinants mentioned before, the models in

this paper possibly contain specification errors. There are different causes of specification

errors:

- Relevant variables are not included into the model

- Irrelevant variables are included into the model

- A wrong functional form of the econometric model is used

- Measurement errors

The test for specification errors can be found in attachment 5 of the appendix.

The Hausman test, which differentiates between the fixed effects model and the random

effects model, shows that the fixed effects model is preferred. The random effects model

however serves as a robustness check. The Hausman test can be found in attachment 6 of

the appendix.

40

5.4 Results

Table 8: Regression results

(1) (2) (3)

VARIABLES OLS Fixed Effects Random Effects Size -0.0190*** -0.0326*** -0.0269*** (0.000747) (0.00185) (0.000966)

Rel -0.121*** -0.0682*** -0.0862*** (0.00377) (0.00416) (0.00350)

Nwc -0.252*** -0.331*** -0.303*** (0.00454) (0.00616) (0.00508)

Div 0.0302*** 0.00857*** 0.0117*** (0.00197) (0.00108) (0.00103)

Lev -0.238*** -0.270*** -0.252*** (0.00483) (0.00652) (0.00532)

Debtmat 0.0134*** 0.156*** 0.111*** (0.00374) (0.00521) (0.00392)

Cflow -0.0234*** 0.0609*** 0.0465*** (0.00707) (0.00513) (0.00484)

Cflowvar 0.116*** 0.0827*** 0.0969*** (0.00923) (0.00832) (0.00763)

Growthopp -0.00655*** -0.00382*** -0.00485*** (0.00100) (0.000693) (0.000674)

Constant 0.596*** 0.784*** 0.689*** (0.0126) (0.0292) (0.0156) Observations 154,075 154,075 154,075 R-squared 0.231 0.242 0.239

Number of firms 25,735 25,735 25,735 Company FE NO YES NO Year FE NO YES YES

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

41

After executing all the regression models, table 8 compares the results of the different

regressions that are executed. All coefficients in the three regression models are significant

at the 5% significance level and even at the 1% significance level.

Starting with the size of the firm, one notes a similar picture for all three regressions.

The pooled OLS regression, the fixed effects regression and the random effects regression all

give a negative relationship between the size of the firm and the cash holdings. The fixed

effects regression measures the largest influence of the size of the firm on the three

regression types with a coefficient of -0.0326. This means that every additional unit of the

size of the firm (logarithm of the total assets) declines the cash holdings ratio with 3.26%.

The negative relationship is in line with hypothesis 1 in part 2. The bigger the firm, the

cheaper it is to obtain funds because of the fixed costs of obtaining funds. Since the projects

of larger firms are bigger, the return needed to offset these fixed transaction costs is

decreasing with the size of the firm (Faulklender, 2002). Bigger firms are also likely to be

more diversified and have a lower probability of financial distress. When there is financial

distress and they are diversified, they could sell their non-core assets in order to obtain cash

when it is needed. Another argument is the presence of asymmetric information. Smaller

firms use to have more asymmetric information and as a result, they have more difficulties

in obtaining funds and accessing capital markets. This assumption is confirmed by Abad,

Sánchez-Ballesta, & Yagüe (2007) who state that smaller firms are more affected by

information asymmetries and need to increase financing from their suppliers. Bigger firms

see the capital markets and loans as a substitute of their cash holdings. For smaller firms,

this is more difficult to assume. Therefore, hypothesis 1 is confirmed so it can be concluded

that small firms have a higher level of cash holdings compared to bigger firms.

A good relationship with the financial institutions is reflected by more bank debt. By

having more bank debt, the Belgian firms are better monitored and banks can more easily

assess the credit quality of the firms by calculating an internal rating for their borrowers. A

high bank debt level means that the bank has confidence in the creditworthiness of the firms

and as a result, firms will have easier access to bank debt. Belgian firms see the bank debt as

a substitute of cash holdings so the relationship with the financial institutions is an

important determinant for the level of cash holdings. Therefore, hypothesis 2 is confirmed so

it can be concluded that firms with a better relationship with the financial institutions have

a lower level of cash holdings.

42

The net working capital hypothesis is also confirmed in all three regression models.

The net working capital ratio yields a negative relationship with each type of regression. The

argument that the net working capital less cash and cash equivalents is a liquid substitute

for cash and cash equivalents is strongly supported by the three regressions. Belgian firms

assume that these liquid substitutes can be easily converted into cash with very low or even

no transaction costs. These assets include for example real estate intended for sale (item 35

in the annual accounts) and commodities (item 30/31). It can be concluded that firms with

a higher net working capital (less cash and cash equivalents) have a lower level of cash

holdings so hypothesis 3 is confirmed.

The three regressions show a positive relationship between cash holdings and dividend

payments. In this hypothesis, the existence of dividend payments was tested but not the size

of the dividend payments. There were two types of theories supporting the relationship

between cash holdings and dividend payments. The first theory expected a negative

relationship between cash holdings and dividend payments. Firms see these dividend

payments as an easy way to free up cash when it is needed. The discontinuation of the

dividend payment frees up liquidity on the balance sheet. The negative relationship is also

supported by the fact that firms that pay out dividends are better monitored and have a

better access to the capital market. The access of capital markets is seen as a substitute of

cash holdings in that case. The other theory expected a positive relationship between cash

holdings and dividend payments as they assumed that firms do not want to squeeze their

periodic dividend payments. Therefore, they want to have enough cash in order to fulfil these

periodic payments. The squeeze of dividend payments could cause anxiety among the

shareholders. It could also lead to a fall of the share price of the firm in case it is a listed firm.

All three regressions suggest that the second theory is more valid for Belgian firms. They will

not just stop dividend payments to obtain extra cash. Hypothesis 4 is confirmed so it can be

concluded that firms that pay out dividends have a higher level of cash holdings.

The results clearly show that there is a negative relationship between the leverage ratio

of the firm and the level of cash holdings. The higher the amount of total debt, the less cash

a firm holds. The high debt level brings the creditor in a good position to control the credit

quality of the debtor and the financial policies of the debtor. This cuts down the agency costs

between the two parties and results in a lower financing cost for the debtor. This is very

interesting because it makes funding become cheaper. In that situation, debt is more

attractive and firms see debt as a substitute of cash. This induces a negative relationship. The

43

pecking order theory also suggested a negative relationship between the leverage ratio and

the level of cash holdings. When the investment needs exceed the retained earnings, firms

first try to finance their investments with internal generated funds by means of cash

holdings. If the internal generated funds are not enough, they take on debt. The reduction of

the cash holdings and the increase of debt induces a negative relationship between the two

variables. Hypothesis 5 is therefore confirmed so it can be concluded that the higher the

leverage ratio, the lower the level of cash holdings.

Debt maturity is also related to the leverage ratio. The more mature debt is, the less

cash is held in a firm. In other words, more short-term debt would result in more cash

holdings and more long-term debt would result in less cash holdings. Short-term debt

obliges the firm to periodically negotiate the renewal of this debt and this causes the risk of

refinancing (Garcia-Teruel et al., 2008). Firms face the risk that changes in market conditions

and capital market imperfections result in refinancing of their debt at a higher interest cost

(Froot, Scharfstein, & Stein, 1993). However, the three regression methods indicate a

positive relationship between debt maturity and the level of cash holdings. This means that

more long-term debt results in more cash holdings and more short-term debt results in less

cash holdings. This is surprising as the majority of literature suggests a negative relationship

between these two variables. The reason for the positive relationship between debt maturity

and cash holdings is explained by the theory of Barclay & Smith (1995). They found that

there are two types of short-term borrowers: firms with a very good rating or credit quality

and firms with a very poor rating or credit quality. Firms with a very good credit quality can

issue short-term debt because the refinancing risk is much lower. Firms with a very bad

credit quality are not able to issue long-term debt because of the costs related to this debt

(Barclay & Smith, 1995) They argue that debt of firms with an intermediate risk mainly

consists of long-term debt. The negative relationship between cash holdings and short-term

debt could be explained by the existence of Belgian firms with a very good credit quality or

very bad credit quality in this sample. If the firms with a good credit quality have an easier

access to short-term debt, they can see this as an immediate substitute of cash holdings

decreasing the level of their cash holdings. Firms with a very bad credit quality can see the

short-term debt as a good substitute of cash holdings since they are not able to issue other

types of debt. Even though the credit quality of the firms is not examined for this sample, this

can still give a possible explanation for why the hypothesis is not confirmed by the regression

44

models. As a result, it can be concluded that for firms with more long-term debt, this leads to

a higher level of cash holdings. Hypothesis 6 is not confirmed.

Literature is not consentient about the relationship between cash flows and cash

holdings. This negative relationship is confirmed by the fixed effects model and the random

effects model. The pooled OLS regression gives a positive relationship between the two

variables. The positive relationship could be explained by the free cash flow theory of Jensen

(1986) who states that managers prefer to build up the level of cash holdings to have control

over a greater amount of assets. It is also supported by the pecking order theory who states

that firms prefer internal financing over external financing. For this purpose, firms will hold

the cash flow as a source of liquidity so higher cash flows result in a higher level of cash

holdings. The fixed effects model and the random effects model on the contrary, indicate a

negative relationship between cash flow and cash holdings. The negative relationship is

supported by the static trade-off theory who considers the cash flow as a substitute of cash

holdings. In this theory, investments can be financed immediately using the current cash

flows. We know that the pooled OLS regression treats all the observations as one cross-

section. This might give a wrong interpretation of the results as it is important to examine

the within-estimator. We assume the within-estimator to give to a more realistic result.

Hypothesis 7 is confirmed so a positive relationship is expected between the level of cash

holdings and the cash flows.

A similar picture is true for the cash flow variability which is related to the uncertainty

about the cash flows. The three regression models all report a positive relationship between

cash flow variability and cash holdings. If the variability of the cash flows is higher, there is

a larger probability of a negative cash flow which can affect the level of cash holdings. As a

result, Belgian firms tend hold a higher cash level to control for this uncertainty for

precautionary reasons. Hypothesis 8 is therefore confirmed.

Finally, for the growth opportunities of the firm a negative relationship between cash

holdings and growth opportunities is reported by all three regressions. This is not as

expected. It is a surprising result as there are almost no explanations available in literature

that support a negative relationship. Firms with growth opportunities are generally

expected to hold more cash to be ready to execute their investment projects. The positive

relationship can however possibly be explained by the free cash flow theory. Managers can

also invest in projects with a negative present value. This would imply that even if they do

not identify valuable projects, they want to hold cash to invest in non-valuable projects. As a

45

result, they increase their discretion in the company. Another possible explanation is given

by the fact that most of the Belgian firms in this sample possibly have a great access to

external financing. They have confidence in the availability of external financing. The size of

the company can also play an important role. This sample consists of firms with full annual

accounts so small firms are excluded. This can have an impact on the impact of the growth

opportunities on cash holdings. The impact may be higher for smaller firms as they have a

more restricted access to debt and they are not seeing debt as a substitute of cash holdings.

The regression has shown that size, leverage and relationship with financial institutions all

have a negative impact on the level of cash holdings.

Another explanation for the negative relationship between growth opportunities and the

level of cash holdings can be the wrong measurement of the growth opportunities. For

example, the sales growth measure of last year could be the results of the investments in

property, plants and equipment and this might be the reason why firms have less cash

holdings on their balance sheet. More investments mean less cash holdings. It is also

assumed that the historical growth will continue in the future but this might not be the case.

The best measurement of growth opportunities is still the market-to-book ratio. It reflects

the future potential of a firm. However, most of the Belgian firms are not listed so these

values were difficult to obtain. In this paper, 5 measurements are used to test the

relationship. All 5 measurements gave a negative relationship between growth

opportunities but one must be careful with these measurements as it is not sure if they really

reflect the growth opportunities. The other regressions can be found in attachment 5 of this

paper. With some caution, it can be concluded that level the level of cash holdings decreases

when growth opportunities are higher. Hypothesis 9 is therefore not confirmed. However, it

is necessary to be aware of the possibility of measurement errors.

46

Table 9: Fixed effects regression high-growth firms versus low-growth firms

(1) (2)

VARIABLES High-growth firms

Low-growth firms

Size -0.0219*** -0.0431*** (0.00295)

(0.00322)

Rel -0.0748*** -0.0748*** (0.00791)

(0.00882)

Nwc -0.346*** -0.339*** (0.0107)

(0.0107)

Div 0.00800*** 0.0119*** (0.00203)

(0.00290)

Lev -0.280*** -0.271*** (0.0111)

(0.0110)

Debtmat 0.167*** 0.182*** (0.0101)

(0.0112)

Cflow 0.0934*** 0.0610*** (0.0113)

(0.00977)

Cflowvar 0.0236 0.0859*** (0.0154)

(0.0155)

Growthopp 0.00190* -0.0189*** (0.00112)

(0.00542)

Constant 0.619*** 0.784*** (0.0461) (0.0504) Observations 38,519 38,519 R-squared 0.261 0.272 Number of firms

17,323 17,496

Company FE YES YES Year FE YES YES


Based on the paper of Gomes (2012), a subsample of low-growth firms (firms below the

25th percentile of the variable growth opportunities) and a subsample of high-growth firms

are made (firms above the 75th percentile of the variable growth opportunities) based on the

47

sales growth of these firms. These subsamples are used to investigate if there are any

differences in the relationship of the determinants and the level of cash holdings between

high-growth firms and low-growth firms. The average sales growth of the low-growth firms

is equal to -32% while the average sales growth of the high-growth firms is equal to 55%.

Table 9 shows the regression results for both type of firms. The firms’ size, the

relationship with the financial institutions, the net working capital, the dividend payments,

leverage, debt maturity and cash flow seems to have the same impact on the cash holdings

policy. However, there are two differences when the high-growth firms are compared with

the low-growth firms. The first difference is the variable cash flow variabilities. This variable

is not significant. This means that cash flow variability is not an important factor in

determining the level of cash holdings. The second difference is the variable growth

opportunities. The fixed effects regression (within estimator) shows that high-growth firms

are holding more cash when growth opportunities are higher and low-growth firms are

holding less cash when growth opportunities are higher. The majority of the observations

have a small or negative sales growth rate. About 50% of the observations have a sales

growth of 2.06% (median) or lower. The 25% highest observations show that some firms in

this sample almost tripled or quadrupled their sales of previous year.

Despite the difference in sales growth of the high-growth firms and the low-growth

firms, the relationships generally remain the same except for the cash flow variability and

the growth opportunities. High-growth firms seem to hold more cash when growth

opportunities are higher which is in line with what is generally expected. Low-growth firms

seem to hold less cash when growth opportunities are higher but this could be explained by

the limited or negative growth expectations. Due to the presence of many low-growth firms,

the full sample regression gave a negative relationship between growth opportunities and

the level of cash holdings.

48

Table 10: Summary of the results

VARIABLE EXPECTED RELATIONSHIP CONCLUSION

Size - -

Relationship with

financial institutions

- -

Net working capital - -

Dividend payments + +

Leverage ratio - -

Debt maturity - +

Cash flow + +

Cash flow variability + +

Growth opportunities + + (high-growth firms)

- (low-growth firms)

The estimated betas in the regression depend on the scale and the standard deviation of

the variables. It is difficult to know which parameters are crucial just by looking at the beta’s.

To know the true relative importance of the independent variables, the economic relevance

is calculated for the three types of regressions in table 11, table 12 and table 13. The

economic relevance is calculated by multiplying the beta of the standard deviation of the

variable. A small standard deviation makes big changes more unlikely. Three of the nine

independent variables have a great impact on the three regression models. Size (10%-15%),

net working capital (32%-34%) and leverage (30-35%) seem to be the most crucial variables

in determining the level of cash holdings of the firm. In total, 70%-80% of the changes in cash

holdings are influenced by the change in these three determinants in this model. However,

the coefficient of determination is equal to 24%. This coefficient presents the proportion of

the variance in the dependent variable that is explained from the variance in the independent

variable. By multiplying the 80% with the R-squared, it can be concluded that 17% to 19%

of the changes in cash holdings are determined by these three variables. Thus, there are

many other variables that cause changes in cash holdings besides the ones that have been

investigated in this thesis.

49

Table 11: OLS regression economic relevance

VARIABLE COEFFICIENT

(absolute

value)

STANDARD

DEVIATION

COEFFICIENT*STANDARD

DEVIATION

ECONOMIC

RELEVANCE

SIZE 0.0190 1.646 0.031274 10.74%

REL 0.121 0.238 0.028798 9.89%

NWC 0.252 0.379 0.095508 32.80%

DIV 0.0302 0.425 0.012835 4.41%

LEV 0.238 0.421 0.10198 34.41%

DEBTMAT 0.0134 0.276 0.003698 1.27%

CFLOW 0.0234 0.129 0.003019 1.04%

CFLOWVAR 0.116 0.106 0.012296 4.22%

GROWTH OPP 0.00655 0.549 0.003596 1.23%

SUM 0.291222 100% Table 12: Fixed effects regression economic relevance


(absolute value)

STANDARD

DEVIATION


DEVIATION

ECONOMIC

RELEVANCE

SIZE 0.0326 1.646 0.05366 14.61%

REL 0.0682 0.238 0.016232 4.42%

NWC 0.331 0.379 0.125449 34.15%

DIV 0.00857 0.425 0.003642 0.99%

LEV 0.270 0.421 0.11367 30.94%

DEBTMAT 0.156 0.276 0.043056 11.72%

CFLOW 0.00609 0.129 0.000786 0.21%

CFLOWVAR 0.0827 0.106 0.008766 2.39%

GROWTH OPP 0.00382 0.549 0.002097 0.57%

SUM 0.367357 100%

Table 13: Random effects regression economic relevance


(absolute value)

STANDARD

DEVIATION


DEVIATION

ECONOMIC

RELEVANCE

SIZE 0.0269 1.646 0.044277 13.01%

REL 0.0862 0.238 0.020516 6.03%

NWC 0.303 0.379 0.114837 33.75%

DIV 0.0117 0.425 0.004973 1.46%

LEV 0.252 0.421 0.106092 31.18%

DEBTMAT 0.111 0.276 0.030636 9.00%

CFLOW 0.0465 0.129 0.005999 1.76%

CFLOWVAR 0.0969 0.106 0.010271 3.02%

GROWTH OPP 0.00485 0.549 0.002663 0.78%

SUM 0.340263 100%

5.5 Impact of the crisis

In the financial crisis of 2008-2009, banks and investors became more prudent in

lending to individuals and corporations. This behaviour drove up the price of debt for the

borrowers because lenders feared bankruptcies and defaults (Berg, 2016). This resulted in

higher lending rates in the financial crisis. The credit supply shock also resulted in more loan

rejections. As a result, firms might have increased their cash holdings for precautionary

50

reasons because of the uncertainty about the loan approvals. They for example increased

their cash holdings by collecting bills earlier or by cutting down investments (Berg, 2016).

For firms with low liquidity this could have a great impact on their asset growth, investment

strategy and employment. Firms with high liquidity could use their cash holdings to continue

their investment strategy. An interesting question therefore is: do firms still see bank debt

as a substitute for cash holdings during the financial crisis of 2008-2009? This can be

investigated by adding an interaction term between relationship with financial institutions

and a dummy variable crisis to the regressions. The dummy variable crisis equals 1 for

observations in the years 2008 and 2009.

Table 14 shows the overall effect of the crisis on the level of cash holdings. A dummy

variable crisis is therefore added to the regression model. The descriptive statistics in

Section 5.2 showed that the level of cash holdings increase with about 2%. An extra term for

the dummy variable is added to check whether the crisis really had an impact on the level of

cash holdings. The fixed effects regression and random effects regression showed that the

relationship is very significant and that there is a positive relationship between cash

holdings and the financial crisis of 2008-2009.

Table 15 reports the results for the regression models that include an interaction effect

between the relationship with financial institutions and the dummy variable that accounts

for the crisis. Table 15 shows that the interaction effect is significant in both the fixed effects

and random effects regression models. This means that during periods of financial crisis cash

holdings increase irrespective of the relationship that firms have with financial institutions.

51

Table 14: Impact crisis on level of cash holdings

(1) (2) (3)


Rel -0.121*** -0.0682*** -0.0862*** (0.00377) (0.00416) (0.00350)

Nwc -0.252*** -0.331*** -0.303*** (0.00454) (0.00616) (0.00508)

Div 0.0301*** 0.00857*** 0.0117*** (0.00197) (0.00108) (0.00103)

Lev -0.238*** -0.270*** -0.252*** (0.00483) (0.00652) (0.00532)

Debtmat 0.0134*** 0.156*** 0.111*** (0.00374) (0.00521) (0.00392)

Cflow -0.0233*** 0.0609*** 0.0465*** (0.00708) (0.00513) (0.00484)

Cflowvar 0.116*** 0.0827*** 0.0969*** (0.00923) (0.00832) (0.00763)

Growthopp -0.00657*** -0.00382*** -0.00485*** (0.00100) (0.000693) (0.000674)

Dummy_crisis -0.00118 0.00939*** 0.00769*** (0.000944) (0.00130) (0.00127)




52

Table 15: Interaction effect of relationship with financial institutions and crisis

(1) (2) (3)


Rel -0.121*** -0.0693*** -0.0876*** (0.00377) (0.00421) (0.00354)

Nwc -0.252*** -0.331*** -0.303*** (0.00454) (0.00616) (0.00508)

Div 0.0302*** 0.00857*** 0.0117*** (0.00197) (0.00108) (0.00103)

Lev -0.238*** -0.270*** -0.252*** (0.00483) (0.00652) (0.00532)

Debtmat 0.0134*** 0.156*** 0.111*** (0.00374) (0.00521) (0.00392)

Cflow -0.0234*** 0.0609*** 0.0465*** (0.00707) (0.00513) (0.00484)

Cflowvar 0.116*** 0.0827*** 0.0968*** (0.00923) (0.00832) (0.00763)

Growthopp -0.00655*** -0.00382*** -0.00485*** (0.00100) (0.000693) (0.000674)

Rel_crisis 0.00116 0.00560** 0.00684*** (0.00235) (0.00248) (0.00244)




53

6. Conclusions

This paper examined the effect of nine different determinants of cash holdings in Belgian

firms for the period 2006 until 2015. These determinants included the size of the firm, the

relationship with the financial institutions, net working capital, dividend payments, leverage,

debt maturity, cash flow, cash flow variability and growth opportunities. For every

determinant, its relationship with cash holdings was determined.

In a first step, an overview of the existing literature was given. Based on that, for every

determinant a hypothesis about their relationship with cash holdings was formulated and a

proxy was determined. In the next step, the hypotheses were tested by the use of three

regression models.

Cash holdings are of a great importance on the balance sheet of a firm. They allow a firm

to grow, to invest and to meet their financial obligations. Although cash and cash equivalents

do not yield high returns, it is important for firms to maintain a certain degree of liquidity.

For the firms in this research, cash and cash equivalents amounted to 14,3% of the total

assets on average.

Although a large number of studies has been conducted on the determinants of cash

holdings, the results can vary considerably across countries. For example, Belgian firms have

easy access to bank debt. Nor venture capital, nor business angels but bank debt is the main

type of external financing for Belgian firms, in particular for SME’s. Belgium is very bank-

oriented just like Germany and Japan. This is different from the market oriented economy

like in the United States where firms rely less on bank debt as a form of external financing.

This is in line with the results obtained in this thesis. Results for example showed that firms

that have a good relationship with financial institutions, which is represented by higher

amounts of bank debt, tend to hold less cash. This is not only valid for bank debt but also for

other types of debt which is represented by a negative relationship between the leverage

ratio and cash holdings. As already mentioned, this is probably due to easy access to external

funds and good credit ratings. Note however, that the debt of Belgian firms mainly consists

of short-term debt. This leads to the conclusion that short-term is more seen as a substitute

than long-term debt.

Besides debt, results also showed that certain assets that are included in the net working

capital are considered as substitutes for cash and cash equivalents. This means that Belgian

companies who have a lot of net working capital, hold less cash on average. These assets can

easily be converted into cash and for example include inventories and accounts receivable.

54

This paper also showed that bigger firms hold less cash on their balance sheet. This is

explained by the fact that bigger companies are usually more diversified allowing them to

sell off assets in times of financial distress and that they have easier and cheaper access to

external financing.

Uncertainty also plays a role in determining the level of cash holdings. Results showed

that the bigger the variability in the cash flow, the more cash a firm holds due to

precautionary reasons. The financial crisis of 2008-2009 was for example a source of

uncertainty. This paper therefore also studied the effect of this crisis on the level of cash

holdings. It was shown that during the financial crisis of 2008-2009, firms held more cash

on average.

Not only uncertainty led to the fact that Belgian firms hold more cash. Firms also hold

more cash to fulfil the periodic dividend payments instead of seeing these dividends as an

easy way to free up cash. This is due to the potentially negative consequences associated

with a change in dividend policy. Another determinant leading to more cash is cash flow. The

higher the cash flows, the more firms save out cash from these cash flows. Firms do not have

the incentive to immediately use their cash flow as source of liquidity but are encouraged to

retain this as excess cash.

The last determinant was growth opportunities. Here, a positive relationship was

expected, but based on the sample data a negative relationship was found between the level

of cash holdings and growth opportunities of a firm. Since this was contra-intuitive, two

subsamples were made between high-growth firms and low-growth firms (small and

negative growth) to test whether the results are valid for the entire sample data. Results

showed that only the determinants growth opportunities and cash flow variability were

different among these two subsamples. High-growth firms tend to have the incentive to hold

more cash when they have higher growth opportunities in order to have enough funds

available when they want to make investments. Low-growth firms do not have the incentive

to hold more cash probably because they do not expect to make big investments in the near

future. It could therefore be concluded that the hypothesis was confirmed for high-growth

firms.

The results of this study can be very interesting for Belgian firms to have a deeper

understanding and appreciation of the role and the importance of the firm characteristics on

the level of cash holdings. It can improve the knowledge of decision makers such as

shareholders, managers, and investors about what motivates firms to hold a certain level of

55

cash holdings. This knowledge may be useful in defining a liquidity policy in their firms.

Determining the level of cash holdings is more than just putting a number, it is the coherence

of many internal and external characteristics. It is finding the right balance between holding

too much and too less cash on the basis of a variety of factors, nine of which have been

discussed in detail in this paper.

7. Limitations

Although a lot of determinants have been investigated in this thesis, there are many

other factors that influence the level of cash holdings in a firm. Agency problems and

corporate governance mechanisms (size and structure of the board of directors, shareholder

protection, etc.) are internal characteristics that can have a large impact on how liquid assets

are managed. In many papers, corporate governance seems to be an important factor for the

level of cash holdings. Data on these variables were however not available. Not only internal

characteristics determine the level of cash holdings. For further research, it would be

interesting to investigate some macro-economic factors such as inflation, unemployment

rate or capital market developments.

Related to the data, it is not sure if all the variables are a good proxy for the determinants.

For the determinant growth opportunities, the market-to-book ratio seems the most

appropriate measure for growth opportunities. However, it was not possible to obtain the

market value of all these firms since most firms in this sample were non-listed firms. The

Bel-first database has no information about the market value of the firms. Therefore,

alternative growth opportunities measures were proposed and served as a robustness

check.

This thesis only focused on a sample of 25,735 Belgian firms with full annual accounts.

The results of this paper cannot be generalized over all firms in Belgium since small firms

may have other factors influencing the level of cash holdings. It may be interesting to have a

look on firms with abridged annual accounts to see if the relationships are the same. One has

to be aware of the fact that small firms have to report less and this could lead to a lack of data

when conducting research on these types of firms. Small firms for example often lack the

time and the resources to deal with their cash policy on a regular basis. Larger companies on

the other hand often have financial specialists who can monitor the specific cash policies of

their firms. The question is whether small companies take all these factors into account or

56

simply keep cash holdings based on their gut feeling. A qualitative study could be carried out

in this respect.

The results are only valid for Belgian firms. Some countries may have a greater access to

capital markets and consider that as a substitute of cash. It is also important to note that this

research is done across all industries. Some industries however may be characterized by

higher investment and higher cash holdings needs.

Another limitation can be the use of the econometric model. This thesis only used static

panel models. It is assumed that firms can instantaneously adjust their level of cash holdings

towards a target cash level following the changes in the determinants (Guizani, 2012). Some

papers also used dynamic panel models. They assume that adjustment costs may delay

adjustment to an optimal level of cash holdings.

V

Bibliography

Journal articles

Abad, D., Sánchez-Ballesta, J. P., & Yagüe, J. (2017). The short-term debt choice under

asymmetric information. SERIEs, 8(3), 261-285.

Almeida, H., Campello, M., & Weisbach, M. S. (2004). The cash flow sensitivity of cash. The

Journal of Finance, 59(4), 1777-1804.

Barclay, M. J., & Smith, C. W. (1995). The maturity structure of corporate debt. The Journal of

Finance, 50(2), 609-631.

Bates, T. W., Kahle, K. M., & Stulz, R. M. (2009). Why do US firms hold so much more cash than

they used to? The Journal of Finance, 64(5), 1985-2021.

Berg, T. (2016). Got rejected? Real effects of not getting a loan.

Bigelli, M., & Sánchez-Vidal, J. (2012). Cash holdings in private firms. Journal of Banking &

Finance, 36(1), 26-35.

Brealey, R., Leland, H. E., & Pyle, D. H. (1977). Informational asymmetries, financial structure,

and financial intermediation. The Journal of Finance, 32(2), 371-387.

Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of

Finance, 63(6), 2899-2939.

Chung, K. H., McInish, T. H., Wood, R. A., & Wyhowski, D. J. (1995). Production of information,

information asymmetry, and the bid-ask spread: Empirical evidence from analysts'

forecasts. Journal of Banking & Finance, 19(6), 1025-1046.

D’Mello, R., Krishnaswami, S., & Larkin, P. J. (2008). Determinants of corporate cash holdings:

Evidence from spin-offs. Journal of Banking & Finance, 32(7), 1209-1220.

Dittmar, A. (2004). Capital structure in corporate spin‐offs. The Journal of Business, 77(1), 9-

43.

Dittmar, A., Mahrt-Smith, J., & Servaes, H. (2003). International corporate governance and

corporate cash holdings. Journal of Financial and Quantitative analysis, 38(1), 111-

133.

Drobetz, W., & Grüninger, M. C. (2007). Corporate cash holdings: Evidence from Switzerland.

Financial Markets and Portfolio Management, 21(3), 293-324.

Faulkender, M. (2002). Cash holdings among small businesses.

VI

Fazzari, S. M., Hubbard, R. G., Petersen, B. C., Blinder, A. S., & Poterba, J. M. (1988). Financing

constraints and corporate investment. Brookings papers on economic activity,

1988(1), 141-206.

Fazzari, S. M., & Petersen, B. C. (1993). Working capital and fixed investment: new evidence

on financing constraints. The RAND Journal of Economics, 328-342.

Ferreira, M. A., & Vilela, A. S. (2004). Why do firms hold cash? Evidence from EMU countries.

European Financial Management, 10(2), 295-319.

Froot, K. A., Scharfstein, D. S., & Stein, J. C. (1993). Risk management: Coordinating corporate

investment and financing policies. The Journal of Finance, 48(5), 1629-1658.

García‐Teruel, P. J., & Martínez‐Solano, P. (2008). On the determinants of SME cash holdings:

Evidence from Spain. Journal of Business Finance & Accounting, 35(1‐2), 127-149.

Gill, A., & Shah, C. (2012). Determinants of corporate cash holdings: Evidence from Canada.

International Journal of Economics and Finance, 4(1), 70.

Gomes, M. d. S. (2012). The financial determinants of corporate cash holdings: evidence from

growing firms.

Guizani, M. (2017). The financial determinants of corporate cash holdings in an oil rich

country: Evidence from Kingdom of Saudi Arabia. Borsa Istanbul Review, 17(3), 133-

143.

Han, S., & Qiu, J. (2007). Corporate precautionary cash holdings. Journal of Corporate Finance,

13(1), 43-57.

Harford, J., Klasa, S., & Maxwell, W. F. (2014). Refinancing risk and cash holdings. The Journal

of Finance, 69(3), 975-1012.

Harris, M., & Raviv, A. (1990). Capital structure and the informational role of debt. The

Journal of Finance, 45(2), 321-349.

Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The

American economic review, 76(2), 323-329.

Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs

and ownership structure. Journal of financial economics, 3(4), 305-360.

Kallapur, S., & Trombley, M. A. (1999). The association between investment opportunity set

proxies and realized growth. Journal of Business Finance & Accounting, 26(3‐4), 505-

519.

Kim, C.-S., Mauer, D. C., & Sherman, A. E. (1998). The determinants of corporate liquidity:

Theory and evidence. Journal of Financial and Quantitative analysis, 33(3), 335-359.

VII

Lang, L., Poulsen, A., & Stulz, R. (1995). Asset sales, firm performance, and the agency costs

of managerial discretion. Journal of Financial Economics, 37(1), 3-37.

Luo, Q., & Hachiya, T. (2005). Bank relations, cash holdings, and firm value: evidence from

Japan. Management Research News, 28(4), 61-73.

Mikkelson, W. H., & Partch, M. M. (2003). Do persistent large cash reserves hinder

performance? Journal of Financial and Quantitative analysis, 38(2), 275-294.

Miller, M. H., & Orr, D. (1966). A Model of the Demand for Money by Firms. The Quarterly

journal of economics, 80(3), 413-435.

Minton, B. A., & Schrand, C. (1999). The impact of cash flow volatility on discretionary

investment and the costs of debt and equity financing. Journal of financial economics,

54(3), 423-460.

Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory

of investment. The American economic review, 48(3), 261-297.

Murphy, K. J. (1985). Corporate performance and managerial remuneration: An empirical

analysis. Journal of accounting and economics, 7(1-3), 11-42.

Myers, S. C. (1984). The capital structure puzzle. The Journal of Finance, 39(3), 574-592.

Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms

have information that investors do not have. Journal of financial economics, 13(2),

187-221.

Opler, T., Pinkowitz, L., Stulz, R., & Williamson, R. (1999). The determinants and implications

of corporate cash holdings. Journal of financial economics, 52(1), 3-46.

Ozkan, A., & Ozkan, N. (2004). Corporate cash holdings: An empirical investigation of UK

companies. Journal of Banking & Finance, 28(9), 2103-2134.

Petersen, M. A., & Rajan, R. G. (1994). The benefits of lending relationships: Evidence from

small business data. The Journal of Finance, 49(1), 3-37.

Pinkowitz, L., & Williamson, R. (2001). Bank power and cash holdings: Evidence from Japan.

The review of financial studies, 14(4), 1059-1082.

Pinkowitz, L., Williamson, R., 2004. What is a dollar worth? the market value of cash holdings.

Unpublished working paper, Georgetown University.

Pinkowitz, L., & Williamson, R. (2007). What is the market value of a dollar of corporate cash?

Journal of Applied Corporate Finance, 19(3), 74-81.

VIII

Račić, Ž., & Stanišić, N. (2017). Analysis of the Determinants of Corporate Cash Holdings:

Examples from Companies in Serbia. The European Journal of Applied Economics,

14(1), 13-23.

Rajan, R. G., & Zingales, L. (1995). What do we know about capital structure? Some evidence

from international data. The Journal of Finance, 50(5), 1421-1460.

Richardson, S. (2006). Over-investment of free cash flow. Review of accounting studies, 11(2-

3), 159-189.

Saddour, K. (2006). The determinants and the value of cash holdings: Evidence from

French firms. CEREG, 1-33. Retrieved from

http://www.dauphine.fr/cereg/cahiers_rech/cereg200606.pdf

Titman, S., & Wessels, R. (1988). The determinants of capital structure choice. The Journal of

Finance, 43(1), 1-19.

Whalen, E. L. (1966). A rationalization of the precautionary demand for cash. The Quarterly

journal of economics, 80(2), 314-324.

Books

Keynes, J.M. (1936). The general theory of employment, interest and money. London:

Macmillan.

Ooghe, Hubert, Vander Bauwhede, H., & Van Wymeersch, C. (2012). Handboek financiële

analyse van de onderneming: theorie en toepassing op de jaarrekening volgens Belgian

GAAP en IFRS (4e ed.). Antwerpen: Intersentia

Web pages

Capital expenditure. (n.d.). Retrieved from

https://www.investopedia.com/terms/c/capitalexpenditure.asp

Corporate governance. (n.d.). Retrieved from

https://www.investopedia.com/terms/c/corporategovernance.asp

Financial distress. (n.d.). Retrieved from

https://www.investopedia.com/terms/f/financial_distress.asp

Refinancing risk. (n.d.). Retrieved from

https://www.investopedia.com/terms/r/refinancing_risk.asp

Why You Need to Check Your Residual Plots for Regression Analysis. (2012). Retrieved

from http://blog.minitab.com/blog/adventures-in-statistics-2/why-you-need-to-

check-your-residual-plots-for-regression-analysis

https://www.investopedia.com/terms/c/capitalexpenditure.asp

https://www.investopedia.com/terms/c/corporategovernance.asp

https://www.investopedia.com/terms/f/financial_distress.asp

https://www.investopedia.com/terms/r/refinancing_risk.asp

http://blog.minitab.com/blog/adventures-in-statistics-2/why-you-need-to-check-your-residual-plots-for-regression-analysis

http://blog.minitab.com/blog/adventures-in-statistics-2/why-you-need-to-check-your-residual-plots-for-regression-analysis

IX

Attachments

Attachment 1: Normality of the residuals

The normality of the residuals was tested by executing the Shapiro-Wilk test. This test

showed that the residuals are not normally distributed. However, the central limit theorem

says that if the sample size is large enough, the violation of this assumption is not a

problem for the OLS regression.

Attachment 2: Multicollinearity

There is perfect multicollinearity if there is an exact linear relationship between the

explanatory variables. If there is perfect multicollinearity, no OLS regression can happen.

Perfect multicollinearity implies that the parameters cannot be identified and not be

estimated. However, a high degree of multicollinearity between the variables can be tested.

This is the case when there is a high correlation between the explanatory variables in our

econometric model. Multicollinearity is caused by using dummy-variables, using insufficient

data or using too many explanatory variables. Therefore, to test our econometric model for

multicollinearity, the variance inflation factor (VIF) can give us an indication of that. This

factor indicates the speed at which the variance increases. A high degree of multicollinearity

does not imply a theoretical problem but a more practical problem. The estimators have a

greater variance and covariance and thus, there are wider confidence intervals and lower t-

statistics. Therefore, it is more difficult to obtain a significant result. The probability of

making a type II-error, where we forget to reject the H0-hypothesis less quickly and assume

that this hypothesis is correct. A common rule of thumb about the variance inflation factor

(VIF) is that the VIF cannot be higher than ten. The VIF can vary between one and infinity. A

value of 1 means that there is no correlation between the different explanatory variables.

Infinity means that there is perfect multicollinearity. A simple linear regression of one of the

explanatory variables to the other explanatory variables gives a multiple determination

coefficient R² as a result. The VIF is calculated by this formula: VIF= 1/(1-R²).

Multicollinearity is a problem of the sample. This is because high correlation between

variables removes a lot of variance and /or because the sample is too small. The OLS

estimators are still unbiased and efficient. However, they cannot be estimated accurately.

Another possibility to test multicollinearity is calculate the pairwise correlation between the

explanatory variables. We can do this by making use of a correlation matrix. Because this

correlation matrix does not take into account indirect correlation (the variable can correlate

X

with different other variables at the same time), we have to make use of the variance inflation

factor. To overcome the problem, we can make use of more data or eliminate one of the

explanatory variables. The last option can be risky because it can lead to a specification error.

None of the variables have a value bigger than 10 so the conclusion is that there is no

problem of multicollinearity in this sample. If we look at the correlation matrix, we can see

that none of the variables are highly correlated with each other. A common rule of thumb

says that there is only high correlation if the correlation coefficient has a value between 0,70

(-0,70) and 0,90 (-0,90). As we can see, the maximum value is -0,6540 between leverage and

net working capital. This will not give a violation of the multicollinearity assumption.

Attachment 3: Heteroscedasticity

To check the assumption of homoscedasticity, the error terms are plotted against the

variable cash holdings by using a scatter plot. In the case of homoscedasticity, no clear

pattern should be visible. As we can see on the graph, a clear pattern is visible. The size of

the residuals increases with the level of cash holdings. By looking at this graph, we assume

that there is heteroscedasticity.

Figure 5: Scatter plot heteroscedasticity

To confirm the results of the scatter plot (graphical analysis), the White heteroscedasticity

test is used. We regress the error terms of our econometric model to all our determinants,

the multiplications of the determinants and the squares of the determinants. This is an

asymptotic test. If n*R² is bigger than the critical value, then there is heteroscedasticity. Since

XI

R² is equal to 0.3447 and the number of observations is equal to 154 075, this multiplication

exceeds the critical value. This test confirms the results of the scatter plot.

XII

Table 16: The White heteroscedasticity test

XIII

Attachment 4: Autocorrelation

The Breusch-Godfrey Serial Correlation LM test is conducted. In addition to the effect of the

normal determinants, the test also checks whether the lagged error terms influence the

current error terms. By regressing the error term on the determinants and the lagged error

terms. If there is no autocorrelation (H0), the coefficients of the lagged error term should not

be significant. (n-p)*R² follows a chi-square distribution when n is large enough. P is the

number of lags included in the regression. As this multiplication exceeds the critical value,

autocorrelation is detected. Some possible causes of autocorrelation are omitting other

relevant variables or an incorrect econometric model.

Table 17: The Breusch-Godfrey Serial Correlation LM test

XIV

Attachment 5: Specification errors

To test this assumption, the Lagrange multiplier test is used. This is the best test to detect

specification errors in a large dataset. The determinants are raised to the second and third

power and then regressed on the residual terms. If the influence of the added variables is

significant, there is a specification error. The statistic is tested by using n*R². This value is

compared to the critical value of the chi-square with a certain number degree of freedom

(dependent on the restrictions). If n*R² > critical value, there is a specification error. To solve

the problem of specification errors, more variables can be added to the econometric model

but then a strong theoretical background is needed to avoid a situation of data mining.

Therefore, it is decided to not do something about this problem however it is known that

more determinants can have an influence on the level of cash holdings.

XV

Table 18: Lagrange multiplier test

XVI

Attachment 6: Hausman test

Table 19: Hausman test

XVII

Attachment 7: Robustness check growth opportunities

Table 20: Sales growth 2

(1) (2) (3)

VARIABLES OLS Fixed Effects Random Effects

Size -0.0188*** -0.0314*** -0.0261*** (0.000749) (0.00186) (0.000957)

Rel -0.121*** -0.0683*** -0.0862*** (0.00377) (0.00416) (0.00350)

Nwc -0.252*** -0.331*** -0.303*** (0.00454) (0.00615) (0.00507)

Div 0.0302*** 0.00853*** 0.0116*** (0.00197) (0.00108) (0.00103)

Lev -0.238*** -0.269*** -0.251*** (0.00482) (0.00651) (0.00531)

Debtmat 0.0131*** 0.155*** 0.110*** (0.00373) (0.00521) (0.00391)

Cflow -0.0185*** 0.0638*** 0.0501*** (0.00710) (0.00515) (0.00486)

Cflowvar 0.108*** 0.0792*** 0.0916*** (0.00917) (0.00825) (0.00756)

Growthopp -0.0140*** -0.00936*** -0.0112*** (0.00156) (0.00113) (0.00111)

Constant 0.605*** 0.920*** 0.786*** (0.0138) (0.0304) (0.0168) Observations 154,075 154,075 154,075

R-squared 0.232 0.243 0.240

Number of firms 25,735 25,735 25,735

Company FE NO YES NO

Year FE NO YES YES


XVIII

Table 21: Capital expenditures

(1) (2) (3)


Size -0.0143*** -0.0143*** -0.0154*** (0.000755) (0.00192) (0.000930)

Rel -0.113*** -0.0645*** -0.0805*** (0.00371) (0.00395) (0.00332)

Nwc -0.242*** -0.311*** -0.282*** (0.00488) (0.00637) (0.00518)

Div 0.0295*** 0.00777*** 0.0109*** (0.00195) (0.00105) (0.00101)

Lev -0.236*** -0.259*** -0.241*** (0.00508) (0.00674) (0.00537)

Debtmat 0.0200*** 0.150*** 0.107*** (0.00368) (0.00511) (0.00378)

Cflow 0.0153** 0.0555*** 0.0464*** (0.00707) (0.00501) (0.00467)

Cflowvar 0.0864*** 0.0580*** 0.0639*** (0.00980) (0.00827) (0.00757)

Growthopp -0.297*** -0.203*** -0.205*** (0.00835) (0.00565) (0.00525)


R-squared 0.232 0.223 0.219

Number of firms 23,281 23,281 23,281


Year FE NO YES YES


XIX

Table 22: Research and development costs

(1) (2) (3)


Size -0.0186*** -0.0296*** -0.0254*** (0.000750) (0.00185) (0.000953)

Rel -0.120*** -0.0676*** -0.0854*** (0.00378) (0.00409) (0.00345)

Nwc -0.250*** -0.331*** -0.302*** (0.00459) (0.00619) (0.00509)

Div 0.0298*** 0.00830*** 0.0113*** (0.00197) (0.00106) (0.00102)

Lev -0.239*** -0.273*** -0.254*** (0.00488) (0.00652) (0.00529)

Debtmat 0.0128*** 0.157*** 0.111*** (0.00374) (0.00514) (0.00386)

Cflow -0.0202*** 0.0623*** 0.0479*** (0.00711) (0.00513) (0.00483)

Cflowvar 0.109*** 0.0760*** 0.0888*** (0.00927) (0.00815) (0.00747)

Growthopp4 -0.282** -0.699*** -0.617*** (0.122) (0.117) (0.0981)


R-squared 0.230 0.239 0.236

Number of firms 25,515 25,515 25,515


Year FE NO YES YES


XX

Table 23: Dummy research and development costs

(1) (2) (3) VARIABLES OLS Fixed Effects Random Effects Size -0.0190*** -0.0332*** -0.0273*** (0.000750) (0.00184) (0.000970)

Rel -0.120*** -0.0679*** -0.0860*** (0.00377) (0.00417) (0.00350)

Nwc -0.252*** -0.331*** -0.303*** (0.00455) (0.00616) (0.00508)

Div 0.0303*** 0.00859*** 0.0117*** (0.00197) (0.00108) (0.00103)

Lev -0.239*** -0.270*** -0.253*** (0.00483) (0.00653) (0.00533)

Debtmat 0.0134*** 0.157*** 0.111*** (0.00374) (0.00521) (0.00392)

Cflow -0.0258*** 0.0588*** 0.0439*** (0.00703) (0.00511) (0.00482)

Cflowvar 0.116*** 0.0819*** 0.0965*** (0.00923) (0.00831) (0.00763)

Growthopp5 -0.00727** -0.00942*** -0.00854*** (0.00301) (0.00255) (0.00222)


R-squared 0.231 0.242 0.239

Number of firms 25,735 25,735 25,735


Year FE NO YES YES


the determinants of cash holdings ... - universiteit gent

Documents