Financial Management Assignement Introduction (1)

7/29/2019 Financial Management Assignement Introduction (1)

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FINANCIALMANAGEMENTASSIGNEMENTAssignment (20% contribution to the module mark)

Done By :

AymanMoussaid.

Mariam Morchid.

Soumaya Hamdouni.

Ayoub Faiz.

2012


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THE CONTENT

1. Download data for the sectorial indices of the last 6 months (3/10/2011 to

30/03/2012) and put the results (called variation) on a side by side basis. Start by

MASI as the main Index.

2. Provide the following analysis of each sector:

*Calculate and analyse the mean return (Highest, lowest return)

*calculate and analyse the variance of returns (Highest, lowest variance)*calculate and analyse the semi variance of each sector including MASI and

MADEX

3. Compare the sectors in terms of return and variance and semi variances

4. Using matrices prepare and analyse the VAR-COVAR matrix

5. Using excel, draw the opportunity set by considering the following:

(a) - 20 risk free rates.

(b) - Optimum portfolios of sectors for each risk free

6. Provide and analyse B for each sector in relation to the market

7. Provide and analyse the Expected Return of each Sector using CAPM


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FINANCIALMANAGEMENTASSIGNEMENT

The purpose of this project is to introduce a good understanding and

analysis regarding Morocco stock exchange regulated market on

whichsecuritiesare traded.ItincludesaCentral Market, whichfaceallsales

ordersandpurchasea securitylisted ontheStock ExchangeandOTC

Marketwhereonecan be negotiatedby private treatytransactions

insecuritieslistedand whichare for quantitiesgreater than or equalto the

minimum sizeof blocksin accordance with conditionsofcoursefromthe Central

Market. So first we download all the important data of the main Market which

is (Masi) and the industries competing in this market. After that, we calculate

and implement different financial concepts (expected return- variances- and

semi variances) which help us to analyze the situation of each sector and

compare all sectors to each other and to the main Market (Masi). In additionthis project gave us a strong knowledge to use Excel in calculating the figures

in professional way by using matrices. Not only this but also Using excel, draw

the opportunity set by considering 20 risk free rates and the optimum

portfolios of sectors for each risk free. And depending on that we provide and

analyze Beta for each sector in relation to the market to examine the volatility,

or systematic risk, of a security or a portfolio in comparison to the market as awhole. In other words, it gives a sense of the stock's market risk compared to

the greater market. Finally, we Provide and analyze the Expected Return of

each Sector using CAPM.


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Sector

ER

Max

ER

Min

ERvariance Highest

variance

Lowest

variance

Semi

vriance

RER

Semi

variance

RER

Masi

-

0,0354

4

1,33 -1,82 0,2976347 6,0496719 0,2976347 47% 53%

Banks

-

0,0352

8

2,07 -2,3 0,5899106 52% 48%

Beverages -

0,0515

2

3,87 -4,08 2,048135574

82%

18%

Chemicals -

0,1468

8

3,81 -3,72 1,666071639 49.5% 50.5%

construction &

building materials

0.0035

2

3,91 -3,5 1,456324606 54% 46%

distributors 0,0323

2

2,38 -2,91 0,894363123 45.6% 54.4%

electrical&electron

icequipments -0.0444

6 -6 5,385382903 82% 18%

engineering

&equipmentindustrialgoods

-

0,16632

3,66 -3,61 2,502189574 49.6% 50.4%

THE SECTORS( ER, MAX ER, MIN ER, VARINCES, SEMI VARIANCES)


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foodproducer and

processors

0,0061

6

4,71 -3,24 1,105646426 38% 62%

Foresty&paper -0,2172 5,86 -5,94 6,049671935

62.4%

37.6%

holding companies -

0,0038

4

5,47 -5,04 3,541122232 53.6% 46.4%

Insurance 0,0774

4

3,85 -3,52 2,1458079 38% 62%

investmentcompan

ies&other finance

0,0478

4

3,71 -3,59 1,173212232 44% 56%

leisures and hotels -

0,0643

2

5,22 -5,85 3,026655381

70%

30%

Utilities 0.1092

8

2,77 -3,24 3.021558348 27% 73%

Transports 0.0023

2

4,72 -6 2.328214735 32.8% 67.2%

Telecommunicatio

n -0.038

2.06 -1.86 0.452862903 52.8% 47.2%


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(Analysis of each sector comparing to Masi)

The Bank sector

During the last 6 months, the performance of the bank sector was better than the market

(MASI), with an average return of -0.3528, a max return of 2.07 and a minimum return of -

2.3. While the market was -0.03544with a max return of 1.33 and a minimum return of -1.82.

We have analyzed that if the variance of the sector is high the risk and the return are high as

well, which explains the difference between the bank and the market as the bank sector have a

higher variance (0.5899106) than the market variance which is 0.2976347.

The semi-variance R< ER have a high variability with a percentage of 52% than the Semi-

variance R>ER with a percentage of 48%, which is not attractive for new investors as returns

have higher probability to move on the negative side compared to the market. However it

doesnt reflect a lot the variance and performance of the industry.

The beverages sector:

real estate -

0.0704

8

4.4 -4.86 1,598691703 55% 45%

pharmaceuticalind

ustry

-

0.1947

2

3.12 -5.8 1,676713832 80% 20%

Oil&Gas -

0,1855

2

5.01 -4.9 3.055562 57% 43%

Mining 0,0090

4

4,72 -5,28 1.64065391

49.6%

50.4%

Material software

& computer service

-

0.1812

8

2.77

-3.24

1.552011252 51% 49%


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During the last six months, the beverages sector performance was not stable, but fluctuating,

as it was below the market performance with a return of - 3.49 in the29th

of December of

2012, and during the same year its return increased to be better than the market performance,

but it continued to decrease after that to reach a return of -1.43 in the 15th

of March 2012.

In general the Mean return of the beverages sector which is -1.0232 was bellow than the

market average return that is -0.07192.

While the variance of the beverages sector is 2.3313864 better than the market variance which

is 0.348335642, which is an odd situation as according to the theory, if we have high

variance, it is usually associated with high returns high risk. But in this case the return of the

industry was lower than the return of the market.

The semi-variance R>ER is higher than the semi-variance RER is lower than the semi-

variance RER have a high variability with a percentage of 54% than the Semi-

variance R


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The distributors sector:

The performance of the distributors sector was better than the market (MASI), with an

average return of 0.03, a max return of 2.38 and a minimum return of -2.91. While the market

was -0.03544with a max return of 1.33 and a minimum return of -1.82.

We have analyzed that if the variance of the sector is high the risk and the return are high as

well, which explains the difference between the distributors sector and the market as

distributorssector have a higher variance (0.89) than the market variance which is 0.2976347.

The semi-variance R< ER have a high variability with a percentage of 54.4%% than the Semi-

variance R>ER with a percentage of 45.6%, which is not attractive for new investors as

returns have higher probability to move on the negative side compared to the market.

However it doesnt reflect a lot the variance and performance of the industry.

The electrical equipments sector:

The performance of the electrical and building equipments sector was better than the market

(MASI), with an average return of -0.044, a max return of 6 and a minimum return of -6.

While the market was -0.03544with a max return of 1.33 and a minimum return of -1.82.

If the variance of the sector is high the risk and the return are high as well, which explains the

difference between the electrical and building materials sector and the market as the electrical

and building equipmentssector have a higher variance (5.38) than the market variance which

is 0.2976347.

The semi-variance R>ER have a high variability with a percentage of 82% than the Semi-

variance R


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The food producer and processors sector:

The performance of the food producer and processors sector was better than the market

(MASI), with an average return of 0,00616, a max return of 4,71 and a minimum return of -

3,24. While the market was -0.03544with a max return of 1.33 and a minimum return of -

1.82.


difference between the food producer and processors sector and the market as the 1st have a

higher variance (1,105646426) than the market variance which is 0.2976347.

The semi-variance RER with a percentage of 32%, which is the opposite compared to the variance.

The forestry and paper sector:

Over 6 month the performance of the forestry and paper sector was constantly lower than the

performance of the market (Masi) with an average return of (-0,2172), max return of 5,86, and

minimum return of (-5,94). Whereas the market had an average return of -0.03544with a max

return of 1.33 and a minimum return of -1.82.

On the other hand, while analyzing the forestry and paper sector we notice that it has high risk

with higher variance of (6.049671935) than the market variance which is 0.2976347, and the

semi variance R>ER have a high variability with a percentage of 62.4% than the Semi-

variance RER have a high variability with a percentage of 53.6% than the Semi-

variance R


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The insurance sector:

The performance of the insurance sector was better than the market (MASI), with an average

return of0,07744, a max return of 3,85 and a minimum return of -3,52. While the marketreturn was -0.03544with a max return of 1.33 and a minimum return of -1.82.


difference between the insurance sector and the market as the 1st have a higher variance

(2.1458079) than the market variance which is 0.2976347, however the semi-variance RER with a

percentage of 32%, which is the opposite compared to the variance, and then it is an odd

situation.

The investment companies & other finance sector:

The performance of the investment companies and other finance sector was better than the

market (MASI), with an average return of0,04784, a max return of 3,71and a minimum return

of -3,59. While the market return was -0.03544with a max return of 1.33 and a minimum

return of -1.82.


difference between the investment companies and other finance sector and the market as the

1st have a higher variance (1,173212232) than the market variance which is 0.2976347,

however the semi-variance RER with a percentage of 44%, which is the opposite compared to the

variance, and then it is an odd situation.

The leisure and hotels sector:

The performance of the leisure and hotels sector wasnt good compared to the market, as the

return of the leisure and hotels sector was -0,06432, with a max return of 5,22 and a minimum

return of -5,85. While the markets return was -0.03544 a max return of 1.33 and a minimum

return of -1.82.

However, when we compare the variances, we find out that the variance of the leisure andhotel sector, which is 3,026655381, is higher than the market, and that the semi variance

R>ER has a high variability with a percentage of 70%, than the semi-variance R


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However, when we compare the variances, we find out that the variance of the software and

computer service sector, which is 1,552011252, is higher than the market, and that the semi

variance R>ER has a high variability with a percentage of 51%, than the semi-variance

RER has a

high variability with a percentage of 57%, than the semi-variance RER has a high variability with a percentage of 80%, than the semi-variance R


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The real estate sector:

The real estate sector has a lower return which is -0.07048 compared to the market that has a

return of -0.03544, while the variance of the real estate sector on the other hand

(1.598691703) is higher than the variance of the market 0.2976347 which is an odd situation,

as normally when there is high return, it I associated with high risk and high variance which is

not the case in here.

However, the semi-variance R>ER has a high variability to move on the positive side with

55%.

The telecommunication sector:

The average return of the telecommunications sector (-0,038) wasnt far from the return of the

market (0.03544), however while comparing the variance, we find out that the variance of the

telecommunication sector, which is (0.452862903), is higher than the variance of the marketthat is 0.2976347, however the semi variance R>ER has a high variability with a percentage

of 52.8%, than the semi-variance R


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(Compare the sectors in terms of return and variance and semi variances)

The beverages sector compared to the bank sector, Number 25:

The bank sector has a higher Returns (-0,03528) compared to the beverages sector (-0,05152),

however the variance of the beverages sector that is 2,048135574, is higher than the bank

sector (0,5899106), and while comparing the semi-variance of both sectors, we find out that

they both have high variability to move towards the positive side, the beverages sector with a

variability of 82% and the bank sector with a variability of 52%, thing that reflect the

difference in variances.

The chemicals sector compared to the bank sector, number 26:

The bank sector has a higher Returns (-0,03528) compared to the chemicals sector (-0,14688),

however the variance of the chemicals sector that is 1,666071639, is higher than the banksector (0,5899106), but if we compare the semi-variances of both sectors, we find out that the

bank sector has a high variability to move towards the positive side, and the chemicals sector

has a high variability to move towards the negative side, which is an odd situation, because

according to the theory, the high variance is usually related to high risk and high return which

is not the case in this comparison.

The construction and building material and the bank sector, number 27:

The construction and building materials sector has a higher Returns (0,00352) compared to

the bank sector (-0,03528), and the variance of the construction and building materials sectorthat is 1,456324606, is higher than the bank sector (0,5899106), and if we compare the semi-

variance of both sectors, we find out that both sectors have a high variability to move towards

the positive side.

The distributors sector compared to the bank sector, number 28:

The distributors sector has a higher Returns (0.03232) compared to the bank sector (-

0.03528), and the variance of the distributors sector that is 0,894363123, is higher than the

bank sector (0.5899106), but if we compare the semi-variance of both sectors, we find out that

we find out that the bank sector has a high variability to move towards the positive side, andthe chemicals sector has a high variability to move towards the negative side, which is an odd

situation

electrical & electronic equipments compared to the bank sector, number 29:

The bank sector has a higher Returns (-0,03528) compared to the electrical and electronic

sector(-0.0444), however the variance of the electrical and electronic sector that is

5,385382903, is higher than the bank sector (0,5899106), and if we compare the semi-

variance of both sectors, we find out that both sectors have a high variability to move towards

the positive side.


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engineering equipments & industrial goods sector compared to the bank sector,

number 30:

The bank sector has a higher Returns (-0,03528) compared to the engineering equipment and

industrial goods sector (-0,16632), however the variance of the engineering equipment andindustrial goods sector that is 2,502189574, is higher than the bank sector (0,5899106), but if

we compare the semi-variance of both sectors, we find out that we find out that the bank

sector has a high variability to move towards the positive side, and the engineering equipment

and industrial goods sector has a high variability to move towards the negative side, which is

an odd situation.

Comparing food producer & processors sector with the bank sector, number 31:

The food producer and processor sector has a higher Returns (0,00616) compared to the bank

sector (-0.03528), and the variance of the food producer and processor sector that is1,105646426, is higher than the bank sector (0.5899106), but if we compare the semi-variance

of both sectors, we find out that we find out that the bank sector has a high variability to move

towards the positive side, and the food producer and processor sector has a high variability to

move towards the negative side, which is an odd situation.

Comparing the forestry and paper sector with the bank sector, number 32:

The bank sector has a higher Returns (-0,03528) compared to the forestry and paper sector

(0,00616), however the variance of the forestry and paper sector that is 1,105646426, is

higher than the bank sector (0,5899106), but if we compare the semi-variance of both sectors,we find out that we find out that the bank sector has a high variability to move towards the

positive side, and the forestry and paper sector has a high variability to move towards the

negative side, which is an odd situation.

Comparing the holding companies sector to the bank sector, number 33:

The holding companies sector has a higher Returns (-0,00384) compared to the bank sector (-

0,03528), and the variance of the holding companies sector that is 3,541122232, is higher than

the bank sector (0,5899106), and if we compare the semi-variance of both sectors, we find out

that both sectors have a high variability to move towards the positive side.

Comparing the insurance sector to the bank sector, number 34:

The insurance sector has a higher Returns (0.07744) compared to the bank sector (-0.03528),

and the variance of the insurance sector that is 2,1458079, is higher than the bank sector

(0.5899106), but if we compare the semi-variance of both sectors, we find out that we find out

that the bank sector has a high variability to move towards the positive side, and the insurance

sector has a high variability to move towards the negative side.


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Comparing the investment companies & other finance to the bank sector,

number 35:

The investment companies and other finance sector has a higher Returns (0,04784) compared

to the bank sector (-0.03528), and the variance of the investment companies and other finance

sector that is 1,173212232, is higher than the bank sector (0.5899106), but if we compare the

semi-variance of both sectors, we find out that we find out that the bank sector has a high

variability to move towards the positive side, and the investment companies and other finance


Comparing the leisure and hotels sector to the bank sector, number 36:

The bank sector has a higher Returns (-0,03528) compared to the leisure and hotels sector (-

0,06432), however the variance of the leisure and hotels sector that is 3,026655381, is higher

than the bank sector (0,5899106), but if we compare the semi-variance of both sectors, we

find that both sectors have high variability to move towards the positive side.

Comparing the material software & computer service and computer service,

number 37:

The bank sector has a higher Returns (-0.03528) compared to the material software and

computer service sector (-0,18128), however the variance of the material software and

computer service sector that is 1,552011252, is higher than the bank sector (0,5899106), but if

we compare the semi-variance of both sectors, we find that both sectors have high variability

to move towards the positive side.

Comparing the mining sector to the bank sector, number 38:

The mining sector has a higher Returns (0.00904) compared to the bank sector (-0.03528),

and the variance of the mining sector that is 1,64065391, is higher than the bank sector


that the bank sector has a high variability to move towards the positive side, and the mining


Comparing the oil & gas sector to the bank sector, number 39:

The bank sector has a higher Returns (-0.03528) compared to the oil a gas sector(-0,18552),however the variance of the oil a gas sector that is 3,055562, is higher than the bank sector

(0,5899106), but if we compare the semi-variance of both sectors, we find that both sectors

have high variability to move towards the positive side.

Comparing the pharmaceutical industry to the bank sector, number 40:

The bank sector has a higher Returns (-0.03528) compared to the pharmaceutical industry

sector (-0,19472), however the variance of the pharmaceutical industry sector that is

1,676713832, is higher than the bank sector (0,5899106), but if we compare the semi-variance

of both sectors, we find that both sectors have high variability to move towards the positiveside.


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Comparing the real estate sector to the bank sector, number 41:

The bank sector has a higher Returns (-0.03528) compared to the real estate sector (-0.07048),

however the variance of the real estate sector that is 1,598691703, is higher than the bank

sector (0,5899106), but if we compare the semi-variance of both sectors, we find that both

sectors have high variability to move towards the positive side

Comparing the telecommunication sector to the bank sector, number 42:

The bank sector has a higher Returns (-0.03528) compared to the telecommunication sector (-

0,038), and also the variance of the bank sector (0,5899106) was higher than the variance of

the telecommunication sector (0,452862903), but if we compare the semi-variance of both

sectors, we find that both sectors have high variability to move towards the positive side.

Comparing the transport sector to the bank sector, number 43:

The transport sector has a higher Returns (0,00232) compared to the bank sector (-0.03528),

and the variance of the transport sector that is 2,328214735, is higher than the bank sector


that the bank sector has a high variability to move towards the positive side, and the transport

sector has a high variability to move towards the negative side, thing that doesnt reflect a lot

the variance of the sector.

Comparing the utilities sector to the bank sector, number 44:

The utilities sector has a higher Returns (0,10928) compared to the bank sector (-0.03528),

and the variance of the utilities sector that is 3,021558348, is higher than the bank sector

(0.5899106), but if we compare the semi-variance of both sectors, we find out that the bank

sector has a high variability to move towards the positive side, and the utilities sector has a

high variability to move towards the negative side.

Comparing the chemicals sector to the beverages sector, number 48:

The beverages sector has a higher Returns (-0,05152) compared to the chemicals sector (-

0,14688), and also the variance of the beverages sector (2,048135574) was higher than the

variance of the chemicals sector (1,666071639), but if we compare the semi-variance of

both sectors, we find out that the beverages sector has a high variability to move towardsthe positive side, and the chemicals sector has a high variability to move towards the

negative side.

Comparing the construction & building materials to the beverages sector,

number 49 :

The construction and building materials sector has a higher Returns (0.00352) compared to

the beverages sector (-0.05152), however the variance of the beverages sector that is

2,048135574, is higher than the construction and building materials sector (1,456324606), but

if we compare the semi-variance of both sectors, we find that both sectors have highvariability to move towards the positive side.


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Comparing the distributors sector to the beverages sector, number 50:

The distributors sector has a higher Returns (0.03232) compared to the beverages sector (-

0.05152), however the variance of the beverages sector that is 2,048135574, is higher than the

distributors sector (0,894363123), but if we compare the semi-variance of both sectors, we

find out that the beverages sector has a high variability to move towards the positive side, and

the distributors sector has a high variability to move towards the negative side.

Comparing the electrical & electronic equipments to the beverages sector,

number 51 :

The electrical and electronic equipments sector has a higher Returns (-0.0444) compared to

the beverages sector (-0.05152), also the variance of the electrical and electronic equipments

(5,385382903), is higher than the variance of the beverages sector that is 2,048135574, and if

we compare the semi-variance of both sectors, we find out that both sectors have high

variability to move towards the positive side with a variability of 82%.

Comparing the engineering equipments & industrial goods to the beverages

sector, number 52:

The beverages sector has a higher Returns (-0,05152) compared to the engineering

equipments and industrial goods sector (-0,16632), however the variance of the engineering

equipments and industrial goods sector (2,502189574) was higher than the variance of the

beverages sector (2,048135574), but if we compare the semi-variance of both sectors, we find

out that the beverages sector has a high variability to move towards the positive side, and the

engineering equipments and industrial goods sector has a high variability to move towards thenegative side, which is an odd situation as it doesnt reflect the reality in this case.

Comparing the food producer & processors to the beverages sector, number 53 :

The food producer and processors sector has a higher Returns (0,00616) compared to the

beverages sector (-0.05152), however the variance of the beverages sector that is

2,048135574, is higher than the food producer and processors sector (1,105646426), but if we

compare the semi-variance of both sectors, we find out that the beverages sector has a high

variability to move towards the positive side, and the food producer and processors sector has

a high variability to move towards the negative side, thing that is different from the theory,and that doesnt reflect the real picture of the sector in this case.

Comparing the forestry & paper sector to the beverages sector, number 54 :

The beverages sector has a higher Returns (-0,05152) compared to the forestry and paper

sector (-0,2172), however the variance of the forestry and paper sector (6,049671935) was

higher than the variance of the beverages sector (2,048135574), and if we compare the semi-

variance of both sectors, we find out that both sectors have high variability to move towards

the positive side.


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Comparing the Holding companies to the beverages sector, number 55:

The holding companies sector has a higher Returns (-0,00384) compared to the beverages

sector (-0.05152), also the variance of the holding companies (3,541122232), is higher than

the variance of the beverages sector that is 2,048135574, and if we compare the semi-variance

of both sectors, we find out that both sectors have high variability to move towards the

positive side with a variability.

Comparing the insurance sector to the beverages sector, number 56:

The insurance sector has a higher Returns (0,07744) compared to the beverages sector (-

0.05152), also the variance of the insurance (2,1458079), is higher than the variance of the

beverages sector that is 2,048135574, but if we compare the semi-variance of both sectors, we

find out that the beverages sector has a high variability to move towards the positive side, and

the insurance sector has a high variability to move towards the negative side, thing that is

different from the theory, and that doesnt reflect the real picture of the sector in this case.

Comparing the investment companies & other finance to the beverages sector,

number 57:

The investment companies and other finance sector has a higher Returns (0,00616) compared

to the beverages sector (-0.05152), however the variance of the beverages sector that is

2,048135574, is higher than the investment companies and other finance sector

(1,105646426), which is an odd situation, as according to the theory high variance is usually

associated with high return, which is not the case in here. But if we compare the semi-

variance of both sectors, we find out that the beverages sector has a high variability to movetowards the positive side, and the investment companies and other finance sector has a high

variability to move towards the negative side.

Comparing the leisure and hotels to the beverages sector, number 58:

The beverages sector has a higher Returns (-0,05152) compared to the leisure and hotels

sector (-0,06432), however the variance of the leisure and hotels sector (3,026655381) was

higher than the variance of the beverages sector (2,048135574), and if we compare the semi-

variance of both sectors, we find out that both sectors have high variability to move towards

the positive side.

Comparing the material software & computer service to the beverages sector,

number 59:

The beverages sector has a higher Returns (-0,05152) compared to the material software and

computer service sector (-0,18128), and also the variance of the beverages sector

(2,048135574) was higher than the variance of the material software and computer service

sector (1,552011252), and if we compare the semi-variance of both sectors, we find out that

both sectors have high variability to move towards the positive side.


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Comparing the mining sector to the beverages sector, number 60:

The mining sector has a higher Returns (0,00904) compared to the beverages sector (-

0.05152), however the variance of the beverages sector that is 2,048135574, is higher than the

mining sector (1,64065391), which is an odd situation, as according to the theory high

variance is usually associated with high return, which is not the case in here. But if we

compare the semi-variance of both sectors, we find out that the beverages sector has a high

variability to move towards the positive side, and the mining sector has a high variability to

move towards the negative side.

Comparing the oil & gas sector to the beverages sector, number 61:

The beverages sector has a higher Returns (-0.05152) compared to oil & gas sector -0,18552),

however the variance of the oil & gas sector (3,055562) was higher than the variance of the

beverages sector (2,048135574), which is an odd situation, and if we compare the semi-

variance of both sectors, we find out that both sectors have high variability to move towardsthe positive side.

Comparing the pharmaceutical industry to the beverages sector, number 62 :

The beverages sector has a higher Returns (-0.05152) compared to the pharmaceutical

industry sector (-0,19472), and also the variance of the beverages sector (2,048135574) was

higher than the variance of the pharmaceutical industry sector (1,676713832), but if we

compare the semi-variance of both sectors, we find out that both sectors have high variability

to move towards the positive side.

Comparing the real estate sector to the beverages sector, number 63:

The beverages sector has a higher Returns (-0.05152) compared to the real estate sector (-

0.07048), and also the variance of the beverages sector (2.048135574) was higher than the

variance of real estate sector (1.598691703), and if we compare the semi-variance of both

sectors, we find out that both sectors have high variability to move towards the positive side.

Comparing the telecommunications sector to the beverages sector, number 64:

The telecommunications sector has a higher Returns (-0,038) compared to the beverages

sector (-0.05152), however the variance of the beverages sector that is 2,048135574, is higherthan telecommunications sector (0,452862903), which is an odd situation, as according to the

theory high variance is usually associated with high return, which is not the case in here. But

if we compare the semi-variance of both sectors, we find out that both sectors have high

variability to move towards the positive side.

Comparing the transport sector to the beverages sector, number 65:

The transport sector has a higher Returns (0,00232) compared to the beverages sector (-

0.05152), also the variance of the transport sector (2,328214735), is higher than the variance

of the beverages sector that is 2,048135574, and if we compare the semi-variance of bothsectors, we find out that the beverages sector has a high variability to move towards the


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positive side, and the transport sector has a high variability to move towards the negative side,

which is an odd situation

Comparing the utilities sector to the beverages sector, number 66:

The utilities sector has a higher Returns (0,10928) compared to the beverages sector (-0.05152), also the variance of the utilities sector (3,021558348), is higher than the variance of

the beverages sector that is 2,048135574, and if we compare the semi-variance of both

sectors, we find out that the beverages sector has a high variability to move towards the

positive side, and the utilities sector has a high variability to move towards the negative side,

which is an odd situation.

Comparing the construction & building materials to the chemicals sector,

number 71 :

The construction and building material sector has a higher Returns (0,00352) compared to thechemicals sector (-0,14688), however the variance of the chemicals sector that is

1,666071639, is higher than construction and building material sector (1,456324606), which

is an odd situation, as according to the theory high variance is usually associated with high

return, which is not the case in here. But if we compare the semi-variance of both sectors, we

find out that the chemicals sector has a high variability to move towards the negative side, and

the construction and building materials sector has a high variability to move towards the

positive side, which is an odd situation.

Comparing the distributors sector to the chemicals sector, number 72:

The distributors sector has a higher Returns (0.03232) compared to the chemicals sector (-

0,14688), however the variance of the chemicals sector that is 1,666071639, is higher than

distributors sector (0,894363123), which is an odd situation, as according to the theory high

variance is usually associated with high return, which is not the case in here. And if we

compare the semi-variance of both sectors, we find out that both sectors tend to have a high

variability to move on the negative side.

Comparing the electrical & electronic equipments to the hemicals sector, number

73 :

The electrical and electronic equipments sector has a higher Returns (-0.0444) compared to

the chemicals sector (-0,14688), also the variance of the electrical and electronic equipments

sector (5,385382903), is higher than the variance of the chemicals sector that is 1,666071639,

and if we compare the semi-variance of both sectors, we find out that the electrical and

electronic equipments sector has a high variability to move towards the positive side, and the

chemicals sector has a high variability to move towards the negative side.


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Comparing the engineering equipments & industrial goods and the chemicals

sector, number 74:

the chemicals sector has a higher Returns (-0,14688) compared to The distributors sector

(0.03232), however the variance of the distributors sector (0,894363123) is higher than the

chemicals sector that is 1,666071639, which is an odd situation, as according to the theory

high variance is usually associated with high return, which is not the case in here. And if we

compare the semi-variance of both sectors, we find out that both sectors tend to have a high


Comparing the food producer & processors to the beverages sector, number 75 :

The food producer and processors sector has a higher Returns (0,00616) compared to the

chemicals sector (-0,14688), however the variance of the chemicals sector that is

1,666071639, is higher than food producer and processors (1,105646426), which is an odd

situation, as according to the theory high variance is usually associated with high return,which is not the case in here. And if we compare the semi-variance of both sectors, we find

out that both sectors tend to have a high variability to move on the negative side.

Comparing the forestry & paper to the beverages sector, number 76 :

the chemicals sector has a higher Returns (-0,14688) compared to The forestry and paper

sector (-0,2172), however the variance of the forestry and paper sector (6,049671935) is

higher than the chemicals sector that is 1,666071639, which is an odd situation, as according

to the theory high variance is usually associated with high return, which is not the case in

here. And if we compare the semi-variance of both sectors, we find out that the forestry andpaper sector has a high variability to move towards the positive side, and the chemicals sector

has a high variability to move towards the negative side.

Comparing the holding companies to the chemicals sector, number 77:

The holding companies sector has a higher Returns (-0,00384) compared to the chemicals

sector (-0,14688), however the variance of the chemicals sector that is 1,666071639, is higher

than holding companies (3,541122232), which is an odd situation, as according to the theory

high variance is usually associated with high return, which is not the case in here. And if we

compare the semi-variance of both sectors, we find out that the holding companies sector hasa high variability to move towards the positive side, and the chemicals sector has a high

variability to move towards the negative side.

Comparing the insurance sector to the chemicals sector, number 78:

The insurance sector has a higher Returns (0,07744) compared to the chemicals sector (-

0,14688), also the variance of the insurance sector (2,1458079), is higher than the variance of

the chemicals sector that is 1,666071639, and if we compare the semi-variance of both

sectors, we find out that the insurance sector has a high variability to move towards the

positive side, and the chemicals sector has a high variability to move towards the negativeside.


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Comparing the investment companies & other finance to the chemicals sector,

number 79:

The investment companies and other finance sector has a higher Returns (0.04784) compared

to the chemicals sector (-0.14688), however the variance of the chemicals sector that is

1,666071639, is higher than the investment companies and other finance (1,173212232),

which is an odd situation, as according to the theory high variance is usually associated with

high return, which is not the case in here. And if we compare the semi-variance of both

sectors, we find out that both sectors tend to have a high variability to move on the negative

side.

Comparing the leisures & hotels sector to the chemicals sector, number 80 :

The leisures and hotels sector has a higher Returns (-0,06432) compared to the chemicals

sector (-0,14688), also the variance of the leisures and hotels sector (3,026655381), is higher

than the variance of the chemicals sector that is 1,666071639, and if we compare the semi-variance of both sectors, we find out that the leisures and hotels sector has a high variability to

move towards the positive side, and the chemicals sector has a high variability to move

towards the negative side.

Comparing the material software & computer service to the chemicals sector,

number 81:

The chemicals sector has a higher Returns (-0.14688) compared to The material software and

computer service sector (-0.18128),and the variance of the chemicals sector that is

1,666071639, is higher than the material software and computer service (1,552011252). Andif we compare the semi-variance of both sectors, we find out that the software and computer

service sector has a high variability to move towards the positive side, and the chemicals


Comparing the mining sector to the chemicals sector, number 82:

The mining sector has a higher Returns (0,00904) compared to the chemicals sector (-

0.14688), however the variance of the chemicals sector that is 1,666071639, is higher than the

mining sector (1,64065391), which is an odd situation, as according to the theory high

variance is usually associated with high return, which is not the case in here. And if wecompare the semi-variance of both sectors, we find out that both sectors tend to have a high


Comparing the oil & gas sector compared to the chemicals sector, number 83:

The oil & gas sector has a higher Returns (-0,18552) compared to the chemicals sector (-

0.14688), however the variance of the chemicals sector that is 1,666071639, is higher than the

oil & gas sector (3,055562), which is an odd situation, as according to the theory high


compare the semi-variance of both sectors, we find out that the oil & gas sector has a high


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variability to move towards the positive side, and the chemicals sector has a high variability to


Comparing the pharmaceutical industry to the chemicals sector, number 84:

the chemicals sector has a higher Returns (-0,14688) compared to The pharmaceuticalindustry sector (-0,19472), however the variance of the pharmaceutical industry sector

(1,676713832) is higher than the chemicals sector that is 1,666071639, which is an odd

situation, as according to the theory high variance is usually associated with high return,

which is not the case in here. And if we compare the semi-variance of both sectors, we find

out that the pharmaceutical industry sector has a high variability to move towards the positive

side, and the chemicals sector has a high variability to move towards the negative side.

Comparing the real estate sector to the chemicals sector, number 85:

The real estate sector has a higher Returns (-0,07048) compared to the chemicals sector (-0.14688), however the variance of the chemicals sector that is 1,666071639, is higher than the

real estate sector (1,598691703), which is an odd situation, as according to the theory high


compare the semi-variance of both sectors, we find out that the real estate sector has a high

variability to move towards the positive side, and the chemicals sector has a high variability to


Comparing the telecommunication sector to the chemicals sector, number 86:

The telecommunications sector has a higher Returns (-0,038) compared to the chemicalssector (-0.14688), however the variance of the chemicals sector that is 1,666071639, is higher

than the telecommunications sector (0,452862903), which is an odd situation, as according to

the theory high variance is usually associated with high return, which is not the case in here.

And if we compare the semi-variance of both sectors, we find out that the telecommunications

sector has a high variability to move towards the positive side, and the chemicals sector has a

high variability to move towards the negative side.

Comparing the transport sector to the chemicals sector, number 87:

The transport sector has a higher Returns (0,00232) compared to the chemicals sector (-

0,14688), also the variance of the transport sector (2,328214735), is higher than the variance

of the chemicals sector that is 1,666071639, and if we compare the semi-variance of both


side.

Comparing the utilities sector to the chemicals sector, number 88:

The utilities sector has a higher Returns (0,10928) compared to the chemicals sector (-

0,14688), also the variance of the utilities sector (3,021558348), is higher than the variance of

the chemicals sector that is 1,666071639, and if we compare the semi-variance of both


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side.

Number 94 :

The Distributors sector has a higher Returns (0.032) compared to the construction sector(0.003), but the variance of the construction sector is (1,456324606), which is higher than the

distributors sector (0, 894363123), but if we compare the semi-variances of both sectors, we

find out that the construction sector has a higher semi variance with variability of (54) to

move towards the positive side, and the distributors sector has a lower variability of

(45,6)with high variability ( 54,4)to move towards the negative side, which is an odd

situation, because according to the theory, the high variance is usually related to high risk and

high return which is not the case in this comparison.

Number 95:

The construction sector has a higher Returns (0.003) compared to the electrical sector (-

0.0444), but the variance of the electrical sector is (5,385382903), which is higher than the

construction sector (1,456324606), but if we compare the semi-variances of both sectors, we

find out that the electrical sector has a higher semi variance with variability of (82) to move

towards the positive side, and the construction sector has a lower variability of (54), which is

an odd situation, because according to the theory, the high variance is usually related to high

risk and high return which is not the case in this comparison.

Number 96:

In this case we see that the engineering sector has a lower Returns (-0,16) compared to the

construction sector (0.003), however the variance of the engineering sector is (2,502189574),

which is higher than the construction sector variance (1,456324606), but if we compare the

semi-variances of both sectors, we find out that the construction sector has a higher semi

variance with variability of (54%) to move towards the positive side, and the engineering

sector has a lower variability of (49.6)to move towards the positive side.so here we notice

there is an odd situation because high variance means high return with high risk, also

according to the theory, the high semi variance is usually related to high risk and high return

which is not the case in this comparison.

Number 97:

In this case we see that the construction sector has a lower Returns (0.003) compared to the

food sector (0.006), but the variance of the construction sector is (1,456324606), which is

higher than the foods sector. Nevertheless, if we compare the semi-variances of both sectors,

we find out that the construction sector has a higher semi variance with variability of (54%) to

move towards the positive side, and the food sector has a lower variability of (38%) to move

towards the positive side, and more variability (62%) to move to the negative side. Which is

an odd situation, because according to the theory, the high semi variance is usually related to

high risk and high return which is not the case in this comparison.


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Number 98:

The construction sector has a higher Returns (0.003) compared to the paper sector (-0.21), but

the variance of the paper sector is (6,049671935), which is higher than the construction sector

(1,456324606), but if we compare the semi-variances of both sectors, we find out that the

food sector has a higher semi variance with variability of (62.4%) to move towards the

positive side, and the construction sector has a lower variability of (54), which is an odd



Number 99:

The construction sector has a higher Returns (0.003) compared to the holding companies

sector (-0.003), but the variance of the holding companies sector is (3,541122232), which is

higher than the construction sector (1,456324606), but if we compare the semi-variances ofboth sectors, we find out that the construction sector has a higher semi variance with

variability of (54%) to move towards the positive side, and the holding companies sector has a

lower variability of (53.6%), which is an odd situation, because according to the theory, the

high variance is usually related to high risk and high return which is not the case in this

comparison.

Number 100:

The insurance sector has a higher Returns (0.07) compared to the constructions sector (0.003),

in addition the variance of the insurance sector is (2, 1458079), which is higher than theconstructions sector, but if we compare the semi-variances of both sectors, we find out that

the construction sector has a higher semi variance with variability of ( 54%) to move towards

the positive side, and the insurance sector has a lower variability of ( 38% )with high

variability ( 62)to move towards the negative side, which is an odd situation, because

according to the theory, the high variance is usually related to high risk and high return which

is not the case in this comparison.

Number 101:

In this case we see that the construction sector has a lower Returns (0.003) compared to theinvestment sector (0.04), but the variance of the construction sector is (1,456324606), which

is higher than the investment sector. Nevertheless, if we compare the semi-variances of both

sectors, we find out that the construction sector has a higher semi variance with variability of

(54%) to move towards the positive side, and the investment sector has a lower variability of

(44%) to move towards the positive side, and more variability (56%) to move to the negative

side. Which is an odd situation, because according to the theory, the high semi variance is

usually related to high risk and high return which is not the case in this comparison.


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Number 102:

The construction sector has a higher Returns (0.003) compared to hotels sector (-0.06), but the

variance of the hotels sector is (3,026655381), which is higher than the construction sector

(1,456324606), but if we compare the semi-variances of both sectors, we find out that the

hotels sector has a higher semi variance with variability of (70%) to move towards the

positive side, and the construction sector has a lower variability of (54%), which is an odd



Number 103:

The construction sector has a higher Returns (0.003) compared to thesoftware sector (-0.18),

but the variance of thesoftware sector is (1.552011252), which is higher than the construction

sector (1,456324606), but if we compare the semi-variances of both sectors, we find out that

theconstruction sector has a higher semi variance with variability of (54%) to move towardsthe positive side, and the software sector has a lower variability of (51%), which is an odd



Number 104:

In this case we see that the mining sector has a higher Returns (0,009) compared to the

construction sector (0.003), however the variance of the mining sector is (1.64065391), which

is higher than the construction sector variance (1,456324606), but if we compare the semi-

variances of both sectors, we find out that the construction sector has a higher semi variancewith variability of (54%) to move towards the positive side, and themining sector has a lower

variability of (49.6%)to move towards the positive side.so here we notice there is an odd

situation because high variance means high return with high risk, also according to the theory,

the high semi variance is usually related to high risk and high return which is not the case in

this comparison.

Number 105:

The construction sector has a higher Returns (0.003) compared to the oil& gas sector (-0.18),

but the variance of the oil& gas sector is (3.055562), which is higher than the constructionsector (1,456324606), but if we compare the semi-variances of both sectors, we find out that

the oil& gas sector has a higher semi variance with variability of (57%) to move towards the

positive side, and the construction sector has a lower variability of (54%), which is an odd



Number 106:

The construction sector has a higher Returns (0.003) compared to the pharmaceutical sector (-

0.19), but the variance of the pharmaceutical sector is (1,676713832), which is higher than theconstruction sector (1,456324606), but if we compare the semi-variances of both sectors, we


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find out that the pharmaceutical sector has a higher semi variance with variability of (80%) to

move towards the positive side, and the construction sector has a lower variability of (54%),

which is an odd situation, because according to the theory, the high variance is usually related

to high risk and high return which is not the case in this comparison.

Number 107:

The construction sector has a higher Returns (0.003) compared to the real estate sector (-

0.07), but the variance of the real estate sector is (1,598691703), which is higher than the

construction sector (1,456324606), but if we compare the semi-variances of both sectors, we

find out that the real estate sector has a higher semi variance with variability of (55%) to

move towards the positive side, and the construction sector has a lower variability of (54%),

which is an odd situation, because according to the theory, the high variance is usually related

to high risk and high return which is not the case in this comparison.

Number 108:

The construction sector has a higher Returns (0.003) compared to the telecommunication

sector (-0.18), in addition the variance of the previous sector is (1,456324606) which is higher

than the telecommunication sector (3, 055562). Also, if we compare the semi-variances of

both sectors, we find out that the construction sector has higher variability (54%) to have

growing returns than the telecommunication sector which has only variability towards

positive side of (52.8%). According to the theory, the high variance is usually related to high

risk and high return which is shown in this comparison.

Number 109:

the construction sector has a higher return (0.003) compared to the transport (0.002), however

the variance of the construction sector that is (1.456) is lower than the transport sectors

(2.32).but if we compare the semi-variances of both sectors, we find out that construction

sector has a high variability to move towards the positive side with variability of (

54%),which is an odd situation, because according to the theory, the high variance is usually

related to high risk and high return which is not the case in this comparison.

Number 110:

The construction sector has a lower return (0.003) compared to the utilities (0.109), also the

variance of theConstrucrion sector that is (1.456) is lower than the utilities (3.02). Which is

logical to the theory that high ER related with high variance but if we compare the semi

variance we find out that construction sectorhas a high variability to move towards the

positive side with variability of (54%),which is an odd situation, because according to the

theory, the high variance is usually related to high risk and high return which is not the case in

this comparison.


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number 122

The insurance sector has a higher Returns (0.07) compared to the distributors sector (0.03), in

addition the variance of the insurance sector is (2, 1458079), which is higher than the

distributors sector, but if we compare the semi-variances of both sectors, we find out that the

distributors sector has a higher semi variance with variability of ( 45.6) to move towards the

positive side, and the insurance sector has a lower variability of ( 38 )with high variability (

62)to move towards the negative side, which is an odd situation, because according to the

theory, the high variance is usually related to high risk and high return which is not the case in

this comparison.

number 123

The investment sector has a higher Returns (0.04) compared to the distributors sector (0.03),

in addition the variance of the investment sector is (1, 173212232), which is higher than the

distributors sector (0, 894363123), but if we compare the semi-variances of both sectors, wefind out that the distributors and investment sectors have nearly similar to each other with a

difference of only (1.6 %)in the variability to move towards the positive side with the higher

possibility to the distributors sector ( 45.6) which is an odd situation again, because according

to the theory, the high variance is usually related to high risk and high return which is not the

case in this comparison.

number 124

In this case we see that the hotels sector has a lower Returns (-0.06) compared to the

distributors sector (0.03),however the variance of the hotels sector is (3,026655381), which ishigher than the distributors sector, so here we notice there is an odd situation because high

variance means high return with high risk. Also, if we compare the semi-variances of both

sectors, we find out that the hotels sector has a higher semi variance with variability of (70%)

to move towards the positive side, and the distributors sector has a lower variability of

(45.6%) to move towards the positive side, and more variability (54.4%) to move to the

negative side. Which is an odd situation, because according to the theory, the high semi

variance is usually related to high risk and high return which is not the case in this

comparison.

number 125

The material software sector has a higher Returns (0.10) compared to the distributors sector

(0.03), in addition the variance of the previous sector is (3, 021558348) which is higher than

the distributors sector (0, 894363123). Also, if we compare the semi-variances of both sectors,

we find out that the material sector has higher variability (77%) to have growing returns than

the distributors sector which has only variability towards positive side of (45.6%). According

to the theory, the high variance is usually related to high risk and high return which is shown

in this comparison.


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number 126

In this case we see that the mining sector has a lower Returns (0.009) compared to the

distributors sector (0.03), however the variance of the mining sector is (2, 328214735), which

is higher than the distributors sector variance (0, 894363123), but if we compare the semi-

variances of both sectors, we find out that the mining sector has a higher semi variance with

variability of (49.6%) to move towards the positive side, and the distributors sector has a

lower variability of (45.6%) to move towards the positive side, and more variability (54.4%)

to move to the negative side.so here we notice there is an odd situation because high variance

means high return with high risk, also according to the theory, the high semi variance is

usually related to high risk and high return which is not the case in this comparison.

number 127

In this case we see that the oil&gas sector has a lower Returns (-0.03) compared to thedistributors sector (0.03), also the variance of the distributors sector is (0, 894363123), which

is higher than the previous sector. Nevertheless, if we compare the semi-variances of both

sectors, we find out that the GAS&oil sector has a higher semi variance with variability of

(57%) to move towards the positive side, and the distributors sector has a lower variability of

(45.6%) to move towards the positive side, and more variability (54.4%) to move to the

negative side. Which is an odd situation, because according to the theory, the high semi

variance is usually related to high risk and high return which is not the case in this

comparison.

number 128

In this case we see that the pharmaceutical sector has a lower Returns (-0.07) compared to the

distributors sector (0.03), however the variance of the previous sector is (1, 598691703), is

higher than the distributors sector variance (0, 894363123), also if we compare the semi-

variances of both sectors, we find out that the pharmaceutical sector has a higher semi

variance with variability of (80%) to move towards the positive side, and the distributors

sector has a lower variability of (45.6%) to move towards the positive side, and more

variability (54.4%) to move to the negative side.so here we notice there is an odd situation

because high variance means high return with high risk, also according to the theory, the highsemi variance is usually related to high risk and high return which is not the case in this

comparison whereas pharmaceutical sector has low return of (-0.07).

number 129

In this case we see that the real estate sector has a lower Returns (-0.19) compared to the

distributors sector (0.03), however the variance of the previous sector is (1, 676713832), is

higher than the distributors sector variance(0,894363123), also if we compare the semi-

variances of both sectors, we find out that the real estate sector has a higher semi variance

with variability of (55%) to move towards the positive side, and the distributors sector has a

lower variability of (45.6%) to move towards the positive side, and more variability (54.4%)


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to move to the negative side.so here we notice there is an odd situation because high variance

means high return with high risk, also according to the theory, the high semi variance is

usually related to high risk and high return which is not the case in this comparison whereas

real estate sector has low return of (-0.19).

number 130

In this case we see that the telecommunication sector has a lower Returns (-0.18) compared to

the distributors sector (0.03), however the variance of the previous sector is (3,055562), is

higher than the distributors sector variance(0,894363123), also if we compare the semi-

variances of both sectors, we find out that the telecommunication sector has a higher semi

variance with variability of (52.8%) to move towards the positive side, and the distributors

sector has a lower variability of (45.6%) to move towards the positive side, and more

variability (54.4%) to move to the negative side.so here we notice there is an odd situation

because high variance means high return with high risk, also according to the theory, the high

semi variance is usually related to high risk and high return which is not the case in this

comparison whereas telecommunication sector has low return of (-0.18).

number 131

In this case we see that the transport sector has a lower Returns (0.009) compared to the

distributors sector (0.03), however the variance of the previous sector is (1,64065391), is

higher than the distributors sector variance(0,894363123), but if we compare the semi-

variances of both sectors, we find out that the transports sector has a lower semi variance with

variability of (32%) to move towards the positive side and higher variability of (67, 2%) to

move to the negative side while the distributors sector has a higher variability of (45.6%) to

move towards the positive side.

number 132

In this case we see that the transport sector has a lower Returns (-0.18) compared to the

distributors sector (0.03), however the variance of the previous sector is (1,552011252), is

higher than the distributors sector variance(0,894363123), but if we compare the semi-

variances of both sectors, we find out that the Utilities sector has a lower semi variance with

variability of (27%) to move towards the positive side and higher variability of (73%) to move

to the negative side while the distributors sector has a higher variability of (45.6%) to move

towards the positive side.

number 134

In this case we see that the banks sector has a lower Returns (-0.03) compared to the electrical

sector (0.003), also the variance of the previous sector (0,5899106), is lower than the

electrical sector variance(5,385382903), however if we compare the semi-variances of both

sectors, we find out that the electrical sector has a higher semi variance with variability of

(82%) to move towards the positive side, and the banks sector has a lower variability of (52%)

to move towards the positive side.so here we notice there is an odd situation because


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which is not the case in this comparison whereas Banks sector has low return of (-0.03).

number 135

In this case we see that the beverages sector has a lower Returns (-0.05) compared to theelectrical sector (0.003), also the variance of the previous sector (5, 385382903), is lower than

the electrical sector variance (5, 385382903), however if we compare the semi-variances of

both sectors, we find out that the both sectors have equal semi variance variability of (82%) to

move towards positive growing returns.

number 136

The electrical sector has a higher Returns (0.003) compared to the chemicals sector (-0.14), in

addition the variance of the previous sector is (5, 385382903) which is higher than the

chemicals sector (1, 666071639). Also, if we compare the semi-variances of both sectors, wefind out that the electrical sector has higher variability (82%) to have growing returns than the

chemicals sector which has only variability towards positive side of (49.5%). According to the

theory, the high variance is usually related to high risk and high return which is shown in this

comparison

number 137

The electrical sector has a higher Returns (0.003) compared to the construction material sector

(-0.14), in addition the variance of the previous sector is (5, 385382903) which is higher than

the construction sector (1, 456324606). Also, if we compare the semi-variances of bothsectors, we find out that the electrical sector has higher variability (82%) to have growing

returns than the chemicals sector which has only variability towards positive side of (54%).

According to the theory, the high variance is usually related to high risk and high return which

is shown in this comparison.

number 140

The electrical sector has a higher Returns (0.003) compared to the engineering sector (-0.16),

in addition the variance of the previous sector is (5, 385382903) which is higher than the

engineering sector (2, 502189574). Also, if we compare the semi-variances of both sectors,

we find out that the electrical sector has higher variability (82%) to have growing returns than

the chemicals sector which has only variability towards positive side of (49.6%). According to

the theory, the high variance is usually related to high risk and high return which is shown in

this comparison.

number 141

In this case we see that the electrical sector has a lower Returns (-0.003) compared to the food

sector (0.006), however the variance of the previous sector is (5, 385382903), is higher than

the food sector variance (1,105646426), but if we compare the semi-variances of both sectors,

we find out that the food sector has a lower semi variance with variability of (38%) to move


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towards the positive side and higher variability of (62%) to move to the negative side while

the Electrical sector has a higher variability of (82%) to move towards the positive side.

Which is an odd situation because high variance of electrical sector means high return with

higher risk which is not fit the reality while electrical return has lower return in this case.

number 142

In this case we see that the paper sector has a lower Returns (-0.21) compared to the electrical

sector (0.003), however the variance of the previous sector is (6,049671935), is higher than

the electrical sector variance (5, 385382903), but if we compare the semi-variances of both

sectors, we find out that the paper sector has a lower semi variance with variability of (62.4%)

to move towards the positive side, while the Electrical sector has a higher variability of (82%)

to move towards the positive side. Which is an odd situation because high variance of

electrical sector means high return with higher risk which is not fit the reality while paper

return has lower return in this case.

number 143

The electrical sector has a higher Returns (0.003) compared to the holding companies sector (-

0.003), in addition the variance of the previous sector is (5, 385382903) which is higher than

the engineering sector (3, 541122232). Also, if we compare the semi-variances of both

sectors, we find out that the electrical sector has higher variability (82%) to have growing

returns than the holding companies sector which has only variability towards positive side of

(53.6%). According to the theory, the high variance is usually related to high risk and high

return which is shown in this comparison.

number 144

In this case we see that the insurance sector has a higher Returns (0.07) compared to the

electrical sector (0.003), but the variance of the previous sector (2,1458079), is lower than the

electrical sector variance(5,385382903), however if we compare the semi-variances of both

sectors, we find out that the electrical sector has a higher semi variance with variability of

(82%) to move towards the positive side, and the insurance sector has a lower variability of

(38%) to move towards the positive side.so here we notice there is an odd situation because


which is not the case in this comparison whereas electrical sector has low return of (0.003).

number 145

In this case we see that the investment sector has a higher Returns (0.04) compared to the

electrical sector (0.003), but the variance of the previous sector (1,173212232), is lower than

the electrical sector variance(5,385382903), however if we compare the semi-variances of

both sectors, we find out that the electrical sector has a higher semi variance with variability

of (82%) to move towards the positive side, and the investment sector has a lower variability

of (44%) to move towards the positive side.so here we notice there is an odd situation because

according to the theory, the high semi variance is usually related to high risk and high returnwhich is not the case in this comparison whereas electrical sector has low return of (0.003).


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number 146

The electrical sector has a higher Returns (0.003) compared to the hotels sector (-0.06), in

addition the variance of the previous sector is (5, 385382903) which is higher than theconstruction sector (3, 026655381).Also, if we compare the semi-variances of both sectors,


the chemicals sector which has only variability towards positive side of (77%). According to


this comparison.

number 147

In this case we see that the material software sector has a higher Returns (0.10)compared to

the electrical sector (0.003), but the variance of the previous sector (3,021558348), is lowerthan the electrical sector variance(5,385382903), however if we compare the semi-variances

of both sectors, we find out that the electrical sector has a higher semi variance with

variability of (82%) to move towards the positive side, and the previous sector has a lower

variability of (51%) to move towards the positive side.so here we notice there is an odd

situation because according to the theory, the high semi variance is usually related to high risk

and high return which is not the case in this comparison whereas electrical sector has low

return of (0.003).

number 148

The electrical sector has a higher Returns (0.003) compared to the mining sector (0.002), in


engineering sector (2, 328214735). Also, if we compare the semi-variances of both sectors,


the mining sector which has only variability towards positive side of (49.6%). According to


this comparison.

number 149

The electrical sector has a higher Returns (0.003) compared to the oil& gas sector (-0.03), in

addition the variance of the previous sector is (5, 385382903) which is higher than the oil&

gas sector (0, 452862903). Also, if we compare the semi-variances of both sectors, we find

out that the electrical sector has higher variability (82%) to have growing returns than the

oil& gas sector which has only variability towards positive side of (57%). According to the


comparison.


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number 150

The electrical sector has a higher Returns (0.003) compared to the pharmaceutical sector (-

0.07), in addition the variance of the previous sector is (5, 385382903) which is higher than

the oil& gas sector (1, 598691703). Also, if we compare the semi-variances of both sectors,


the pharmaceutical sector which has only variability towards positive side of (80%).



Number 151

The electrical sector has a higher Returns (0.003) compared to the real estate sector (-0.19), in

addition the variance of the previous sector is (5, 385382903) which is higher than the real

estate sector (1, 676713832). Also, if we compare the semi-variances of both sectors, we find

out that the electrical sector has higher variability (82%) to have growing returns than the realestate sector which has only variability towards positive side of (55%). According to the


comparison.

Number 152

The electrical sector has a higher Returns (0.003) compared to the telecommunication sector

(-0.18), in addition the variance of the previous sector is (5, 385382903) which is higher than

the telecommunication sector (3, 055562). Also, if we compare the semi-variances of both

sectors, we find out that the electrical sector has higher variability (82%) to have growingreturns than the telecommunication sector which has only variability towards positive side of

(52.8%). According to the theory, the high variance is usually related to high risk and high

return which is shown in this comparison.

Number 153

In this case we see that the transports sector has a higher Returns (0.009)compared to the

electrical sector (0.003), but the variance of the previous sector (1, 64065391), is lower than

the electrical sector variance(5,385382903), however if we compare the semi-variances of

both sectors, we find out that the electrical sector has a higher semi variance with variabilityof (82%) to move towards the positive side, and the previous sector has a lower variability of

(32.8%) to move towards the positive side.so here we notice there is an odd situation because


which is not the case in this comparison whereas electrical sector has low return of (0.003).

Number 154

The electrical sector has a higher Returns (0.003) compared to the utilities sector (-0.18), in


telecommunication sector (1, 552011252). Also, if we compare the semi-variances of bothsectors, we find out that the electrical sector has higher variability (82%) to have growing


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returns than the utilities sector which has only variability towards positive side of (27%).



Number 156

In this case we see that the Engineering sector has a lower Returns (-0.16) compared to the

Banks sector (-0.03), however the variance of the previous sector is (2, 502189574), is higher

than the banks sector variance (0,5899106), but if we compare the semi-variances of both

sectors, we find out that the engineering sector has a lower semi variance with variability of

(49.6%) to move towards the positive side, while the Banks sector has a higher variability of

(52%) to move towards the positive side. Which is an odd situation because higher variance

of Engineering sector means high return with higher risk which is not fit the reality where

engineering has lower return in this case.

Number 209

The forestry & paper sector has a lower Returns (-0.217) compared to the holding company

sector (-0.003), however the variance of the forestry & paper sector that is 6.04, is higher than

the holding company sector, but if we compare the semi-variances of both sectors, we find

out that the both sector have a high variability to move towards the positive side, which is an

odd situation, because according to the theory, the high variance is usually related to high risk

and high return which is not the case in this comparison.

Number 210

The forestry & paper sector has a lower Returns (-0.217) compared to the insurance sector

(0.077), however the variance of the forestry & paper sector that is 6.04, is higher than the

insurance sector, but if we compare the semi-variances of both sectors, we find out that the

forestry & paper sector has a high variability to move towards the positive side, and the

insurance sector has a high variability to move towards the negative side, which is an odd



Number 211

The forestry & paper sector has a lower Returns (-0.217) compared to the investment

companies & other finance sector (0.04), however the variance of the forestry & paper sector

that is 6.04, is higher than the investment companies & other finance sector, but if we

compare the semi-variances of both sectors, we find out that the forestry & paper sector has a

high variability to move towards the positive side, and the investment companies & other

finance sector has a high variability to move towards the negative side, which is an odd




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Number 212

The forestry & paper sector has a lower Returns (-0.217) compared to the leisures& hotels

sector (-0.064), however the variance of the forestry & paper sector that is 6.04, is higher than

the leisures& hotels sector, but if we compare the semi-variances of both sectors, we find out

that the both sector have a high variability to move towards the positive side, which is an odd



Number 213

The forestry & paper sector has a lower Returns (-0.217) compared to the material software &

computer service sector (-0.181), however the variance of the forestry & paper sector that is

6.04, is higher than the material software & computer service sector, but if we compare the

semi-variances of both sectors, we find out that the both sector have a high variability to

move towards the positive side, which is an odd situation, because according to the theory, thehigh variance is usually related to high risk and high return which is not the case in this

comparison.

Number 214

The forestry & paper sector has a lower Returns (-0.217) compared to the Mining sector

(0.009), however the variance of the forestry & paper sector that is 6.04, is higher than the

Financial Management Assignement Introduction (1)

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