Assessment of Companies Listed on ASX with Sector Area and Financial Information

The research was focused on assessing the companies listed on the ASX with their sector area and financial information. It was established that most of the companies listed on the ASX were from the material sector, although about 52.00% were either suspended or delisted. All the continuous variables in the sample data were highly positively skewed. The analysis indicated that the sample data used could not estimate the population parameter for the total asset for all companies that are trading. However, the sample could estimate the population parameter for the total revenue for the GICS sector of material only. It was found that total revenue for financial (GICS) is not more than the average total revenue for Health Care (GICS) and the average market capitalization, for financial and materials, did not differ. Lastly, it was found that there a significant relationship between net profit after tax and total assets.

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FIN10002: Financial Statistics
Name
The Name of the Class (Course)
Professor (Tutor)
The Name of the School (University)
The City and State where it is located
Date

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Executive Summary
The research was focused on assessing the companies listed on the ASX with their sector area
and financial information. It was established that most of the companies listed on the ASX
were from the material sector, although about 52.00% were either suspended or delisted. All
the continuous variables in the sample data were highly positively skewed. The analysis
indicated that the sample data used could not estimate the population parameter for the total
asset for all companies that are trading. However, the sample could
estimate the population parameter for the total revenue for the GICS
sector of material only. It was found that total revenue for financial (GICS) is not
more than the average total revenue for Health Care (GICS) and the average market
capitalization, for financial and materials, did not differ. Lastly, it was found that
there a significant relationship between net profit after tax and total assets.

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Introduction
In this study, the focus was on assessing the companies listed on the ASX with their sector
area and financial information. The main objective was to determine the distribution of the
data. Both descriptive statistics and plots will be used to illustrate how the variables were
distributed. For the nominal variables, pie chart and bar plots will be used. A histogram will
be used to show the distribution of the continuous variables (Lowry, 2014). The independent
sample t-test will be carried out to determine whether the mean difference between two
groups is different. Lastly, a regression model will be fitted to determine whether there exists
a significant relationship between net profit after tax and total assets.
To achieve this, the sample data of companies listed on the ASX (n = 50) was used. A simple
random process was used to select a random sample size 50 of customers from the 1536
companies in the Major Assignment Data Company Profits file. The sample data are located
in .
Analysis
Using this sample data, we carry out data analysis as follows:
Descriptive analysis
The distribution of Status was carried out and the summary table is as follows:
Row Labels Count of Status
Delisted 44.00%
Suspended 8.00%
Trading 48.00%
Grand Total 100.00%

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Delisted
44%
Suspended
8%
Trading
48%
Status Distibution
Delisted
Suspended
Trading
The summary shows that 48.00% of the companies in the data are currently trading, 8.00%
are suspended and 44.00% are delisted (Ott & Longnecker, 2015).
The distribution of the GICS sector is as illustrated below:
Communication Services
Consumer Discretionary
Consumer Staples
Energy
Financials
Health Care
Industrials
Information Technology
Materials
Real Estate
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
GICS sector distribution
GICS sector
Percentage
The summary indicates that 30.0% of the companies were from the material sector which was
the largest sector. The second largest sector was financial sector which covered
approximately 14.00% of the GICS Sector.

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Materials
Diversified Financials
Real Estate
Commercial & Professional Services
Energy
Consumer Services
Telecommunication Services
Pharmaceuticals, Biotechnology & Life Sciences
Software & Services
Capital Goods
Retailing
Household & Personal Products
Technology Hardware & Equipment
Food, Beverage & Tobacco
Automobiles & Components
Health Care Equipment & Services
0.0%
10.0%
20.0%
30.0% 30.0%
14.0%10.0%10.0%10.0%
4.0% 4.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0%
GICS Industry group distribution
Total
GICS Industry group
Percentage
The chart indicates that 30.0% of the companies are in the GICS industry group. The second
largest sector was diversified Financials with 14.0% (Keller, 2014).
The market capitalization was analyzed and the results are as follows.
Descriptive statistics
Market Capitalization
count 50
mean 209,562,715.76
sample standard
deviation 511,366,715.80
sample variance
261,495,918,025,340,000.0
0
minimum 283648
maximum 3339177936
range 3338894288
On average, the capital capitalization is $209,562,715.76 (SD = $511,366,715.80). The
minimum value of the company from the data sample was $283,648 and the maximum value
was $3,339,177,936. The distribution of this factor is as illustrated below.

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0
300,000,000
600,000,000
900,000,000
1,200,000,000
1,500,000,000
1,800,000,000
2,100,000,000
2,400,000,000
2,700,000,000
3,000,000,000
3,300,000,000
0
10
20
30
40
50
60
70
80
Histogram of Market capitalization
Market Capitalisation
Percent
The plot shows that the data is very skewed, with most of the company’s worth between a
minimum of $283,648 and $1,000,000,000.
The total asset as per the balance sheet descriptive statistics summary are as follows.
Descriptive statistics
Total Assets
count 50
mean 267,252,120.72
sample standard
deviation 666,564,496.99
sample variance 444,308,228,653,139,000.00
minimum 33845
maximum 4451900000
range 4451866155
1st quartile 9,038,254.25
3rd quartile 255,639,715.75
The company’s total asset average was valued to be $504,360,999.66 (SD=
$1,819,939,027.36). The company with the least total asset was worth $33,845 and the largest
company had assets worth $12,240,156,947. This means that the range of assets as per the
balance sheet was $12,240,123,102. It was estimated that the 25% percentile of the
companies had total assets worth $9,038,254.25, and approximately 75% percentile was

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$255,639,715.75. The total asset distribution was summarized using a chart and the results
are as follows.
0
300,000,000
600,000,000
900,000,000
1,200,000,000
1,500,000,000
1,800,000,000
2,100,000,000
2,400,000,000
2,700,000,000
3,000,000,000
3,300,000,000
3,600,000,000
3,900,000,000
4,200,000,000
4,500,000,000
0
10
20
30
40
50
60
70
Histogram of total asset
Total Assets
Percent
The data seem to be very skewed, with a few observations on the upper side of the plot. The
plot indicates that most of the companies have total assets worth between $0.00 and
$100,000.
Descriptive analysis was carried out for the total revenue and results are tabulated below.
Descriptive statistics
Total Revenue
count 48
mean 116,075,628.02
sample standard deviation 288,412,111.85
sample variance 83,181,546,263,624,500.00
minimum 0
maximum 1851985000
range 1851985000
On average, the companies get a total revenue of $116,075,628.02 (SD = $288,412,111.85).
There are some companies that did not get any revenue from their operation. The maximum

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revenue generate was $1,851,985,000. This means that the revenue generated had a range of
$1,851,985,000. The distribution of the total revenue is as illustrated in the chart below.
0
150,000,000
300,000,000
450,000,000
600,000,000
750,000,000
900,000,000
1,050,000,000
1,200,000,000
1,350,000,000
1,500,000,000
1,650,000,000
1,800,000,000
0
10
20
30
40
50
60
70
Histogram
Total Revenue
Percent
The plot suggests that the data are very skewed which one value which is very extreme. The
plot indicates that most of the companies make a total revenue between $0.00 and $50,000.
Descriptive statistics of the net profit after tax is as summarized below.
Net profit after tax
count 50
mean 11,044,466.38
sample standard
deviation 81,448,985.09
sample variance 6,633,937,172,181,100.00
minimum -87826000
maximum 521600000
range 609426000
1st quartile -2,949,164.00

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3rd quartile 3,761,688.75
interquartile range 6,710,852.75
The summary indicates that on average companies made $11,044,466.38 net profit after tax
(SD = $81,448,985.09). The company that made the largest loss made a loss of $87,826,000
and the highest profit was $521,600,000. This means that the range of the net profit after tax
was $609,426,000. The 25th percent of the company made a loss of $2,949,164.00 and the 75th
percent made a profit of $3,761,688.75 (Silvey, 2017). The distribution of net profit is as
illustrated below.
-96,000,000
-48,000,000
0
48,000,000
96,000,000
144,000,000
192,000,000
240,000,000
288,000,000
336,000,000
384,000,000
432,000,000
480,000,000
528,000,000
0
5
10
15
20
25
30
35
40
45
Histogram
Net profit after tax
Percent
The chart shows that the net profit after tax was evenly distributed around zero. However,
there was a very extreme case.
Confidence interval
The 95% confidence intervals for the total revenue for GICS Sector of Materials only, is as
follows.
Lower limit Upper limit
-901,360,452.80 2,725,072,076.00

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The table indicates that, we are 95% confident that total revenue for the GICS
sector of material only is expected to be between -$901,360,452.80 and
$2,725,072,076.00 (Lee, 2016).
The population summary statistics are as follows:
GICS Sector Materials population
Count of Total
Assets
Average of Total
Assets
StdDev of Total
Assets
500 774,969,415.1 7326046170
The population total revenue for the GICS sector of material only average is
$774,969,415.1, which is within the constructed confidence interval. This
implies that the sample average is not significantly different from that of
the population (Ott & Longnecker, 2015).
The total asset confidence interval for all companies that are trading from
the data sample is as summarized in Appendix 2. We are 95% confident
that the average total asset is between -$352,006,208.66 and
$1,740,055,410.99. The population total asset for all companies that are
trading is as summarized in Appendix 2. The average $3,242,345,887.59 is not
within the sample confidence interval, which means that the sample data could not estimate
the population parameter (Azzalini, 2017).
Hypothesis testing
Independent sample T-test was carried out to determine whether the average difference
between the average total assets are different between the health care industries and financial
industries. The results indicate that there is inadequate evidence to support the claim that the
average total revenue for financial (GICS) is more than the average total revenue for Health
Care (GICS) (t (7) = 0.9663, p-value = 0.1830) (see Appendix 3) (Chatfield, 2018). This

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means that the average between the average total asset for the health care industries and
financial industries is not different. However, the test, in this case, might be unreliable since
the sample used was too small. Nonetheless, one can conclude that the average total revenue
for Financials (GICS) is not more than the average total revenue for Health Care (GICS).
An analysis was carried out to determine whether the average market capitalization, differs
for financial and materials. The results indicate that there is inadequate evidence to support
that claim (t (20) = -0.7233, p-value = 0.4779) (see Appendix 4) (Sullivan III, 2015). This
implies that the average market capitalization does not differ for Financials and materials.
Therefore, when an investor is planning to venture into either of the two, he/she should know
that the two require an equal amount of capital.
Correlation and Regression
A model was fitted to investigate the relationship between total assets and the net profit after
tax. The linear regression model was developed to predict net profit after tax from the total
assets (Cohen, et al., 2014). First, we carry out the correlation analysis to determine the
strength and direction of the association. The results are as follows.
0 1,000,000,000 2,000,000,000 3,000,000,000 4,000,000,000 5,000,000,000
-200,000,000
-100,000,000
0
100,000,000
200,000,000
300,000,000
400,000,000
500,000,000
600,000,000
f(x) = 0.102437845548601 x − 16332265.0848515
R² = 0.702803432894614
Net profit after tax against Total asset
Total Asset
Net profit after tax

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The plot indicates that there is a strong positive relationship between total assets and net
profit after tax (Bland, 2015). Further, an analysis was carried out to determine whether there
is a linear relationship between total assets and net profit after tax. The hypothesis was as
follows:
H0: there is no linear relationship between total assets and net profit after tax.
Ha: there is a linear relationship between total assets and net profit after tax.
The analysis was carried out and the results summary is as follows:
Regression Statistics
Multiple R 0.838333724
R Square 0.702803433
Adjusted R
Square 0.696611838
Standard Error 44862657.16
Observations 50
ANOVA
df SS MS F
Significanc
e F
Regression 1 2.28455E+17 2.28455E+17 113.509268 3.06E-14
Residual 48 9.66076E+16 2.01266E+15
Total 49 3.25063E+17
Coefficients Standard Error t Stat P-value Lower 95%
Upper
95%
Intercept -16332265.08 6845145.492 -2.385963177 0.021027202 -3E+07 -2569178
Total Assets 0.102437846 0.0096149 10.65407284 3.05555E-14 0.083106 0.12177
The summary indicates that the fitted model is:
Net profit after tax = -16332265.08 + 0.102437846(total assets)
The fitted model is significant (F (1, 48) = 113.509, p-value < .05) (Keller, 2014). Therefore,
the fitted model is ideal in predicting the net profit after tax from the total assets. This simply
means that the coefficient of total assets is not equal to zero or the slope is not equal to zero.
In this case, the correlation coefficient value is 0.8383, which shows that there is a strong
positive relationship between net profit after tax and the total assets. The R-squared value

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which is also called the coefficient of variation indicates that the fitted model can account for
70.28% of the variation (Rohatgi & Saleh, 2015). The remaining 29.12% is unexplained.
Conclusion
The summary indicates that most of the companies were from the material sector. The
confidence interval indicated that the sample data estimated the true population parameter for
the total revenue for the GICS sector of material only. On the other hand,
the sample data could not estimate the population for the total asset for
all companies that are trading. The analysis further pointed out that the total revenue
for Financials (GICS) is not more than the average total revenue for Health. Also, the results
indicated that the average market capitalization does not differ for Financials and materials
industries. When the analysis of the relationship between total assets and net profit after tax
was carried out, it was found that the two had a strong positive association.
In this report, the results might not be generalized to other data since the sample was quite
small. Like when comparing the averages between total revenue for Financials (GICS) and
the total revenue for Health, the sample for Financials was seven and that of health care was
two which was quite small. Thus, the validity and reliability of that test results are in
question.

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References
Anderson, D. R. et al., 2016. Statistics for business & economics. s.l.:Nelson Education.
Azzalini, A., 2017. Azzalini, A. (2017). Statistical inference based on the likelihood.
s.l.:Routledge.
Bland, M., 2015. n introduction to medical statistics. s.l.:Oxford University Press (UK).
Chatfield, C., 2018. Statistics for technology: a course in applied statistics. 3rd Edition ed.
New York: Routledge.
Cohen, P., West, S. G. & Aiken, L. S., 2014. Applied multiple regression/correlation
analysis for the behavioral sciences. 2nd ed. s.l.:Psychology Press.
Keller, G., 2014. Statistics for Management and Economics. 10th ed. Stamford: Cengage
Learning.
Lee, D. K., 2016. Alternatives to P value: confidence interval and effect size. Korean journal
of anesthesiology, 69(6), p. 555.
Lowry, R., 2014. Concepts and applications of inferential statistics. s.l.:s.n.
Ott, R. L. & Longnecker, M. T., 2015. An introduction to statistical methods and data
analysis. s.l.:Nelson Education.
Rohatgi, V. K. & Saleh, A. M. E., 2015. An introduction to probability and statistics.
s.l.:John Wiley & Sons.
Silvey, S. D., 2017. Statistical inference. s.l.:Routledge.
Sullivan III, M., 2015. Fundamentals of statistics. s.l.:Pearson.

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Appendix 1: Data
ASX
Code Company Name Status GICS Sector GICS Industr
AED AED Oil Limited Delisted Energy Energy
AEG
Absolute Equity Performance Fund
Limited Trading Financials Diversified Financials
AFQ African Petroleum Corporation Limited Delisted Energy Energy
JMS Jupiter Mines Limited. Trading Materials Materials
ARC Australian Rural Capital Limited Trading Financials Diversified Financials
ARV Artemis Resources Limited Trading Materials Materials
AYS Amaysim Australia Limited Trading
Communication
Services Telecommunication Servi
AZA Anzon Australia Limited Delisted Energy Energy
AZV Azure Healthcare Limited Trading Health Care Health Care Equipment &
BAF
Blue Sky Alternatives Access Fund
Limited Trading Financials Diversified Financials
BIR Bir Financial Limited Trading Financials Diversified Financials
BIS Bisalloy Steel Group Limited Trading Materials Materials
BRZ Brazin Limited Delisted
Consumer
Discretionary Retailing
BUE Bluestone Global Limited Delisted Industrials Commercial & Profession
BWX BWX Limited Trading Consumer Staples Household & Personal Pr
BYE Byron Energy Limited Trading Energy Energy
C6C Copper Mountain Mining Corporation Trading Health Care Health Care Equipment &
CDP Carindale Property Trust Trading Real Estate Real Estate
CIP Centuria Industrial REIT Trading Real Estate Real Estate
CMV CMA Corporation Limited Delisted Industrials Commercial & Profession
COV Corvette Resources Limited Delisted Materials Materials
COY Coppermoly Limited Trading Materials Materials
CPS Computronics Holdings Limited Delisted
Information
Technology Technology Hardware & E
CRU Catalyst Recruitment Systems Limited Delisted Industrials Commercial & Profession
CTI Chariot Limited Delisted
Information
Technology Software & Services
CVY Coventry Resources Limited Delisted Materials Materials
DDT DataDot Technology Limited Trading
Consumer
Discretionary Automobiles & Componen
DIO Dioro Exploration NL Delisted Materials Materials
DTM Dart Mining NL Trading Materials Materials
E3S E3Sixty Limited
Suspende
d Materials Materials
ECT
Environmental Clean Technologies
Limited
Suspende
d Industrials Commercial & Profession
EGH Eureka Group Holdings Limited Trading Real Estate Real Estate
EPA Ephraim Resources Limited
Suspende
d Consumer Staples Food, Beverage & Tobacc
EQI Equigold NL Delisted Materials Materials
ETR Etrade Australia Limited Delisted Financials Diversified Financials
FIJ Fiji Kava Limited Trading Health Care
Pharmaceuticals, Biotech
Sciences
FLK Folkestone Limited Delisted Real Estate Real Estate
FSG Field Solutions Holdings Limited Trading
Communication
Services Telecommunication Servi

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GBA Grandbridge Limited
Suspende
d Financials Diversified Financials
GGU GEC Global Fund Delisted Financials Diversified Financials
GIR Giralia Resources NL Delisted Materials Materials
GUL Gullewa Limited Trading Materials Materials
HE8 Helios Energy Ltd Trading Energy Energy
HST Hastie Group Limited Delisted Industrials Capital Goods
ICL International Coal Holdings Limited Delisted Materials Materials
ICT iCollege Limited Trading
Consumer
Discretionary Consumer Services
IOF Investa Office Fund Delisted Real Estate Real Estate
IPO International Petroleum Limited Delisted Materials Materials
IQE Intueri Education Group Limited Delisted
Consumer
Discretionary Consumer Services
IVT Inventis Limited Trading Industrials Commercial & Profession
Appendix 2: Confidence interval summaries
GICS Sector Materials sample
Count of Total
Revenue
Average of Total
Revenue
StdDev of Total
Revenue
15 911855811.6 3274244676
lower limit upper limit
(901,360,452.80) 2,725,072,076.00
GICS Sector Materials population
Count of Total
Assets
Average of Total
Assets
StdDev of Total
Assets
500 774969415.1 7326046170
Status Trading sample
Count of Total
Assets
Average of Total
Assets
StdDev of Total
Assets
24 694024601.2 2477202343
lower limit upper limit
(352,006,208.66) 1,740,055,410.99
Status Trading population
Count of Total
Assets
Average of Total
Assets
StdDev of Total
Assets
943.00 3,242,345,887.59 44,835,566,214.29

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Appendix 3: t Test for the Difference Between financials and health care total revenue
Pooled-Variance t Test for the Difference Between Two Means
(assumes equal population variances)
Data
Hypothesized Difference 0
Level of Significance 0.05
Financials Sample
Sample Size 7
Sample Mean 134571822.3
Sample Standard Deviation 167694332.3
Health care Sample
Sample Size 2
Sample Mean 14287612
Sample Standard Deviation 3572852.173
Intermediate Calculations
Financials Sample Degrees of
Freedom 6
Health care Sample Degrees of
Freedom 1
Total Degrees of Freedom 7
Pooled Variance 24105871402081000.0000
Standard Error 124485467.4877
Difference in Sample Means 120284210.2857
t Test Statistic 0.9663
Two-Tail Test
Lower Critical Value -2.3646
Upper Critical Value 2.3646
p-Value 0.3661
Do not reject the null hypothesis
Appendix 4: t Test for the Difference Between financials and materials market capitalization
Pooled-Variance t Test for the Difference Between Two Means
(assumes equal population variances)
Data
Hypothesized Difference 0
Level of Significance 0.05
Financials Sample
Sample Size 7
Sample Mean 103586325.3
Sample Standard Deviation 158288631.7
Materials Sample
Sample Size 15

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Sample Mean 1381334053
Sample Standard Deviation 4611584734
Intermediate Calculations
Financials Sample Degrees of
Freedom 6
Materials Sample Degrees of
Freedom 14
Total Degrees of Freedom 20
Pooled Variance
14894216219739600000.000
0
Standard Error 1766548307.3585
Difference in Sample Means -1277747727.3143
t Test Statistic -0.7233
Two-Tail Test
Lower Critical Value -2.0860
Upper Critical Value 2.0860
p-Value 0.4779
Do not reject the null hypothesis