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IJRAR19L1902 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 167

THE RELATIONSHIP BETWEEN FACEBOOK

USAGE AND ACADEMIC PERFORMANCE OF

ADIGRAT UNIVERSITY STUDENT

Birhane Gebrekidan Tekle

DEPARTMENT OF PSYCHOLOGY

ADIGRAT UNIVERSITY, ADIGRAT, ETHIOPIA

Abstract: The main purpose of this study was to investigate the influence of Facebook usage on

students’ academic performance in ADU (college of business and economics). The data were

collected from randomly selected three hundred twenty-four students through self-reported

questionnaire. The analysis was done by using SPSS version 20. This study used both descriptive

statistics and inferential statistic the descriptive statistic was used to analyze the prevalence of face

book use in the college and the purpose why the students used face book. At the same time the

inferential statistic was used to measure the difference of students face book usage based on their

sex and the effect of face book on academic performance by comparing with non-users using

independent T test.

The finding of the present study revealed that the prevalence of face book usage was relatively

more at male students than the female students, in terms of purpose the students use Facebook for

the following purposes such as entertainment, chatting with friends and education respectively.

when comparing Facebook usage in terms of time between genders, the finding of the current

study showed that there was no significant difference between females and male usage of face

book. Finally, the study revealed that there is a statistically significant difference in academic

performance between Facebook users and non-users. Those non user have better performance than

Facebook users.

Index Terms - Facebook, Academic performance,

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Introduction

Social Media is a form of computer mediated communication which runs through web 2.0 technology. Web 2.0

facilitate the creation and up gradation of online social network sites in digital environment. According to Kaplan

and Haenlein, (2010) social media” are “a group of Internet-based applications that build on the ideological and

technological foundations of Web 2.0 which allows the creation and exchange of user-generated content”. Social

media refers to activities, practices, and behaviors among communities, who gather online information,

knowledge, and feedback to share and it is based on web- based applications that provide the utility to create and

transmit content in the form of text, picture, videos, and audios (Safko and Brake, 2009).

Therefore, social media are just like other media, a means of communicating and exchanging information. Most

social media services provide features like chatting, commenting, voting, updating status and sharing of

information for their users. The primary feature of the social media is staying connected continuously with more

than one person at the same time. Social media made it easy to share photos, videos, ideas, likes and dislikes, with

the world and also made it fast to know what people commented on them. Clearly the term „social networking

sites‟ and social media‟ are used interchangeably (Boyd & Ellison, 2007).

Lenhart and Madden (2007) define social networks as “spaces on the internet where users can create a profile and

connect that profile to others to create a personal network.” The first recognizable social network site was

launched in 1997. Currently there are hundreds of social networking sites across the globe, supporting a spectrum

of practices, interests and users.

Social networking websites, such as Facebook, Myspace, Whatsapp, Linkedin, Friendster, Live Journal, and Bubo,

are member-based internet communities that allow users to post profile information such as a username and

photography, and to communicate with others in innovative ways such as sending public or private online

messages or sharing photos online (Ellison & Steinfield, 2007). Social networking sites have implemented a wide

variety of technical features; their backbone consists of visible profiles that display an articulated list of friends

who are also users of the system. Profiles are unique pages where one can “type

oneself into being” (Sunden, 2003). After joining a social networking site, an individual is asked to fill out forms

containing a series of questions. The profile is generated using the answers to these questions, which typically

include descriptors such as age, location, interest, and “about me” section. Most sites also encourage users to

upload a profile photo. Some sites allow users to enhance their profile by adding multimedia content or modifying

their profile’s look and feel. Others, such as Facebook, allow users to add applications that enhance their profile

(Boyd, 2006)

The way human beings interact has rapidly changed over the last decade due to online social networking sites such

as Facebook. These Web-based systems allow members to connect with other members electronically, while also

allowing them to make these connections and interactions publicly. Facebook is the leading site among social

media (Duggan, Ellison, Lampe, Lenhart & Madden, 2015) First it was founded by Mark Zuckerberg and college

cohorts in 2004. It was limited to Harvard University students (Croft, 2007). But currently above 2.23 billions of

peoples are Facebook users in the world (Internet World Stats, 2018). It allows people to collaborate with each




other, create profiles, share pictures and information, write comments and likes or shares posts. People usually add

and follow their friends, family, and browsing the flow of updates on their profiles. Similarly, even when people

are a thousand miles away from their loved ones, they can still share their daily stories with others (Boyd and

Ellison et al, 2007). Since Facebook has made it so easy for students to present themselves as well as learn about

another person online, exposure to this type of media has influences on student’s academic performance positively

or adversely (Banquil and Chua, 2009).

According to Tuckman (1975) Academic performance defined as the apparent demonstration of understanding,

concepts, skills, ideas and knowledge of a person and proposed that grades clearly depict the performance of

students. Hence, their academic performance must be managed efficiently keeping in view all the factors that can

positively and negatively affect their educational performance. For example, a study conducted by Moghavvemi,

Aziz and Sulaiman (2017) examines how spending time on Facebook affect students’ academic performance.

They found that spending time on Facebook has a positive effect on their academic performance, which indicated

the time spent on Facebook did not affect their education.

According to a study conducted by Hargitta (2008) there is no evidence correlating Facebook with academic

achievement (as cited in Kirschner, Aryn & Karpinsk, 2010). Similarly, Pasek (2009) found that there was no

relation between Facebook use and academic performance of students. In contrary of, Owusu-Acheaw, & Larson

(2015) did a study to assess students‟ use of social media and its effect on their academic performance. The study

revealed that majority of the respondents had mobile phones and visited their social media sites using their phones

and spent between thirty minutes to three hours per day. In addition, the study revealed that the use of social media

sites had affected academic performance of the respondents negatively and that there was indirect relationship

between the use of social media sites and academic performance (Owusu-Acheaw, & Larson et al, 2015).

In the context of Ethiopia, more researchers are interested in college and university students. For example,

Negussie and Ketema (2014) examined the relationship between Facebook practices on academic performance of

students and found no significant relationship between times spent on social networks such as Facebook with

students. Similarly, studies carried out by Bedassa (2014) to assess the impact of social networking sites i.e.

Facebook on students’ academic performance, administering a questionnaire on 384 regulars under graduate

Wollega University students, revealed that time spent on Facebook and addiction to it negatively and significantly

affects students‟ academic performance.

Today, a number of Ethiopians are joining and using Facebook every day. It has been the largest visited social

networking site in Ethiopia. According to the Internet World Stats (2018) Ethiopia has an estimated of

107,534,882 populations, out of which 16,437, 881 are internet users. Also, currently, there are more than

4,500,000 Facebook users in Ethiopia, and this makes the 10th in the rank of Africa Facebook statistics by

country. But still Ethiopia has the least Facebook penetration rate that is 15.3% (Internet World Stats, 2018).

According to Digital World Statistics (2017) the total numbers of monthly active Facebook users in Ethiopia are

3.3 million, and 22% of users are using Facebook each day. Also 91% of users are accessing via mobile phone.

Regarding Facebook usage a study by Lenhart and Madden (2007) indicated that 73% of adolescents between 18

above years had their own profile in social network media sites. In the frequency of use, 59.4% of students visited




a social networking site several times a day (Sponsil and Gitimu, 2012). In Ethiopia the largest age group

proportions of Facebook users are currently 25-34 followed by the users in the age of 18-24 and live in the urban

areas of the country (social baker, 2018). Local researches, also revealed that compared to other youth sectors

found in Addis Ababa, high school students are found to be the major Facebook users. This revealed that more

95% half of the high school students were using social networking sites specially Facebook (Gedion, 2011).

Another study conducted by Mohammed (2014) indicated that secondary and preparatory school students of 15 –

21 age in general Addis Ababa, particularly in Bole sub city, using Facebook whether at the internet café or via

mobile phone.

According to Kuppuswamy and Shankar (2010), Facebook grasps the total attention and concentration of the

students and divert them towards non educational, unethical and inappropriate actions such as useless chatting and

posting, time killing by random searching and not doing their jobs on time. Also, they use Facebook for fun,

posting photo, for entertainment, playing games and reading the news story in very short sentences (Ellison,

Steinfield, and Lampe 2007). Similarly, Olubiyi (2012) noted that these days‟ students are engaged in the social

networking sites that they are almost 24 hours online.

Since its existence, the numbers of users have been increasing day by day and especially among the students, they

are facing a lot of neglect and challenges on their academic performance and have certainly brought about rapid

decline in education (Kuppuswamy and Shankar et al, 2010). Students give more attention and time to Facebook

than they do to their studies and they spend their time chatting and making friends via Facebook and this might

definitely have influence on their academic performance, because when you do not read, there was no way you can

perform academically (Osharive, 2015) .

It was therefore of great importance to explore some of the issues facing students’ academic performance as a

result of Facebook usage. Thus, this study is investigating the relationship between Facebook usage and the

academic performance of the students. Hence the article explores the following problems:

What is the prevalence of social media Adigrat university students?

What is the purpose of Facebook use for the students?

Is there any significant difference based on sex on Facebook usage?

Is there any academic performance difference between Facebook users and non-users?

RESEARCH AREA

This study was delimited to Adigrat university. Adigrat university (ADU) is one among the new university opened

in Ethiopia in 2000 it is located in the north of Ethiopia in Tigray reginal state which is about one thousand K.m

far from Addis Ababa capital of Ethiopia.

The university was selected because the researcher worked in the selected university and the situations were

assumed to be relatively convenient for data collection. Also, this study focuses on Facebook than other type of

social media networking sites because today Facebook is constantly available through portable mobile devices

such as smart phones and has become an integrated part of adolescents‟ social life (Crone and Konijn, 2018).




METHOD

In this study, a quantitative method was employed to gather the data. It allows the researcher to investigate the

relationship between variables and generalized real world settings and it also can analyze the data using

mathematically based method, in particular Statistics (Muijs, 2004). The researcher used descriptive statistics with

correlational method as a design and inferential statistic because this study was aimed to describe the prevalence

and purpose of Facebook use, and also to examine the difference between Facebook users and non-usurers at

academic performance, and see which group of sex is more user face book. In Adigrat university there is about

twenty thousand students in about six colleges naming: Technology, natural and computational science,

agriculture, social and humanity science, business and economics. Hence for the purpose of feasibility the

researcher used simple lottery method and select one college that is business and economics.

in the college there are about 2495 total students in the in the academic of 2020. Then to determine the sample

size Yamane (1967) formula is used.

• 𝐧 =𝑵

𝟏+𝑵∗(𝒆)𝟐

• n=sample size

• N=Population

• E=acceptable sampling error

• *=95%confidence level and p=0.05 is assumed

• Then base on the above formula the sample size is 324 taken.

As the fact the instrument was self-developed tools to ensure the validity and reliability the researcher used

different measures. For example, Validity was checked by the help of ADU psychology department staff then such

as face validity and content validity were checked in relation the purpose of the research similarly pilot test was

conducted to ensure the reliability.

The pilot study was up on another college 30 students. The pilot test showed that the questionnaires were

unambiguous and did not create any confusion. Therefore, all 30 students responded and accomplished the

questionnaire.

The response of the participants was analyzed to calculate reliability.

The reliability of the questionnaires (scale) was checked by scoring and tabulating the participants‟ response using

SPSS version 20. In order to determine the reliability coefficient of the instrument, the Cronbach Alpha Reliability

Coefficient was used. This value was found to be α =.89. This coefficient value indicates that the instrument was

reliable.




Data analysis and Interpretation

Table 1: Demographic Characteristic of Participants

Demographic

characteristics

Categories Frequency Percent

Sex Male 262 77.7

Female 72 22.3

Total 324 100.0

Facebook Users 227 70.1

Non users 97 29.9

Total 324 100.0

As indicated in the table 1 above, the total number of participants in the distributed questionnaire was 324 and all

questionnaires were filled completely and consistently with a response rate of 100%. Among the total respondents

who filled out the questionnaire, the two sexes were distributed with 252 (78%) males and 72 (22%) females.

Furthermore, the majority of the respondents in ADU (college of business and economics) used Facebook 227

(70.1%) and the rest 97 (29.9%) did not use Facebook.

Table 2: Purposes of Facebook Use for the Students

Alternatives Frequency Percent

For entertainment 80 35.2

Education 26 11.5

Passing time 17 7.5

Chatting with friends 34 15

Making new friendship 17 7.5

Entertainment, passing time and chatting 22 9.7

Entertainment, education, passing time, chatting and 11 4.8

Other reasons 20 8.8

Total 227 100

As it is displayed in the above table 2, a high number of the students stated that they use face book for

entertainment, with 80 students (35.2%) cited this reason. Other 34 (15%) respondents reported using face book

for communication chatting with friends and this is the second most stated purpose for using face book. The

number of students who use Facebook for education were 26 (11.5%) and 22 (9.7%) used Facebook for

Entertainment, passing time and chatting, and 17 (7.5%) use it for Passing time. The number of respondents who




sated that they used Facebook for Entertainment, education, passing time, chatting and making new friendship

were 11(4.8) and the last 20 (8.8%) were used Facebook for other purposes.

Table 3: Independent sample t-test for differences in the usage of Facebook as a function of

sex Category (n = 252 males and 72 females).

Variable Sex M SD t df p

Facebook usage

Male

33.19

11.16

-.702 225 .484

Female 34.26 11.73

As the above table shows that, an independent t-test was conducted to compare the Facebook usage scores for

male and females. There was no significant difference in scores for males (M = 33.19, SD = 11.16) and females

(M = 34.26, SD = 11.73; t (227) = -.702, p = .484, two-tailed). The magnitude of the difference in the means

(mean difference = -1.068, 95% CI: -4.07 to 1.93) was very small (eta squared = .002).

Table 4: Comparison between Facebook Users and Non users in their Academic

Performance (Difference among face book users and non-users N = 324)

Variable Facebook M SD t df p

Academic performance

User

70.28

10.61

-3.89 322 .000

Non user 75.02 8.50

As the above table shows, an independent t-test was conducted to compare the academic performance of face book

users and non-users. There was statistically significant difference in academic performance scores for users and

non-users (M = 75.02, SD = 8.50; t (227) = - 3.899, p = .001, two-tailed). The magnitude of the difference in the

means (mean difference = -4.744, 95% CI: -7.138 to -2.350) was very small (eta squared = .045). So, only 4.5% of

the variance in academic performance was explained by face book usage.

Discussion

Prevalence of Facebook Use based on Sex

The first question of this study was focused on the prevalence of Facebook usage based on sex. Firstly, the finding

of the present study revealed that male students were relatively more users of Facebook than the female students.

Thus, this finding is supported by Lin and Subrahmanyam, (2007) who conclude that boys have been online more

than girls. Similarly, Hsu and Chuang (2008) conducted a study on secondary school students and reported that

male students used social networking sites more than female students did.




Purposes of Facebook Usage

The second question of this study focused on the purpose of Facebook used by ADU (college of business and

economic) students. In this study the majority of participants indicated using Facebook for the following purposes

such as entertainment, chatting with friends and education respectively. Hence, this finding supports the previous

findings of Sheldon (2008) and Singh and Gill (2015) which studied that the main purpose of using face book for

students was entertainment and to pass time. Similar results from a local study conducted by Mohammed (2014)

also revealed that majority of students used Facebook for entertainment, communication with people far away and

making companionship respectively. That means students used Facebook for entertainment than educational

purpose. However, this finding does not support the previous findings of Kassahun (2014), Eke, Omekwu and

Odoh (2014), Sponcil and Gitimu (2013), Nazir (2014) and Madhusudhanv (2013) who found that students use the

Facebook as a way to keep contact with friends and family, chatting with friends and academic related activities.

The possible reasons for this difference might be the purpose that students using Facebook are in line with Use and

Gratification theory which says that audience purposively selects media to satisfy their needs. Therefore,

adolescents used social networking sites like Facebook because adolescents use media for entertainment, tension

relief, information sharing, passing time, social interaction, learning about the world, sensation seeking and escape

from loneliness as the major reasons to use media (Whiting & Williams, 2013, Papacharissi & Mendelson, 2011).

Sex difference in Facebook Usage among students

The fourth question of this study focused on students‟ Facebook usage difference based on sex. First, when

comparing Facebook usage between genders, the finding of the current study shows that there was no significant

difference between females and male usage of face book. This implies that gender does not determine face book

usage. This finding is in line with the finding of Omolayo, Balogun & Omole (2013) which noted that, there is no

significant difference between males and females‟ usage of face book. However, this finding does not support the

previous findings of Khan (2010), Aghazamani (2010), and Shen and Khalifa (2010) who found that male students

use face book than females. Other researchers, Tufekci (2008), Rudi & Dworkin (2014), Kittinger, Correia, &

Irons (2012) suggest that females are more likely to use Facebook than males. This is also not consistent with the

findings of the current study.

Difference between Facebook Users and Non users in their

Academic Performance

To examine whether there is an academic performance difference between Facebook users and non-users. Hence,

the present study revealed that there is a statistically significant difference in academic performance between

Facebook users and non-users. This finding is in keeping with the study of San Miguel (2009) and Kirschner and

Karpinski (2010) who found out Facebook users reporting lower academic performance than face book non users.




Reference

Aghazamani, A. (2010). How Do University Students Spend Their Time on Facebook? An

Exploratory Study. Journal of American Science , 6 (12), 730-735.

Ainin, S., Naqshbandi, N. M., Moghavvemi, S., Jaafar, N. I. (2015). Facebook Usage,

Socialization and Academic Performance. Computers & Education , 64 (2015), 64-

73.

Akhtar, N. (2013). Relationship between internet addiction and Academic Performance

among University Undergraduates. Educational Research and Reviews , 8 (19), 1797-

1796.

Alexander, M. (2012). Facebook usage and Academic Achievement of High School

Students: a Quantitative Analysis (Doctoral dissertation) unpublished. Pepperdine

University

Araya, T. (2018). Motivators and Challenges of Social Media Use for Political

Communication in Addis Ababa. Addis Ababa University. Addis Ababa: (Master‟s

thesis) unpublished.

Ball -Rokeach, S.J., & DeFl eur, M. L. (1976). A dependency model or mass -media effects.

Communication Research , 3 (1), 3-21.

Barker,C., Pistrang, N., & Elliott, R. (2002). Research methods in Clinical Psychology: An

Introduction for students and practioners (2nded.). England: John Wiley &Sons Ltd

Burke M., Marlow C., & Lento T. (2010). Social Network Activity and Social Well-Being.

CHI2010: Social Media Users, 1909-12.

Duggan, M., Ellison, N., Lampe, C., Lenhart, A., & Madden, M. (2015). Social Media

Update 2014.

Ellison, N. B. , Steinfield, C., & Lampe, C. (2007). The benefits of Facebook "friends:"Social

capital and college students use of online social network sites. Journal of ComputerMediated Communication, 12,

1143-1168.

Facebook. (2018). Facebook about: Info. Retrieved from http://www.facebook.

com/facebook#!/facebook=info

Facebook by the numbers: Stats, Demographics & Fun Facts. (2019). Retrieved from;

https://www.omnicoreagency.com/facebook-statistics/

Gedion, T. (2011).The role of Facebook in building the social capital of youth living in Addis

Ababa.(Master‟s thesis).Addis Ababa University, Addis Ababa.


https://www.omnicoreagency.com/facebook-statistics/



Internet World Stats. (2018). Internet users, population and Facebook statisticsinthe World.

Retrieved from http://www.internetworldstats.com

Internet World Stats. (2018). Internet users, population and Facebook statistics forAfrica.


Internet World Stats. (2018). Internet users,population andFacebook statistics forEthiopia.


Junco, R. (2012). Too much face and not enough books: The relationship between multiple

indices of facebook use and academic performance. Computers & Education, 28,187-

198.

Kaplan A. &Haenlien M. (2010) Users of the world, unite!; The challenges and opportunities

of Social media; Business Horizons (53) pp. 59-68

Kasshun, G. (2014).The Relationship of Social Networking to Adolescent Psychosocial

Adjustment in Selected Preparatory Schools of Addis Ababa (Master‟s thesis)

unpublished. Addis Ababa University. Addis Ababa.

Lenhart, A., Purcell, K., Smith, A., &Zickuhr, K. (2010).Social media and mobile Internet

use among teens and young adults. Pew Internet & American Life Project. Retrieved

from: http://www.pewinternet.org/~/media//Files

Lerner, M., & Steinberg, L. (Eds). (2004). Handbook of adolescent psychology (2nd ed).

John Wiley & Sons INC.

Madhusudhan,M. (2013). Use of social networking sites by research scholars of the

University of Delhi: A study. The International Information & Library Review, 44(2).

DOI:10.1080/10572317.2012.10762919

Mansfield, P. (2009). College students and academic performance: A case of taking control.

NASPA Journal, 46(3), 502-518.

Moghavvemia,S., Sulaimana, A., and Aziz, A. (2017). The Impact of Facebook Usage on

Academic Performance. Retrieved from https://www.researchgate.net.

Nazir, T. (2014). Use of Social Networking Sites by the Secondary and Higher Secondary

School Students of Srinagar, Kashmir. International Journal of Digital Library

Services , 231-242. International Journal of Digital Library Services, 4 (-3), 231-242.

Omolayo, B. O., Balogun, S. K., & Omole, O. C. (2013). Influence of exposure to Facebook

on self-esteem. European Scientific Journal, 9(11)


http://www.internetworldstats.com/



http://www.pewinternet.org/~/media/Files

https://www.researchgate.net/



Osharie, P. (2015). Social media and academic performance of students, conference paper of

January 2015. Retrieved September, 2016 from https://www.researchgate.net/

publication/273765340.

Pasek, J., More, E., & Hargittai, E. (2009). Facebook and academic performance:

Reconciling a media sensation with data. http://www.uic.edu/htbin/cgiwrap

Paul, J. A., Baker, H. M. and Cochran, J. D. (2012). Effect of online social networking on

Student Academic Performance. Computers in Human Behaviour, 28(6), 2117-2127.

Rudi, J. & Dworkin.J. (2014). Youth online media use: Associations with youth

demographics, parental monitoring, and parent-child relationships. Journal of Youth

and Development, 9(1), 60-70.

Ruggiero, T. (2000).Uses and gratifications theory in the 21st Century. Mass Communication

& Society, 3(1), 3-37.

Safko, L. and Brake, D. (2009).The Social Media Bible. Tactics, Tools and Strategies for

business Success. Publisher, John Wiley and Sons. Hoboken, NJ, USA.

Sheldon, P. (2008). The relationship between unwillingness-to-communicate and students‟

Facebook use. Journal of Media Psychology, 20, 67-75.

Worku (2005). A Background Paper on Telecom and Telecom Statistics in Ethiopia.

Ethiopian Telecommunications Corporation.

.


http://www.uic.edu/htbin/cgiwrap



I. EASE OF USE

Thetemplateisusedtoformatyourpaperandstylethetext.Allmargins,columnwidths,linespaces,andtextfontsareprescribed;pleasedonotalterth

em.Youmaynotepeculiarities.Forexample,theheadmargininthistemplatemeasuresproportionatelymorethaniscustomary.Thismeasurementand

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otreviseanyofthecurrentdesignations.

II. PREPARE YOUR PAPER BEFORE STYLING

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hasbeenformattedandstyled.Donotusehardtabs,andlimituseofhardreturnstoonlyonereturnattheendofaparagraph.Donotaddanykindofpaginatio

nanywhereinthepaper.Donotnumbertextheads—thetemplatewilldothatforyou.

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mmar.

AbbreviationsandAcronyms(Heading2)

Defineabbreviationsandacronymsthefirsttimetheyareusedinthetext,evenaftertheyhavebeendefinedintheabstract.AbbreviationssuchasIEE

EandSIdonothavetobedefined.Donotuseabbreviationsinthetitleorheadsunlesstheyareunavoidable.

3.1Population and Sample

KSE-100 index is an index of 100 companies selected from 580 companies on the basis of sector leading and market

capitalization. It represents almost 80% weight of the total market capitalization of KSE. It reflects different sector company’s

performance and productivity. It is the performance indicator or benchmark of all listed companies of KSE. So it can be regarded as

universe of the study.Non-financial firms listed at KSE-100 Index (74 companies according to the page of KSE visited on 20.5.2015) are

treated as universe of the study and the study have selected sample from these companies.

The study comprised of non-financial companies listed at KSE-100 Index and 30 actively traded companies are selected on the

bases of market capitalization.And 2015 is taken as base year for KSE-100 index.

3.2 Data and Sources of Data For this study secondary data has been collected. From the website of KSE the monthly stock prices for the sample firms are

obtained from Jan 2010 to Dec 2014. And from the website of SBP the data for the macroeconomic variables are collected for the period

of five years. The time series monthly data is collected on stock prices for sample firmsand relative macroeconomic variables for the

period of 5 years. The data collection period is ranging from January 2010 to Dec 2014. Monthly prices of KSE -100 Index is taken from

yahoo finance.

3.3 Theoretical framework

Variables of the study contains dependent and independent variable. The study used pre-specified method for the selection

ofvariables. The study used the Stock returns are as dependent variable. From the share price of the firm the Stock returns are calculated.

Rate of a stock salable at stock market is known as stock price.

Systematic risk is the only independent variable for the CAPM and inflation, interest rate, oil prices and exchange rate are the

independent variables for APT model.

Consumer Price Index (CPI) is used as a proxy in this study for inflation rate. CPI is a wide basic measure to computeusualvariation in

prices of goods and services throughout a particular time period. It is assumed that arise in inflation is inversely associated to security

prices because Inflation is at lastturned into nominal interest rate andchange in nominal interest rates caused change in discount rate so

discount rate increase due to increase in inflation rate and increase in discount rateleads todecreasethe cash flow’s present value

(Jecheche, 2010). The purchasing power of money decreased due to inflation, and due to which the investors demand high rate of return,

and the prices decreased with increase in required rate of return (Iqbal et al, 2010).

Equations

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pedusingeithertheTimesNewRomanortheSymbolfont(pleasenootherfont).Tocreatemultileveledequations,itmaybenecessarytotreattheequatio

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1,usingarighttabstop.Tomakeyourequationsmorecompact,youmayusethesolidus(/),theexpfunction,orappropriateexponents.ItalicizeRomansy

mbolsforquantitiesandvariables,butnotGreeksymbols.Usealongdashratherthanahyphenforaminussign.Punctuateequationswithcommasorperi

odswhentheyarepartofasentence,asin

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theequation.Use “Eq.1” or “Equation1”, not “(1)”, especially atthebeginningofasentence: “Equation1is...”

I. RESEARCH METHODOLOGY

The methodology section outline the plan and method that how the study is conducted. This includes Universe of the study, sample of the

study,Data and Sources of Data, study’s variables and analytical framework. The detailsare as follows;




3.1Population and Sample

KSE-100 index is an index of 100 companies selected from 580 companies on the basis of sector leading and market

capitalization. It represents almost 80% weight of the total market capitalization of KSE. It reflects different sector company’s

performance and productivity. It is the performance indicator or benchmark of all listed companies of KSE. So it can be regarded as

universe of the study.Non-financial firms listed at KSE-100 Index (74 companies according to the page of KSE visited on 20.5.2015) are

treated as universe of the study and the study have selected sample from these companies.

The study comprised of non-financial companies listed at KSE-100 Index and 30 actively traded companies are selected on the

bases of market capitalization.And 2015 is taken as base year for KSE-100 index.

3.2 Data and Sources of Data For this study secondary data has been collected. From the website of KSE the monthly stock prices for the sample firms are

obtained from Jan 2010 to Dec 2014. And from the website of SBP the data for the macroeconomic variables are collected for the period

of five years. The time series monthly data is collected on stock prices for sample firmsand relative macroeconomic variables for the

period of 5 years. The data collection period is ranging from January 2010 to Dec 2014. Monthly prices of KSE -100 Index is taken from

yahoo finance.

3.3 Theoretical framework

Variables of the study contains dependent and independent variable. The study used pre-specified method for the selection

ofvariables. The study used the Stock returns are as dependent variable. From the share price of the firm the Stock returns are calculated.

Rate of a stock salable at stock market is known as stock price.

Systematic risk is the only independent variable for the CAPM and inflation, interest rate, oil prices and exchange rate are the

independent variables for APT model.

Consumer Price Index (CPI) is used as a proxy in this study for inflation rate. CPI is a wide basic measure to computeusualvariation in

prices of goods and services throughout a particular time period. It is assumed that arise in inflation is inversely associated to security

prices because Inflation is at lastturned into nominal interest rate andchange in nominal interest rates caused change in discount rate so

discount rate increase due to increase in inflation rate and increase in discount rateleads todecreasethe cash flow’s present value

(Jecheche, 2010). The purchasing power of money decreased due to inflation, and due to which the investors demand high rate of return,

and the prices decreased with increase in required rate of return (Iqbal et al, 2010).

Exchange rate is a rate at which one currency exchanged with another currency. Nominal effective exchange rate (Pak

Rupee/U.S.D) is taken in this study.This is assumed that decrease in the home currency is inverselyassociated to share prices

(Jecheche,2010). Pan et al. (2007) studied exchange rate and its dynamic relationship with share prices in seven East Asian Countries

and concludethat relationshipof exchange rate and share prices varies across economies of different countries. So there may be both

possibility of either exchange rate directly or inverselyrelated with stock prices.Oil prices are positively related with share prices if oil

prices increase stock prices also increase (Iqbal et al, 1012).Ataullah (2001) suggested that oil prices cause positive change in the

movement of stock prices. The oil price has no significant effect on stock prices (Dash & Rishika, 2011).Six month T-bills rate is used as

proxy of interest rate. As investors arevery sensitive about profit and where the signals turn into red they definitely sell the shares. And

this sensitivity of the investors towards profit effects the relationship of the stock prices and interest rate, so the more volatility will be

there in the market if the behaviors of the investors are more sensitive. Plethora (2002)has tested interest rate sensitivity to stock market

returns, and concluded an inverse relationship between interest rate and stock returns. Nguyen (2010) studies Thailand market and found

thatInterest rate has aninverse relationship with stock prices.

KSE-100 index is used as proxy of market risk. KSE-100 index contains top 100 firms which are selected on the bases of their

market capitalization. Beta is the measure of systematic risk and has alinear relationship with return (Horn, 1993). High risk is associated

with high return (Basu, 1977, Reiganum, 1981 and Gibbons, 1982). Fama and MacBeth (1973) suggested the existence of a significant

linear positive relation between realized return and systematic risk as measured by β. But on the other side some empirical results showed

that high risk is not associated with high return (Michailidis et al. 2006, Hanif, 2009). Mollah and Jamil (2003) suggested thatrisk-return

relationship is notlinear perhaps due to high volatility.

3.4Statistical tools and econometric models

This section elaborates the proper statistical/econometric/financial models which are being used to forward the study from data

towards inferences. The detail of methodology is given as follows.

3.4.1 Descriptive Statistics

Descriptive Statics has been used to find the maximum, minimum, standard deviation, mean and normally distribution of the

data of all the variables of the study. Normal distribution of data shows the sensitivity of the variables towards the periodic changes and

speculation. When the data is not normally distributed it means that the data is sensitive towards periodic changes and speculations which

create the chances of arbitrage and the investors have the chance to earn above the normal profit. But the assumption of the APT is that

there should not be arbitrage in the market and the investors can earn only normal profit. Jarque bera test is used to test the normality of

data.




3.4.2 Fama-Mcbeth two pass regression

After the test statistics the methodology is following the next step in order to test the asset pricing models. When testing asset

pricing models related to risk premium on asset to their betas, the primary question of interest is whether the beta risk of particular factor

is priced. Fama and McBeth(1973)develop a two pass methodology in which the beta of each asset with respect to a factor is estimated in

a first pass time series regression and estimated betas are then used in second pass cross sectional regression to estimate the risk premium

of the factor. According to Blum (1968) testing two-parameter models immediately presents an unavoidable errors-in-the variables

problem.It is important to note that portfolios (rather than individual assets) are used for the reason of making the analysis statistically

feasible.Fama McBeth regression is used to attenuate the problem of errors-in-variables (EIV) for two parameter models (Campbell, Lo

and MacKinlay, 1997).If the errors are in the β (beta)of individual security are not perfectly positively correlated, the β of portfolios can

be much more precise estimates of the true β (Blum, 1968).

The study follow Fama and McBeth two pass regressionto test these asset pricing models.The Durbin Watson is used to check

serial correlation and measures the linear association between adjacent residuals from a regression model. If there is no serial correlation,

the DW statistic will be around 2. The DW statistic will fall if there is positive serial correlation (in worst case, it will be near zero). If

there is a negative correlation, thestatistic will lie somewhere between 2 and 4. Usually the limit for non-serial correlation is considered to

be DW is from 1.8 to 2.2. A very strong positive serial correlation is considered at DW lower than 1.5 (Richardson and smith, 1993).

According to Richardson and smith(1993) to make the model more effective and efficient the selection criteria for the shares in

the period are: Shares with no missing values in the period, Shares with adjusted R2 < 0 or F significant (p-value) >0.05of the first pass

regression of the excess returns on the market risk premium are excluded. And Shares are grouped by alphabetic order into group of 30

individual securities (Roll and Ross, 1980).

3.4.2.1 Model for CAPM

In first pass the linear regression is used to estimate beta which is the systematic risk.

𝑅𝑖 − 𝑅𝑓 = (𝑅𝑚 − 𝑅𝑓)𝛽 (3.1)

Where RiisMonthly return of thesecurity, Rf isMonthly risk free rate, Rm isMonthly return of market and βis systematic risk (market risk).

The excess returns Ri - Rf of each security is estimated from a time series share prices of KSE-100 index listed shares for each

period under consideration. And for the same periodthe market Premium Rm - Rfalso estimated. After that regress the excess returns Ri -

Rf on the market premium Rm - Rfto find the beta coefficient (systematic risk).

Then a cross sectional regression or second pass regression is used on average excess returns of the shares and estimated betas.

Ȓ𝑖 = 𝛾0 + 𝛾1𝛽1 + є (3.2) Where ƛ0= intercept, ȒIis average excess returns of security i,βIisestimated be coefficient of security I and Є is error term.

3.4.2.2 Model for APT

In first pass the betas coefficients are computed by using regression.

𝑅𝑖 − 𝑅𝑓 = 𝛽𝑖𝑓1 + 𝛽𝑖2𝑓2 + 𝛽𝑖3𝑓3 + 𝛽𝑖4𝑓4 + 𝜖 (3.3)

Where Ri is the monthly return of stock i,Rf is risk free rate, βi is the sensitivity of stock i with factors and 𝜖 is the error term.

Then a cross sectional regression or second pass regression is used on average excess returns of the shares on the factor scores.

Ȓ = γ0 + γ1𝛽1 + γ2𝛽2 + γ3𝛽3 + γ4𝛽4 + 𝜖𝑖 (3.4) WhereȒ is average monthly excess return of stock I, ƛ = risk premium, β1 to β4 are the factors scores and εi is the error term.

3.4.3 Comparison of the Models

The next step of the study is to compare these competing models to evaluate that which one of these models is more supported

by data.This study follows the methods used by Chen (1983), the Davidson and Mackinnon equation (1981) and the posterior odds ratio

(Zellner, 1979) for comparison of these Models.

3.4.3.1 Davidson and MacKinnon Equation

CAPM is considered the particular or strictly case of APT. These two models are non-nested because by imposing a set of linear

restrictions on the parameters the APT cannot be reduced to CAPM. In other words the models do not have any common variable.

Davidson and MacKinnon (1981) suggested the method to compare non-nested models. The study used the Davidson and MacKinnon

equation (1981) to compare CAPM and APT.

This equation is as follows;

𝑅𝑖 = 𝛼𝑅𝐴𝑃𝑇 + (1 − 𝛼)𝑅𝐶𝐴𝑃𝑀 + 𝑒𝑖 (3.5)

WhereRi= the average monthly excess returns of the stock i, RAPT= expected excess returns estimated by APT, RCAPM= expected excess

returns estimated by CAPM and α measure the effectiveness of the models. The APT is the accurate model to forecast the returns of the

stocks as compare to CAPMif α is close to 1.

3.4.3.2 Posterior Odds Ratio

A standard assumption in theoretical and empirical research in finance is that relevant variables (e.g stock returns) have

multivariate normal distributions (Richardson and smith, 1993). Given the assumptionthat the residuals of the cross-sectional regression

of the CAPM and the APT satisfy the IID (Independently and identically distribution) multivariate normal assumption (Campbell, Lo and

MacKinlay, 1997), it is possible to calculate the posterior odds ratio between the two models.In general the posterior odds ratio is a more

formal technique as compare to DM equation and has sounder theoretical grounds (Aggelidis and Maditinos, 2006).

The second comparison is done using posterior odd radio. The formula for posterior odds is given by Zellner (1979) in favor of model 0

over model 1.

The formula has the following form;

𝑅 = [𝐸𝑆𝑆0/𝐸𝑆𝑆1]𝑁/2𝑁𝐾0−𝐾1/2 (3.6)




WhereESS0iserror sum of squares of APT, ESS1iserror sum of squares of CAPM, Nisnumber of observations, K0is number of

independent variables of the APT and K1 isnumber of independent variables of the CAPM.As according to the ratio when;

R> 1 means CAPM is more strongly supported by data under consideration than APT.

R < 1 means APT is more strongly supported by data under consideration than CAPM.

IV. RESULTS AND DISCUSSION

4.1 Results of Descriptive Statics of Study Variables

Table 4.1: Descriptive Statics

Table 4.1 displayed mean, standard deviation, maximum minimum and jarque-bera test and its p value of the macroeconomic variables of

the study. The descriptive statistics indicated that the mean values of variables (index, INF, EX, OilP and INT) were 0.020, 0.007, 0.003,

0.041 and 0.047 respectively. The maximum values of the variables between the study periods were 0.14, 0.02, 0.04, 0.41, 0.11 and 0.05

for the KSE- 100 Index, inflation, exchange rate, oil prices and interest rate.

The standard deviations for each variable indicated that data were widely spread around their respective means.

Column 6 in table 4.1 shows jarque bera test which is used to checkthe normality of data. The hypotheses of the normal distribution are

given;

H0 : The data is normally distributed.

H1 :The data is not normally distributed.

Table 4.1 shows that at 5 % level of confidence, the null hypothesis of normality cannot be rejected. KSE-100 index and macroeconomic

variables inflation, exchange rate, oil prices and interest rate are normally distributed.

The descriptive statistics from Table 4.1 showed that the values were normally distributed about their mean and variance. This indicated

that aggregate stock prices on the KSE and the macroeconomic factors, inflation rate, oil prices, exchange rate, and interest rate are all not

too much sensitive to periodic changes and speculation. To interpret, this study found that an individual investor could not earn higher

rate of profit from the KSE. Additionally, individual investors and corporations could not earn higher profits and interest rates from the

economy and foreign companies could not earn considerably higher returns in terms of exchange rate. The investor could only earn a

normal profit from KSE.

FiguresandTables

Placefiguresandtablesatthetopandbottomofcolumns.Avoidplacingtheminthemiddleofcolumns.Largefiguresandtablesmayspanacrossboth

columns.Figurecaptionsshouldbebelowthefigures;tablecaptionsshouldappearabovethetables.Insertfiguresandtablesaftertheyarecitedinthetext

.Usetheabbreviation“Fig.1” in the text, and “Figure 1” atthebeginningofasentence.

Use10pointTimesNewRomanforfigurelabels.Usewordsratherthansymbolsorabbreviationswhenwritingfigure-

axislabelstoavoidconfusingthereader.Asanexample,writethequantity “Magnetization”,or “Magnetization,M”,notjust “M”.

Table 1 Table Type Styles

Table

Head TableColumnHead

Tablecolumnsubhead Subhead Subhead

copy Moretablecopya

III. ACKNOWLEDGMENT

Thepreferredspellingoftheword “acknowledgment” inAmericaiswithoutan “e” afterthe “g”.Avoidthestiltedexpression,

“Oneofus(R.B.G.)thanks...”

Instead,try“R.B.G.thanks”.Putapplicablesponsoracknowledgmentshere;DONOTplacethemonthefirstpageofyourpaperorasafootnote.

REFERENCES

[1] Ali, A. 2001.Macroeconomic variables as common pervasive risk factors and the empirical content of the Arbitrage Pricing Theory.

Journal of Empirical finance, 5(3): 221–240.

[2] Basu, S. 1997. The Investment Performance of Common Stocks in Relation to their Price to Earnings Ratio: A Test of the Efficient

Markets Hypothesis. Journal of Finance, 33(3): 663-682.

[3] Bhatti, U. and Hanif. M. 2010. Validity of Capital Assets Pricing Model.Evidence from KSE-Pakistan.European Journal of

Economics, Finance and Administrative Science, 3 (20).

Variable Minimum Maximum Mean

Std.

Deviation

Jarque-Bera test Sig

KSE-100 Index -0.11

0.14 0.020 0.047

5.558

0.062

Inflation -0.01 0.02 0.007 0.008 1.345 0.510

Exchange rate -0.07 0.04 0.003 0.013 1.517 0.467

Oil Prices -0.24 0.11 0.041 0.060 2.474 0.290

Interest rate -0.13 0.05 0.047 0.029 1.745 0.418


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