The Effect of Cash Holdings to Company Performance Evidence from Mining Companies in Australia

8/15/2019 The Effect of Cash Holdings to Company Performance Evidence from Mining Companies in Australia

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Research Method in Finance

CMSE11085

The Effect of Cash Holdings to Company Performance

Evidence from Mining Companies in Australia

Exam Number

B087854


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Introduction

Financial performances have been key indicators of how well companies strive. Regarding

that matter, the internal factors which are indicated by financial decisions might have impact

on performances. Previously, there have been a great number of academic papers covering

factors i.e. cash holdings, leverage, company size, and riskiness affecting financial performances.

Firstly, level of cash and equivalents has been regarded as two sides of a coin, having both

advantage and disadvantage. The advantage of holding a high cash ratio, which is defined as

total cash and equivalents over its total assets, is enabling a company get higher ability in

meeting its liabilities. However, high cash holdings ratio is considered as less productive

since it restrain sources of earning from working optimally. Empirical studies by O’Brien &

Volta (2009) and Abushammala & Sulaiman (2014) suggested that cash holdings couldn’t be

overlooked by managers – as they positively impact performances. However, they also

claimed that high cash holdings might not be the basis of a competitive advantages and

holding a large number of it could increase agency cost.

Another financial factor is leverage which be defined as company’s total liabilities compared

to its shareholder’s equity. It indicates whether a company’s capital is heavier on internal

sources (as in stocks) or external funding (as in debts). Although capital structure was

proposed to be irrelevant on company’s performance by Modigliani & Miller (1958), several

studies proved the significant impact of capital structure. One empirical study by Weill

(2008) argued that leverage has positively significant impact on turnover as proxy for

company’s performance using samples of company in the European area: Belgium, France,

Germany, Italy, Norway, Portugal, and Spain for 3-year period of observations. Leland and

Pyle (1977) claimed that relatively higher leverage ratio indicated good characteristics ofcompany’s credibility. This argument can be justified by the case that creditors prefer well -

managed and well-operated companies to those of less performed ones.

The third internal factor considered to impact company performances is risk-taking behaviour

of management. One way to indicate risk-taking behaviour of a company is by taking

consideration of investment in long term assets other than the conventional fixed assets –

such as plant, property, and equipment – as proposed by Apergis and Sorros (2011). Lastly,

financial factor that could impact performance is company’s size which is measured by total

assets. The argument of total assets affecting performance is that the bigger company size, the

more resources can be allocated to the business-operating activities. An empirical study by

Fresard (2010) argued that company’s size significantly impact on ROA in negative way.

However, Benito-Osorio (2016) claimed that company size has significant positive impact on

ROA.

In general, this paper mainly discusses about the impact of cash holdings to company

performance using controlled variables such as: leverage, risk-taking behaviour, and size of

companies with regard to its Return on Assets (ROA) and Profit Margin as indicator of

performance. ROA is defined as company’s net profit divided by total assets, whereas Profit

Margin is specified as company’s net profit over the net sales or revenues. In examining the

research question, the samples are selected including mining companies listed in Australia

Stock Exchange within five-year period that been averaged into cross-sectional data.


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Overall, this study provides evidence that Cash Holdings does not impact company

performances, as well as the interaction term of Cash Holdings and Leverage. On the other

hand, this study suggests that the controlled variables – Leverage and Total Assets – instead

impact ROA. To be more precise, this paper presents indication suggesting the exponential

relationship between those variables and ROA.

Due to time limitations, this paper couldn’t pursue the best model by only including squared

and cubed terms without including further interaction terms. In addition, the sample selection

which incorporates only mining companies in Australia might not be able to represent the

true condition of all companies in Australia, let alone to be generally used to describe the

companies worldwide. Another shortcomings of this study is the endogeneity problem arises

in which there are other factors – other than regressors used – that could explain

performances but included in the model. The other limitation is that there are many other

financial performances indicators that might be considered important by managers, yet not

included in this study. Those limitations might become future references in examining the

effect of Cash Holdings to company performances.

Lastly, implication of this study in practice is to provide understanding of how company

performances would likely to behave if managers take a certain financial decision – including

how high (or low) cash level should be maintained; to decide the how the company funded;

or to take any risky long-term investments – in order to boost performances. Although there

are lots of consideration which performance indicators suit best to be considered by the

stakeholders, this study presents two performance indicator of ROA and Profit Margin as the

dependent variables. Hopefully, this study could be a suitable reference to management’s

financial decision making process.


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Methodology

This study employs multiple regression models for cross sectional data, in which the initially

five-year panel data being averaged so that every observation will provide only one data. This

model allows controlling for heteroscedasticity and miss-specification model testing. The

result will be in robust standard error to aim the reliability of t-statistic. In order to examinethe effect of corporate Cash Holdings, other controls are employed, such as: capital structure;

risk taking behaviour; company size; and interaction between cash held and capital structure.

The capital structure is represented by leverage ratio, whereas company’s risk taking

behaviour is indicated by investment in other long term investment in other fixed assets.

Furthermore, company size is expressed by company’s total assets.

Below equations are model formulation of the model employed, while the variable’s

elaboration is specified in Table 1.

= 0 + 1ℎ + 2 + 3 Cash Holdings

∗ Leverage + 4 log + 5 + ε (1)

= 0 + 1ℎ + 2 + 3 Cash Holdings

∗ Leverage + 4 log + 5 + ε (2)

Table 1 Description of Variables

Variables

Unit of

Measurements

Predicted

Sign RationaleDependent:

Return on Assets (ROA) Decimal N/A Commonly used financial performance indicator,calculated by dollar profitavailable to commonshareholder divided by totalassets. It expresses theability of $1 of company’sassets to generate profit.The higher ROA, the better acompany perform.

Profit Margin Decimal N/A Another performanceindicator, measured by profitavailable to commonshareholders divided by netsales (revenues).The higher Profit Margin,the better a company

perform.


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Independent:Cash Holdings Decimal (+) Percentage of cash and

equivalent holdings overtotal assets.The more company hold

cash the more likely acompany perform better

because it has more ability interm of meeting itsobligations. In addition, themore liquid a company, themore it likely to expand its

business or investing in a potential project.

Leverage N/A (+) Capital structure ratio,

comparing number of totalliabilities and number oftotal shareholder’s equity. A

ratio greater than one showsthat a company is heavier ondebt rather than internalfunding.The higher companyleverage, the more liabilitiescompared to its equity whichindicates either it’s a

reputable company or a riskycompany. Leverage allows acompany to expand the

business or financing potential project which leadto potential higher

performance.

Cash Holdings x Leverage Decimal (+) Interaction between percentage of cash holdings

and leverage ratio of acompany. When a companyhas higher leverage ratio,they usually have highercash holdings since they willhave to meet theirobligations towardscreditors. In addition, inorder to get external funding,a company might have tokeep its liquidity at a certain

point. A bond covenant alsocontains certain points that


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debtors should and shouldn’t

do, maintaining cashholdings for example.

Log Total Assets N/A (+) Total assets indicate the size

of company with employed by natural logarithm toovercome heteroscedasticity.The bigger a company, the

bigger chance of it to employall the resources a companyhas, allowing them tooptimally earn higher

performances.

Riskiness N/A (+) Describing risk-taking

behaviour of a company bylooking at its long terminvestment other thanconventional fixed assets,such as: property, plant, andequipment.Assuming managers takeonly good projects orinvestments, the riskier acompany, the more it has

potential to perform betterthan letting its money idle.


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Data

This empirical study occupies the average of five years financial data of companies in the

mining sector that listed in Australia Stock Exchange. In other words, cross sectional data

sample were constructed from panel data in order to have only one data set per observation.

The sample selection covers companies of different sizes (i.e., small, medium, and largecompanies), even though there are larger portion of small and medium companies.

All data needed to construct variables are downloaded from Thomson One. In total, there are

641 companies. However, there are missing data in one type of financial data or incomplete

five-year data. Those incomplete data are dropped from sample, resulting only 517 complete

data of observation. Moreover, outliers in ROA, Total Assets, ad Leverage were spotted after

constructing scatter plots (see Appendix). Rather than deleting them from becoming sample,

those extreme value of variables are winsorized at 5% level. Therefore, the final sample size

used for the estimation remain cross-sectional data of 517 companies with four independent

regressors plus one interaction variables.

Table 2

Descriptive Statistics

Variable Mean SD Min Max p25 p50 p75

ROA -0.348 0.321 -1.180 0.039 -0.493 -0.269 -0.114Cash Holdings 0.285 0.244 0.002 0.986 0.104 0.204 0.393Leverage 0.319 1.357 -8.353 22.671 0.048 0.113 0.356Log-Total Assets 16.555 1.581 14.253 20.126 15.385 16.284 17.350Other Investment 4.21e+07 5.71e+08 -3.59e+07 1.18e+10 -4.31e+04 0 4.59e+05

Table 2 illustrates the descriptive statistic of data observed in the study. In general, the

statistic of data variable observed is widely varied. ROA of which left skewed, has negative

mean and 50th quantile, indicating that more than half of the sample has negative return. At

the same time, percentage of cash held by companies is almost normally distributed since the

value of Cash Holdings’ mean is almost the same as 50 th quantile of it. However, Leverage is

skewed to the right, while its standard deviation is much larger than the mean and the value

ranged widely. After logging the Total Assets, it has almost normal distribution, whereas

Other Investment shows the majority of sample did not have other long-term investment

other than their fixed assets. However, the dummy for Riskiness remained same afterwinsorized the other fixed assets.

Table 3

Correlation Table

ROA Cash Holdings Leverage Log Total Assets Other Investment

ROA 1.0000Cash Holdings -0.3959 1.0000Leverage -0.0996 -0.0591 1.0000

Log Total Assets 0.6460 -0.4698 0.0252 1.0000Other Investment 0.0815 -0.0617 0.0329 0.1636 1.0000


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Table 3 illustrates the correlation between variables. It is observed that the highest positive

correlation is between Log Total Assets and ROA since the ROA value is determined by Log

Total Assets. Meanwhile, the greatest negative correlation is between Log Total Assets and

Cash Holdings, indicating that the smaller size of a company, the more it becomes liquid.Regarding relationship between independent and dependent variables, the greatest correlation

belongs to Log Total Assets to ROA, while the smallest correlation is between Other

Investment and ROA.


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Results

Table 4 Regression Results

(1) (2) (3) (4) (5) (6) (7) ROA_cl_11 ROA_cl_2 ROA_rob ROA_exp2 PM_rob PM_exp1 PM_exp2

Cash Holdings -0.277*** -0.0642 -0.0642 -0.0975 938.4 3950.3 2310.2(-4.27) (-1.10) (-0.94) (-0.63) (1.20) (1.06) (1.10)

Leverage -0.0466 -0.0937** -0.0937** -0.464*** -412.7 -3342.5 -2245.7(-0.92) (-2.14) (-2.03) (-3.83) (-0.92) (-1.04) (-1.03)

Cash Holdings #Leverage

-0.739*** -0.514*** -0.514** -0.245 1029.7 2072.0 2129.1

(-4.10) (-3.27) (-2.29) (-1.55) (0.85) (0.92) (0.92)

Total Assets 8.25e-10*** (8.58)

Riskiness 0.0206 0.00603 0.00603 0.0281 278.8 346.5 344.0(0.84) (0.28) (0.29) (1.37) (0.99) (0.99) (0.99)

Log Total Assets 0.125*** 0.125*** 0.873*** 130.5 -224.1 61.81(16.35) (15.77) (6.30) (1.06) (-0.51) (0.22)

Cash Holdings2 0.112 -3571.2(0.70) (-1.03)

Leverage2 0.317*** 2410.2(3.60) (1.05)

Log Total Assets2 -0.0217*** 11.02

(-5.39) (0.84)

Cash Holdings3 -2049.5(-1.05)

Leverage3 1242.7(1.05)

Log TotalAssets3 0.108(0.45)

_cons -0.272*** -2.338*** -2.338*** -8.724*** -2675.1 26.66 -2079.6(-10.17) (-17.97) (-17.36) (-7.29) (-1.08) (0.01) (-0.51)

N 517 517 517 517 512 512 512 R2 0.308 0.480 0.480 0.529 0.010 0.024 0.021

1

Term “_cl” refers to classic model, whereas “_rob” indicates that robust standard error is employed. 2 Term “_exp” specifies the exponential form of model, employing squared and/or cubed of variables(regressors)


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Analysis

Table 4 illustrates the results of cross-sectional data regression examining the effect of cash

holdings to company’s performance. Column (1) – (4) show the regression results of ROA,

whereas Column (5) – (7) present the result of regress Profit Margin on almost exact

regressors that employed to ROA. Column (1) describes the simplest regression’s result ofROA on Cash Holdings, Leverage; Total Assets; and Riskiness. The difference between

Column (1) and (2) is that the latter employs Log Total Assets instead of level. Then, log

Total Assets is employed to the rest of models. Furthermore, Column (3) is basically the same

model as Column (2) with robust standard error. Meanwhile, Column (4) contains the same

model as the two previous Columns plus squared term of each regressors – except dummy

Riskiness. Meanwhile, Column (5) is basically the same as Column (3) with Profit Margin as

the dependent variable. In addition, Column (6) and (7) contain non-linear relationship by

adding squared and cubed term of regressors.

First of all, variation of ROA that can be explained by the variation of its regressors is

expressed by R 2 which is 30.8% in Column (1) becoming less reliable because of

heteroscedasticity issue. However, Column (2) and (3) produce higher R 2 of 48% using the

same number of regressors as the first model of Total Assets in level instead of Log.

Furthermore, the value of R 2 in Column (4) is greater than the previous Column because it

employs three more regressors. On the other hand, the less than 1% of variation on Profit

Margin is explainable by the variation of regressors compared to ROA’s models despite of

the same variables employed. Nevertheless, it can’t be concluded that model in Column (1) –

(4) are better model than Column (5) – (7) just because of the higher value of its respective

R 2. In other words, the significance of variables in affecting ROA or Profit Margin will be

more determinant than R 2 alone. Therefore, the next paragraphs will discuss about the

significance of each variables.

Generally speaking, all Columns of regressing ROA on the all regressors show the constant

(β0) being negatively significant. This significance of β0 can be justified because of dummy

variable used. Therefore, assuming everything else being held constant, the impact of

Riskiness – which only has two possible of Riskiness (0 or 1) – to ROA is highly determined

by the constant. However, the completely opposite results obtained in regressing Profit

Margin despite of using the same regressors. In other words, although Riskiness significantly

affects ROA, it does not have the same impact on Profit Margin.

Regarding the main question whether Cash Holdings impact the company performance, the

results are completely the opposite from what’s expected as well as from Abushammala &

Sulaiman (2014) who argued that Cash Holdings significantly impact ROA in positive

direction. Although the linear model regression of Column (1) shows significant result under

99% confidence level which means that holding everything else constant, if Cash Holdings

increases from 25% to 50% (in other words, increases 100%), ROA will likely to decrease by

27.7%. However, Cash Holdings no longer affects ROA after test for heteroscedasticity is

conducted and robust standard error is employed. In addition, the same insignificant impact

of Cash Holdings to ROA is observed even after testing for model miss-specification (see

Column 2 – 4) in which Cash Holdings squared is added.

This section forward will discuss the control variables, including Leverage; Total Assets (aswell as the non-linear form of them); and Riskiness. The first controlled variable is the capital


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structure indicated by leverage ratio. This study suggests that the impact of Leverage to

performance is the opposite of what Weill (2008) suggested. Although Column 1 indicates

that Leverage does not affect ROA, it then does have significant impact on ROA under 95%

confidence level after heteroscedasticity is corrected and robust standard error is employed.

Furthermore, Leverage has significant impact on ROA under 99% confidence level after

employing test for model miss-specification and adding squared term. In other words,

Leverage exponentially affect ROA in which impact’s magnitude is different for every level

of Leverage. In ceteris paribus environment, the impact of Leverage to ROA can be defined

as -0.464 + (1.585xLeverage). Intuitively, holding everything else being equal, a company

that previously had full internal funding then go to 50:50 in its liabilities and equity, it will

decrease its ROA by 1.121%. Furthermore, marginal effect (dydx) of Leverage on ROA will

steadily negative until the Leverage is on level 1 – or liabilities equals to equity. If Leverage

goes up further, it will impact ROA in positive direction under 95% confidence level, as

shown in Figure 1.

Figure 1 Marginsplot: the Impact of Leverage to ROA

Source: Thomson One

Secondly, Interaction between Cash Holdings and Leverage is proven having significant

effect to ROA under 99% confidence level as shown in Column (1) and (2). However, its

impact lower to under 95% confidence level after robust standard error is employed (Column

3). Furthermore, the impact become insignificant after squared term of regressors are added.

In other words, the coefficient of interaction term encounters omitted variable bias, where the

correlation between interaction regressors and omitted variable have negative correlation. On

the other hand, interaction term between Cash Holdings and Leverage is observed to

insignificantly impact Profit Margin as shown in Column (5) – (7). Overall, the results of

Table 4 suggest that that holding either small or large portion of cash to total assets while

having either high or low leverage do not impact on how the company perform.

The third controlled variable is company size which is represented by its Total Assets.According to Table 4, the level of Total Assets significantly affect ROA under 99%

-10

0

10

20

-10 0 10 20 30Leverage, Winsorized fraction .05

Average Marginal Effects of wLeverage with 95% CIs


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confidence level in any models. However, the first model in Column (1) contains

heteroscedasticity so log Total Assets is employed to the rest of the model as shown in

Column (2)-(7). This positive significant results are consistent with Benito-Osorio (2016).

The effect of company size to ROA is observed to be exponential after squared term of Total

Assets is found to be significant. In other words, holding everything else constant, the impact

of company size to ROA equals to (0.873/100) + ((-0.0217/100) x % ΔTotal Assets).

Furthermore, the marginal effect (dydx) of company size to ROA will have positive until the

log Total Assets reaches 21 as shown in Figure 2.

Figure 2 Marginsplot: the Impact of Log Total Assets to ROA

Source: Thomson One

The last controlled variable is Riskiness which represent a company’s investments on long-

term assets other than conventional fixed assets. According to Table 4, company

performances are not affected by the fact that they are taking risky financial decision or not.

This result is not what is previously expected. It can be explained by the ROA data

distribution and behaviour which they are mostly in negative values no matter the Riskiness

value of 0 or 1, see Appendix-Figure 3. In other words, the variation in Riskiness couldn’t

explain variation on ROA because of data mining issues.

To compare, all regressors also do not have significant impact on Profit Margin. After testing

for model miss-specification, it is shown that model with exponential form of variables has

significantly impact Profit Margin. Unlike ROA, the model miss-specification test indicated

that only the cubed form of model’s fitted value significantly affect Profit Margin. However,

there is still no significant relationships found when the cubed form of all regressors were

added to the equation. In other words, there are omitted variable bias, especially in interaction

terms between regressors.

0

.5

1

1.5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21log_wTotalAssets

Average Marginal Effects of log_wTotalAssets with 95% CIs


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Implication related to the existing studies

The implication of this study to the already existing papers including these three following

points: controlled variables; company performance proxy; and marginal effects. Firstly, this

paper cover more controlled variables than some papers in order to obtain the effect of Cash

Holdings to company performance. It gives deeper explanation of why Cash Holdingscouldn’t determine company performance. Another point to consider , this study also

examines two proxies of company performance: ROA and Profit Margin. Therefore, we can

identify which of financial performance is impacted by those variables because every

management yearns different performance indicator to be focused on. Lastly, this paper also

pinpoints that the effect of some variables are not linear, but exponential. There are some

papers that had not concluded that one yet. Overall, this study could contribute to the existing

papers by providing more controlled regressors, more performance indicators, and more

explanatory effect on how Cash Holdings are impacting performances.


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Limitations and Future Avenues of Research

This study also has limitations that should be addressed in the future. First, fact that sample

construction only covering companies of Mining sector in Australia. The results will

probably not be able to represent the population regarding the impact of internal financial

decision to company’s performance. In other words, the sample used might not be able torepresent the true population of all companies. Therefore, future studies should include

companies from all industries (sectors) in order to produce more reliable results.

The second shortcomings of this study is related to the endogeneity problem in which the

regressors are related to the error term. Endogeneity is highly possible encountered by having

error term emerged because the dependant variables can’t be fully explained by regressors

used in the model. This problem could arise as the result of omitted variable biases. The

omitted variable bias could develop because there are factors other than Cash Holdings,

Leverage, interaction between Cash Holdings and Leverage, Log Total Assets, and Riskiness

that should be able to explain ROA and Profit Margin. One possibility should cause the

endogeneity problem is the external factors which are completely not included in the model.

These external factors could be anything outside managers’ control that could affect company

performance, such as government regulation related export or the volatility of commodity

prices. Another possibility leading to endogeneity is excluding some variables because of

data unavailability, such as Research and Development data, that is finally not incorporated

in the model.

Another consideration worth contemplated is the average data of five-year observation period

which make this observation only covering events in the recent years. It might not be able to

represent the true condition since the observation period is not long enough. For future

reference, longer period of observation may be considered. Moreover, instead of having oneobservation period, next studies could divide the period into short, medium, and long term

period to examine their robustness’ stability in order to determine the statistical significances

of variables employed.

The fourth drawbacks of this study is the use of company’s financial performance’s

indicators of ROA and Profit Margin while there are other proxies that might be significantly

affected by Cash Holdings. Furthermore, it is recommended to employ other performance

indicators, such as Return on Equity and Tobin’s Q. Lastly, the model misspecification still

exists after adding squared and/or cubed term of Cash Holdings and other controlled

variables. In other words, there are possibilities that another interaction terms other than Cash

Holdings and Leverage that might significantly affect company performances.


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Conclusion

The main purpose of this study is to observe the effect of percentage of cash held by

company determining its financial performance. This study uses sample from mining

companies listed in Australia Stock Exchange in the last five year calendar period. In the

examining the impact of Cash Holdings, some controlled variables are added to the model,including interaction between them. Cash Holdings is calculated by the percentage of cash

held by company over its total assets. Meanwhile, the controlled variables include company

size; leverage ratio; and riskiness which are represented by the total assets, liabilities to

equity, and other long-term investments. The company’s financial performance is represented

by ROA and Profit Margin.

In general, according to the regression results, this study suggests that Cash Holdings does

not have significant impact on ROA and Profit Margin. Moreover, this study also examines if

the relationship between Cash Holdings and performance is exponential instead of linear.

However, the same insignificant effect is observed. In other words, how well a company

perform is not affected by how much percentage of cash they hold. Furthermore, this study

also claims that not only cash but also the controlled variables do not affect Profit Margin. On

the other hand, some controlled variables are observed to significantly impact ROA. The next

paragraphs will elaborate how those variables instead of Cash Holdings impact ROA.

Firstly, this study suggests that company’s capital structure which represented by leverage

ratio exponentially affect ROA under 99% confidence level by -0.464 + (1.585xLeverage).

Since leverage’s impact on ROA is exponential, the marginal effect of Leverage will be

positive starting the leverage level of 1. In other words, the having less liabilities than equity

will only negatively impact ROA.

The second controlled variables is interaction term between Cash Holdings and Leverage

which was initially significantly impact ROA before squared terms are added. In other words,

having a certain level of Cash Holdings regardless how much liabilities to equity a company

has do not impact performance. In addition, the third controlled variable, the risky-taking

behaviour, also observed to be insignificant affecting company performance. Particularly, this

study claims that the company performance is insignificant with the fact that a company

taking risky investment or not.

Lastly, another controlled variables of Log Total Assets, which represents company size, is

observed to significantly affect ROA. To be more precise, the effect of company size to its

performance is distinguished to be exponential (0.873/100) + ((-0.0217/100) x % ΔTotalAssets) under 95% confidence level. Furthermore, since company size’s effect is exponential,

the marginal effect of Log Total Assets will have positive impact on ROA until it reaches 21.

Word counts: 4,143


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References

Abushammala, S., & Sulaiman, J. (2014). Impact of Macroeconomic Performance onCorporate Cash Holdings: Some Evidences from Jordan. Asian Economic and

Financial Review, 4(10), 1363-1377.

Apergis, N., & Sorros, J. (2011). Long-Term Debt and the Value of the Firm, Evidence fromInternational Listed Manufacturing Firms. Review of Economics & Finance, 1, 60-72.

Benito-Osorio, D., Colino, A., Guerras-Martin, L., Zuniga-Vicente, J., Angel Guerras-Martin,D., & Angel Zuniga-Vicente, A. (2016). The international diversification-performancelink in Spain: Does firm size really matter? International Business Review, 25(2), 548-558.

Modigliani, F., & Miller, M. (1958). The Cost of Capital, Corporation Finance and theTheory of Investment. The American Economic Review, 48(3), 261-297.

O'Brien, J., & Folta, T. (2009). A Transaction Cost Perspective on Why, How, and WhenCash Impacts Firm Performance. Managerial and Decision Economics,30(7), 465-479.

Weill, L.. (2008). Leverage and Corporate Performance: Does Institutional EnvironmentMatter?. Small Business Economics, 30(3), 251 – 265. Retrieved fromhttp://www.jstor.org/stable/40650910

http://www.jstor.org/stable/40650910http://www.jstor.org/stable/40650910http://www.jstor.org/stable/40650910


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Appendix

Table 5 Heteroscedasticity Tests

(1) (2) (3) (4) (5)ROA_cl_1 Heteros_1 ROA_cl_2 Heteros_2 ROA_rob

Cash Holdings -0.277*** 0.0949*** -0.0642 0.0384** -0.0642(-4.27) (3.51) (-1.10) (2.04) (-0.94)

Leverage -0.0466 0.0611*** -0.0937** 0.0270* -0.0937**

(-0.92) (2.88) (-2.14) (1.91) (-2.03)

Cash Holdings # Leverage -0.739*** 0.0177 -0.514*** 0.0571 -0.514** (-4.10) (0.24) (-3.27) (1.12) (-2.29)

Total Assets 8.25e-10*** -1.46e-10*** (8.58) (-3.65)

Riskiness 0.0206 0.000712 0.00603 -0.00127 0.00603(0.84) (0.07) (0.28) (-0.19) (0.29)

Log Total Assets 0.125*** -0.0168*** 0.125*** (16.35) (-6.82) (15.77)

_cons -0.272*** 0.0351*** -2.338*** 0.310*** -2.338***

(-10.17) (3.15) (-17.97) (7.38) (-17.36) N 517 517 517 517 517 R2 0.308 0.091 0.480 0.174 0.480


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Table 6 ROA – Model Miss-specification Results

(1) (2) (3) (4)ROA1 ROA1_MissSpec ROA_exp ROA_exp_MissSpec

Cash Holdings -0.0642 0.134** -0.0975 0.0570

(-0.94) (2.14) (-0.58) (0.35)

Leverage -0.0937** 0.0566 -0.464*** 0.287(-2.03) (1.16) (-3.65) (1.39)


-0.514** 0.0443 -0.245 0.193

(-2.29) (0.18) (-1.17) (0.88)

Log Total Assets 0.125*** -0.0157 0.873*** -1.225*** (15.77) (-0.76) (5.74) (-2.61)

Riskiness 0.00603 0.0169 0.0281 -0.0177(0.29) (0.84) (1.36) (-0.79)

Fitted 2 -3.394*** (-6.96)

Fitted 3 -2.270*** (-5.48)

Cash Holdings2 0.112 -0.0554

(0.61) (-0.34)

Leverage2 0.317*** -0.207(3.36) (-1.44)

Log Total Assets2 -0.0217*** 0.0331*** (-5.03) (2.67)

SQ_Fitted 2 -4.149*** (-4.33)

SQ_Fitted 3 -2.555*** (-3.74)

_cons -2.338*** 0.209 -8.724*** 11.18** (-17.36) (0.57) (-6.51) (2.53)

N 517 517 517 517 R2 0.480 0.529 0.529 0.549


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Table 7 Profit Margin – Model Miss-specification Results

(1) (2) (3) (4)PM_rob PM_MissSpec PM_exp1 PM_exp2

Cash Holdings 938.4 -0.112* 3950.3 2310.2

(1.20) (-1.74) (1.06) (1.10)

Leverage -412.7 -0.0224 -3342.5 -2245.7(-0.92) (-0.41) (-1.04) (-1.03)


1029.7 -0.657*** 2072.0 2129.1

(0.85) (-3.80) (0.92) (0.92)

Log Total Assets 130.5 0.113*** -224.1 61.81(1.06) (11.85) (-0.51) (0.22)

Riskiness 278.8 -0.0175 346.5 344.0(0.99) (-0.74) (0.99) (0.99)

PM_Fitted 2 -0.000000106(-1.11)

PM_Fitted 3 1.71e-10(1.16)

Cash Holdings2 -3571.2

(-1.03)

Leverage2 2410.2(1.05)

Log Total Assets2 11.02(0.84)

Cash Holdings3 -2049.5(-1.05)

Leverage3 1242.7(1.05)

Log Total Assets3 0.108(0.45)

_cons -2675.1 -2.113*** 26.66 -2079.6(-1.08) (-12.43) (0.01) (-0.51)

N 512 517 512 512 R2 0.010 0.485 0.024 0.021


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Figure 3: Scatterplot ROA and Risky Behaviour

Source: Thomson One

Figure 4: Scatterplot ROA and Size

Source: Thomson One


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Figure 5: Scatterplot ROA and Leverage

Source: Thomson One

Figure 6: Scatterplot ROA and Cash Holdings

Source: Thomson One

The Effect of Cash Holdings to Company Performance Evidence from Mining Companies in Australia

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