Report (Perfect)

8/8/2019 Report (Perfect)

http://slidepdf.com/reader/full/report-perfect 1/58

ANALYSIS OF FTSE-100 (USA), DOW Jones (USA),

DAX (GERMAN), SHANGHAI COMPOSITE

AND NIKKEI (JAPAN)

15TH SEPT 2009 - 8TH DEC 2009

Ng Kim Soon, Chin Kai Chong and Wong Wei Ling

To: Joanna Molyn

Tutor

Date: 1st April 2010

1



TABLE OF CONTENT

PAGE

1.0 Introduction 3

2.1 Task 1

Data Presentation

a) Relative Frequency Distribution for the value given 3

b) Descriptive Statistics 14

2.2 Task 2

Correlation and Regression

a) Correlation and Coefficient of Determinants 16

b) Linear Regression 51

c) Scatterplot and Line Chart

i) Scatterplot 55

ii) Line Chart 62

2.3 Task 3 65

2.4 Task 4 66

3.0 Peer Review 67

4.0 Executive Summary 70

5.0 List of Refferences 71

1. INTRODUCTION

2



In this report, an analysis will be done on the history prices of 5 stock markets that is

FTSE-100 (UK), Dow Jones (USA), DAX (German), Shanghai Composite and Nikkei

(Japan). The analysis will be done by using data presentation in a form of relative

frequency distribution, it will all be review using mean, media, mode and standard

deviation as well as the relationship between the data from the 5 markets using

correlation and regression.

2.1 TASK 1

DATA PRESENTATION

a) Relative Frequency Distribution for the value given

The data given are the historical prices of the stock on 5 markets that is FTSE-100

(UK), Dow Jones Industrial average, German DAX, Shanghai Composite and Nikkei

between 15th of September to 8th of December 2009. Each market consists of 61 data

and it has been shown in a group data and relative frequency distribution form. Relative

frequency distribution is “a tabular summary of a set of data showing the relative

frequency of items in each of several non-overlapping classes. The relative frequency is

the fraction or proportion of the total number of items belonging to a class”

(Stastitics.com 2004). The graphing of histogram are using relative frequency rather

than frequency. The relative frequency is “the ratio of the number of times an event

occurs to the number of occasions on which it might occur in the same period”

(dictionary.com 2010). Each data will be arranged into a group data and each of it has

its own frequency that is the time of occurrences for each data (electrical-res 2009).

i) FTSE-100 (UK)

3



This is the result of group data for FTSE-100 (UK) when it has been rearrange with the

relative frequency:

Prices (£)

Frequenc

y

Upper

Boundary

Lower

Boundary

mid

point

relative

frequency

4950-5000 1 5000 4950 4975 0.017

5000-5050 5 5050 5000 5025 0.082

5050-5100 3 5100 5050 5075 0.049

5100-5150 12 5150 5100 5125 0.197

5150-5200 11 5200 5150 5175 0.180

5200-5250 10 5250 5200 5252 0.164

5250-5300 8 5300 5250 5275 0.131

5300-5350 8 5350 5300 5325 0.131

5350-5400 3 5400 5350 5375 0.049

Total 61 1.000

As for the relative frequency distribution, this is the result of the FTSE-100 (UK)

presentation when the mid point of group data has been implemented:

4



ii) Dow Jones Industrial (USA)

This is the result of group data for Dow Jones Industrial (USA) when it has been

rearrange with the relative frequency:

Prices ($)Frequenc

yUpper

BoundaryLower

BoundaryMid

pointRelative

frequency

9450-9500 1 9500 9450 9475 0.0164

9500-9550 1 9550 9500 9525 0.0164

9550-9600 1 9600 9550 9575 0.0164

5



9600-9650 1 9650 9600 9625 0.0164

9650-9700 2 9700 9650 9675 0.0328

9700-9750 7 9750 9700 9725 0.1148

9750-9800 8 9800 9750 9775 0.1311

9800-9850 3 9850 9800 9825 0.0492

9850-9900 5 9900 9850 9875 0.0820

9900-9950 1 9950 9900 9925 0.0164

9950-10000 3 10000 9950 9975 0.0492

10000-10050 4 10050 10000 10025 0.0656

10050-10100 3 10100 10050 10075 0.0492

10100-10150 0 10150 10100 10125 0.0000

10150-10200 1 10200 10150 10175 0.0164

10200-10250 2 10250 10200 10225 0.0328

10250-10300 3 10300 10250 10275 0.0492

10300-10350 4 10350 10300 10325 0.0656

10350-10400 3 10400 10350 10375 0.0492

10400-10450 4 10450 10400 10425 0.0656

10450-10500 4 10500 10450 10475 0.0656

Total 61 1.0000

As for the relative frequency distribution, this is the result of the Dow Jones (USA)


6



iii) DAX (German)

This is the result of group data for DAX (German) when it has been rearrange with the

relative frequency:

Prices (€)

Frequenc

y

Upper

Boundary

Lower

Boundary

Mid

Point

Relative

Frequency

5350-5400 1 5400 5350 5375 0.0164

5400-5450 3 5450 5400 5425 0.0492

5450-5500 4 5500 5450 5475 0.0656

5500-5550 1 5550 5500 5525 0.0164

7



5550-5600 3 5600 5550 5575 0.0492

5600-5650 9 5650 5600 5625 0.1475

5650-5700 9 5700 5650 5675 0.1475

5700-5750 13 5750 5700 5725 0.2131

5750-5800 9 5800 5750 5775 0.1475

5800-5850 7 5850 5800 5825 0.1148

5850-5900 2 5900 5850 5875 0.0328

Total 61 1.0000

As for the relative frequency distribution, this is the result of the DAX (German)


iv) Shanghai Composite (China)

8



This is the result of group data for Shanghai Composite (China) when it has been

rearrange with the relative frequency:

Prices

(remembi)

Frequenc

y

Upper

Boundary

Lower

Boundary

Mid

Point

Relative

Frequency

2700-2750 1 2750 2700 2725 0.0164

2750-2800 3 2800 2750 2775 0.0492

2800-2850 3 2850 2800 2825 0.0492

2850-2900 5 2900 2850 2875 0.0820

2900-2950 5 2950 2900 2925 0.0820

2950-3000 9 3000 2950 2975 0.1475

3000-3050 5 3050 3000 3025 0.0820

3050-3100 6 3100 3050 3075 0.0984

3100-3150 4 3150 3100 3125 0.0656

3150-3200 8 3200 3150 3175 0.1311

3200-3250 2 3250 3200 3225 0.0328

3250-3300 4 3300 3250 3275 0.0656

3300-3350 6 3350 3300 3325 0.0984

Total 61 1.0000

As for the relative frequency distribution, this is the result of the Shanghai Composite

(China) presentation when the mid point of group data has been implemented:

9



v) Nikkei (Japan)

This is the result of group data for Nikkei (Japan) when it has been rearrange with the

relative frequency:

Prices (¥)

Frequenc

y

Upper

Boundary

Lower

Boundary

Mid

Point

Relative

Frequency

9050-9100 1 9100 9050 9075 0.0164

9100-9150 0 9150 9100 9125 0.0000

9150-9200 0 9200 9150 9175 0.0000

9200-9250 0 9250 9200 9225 0.0000

9250-9300 0 9300 9250 9275 0.0000

9300-9350 1 9350 9300 9325 0.0164

9350-9400 1 9400 9350 9375 0.01649400-9450 2 9450 9400 9425 0.0328

9450-9500 1 9500 9450 9475 0.0164

9500-9550 1 9550 9500 9525 0.0164

9550-9600 1 9600 9550 9575 0.0164

10



9600-9650 1 9650 9600 9625 0.0164

9650-9700 3 9700 9650 9675 0.0492

9700-9750 3 9750 9700 9725 0.0492

9750-9800 4 9800 9750 9775 0.0656

9800-9850 5 9850 9800 9825 0.0820

9850-9900 3 9900 9850 9875 0.0492

9900-9950 0 9950 9900 9925 0.0000

9950-10000 2 10000 9950 9975 0.0328

10000-10050 4 10050 10000 10025 0.0656

10050-10100 3 10100 10050 10075 0.0492

10100-10150 4 10150 10100 10125 0.0656

10150-10200 0 10200 10150 10175 0.0000

10200-10250 5 10250 10200 10225 0.0820

10250-10300 5 10300 10250 10275 0.0820

10300-10350 5 10350 10300 10325 0.0820

10350-10400 2 10400 10350 10375 0.0328

10400-10450 2 10450 10400 10425 0.0328

10450-10500 0 10500 10450 10475 0.0000

10500-10550 2 10550 10500 10525 0.0328

61 1.0000

11



As for the relative frequency distribution, this is the result of the Nikkei (Japan)


b) Descriptive Statistics

In a business, descriptive data are often use in order to “summarize a collection of data

in a clear and understandable way but do not infer the properties of the population from

which the sample was drawn” (Business Dictionary 2010) There are two types of

descriptive statistics calculation and that is measures of central tendency and measures

of dispersion (Wui, Chye, Kar and Hai 2003. In order to calculate the central value of the

data, descriptive statistics such as mean, mode and median are compulsory to be used

(Wui, Chye, Kar and Hai 2003).

12

http://www.businessdictionary.com/definition/population.html

http://www.businessdictionary.com/definition/population.html



Mean: The mathematical and statistics of average value of a set of data.

(Cherry 2010)

Formula:

Σx = sum of the data

n = total number of samples

(Wui, Chye, Kar and Hai 2003)

Median: mathematical result that indicates that one half of the group is higher and one

half lower (Kimmons J. 2010)

Formula:

L = Lower boundary of median class

n = Total number of samples

F = Cumulative Frequency before median

c = width of median class


Mode: surveillance which occur the occasionally number of times compared to the

others (Wui, Chye, Kar and Hai 2003).

13



Meanwhile, there are certain descriptive statistics such as standard deviation are used

to ascertain the distribution and differences between each pair of data inside the set of

data (Wui, Chye, Kar and Hai 2003). Standard deviation is “a measure of the extent to

which numbers are spread around their average” (Investorwords 2010).

Formula:

Σ= the sum of

x = the given data

= mean

n = the total of the data


Market Stock

Prices Mean Median Mode

Standard

deviation

FTSE-100 (UK) 5196.44 5191.70 5150-5200 95.78

Dow Jones (USA) 10007.34 9962.58 9750-9800 287.40

DAX (German) 5675.77 5700.26 5700-5750 118.09

Shangai

Composite 3054.58 3038.27 2950-3000 164.23

Nikkei (Japan) 9977.08

10016.3

9

9800-9850,

10200-10250,

10250-10300 329.34

14

http://www.investorwords.com/4668/spread.html

http://www.investorwords.com/347/average.html

http://www.investorwords.com/4668/spread.html

http://www.investorwords.com/347/average.html



2.2 TASK 2

CORRELATION AND REGRESSION

a) Correlation and Coefficient of Determinants

Correlation coefficient is defined as statistical measure of the linear relationship

(correlation) between a dependent-variable and an independent variable

(Businessdictionary 2010). It is represented by the word ‘r’ (Wui, Chye, Kar and Hai

2003). Dependent variable is the data that we have expected ourselves and can be

control by us (Wui, Chye, Kar and Hai 2003) and independent variable is where the data

the variable is react with the experiment and can’t control by us because it is control by

the nature (Wui, Chye, Kar and Hai 2003).

Formula: r

n = The total of data given

x and y means the pairs of data of two variables x and y


Coefficient of determinants is defined as the percent of the dissimilarity that can be

explained by the regression equation(Jones 2009).The formula are simple that is

correlation coefficient to the power of two (r²).

In order to search and measure the correlation and coefficient of determinants of the

data, each of the stock value was pair up. The data are consist from 5 stock value that

is FTSE-100 from United Kingdom, Dow Jones from USA, DAX from German, Shanghai15

http://www.businessdictionary.com/definition/measure.html

http://www.businessdictionary.com/definition/linear-relationship.html

http://www.businessdictionary.com/definition/correlation.html

http://www.businessdictionary.com/definition/dependent-variable.html

http://www.businessdictionary.com/definition/independent-variable.html

http://www.businessdictionary.com/definition/measure.html

http://www.businessdictionary.com/definition/linear-relationship.html

http://www.businessdictionary.com/definition/correlation.html

http://www.businessdictionary.com/definition/dependent-variable.html

http://www.businessdictionary.com/definition/independent-variable.html





5230.50 10226.94 27358130.25 104590301.76 53492009.67

5235.20 10023.42 27407319.04 100468948.50 52474608.38

5142.70 10005.96 26447363.29 100119235.52 51457650.49

5125.60 9802.14 26271775.36 96081948.58 50241848.78

5107.90 9771.91 26090642.41 95490225.05 49913939.09

5037.20 9789.44 25373383.84 95833135.51 49311367.17

5104.50 9712.73 26055920.25 94337124.05 49578630.29

5044.50 9962.58 25446980.25 99253000.26 50256234.81

5137.70 9762.69 26395961.29 95310116.04 50157772.41

5080.40 9882.17 25810464.16 97657283.91 50205376.47

5201.00 9867.96 27050401.00 97376634.56 51323259.96

5191.70 9972.18 26953748.89 99444373.95 51772566.91

5242.60 10081.31 27484854.76 101632811.32 52852275.81

5207.40 9949.36 27117014.76 98989764.41 51810297.26

5257.90 10041.48 27645512.41 100831320.59 52797097.69

5243.40 10092.19 27493243.56 101852299.00 52917389.05

5281.50 9995.91 27894242.25 99918216.73 52793398.67

5190.20 10062.94 26938176.04 101262761.44 52228671.19

5223.00 10015.86 27279729.00 100317451.54 52312836.78

5256.10 9871.06 27626587.21 97437825.52 51883278.47

5154.10 9885.80 26564746.81 97729041.64 50952401.78

5210.20 9864.94 27146184.04 97317041.20 51398310.39

5161.90 9786.87 26645211.61 95782824.40 50518844.25

5154.60 9725.58 26569901.16 94586906.34 50131474.67

5108.90 9731.25 26100859.21 94697226.56 49715983.13

5138.00 9599.75 26399044.00 92155200.06 49323515.50

5024.30 9487.67 25243590.49 90015882.03 47668900.38

4988.70 9509.28 24887127.69 90426406.12 47438945.14

5047.80 9712.28 25480284.84 94328382.80 49025646.98

17



5133.90 9742.20 26356929.21 94910460.84 50015480.58

5159.70 9789.36 26622504.09 95831569.21 50510160.79

5165.70 9665.19 26684456.49 93415897.74 49927471.98

5082.20 9707.44 25828756.84 94234391.35 49335151.57

5079.30 9748.55 25799288.49 95034227.10 49515810.02

5139.40 9829.87 26413432.36 96626344.22 50519633.88

5142.60 9778.86 26446334.76 95626102.90 50288765.44

5134.40 9820.20 26362063.36 96436328.04 50420834.88

5172.90 9783.92 26758894.41 95725090.57 50611239.77

5164.00 9791.71 26666896.00 95877584.72 50564390.44

5124.10 9683.41 26256400.81 93768429.23 49618761.18

5042.10 9626.80 25422772.41 92675278.24 48539288.28

316982.90 610447.83 1647733343.85 6113915876.81 3173552349.33

Hence,

Correlation coefficient for FTSE-100 and DOW Jones, r = 0.8454

Coefficient of determination for FTSE-100 and DOW Jones, r² = 0.7147

ii) FTSE-100 (UK) and DAX (German)

x= FTSE-100 (UK)

y= DAX (German)

x y x² y² xy

18





5080.40 5496.27 25810464.16 30208983.91 27923250.11

5201.00 5635.02 27050401.00 31753450.40 29307739.02

5191.70 5642.16 26953748.89 31833969.47 29292402.07

5242.60 5740.25 27484854.76 32950470.06 30093834.65

5207.40 5762.93 27117014.76 33211362.18 30009881.68

5257.90 5833.49 27645512.41 34029605.58 30671907.07

5243.40 5811.77 27493243.56 33776670.53 30473434.82

5281.50 5852.56 27894242.25 34252458.55 30910295.64

5190.20 5743.39 26938176.04 32986528.69 29809342.78

5223.00 5830.77 27279729.00 33997878.79 30454111.71

5256.10 5854.14 27626587.21 34270955.14 30769945.25

5154.10 5714.31 26564746.81 32653338.78 29452125.17

5210.20 5783.23 27146184.04 33445749.23 30131784.95

5161.90 5711.88 26645211.61 32625573.13 29484153.37

5154.60 5716.54 26569901.16 32678829.57 29466477.08

5108.90 5640.75 26100859.21 31818060.56 28818027.68

5138.00 5657.64 26399044.00 32008890.37 29068954.32

5024.30 5508.85 25243590.49 30347428.32 27678115.06

4988.70 5467.90 24887127.69 29897930.41 27277712.73

5047.80 5554.55 25480284.84 30853025.70 28038257.49

5133.90 5675.16 26356929.21 32207441.03 29135703.92

5159.70 5713.52 26622504.09 32644310.79 29480049.14

5165.70 5736.31 26684456.49 32905252.42 29632056.57

5082.20 5581.41 25828756.84 31152137.59 28365841.90

5079.30 5605.21 25799288.49 31418379.14 28470543.15

5139.40 5702.05 26413432.36 32513374.20 29305115.77

5142.60 5709.38 26446334.76 32597019.98 29361057.59

5134.40 5668.65 26362063.36 32133592.82 29105116.56

5172.90 5703.83 26758894.41 32533676.67 29505342.21

20



5164.00 5731.14 26666896.00 32845965.70 29595606.96

5124.10 5700.26 26256400.81 32492964.07 29208702.27

5042.10 5628.98 25422772.41 31685415.84 28381880.06

316982.90 346221.69 1647733343.85 1965909774.10 1799634434.51

Hence,

Correlation coefficient for FTSE-100 and DAX, r = 0.7572

Coefficient of determination for FTSE-100 and DAX, r² = 0.5734

iii) FTSE-100 (UK) and Shanghai Composite Index

x= FTSE-100 (UK)

y= Shanghai Composite Index

x y x² y² xy

5223.10 3331.90 27280773.61 11101557.61 17402846.89

5310.70 3317.04 28203534.49 11002754.36 17615804.33

21



5322.40 3269.75 28327941.76 10691265.06 17402917.40

5313.00 3235.36 28227969.00 10467554.33 17189467.68

5327.40 3195.30 28381190.76 10209942.09 17022641.22

5312.20 3096.26 28219468.84 9586825.99 16447952.37

5190.70 3170.98 26943366.49 10055114.16 16459605.89

5245.70 3290.17 27517368.49 10825218.63 17259244.77

5194.10 3223.53 26978674.81 10391145.66 16743337.17

5364.80 3338.66 28781079.04 11146650.60 17911243.17

5324.00 3308.35 28344976.00 10945179.72 17613655.40

5355.50 3320.61 28681380.25 11026450.77 17783526.86

5251.40 3303.23 27577201.96 10911328.43 17346582.02

5267.70 3282.89 27748663.29 10777366.75 17293279.65

5342.10 3275.05 28538032.41 10725952.50 17495644.61

5345.90 3187.65 28578646.81 10161112.52 17040858.14

5382.70 3175.19 28973459.29 10081831.54 17091095.21

5296.40 3178.61 28051852.96 10103561.53 16835190.00

5276.50 3175.58 27841452.25 10084308.34 16755947.87

5266.80 3164.04 27739182.24 10011149.12 16664365.87

5230.50 3155.05 27358130.25 9954340.50 16502489.03

5235.20 3128.54 27407319.04 9787762.53 16378532.61

5142.70 3114.23 26447363.29 9698428.49 16015550.62

5125.60 3076.65 26271775.36 9465775.22 15769677.24

5107.90 2995.85 26090642.41 8975117.22 15302502.22

5037.20 2960.47 25373383.84 8764382.62 14912479.48

5104.50 3031.33 26055920.25 9188961.57 15473423.99

5044.50 3021.46 25446980.25 9129220.53 15241754.97

5137.70 3109.57 26395961.29 9669425.58 15976037.79

5080.40 3107.85 25810464.16 9658731.62 15789121.14

5201.00 3051.41 27050401.00 9311102.99 15870383.41

22



5191.70 3070.59 26953748.89 9428522.95 15941582.10

5242.60 3084.45 27484854.76 9513831.80 16170537.57

5207.40 3038.27 27117014.76 9231084.59 15821487.20

5257.90 2976.63 27645512.41 8860326.16 15650822.88

5243.40 2979.79 27493243.56 8879148.44 15624230.89

5281.50 2970.53 27894242.25 8824048.48 15688854.20

5190.20 2936.19 26938176.04 8621211.72 15239413.34

5223.00 2894.48 27279729.00 8378014.47 15117869.04

5256.10 2911.72 27626587.21 8478113.36 15304291.49

5154.10 2779.43 26564746.81 7725231.12 14325460.16

5210.20 2754.54 27146184.04 7587490.61 14351704.31

5161.90 2763.52 26645211.61 7637042.79 14265013.89

5154.60 2838.84 26569901.16 8059012.55 14633084.66

5108.90 2853.55 26100859.21 8142747.60 14578501.60

5138.00 2842.72 26399044.00 8081057.00 14605895.36

5024.30 2897.55 25243590.49 8395796.00 14558160.47

4988.70 2967.01 24887127.69 8803148.34 14801522.79

5047.80 2962.67 25480284.84 8777413.53 14954965.63

5133.90 3060.26 26356929.21 9365191.27 15711068.81

5159.70 2999.71 26622504.09 8998260.08 15477603.69

5165.70 3033.73 26684456.49 9203517.71 15671339.06

5082.20 3026.74 25828756.84 9161155.03 15382498.03

5079.30 2989.79 25799288.49 8938844.24 15186040.35

5139.40 2924.88 26413432.36 8554923.01 15032128.27

5142.60 2946.26 26446334.76 8680447.99 15151436.68

5134.40 2930.48 26362063.36 8587713.03 15046256.51

5172.90 2881.12 26758894.41 8300852.45 14903745.65

5164.00 2861.61 26666896.00 8188811.79 14777354.04

5124.10 2845.02 26256400.81 8094138.80 14578166.98

23



5042.10 2714.97 25422772.41 7371062.10 13689150.24

316982.90 186329.61 1647733343.85 570777677.64 968847344.86

Hence,

Correlation coefficient for FTSE-100 and Shanghai Composite, r = 0.6321

Coefficient of determination for FTSE-100 and Shanghai Composite, r² = 0.3995

iv) FTSE-100 (UK) and Nikkei (Japan)

x= FTSE-100 (UK)

y=Nikkei (Japan)

x y x² y² xy

5223.10 10140.47 27280773.61 102829131.82 52964688.86

5310.70 10167.60 28203534.49 103380089.76 53997073.32

5322.40 10022.59 28327941.76 100452310.31 53344233.02

5313.00 9977.67 28227969.00 99553898.63 53011360.71

5327.40 9608.94 28381190.76 92331727.92 51190666.96

5312.20 9572.20 28219468.84 91627012.84 50849440.84

5190.70 9345.55 26943366.49 87339304.80 48509946.39

5245.70 9081.52 27517368.49 82474005.51 47638929.46

5194.10 9383.24 26978674.81 88045192.90 48737486.88

5364.80 9441.64 28781079.04 89144565.89 50652510.27

5324.00 9401.58 28344976.00 88389706.50 50054011.92

5355.50 9497.68 28681380.25 90205925.38 50864825.24

24



5251.40 9549.47 27577201.96 91192377.28 50148086.76

5267.70 9676.80 27748663.29 93640458.24 50974479.36

5342.10 9729.93 28538032.41 94671537.80 51978259.05

5345.90 9791.18 28578646.81 95867205.79 52342669.16

5382.70 9770.31 28973459.29 95458957.50 52590647.64

5296.40 9804.49 28051852.96 96128024.16 51928500.84

5276.50 9871.68 27841452.25 97450066.02 52087919.52

5266.80 9870.73 27739182.24 97431310.73 51987160.76

5230.50 9808.99 27358130.25 96216284.82 51305922.20

5235.20 9789.35 27407319.04 95831373.42 51249205.12

5142.70 9717.44 26447363.29 94428640.15 49973878.69

5125.60 9844.31 26271775.36 96910439.38 50457995.34

5107.90 9802.95 26090642.41 96097828.70 50072488.31

5037.20 10034.74 25373383.84 100696006.87 50546992.33

5104.50 9891.10 26055920.25 97833859.21 50489119.95

5044.50 10075.05 25446980.25 101506632.50 50823589.73

5137.70 10212.46 26395961.29 104294339.25 52468555.74

5080.40 10362.62 25810464.16 107383893.26 52646254.65

5201.00 10282.99 27050401.00 105739883.34 53481830.99

5191.70 10267.17 26953748.89 105414779.81 53304066.49

5242.60 10333.39 27484854.76 106778948.89 54173830.41

5207.40 10336.84 27117014.76 106850261.19 53828060.62

5257.90 10236.51 27645512.41 104786136.98 53822545.93

5243.40 10257.56 27493243.56 105217537.15 53784490.10

5281.50 10238.65 27894242.25 104829953.82 54075429.98

5190.20 10060.21 26938176.04 101207825.24 52214501.94

5223.00 10076.56 27279729.00 101537061.43 52629872.88

5256.10 10016.39 27626587.21 100328068.63 52647147.48

5154.10 9832.47 26564746.81 96677466.30 50677533.63

25



5210.20 9799.60 27146184.04 96032160.16 51057875.92

5161.90 9691.80 26645211.61 93930987.24 50028102.42

5154.60 9674.49 26569901.16 93595756.76 49868126.15

5108.90 9731.87 26100859.21 94709293.70 49719150.64

5138.00 9978.64 26399044.00 99573256.25 51270252.32

5024.30 10133.23 25243590.49 102682350.23 50912387.49

4988.70 10100.20 24887127.69 102014040.04 50386867.74

5047.80 10009.52 25480284.84 100190490.63 50526055.06

5133.90 10265.98 26356929.21 105390345.36 52704514.72

5159.70 10544.22 26622504.09 111180575.41 54405011.93

5165.70 10370.54 26684456.49 107548099.89 53571098.48

5082.20 10443.80 25828756.84 109072958.44 53077480.36

5079.30 10270.77 25799288.49 105488716.39 52168322.06

5139.40 10217.62 26413432.36 104399758.46 52512436.23

5142.60 10202.06 26446334.76 104082028.24 52465113.76

5134.40 10444.33 26362063.36 109084029.15 53625367.95

5172.90 10513.67 26758894.41 110537256.87 54386163.54

5164.00 10312.14 26666896.00 106340231.38 53251890.96

5124.10 10393.23 26256400.81 108019229.83 53255949.84

5042.10 10320.94 25422772.41 106521802.48 52039211.57

316982.90 608601.67 1647733343.85 6078573397.08 3161757588.59

Hence,

Correlation coefficient for FTSE-100 and Nikkei, r = -0.4254

Coefficient of determination for FTSE-100 and Nikkei, r² = 0.1809

26



v) Dow Jones (USA) and DAX (German)

x= Dow Jones (USA)

y= DAX (German)

x y x² y² xy

10,285.97 5,688.58 105,801,178.84 32,359,942.42 58,512,563.22

10,390.11 5,784.75 107,954,385.81 33,463,332.56 60,104,188.82

10,388.90 5,817.65 107,929,243.21 33,845,051.52 60,438,984.09

10,366.15 5,770.35 107,457,065.82 33,296,939.12 59,816,313.65

10,452.68 5,781.68 109,258,519.18 33,427,823.62 60,434,050.90

10,471.58 5,776.61 109,653,987.70 33,369,223.09 60,490,233.74

10,344.84 5,625.95 107,015,714.63 31,651,313.40 58,199,552.60

10,309.92 5,685.61 106,294,450.41 32,326,161.07 58,618,184.25

10,464.40 5,614.17 109,503,667.36 31,518,904.79 58,748,920.55

10,433.71 5,803.02 108,862,304.36 33,675,041.12 60,547,027.80

10,450.95 5,769.31 109,222,355.90 33,284,937.88 60,294,770.34

10,318.16 5,801.48 106,464,425.79 33,657,170.19 59,860,598.88

27



10,332.44 5,663.15 106,759,316.35 32,071,267.92 58,514,157.59

10,426.31 5,702.18 108,707,940.22 32,514,856.75 59,452,696.36

10,437.42 5,787.61 108,939,736.26 33,496,429.51 60,407,716.37

10,406.96 5,778.43 108,304,816.44 33,390,253.26 60,135,889.87

10,270.47 5,804.82 105,482,554.02 33,695,935.23 59,618,229.67

10,197.47 5,686.83 103,988,394.40 32,340,035.45 57,991,278.32

10,291.26 5,663.96 105,910,032.39 32,080,442.88 58,289,284.99

10,246.97 5,668.35 105,000,394.18 32,130,191.72 58,083,412.40

10,226.94 5,613.20 104,590,301.76 31,508,014.24 57,405,859.61

10,023.42 5,619.72 100,468,948.50 31,581,252.88 56,328,813.84

10,005.96 5,488.25 100,119,235.52 30,120,888.06 54,915,209.97

9,802.14 5,480.92 96,081,948.58 30,040,484.05 53,724,745.17

9,771.91 5,444.23 95,490,225.05 29,639,640.29 53,200,525.58

9,789.44 5,353.35 95,833,135.51 28,658,356.22 52,406,298.62

9,712.73 5,430.82 94,337,124.05 29,493,805.87 52,748,088.34

9,962.58 5,414.96 99,253,000.26 29,321,791.80 53,946,972.20

9,762.69 5,587.45 95,310,116.04 31,219,597.50 54,548,542.24

9,882.17 5,496.27 97,657,283.91 30,208,983.91 54,315,074.51

9,867.96 5,635.02 97,376,634.56 31,753,450.40 55,606,151.96

9,972.18 5,642.16 99,444,373.95 31,833,969.47 56,264,635.11

10,081.31 5,740.25 101,632,811.32 32,950,470.06 57,869,239.73

9,949.36 5,762.93 98,989,764.41 33,211,362.18 57,337,465.22

10,041.48 5,833.49 100,831,320.59 34,029,605.58 58,576,873.17

10,092.19 5,811.77 101,852,299.00 33,776,670.53 58,653,487.08

9,995.91 5,852.56 99,918,216.73 34,252,458.55 58,501,663.03

10,062.94 5,743.39 101,262,761.44 32,986,528.69 57,795,388.97

10,015.86 5,830.77 100,317,451.54 33,997,878.79 58,400,176.01

9,871.06 5,854.14 97,437,825.52 34,270,955.14 57,786,567.19

9,885.80 5,714.31 97,729,041.64 32,653,338.78 56,490,525.80

28



9,864.94 5,783.23 97,317,041.20 33,445,749.23 57,051,216.96

9,786.87 5,711.88 95,782,824.40 32,625,573.13 55,901,427.02

9,725.58 5,716.54 94,586,906.34 32,678,829.57 55,596,667.09

9,731.25 5,640.75 94,697,226.56 31,818,060.56 54,891,548.44

9,599.75 5,657.64 92,155,200.06 32,008,890.37 54,311,929.59

9,487.67 5,508.85 90,015,882.03 30,347,428.32 52,266,150.88

9,509.28 5,467.90 90,426,406.12 29,897,930.41 51,995,792.11

9,712.28 5,554.55 94,328,382.80 30,853,025.70 53,947,344.87

9,742.20 5,675.16 94,910,460.84 32,207,441.03 55,288,543.75

9,789.36 5,713.52 95,831,569.21 32,644,310.79 55,931,704.15

9,665.19 5,736.31 93,415,897.74 32,905,252.42 55,442,526.05

9,707.44 5,581.41 94,234,391.35 31,152,137.59 54,181,202.69

9,748.55 5,605.21 95,034,227.10 31,418,379.14 54,642,669.95

9,829.87 5,702.05 96,626,344.22 32,513,374.20 56,050,410.23

9,778.86 5,709.38 95,626,102.90 32,597,019.98 55,831,227.71

9,820.20 5,668.65 96,436,328.04 32,133,592.82 55,667,276.73

9,783.92 5,703.83 95,725,090.57 32,533,676.67 55,805,816.41

9,791.71 5,731.14 95,877,584.72 32,845,965.70 56,117,660.85

9,683.41 5,700.26 93,768,429.23 32,492,964.07 55,197,954.69

9,626.80 5,628.98 92,675,278.24 31,685,415.84 54,189,064.66

610,447.83 346,221.69 6,113,915,876.81 1,965,909,774.10 3,465,688,492.56

Hence,

Correlation coefficient for DOW Jones (USA) and DAX (German), r = 0.4566

Coefficient of determination for DOW Jones (USA) and DAX (German), r² = 0.2085

29



vi) Dow Jones (USA) and Shanghai Composite Index

x= Dow Jones (USA)


x y x² y² xy

10,285.97 3,331.90 105,801,178.84 11,101,557.61 34,271,823.44

10,390.11 3,317.04 107,954,385.81 11,002,754.36 34,464,410.47

10,388.90 3,269.75 107,929,243.21 10,691,265.06 33,969,105.78

10,366.15 3,235.36 107,457,065.82 10,467,554.33 33,538,227.06

10,452.68 3,195.30 109,258,519.18 10,209,942.09 33,399,448.40

10,471.58 3,096.26 109,653,987.70 9,586,825.99 32,422,734.29

10,344.84 3,170.98 107,015,714.63 10,055,114.16 32,803,280.74

10,309.92 3,290.17 106,294,450.41 10,825,218.63 33,921,389.49

10,464.40 3,223.53 109,503,667.36 10,391,145.66 33,732,307.33

10,433.71 3,338.66 108,862,304.36 11,146,650.60 34,834,610.23

10,450.95 3,308.35 109,222,355.90 10,945,179.72 34,575,400.43

10,318.16 3,320.61 106,464,425.79 11,026,450.77 34,262,585.28

10,332.44 3,303.23 106,759,316.35 10,911,328.43 34,130,425.78

10,426.31 3,282.89 108,707,940.22 10,777,366.75 34,228,428.84

10,437.42 3,275.05 108,939,736.26 10,725,952.50 34,183,072.37

10,406.96 3,187.65 108,304,816.44 10,161,112.52 33,173,746.04

10,270.47 3,175.19 105,482,554.02 10,081,831.54 32,610,693.64

10,197.47 3,178.61 103,988,394.40 10,103,561.53 32,413,780.12

10,291.26 3,175.58 105,910,032.39 10,084,308.34 32,680,719.43

10,246.97 3,164.04 105,000,394.18 10,011,149.12 32,421,822.96

10,226.94 3,155.05 104,590,301.76 9,954,340.50 32,266,507.05

10,023.42 3,128.54 100,468,948.50 9,787,762.53 31,358,670.41

30



10,005.96 3,114.23 100,119,235.52 9,698,428.49 31,160,860.81

9,802.14 3,076.65 96,081,948.58 9,465,775.22 30,157,754.03

9,771.91 2,995.85 95,490,225.05 8,975,117.22 29,275,176.57

9,789.44 2,960.47 95,833,135.51 8,764,382.62 28,981,343.44

9,712.73 3,031.33 94,337,124.05 9,188,961.57 29,442,489.83

9,962.58 3,021.46 99,253,000.26 9,129,220.53 30,101,536.97

9,762.69 3,109.57 95,310,116.04 9,669,425.58 30,357,767.94

9,882.17 3,107.85 97,657,283.91 9,658,731.62 30,712,302.03

9,867.96 3,051.41 97,376,634.56 9,311,102.99 30,111,191.82

9,972.18 3,070.59 99,444,373.95 9,428,522.95 30,620,476.19

10,081.31 3,084.45 101,632,811.32 9,513,831.80 31,095,296.63

9,949.36 3,038.27 98,989,764.41 9,231,084.59 30,228,842.01

10,041.48 2,976.63 100,831,320.59 8,860,326.16 29,889,770.61

10,092.19 2,979.79 101,852,299.00 8,879,148.44 30,072,606.84

9,995.91 2,970.53 99,918,216.73 8,824,048.48 29,693,150.53

10,062.94 2,936.19 101,262,761.44 8,621,211.72 29,546,703.80

10,015.86 2,894.48 100,317,451.54 8,378,014.47 28,990,706.45

9,871.06 2,911.72 97,437,825.52 8,478,113.36 28,741,762.82

9,885.80 2,779.43 97,729,041.64 7,725,231.12 27,476,889.09

9,864.94 2,754.54 97,317,041.20 7,587,490.61 27,173,371.83

9,786.87 2,763.52 95,782,824.40 7,637,042.79 27,046,210.98

9,725.58 2,838.84 94,586,906.34 8,059,012.55 27,609,365.53

9,731.25 2,853.55 94,697,226.56 8,142,747.60 27,768,608.44

9,599.75 2,842.72 92,155,200.06 8,081,057.00 27,289,401.32

9,487.67 2,897.55 90,015,882.03 8,395,796.00 27,490,998.21

9,509.28 2,967.01 90,426,406.12 8,803,148.34 28,214,128.85

9,712.28 2,962.67 94,328,382.80 8,777,413.53 28,774,280.59

9,742.20 3,060.26 94,910,460.84 9,365,191.27 29,813,664.97

9,789.36 2,999.71 95,831,569.21 8,998,260.08 29,365,241.09

31



9,665.19 3,033.73 93,415,897.74 9,203,517.71 29,321,576.86

9,707.44 3,026.74 94,234,391.35 9,161,155.03 29,381,896.95

9,748.55 2,989.79 95,034,227.10 8,938,844.24 29,146,117.30

9,829.87 2,924.88 96,626,344.22 8,554,923.01 28,751,190.17

9,778.86 2,946.26 95,626,102.90 8,680,447.99 28,811,064.06

9,820.20 2,930.48 96,436,328.04 8,587,713.03 28,777,899.70

9,783.92 2,881.12 95,725,090.57 8,300,852.45 28,188,647.59

9,791.71 2,861.61 95,877,584.72 8,188,811.79 28,020,055.25

9,683.41 2,845.02 93,768,429.23 8,094,138.80 27,549,495.12

9,626.80 2,714.97 92,675,278.24 7,371,062.10 26,136,473.20

610,447.83 186,329.61 6,113,915,876.81 570,777,677.64 1,866,949,506.00

Hence,

Correlation coefficient for DOW Jones and Shanghai Composite, r = 0.8070

Coefficient of determination for DOW Jones and Shanghai Composite, r² = 0.6513

vii) Dow Jones (USA) and Nikkei (Japan)

x= Dow Jones (USA)

y= Nikkei (Japan)

x y x² y² xy

10,285.97 10,140.47 105,801,178.84 102,829,131.82 104,304,570.21

10,390.11 10,167.60 107,954,385.81 103,380,089.76 105,642,482.44

10,388.90 10,022.59 107,929,243.21 100,452,310.31 104,123,685.25

32



10,366.15 9,977.67 107,457,065.82 99,553,898.63 103,430,023.87

10,452.68 9,608.94 109,258,519.18 92,331,727.92 100,439,174.96

10,471.58 9,572.20 109,653,987.70 91,627,012.84 100,236,058.08

10,344.84 9,345.55 107,015,714.63 87,339,304.80 96,678,219.46

10,309.92 9,081.52 106,294,450.41 82,474,005.51 93,629,744.68

10,464.40 9,383.24 109,503,667.36 88,045,192.90 98,189,976.66

10,433.71 9,441.64 108,862,304.36 89,144,565.89 98,511,333.68

10,450.95 9,401.58 109,222,355.90 88,389,706.50 98,255,442.50

10,318.16 9,497.68 106,464,425.79 90,205,925.38 97,998,581.87

10,332.44 9,549.47 106,759,316.35 91,192,377.28 98,669,325.81

10,426.31 9,676.80 108,707,940.22 93,640,458.24 100,893,316.61

10,437.42 9,729.93 108,939,736.26 94,671,537.80 101,555,365.98

10,406.96 9,791.18 108,304,816.44 95,867,205.79 101,896,418.61

10,270.47 9,770.31 105,482,554.02 95,458,957.50 100,345,675.75

10,197.47 9,804.49 103,988,394.40 96,128,024.16 99,980,992.64

10,291.26 9,871.68 105,910,032.39 97,450,066.02 101,592,025.52

10,246.97 9,870.73 105,000,394.18 97,431,310.73 101,145,074.19

10,226.94 9,808.99 104,590,301.76 96,216,284.82 100,315,952.19

10,023.42 9,789.35 100,468,948.50 95,831,373.42 98,122,766.58

10,005.96 9,717.44 100,119,235.52 94,428,640.15 97,232,315.94

9,802.14 9,844.31 96,081,948.58 96,910,439.38 96,495,304.82

9,771.91 9,802.95 95,490,225.05 96,097,828.70 95,793,545.13

9,789.44 10,034.74 95,833,135.51 100,696,006.87 98,234,485.15

9,712.73 9,891.10 94,337,124.05 97,833,859.21 96,069,583.70

9,962.58 10,075.05 99,253,000.26 101,506,632.50 100,373,491.63

9,762.69 10,212.46 95,310,116.04 104,294,339.25 99,701,081.12

9,882.17 10,362.62 97,657,283.91 107,383,893.26 102,405,172.49

9,867.96 10,282.99 97,376,634.56 105,739,883.34 101,472,134.00

9,972.18 10,267.17 99,444,373.95 105,414,779.81 102,386,067.33

33



10,081.31 10,333.39 101,632,811.32 106,778,948.89 104,174,107.94

9,949.36 10,336.84 98,989,764.41 106,850,261.19 102,844,942.42

10,041.48 10,236.51 100,831,320.59 104,786,136.98 102,789,710.43

10,092.19 10,257.56 101,852,299.00 105,217,537.15 103,521,244.46

9,995.91 10,238.65 99,918,216.73 104,829,953.82 102,344,623.92

10,062.94 10,060.21 101,262,761.44 101,207,825.24 101,235,289.62

10,015.86 10,076.56 100,317,451.54 101,537,061.43 100,925,414.24

9,871.06 10,016.39 97,437,825.52 100,328,068.63 98,872,386.67

9,885.80 9,832.47 97,729,041.64 96,677,466.30 97,201,831.93

9,864.94 9,799.60 97,317,041.20 96,032,160.16 96,672,466.02

9,786.87 9,691.80 95,782,824.40 93,930,987.24 94,852,386.67

9,725.58 9,674.49 94,586,906.34 93,595,756.76 94,090,026.45

9,731.25 9,731.87 94,697,226.56 94,709,293.70 94,703,259.94

9,599.75 9,978.64 92,155,200.06 99,573,256.25 95,792,449.34

9,487.67 10,133.23 90,015,882.03 102,682,350.23 96,140,742.27

9,509.28 10,100.20 90,426,406.12 102,014,040.04 96,045,629.86

9,712.28 10,009.52 94,328,382.80 100,190,490.63 97,215,260.91

9,742.20 10,265.98 94,910,460.84 105,390,345.36 100,013,230.36

9,789.36 10,544.22 95,831,569.21 111,180,575.41 103,221,165.50

9,665.19 10,370.54 93,415,897.74 107,548,099.89 100,233,239.50

9,707.44 10,443.80 94,234,391.35 109,072,958.44 101,382,561.87

9,748.55 10,270.77 95,034,227.10 105,488,716.39 100,125,114.88

9,829.87 10,217.62 96,626,344.22 104,399,758.46 100,437,876.31

9,778.86 10,202.06 95,626,102.90 104,082,028.24 99,764,516.45

9,820.20 10,444.33 96,436,328.04 109,084,029.15 102,565,409.47

9,783.92 10,513.67 95,725,090.57 110,537,256.87 102,864,906.19

9,791.71 10,312.14 95,877,584.72 106,340,231.38 100,973,484.36

9,683.41 10,393.23 93,768,429.23 108,019,229.83 100,641,907.31

9,626.80 10,320.94 92,675,278.24 106,521,802.48 99,357,625.19

34



610,447.83 608,601.67 6,113,915,876.81 6,078,573,397.08 6,087,122,199.31

Hence,

Correlation coefficient for DOW Jones (USA) and Nikkei (Japan), r = -0.5921

Coefficient of determination for DOW Jones (USA) and Nikkei (Japan), r² = 0.3506

viii) DAX (German) and Shanghai Composite Index

x= DAX (German)


x y x² y² xy

5,688.58 3,331.90 32,359,942.42 11,101,557.61 18,953,779.70

5,784.75 3,317.04 33,463,332.56 11,002,754.36 19,188,247.14

5,817.65 3,269.75 33,845,051.52 10,691,265.06 19,022,261.09

5,770.35 3,235.36 33,296,939.12 10,467,554.33 18,669,159.58

5,781.68 3,195.30 33,427,823.62 10,209,942.09 18,474,202.10

5,776.61 3,096.26 33,369,223.09 9,586,825.99 17,885,886.48

5,625.95 3,170.98 31,651,313.40 10,055,114.16 17,839,774.93

5,685.61 3,290.17 32,326,161.07 10,825,218.63 18,706,623.45

5,614.17 3,223.53 31,518,904.79 10,391,145.66 18,097,445.42

5,803.02 3,338.66 33,675,041.12 11,146,650.60 19,374,310.75

5,769.31 3,308.35 33,284,937.88 10,945,179.72 19,086,896.74

5,801.48 3,320.61 33,657,170.19 11,026,450.77 19,264,452.50

5,663.15 3,303.23 32,071,267.92 10,911,328.43 18,706,686.97

5,702.18 3,282.89 32,514,856.75 10,777,366.75 18,719,629.70

35



5,787.61 3,275.05 33,496,429.51 10,725,952.50 18,954,712.13

5,778.43 3,187.65 33,390,253.26 10,161,112.52 18,419,612.39

5,804.82 3,175.19 33,695,935.23 10,081,831.54 18,431,406.42

5,686.83 3,178.61 32,340,035.45 10,103,561.53 18,076,214.71

5,663.96 3,175.58 32,080,442.88 10,084,308.34 17,986,358.10

5,668.35 3,164.04 32,130,191.72 10,011,149.12 17,934,886.13

5,613.20 3,155.05 31,508,014.24 9,954,340.50 17,709,926.66

5,619.72 3,128.54 31,581,252.88 9,787,762.53 17,581,518.81

5,488.25 3,114.23 30,120,888.06 9,698,428.49 17,091,672.80

5,480.92 3,076.65 30,040,484.05 9,465,775.22 16,862,872.52

5,444.23 2,995.85 29,639,640.29 8,975,117.22 16,310,096.45

5,353.35 2,960.47 28,658,356.22 8,764,382.62 15,848,432.07

5,430.82 3,031.33 29,493,805.87 9,188,961.57 16,462,607.59

5,414.96 3,021.46 29,321,791.80 9,129,220.53 16,361,085.04

5,587.45 3,109.57 31,219,597.50 9,669,425.58 17,374,566.90

5,496.27 3,107.85 30,208,983.91 9,658,731.62 17,081,582.72

5,635.02 3,051.41 31,753,450.40 9,311,102.99 17,194,756.38

5,642.16 3,070.59 31,833,969.47 9,428,522.95 17,324,760.07

5,740.25 3,084.45 32,950,470.06 9,513,831.80 17,705,514.11

5,762.93 3,038.27 33,211,362.18 9,231,084.59 17,509,337.33

5,833.49 2,976.63 34,029,605.58 8,860,326.16 17,364,141.34

5,811.77 2,979.79 33,776,670.53 8,879,148.44 17,317,854.13

5,852.56 2,970.53 34,252,458.55 8,824,048.48 17,385,205.06

5,743.39 2,936.19 32,986,528.69 8,621,211.72 16,863,684.28

5,830.77 2,894.48 33,997,878.79 8,378,014.47 16,877,047.15

5,854.14 2,911.72 34,270,955.14 8,478,113.36 17,045,616.52

5,714.31 2,779.43 32,653,338.78 7,725,231.12 15,882,524.64

5,783.23 2,754.54 33,445,749.23 7,587,490.61 15,930,138.36

5,711.88 2,763.52 32,625,573.13 7,637,042.79 15,784,894.62

36



5,716.54 2,838.84 32,678,829.57 8,059,012.55 16,228,342.41

5,640.75 2,853.55 31,818,060.56 8,142,747.60 16,096,162.16

5,657.64 2,842.72 32,008,890.37 8,081,057.00 16,083,086.38

5,508.85 2,897.55 30,347,428.32 8,395,796.00 15,962,168.32

5,467.90 2,967.01 29,897,930.41 8,803,148.34 16,223,313.98

5,554.55 2,962.67 30,853,025.70 8,777,413.53 16,456,298.65

5,675.16 3,060.26 32,207,441.03 9,365,191.27 17,367,465.14

5,713.52 2,999.71 32,644,310.79 8,998,260.08 17,138,903.08

5,736.31 3,033.73 32,905,252.42 9,203,517.71 17,402,415.74

5,581.41 3,026.74 31,152,137.59 9,161,155.03 16,893,476.90

5,605.21 2,989.79 31,418,379.14 8,938,844.24 16,758,400.81

5,702.05 2,924.88 32,513,374.20 8,554,923.01 16,677,812.00

5,709.38 2,946.26 32,597,019.98 8,680,447.99 16,821,317.92

5,668.65 2,930.48 32,133,592.82 8,587,713.03 16,611,865.45

5,703.83 2,881.12 32,533,676.67 8,300,852.45 16,433,418.69

5,731.14 2,861.61 32,845,965.70 8,188,811.79 16,400,287.54

5,700.26 2,845.02 32,492,964.07 8,094,138.80 16,217,353.71

5,628.98 2,714.97 31,685,415.84 7,371,062.10 15,282,511.83

346,221.69 186,329.61 1,965,909,774.10 570,777,677.64 1,057,716,982.69

Hence,

Correlation coefficient for DAX (German) and Shanghai Composite, r = 0.1322

Coefficient of determination for DAX (German) and Shanghai Composite, r² = 0.0175

ix) DAX (German) and Nikkei (Japan)

x= DAX (German)

y= Nikkei (Japan)37



x y x² y² xy

5,688.58 10,140.47 32,359,942.42 102,829,131.82 57,684,874.83

5,784.75 10,167.60 33,463,332.56 103,380,089.76 58,817,024.10

5,817.65 10,022.59 33,845,051.52 100,452,310.31 58,307,920.71

5,770.35 9,977.67 33,296,939.12 99,553,898.63 57,574,648.08

5,781.68 9,608.94 33,427,823.62 92,331,727.92 55,555,816.22

5,776.61 9,572.20 33,369,223.09 91,627,012.84 55,294,866.24

5,625.95 9,345.55 31,651,313.40 87,339,304.80 52,577,597.02

5,685.61 9,081.52 32,326,161.07 82,474,005.51 51,633,980.93

5,614.17 9,383.24 31,518,904.79 88,045,192.90 52,679,104.51

5,803.02 9,441.64 33,675,041.12 89,144,565.89 54,790,025.75

5,769.31 9,401.58 33,284,937.88 88,389,706.50 54,240,629.51

5,801.48 9,497.68 33,657,170.19 90,205,925.38 55,100,600.57

5,663.15 9,549.47 32,071,267.92 91,192,377.28 54,080,081.03

5,702.18 9,676.80 32,514,856.75 93,640,458.24 55,178,855.42

5,787.61 9,729.93 33,496,429.51 94,671,537.80 56,313,040.17

5,778.43 9,791.18 33,390,253.26 95,867,205.79 56,577,648.25

5,804.82 9,770.31 33,695,935.23 95,458,957.50 56,714,890.89

5,686.83 9,804.49 32,340,035.45 96,128,024.16 55,756,467.87

5,663.96 9,871.68 32,080,442.88 97,450,066.02 55,912,800.65

5,668.35 9,870.73 32,130,191.72 97,431,310.73 55,950,752.40

5,613.20 9,808.99 31,508,014.24 96,216,284.82 55,059,822.67

5,619.72 9,789.35 31,581,252.88 95,831,373.42 55,013,405.98

5,488.25 9,717.44 30,120,888.06 94,428,640.15 53,331,740.08

5,480.92 9,844.31 30,040,484.05 96,910,439.38 53,955,875.57

5,444.23 9,802.95 29,639,640.29 96,097,828.70 53,369,514.48

5,353.35 10,034.74 28,658,356.22 100,696,006.87 53,719,475.38

38



5,430.82 9,891.10 29,493,805.87 97,833,859.21 53,716,783.70

5,414.96 10,075.05 29,321,791.80 101,506,632.50 54,555,992.75

5,587.45 10,212.46 31,219,597.50 104,294,339.25 57,061,609.63

5,496.27 10,362.62 30,208,983.91 107,383,893.26 56,955,757.43

5,635.02 10,282.99 31,753,450.40 105,739,883.34 57,944,854.31

5,642.16 10,267.17 31,833,969.47 105,414,779.81 57,929,015.89

5,740.25 10,333.39 32,950,470.06 106,778,948.89 59,316,241.95

5,762.93 10,336.84 33,211,362.18 106,850,261.19 59,570,485.34

5,833.49 10,236.51 34,029,605.58 104,786,136.98 59,714,578.72

5,811.77 10,257.56 33,776,670.53 105,217,537.15 59,614,579.48

5,852.56 10,238.65 34,252,458.55 104,829,953.82 59,922,313.44

5,743.39 10,060.21 32,986,528.69 101,207,825.24 57,779,709.51

5,830.77 10,076.56 33,997,878.79 101,537,061.43 58,754,103.75

5,854.14 10,016.39 34,270,955.14 100,328,068.63 58,637,349.35

5,714.31 9,832.47 32,653,338.78 96,677,466.30 56,185,781.65

5,783.23 9,799.60 33,445,749.23 96,032,160.16 56,673,340.71

5,711.88 9,691.80 32,625,573.13 93,930,987.24 55,358,398.58

5,716.54 9,674.49 32,678,829.57 93,595,756.76 55,304,609.06

5,640.75 9,731.87 31,818,060.56 94,709,293.70 54,895,045.70

5,657.64 9,978.64 32,008,890.37 99,573,256.25 56,455,552.81

5,508.85 10,133.23 30,347,428.32 102,682,350.23 55,822,444.09

5,467.90 10,100.20 29,897,930.41 102,014,040.04 55,226,883.58

5,554.55 10,009.52 30,853,025.70 100,190,490.63 55,598,379.32

5,675.16 10,265.98 32,207,441.03 105,390,345.36 58,261,079.06

5,713.52 10,544.22 32,644,310.79 111,180,575.41 60,244,611.85

5,736.31 10,370.54 32,905,252.42 107,548,099.89 59,488,632.31

5,581.41 10,443.80 31,152,137.59 109,072,958.44 58,291,129.76

5,605.21 10,270.77 31,418,379.14 105,488,716.39 57,569,822.71

5,702.05 10,217.62 32,513,374.20 104,399,758.46 58,261,380.12

39



5,709.38 10,202.06 32,597,019.98 104,082,028.24 58,247,437.32

5,668.65 10,444.33 32,133,592.82 109,084,029.15 59,205,251.25

5,703.83 10,513.67 32,533,676.67 110,537,256.87 59,968,186.36

5,731.14 10,312.14 32,845,965.70 106,340,231.38 59,100,318.04

5,700.26 10,393.23 32,492,964.07 108,019,229.83 59,244,113.24

5,628.98 10,320.94 31,685,415.84 106,521,802.48 58,096,364.84

346,221.69 608,601.67 1,965,909,774.10 6,078,573,397.08 3,454,163,596.92

Hence,

Correlation coefficient for DAX and Nikkei, r = -0.05

Coefficient of determination for DAX and Nikkei, r² = 0.0025

x) Shanghai Composite Index and Nikkei (Japan)

x= Shanghai Composite Index

y= Nikei (Japan)

x y x² y² xy

3,331.90 10,140.47 11,101,557.61 102,829,131.82 33,787,031.99

3,317.04 10,167.60 11,002,754.36 103,380,089.76 33,726,335.90

3,269.75 10,022.59 10,691,265.06 100,452,310.31 32,771,363.65

3,235.36 9,977.67 10,467,554.33 99,553,898.63 32,281,354.41

3,195.30 9,608.94 10,209,942.09 92,331,727.92 30,703,445.98

3,096.26 9,572.20 9,586,825.99 91,627,012.84 29,638,019.97

3,170.98 9,345.55 10,055,114.16 87,339,304.80 29,634,552.14

3,290.17 9,081.52 10,825,218.63 82,474,005.51 29,879,744.66

3,223.53 9,383.24 10,391,145.66 88,045,192.90 30,247,155.64

40



3,338.66 9,441.64 11,146,650.60 89,144,565.89 31,522,425.80

3,308.35 9,401.58 10,945,179.72 88,389,706.50 31,103,717.19

3,320.61 9,497.68 11,026,450.77 90,205,925.38 31,538,091.18

3,303.23 9,549.47 10,911,328.43 91,192,377.28 31,544,095.79

3,282.89 9,676.80 10,777,366.75 93,640,458.24 31,767,869.95

3,275.05 9,729.93 10,725,952.50 94,671,537.80 31,866,007.25

3,187.65 9,791.18 10,161,112.52 95,867,205.79 31,210,854.93

3,175.19 9,770.31 10,081,831.54 95,458,957.50 31,022,590.61

3,178.61 9,804.49 10,103,561.53 96,128,024.16 31,164,649.96

3,175.58 9,871.68 10,084,308.34 97,450,066.02 31,348,309.57

3,164.04 9,870.73 10,011,149.12 97,431,310.73 31,231,384.55

3,155.05 9,808.99 9,954,340.50 96,216,284.82 30,947,853.90

3,128.54 9,789.35 9,787,762.53 95,831,373.42 30,626,373.05

3,114.23 9,717.44 9,698,428.49 94,428,640.15 30,262,343.17

3,076.65 9,844.31 9,465,775.22 96,910,439.38 30,287,496.36

2,995.85 9,802.95 8,975,117.22 96,097,828.70 29,368,167.76

2,960.47 10,034.74 8,764,382.62 100,696,006.87 29,707,546.73

3,031.33 9,891.10 9,188,961.57 97,833,859.21 29,983,188.16

3,021.46 10,075.05 9,129,220.53 101,506,632.50 30,441,360.57

3,109.57 10,212.46 9,669,425.58 104,294,339.25 31,756,359.24

3,107.85 10,362.62 9,658,731.62 107,383,893.26 32,205,468.57

3,051.41 10,282.99 9,311,102.99 105,739,883.34 31,377,618.52

3,070.59 10,267.17 9,428,522.95 105,414,779.81 31,526,269.53

3,084.45 10,333.39 9,513,831.80 106,778,948.89 31,872,824.79

3,038.27 10,336.84 9,231,084.59 106,850,261.19 31,406,110.87

2,976.63 10,236.51 8,860,326.16 104,786,136.98 30,470,302.76

2,979.79 10,257.56 8,879,148.44 105,217,537.15 30,565,374.71

2,970.53 10,238.65 8,824,048.48 104,829,953.82 30,414,216.98

41



2,936.19 10,060.21 8,621,211.72 101,207,825.24 29,538,688.00

2,894.48 10,076.56 8,378,014.47 101,537,061.43 29,166,401.39

2,911.72 10,016.39 8,478,113.36 100,328,068.63 29,164,923.09

2,779.43 9,832.47 7,725,231.12 96,677,466.30 27,328,662.09

2,754.54 9,799.60 7,587,490.61 96,032,160.16 26,993,390.18

2,763.52 9,691.80 7,637,042.79 93,930,987.24 26,783,483.14

2,838.84 9,674.49 8,059,012.55 93,595,756.76 27,464,329.19

2,853.55 9,731.87 8,142,747.60 94,709,293.70 27,770,377.64

2,842.72 9,978.64 8,081,057.00 99,573,256.25 28,366,479.50

2,897.55 10,133.23 8,395,796.00 102,682,350.23 29,361,540.59

2,967.01 10,100.20 8,803,148.34 102,014,040.04 29,967,394.40

2,962.67 10,009.52 8,777,413.53 100,190,490.63 29,654,904.62

3,060.26 10,265.98 9,365,191.27 105,390,345.36 31,416,567.95

2,999.71 10,544.22 8,998,260.08 111,180,575.41 31,629,602.18

3,033.73 10,370.54 9,203,517.71 107,548,099.89 31,461,418.31

3,026.74 10,443.80 9,161,155.03 109,072,958.44 31,610,667.21

2,989.79 10,270.77 8,938,844.24 105,488,716.39 30,707,445.44

2,924.88 10,217.62 8,554,923.01 104,399,758.46 29,885,312.39

2,946.26 10,202.06 8,680,447.99 104,082,028.24 30,057,921.30

2,930.48 10,444.33 8,587,713.03 109,084,029.15 30,606,900.18

2,881.12 10,513.67 8,300,852.45 110,537,256.87 30,291,144.91

2,861.61 10,312.14 8,188,811.79 106,340,231.38 29,509,322.95

2,845.02 10,393.23 8,094,138.80 108,019,229.83 29,568,947.21

2,714.97 10,320.94 7,371,062.10 106,521,802.48 28,021,042.47

186,329.61 608,601.67 570,777,677.64 6,078,573,397.08 1,857,534,743.13

Hence,

Correlation coefficient for Shanghai Composite and Nikkei, r = -0.4591

42



Coefficient of determination for Shanghai Composite and Nikkei, r² = 0.2108

b) Linear Regression

Linear Regression is often use to organize a data. It often used by the organisation to

set a predetermined value of how the stock varies every period of time (Trade Ideas

2010). Hence, linear regression is defined as a technique for finding the straight line

that best-fits the values of a linear function, plotted on a scatter graph as data points. If

a 'best fit' line is found, it can be used as the basis for estimating the future values of the

function (BusinessDictionary 2010).

The linear regression line is often written in a form of linear equation that is

where: x and y are the linked variables

a and b are the pairs to used to predict the value of x and y

(Wu, Chye, Kar and Hai 2003, p305)

In order to find the value of a and b. Certain formula must use to calculate the value of a

and b that is:

a = - b

Where:

n = number of pairs

= mean for x value

43

http://www.businessdictionary.com/definition/technique.html

http://www.businessdictionary.com/definition/values.html

http://www.businessdictionary.com/definition/linear-function.html

http://www.businessdictionary.com/definition/scatter-graph.html

http://www.businessdictionary.com/definition/data.html

http://www.businessdictionary.com/definition/point.html

http://www.businessdictionary.com/definition/estimating.html

http://www.businessdictionary.com/definition/future-value.html

http://www.businessdictionary.com/definition/function.html

http://www.businessdictionary.com/definition/technique.html

http://www.businessdictionary.com/definition/values.html

http://www.businessdictionary.com/definition/linear-function.html

http://www.businessdictionary.com/definition/scatter-graph.html

http://www.businessdictionary.com/definition/data.html

http://www.businessdictionary.com/definition/point.html

http://www.businessdictionary.com/definition/estimating.html

http://www.businessdictionary.com/definition/future-value.html

http://www.businessdictionary.com/definition/function.html



= mean for y value

(Wu, Chye, Kar and Hai 2003, p312)

i) FSTE-100 (UK) and Dow Jones (USA)

x= FTSE-100 (UK)

y= Dow Jones (USA)

y = -3174.462+2.537x


x= FTSE-100 (UK)

y= DAX (German)

y = 824.45+0.934x

iii) FTSE-100 (UK) and Shanghai Composite

x= FTSE-100 (UK)

y= Shanghai Composite

y = -2577.2+1.084x


x= FTSE-100 (UK)

y= Nikkei (Japan)

44



y = 17577.775-1.463x


x= Dow Jones (USA)

y= DAX (German)

y = 3798.245+0.188x

vi) Dow Jones (USA) and Shanghai Composite

x= Dow Jones (USA)


y = -1560.337+0.461x


x= Dow Jones (USA)

y= Nikkei (Japan)

y = 16766.848 -0.678x

viii) DAX (German) and Shanghai Composite

x= DAX (German)


45



y = 2011.071+0.184x


x= DAX (German)

y= Nikkei (Japan)

y = 10768.798-0.139x

x) Shanghai Composite and Nikkei (Japan)

x= Shanghai Composite

y= Nikkei (Japan)

y = 12789.611-0.921x

c) Scatterplot and Line Chart

i) Scatterplot

It is a diagram represent of bivariate data as a set of points in the Cartesian

Coordinates where it is equal to corresponding values of the two variables

(Dictionary.com 2010). Bivariate is refer to as the study of correlationship that occupytwo independent variable (Wu, Chye, Kar and Hai 2003).

46



i) FTSE-100 (UK) and Dow Jones (USA)

From the scatterplot above, it shows that there are positive relation between FTSE-100

(UK) and Dow Jones (USA). It shows a strong positive correlation where r = 0.7147.


47



From the scatterplot above, it shows that there are spositive relation between FTSE-100

(UK) and DAX (German). It shows a strong positive correlation as well where r =

0.7572.

iii) FTSE-100 (UK) and Shanghai Composite

From the scatterplot above, it shows that there are positive relation between FTSE-100

(UK) and Shanghai Compsite. It shows a strong positive correlation as well where r =

0.6321.


48



From the scatterplot above, it shows that there negative relation between FTSE-100

(UK) and Nikkei (Japan). It shows a partial negative correlation as well where

r = -0.4253.


From the scatterplot above, it shows that there positive relation between Dow Jones

(USA) and DAX (German). It shows a partial positive correlation as well where r =

0.4566.

vi) Dow Jones (USA) and Shanghai Composite

49



From the scatterplot above, it shows that there positive relation between Dow Jones

(USA) and Shanghai Composite. It shows a strong positive correlation as well where r =

0.8070.


From the scatterplot above, it shows that there negative relation between Dow Jones

(USA) and Nikkei (Japan). It shows a partial negative correlation as well where

r = -0.5920.

viii) DAX (German) and Shanghai Composite

50



From the scatterplot above, it shows that there positive relation between DAX (German)

and Shanghai Composite. It shows a small partial positive correlation as well where r =

0.0174.


51



From the scatterplot above, it shows that there negative relation between DAX

(German) and Nikkei (Japan). It shows a small partial negative correlation as well where

r = -0.05.

x) Shanghai Composite and Nikkei (Japan)

From the scatterplot above, it shows that there negative relation between Shanghai

Composite and Nikkei (Japan). It shows a partial negative correlation as well where

r = -0.4091.

52



ii) LINE CHART

Line chart is an option term for line graph (Business Dictionary 2010). This the result on

line chart that plot with the data of the 5 stock market values.

i) FTSE-100 (UK)

ii) Dow Jones (USA)

53



iii) DAX (German)

iv) Shanghai Composite

54



v) Nikkei (Japan)

2.3 TASK 3

55



SUBSTITUTION VALUE

Using the linear regression between FTSE-100(UK) and Dow Jones (USA) that is:

y = -3.174.462+2.537x where:

y= Dow Jones (USA)

x= FTSE-100 (UK)

If the stock value of FTSE-100(UK) are:

5400, 5478, 5389, 5000, 5904 then the value of Dow Jones (USA) are:

Stock Value (£)

of FTSE-100

(UK)

Stock Value ($) of Dow

Jones (USA)

5400

y =-3174.462+2.537(5400)

y = 9510.538

5478

y =

-3174.462+2.537(5478)

y = 10723.224

5389

y =-3174.462+2.537(5389)

y = 10497.431

5000 y =

56



-3174.462+2.537(5000)

y = 9510.538

5904

y =

-3174.462+2.537(5904)

y = 11803.986

2.4 TASK 4

COMMENT

From all the data that has been arranged into pairs for calculating the correlationship,

the pair of indices that has the strogest correlation are Dow Jones (USA) and Shanghai

Composite where the correlation was 0.8070 where it has a positive value. Since the

value for this pair is the most nearest to +1, then it has a strongest correlation

(Wu, Chye, Kar and Hai 2003). The value stock of Shanghai Index was decrease when

the value stock of Dow Jones (USA) decrease but the change of decline was not very

strong. For example, when the value of Dow Jones (USA) decrease from $9,683.41 in

7th of December 2009 to $9,626.80 in 8th December 2009, the value of Shanghai

Composite was decrease in accordance of the decline in Dow Jones that is 2,845.02

yuan in 7th to 2,714.97 yuan in 8th. The reason why it happens is because the low

value or high value of Shanghai Composite (China) are associated with the low value or

57



high value of Dow Jones (USA) (CIMA 2005). This happen mostly because most of the

investments on China was from the United States of America and the export and trade

of China are mostly deal with the USA as well. This will give an impact on the stock

value of China when there are any changes happen on business of USA. Example,

“many US investors have been flocking to mutual funds which invest in the Shanghai

market. The reasons are obvious. The Shanghai Index was the best-performing in the

world, with gains that peaked above 90% for the year 2009” (SMR 2009). That is why,

USA has a big relation with the stock value of Shanghai Composite Index. The

significant example was stated by “director of technical research at Janney Montgomery

that the US can continue to pull away from the market trends of China and this will

continue to boost our relative attractiveness to global competitors" (peopledaily 2010).

Date post:	10-Apr-2018
Category:	Documents
Upload:	kim-soon
View:	219 times
Download:	0 times

Report (Perfect)

Documents