Using vector measure construction method (VMCM)

Using vector measure construction method (VMCM) in classification Gulf Cooperation Council (GCC) stock market companies Salam Al-augby1, Kesra Nermend2, Prof. inz. dr hab., Sebastian Majewski3, Prof. dr hab., Agnieszka Majewska4, dr hab.

Abstract: This work aims to use vector measure construction method (VMCM) in classification Gulf Cooperation Council (GCC) stock markets ratios movement in term of its response to the media news indicator. VMCM is used for classified dynamic set of data such as showing dynamic stock market changes related to media influence as in this article. Two types of data are used in this article throughout October, November, and December 2012. The first type is the news indicator Good News Indicator (GNI), Bad News Indicator (BNI) and, Neutral News Indicator (NNI) as a media expansiveness measurement. The headlines collected from Alarabia.net and Reuters.com. The second kind of data is the stock market ratios of banking and energy companies on the GCC stock markets. This work shows the dynamic changes of the quieted stock to the news that used here as a noise source. Keywords: News, VMCM, Media, dynamic classification, stock market 1

University of Kufa, Research and Information Qualifying Centre, PhD Student at Szczecin University, [email protected]. 2 University of Szczecin. Faculty of Economics and Management, Institute of

IT in Management, Department of Computer Methods in Experimental Economic, [email protected]. 3

University of Szczecin, Faculty of Economics and Management, Institute of Finance Department of Insurance and Capital Market, [email protected]. 4 University of Szczecin, Faculty of Economics and Management, Institute of Finance Department of Insurance and Capital Market, [email protected].

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1. Introduction Research over several decades has shown that the media does have an impact on individual perceptions of what issues and events are important, and it follows that online news would have a similar impact [Pope, 2007]. Easy and quick availability to news information was not possible until the beginning of the last decade. In this age of information, news is now easily accessible, as content providers and content locators such as online news services have sprouted on the World Wide Web [Hariharan, 2004]. Investors’ attention grabbing event is likely to be reported in the news [Barber & Odean, 2008]. Possibly investors will recognize this news to be irrelevant to the firm’s future earnings and not trade or investors will all interpret the news similarly and not trade. But significant news will often affect investors’ beliefs and portfolio goals heterogeneously, resulting in greater than usual trading [Barber & Odean, 2008]. Whereas newspapers are updated once or twice a day, the real-time news sources are frequently updated on the spot. All these information sources contain global and regional political and economic news, citations from influential bankers and politicians, as well as recommendations from financial analysts [Cho V. W.-S., 1999]. Financial markets contain many uncertainties, and they interact with various economic, political, and social factors. Since change in the stock market is more disorderly, the system is hard to define as merely a “linear” or “nonlinear” system [Luo, Wu, & Yan, 2010]; this is why predictions of stock market prices and their direction are so difficult [Mhmoud & Ali, 2013]. Our case study is the stock markets of the Gulf Cooperation Council (GCC) which represent the most developed in the Middle East in terms of economic reforms; by the beginning of the 2000s, they had made progress on regional integration [Simpson, 2008]. The GCC includes six countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates), which considers to be encouraging and emerging markets [Reports, 2012]. The banking sector is still the dominant financial sector [Hertog, 2012]. One of the most important policies for most of the countries on the Arabian Peninsula during the last decade has been to develop their financial markets. Aspirations for the region’s financial centers have been high [Kern, November 2012]. One important step taken by these countries to give them a competitive edge is in their infrastructure development policy (on both the technical and administrative sides), where foreign expertise plays an assisting role [Simpson, 2008]. GCC banks, in turn, are frequently partly state-owned, reflecting the continuing large role of GCC governments in local economic development [Hertog, 2012]. The difference between the GCC markets and both developed and

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Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

emerging markets is due to the great segmentation of the world equity markets, and their excessive sensitivity to regional political events [Hammoudeh & Choi, 2006]. Classification is the most widely used DM methods and it is one of the functions of DM to map raw data into one of several predefined categorical classes [Rahman, 2006]. Classification may be defined as a process of analyzing data from different viewpoints and summarizing them into useful information, which is the primary goal of DM [Luo, 2008]. Classification is also one of the important techniques used in DM. Classification predicts group membership for data instances [Rani, Rao, & Lakshmi, 2014]. Various classification methodologies are used in nearly every discipline in which the task of classification, due to the huge amount of data, requires process automation. The classification of financial market trends and the identification of objects in large image databases represent an example of classification methods that are used as part of a DM application [Kantardzic, 2011]. Many of classification techniques such as SVM, neural network, genetic algorithms, knearest-neighbor methods, and etcetera are designed to build classification models from static datasets when several passes over the stored data are possible [Gaber, Zaslavsky, & Krishnaswamy, 2007]. For classified dynamic set of data the vector measure construction method (VMCM) is used to such as showing dynamic stock market changes related to media influence as in this article. Vectorial synthetic measure belongs to the group of the linear objects ordering methods, forming the branch of taxonomy. Hellwig [1968] proposed one of the most popular methods of linear objects ordering, so-called Hellwig's synthetic measure. Hellwig introduced the basic notions of the method, e.g. stimulants and de-stimulants. The method proposed by Kolenda [2006] is an improvement of Hellwig’s method. Kolenda method uses the real patterns, which may not be the best objects in the present set of objects. Kolenda proposed in her work the use of orthogonal projection for the construction of synthetic measure. The Hellwig's method finds many applications, e.g. in construction of synthetic variables in the process of econometric modeling [Bartosiewicz, 1984], determination of the product quality [Borys, 1984], survey of regions development [Młodak, 2006], investigations of stock investment attractiveness [Tarczyński & Łuniewska, 2006], the study of regional development [Nermend 2006 ; Nermend, 2008a; Strahl, 2006], examining the level of socio-economic development [Kompa & Witkowska, 2010], to evaluate the effectiveness of financial exchanges [Kompa, 2014], examining the attractiveness of equity investments [Tarczyński & Łuniewska, 2006], examining effectiveness Chinese banks [Witkowska, 2010], examining the demand for electronic durables in Poland [Dittman & Pisz, 1975], examine the need for qualified personnel [Cieślak, 1974; Cieślak & Maria, 1976],

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a synthetic evaluation of the activities of enterprises [Pluta, 1977 ], the study of socio-economic development of countries in the world [Grabiński et al., 1989], examining the level of agricultural production in selected European countries [Smith, 1990], in investigations the spatial differentiation of agriculture using the taxonomic method [Borkowski & Szczesny, 2002], the attractiveness of investments on the example of companies listed on the Warsaw Stock Exchange [Tarczyński, 2004], in using radar charts to measure concentration of a distribution [Binderman, Borkowski & Szczesny, 2012] and, in the studying of tourism differentiation economy of Polish voivodeships between 2002-2008 using modified radar methods [Binderman et al., 2010].

Selection of

Elimination of

Variable

variable

Normalization

Determination of the pattern

Determination the

and anti-pattern

synthetic measure

Figure 1. Stage diagram procedure for the construction measuring method.

Source: Nermend et al., Comparative analysis of quality of the international versions of municipal in websites in Poland, 2012. Binderman, Borkowski & Szczesny [2013] as a continual to their research in [2010a] they conducted the using of synthetic measure that use radar charts of vectors in the analysis of regional differentiation of agriculture in Poland (20012010) in an attempt to generalize the synthetic measure . Three methods were used, inter alia, to develop synthetic measures to the analysis of regional differences in the level agriculture of Poland in 2009 [Binderman, 2011]. In his works Nermend [2008b; 2007] proposed to use the properties of vector to vector construction of synthetic measure (called. Vector Measure Construction Method - VMCM) based on the definition of scalar product without resorting to measure distances. Figure 1 illustrates a stage diagram procedure for the construction measuring method.

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Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

A typical test procedure for the construction of such a measuring method consists of five stages: selection of variables, elimination of variables, normalization of variables, determination of the pattern, and determination of the anti-pattern (in addition to a VMCM) [Nermend, 2007], [Nermend, 2009].

2. Methodology There were no easily available data sets for this problem, and therefore a new one was created. The new data set is a combination of financial data and textual data. This section will present how the data set was created and combined and how it was processed. In this case study, press economic information, taken from Alarabia.net [Al Arabiya News, 2012], which is a semi-official press agency, and Reuters.com [Reuters, 2012], which is one of the world's largest international multimedia news agencies, is treated as a source of media noise that has an influence on the value of stock quoted on the GCC stock market (SMs) for top market capitalization (mark cap) companies of the bank sector. The chosen period to conduct this type of research was October, November, and December 2012. All economic news was categorized into three types of information: neutral, positive, and negative, after which another group, called the most tragic news in the economic sense, was created by selecting this kind of news from the negative information group. To determine the most tragic news, the researchers selected news that contained words such as “crisis,” “depression,” and “collapse” in its title. An indicator of media expansiveness was constructed on the basis of these data [Majewski S., 2009], [Al-augby, Majewski, & Nermend, 2013]. This indicator is a quotient of the number of articles in a chosen information group and the number of all Alarabia.net and Reuters.com information pieces published on the same day (t).

BNI 

NBN  100% , TNN

(1) where BNI is the bad news indicator, NBN is the number of bad headlines, and TNN is the total number of headlines. This indicator was calculated with daily frequency and it provides information on the strength of the negative information obtained from press coverage. Analogous tragic news indicators and a good news indicator (GNI) were also calculated. The collected news headlines were analyzed by economic specialists, and the news indicators (GNI, BNI, and NNI) were calculated. The second kind of data was used in this research in addition to the news indicators is the economic ratios

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(Rt, Mar Cap, P/E, P/B, β, and DY) for all the companies of the bank and energy sectors of the GCC stock markets are quoted for the last three months of 2012 (October, November, and December). The second step of calculation was determining the correlation between the news indicators with the stock market ratios. Based on these calculation the these calculation shows us that the correlation coefficient and t-test values for the whole period of study (92 days, 63 trading days) was statically insignificance therefore, we divide the period of study into sub periods (25, 26, 27, 28, 29 and, 30) days [Al-augby, Majewski, Majewska, & Nermend, 2014]. The third step of calculation is choosing the best period of time that shows the best reaction of stock market ratios movement to the news indicators. This step was done by using Euclidean distance measure. In order to classify the stock market objects (companies), the Vector Measure Construction Method (VMCM) was used for the current study. The VMCM was built by means of the vector aggregate measure. Typically, the research procedure for constructing such a measure consists of five stages: I) selection and II) elimination of variables; III) variable normalization; IV) determination of a pattern and an anti-pattern; and V) is determining the synthetic measure. These stages are described below.

Stage I—Selection of variables This step is crucial for the success of synthetic vector measure application, and simultaneously is less formalized than the other steps used in measure construction. Which of the variables are potentially most useful in the representation (or discrimination) of the analyzed phenomena depends on the subject matter at hand, and should be decided by an expert in this domain. Phenomena/objects are characterized by a set of n attributes (variables, features); results of their measurements (observations) on all m objects are organized as M x N data matrix X [Nermend, 2008c], as follows:

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Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

 x1 1  x1 2     X   x1 i       x1 N

    xM  1  x2     xk     xM  2 2  2                      x2     xk     xM  i i  i                      x2     xk     xM  N N  N x2

    xk

1

1

where: N = the number of objects;

M = the number of variables; and xi = the value of the i -th variable for the j -th object. j

Stage II—Elimination of variables This step consists of the assessment of individual variable usefulness based on its variability. The elimination of variables is usually performed by using significance coefficient characteristics [Kukuła, 2000], as follows:

V xi 

i xi

,

(3)

where:

xi = the i -th variable;

 i = the standard deviation of the i - th variable; and xi = the mean value of the i - th variable, where: N

xi 

x j 1 j

N

i

(4) ,

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and: 2

   x i  x i   j 1  j  . i  N 1 N

(5)

Nermend and Kukula specified in their respective papers [Nermend, 2007] [Kukuła, 2000] that the variables whose significance factors values are within the range constitute quasi-constant variables. Such variables should be discarded from the set of variables under consideration.

Stage III—Normalization The variables used in studies are “heterogeneous” if they describe different properties of the objects [Nermend, 2009]. Normalization creates new attributes with a common scale, and where differences in values in sparse data are minimized [Ghazal, 2011]. They can be illustrated in various units of measure, which additionally hinders any arithmetical calculations that are necessary for individual procedures. This is why the next necessary stage of constructing the development measure is variable normalization. This process leads not only to the elimination of the measurement units, but to the equalization of the variable values as well. The process ends when the statistical mean (μ) (here it is x , and it is the first step) of all values in one column is made equal to zero, and the standard deviation (σ) equals one [Ghazal, 2011]. The most common normalizing method is standardizing [Nermend, 2009]. Several techniques can be used to standardize or normalize variables [Freudenberg, 2003], [Jacobs, Smith, & Goddard, 2004], [Nardo, et al., 2005], such as standardization (or z-scores), distance from the group leader, distance from the mean, distance from the best and worst performers, categorical scale, ranking, number of variables above the mean minus number below the mean, or percentage of annual differences over time. The method used in the VMCM in the normalization stage of this study was the standard deviation approach (z-score); the reason for choosing this method is its eligible features in variable aggregation. This technique provides standardized deviations from the means, provided that there is standard normal distribution (i.e. a mean equal to 0 and a standard deviation equal to 1). Consequently, positive (negative) values of a given indicator indicate above (below)-average performance:

yij 

xij  x j

j

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Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

where: yij = standardized j variable for object i (the so-called z-score); xij = original j variable value for object i; x j  mean value of variable j ; and

 j  standard deviation of variable j The normal distribution of variables was converted to a common scale. This method has an average of zero, i.e. it will exclude the variable means from differences by avoiding the introduced aggregation distortions. The scaling factor was taken instead of standard deviation as a range of distribution in the different approaches, which leads to the assumption that the extreme values have a significant influence on the composite index. For example, the “distance from the best and worst performers” is taken from the outliers, which may be considered to be unreliable. For obtaining better performance and getting less outlier or variable distortion, the median is taken instead of the mean as the threshold after experiencing large variance. The arbitrary choice of thresholds and scales in categorical scales gives it a high degree of subjectivity. On the other hand, the ranking will exclude a significant amount of information on the amount of variance between variables.

Stage IV—Determination of a pattern and an anti-pattern The next stage, which follows the normalization of variables, is the construction of a pattern (a reference point). The collected variables fall into two types: stimulants and destimulants [Hellwig, 1968]. The criterion of division is the impact of each of the selected variables on the level of development of the units. Variables that have a positive, stimulating effect on the level of units are called stimulants, as opposed to inhibitory variables, or so-called destimulants. Sometimes the optimal level of development for a given variable is achieved, which is then called the nominant. In the Hellwig’s measure, a pattern is commonly understood according to the values of variables. The coordinates of the pattern in Hellwig’s measure, by definition, are the maximum value of stimulants and the minimum value of destimulants. The nominants are usually transformed into stimulants or destimulants. In vector measures, it is not the position of the pattern that is important, but rather the direction (vector) indicating the positions of the best objects. The direction is determined according to the pattern that is characterized by high values of both stimulants and destimulants. Anti-pattern and pattern can be taken as real objects. Based on the first and third quartiles [Kolenda, 2006], it is also possible to automatically determine both the pattern and the anti-pattern. At the same time, variables for stimulants in the third quartile and variables for

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destimulants in the first quartile are considered to be the coordinates of the pattern:

 q3, j for stimulants y w, j    q1, j for destimulants

(7)

where:

yw, j  coordinate j of pattern vector q3, j - coordinate j of 3rd quartile

q1, j - coordinate j of 1st quartile In case of anti-pattern the procedure is reversed. To be more exact, the values of the stimulants from the first quartile and the values of destimulants from the third quartile constitute the coordinates of the anti-pattern:

 q1, j for stimulants yaw, j    q3, j for destimulants

(8)

where:

yaw, j  coordinate j of anti - pattern 25%

25%

1st quartile

25%

2nd quartile

25%

3rd quartile

quartile Figure 2. Location of quartiles in the set of all observations.

Source: Dodge, Y.; The concise encyclopedia of statistics, Springer Science & Business Media (2008) The first and third quartiles are the values of the 25 th and the 75th percentile, where quartiles are location measures of a distribution of observations. Quartiles separate a distribution into four parts. Thus there are three quartiles for a given distribution. Between each quartile we find 25% of the total observations.

Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

122

According to this supposition the second quartile equals the median. When we have all the observations, quartiles are calculated as follows [Dodge, 2008]: 1.

The n observations must be arranged in the form of a frequency distribution. Quartiles correspond to observations for which the relative cumulated frequency exceeds 25%, 50%, and 75%.

2.

Computation of jth quartile: Let i be the integer part of

𝑗.(𝑛+1) 4

and l the fraction part of

𝑗.(𝑛+1) 4

.

Let xi and xi +1 be the values of the observations respectively in the ith and (i + 1)th position (when the observations are arranged in increasing order). The jth quartile is Qj = xi + l · (xi+1 − xi).

(9)

The patterns specified in this way are insensitive to the values of variables in atypical objects. On the contrary, in the Hellwig measure, they are not “ideal” objects to which other items should drift. They only provide direction in which all the objects should evolve. Another way of determining this direction could also be to adopt a real object as both the pattern and anti-pattern. It is important to note that these do not need to be the best and the worst objects—they should simply be characterized by suitable proportions of the variables.

Stage V—Determining a synthetic measure In the vector space, the values of the variables in the examined objects are interpreted as coordinates of the vectors. Each object represents a specific direction in space. The difference in pattern and anti-pattern is also a vector designating the direction in space. Along this direction, the value of synthetic measure is calculated for each object. This measure could be seen as a onedimensional coordinate system. Given this, the process of determining the measure becomes the process of determining the coordinate in the coordinates system, which can be illustrated by the following formula [Nermend, 2007]; [Nermend, 2006]: n

mi 

(y j 1

ij

n

 yaw, j )( yw, j  yaw, j ) 1/ 2

[ ( yw, j  yaw, j ) 2 ] i 1

(10)

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where:

mi  synthetic measure of object i ;

yij  coordinate j of object i ;

yw, j  coordinate j of pattern vector ; and yaw, j  coordinate j of anti - pattern . For a measure constructed in such a way, all objects that are better than the antipattern and worse than the pattern will be characterized by a value of the measure ranging from zero to one. Subsequently, the pattern will have the value of a measure equal to one, while the anti-pattern will equal zero. It is feasible to determine the values of measurement in objects that are better than the pattern; they will have a value of measure that is greater than one. Objects that are worse than the anti-pattern will have a negative value. Thus, it is easy to determine the object’s position in the ranking in reference to the pattern and the anti-pattern. The range of synthetic vector measure is usually divided into four parts (c1 [the best], c2, c3, and c4 [the worst]), as follows:

c1  {mi | mi  m   }

c2  {mi | m  mi  m   } c3  {mi | m    mi  m}

(11)

c4  {mi | mi  m   } where: mi is the value of the synthetic vector measure of object i; m is the mean value of all mi; and  is the standard deviation of all mi.

3. Empirical results The dynamic measure method, a method built by using the vector measure construction method VMCM, has been applied to all the weekly data of six variables. These variables are: X1 = the absolute maximum correlation coefficient between GNI and the economic ratios of the best period of their reaction to news indicators (Rt, P/E, P/B, β, and DY) for each week starting from the first week (except for Mar Cap,

Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

124

because the selection data of the first week were zeros, therefore we used the second week as reference data), which will be the same for the other variables; X2 = the absolute minimum correlation coefficient between GNI and the economic ratios for each week; X3 = the absolute maximum coefficient between BNI and the economic ratios for each week; X4 = the absolute minimum correlation coefficient between BNI and the economic ratios for each week; X5 = the maximum absolute value of the t-stat of the statistically significant correlation coefficient of GNI and BNI with economic ratios for each week; and X6 = the minimum or maximum values of GNI and BNI depending on the alpha value.

Table 1. List of tested companies. No.

Company

No.

Company

No.

Company

No.

Company

1

EIBANK

28

DANA

55

KCBK

82

BSC

2

AJMANBANK

29

NBK

56

NLCS

83

ABC

3

ALSALAMSUDAN

30

GBK

57

DBIS

84

BMB

4

AMLAK

31

CBK

58

QOIS

85

BARKA

5

CBD

32

ABK

59

IHGS

86

GFH

6

DIB

33

ALMUTAHED

60

IQCD

87

INVCORP

7

EIB

34

KIB

61

QEWS

88

ITHMR

8

EMIRATESNBD

35

BURG

62

MCCS

89

TAIB

9

GFH

36

KFIN

63

GISS

90

UGB

10

MASQ

37

BOUBYAN

64

91

BKSB

11

SALAM_BAH

38

UGB

65

92

SIHC

12

TAMWEEL

39

AUB

66

93

ABOB

13

ADIB

40

ITHMR

67

94

BKDB

14

ADCB

41

IKARUS

68

95

BKMB

15

BOS

42

MARIN

69

96

DIDI

16

CBI

43

IPG

70

97

MNHI

17

FGB

44

NAPESCO

71

98

NBOB

18

INVESTB

45

AREFENRGY

72

99

HBMO

19

FH

46

GPI

73

100

ONIC

20

NBAD

47

ABAR

74

1010 - Riyad Bank 1020 - Bank AlJazira 1030 - The Saudi Investment Bank 1040 - Saudi Hollandi Bank 1050 - Banque Saudi Fransi 1060 - The Saudi British Bank 1080 - Arab National Bank 1090 - Samba Financial Group 1120 - Al Rajhi Bank 1140 - BANK ALBILAD 1150 - Alinma Bank

101

TGII

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125

No.

Company

No.

Company

No.

Company

No.

Company

21

NBQ

48

QNBK

75

102

MHAS

22

RAKBANK

49

QIBK

76

103

MGMC

23

NBF

50

CBQK

77

2080 - National Gas & Industrialization Co. 5110 - Saudi Electricity Company AUB

104

OOMS

24

SIB

51

DHBK

78

BISB

105

NGCI

25

UAB

52

ABQK

79

BBK

26

UNB

53

QIIK

80

NBB

27

TAQA

54

MARK

81

SALAM

Source: own research

Table 1 shows the list of all the companies that were used in our article. These companies include the companies of the bank and energy sectors for all GCC stock markets. The classification was done for all the economic ratios. The classification of the GCC stock market companies (DFM, ADX, KSE, DSM, Tadawul, BSE, and MSM) for the bank and energy sectors are done according to their coordinates in four groups. The groups were determined in the basis of three threshold values, where class4 = (the worst) below (the mean of the coordinates minus standard deviation), class3 = between this value and mean, class2 = between mean and (mean plus standard deviation), class1 = (the best) above the mean plus standard deviation. Table 2 shows the first week classification of DY, while Table 4 shows the first week classification of P/B. Table 3 shows the last week classification of DY, while Table 5 shows the last week classification of P/B. Matlab R2009a software was used for the estimation of these measurements. Table 2. First week classification of DY.

Class1

Class2

Class3

Class4

CBD

ALSALAMSUDAN

EIBANK

Empty cell

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Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

DIB

EMIRATESNBD

AJMANBANK

BOS

ADIB

AMLAK

FGB

ADCB

EIB

INVESTB

NBAD

GFH

FH

SIB

MASQ

NBQ

UAB

SALAM_BAH

RAKBANK

ALMUTAHED

TAMWEEL

NBF

KIB

CBI

UNB

BURG

DANA

Class1

Class2

Class3

TAQA

KFIN

NBK

ABK

AUB

GBK

CBQK

IKARUS

CBK

KCBK

QNBK

BOUBYAN

NLCS

QIBK

UGB

DBIS

DHBK

ITHMR

IHGS

QIIK

MARIN

MARK

IPG

QOIS

NAPESCO

IQCD

AREFENRGY

GISS

GPI ABAR ABQK QEWS MCCS 1010 - Riyad Bank 1020 - Bank AlJazira 1030 - The Saudi Investment Bank 1040 - Saudi Hollandi Bank 1050 - Banque Saudi Fransi

Class4

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1060 - The Saudi British Bank 1080 - Arab National Bank 1090 - Samba Financial Group 1120 - Al Rajhi Bank

Class1

Class2

1140 ALBILAD Class3

BANK

1150 - Alinma Bank 2080 - National Gas & Industrialization Co. 5110 - Saudi Electricity Company AUB BISB BBK NBB SALAM BSC ABC BMB BARKA GFH' INVCORP ITHMR TAIB UGB BKSB SIHC' ABOB

Class4

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Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

BKDB BKMB DIDI MNHI NBOB HBMO ONIC TGII MHAS MGMC OOMS NGCI Source: own research

Table 2 shows the first week classification of DY. In this table, it is obvious that there is no company in the first week that is not influenced by news indicators, which means that all the companies are influenced by news indicators but to different degrees, with a minimum number of companies in class1, a higher number in class2, and the highest number of companies in class3. Table 3. Last week classification of DY.

Class1 DIB

Class2 ALSALAMSUDAN

Class3 CBD

Class4 EIBANK

ADIB

EMIRATESNBD

INVESTB

NBAD

ADCB

FH

AJMANBAN K AMLAK

TAQA

BOS

SIB

EIB

AUB

FGB

UNB

GFH

DBIS

NBQ

NBK

MASQ

1010 - Riyad Bank 1020 - Bank AlJazira 1060 - The Saudi British Bank

RAKBANK

KFIN

NBF

IKARUS

SALAM_BA H TAMWEEL

UAB

DHBK

CBI

Chapter title

129

Class1 1080 - Arab National Bank 1090 - Samba Financial Group NBB

Class2 ABK

Class3 KCBK

Class4 DANA

ALMUTAHED

IHGS

GBK

KIB

MCCS

CBK

BKSB

BURG

BOUBYAN

DIDI

NAPESCO

1030 - The Saudi Investment Bank ONIC

NBOB

QNBK

ITHMR

MHAS

QIBK

MARIN

NGCI

CBQK

IPG

QIIK MARK

AREFENRG Y GPI

NLCS

ABAR

QOIS

ABQK

IQCD

1140 BANK ALBILAD 1150 Alinma Bank BISB

QEWS GISS

UGB

1040 - Saudi Hollandi Bank 1050 - Banque Saudi Fransi 1120 - Al Rajhi Bank

SALAM

2080 - National Gas & Industrialization Co. 5110 Saudi Electricity Company AUB

BMB

BSC ABC

GFH INVCORP

130

Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

Class1

Class2 BBK

Class3

Class4 ITHMR

BARKA

TAIB

BKDB

UGB

BKMB

SIHC

MNHI

ABOB

HBMO

TGII

MGMC OOMS Source: own research

Table 3 shows the last week classification of DY. In this table, it is clear that class1 has the minimum number of companies that are influenced the most by news indicators in the first week. The other companies are influenced by news indicators, but at different levels, with a minimum number in class3, a higher number in class4, and the highest number of companies in class2. Based on the first week and last week classifications, it is concluded that some of these companies are influenced the same by news indicators, such as DIB and TAQA, and others are not. Table 4. First week classification of P/B.

Class1 DIB

Class2 AJMANBA NK CBD

Class3 SALAM_BAH

Class4 EIBANK

CBI

ALSALAMSUDAN

GFH

NBQ

AMLAK

ADCB

ADIB

NBK

EIB

FGB

BOS

CBK

MASQ

INVESTB

NBAD

BURG

UGB

FH

RAKBANK

KFIN

1010 - Riyad Bank

NBF

SIB

BOUBYAN

1020 - Bank AlJazira

UNB

UAB

IKARUS

TAQA

GBK

NAPESCO

1030 The Saudi Investment Bank 1040 - Saudi Hollandi Bank

DANA

ALMUTAH

GPI

1050 - Banque Saudi Fransi

EMIRATES NBD TAMWEEL

Chapter title

131

Class1

Class2 ED

Class3

Class4

ABK

AUB

ABAR

KIB

ITHMR

MARK

1060 - The Saudi British Bank 1080 - Arab National Bank

ITHMR

MARIN

KCBK

BKDB

IPG

QOIS

1090 - Samba Financial Group 1120 - Al Rajhi Bank

NBOB

MCCS

1140 - BANK ALBILAD

HBMO

AREFENR GY QNBK

AUB

1150 - Alinma Bank

MHAS

QIBK

NBB

CBQK

SALAM

DHBK

SIHC

2080 - National Gas & Industrialization Co. 5110 - Saudi Electricity Company BSC

ABQK

BKMB

ABC

QIIK

TGII

BMB

NLCS

INVCORP

DBIS

TAIB

IHGS

UGB

IQCD

MNHI

QEWS

NGCI

GISS BISB BBK BARKA GFH BKSB ABOB DIDI ONIC

132 Class1

Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

Class2 MGMC

Class3

Class4

OOMS Source: own research

Table 4 shows the first week classification of P/B. In this table, it is obvious that class1 has the minimum number of companies that are influenced the most by news indicators in the first week. The other companies are influenced by news indicators, but at different levels, with a minimum number in class3, a higher number in class4, and the highest number of companies in class2. Table 5. Last week classification of P/B.

Class1

Class2

Class3

Class4

DIB

EIBANK

AJMANBANK

AMLAK

ADIB

GFH

EIB

IPG

MASQ

ALSALAMSUD AN CBD

DBIS

TAMWEEL

EMIRATESNBD

FH

1020 - Bank AlJazira 1060 - The Saudi British Bank 1140 - BANK ALBILAD 2080 - National Gas & Industrialization Co. SALAM

FGB

SALAM_BAH

NBAD

NBF

ADCB

NBQ

TAQA

CBI

UAB

GBK

INVESTB

DANA

ABK

RAKBANK

ALMUTAHED

BSC

KIB

SIB

BOUBYAN

ITHMR

BURG

UNB

ITHMR

BKSB

UGB

NBK

ABAR

SIHC

AUB

CBK

DIDI

MARIN

KFIN

1010 - Riyad Bank INVCORP

NBOB

NAPESCO

IKARUS

ABOB

HBMO

AREFENRGY

QNBK

OOMS

BOS

Chapter title

133

Class1

Class2

Class3

TGII

GPI

DHBK

QIBK

ABQK

CBQK

KCBK

QIIK

QOIS

MARK

IHGS

NLCS

QEWS

IQCD

MCCS

1030 - The Saudi Investment Bank 1090 - Samba Financial Group 1120 - Al Rajhi Bank 1150 - Alinma Bank NBB

GISS

ABC

1040 - Saudi Hollandi Bank 1050 - Banque Saudi Fransi 1080 Arab National Bank 5110 - Saudi Electricity Company AUB

BMB

BISB

BARKA

BBK

GFH

BKDB

TAIB

BKMB

UGB

MNHI

NGCI

ONIC

Class4

MHAS MGMC Source: own research

Table 5 shows the last week classification of P/B. In this table, it is clear that class4 has the minimum number of companies that are influenced the least by news indicators in the first week. The other companies are influenced by news indicators, but at different levels, with a minimum number in class1, a higher

134

Salam Al-augby, Kesra Nermend, Sebastian Majewski, Agnieszka Majewska

number in class2, and the highest number of companies in class3. Based on the first week and last week classifications, it is concluded that some of these companies are influenced the same by news indicators, such as DIB and TAQA, and others are not. The level of influence by the news indicators are varied along all the weeks.

4. Conclusions Stock market is a complex system and very difficult to predict and there are many factors can be affected the stock market movement. News can be considered as a one of the influencing factors that plays a significant role in individual perception and how individuals react to these events is the media. In this article the news indicators GNI, BNI and, NNI are used to evaluate the influence of news in GCC stock market ratios of banking and energy sectors. VMCM is used as classification method for classified dynamic changes of stock market related to media influence. In this article the classification method is used for estimating the dynamic changes of stock market ratios for the last quarter of 2012 by dividing this period into 9 weeks, starting from the first week (except for Mar Cap, because the selection data of the first week were zeros, therefore we used the second week as reference data). According to the results of this estimation one can observed that some of the companies are influenced the same by news indicators, such as DIB and TAQA, and others are not. The level of influence by the news indicators are varied along all the weeks. This dynamic classification can give as indications of the stable and non-stable companies in term of its influence by tragic news.

Acknowledgment: The authors would like to thank Iraqi ministry of higher education and scientific research and faculty of economic and management in Szczecin University for their support and helped in shaping this final draft.

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