volume 03 july 2014 number 01 - gla...

Volume 03 July 2014 Number 01

Importance of Services in Organised Retail -

An Empirical Study

Volatility Forecast of BSE Ltd., Broad Indices

Analysis of it Infrastructure as A Factor Affecting

E-Commerce and Its Impact on Consumer Satisfaction

Impact of Social Networking on Employee Engagement

at Workplace: an Empirical Study Based on it

Industry in India

Impact of The Economic Crisis on Corporate

Financial Reporting: Stakeholders’ Perceptions

Causal Loop Modeling of Macroeconomic

Determinants of Stock Market Volatility

Abhaya Ranjan Srivastava

Dr. Saumya Singh

Dr. Anand Mohan Agrawal

Dr. Y. V. Ramana Murthy

Dr. K. Kameshwari

Dr. Shailendra Kumar, Vikalp

Shubham Arya, Shreyash Bharadwaj

Bhavya Mathur

Divyanhsu Ojha

Dr. Shailendra Kumar

Hina Agarwal

Sonam Bhadauriya

Recognised by UGC Under Section 2(f)

Executive Editor

Keeping an eye on Kaleidoscopic range of issues influencing the business management and administration Prastuti - Journal of Management and Research forays into the realm of advances and amelioration being made in the field of management practices and studies. It provides a podium for the dissemination and experimentation for practices and policies of the business management and administration. Prastuti (ISSN 2320-2262) is published biannually in January & July by the Institute of Business Management, GLA University, Mathura.

GLA University of PRASTUTI, Journal of Management & Research assumes no responsibility for the views expressed or information furnished by the authors.

Please direct all the manuscripts, editorial correspondence and subscriptions by D.D. payable at Mathura to the EDITOR-IN-CHIEF, PRASTUTI, Journal of Management & Research, Institute of Business Management, GLA University, Mathura-281406, Ph. No.: +91-5662-250718, E-mail: [email protected]

Vol. 03 - No. 01 © 2014 Institute of Business Management, GLA University, Mathura

Prof. D.S. Chauhan, Vice-Chancellor, GLA University

Prof. Anand Mohan Agrawal

Dr. Ankit Saxena

Editor’s Preface

The world economic spectrum has gone under tremendous transformation in

last few months in all aspects. The world economy is in the progression of resurgence,

combating the several issues like international terrorism and inter-continental political

issues. In India, we witnessed a historical political transformation. As always, the

hopes from the new government are elevated and next few years are quite challenging

for Indian economy.

Coming to the current issue of this journal, I am pleased to inform that, we have

published six quality research publications spreading different functional areas of

Management domain. The papers have approached to various domains of economy.

Srivastava et. al. in their contribution entitled “Importance of Services in

Organised Retail – An Empirical Study” have derived that services provided to

customers are one very essential factor which leads to more number of customers to an

organisation. Jharkhand a comparatively new state is also witnessing a significant shift

from unorganised retailing to organised retailing. The study aims to assess the

importance of service in attracting and increasing customers for the organised retail

outlets. Need for good service as an important factor in increasing and attracting

customers to the organised retail outlets cannot be negated and the same has been seen

expressed by the respondents in this study.

Murthy Y. V. R and Kameshwari K. in their empirical work entitled “Volatility

Forecast of BSE Ltd., Broad Indices” have underlined that volatility plays a very

important role in any financial market around the world. This research attempts to

present risk metrics which helps to manage risks such as to measure the difference in

actual and expected volatility which can act as an effective hedging tool, portfolio

diversification and overall risk management.

Kumar et. al. in “Analysis of IT Infrastructure as a Factor Affecting E-Commerce

and its Impact on Consumer Satisfaction”, have correctly stated that the economic

reforms of 1991 have completely changed the scenario in which business is being done

in India. The advancement in Information Technology (IT) also helps the e-commerce

to grow rapidly. This paper extends the research on e-commerce in India. The paper

uses a survey based approach to draw the conclusions. Using data from 300

respondents, they concluded that Internet unavailability, slow access of internet,

security concerns and the transaction process are the major concerns exist. The paper

shows the impact of these factors on consumer satisfaction on e-commerce.

Mathur et. al. in their research on a quite contemporary emerging phenomena

viz. “Impact of Social Networking on Employee Engagement at Workplace: An

Empirical Study Based on IT Industry in India” have derived that organizations across

the globe struggling with how best to introduce social networking tools - blogs, micro

blogs like Twitter, Collaboration platforms like Facebook to internal audiences. Social

networking has always been stated for distracting employees but with its proper and

regulated use, it can have the obverse effect. This paper examines, explores and gains a

quantitative insight of the same in detail and establishes that employee engagement is

a function of use of social networking at workplace. People often make use of social

networking to gain work related ideas, enhance accuracy in decision making and as a

medium of knowledge and information sharing.

Hina Agarwal in her research paper entitled “Impact of the Economic Crisis on

Corporate Financial Reporting: Stakeholders’ Perceptions” underlines that in the age

of globalization, no country can remains isolated from the fluctuations of world

economy. Heavy losses suffered by major International Banks affect all countries of the

world as they have their investment interest in almost all countries. This study aimed

to stand on the opinions of relevant stakeholders in the field of corporate financial

reporting practices after economic crisis in India. In the opinion of stakeholders there is

need to apply global reporting practices to smoothen the corporate’s working and for

making the system more transparent.

Sonam Bhadauriya in her contribution entitled “Causal Loop Modeling of

Macroeconomic Determinants of Stock Market Volatility” explores logically

structured framework for interrelationship among the stock market returns and the

macroeconomic determinants via a causal loop diagramming. Stock market dynamics

or volatility refers to the variation in the stock price changes during a period of time.

The volatility of stock market indicators goes beyond anyone’s reasonable

explanations. The modeling of stock market volatility is one of the key areas of present

financial research as stock market is the main determinant of economic development of

a country.

I take this opportunity to invite all the professionals, researchers and

academicians to send their conceptual or empirical papers, case studies and book

reviews for publishing in this journal. Finally, I thank all the reviewers for their time

and valuable suggestions and also congratulate all the contributors for their research.

Anand Mohan AgrawalEditor-in-ChiefPrastuti

Contents

1. Importance of Services in Organised Retail - An Empirical Study 01-07

Abhaya Ranjan Srivastava, Dr. Saumya Singh, Dr. Anand Mohan Agrawal

2. Volatility Forecast of BSE Ltd., Broad Indices 08-34

Dr. Y. V. Ramana Murthy, Dr. K. Kameshwari

3. Analysis of it Infrastructure as A Factor Affecting E-commerce and

its Impact on Consumer Satisfaction 35-45

Dr. Shailendra Kumar, Vikalp, Shubham Arya, Shreyash Bharadwaj

4. Impact of Social Networking on Employee Engagement at Workplace:

An Empirical Study Based on it Industry in India 46-52

Bhavya Mathur, Divyanhsu Ojha, Dr. Shailendra Kumar

5. Impact of The Economic Crisis on Corporate Financial Reporting:

Stakeholders’ Perceptions 53-63

Hina Agarwal

6. Causal Loop Modeling of Macroeconomic Determinants of

Stock Market Volatility 64-71

Sonam Bhadauriya

Retailing is one of the most active and attractive sector of the last decade. While retailing itself has been present through history in our country, it is only the recent past that has witnessed so much dynamism in India. Organised retail has been preferred by customers because of various features such as variety, ambience, convenience, better services, etc. Services provided to customers are one very essential factor which leads to more number of customers to an organisation. Since their starting organised retailers have used customer service as one of the important tool to attract new and retain existing customers. Jharkhand a comparatively new state is also witnessing a significant shift from unorganised retailing to organised retailing.

This study aims to assess the importance of service in attracting and increasing customers for the organised retail outlets. A total of seven statements signifying services have been considered in the study. The various services focussed in the study are – the faster billing procedures, better customer relationship management practices, free gift packaging facility, free alteration, child care facility, exchange facility and personal attention to customers.

Keywords: Organised Retail, Services, Customer Relationship Management Practices

Importance of Services in Organised Retail - An Empirical Study

Abstract

Preamble

Retailing is one of the most active and attractive sector

of the last decade. While retailing itself has been present

through history in our country, it is only the recent past

that has witnessed so much dynamism in India. The

world ‘retail’ means selling directly to customers in small

quantities as demanded by them. In India for

generations the nearby grocery stores were the

convenient options for the customers to purchase goods

for themselves. As organised retailing ventured in the

Indian market it changed the buying patterns of the

Indians. Over the past few years there has been a

proliferation of organised retail players from abroad.

Existing players have been trying to increase their

presence in the retail market. A number of large

domestic business groups such as Tata, Reliance, ITC,

RPG, Raheja and Piramal have setup malls and built

businesses within retail. Organised retailers provide

many distinctive advantages as compared to the

traditional retailers like - pleasant ambience,

convenience, variety, good infrastructure, better

services etc.

India has been ranked on fourteenth position on the

Global Retail Development Index in the AT Kearney

Report 2013 and fourth in Asia. As an affect of the global

slowdown India’s growth rate has slipped to 5 percent

from a 10 year average of 7.8 percent. On the GRDI

India’s position has gone down by nine spots in

comparison to the ranking of 2012 but it still holds a

strong position. The world's largest developing markets -

particularly the BRIC nations (Brazil, Russia, India, and

China) still allure the largest global retailers because of

the anemic growth in European and North American

markets. But it has become tough for them to have a

Abhaya Ranjan Srivastava*Dr. Saumya Singh**

Dr. Anand Mohan Agrawal***

*Assistant Professor, Department of Management, Birla Institute of Technology, Lalpur, Ranchi**Associate Professor, Department of Management Studies, ISM Dhanbad***Pro Vice Chancellor, GLA University, Mathura

01

global expansion strategy in retail. Every market has its

own characteristics which require unique strategies for

success. The GRDI 2013 report highlights that global

retailers have become more cautious and have taken a

step back from aggressive expansion.

India still remains a high-potential market with an

accelerated retail market growth of 14 to 15 percent by

2015. India’s GDP growth rate of 6 to 7 percent, the rising

disposable income particularly of the Indian middle-class

and the rapid urbanisation signifies the retail growth in

India. The changes made in the FDI regulations in October

2012 by the Government of India indicate a positive

environment for the international retailers and retail

growth in general. Organised retail share in India is still

around 5percent which indicates a room of high growth

opportunity for the organised retailers. Retailers are

presently expanding their presence in tier 2 and tier 3

cities because the cost of real estate has skyrocketed in

the metro areas. Metro, Bharti-Walmart and Carrefour

have increased their presence in these markets.

Jharkhand also witnessed the entry of some of the retail

chains like- Spencer, Reliance, Big Bazaar and Vishal

Megamart Most of these started with multiple number of

outlets but all of them could not sustain. Closure of some

of the retail outlets in Jharkhand sent a warning signal to

the existing ones and also to those who were looking to

enter in this market. It has become now clear to the retail

players that only opening the stores will not lead to

success rather one has to catch the pulse of the market

and act accordingly within time. The companies have to

be update and upgrade them with the latest

developments to be successful. This study was conducted

in Jharkhand witnessing the changes going in its retail

market. Before moving further let us have a brief look on

the factors advocated as important by researchers in the

past.

Price: It is one of the most important factors for Indian

consumers in their purchase decision. Organised retailers

have large presence due to which they achieve economies

of scale in their operations. So they are able to offer

products at cheaper rates as compared to the traditional

retailers. This has helped them in attracting new

customers and retaining the existing ones.

Variety: Another strong point of organised retailers has

been variety which is much higher as compared to the

traditional retailers. The availability of products even in

various sizes and quantities has increased their customer

base. Nuclear families, bachelors and persons living alone

away from their family prefer retail outlets because they

are able to buy products in small and varied quantities.

Brand Image: Their brand image has also helped in getting

more customers. Entry of international players and big

business houses in the retail sector has increased the

confidence of the customers in their purchases. The

customer feeling of getting reasonable or better quality,

equal treatment and the confidence of not getting

cheated has definitely increased their customer base.

Convenience: Proper locations, long opening hours, pick

and choose facility and pleasant ambience has added

convenience in shopping to the Indian consumers and is

leading to more customers for the organised retailers.

Promotions: Ability to advertise which is not possible

with the neighbourhood stores has differentiated them

and attracted customers. Use of technology to inform

customers by sms, e-mail has helped them to inform

customers in advance about their new schemes and

offers. This has made their promotional efforts more

attractive and organised as compared to the traditional

retailers.

Different Payment Options: The option of paying in cash

or through debit & credit cards has also attracted more

customers. The choice of not carrying cash while

purchasing has increased convenience and safety to the

customers. This has also led to more impulse and

unplanned purchases by customers thus increasing the

share of organised retailers in the Indian retail.

Infrastructure: Shopper friendly store design, air

conditioned environment, trolleys, big size stores have

resulted in a good infrastructure which has brought

customers in organised retail outlets. This is definitely

distinctive and attractive as compared to the traditional

retailers. In this infrastructure Customers never feel

bored or tired while purchasing leading to higher

purchases by them.

02

Prastuti: Vol. 3, No. 1, July 2014


03

Service: Services provided to customers are one very

essential factor which leads to more number of customers

to an organisation. As customers one can easily deduce

that customer service provided by the organised retailers

have been better as compared to the traditional retailers

in India. Since their starting organised retailers have used

customer service as one of the important tool to attract

new and retain existing customers. Organised retailers

have also used technology as a tool to differentiate their

services as compared to the traditional retailers. Use of

optical scanners at billing counters has made the billing

process faster. They have used the services not only to

differentiate but also to add value in the shopping

experience of the customers. Provision of free alterations

in garments, personal attention provided to customers by

sales staff to help and assist if required has definitely

added higher value in the shopping experience.

The Indian customers are very busy in their office & house

hold activities due to which they are in lack of time

particularly in big cities. The services offered by the

organised retailers has not only taken care for this rather

they have also tried that the customers do their

purchasing in less time with ease and comfort. Availability

of prams in bigger outlets has increased the ease in

purchasing. Free alteration, free gift packaging, etc. are

some of the other services which are distinctive when we

compare them with the traditional retailers.

Literature Review

The emergence of retailing in India has more to do with

the increasing purchasing power of the buyers, especially

in the post liberalization era (Prakash, 2007). An

improvement could be seen in the quality of life of urban

Indian consumers. The growing affluence of the Indian

middle class, a flood of imported products in the fashion

and food categories, the increasing space for groceries

and the emergence of a new breed of entrepreneurs are

drivers of boom in retail sector of India. (Krishnan &

Venkatesh, 2008). Upsurging Consumerism, changing

lifestyle, increasing access to information and ever

improving technology, made the last decade observe an

enormous development in the retail sector around the

globe (Lahiri & Samanta, 2010). Customers receive

relational benefits from service relationships (Gwinner,

Gremler, & Bitner, 1998). Good service and good selling

help in retaining, enhancing and cementing relationship

resulting in relationship management which finally leads

to competitive advantage for the firm (Kar & Nanda,

2011).

Good Customer Service attracts more customers and

increases consumer satisfaction (C & Hariharan, 2008).

Organised retailer should implement various value-added

services to provide pleasant shopping experiences to

consumers (Ramanathan & Hari, 2011). The authors

indicate that alert staff helps in building this relationship

by being courteous and giving personal attention to the

customers. More than 60 percent of the customers

perceive that customer service to be good in the

organised retail outlets (Dalwadi, Rathod, & Patel, 2010).

CRM practices have gained attention from both

academics and practitioners in the recent years due to the

intense competition in the retail market. The product-

centric business has transformed into a customer-centric

business in this intense competitive environment (Prasad

& Aryasri, 2008). Regular entry of new retailers could be

seen with new formats. The present models which are

successful highly in certain parts of the country are only

moderately successful in other areas. Better services are

used as one of the important driver to bridge this gap

(Krishnan & Venkatesh, 2008). Organised retailers have

tried to meet the expectations of the customers by

providing superior products and services.

Billing system acts as one of the important determinants

for preference of Mega Marts (Sonia, 2008). Use of optical

scanners at billing counters has made the billing process

faster. This has facilitated in completing the purchasing

exercise faster for the customers in today’s busy life. They

have used the services not only to differentiate but also to

add value in the shopping experience of the customers.

Fast processing is welcomed and appreciated by the

modern housewives (Krishnan & Venkatesh, 2008). Old

customers enjoy interactions and prefer those retail

stores where they receive special assistance services like

valet parking, carry-out assistance and delivery assistance

(Das, 2011). Technology would be the primary driver in

future for differentiating services. Application of

technology will revamp the stores and the shopping

experience for the customers (Misra & Khan, 2008). To

date there is a lack of studies that examine the various

aspects of service that are important for customer

retention (Zeithmal, 2000). Since still there is lack of

studies justifying the role of services, more work is

required to be carried out in this direction.


04

Research Methodology

This study tries to assess the importance of service in

attracting and increasing customers for the organised

retail outlets in Jharkhand. The present study was carried

out using stratified purposive sampling. Questionnaires

were distributed to 550 people in the districts of Ranchi,

Dhanbad and Jamshedpur of Jharkhand. A total of 465

filled questionnaires were received. A five point likert

scale was used in the questionnaire to know the ratings of

the respondents. Seven statements have been

considered to represent Services in this study. The various

services variables focussed in the study are - the billing

procedures are faster, customer relationship

management practices are good, free gift packaging

facility, free alteration, child care facility, exchange facility,

and personal attention to customers.

The respondents had varied preferences regarding the

customer services offered by the organised retailers. SPSS

17 was applied to analyse the data collected for the study.

It identified the relative impact levels and the KMO and

Bartlett’s test of Sphericity. Communality method of

principal Component Extraction was done to identify the

key factors contributing to the effectiveness of organised

retail. Expert’s opinion was also taken in the designing of

questionnaire.

Discussion of Research Findings

Table 1 presents the percentage of responses on all the 7

statements representing service. Customers have

indicated that all the seven service variables are

important and have shown their preference. Child care

facility has not received a good response because it is not

available at all the retail outlets. But this service variable

has received a substantial response which indicates it as

an important. Child care facility can act as a distinguishing

service variable which adds comfort to the purchasing so

it needs to be improved to increase the customer

satisfaction.

Cronbach’s Alpha: A value of 0.628 for Croanbach Alpha

indicates the reliability of the construct.

KMO Measure of Sampling Adequacy: As the KMO test

value is 0.646 which is more than 0.5 it indicates that we

can go for factor analysis.

Bartlett’s Test of Sphericity: Since the Chi-Square value is

higher, i.e. - 379.729 and significance level is 0.000 it

means we can definitely go for factor analysis.

Table 4 shows the 7 variable which are representing the

different services offered to consumers. These 7 variables

were put for factor analysis and have resulted into

extraction of two factors. The first factor consists of

4variables and has been named as ‘Basic Services’ and the

second factor consists of 3 variables and has been named

as ‘Extra Services’. The extracted factors support the

researches done in past. This justifies the contributions of

the earlier researches which say that service is one of the

important determinants in the success of organised retail.

Previous studies by Gwinner, Gremler, & Bitner (1998),

Prasad & Aryasri (2007), C & Hariharan (2008), Dalwdi,

Rathore, & Patel (2010), Karadeniz (2010), Kar & Nanda

(2011), Ramanathan & Hari (2011), support this view. Let

us have a brief look on the extracted factors.

Basic Services: This has emerged as the dominant factor

in case of services. It contains the variables which are

definitely demanded by customers. Most of the

customers feel that Customer Relationship Management

practices are good but because of the regular inflow of

new schemes floated by new and existing players the

updating and upgradation in Customer Relationship

Management practices are constantly desired. Customers

feel that the billing procedures are not fast although

application of technology is used by organised retailers in

the billing process. It signifies that organised retailers

need to improve on this service variable if they want to

use it as a differentiated service over traditional retailers.

The efforts of the staff have been appreciated as a service

element by the respondents which convey a positive

feeling towards organised retailers and would ensure a

promising future for them. The Exchange facility provided

by the organised retailers is a very important service

feature in terms of the value it holds in the minds of the

customers. A time period is specified in the bill provided

to the customers during which they can come and

exchange their products.

Extra Services: The service variables representing this

factor are not provided by all retailers. Unorganised

retailers generally do not provide them and is also not

available with all organised retailers. Free gift packaging if

provided by all the organised retailers would act as a

service element to bring more customers. This is already


05

acting as a positive element for those who are providing

it. Free Alteration is provided by almost all organised

retailers but by only few unorganised retailers provide it.

It has created a positive impact as a service element. In

cases where customers are purchasing during vacations

or outside their home town it has emerged important

because in lack of time they could get their products

altered in the showrooms of the same company in any city

of India. Child care facility is a distinguishing service

feature provided by the organised retailers which has

added value in their service basket and also made the

purchasing comfortable for the customers. Availability of

prams in retail outlets, proper washroom facility

especially for small kids, etc. are a part of this. It should be

treated as an important element of service for increasing

the customer base

Conclusions

Need for good service as an important factor in increasing

and attracting customers to the organised retail outlets

cannot be negated and the same has been seen expressed

by the respondents in this study. A good service has its

own benefits for any business be it a retail business or any

other. Factor analysis applied to the service variables has

grouped the seven variables into two factors. These

factors have been named as ‘BASIC SERVICES’ and ‘EXTRA

SERVICES’.

Recommendations

Based on the results of the present research the following

recommendations could be made:

• Service is an important factor which influences the

purchase from organised retail outlets. In this

changing environment having a uniform service

strategy throughout the stores in India would not be

possible so organised retailers should try to

customise their services to the local needs which are

unique. This could be achieved by developing a

deeper insight about consumer preferences.

• Out of the two factors extracted the factor ‘Basic

Services’ has emerged as the dominant factor but

the second factor ‘Extra Services’ also needs

reasonable attention by the organised retailers to be

successful.

Limitations

As all studies have suffers limitations the present study

also has certain limitations. This analysis is based on the

questionnaires which were filled by respondents in

Jharkhand only. A bigger sample and broader market

coverage would help in generalising the results for the

whole of India.

Appendix

Table 1: Percentage response on Statements/Variables

Statements/ Variables Strongly Agree Agree Not Known Disagree Strongly Disagree

The billing procedures are faster 12.1% 42.3% 3.5% 36.3% 5.7%

Customer Relationship Management practices are good

7.0% 63.2% 13.9% 13.9% 2.0%

Personal Attention to customer 13.2% 42.7% 13.7% 26.9% 3.5%

Exchange facility 16.3% 54.4% 16.3% 11.9% 1.1%

Free Gift Packaging 7.5% 35.5% 16.7% 33.3% 7.0%

Free Alteration 10.4% 58.4% 14.8% 13.0% 3.5%

Child care facility 6.2% 22.3% 25.1% 35.7% 10.8%

Statements/ Variables Components (Loading Criteria>0.4)

1 2

Personal Attention to Customers 0.483

Customer Relationship Practices are good 0.769

Exchange Facility 0.674

The billing procedures are faster 0.510

Child Care facility 0.568

Free Alteration 0.687

Free Gift Packaging Facility 0.846

Kaiser-Meyer-Olkin Measure of Sampling Adequacy

Bartlett’s Test of Sphericity !pprox. Chi -Square

Df

Sig.

0.646

379.729

464

0.000

Cronbach’s Alpha N of Items

0.628 7

N %

Cases Valid

Excluded

Total

465

0

465

100.0

0.0

100.0


06

Table 2: Case Processing Summary

Table 3: Reliability Statistics

Table 3: KMO and Bartlett’s Test

Table 4: Rotated Component Matrices of 7 Variables

Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization; a. Rotation Converged in 3 Iterations.

Table 5: Naming of the extracted Factors (Loading Criteria>0.4)

Factor No. Statements/ Variables Factor Loading Naming of Factors

1 Personal Attention to Customers 0.483 Basic Services

Customer Relationship Management Practices are good 0.769

Exchange Facility 0.674

The billing procedures are faster 0.510

2 Child Care facility 0.568 Extra Services

Free Alteration 0.687

Free Gift Packaging Facility 0.846

Bibliography

• C, A., & Hariharan, G. (2008, February). Profile and

Perception of Retail Customers-An Empirical Study in

Palakkad District. Indian Journal of Marketing,

24-29.

• Dalwadi, R., Rathod, H. S., & Patel, A. (2010, April-

August). Key Retail Store Attributes Determining

Consumers' Perceptions: An Empirical Study of

Consumers of Retail Stores Located in Ahmedabad

(Gujrat). SIES Journal of Management, 7(1), 20-34.

• Das, G. (2011, July). Effect of Retail Sales Promotion

on Buying Behaviour of Customers: An Empirical

Study. Indian Journal of Marketing, 25-35.

• Gwinner, K. P., Gremler, D. D., & Bitner, M. J. (1998).

Relational Benefits in Services Industries: The

Customer's Perspective. Academy of Marketing

Scince, 26(2), 101-114.

• Kar, S. K., & Nanda, S. (2011). Relationship Marketing

and Indian Shoppers. The IUP Journal of Marketing

Management, X(4), 7-32.

• Karadeniz, M. (2010). The Relationship marketing

Approach and Strategiesin Retailing Management to

Constitute Customer andBrand Loyalty. Journal of

Naval Science and Enginerring, 6(1), 15-26.

• Krishnan, N. V., & Venkatesh, S. P. (2008, March).

Challenges of Merchandising Strategies in Fashion

Retailing - A Study on Private Labels Vs

Manufactured Brands. Indian Journal of Marketing,

14-21.

• Lahiri, I., & Samanta, P. K. (2010). Factors Influencing

Purchase of Apparels from Organized Retail Outlets.

The IUP Journal of Marketing Management, IX(1&2),

73-87.

• Misra, A., & Khan, M. Y. (2008, March). FDI in

Retailing. Indian Journal of Marketing, 22-29.

• Patil, P. P., & Vedak, V. (2011, February). The Private

Label: Retailer's Value Proposition and Strategic Tool

of Differentiation. Indian Journal of Marketing,

54-60.

• Prakash, N. (2007). Retailing Revolution in India-An

Overview. The Management Accountant, 42(10),

764-66.

• Prasad, J. S., & Aryasri, A. R. (2008). Study of

Customer Relationship Marketing Practices in

Organised Retailing in Food and Grocery Sector in

India: AnEmpirical Analysis. VISION-The Journal of

Business Perspective, 12(4), 33-43.

• Ramanathan, V., & Hari, K. (2011, December). A

Study On Consumer Perception About Organized Vs

Unorganized Retailers at Kanchipuram, Tamil Nadu.

Indian Journal of Marketing, 11-23.

• Sonia. (2008). Customer Perception Towards Mega

Mart. Services Marketing, 6(4), 38-48.

• Zeithmal, V. A. (2000). Service Quality, Profitability

and the Economic Worth of the Customers: What we

Know and What we Need to Learn. Journal of

Academy of Marketing Sciences, 28(1), 67-85.

• http://www.grdi.atkearney.com/accessed on 2013-

01-2012

• http://www.grdi.atkearney.com/accessed on 2013-

11-28


07

Volatility plays a very important role in any financial market around the world. Accurate forecasting of volatility is essential for taking wise and timely decisions for transacting financial products and to manage other financial applications. The goal of any volatility model is to be able to forecast volatility. In this paper “VOLATILITY FORECAST OF BSE LTD. BROAD INDICES”, focuses on volatility forecasting through three widely used time series volatility models namely, the Historical Variance, time series of univariate data, the Generalized Autoregressive Conditional Heteroscedastic Model (GARCH) and the Risk Metrics, Exponential Weighted Moving Average (EWMA) . The characteristics of these volatility models are explored using monthly data on the BSE broad indices for a period of 4 years. (Jan 2010 to Jan 2014). “VOLATILITY FORECAST OF BSE LTD. BROAD INDICES”, analysis through GARCH(1,1) of BSE S&P, BSE MIDCAP, BSE SMALL CAP, BSE 100, BSE 200 and S&P 500 have shown the volatility ranging from 1.52 to 6.87%, whereas the same indices through Exponential Generalized Autoregressive Conditional Heteroscedastic Model (EGARCH )model has shown the volatility range from 4.55 to 10.59%.

Keywords: Volaitity forecast, BSE broad indices, Time series, Generalized Autoregressive Conditional Heteroscedastic Model (GARCH), Risk metrics, Exponential Weighted Moving Average (EWMA)


Abstract

Introduction

The paper entitled “VOLATILITY FORECAST OF BSE LTD.

BROAD INDICES” attempts to present the volatility

forecast of the BSE broad indices for a period of one year

from Jan 2014. The six BSE broad indices are widely

chosen by the investors and forecasting volatility for the

same carries significance for investment decisions.

Hence the study is focused on forecasting the volatility of

BSE S&P SENSEX, BSE SMALL CAP INDICES, BSE MID CAP

INDICES, BSE 100, BSE 200 AND BSE 500 INDICES. BSE

was established in 1875, BSE Ltd. (formerly known as

Bombay Stock Exchange Ltd.), which is presently Asia’s

first Stock Exchange and one of India’s leading exchange

groups. Over the past 137 years, BSE has facilitated the

growth of the Indian corporate sector by providing it an

efficient capital-raising platform. More than 5000

companies are listed on BSE making it world's No. 1

exchange in terms of listed members. The companies

listed on BSE Ltd command a total market capitalization

of USD 1.32 Trillion as of January 2013. It is also one of

the world’s leading exchanges (3rd largest in December

2012) for Index options trading (Source: World

Federation of Exchanges).


• Source of Data: The data is collected from the

bseindia.com and analyzed by using the NUMXL

software Excel addin.

• Objective: To find out the BSE broad indices

volatility.

• Study period: Period of 4 years from 2010 January

to 2014 January is taken.

• Techniques used:

• Log returns: log returns are calculated for the

Dr. Y. V. Ramana Murthy*Dr. K. Kameshwari**

*Asstt Prof, Centre for Management Studies, NALSAR University of Law, Hyderabad. E-mail: [email protected]**Asstt Prof, Integral Institute of Advanced Management, Visakhapatnam, AP, India. Email:[email protected]

08

monthly returns which can provide better values

distribution prices between January 2010 and July

2014.

• WMA: A Weighted Moving Average (WMA) assigns a

weighting factor to each value in the data series

according to its age. The most recent data gets the

greatest weight and each price value gets a smaller

weight as it counts backward in the series.

• EWMA: is computed to estimate the next-day (or

period) volatility of a time series and closely track the

volatility as it changes. EWMA is basically a special

form of an ARCH() model where the ARCH order is

equal to the sample data size and the weights are

exponentially declining at rate throughout time.

(lambda 0.94).

• Correlogram: To find out the ACF and PACF to

understand the lag order position and decide the

model.

• EGARCH (1,1): Volatility forecasting through

econometric models have gained wide popularity.

Egarch analysis, the exponential general

autoregressive conditional Heteroscedasticity

(model by Nelson (1991) is another form of the

GARCH model . The exponent ia l genera l

autoregressive conditional heteroskedastic (E-

GARCH) model by Nelson (1991) is another form of

the GARCH model. Formally, an EGARCH(p,q):

Where:

is the time series value at time t.

is the mean of GARCH model.

is the model's residual at time t.

is the conditional standard deviation (i.e. volatility)

at time t.

is the order of the ARCH component model.

are the parameters of the ARCH

component model.

is the order of the GARCH component model.

are the parameters of the GARCH component

model.

are the standardized residuals:

is the probability distribution function for .

Currently, the following distributions are supported:

Normal distribution P_{\nu} = N(0,1)

Student's t-distribution

Generalized error distribution (GED)

The E-GARCH model differs from GARCH in several ways.

For instance, it used the logged conditional variances to

relax the positiveness constraint of model coefficients.

EGARCH (p,q) model has 2p+q+2 estimated parameters.

EGARCH_VL (alphas, betas, innovation, v)

Alphas are the parameters of the ARCH(p) component

model (starting with the lowest lagi).

Betas are the parameters of the GARCH(q) component

model (starting with the lowest lag).

Innovation is the probability distribution model for the

innovations/residuals (1=Gaussian, 2=t-Distribution,

3=GED). If missing, a gaussian distribution is assumed.

1 Gaussian or Normal Distribution (default)

2 Student's t-Distribution

3 Generalized Error Distribution (GED)

V is the shape parameter (or degrees of freedom) of the

innovations/residuals probability distribution function.

The EGARCH long-run average log variance is defined as:

Where:

Gaussian distributed innovations/shocks:

GED distributed innovations/shocks.

09


is the probability distribution function for .

Normal distribution:

Student's t-distribution:

Generalized error distribution (GED):

Review of Literature

Padhi (2005) explained the stock market volatility at the

individual script level and at the aggregate indices level.

The empirical analysis has been done by using ARCH,

GARCH model and ARCH in Mean model and it is based on

daily data for the time period from January 1990 to

November 2004. The analysis reveals the same trend of

volatility in the case of aggregate indices and five different

sectors such as electrical, machinery, mining, non-

metallic and power plant sector. The GARCH (1, 1) model

is persistent for all the five aggregate indices and

individual company. Karmakar (2006) measured the

volatility of daily stock return in the Indian stock market

over the period of 1961 to 2005. Using GARCH model, he

found strong evidence of' time varying volatility. He also

used the TGARCH model to test the asymmetric volatility

effect and the result suggests the asymmetry in volatility.

Rao, Kanagaraj and Tripathy (2008) attempts to

determine the impact of individual stock futures on the

underlying stock market volatility in India by applying

both GARCH and ARCH model for a period of seven years

from June 1999 to July 2006. This study includes stock of

10 companies i.e Reliance, SBI, TISCO, ACC, MTNL, TATA

Power, TATA Tea, BHEL, MAHINDRA & MAHINDRA and ITC.

The results suggest that stock future derivatives are not

responsible for increase or decrease in spot market

volatility and conclude that there could be other market

factors that have helped the increase in Nifty volatility.

Vuyyuri and Roy (2003) modelled the monthly volatility of

market indices (Sensex & S&PCNX-Nifty) of Indian capital

markets using eight different univariate models. Out-of-

sample forecasting performance of these models has

been evaluated using different symmetric, as well as

asymmetric loss functions. The GARCH (1, 1) model has

been found to be the overall superior model based on

most of the symmetric loss functions, though ARCH has

been found to be better than the other models for

investors who are more concerned about under

predictions than over predictions.

Student's t-Distributed innovations/shocks.

The time series is homogeneous or equally spaced.

The number of gamma-coefficients must match the

number of alpha-coefficients.

The number of parameters in the input argument - alpha -

determines the order of the ARCH component model.

The number of parameters in the input argument - beta -

determines the order of the GARCH component model.

GARCH MODEL (1,1):

An autoregressive moving average model (ARMA model)

is assumed for the error variance, the model is a

g e n e r a l i z e d a u t o r e g r e s s i v e c o n d i t i o n a l

heteroskedasticity (GARCH in Excel, Bollerslev(1986))

model.

Where:

is the time series value at time t.

is the mean of GARCH in Excel model.

is the model's residual at time t.

is the conditional standard deviation (i.e. volatility)

at time t.

is the order of the ARCH component model.

are the parameters of the ARCH

component model.

is the order of the GARCH component model.

are the parameters of the GARCH

component model.

are the standardized residuals:

10


The data analysis is presented in the following order:

1. Tables showing monthly log returns of all six BSE

BROAD INDICES followed by graphs. WMA and

EWMA are calculated for finding out the stationarity.

2. Descriptive statistics of the selected sample.

3. Stationarity, distribution and Arch values are

computed to find out white noise, distribution and

arch effect.

4. Corrrelogram analysis is made to find the best fit

model.

5. Garch and EGARCH models are used and calibrated

to understand the goodness of fit.

6. Volatility forecasting is done through GARCH_vl and

EGARCH_vl functions.

7. Graphs are prepared to present the term structure

and standard deviation.

Month Open High Low Close % RET WMA EWMA

10-Jan 17,473.45 17,790.33 15,982.08 16,357.96 #N/A #N/A

10-Feb 16,339.32 16,669.25 15,651.99 16,429.55 0.44% #N/A

10-Mar 16,438.45 17,793.01 16,438.45 17,527.77 6.47% 0.44% 0.00%

10-Apr 17,555.04 18,047.86 17,276.80 17,558.71 0.18% 3.45% 3.02%

10-May 17,536.86 17,536.86 15,960.15 16,944.63 -3.56% 2.36% 2.12%

10-Jun 16,942.82 17,919.62 16,318.39 17,700.90 4.37% 0.88% 1.40%

10-Jul 17,679.34 18,237.56 17,395.58 17,868.29 0.94% 1.58% 2.01%

10-Aug 17,911.31 18,475.27 17,819.99 17,971.12 0.57% 1.47% 2.03%

10-Sep 18,027.12 20,267.98 18,027.12 20,069.12 11.04% 1.34% 1.92%

10-Oct 20,094.10 20,854.55 19,768.96 20,032.34 -0.18% 2.56% 2.54%

10-Nov 20,272.49 21,108.64 18,954.82 19,521.25 -2.58% 2.25% 3.12%

10-Dec 19,529.99 20,552.03 19,074.57 20,509.09 4.94% 1.77% 2.97%

11-Jan 20,621.61 20,664.80 18,038.48 18,327.76 -11.25% 2.06% 3.15%

11-Feb 18,425.18 18,690.97 17,295.62 17,823.40 -2.79% 0.95% 3.05%

11-Mar 17,982.28 19,575.16 17,792.17 19,445.22 8.71% 0.68% 4.18%

11-Apr 19,463.11 19,811.14 18,976.19 19,135.96 -1.60% 0.87% 4.23%

11-May 19,224.05 19,253.87 17,786.13 18,503.28 -3.36% 0.72% 4.48%

11-Jun 18,527.12 18,873.39 17,314.38 18,845.87 1.83% 0.73% 4.37%

11-Jul 18,974.96 19,131.70 18,131.86 18,197.20 -3.50% 0.52% 4.36%

11-Aug 18,352.23 18,440.07 15,765.53 16,676.75 -8.73% 0.15% 4.23%

11-Sep 16,963.67 17,211.80 15,801.01 16,453.76 -1.35% -0.62% 4.21%

11-Oct 16,255.97 17,908.13 15,745.43 17,705.01 7.33% -1.66% 4.62%

Table 1: BSE S&P INDEX

11


11-Nov 17,540.55 17,702.26 15,478.69 16,123.46 -9.36% -1.03% 4.53%

11-Dec 16,555.93 17,003.71 15,135.86 15,454.92 -4.23% -1.59% 4.71%

12-Jan 15,534.67 17,258.97 15,358.02 17,193.55 10.66% -2.36% 5.09%

12-Feb 17,179.64 18,523.78 17,061.55 17,752.68 3.20% -0.53% 5.09%

12-Mar 17,714.62 18,040.69 16,920.61 17,404.20 -1.98% -0.03% 5.56%

12-Apr 17,429.96 17,664.10 17,010.16 17,318.81 -0.49% -0.92% 5.44%

12-May 17,370.93 17,432.33 15,809.71 16,218.53 -6.56% -0.83% 5.30%

12-Jun 16,217.48 17,448.48 15,748.98 17,429.98 7.20% -1.10% 5.14%

12-Jul 17,438.68 17,631.19 16,598.48 17,236.18 -1.12% -0.65% 5.25%

12-Aug 17,244.44 17,972.54 17,026.97 17,429.56 1.12% -0.45% 5.37%

12-Sep 17,465.60 18,869.94 17,250.80 18,762.74 7.37% 0.37% 5.22%

12-Oct 18,784.64 19,137.29 18,393.42 18,505.38 -1.38% 1.09% 5.08%

12-Nov 18,487.90 19,372.70 18,255.69 19,339.90 4.41% 0.37% 5.21%

12-Dec 19,342.83 19,612.18 19,149.03 19,426.71 0.45% 1.52% 5.07%

13-Jan 19,513.45 20,203.66 19,508.93 19,894.98 2.38% 1.91% 5.01%

13-Feb 19,907.21 19,966.69 18,793.97 18,861.54 -5.33% 1.22% 4.85%

13-Mar 18,876.68 19,754.66 18,568.43 18,835.77 -0.14% 0.50% 4.74%

13-Apr 18,890.81 19,622.68 18,144.22 19,504.18 3.49% 0.66% 4.80%

13-May 19,459.33 20,443.62 19,451.26 19,760.30 1.30% 0.99% 4.66%

13-Jun 19,859.22 19,860.19 18,467.16 19,395.81 -1.86% 1.65% 4.58%

13-Jul 19,352.48 20,351.06 19,126.82 19,345.70 -0.26% 0.89% 4.44%

13-Aug 19,443.29 19,569.20 17,448.71 18,619.72 -3.82% 0.96% 4.34%

13-Sep 18,691.83 20,739.69 18,166.17 19,379.77 4.00% 0.55% 4.22%

13-Oct 19,452.05 21,205.44 19,264.72 21,164.52 8.81% 0.27% 4.21%

13-Nov 21,158.81 21,321.53 20,137.67 20,791.93 -1.78% 1.12% 4.19%

13-Dec 20,771.27 21,483.74 20,568.70 21,170.68 1.81% 0.60% 4.54%

14-Jan 21,222.19 21,409.66 20,343.78 20,513.85 -3.15% 0.72% 4.43%

12


Table 2: BSE 100

Graph 1: BSE S&P INDEX

0

0.05

0.1

-5.00%

0.00%

5.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

wma ewma

Month Open High Low Close % ret wma ewma

10-Jan 5,343.39 5,479.32 4,924.25 5,050.54 #N/A #N/A

10-Feb 5,021.36 5,145.20 4,856.17 5,079.94 0.58% #N/A

10-Mar 5,131.29 5,459.67 5,128.21 5,394.12 6.00% 0.58% 0.00%

10-Apr 5,423.04 5,551.48 5,316.13 5,439.84 0.84% 3.29% 2.71%

10-May 5,410.90 5,423.35 4,935.68 5,243.91 -3.67% 2.48% 2.03%

10-Jun 5,231.30 5,509.89 5,098.68 5,476.70 4.34% 0.94% 1.25%

10-Jul 5,447.33 5,644.63 5,400.14 5,542.87 1.20% 1.62% 1.90%

10-Aug 5,583.58 5,739.15 5,536.06 5,584.08 0.74% 1.55% 1.94%

10-Sep 5,612.42 6,230.59 5,606.04 6,163.86 9.88% 1.43% 1.84%

10-Oct 6,193.48 6,432.74 6,097.53 6,171.18 0.12% 2.49% 2.35%

10-Nov 6,229.05 6,491.89 5,747.61 5,962.87 -3.43% 2.23% 2.82%

10-Dec 5,967.21 6,201.86 5,787.93 6,191.51 3.76% 1.66% 2.66%

11-Jan 6,219.56 6,241.93 5,456.59 5,550.03 -10.94% 1.85% 2.91%

11-Feb 5,574.59 5,643.69 5,209.44 5,370.50 -3.29% 0.79% 2.82%

11-Mar 5,408.53 5,889.22 5,393.29 5,855.53 8.65% 0.46% 3.95%

11-Apr 5,858.31 5,989.65 5,753.91 5,795.29 -1.03% 0.68% 4.02%

11-May 5,817.32 5,825.72 5,403.47 5,638.16 -2.75% 0.53% 4.31%

11-Jun 5,644.23 5,694.20 5,275.65 5,686.26 0.85% 0.60% 4.19%

11-Jul 5,717.27 5,791.78 5,524.40 5,531.70 -2.76% 0.31% 4.15%

11-Aug 5,568.18 5,602.55 4,797.15 5,062.17 -8.87% -0.02% 4.02%

11-Sep 5,132.47 5,232.35 4,833.74 4,995.67 -1.32% -0.82% 3.98%

13


11-Oct 4,950.13 5,372.23 4,794.95 5,334.14 6.56% -1.75% 4.43%

11-Nov 5,295.02 5,349.84 4,651.21 4,831.73 -9.89% -1.21% 4.34%

11-Dec 4,938.91 5,093.14 4,516.41 4,598.21 -4.95% -1.75% 4.48%

12-Jan 4,616.42 5,215.05 4,560.67 5,202.65 12.35% -2.48% 4.94%

12-Feb 5,198.68 5,658.18 5,171.83 5,406.46 3.84% -0.54% 4.99%

12-Mar 5,393.04 5,520.13 5,155.64 5,315.15 -1.70% 0.06% 5.69%

12-Apr 5,320.66 5,411.04 5,177.60 5,268.41 -0.88% -0.81% 5.59%

12-May 5,296.69 5,302.89 4,811.28 4,942.13 -6.39% -0.79% 5.44%

12-Jun 4,931.16 5,283.02 4,786.41 5,279.22 6.60% -1.10% 5.28%

12-Jul 5,290.08 5,356.98 5,046.93 5,229.16 -0.95% -0.62% 5.36%

12-Aug 5,225.41 5,426.50 5,175.70 5,251.07 0.42% -0.47% 5.43%

12-Sep 5,260.07 5,733.12 5,200.44 5,701.39 8.23% 0.31% 5.28%

12-Oct 5,706.23 5,825.42 5,583.93 5,620.99 -1.42% 1.10% 5.13%

12-Nov 5,619.91 5,914.16 5,567.27 5,908.97 5.00% 0.44% 5.33%

12-Dec 5,910.31 6,010.14 5,881.67 5,975.74 1.12% 1.68% 5.18%

13-Jan 5,998.53 6,172.30 5,997.95 6,091.49 1.92% 2.18% 5.15%

13-Feb 6,094.62 6,117.33 5,698.19 5,720.10 -6.29% 1.31% 4.99%

13-Mar 5,723.61 5,988.12 5,595.58 5,678.70 -0.73% 0.47% 4.86%

13-Apr 5,694.03 5,969.74 5,490.97 5,941.35 4.52% 0.55% 4.99%

13-May 5,931.90 6,246.37 5,928.66 5,991.11 0.83% 1.00% 4.84%

13-Jun 6,015.01 6,015.01 5,546.07 5,802.30 -3.20% 1.60% 4.80%

13-Jul 5,799.54 6,067.37 5,630.83 5,707.16 -1.65% 0.79% 4.66%

13-Aug 5,737.99 5,773.14 5,116.81 5,447.15 -4.66% 0.73% 4.60%

13-Sep 5,470.19 6,095.95 5,321.17 5,723.40 4.95% 0.31% 4.49%

13-Oct 5,739.12 6,280.66 5,684.77 6,270.72 9.13% 0.03% 4.52%

13-Nov 6,270.99 6,330.48 5,973.17 6,177.75 -1.49% 0.91% 4.53%

13-Dec 6,177.55 6,399.06 6,133.37 6,326.72 2.38% 0.37% 4.88%

14-Jan 6,343.75 6,381.76 5,998.01 6,071.02 -4.13% 0.48% 4.75%

14


0

0.02

0.04

0.06

-5.00%

0.00%

5.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

wma ewma

Table 3: BSE 200

Graph 2: BSE 100

Month Open High Low Close %ret wma ewma

10-Jan 2,178.01 2,242.47 2,011.14 2,065.21 #N/A #N/A

10-Feb 2,053.93 2,103.13 1,990.25 2,071.72 0.31% #N/A

10-Mar 2,091.42 2,224.31 2,091.42 2,199.50 5.99% 0.31% 2.84%

10-Apr 2,210.53 2,267.07 2,175.22 2,230.17 1.38% 3.15% 2.84%

10-May 2,218.91 2,224.56 2,026.84 2,152.21 -3.56% 2.56% 2.33%

10-Jun 2,147.54 2,257.99 2,099.94 2,248.06 4.36% 1.03% 1.36%

10-Jul 2,237.38 2,319.47 2,219.05 2,281.63 1.48% 1.70% 2.06%

10-Aug 2,297.17 2,371.08 2,283.12 2,302.88 0.93% 1.66% 2.08%

10-Sep 2,313.81 2,557.24 2,313.75 2,530.47 9.42% 1.56% 1.97%

10-Oct 2,541.63 2,644.37 2,512.75 2,541.85 0.45% 2.54% 2.50%

10-Nov 2,562.44 2,671.95 2,354.94 2,451.45 -3.62% 2.31% 2.86%

10-Dec 2,453.00 2,538.19 2,366.71 2,533.90 3.31% 1.71% 2.64%

11-Jan 2,543.96 2,557.05 2,229.16 2,270.22 -10.99% 1.86% 2.91%

11-Feb 2,279.03 2,303.24 2,122.78 2,185.86 -3.79% 0.79% 2.75%

11-Mar 2,199.48 2,391.35 2,198.60 2,378.69 8.45% 0.45% 3.90%

11-Apr 2,379.69 2,441.31 2,349.55 2,363.68 -0.63% 0.65% 4.01%

11-May 2,371.58 2,375.99 2,204.88 2,301.65 -2.66% 0.48% 4.29%

11-Jun 2,303.82 2,319.15 2,155.87 2,314.65 0.56% 0.56% 4.16%

11-Jul 2,325.75 2,361.08 2,253.99 2,256.48 -2.55% 0.24% 4.11%

11-Aug 2,269.53 2,284.75 1,955.28 2,061.08 -9.06% -0.09% 3.98%

11-Sep 2,086.41 2,131.58 1,968.11 2,028.27 -1.60% -0.92% 3.92%

11-Oct 2,011.86 2,167.52 1,947.72 2,155.58 6.09% -1.84% 4.39%

15


11-Nov 2,141.49 2,163.27 1,880.74 1,953.03 -9.87% -1.37% 4.31%

11-Dec 1,991.63 2,053.99 1,819.80 1,850.89 -5.37% -1.89% 4.42%

12-Jan 1,857.46 2,102.54 1,835.84 2,097.94 12.53% -2.62% 4.87%

12-Feb 2,096.51 2,289.67 2,087.61 2,190.92 4.34% -0.66% 4.96%

12-Mar 2,186.05 2,237.66 2,092.37 2,157.89 -1.52% 0.02% 5.70%

12-Apr 2,161.45 2,197.57 2,100.07 2,136.82 -0.98% -0.81% 5.62%

12-May 2,148.08 2,150.62 1,953.17 2,003.10 -6.46% -0.84% 5.46%

12-Jun 1,998.91 2,139.50 1,940.89 2,138.10 6.52% -1.16% 5.30%

12-Jul 2,142.59 2,171.00 2,046.69 2,114.47 -1.11% -0.66% 5.39%

12-Aug 2,113.29 2,193.84 2,097.30 2,124.06 0.45% -0.54% 5.45%

12-Sep 2,126.75 2,320.21 2,106.35 2,307.58 8.29% 0.25% 5.29%

12-Oct 2,312.66 2,358.00 2,260.96 2,276.15 -1.37% 1.08% 5.15%

12-Nov 2,276.46 2,391.64 2,254.73 2,389.51 4.86% 0.45% 5.36%

12-Dec 2,391.68 2,436.97 2,384.74 2,424.38 1.45% 1.68% 5.21%

13-Jan 2,435.31 2,498.11 2,435.31 2,461.12 1.50% 2.25% 5.16%

13-Feb 2,463.69 2,471.54 2,299.53 2,307.98 -6.42% 1.33% 5.01%

13-Mar 2,311.98 2,412.78 2,253.50 2,287.96 -0.87% 0.43% 4.88%

13-Apr 2,295.20 2,399.33 2,215.44 2,388.98 4.32% 0.49% 5.01%

13-May 2,384.85 2,509.17 2,384.85 2,409.22 0.84% 0.93% 4.87%

13-Jun 2,416.46 2,416.46 2,223.69 2,323.83 -3.61% 1.54% 4.82%

13-Jul 2,324.56 2,419.77 2,238.98 2,270.93 -2.30% 0.69% 4.67%

13-Aug 2,285.47 2,295.98 2,041.82 2,167.96 -4.64% 0.59% 4.63%

13-Sep 2,177.15 2,417.42 2,121.30 2,281.93 5.12% 0.17% 4.53%

13-Oct 2,288.56 2,494.21 2,267.48 2,490.49 8.75% -0.09% 4.55%

13-Nov 2,492.65 2,515.46 2,383.74 2,463.86 -1.08% 0.75% 4.58%

13-Dec 2,467.32 2,547.57 2,449.47 2,530.58 2.67% 0.26% 4.89%

14-Jan 2,537.73 2,552.71 2,394.65 2,425.46 -4.24% 0.36% 4.75%

16


Table 4: S&P 500

Graph 3: BSE 200

0

0.05

0.1

-5.00%

0.00%

5.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

wma ewma


10-Jan 6,839.38 7,070.37 6,338.25 6,509.90 #N/A #N/A

10-Feb 6,477.83 6,639.59 6,280.99 6,518.38 5.97% #N/A

10-Mar 6,576.07 6,987.88 6,576.07 6,919.55 1.76% 5.97% 2.10%

10-Apr 6,952.74 7,140.21 6,863.81 7,042.68 -3.77% 3.87% 2.10%

10-May 7,009.94 7,028.13 6,396.74 6,782.37 4.47% 1.32% 4.51%

10-Jun 6,769.03 7,119.58 6,634.30 7,092.20 1.58% 2.11% 3.91%

10-Jul 7,061.59 7,321.41 7,009.83 7,205.22 1.17% 2.00% 3.92%

10-Aug 7,249.77 7,514.63 7,227.39 7,289.74 9.10% 1.86% 3.90%

10-Sep 7,322.52 8,064.87 7,322.52 7,984.45 0.65% 2.90% 3.10%

10-Oct 8,018.42 8,344.12 7,950.12 8,036.88 -4.00% 2.62% 3.55%

10-Nov 8,095.04 8,434.05 7,411.68 7,722.05 3.05% 1.88% 3.95%

10-Dec 7,726.39 7,975.22 7,421.12 7,961.06 -11.05% 2.00% 4.03%

11-Jan 7,989.28 8,038.74 6,999.44 7,128.29 -3.98% 0.81% 4.66%

11-Feb 7,152.97 7,222.02 6,647.92 6,850.40 8.22% 0.41% 5.56%

11-Mar 6,888.55 7,471.35 6,888.55 7,437.26 -0.14% 0.60% 5.27%

11-Apr 7,440.05 7,651.27 7,381.56 7,427.14 -2.64% 0.44% 5.44%

11-May 7,449.52 7,463.28 6,932.82 7,233.85 0.43% 0.54% 5.37%

11-Jun 7,240.14 7,291.32 6,789.01 7,265.32 -2.14% 0.20% 5.28%

11-Jul 7,296.61 7,417.00 7,103.90 7,111.31 -9.19% -0.11% 5.17%

11-Aug 7,148.11 7,197.91 6,165.06 6,487.22 -1.58% -0.97% 5.26%

11-Sep 6,559.20 6,711.06 6,208.73 6,385.76 5.74% -1.86% 5.59%

11-Oct 6,338.96 6,796.79 6,135.65 6,763.26 -10.04% -1.44% 5.37%

11-Nov 6,723.25 6,787.42 5,899.25 6,117.00 -5.69% -1.94% 5.52%

17


11-Dec 6,226.60 6,416.65 5,683.02 5,778.68 12.52% -2.67% 5.92%

12-Jan 5,797.33 6,562.69 5,734.21 6,549.31 4.60% -0.71% 5.80%

12-Feb 6,545.14 7,166.28 6,522.13 6,857.28 -1.43% 0.01% 6.37%

12-Mar 6,844.63 7,001.32 6,556.03 6,759.63 -0.91% -0.80% 6.28%

12-Apr 6,769.94 6,887.06 6,585.99 6,698.51 -6.45% -0.86% 6.11%

12-May 6,732.03 6,741.87 6,129.37 6,280.04 6.21% -1.18% 5.98%

12-Jun 6,268.76 6,686.19 6,088.62 6,682.47 -1.16% -0.70% 5.97%

12-Jul 6,695.99 6,797.05 6,407.78 6,605.70 0.40% -0.61% 5.99%

12-Aug 6,602.82 6,848.80 6,560.62 6,632.34 8.30% 0.18% 5.81%

12-Sep 6,640.17 7,243.40 6,582.88 7,206.51 -1.22% 1.01% 5.60%

12-Oct 7,221.74 7,364.54 7,070.76 7,118.77 4.85% 0.43% 5.78%

12-Nov 7,120.24 7,478.35 7,057.34 7,472.45 1.45% 1.67% 5.60%

12-Dec 7,480.17 7,627.07 7,460.59 7,581.57 1.10% 2.26% 5.53%

13-Jan 7,613.36 7,792.70 7,600.10 7,665.74 -6.77% 1.31% 5.36%

13-Feb 7,673.22 7,697.72 7,138.74 7,163.69 -1.11% 0.36% 5.24%

13-Mar 7,175.22 7,478.62 6,976.75 7,084.96 4.15% 0.39% 5.37%

13-Apr 7,105.97 7,413.56 6,872.16 7,385.25 0.76% 0.81% 5.21%

13-May 7,374.61 7,748.63 7,374.61 7,441.89 -3.80% 1.41% 5.13%

13-Jun 7,463.18 7,465.12 6,868.43 7,164.06 -2.52% 0.58% 4.99%

13-Jul 7,166.52 7,444.46 6,888.21 6,985.56 -4.56% 0.47% 4.95%

13-Aug 7,027.92 7,060.53 6,301.27 6,673.96 5.05% 0.05% 4.85%

13-Sep 6,700.73 7,413.62 6,539.15 7,019.96 8.68% -0.22% 4.83%

13-Oct 7,040.23 7,667.42 6,978.73 7,656.62 -0.77% 0.61% 4.83%

13-Nov 7,663.98 7,737.66 7,348.20 7,598.21 2.98% 0.14% 5.11%

13-Dec 7,609.06 7,862.72 7,558.21 7,828.34 -4.30% 0.27% 4.96%

14-Jan 7,850.35 7,902.67 7,401.20 7,499.02 #N/A 0.27% 4.86%

18


Table 5: BSE MID CAP

Graph 4: S&P 500

0

0.05

0.1

-5.00%

0.00%

5.00%

10.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

wma ewma


10-Jan 6,746.69 7,153.87 6,276.91 6,509.80 #N/A #N/A

10-Feb 6,499.97 6,730.62 6,294.53 6,397.82 6.19% 0.65%

10-Mar 6,429.54 6,839.83 6,429.54 6,806.18 5.41% 6.19% 0.39%

10-Apr 6,830.62 7,207.44 6,830.62 7,184.78 -4.99% 5.41% 3.94%

10-May 7,177.56 7,202.94 6,466.33 6,834.87 4.50% 0.21% 3.94%

10-Jun 6,830.92 7,198.91 6,734.06 7,149.21 3.55% 1.64% 3.78%

10-Jul 7,138.43 7,519.18 7,106.00 7,407.91 2.52% 2.12% 3.58%

10-Aug 7,438.57 7,918.03 7,438.57 7,596.84 6.22% 2.20% 3.52%

10-Sep 7,622.61 8,202.91 7,622.61 8,084.14 2.67% 2.87% 3.12%

10-Oct 8,112.29 8,521.43 8,112.29 8,302.56 -6.71% 2.84% 3.15%

10-Nov 8,302.56 8,791.10 7,339.55 7,764.02 0.50% 1.65% 3.82%

10-Dec 7,764.02 8,105.73 7,176.49 7,802.71 -12.75% 1.52% 4.38%

11-Jan 7,802.71 7,929.37 6,722.59 6,868.35 -7.48% 0.09% 5.06%

11-Feb 6,868.35 6,922.12 6,182.86 6,373.23 7.56% -0.60% 6.22%

11-Mar 6,373.23 6,894.10 6,373.23 6,873.40 3.16% 0.08% 6.09%

11-Apr 6,873.40 7,309.29 6,873.40 7,094.26 -2.63% -0.11% 6.08%

11-May 7,094.56 7,117.32 6,607.78 6,910.24 -0.82% 0.09% 6.01%

11-Jun 6,911.20 6,987.72 6,475.70 6,854.05 0.89% -0.35% 5.91%

11-Jul 6,854.13 7,115.91 6,854.13 6,915.31 -9.74% -0.57% 5.73%

11-Aug 6,915.46 6,987.82 6,014.18 6,273.60 -2.32% -1.59% 5.75%

11-Sep 6,273.56 6,534.66 6,066.34 6,129.59 2.71% -2.31% 6.09%

11-Oct 6,128.21 6,313.30 5,871.68 6,297.99 -11.25% -2.30% 5.89%

11-Nov 6,297.99 6,341.71 5,459.92 5,627.69 -9.16% -2.68% 5.90%

19


11-Dec 5,627.75 5,804.38 5,073.25 5,135.05 13.41% -3.49% 6.38%

12-Jan 5,135.05 5,895.72 5,101.95 5,871.70 8.41% -1.31% 6.43%

12-Feb 5,870.43 6,654.98 5,870.09 6,386.82 -0.64% 0.02% 7.04%

12-Mar 6,384.39 6,534.36 6,149.74 6,346.38 -0.48% -0.66% 7.13%

12-Apr 6,357.35 6,512.72 6,213.98 6,315.85 -6.68% -0.97% 6.92%

12-May 6,343.48 6,370.98 5,802.33 5,907.95 4.08% -1.31% 6.75%

12-Jun 5,907.96 6,156.07 5,734.24 6,153.72 -2.33% -0.90% 6.71%

12-Jul 6,169.76 6,362.03 5,877.38 6,012.28 -0.12% -1.17% 6.60%

12-Aug 6,014.73 6,208.92 5,936.51 6,005.02 9.56% -0.36% 6.42%

12-Sep 6,008.33 6,628.85 6,004.87 6,607.29 -0.63% 0.63% 6.19%

12-Oct 6,618.44 6,778.70 6,495.04 6,565.99 4.99% 0.35% 6.44%

12-Nov 6,569.64 6,910.65 6,530.06 6,901.99 3.01% 1.70% 6.23%

12-Dec 6,922.96 7,157.66 6,919.55 7,112.89 -2.02% 2.72% 6.14%

13-Jan 7,123.32 7,391.34 6,831.14 6,970.88 -10.08% 1.43% 6.00%

13-Feb 6,973.52 7,016.83 6,283.68 6,302.78 -2.58% -0.11% 5.89%

13-Mar 6,312.99 6,524.94 6,022.77 6,142.06 3.24% -0.27% 6.22%

13-Apr 6,157.61 6,368.11 6,029.10 6,344.04 0.71% 0.04% 6.06%

13-May 6,350.41 6,661.12 6,332.82 6,389.47 -6.88% 0.65% 5.93%

13-Jun 6,409.37 6,468.98 5,778.97 5,964.50 -7.33% -0.26% 5.77%

13-Jul 5,972.47 6,111.29 5,441.93 5,543.13 -4.48% -0.68% 5.84%

13-Aug 5,553.96 5,606.72 5,118.74 5,300.40 5.61% -1.04% 5.91%

13-Sep 5,312.48 5,744.17 5,269.87 5,605.98 8.57% -1.37% 5.82%

13-Oct 5,624.30 6,115.28 5,593.99 6,107.35 3.51% -0.60% 5.83%

13-Nov 6,117.51 6,355.01 6,063.27 6,325.58 5.83% -0.73% 6.03%

13-Dec 6,339.90 6,707.87 6,297.65 6,705.56 -6.11% -0.49% 5.90%

14-Jan 6,719.03 6,802.60 6,185.62 6,308.05 #N/A -0.49% 5.91%

20


Table 6: BSE SMALL CAP

Graph 5: BSE MID CAP

0

0.1

-20.00%

0.00%

20.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

wma ewma

% ret wma ewma

10-Jan 8,393.77 9,118.00 7,926.82 8,232.68 #N/A #N/A

10-Feb 8,248.53 8,631.19 7,973.57 8,067.40 5.19% #N/A 0.00%

10-Mar 8,085.87 8,634.96 8,085.87 8,497.43 8.02% 5.19% 1.41%

10-Apr 8,523.51 9,293.70 8,523.51 9,207.14 -7.44% 6.61% 3.50%

10-May 9,201.89 9,260.50 8,160.50 8,547.16 5.95% 1.93% 6.14%

10-Jun 8,555.98 9,132.52 8,451.15 9,071.20 3.02% 2.93% 5.85%

10-Jul 9,072.30 9,558.99 9,064.30 9,348.97 2.03% 2.95% 5.73%

10-Aug 9,375.33 10,022.13 9,375.33 9,540.56 7.13% 2.80% 4.98%

10-Sep 9,561.98 10,375.90 9,561.98 10,245.71 3.38% 3.41% 4.90%

10-Oct 10,268.65 10,918.41 10,268.65 10,597.59 -8.39% 3.41% 5.82%

10-Nov 10,597.59 11,366.68 9,233.62 9,744.71 -0.77% 2.10% 6.34%

10-Dec 9,744.71 10,229.86 8,617.43 9,670.31 -13.16% 1.81% 7.13%

11-Jan 9,670.31 9,920.58 8,333.93 8,477.82 -8.11% 0.45% 8.04%

11-Feb 8,477.82 8,551.45 7,471.77 7,817.32 4.48% -0.26% 7.79%

11-Mar 7,817.32 8,228.02 7,730.46 8,175.89 6.39% -0.32% 7.41%

11-Apr 8,175.89 8,976.17 8,175.89 8,715.31 -5.66% -0.46% 7.54%

11-May 8,713.47 8,744.52 7,999.23 8,235.72 -0.97% -0.31% 7.45%

11-Jun 8,237.06 8,381.73 7,753.00 8,156.60 1.81% -0.89% 7.17%

11-Jul 8,159.30 8,536.87 8,159.30 8,305.58 -15.24% -0.99% 7.37%

11-Aug 8,306.84 8,377.62 6,892.98 7,131.48 -3.57% -2.43% 8.00%

11-Sep 7,131.90 7,421.17 6,873.20 6,881.08 1.35% -3.32% 7.74%

11-Oct 6,879.21 6,997.39 6,638.86 6,974.61 -13.44% -3.49% 7.74%

11-Nov 6,974.90 7,007.29 5,914.55 6,097.26 -9.40% -3.91% 8.14%

21


11-Dec 6,099.34 6,248.81 5,460.31 5,550.14 15.23% -4.63% 7.96%

12-Jan 5,551.77 6,504.14 5,540.30 6,463.30 5.96% -2.26% 8.64%

12-Feb 6,464.29 7,263.11 6,464.29 6,859.97 -3.42% -1.09% 8.57%

12-Mar 6,870.15 6,914.90 6,434.17 6,629.38 2.02% -1.75% 8.30%

12-Apr 6,641.72 6,982.30 6,641.72 6,764.62 -7.58% -2.11% 8.14%

12-May 6,787.38 6,844.92 6,202.13 6,271.00 4.26% -2.27% 8.01%

12-Jun 6,283.46 6,547.61 6,132.10 6,543.75 -1.48% -1.84% 7.88%

12-Jul 6,552.00 6,870.17 6,355.15 6,447.89 -0.82% -2.11% 7.64%

12-Aug 6,456.47 6,687.31 6,336.09 6,395.09 9.29% -0.91% 7.35%

12-Sep 6,399.94 7,045.06 6,388.01 7,017.89 -0.41% 0.16% 7.53%

12-Oct 7,026.62 7,252.49 6,949.96 6,989.17 4.02% 0.02% 7.27%

12-Nov 6,993.11 7,287.09 6,975.15 7,275.65 1.42% 1.47% 7.12%

12-Dec 7,283.14 7,525.68 7,283.14 7,379.94 -4.23% 2.37% 6.94%

13-Jan 7,388.39 7,696.74 7,049.69 7,074.07 -13.09% 0.75% 6.87%

13-Feb 7,081.69 7,114.58 6,192.07 6,206.22 -6.69% -0.83% 7.32%

13-Mar 6,198.16 6,378.13 5,708.41 5,804.65 3.66% -1.11% 7.22%

13-Apr 5,812.49 6,137.88 5,812.49 6,021.16 -1.30% -0.97% 7.09%

13-May 6,027.98 6,243.54 5,935.92 5,943.46 -5.18% -0.45% 6.89%

13-Jun 5,950.67 6,018.92 5,544.60 5,643.52 -6.07% -1.23% 6.76%

13-Jul 5,647.85 5,787.89 5,257.96 5,311.06 -2.28% -1.62% 6.67%

13-Aug 5,328.60 5,407.88 5,085.56 5,191.25 5.16% -1.74% 6.47%

13-Sep 5,201.42 5,557.91 5,185.13 5,466.24 7.57% -2.08% 6.44%

13-Oct 5,489.69 5,905.11 5,468.09 5,896.11 3.39% -1.42% 6.57%

13-Nov 5,913.90 6,140.96 5,880.96 6,099.52 7.14% -1.47% 6.43%

13-Dec 6,117.84 6,567.03 6,117.84 6,551.13 -4.49% -0.99% 6.53%

14-Jan 6,570.08 6,716.80 6,164.27 6,263.35 #N/A -0.99% 6.53%

0

0.1

-20.00%

0.00%

20.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

wma ewma

Graph 6: BSE SMALL CAP

22


As shown in the above tables and graphs the BSE indices

exhibit both positive and negative values of the mean and

standard deviation but are significantly not different as

the p values are higher at 5% significance level. The table

below presents the descriptive statistics of the six indices

of BSE LTD.

Table 7: Descriptive Statistics of BSE Broad Indices

ave

rage

std

ev

skew

ne

ss

exce

ss

kurt

osi

s

me

dia

n

min

max

Q 1

:

Q 3

:

1 BSE S&P 0.00472

0.05

0.08

0.02

0.02%

-11.25%

11.04%

-2.64%

3.62%

P values 25.80% 41.79% 37.38%

2 BSE 100 0.003834

0.051378 0.12 -0.06 0.002684 -0.10938 0.123501 -0.02867 0.039679

P values 30.38%

38% 34%

3 BSE 200 0.00335

0.051418

0.09 -0.08 0.003817 -0.10988 0.125289 -0.02884 0.043246

P values 32.69%

41% 33%

4 S&P 500 0.002982

0.052057

0.04

-14% 0.004025 -0.11049 0.125184 -0.03202 0.04309

P values

34.82%

46%

30%

5 BSE MIDCAP

-0.0003

0.051418

-0.18

-0.60

0.004971

-0.12755

0.134055

-0.04735 0.042861

P values

48.66%

31.42%

14.42%

6BSE SMALL

CAP

-0.00539

0.067855

-0.26

-0.34

-0.0041

-0.15241

0.152317

-0.05419 0.043711

P values 29.45% 0.25 0.23

The table no. 7 presents the descriptive statistics of the

BSE Broad Indices. BSE S&P’s average is 0.05, standard

deviation is 0.05 and skewness is 0.08 and excess kurtosis

is positive which indicates distribution has a slightly

leptokurtic distribution. The BSE MID CAP AND BSE

SMALL CAP has shown negative averages and negative

excess kurtosis which indicates platykurtic distribution.

The remaining indices also have recorded positive mean

and negative mean values negative excess skewness and

are representing slightly platykurtic distribution. In sum,

it can be concluded that the data represents the

distribution is positively skewed and the density

distribution has negative excess kurtosis for all indices

selected in the sample except BSE S&P Sensex indices has

positive density distribution.

Table 8: showing stationarity, distribution and Arch effect

White-noise Normal Distributed ARCH Effect?

BSE S&P 2.16% 96.59% 74.31%

FALSE TRUE FALSE

BSE MIDCAP 25.26% 57.61% 75.13%

TRUE TRUE FALSE

BSE SMALL CAP 40.85% 64.97% 99.91%

TRUE TRUE FALSE

23


BSE 100 5.89% 91.97% 70.78%

TRUE TRUE FALSE

BSE 200 9.20% 93.79% 76.02%

TRUE TRUE FALSE

S&P 500 12.28% 93.38% 78.21%

TRUE TRUE FALSE

The distribution of the data is further analyzed for

studying the stationarity and trend. The reason for non

stationarity is the presence is trend and integration (Unit

root) between the observations themselves. Hence white

noise is tested and the results indicated significant serial

correlation for BSE S&P and the remaining have no serial

correlation. The data is normally distributed which is

proved through Jarque Bera test presented in the above

table. The arch effect of all the samples included in the

study reveals that there is no conditional heteroskedacity.

The correlogram analysis is made to find out the ACF and

PACF of the selected samples to fit in the appropriate

volatility forecasting model. The data has shown auto

correlation only for the first two lags, but however exhibit

no auto correlation which is evident from the arch effect.

The following tables are presented to visualize the ACF

and PACF values at different lag orders.

Table 9: Correlogram Analysis of BSE S&P

Lag ACF UL LL PACF UL LL

1 -15.63% 28.59% -28.59% -15.72% 28.59% -28.59%

2 -31.66% 28.90% -28.90% -34.58% 28.90% -28.90%

3 28.37% 29.91% -29.91% 18.76% 29.22% -29.22%

4 -15.64% 32.91% -32.91% -23.77% 29.55% -29.55%

5 -11.21% 35.28% -35.28% 0.88% 29.89% -29.89%

6 19.98% 36.24% -36.24% 1.45% 30.24% -30.24%

7 4.34% 36.95% -36.95% 15.19% 30.61% -30.61%

8 -9.38% 38.27% -38.27% -2.22% 30.99% -30.99%

9 0.35% 38.80% -38.80% -1.08% 31.38% -31.38%

10 8.66% 39.48% -39.48% 5.29% 31.79% -31.79%

Graph 7: PACF of BSE S&P

-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

PACF

UL

LL

24



1 -9.89% 28.59% -28.59% -9.97% 28.59% -28.59%

2 -32.17% 28.90% -28.90% -33.28% 28.90% -28.90%

3 20.37% 29.50% -29.50% 14.64% 29.22% -29.22%

4 -16.72% 32.60% -32.60% -30.31% 29.55% -29.55%

5 -6.74% 34.02% -34.02% 4.84% 29.89% -29.89%

6 16.75% 35.07% -35.07% -2.93% 30.24% -30.24%

7 3.50% 35.59% -35.59% 14.95% 30.61% -30.61%

8 -7.90% 36.66% -36.66% -7.54% 30.99% -30.99%

9 -1.98% 37.15% -37.15% -0.38% 31.38% -31.38%

10 12.16% 37.77% -37.77% 8.67% 31.79% -31.79%

-50%

0%

50%

1 2 3 4 5 6 7 8 9 10

ACF

UL

LLGraph 8: ACF of BSE S&P

Table 10: Correlogram Analysis of BSE 100

-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

ACF

UL

LL

Graph 9: ACF of BSE100

Graph 10: PACF of BSE 100

-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

PACF

UL

LL

25


-50%

0%

50%

1 2 3 4 5 6 7 8 9 10

PACF

UL

LL

-50%

0%

50%

1 2 3 4 5 6 7 8 9 10

ACF

UL

LL


1 -6.49% 28.59% -28.59% -6.55% 28.59% -28.59%

2 -31.42% 28.90% -28.90% -31.82% 28.90% -28.90%

3 17.64% 29.34% -29.34% 14.57% 29.22% -29.22%

4 -16.40% 32.32% -32.32% -30.78% 29.55% -29.55%

5 -6.11% 33.49% -33.49% 6.22% 29.89% -29.89%

6 15.10% 34.52% -34.52% -4.20% 30.24% -30.24%

7 3.59% 35.02% -35.02% 14.44% 30.61% -30.61%

8 -7.08% 35.97% -35.97% -8.71% 30.99% -30.99%

9 -2.55% 36.46% -36.46% 0.19% 31.38% -31.38%

10 11.93% 37.04% -37.04% 8.11% 31.79% -31.79%

Table 11: Correlogram Analysis of BSE 200

Graph 11: ACF of BSE 200

Graph No.12: PACF OF BSE 200


1 -4.53% 28.90% -28.90% -4.51% 28.90% -28.90%

2 -30.90% 29.22% -29.22% -31.05% 29.22% -29.22%

3 15.89% 29.61% - 14.26% 29.55% -29.55%

4 -16.65% 32.55% -32.55% -31.81% 29.89% -29.89%

5 -6.92% 33.58% -33.58% 4.93% 30.24% -30.24%

Table 12: Correlogram Analysis of S&P 500

26



1 15.39% 28.90% -28.90% 15.38% 28.90% -28.90%

2 -24.64% 29.22% -29.22% -27.96% 29.22% -29.22%

3 -3.06% 30.20% -30.20% 6.70% 29.55% -29.55%

4 -17.38% 32.14% -32.14% -30.99% 29.89% -29.89%

5 -12.49% 32.55% -32.55% -3.02% 30.24% -30.24%

6 8.57% 33.64% -33.64% -3.30% 30.61% -30.61%

7 4.91% 34.40% -34.40% -1.49% 30.99% -30.99%

8 -8.72% 35.01% -35.01% -11.48% 31.38% -31.38%

9 -2.61% 35.53% -35.53% -4.14% 31.79% -31.79%

10 7.25% 36.17% -36.17% -0.45% 32.22% -32.22%

-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

PACF

UL

LL

-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

ACF

UL

LL

Graph 13: ACF of S&P 500

6 14.77% 34.64% -34.64% -5.20% 30.61% -30.61%

7 3.82% 35.17% -35.17% 10.43% 30.99% -30.99%

8 -7.54% 36.13% -36.13% -9.51% 31.38% -31.38%

9 -2.06% 36.63% -36.63% -0.23% 31.79% -31.79%

10 11.58% 37.24% -37.24% 6.67% 32.22% -32.22%

Graph 14: PACF of S&P 500

Table 13: Correlogram Analysis of BSE MID CAP

27


-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

ACF

UL


1 11.31% 28.90% -28.90% 11.26% 28.90% -28.90%

2 -17.07% 29.22% -29.22% -18.40% 29.22% -29.22%

3 12.23% 29.91% -29.91% 17.04% 29.55% -29.55%

4 -17.33% 31.03% -31.03% -29.09% 29.89% -29.89%

5 -22.03% 31.78% -31.78% -8.89% 30.24% -30.24%

6 10.86% 32.87% -32.87% 2.35% 30.61% -30.61%

7 0.71% 34.35% -34.35% -5.46% 30.99% -30.99%

8 -13.74% 35.09% -35.09% -9.77% 31.38% -31.38%

9 5.81% 35.55% -35.55% -0.35% 31.79% -31.79%

10 0.77% 36.41% -36.41% -10.51% 32.22% -32.22%

-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

PACF

UL

LL

Graph 15: ACF of BSE MID CAP

-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

ACF

UL

LL

Graph 16: PACF of BSE MID CAP

Table 14: Correlogram Analysis of BSE SMALL CAP

Graph 17: ACF of BSE SMALL CAP

28


Table 15: Egarch (1,1) analysis

-100%

0%

100%

1 2 3 4 5 6 7 8 9 10

PACF

UL

LL

Graph 18: PACF of BSE SMALL CAP

BSE S&P Param Value Goodness-of-fit

µ -0.01 LLF AIC CHECK

á0 -1.46 84.05 -157.10 1.00

á1 -1.22

ã1 0.52

â1 0.60

BSE MIDCAP Param Value Goodness-of-fit

µ 0.00 LLF AIC CHECK

á0 -7.01 52.247947 -93.495894 1

á1 1.55

ã1 -0.13

â1 -0.28

BSE SMALL CAP Param Value Goodness-of-fit


á0 -7.05 52.61080854 -94.22161707 1

á1 1.24

ã1 -0.08

â1 -0.28

BSE 100 Param Value Goodness-of-fit


á0 -1.04 81.28203341 -151.5640668 1

á1 -1.00

ã1 0.26

â1 0.70



á0 -6.28 74.34 -137.69 1

á1 0.00

ã1 -0.56

â1 -0.06

S&P 500 Param Value Goodness-of-fit


á0 -1.03 80.77601833 -150.5520367 1

á1 -1.08

ã1 0.45

â1 0.69

29


Table 16: Showing the Residual Analysis

The above table no. 15 represents the EGARCH (1, 1)

model which is used to forecast the monthly volatility and

calibrated to find out the exact values. Though the alpha

and beta values are showing negative values but are still

considered for the test because the egarch after

calibration returned check value as 1.

BSE S&P AVG STDEV SKEW KURTOSIS Noise? Normal? ARCH?

0.12 1.13 -0.547 0.275051 TRUE TRUE FALSE

Target 0.00 1 0 0

SIG? FALSE FALSE FALSE FALSE

BSE MIDCAP AVG STDEV SKEW KURTOSIS Noise? Normal? ARCH?

0.03 0.817 -0.245 1.113813 TRUE TRUE TRUE

Target 0.00 1 0 0

SIG? FALSE TRUE FALSE FALSE

BSE SMALL CAP AVG STDEV SKEW KURTOSIS Noise? Normal? ARCH?

0.02 0.88 -0.32 0.816192 TRUE TRUE FALSE

Target 0.00 1 0 0


BSE 100 AVG STDEV SKEW KURTOSIS Noise? Normal? ARCH?

0.19 1.022 -0.0016 -0.42858 TRUE TRUE FALSE

Target 0.00 1 0 0


BSE 200 AVG STDEV SKEW KURTOSIS Noise? Normal? ARCH?

0.00 1 0.085 -0.07689 TRUE TRUE FALSE

Target 0.00 1 0 0


S&P 500 AVG STDEV SKEW KURTOSIS Noise? Normal? ARCH?

-0.10 1.114 -0.0318 -0.62645 TRUE TRUE FALSE

Target 0.00 1 0 0


The residual analysis has shown average and standard

deviations are not significantly different from zero and

the skewness is also not different from zero. The residuals

skewness also shows that the data is symmetrical. The

kurtosis figures in the residual analysis shows that the

tails are normal. So far, the residuals have shown a

Gaussian distribution. Examining the residual

interdependence concern among the variables, serial

correlation or linear first order dependence exhibited no

significant serial correlation. The second order

dependence (quadratic) shows insignificant correlation in

the squared residuals or absence of an arch effect. As a

result, the standardized residuals are independent and

identically Gaussian distributed. Thus, the EGARH model

assumption is met.

Table 17: Garch (1,1)

BSE S&P Param Value Goodness-of-fit


á0 0.0029 75.48 -144.96 1

á1 0.0000

30


Table 18: Showing the Residual Analysis (GARCH (1,1)

â1 0.0000

BSE MIDCAP Param Value Goodness-of-fit


á0 0.003 65.38783494 -124.7756699 1

á1 0.050

â1 0.112

BSE SMALL CAP Param Value Goodness-of-fit


á0 0.005 60.11 -114.21 1

á1 0.000

â1 0.000



á0 0.00306 74.12374497 -142.2474899 1

á1 0.00000

â1 0.00000



á0 0.00300 74.1574746 -142.3149492 1

á1 0.00000

â1 0.00000

S&P 500 Param Value Goodness-of-fit


á0 0.0000 149.80 -293.59 1

á1 0.8706

â1 0.0450

BSE S&P AVG STDEV SKEW KURTOSIS Noise? Normal? ARCH?

0.00 0.93 0.08 0.02 FALSE TRUE FALSE

Target 0.00 1.00 0.00 0.00


31


The highest volatility is recorded by BSE MID CAP followed

by BSE SMALL CAP as per the Egarch(1,1) analysis. The

remaining four indices are in a range of 4.55% to 5.14%.

As per the Garch (1, 1) analysis, a BSE SMALL CAP index

has shown the highest volatility followed BSE MID CAP

indices. The lowest volatility is recorded by S&P 100 as per

both the models. The long run volatility is calculated by

multiplying the monthly volatility with square root of 12.

(one year).

Table 19: Monthly Volatility forecast Annual Volatility


BSE MIDCAP 0.02 1.012 -0.232 -0.596 TRUE TRUE FALSE

Target 0.00 1 0 0


BSE SMALL CAP 0.00 0.99 -0.26 -0.34 TRUE TRUE FALSE

Target 0.00 1.00 0.00 0.00


BSE 100 0.00 0.94 0.09 -0.08 TRUE TRUE FALSE

Target 0.00 1.00 0.00 0.00


BSE 200 0.00 1.131 0.777 1.086 FALSE TRUE FALSE

Target 0.00 1 0 0

SIG? FALSE FALSE TRUE FALSE

Egarch(1,1) Garch(1,1) Egarch(1,1) Garch(1,1)

BSE S&P 4.85% 5.40% 16.80 18.71%

BSE MIDCAP 10.59% 6.02 36.68% 20.85

BSE SMALL CAP 9.36% 6.87 32.42% 23.80

BSE 100 4.88% 5.54 16.90% 19.19

BSE 200 5.14% 5.48 17.81% 18.98

S&P 500 4.55% 1.52 15.76% 5.27

Graph 19: BSE S&P SENSEX

4.00%

5.00%

6.00%

7.00%

1 2 3 4 5 6 7 8 9 10 11 12

STD

TS

32


4.50%

4.70%

4.90%

5.10%

1 2 3 4 5 6 7 8 9 10 11 12

STD

TS

7.00%8.00%9.00%

10.00%11.00%

1 2 3 4 5 6 7 8 9 10 11 12

STD TS

9.00%

9.50%

10.00%

10.50%

11.00%

1 2 3 4 5 6 7 8 9 10 11 12

STD TS

Graph 20: BSE MIDCAP

Graph 21: BSE SMALL CAP

Graph 22: S&P500

Graph 23: BSE 100

4.70%

5.20%

5.70%

1 2 3 4 5 6 7 8 9 10 11 12

STD

TS

33


0.05141

0.051412

0.051414

0.051416

0.051418

0.05142

1 2 3 4 5 6 7 8 9 10 11 12

STD

TS

Conclusion

The analysis through GARCH (1,1) and EGARCH (1,1)

forecasted volatility of the selected BSE BROAD indices

and indicated BSE MIDCAP and BSE SMALL CAP has

highest volatility. GARCH (1,1) model has shown low

volatility than the EGARCH (1,1). Volatility forecasting

through econometric techniques presents the forecast

based on the past data, however, the volatility of stock

markets are affected by many exogenous variables which

are highly unpredictable. This analysis attempts to

present risk metrics which helps to manage risks such as

to measure the difference in actual and expected

volatility which can act as an effective hedging tool,

portfolio diversification and overall risk management.

References

• Deb, S. S., Vuyyuri, S and Roy, B. (2003), "Modelling

Stock Market Volatility in India: A Comparison of

Univariate Deterministic Models", ICFAI Journal of

Applied Finance, Vol. 9, No. 7, 19-33.

• Engle, R. (1982), "Autoregressive Conditional

Heteroscedasticity with Estimates of the Variance of

UK Inflation", Econometrica, Vol. 50, 987-1008.

• Mishra, P. K., K. B. Das, and B. B. Pradhan, (2009),

"Capital Market Volatility--An Econometric

Analysis", The Empirical Economics Letter, Vol. 8, No.

5, pp. 739-746.

• Nelson, D. B. (1991), "Conditional Heteroscedasticity

in Asset Returns: A New Approach", Econometrica,

Vol. 59, No. 2, 347-370.

• Liu, S. and Brorsen, B. (1995) Maximum

likelihoodestimation of a GARCH-stable model,

Journal of Applied Econometrics, 10, 273–85.

• Loudon, G., Watt, W. and Yadav, P. (2000) An

empiricalanalysis of alternative parametric ARCH

models, Journal of Applied Econometrics, 15,

117–36.

• Mandelbrot, B. (1963) The variation of certain

speculative prices, Journal of Business, 36, 394–419.

• McMillan, D., Speight, A. and Apgwilym, O. (2000)

Forecasting UK stock market volatility, Applied

Financial Economics, 10, 435–48.

• Nelson, D. (1991) Conditional heteroskedasticity in

asset returns: a new approach, Econometrica, 59,

349–70.

• Pagan, A. and Schwert, G. (1990) Alternative models

for conditional stock volatility, Journal of

Econometrics, 45, 267–90.

• Pena, J. (1995) Daily seasonalities and stock market

reforms in Spain, Applied Financial Economics,

5,419–23.

• Poon, S. and Granger, C. (2003) Forecasting financial

market volatility: a review, Journal of Economic

Literature, 41, 478–539.

• Siourounis, D. (2002) Modeling volatility and testing

for efficiency in emerging capital markets: the case

of the Athens stock exchange, Applied Financial

Economics, 12, 47–55.

• Yu, J. (2002) Forecasting volatility in the New Zealand

stock market, Applied Financial Economics, 12,

193–202.

Graph 24: BSE 200

34


The economic reforms of 1991 have completely changed the scenario in which business is being done in India. The reform policies include opening-up for international trade and investment, deregulation, initiation of privatization, tax reforms, and inflation-controlling measures. This has been done to integrate the Indian economy with the world economy. The rise of business over the internet is one of the outcomes of such economic reforms. The advancement in Information Technology (IT) also helps the e-commerce to grow rapidly. This paper extends the research on e-commerce in India. We try to analyze how IT infrastructure acts as a barrier to e-commerce. The paper uses a survey based approach to draw the conclusions. Using data from 300 respondents, we came to the conclusion that Internet unavailability, slow access of internet, security concerns and the transaction process are the major concerns exist. The paper shows the impact of these factors on consumer satisfaction on e-commerce. Finally, we try to give some suggestions to improve the IT infrastructure so that e-commerce business can achieve their potential.

Keywords: E-commerce, IT Infrastructure, India, Internet.

Analysis of it Infrastructure As A Factor AffectingE-commerce and its Impact on Consumer Satisfaction

Abstract

Introduction

With the advancement in the Information Technology

and the explosive increase of Internet users, companies

all around the globe saw this development as an

opportunity to reach to their customers. They have been

shifting from the traditional offline mode of transaction

to the latest new online mode of transaction. The

concept of online transaction relates to e-commerce as a

practice of buying and selling products over the internet

(Lee, 2008). In the present era of Globalization and the

dot-com burst, E-commerce business has grown

significantly. The geographical distance between buyer

and seller offers no problem in doing business.

According to a survey the India’s e-commerce market is

of $16 billion in year 2013 and is expected to reach

Rs 1,07,800 crores (US$ 24 billion) by the year 2015 and

further expected to reach $56 billion in year 2023 as per

survey conducted by industry body ASSOCHAM [W1]. So

the e-commerce industry in India is growing day by day

on a rapid rate. India is currently third largest internet

user in the world and expected to become the second

with 243 million internet user by June, 2014 as per

report of I-Cube 2013 report [W2] (Internet and Mobile

Association of India (IAMAI) and IMRB International).

With the technological advancement in the Mobile

telephony the 3G and 4G services which provide a fast

access to the internet is also going to increase and also

going to increase the users day by day. By 2015 India 4G

services is projected to be 28 million source AVENDUS

[W3]. More is the users on Internet; more is the

opportunity for e-commerce.

E-commerce provides ease and convenience to the

users. Consumers can enjoy benefits such as availability

Dr. Shailendra Kumar*Vikalp**

Shubham Arya**Shreyash Bharadwaj**

*Assistant Professor, MSCLIS Divison, Indian Institute of Information Technology, Allahabad, UP. E-mail: [email protected]**Students, MBA, Indian Institute of Information Technology, Allahabad, UP. E-mail: [email protected], [email protected], [email protected] 35

of goods at lower cost, wider choice and more important

time safety. People can buy goods with a click of mouse

button without going to shops physically. Various online

services such as banking, ticketing (including airlines, bus,

railways), bill payments, hotel booking etc. also provides

tremendous benefit to the customers. E-commerce also

includes electronic advertising, electronic payment

system, electronic marketing, electronic customer

support service and electronic order and delivery.

Customer satisfaction must be the main focus of any

business now days. If the customer is not satisfied with

the interaction, he/she may lose faith and confidence on

e-commerce. They start avoiding visiting such websites.

Success of e-commerce business depends on how easily

the website can be accessed; how much reliable is the

server on which website is hosted; how much security

related issues are being taken care of; and most important

how much secure the transaction process is. Absence of

reliability and security make the websites vulnerable to

various e-attacks. Customer may feel shy to visit these e-

commerce websites and hence, the business gets

affected; the essence of e-commerce can never be

realized for both consumer and businessman. The

interaction with the websites according to a user

prospective relate to EES i.e. Effectiveness, Efficiency and

Satisfaction.

E-commerce and India

There are basically two types of e-commerce market

which can be classified as a) Business-to-Business (B2B)

and b) Business to-Consumer (B2C).

Business-to-Business (B2B):

In this type of market, the transactions happen between

organizations or businesses such as transaction between

manufacturer and wholesaler or between wholesaler and

retailer. It includes purchasing, procurement, retail

management, inventory management, payment

management, services and support etc. India’s largest

B2B portal Tradeindia, maintained by Infocom Network

Ltd, stated that e-commerce transactions in India show a

growth rate of 30 percent to 40 percent and will soon

reach the $100 billion mark. Seeing the growing Indian

market, many foreign branded companies are willing to

enter the market to take full advantage. Some of B2B

exchanges in India are tradeindia.com, Alibaba.com,

AuctionIndia.com, Indiamart.com, TeaAuction.com,

MetalJunction.com, Chemdex (www.chemdex.com),

Fastparts (www.fastparts.com), and FreeMarkets

(www.freemarkets.com) etc. [P1]

Business to-Consumer (B2C):

In this type of market, the transactions happen between

business and customer. The products are sold to the end

customer. It includes business such as e-retailing,

banking, tax payment, bill payment, hotel room booking,

entertainment, matrimonial sites, job sites, etc. Some of

B2C businesses in India are flipkart.com, jabong.com,

irctc.co.in, shaadi.com, makemytrip.com etc. [P1]

Customers are the backbone for the success of any

business. Any type of industry whether manufacturing or

service requires customers to sell their products or

utilizes their services in order to achieve the desired profit

target. India, the second fastest growing economy is also

the second most populous country in the world. Hence,

India offers a great opportunity for the e-commerce

market to grow and help in reviving the Indian economy.

The role of government is to provide a legal framework for

E Commerce so that basic rights such as privacy,

intellectual property, Prevention of fraud, consumer

protections etc are all taken care of. [P2]

Literature Review

The term IT INFRASTRUCTURE is defined in a standard

called Information Technology Infrastructure Library (ITIL)

v3 as a combined set of hardware, software, networks,

facilities, etc. (including all of the information

technology), in order to develop, test, deliver, monitor,

control or support IT services. Associated people,

processes and documentation are not part of IT

Infrastructure. [W4] Thus for our research, we link IT

infrastructure as follows:

36


HARDWARE

NETWORK

SOFTWARE

FACILITIES

Speed or Slow Access of Internet

Low Network Connectivity

Absence of Virtual Keyboard

Security Issues

Server Reliability

Computer Illiteracy

Transaction Process

Expectation-Confirmation Theory (ECT) was proposed by

Oliver [1980] to study consumer satisfaction and

repurchase behavior. The ECT theory states that

consumers firstly form an initial expectation about any

product/service before purchasing. After some period of

consumption, they start building perceptions about the

performance of the consumed product/service. Next, the

consumers start deciding on their level of satisfaction.

The level of satisfaction is obtained by comparing the

actual performance of the product/service against their

initial expectation of the performance. Consequently,

satisfied consumers will form repurchasing intentions.

Jakob Nielsen [W5] the famous web usability consultant

finds satisfaction as one of the quality attribute of the

usability of websites for e-commerce:

Analysis of it Infrastructure As A Factor Affecting E-commerce and its Impact on Consumer Satisfaction

37

Tagrul U. Daim (2011) describes how IT infrastructure

refreshing can provide larger return on investment over

time.

Elizabeth Goldsmith and Sue L.T. McGregor (2000)

analyzed the impact of e-commerce on consumers, public

policy, business and education.

Arvind panagariya (1999) describes the opportunities

offered by e-commerce for the WTO and developing

countries.

Nir B.kshetri (2001) use three categories of feedback

systems–economic, sociopolitical and cognitive—to offer

a simple model of e-commerce barriers in the developing

world.

Mustafa I Eid (2011) examines the determinants of

customer satisfaction, trust and loyalty towards e-

commerce.

38


A descriptive research design is used for the study. The

paper uses a survey based approach to draw the

conclusions. Random sampling has been done to collect

the data. Questionnaire was the instrument used for data

collection in order to understand the status of the subject

under study. The questionnaire consisted of 16 questions

being prepared in such a way that they enhance the

validity of responses. A five-point Likert-type scale (1 =

Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and

5 = Strongly Agree) was used to reach the conclusion.

Firstly, we conducted an online survey through the

questionnaire in order to get responses from the

population. The questionnaire consisted of various

questions relevant to the subject under study.

Than we code the variables for performing Regression

analysis and Correlation analysis. The various variables

are coded as:

1) How was the interaction between you and online

shopping website As INTER_SHOP

2) “Speed or Slow Access of Internet” plays a vital role

in online shopping As SPEED_ROLE

3) “Low Network Connectivity” restrains the consumer

from online shopping As LOW_NETWORK

4) “Transactions process” should be very smooth and

transparent As TRANSAC_PROCESS

5) “Security issues” should be give higher priority than

any other feature As SECURITY

6) Does absence of “Virtual keyboard” makes you feel

unsecure while payments As VIRTUAL_KEYWORD

7) Number of feedbacks given by the consumer makes

the website trustable As FEEDBACK

Among the above variables INTER_SHOP that relates to

the customer satisfaction as how was the interaction

between customers and online shopping website is the

dependent variable.

While the other variables i .e. SPEED_ROLE,

LOW_NETWORK, TRANSAC_PROCESS, SECURITY,

VIRTUAL_KEYWORD and FEEDBACK that relates to the IT

infrastructure acts as an independent variables.

To assess the degree of correctness of the results,

questionnaire Reliability Analysis is done. Cronbach's

Alpha is the most common measure to determine the

reliability. It is used when the survey/questionnaire has

multiple Likert scale type questions and we want to

determine if the scale is reliable or not.

After performing the Reliability analysis we find that

Cronbach's alpha is 0.805, which indicates a high level of

internal consistency for our scale.

Secondly, we took an insight on how closely these

variables are associated with each other. Correlation

analysis is being performed on these variables to

determine the association between them. The Value of

the correlation coefficient is always between -1 and +1. A

correlation coefficient of +1 indicates that two variables

are perfectly related in a positive linear sense,as variable

X increases, variable Y increases. A correlation coefficient

of -1 indicates that two variables are perfectly related in a

negative linear sense, as variable X decreases, variable Y

decreases and a correlation coefficient of 0 indicates that

there is no linear relationship between the two variables.

Finally, we performed Regression analysis to analyze the

impact of these independent variables on the dependent

USABILITY

EFFICIENCY MEMORABILITY SATISFACTION LEARNABILITY ERRORS


variable. Regression analysis is used to identify the

relationship between a dependent variable and one or

more independent variables. The results are interpreted

through the values of R squared (Coefficient of

determination), coefficients and the p-value obtained

from regression analysis.

Results and Interpretation

From the above analysis we find that our result is reliable.

The value of F is .076868 which is less than .10 at 90% level

of confidence.

The p-value is 0.076868 which is less than .1 means that

the speed and slow access of internet definitely has

impact on the customer satisfaction.

R Square is .011136 means that 1.136% of customer

satisfaction can be obtained by improving the speed and

slow access of internet.

From the coefficients, it is clear that for every percent

increase in speed of internet, customer satisfaction

increases by 10.05 %.

CORRELATION

Inter_shop Speed_role Low_network Transac_process Security Virtual_keyword Feedback

Inter_shop 1

Speed_role 0.105526145 1

Low_network 0.1211690 0.393929275 1

Transac_process 0.106287193 0.278562589 0.469044623 1

Security 0.121207779 0.318894617 0.738276985 0.73598141 1

Virtual_keyword -0.02987376 0.19058036 0.224715213 0.173430293 0.1945515 1

Feedback -0.00835337 0.204421236 0.062177255 0.062847007 0.0380863 0.029740042 1

REGRESSION

Impact of speed and slow access of internet on customer satisfaction

SUMMARY OUTPUT

Regression Statistics

R Square 0.011136

Standard Error

0.656716

Observations

282

ANOVA

df SS MS F Significance F

Regression 1 1.359867 1.359867 3.153127 0.076868*

Residual 280

120.7572

0.431276

Total 281 122.117

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

3.337694

0.238417

13.99937

4.05E-34

2.868376

3.807012

2.868376

3.807012

Speed_role 0.100572* 0.056638 1.775705 0.076868 -0.01092 0.212063 -0.01092 0.212063

*Significant at 90% level of confidence

39


Impact of low network connectivity on customer satisfaction

SUMMARY OUTPUT

ANOVA

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 3.406591

0.17513367

19.45138

6.53E-54

3.061845534

3.75

3.0618

3.751337

Low_network 0.089077* 0.043609788 2.042597 0.042029 0.003232547 0.17 0.0032 0.174922




of confidence.


the low network connectivity definitely has impact on the

customer satisfaction.

R Square is .014682 means that 1.46 % of customer

satisfaction can be obtained by improving the network

connectivity.


improvement in network connectivity, customer

satisfaction increases by 8.9 %.

Impact of Transaction Process on customer satisfaction

SUMMARY OUTPUT

ANOVA

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 3.359986

0.224455

14.96953

1.27E-37

2.918152

3.801819

2.918152

3.801819

Transac_process 0.09282* 0.051897 1.788657 0.074751 -0.00933 0.194984 -0.00933 0.194984




R Square 0.014682

Standard Error

0.655537

Observations

282


Regression 1 1.792915815 1.792916 4.172202 0.04202882*

Residual 280

120.3241055

0.429729

Total 281 122.1170213


R Square 0.011297

Standard Error

0.656662

Observations

282


Regression 1 1.379552 1.379552 3.199293 0.07475*

Residual 280

120.7375

0.431205

Total 281 122.117


Regression 1 0.108982 0.108982 0.250107 0.61739*

Residual 280

122.008

0.435743

Total 281 122.117

ANOVA


R Square 0.000892

Standard Error

0.660108

Observations

282

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 3.38572

0.185051

18.29613

9.84E-50

3.021452

3.749988

3.021452

3.749988

Security 0.08946* 0.043786 2.043259 0.041963 0.003275 0.175656 0.003275 0.175656



Regression 1 1.794061 1.794061 4.174906 0.04196*

Residual 280

120.323

0.429725

Total 281 122.117

Impact of Security issues on customer satisfaction



of confidence.


the transaction process definitely has impact on the

customer satisfaction.


satisfaction can be obtained by improving the transaction

process.


improvement in the transaction process, customer

satisfaction increases by 9.28%.

41


SUMMARY OUTPUT


R Square 0.014691

Standard Error

0.655534

Observations

282

ANOVA



of confidence.


the security issues definitely have impact on the customer

satisfaction.


satisfaction can be obtained by improving upon the

transaction process.


improvement in the security issues, customer satisfaction

increases by 8.94 %.

Impact of absence of virtual keyboard on customer satisfaction

SUMMARY OUTPUT

Coefficients

Standard

Error

t Stat

P-value

Lower 95%

Upper 95%

Lower

95.0%

Upper 95.0%

Intercept 3.779773

0.179307

21.07984

9.81E-60

3.426811

4.132735

3.426811

4.132734984

Feedback -0.00626* 0.044767 -0.13978 0.888932 -0.09438 0.081866 -0.09438 0.081865609


Regression 1 0.008521 0.008521 0.019539 0.88893*

Residual 280 122.1085 0.436102

Total 281

122.117


R Square 6.98E-05

Standard Error

0.66038

Observations

282

Impact of customer feedback on customer satisfaction

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 3.813817

0.123398

30.90657

3.1E-92

3.570911

4.056723

3.570911

4.056723

Virtual_keyword -0.01699* 0.033971 -0.50011 0.617393 -0.08386 0.049881 -0.08386 0.049881


From the above analysis we find that the result is not

reliable. The value of F is .61739 which is greater than .10

at 90% level of confidence.

Hence we can say that the absence of virtual keyboard

does not pose any impact on the customer satisfaction

while transacting with the e-commerce websites.


From the above analysis we find that the result is not

reliable. The value of F is .88893 which is greater than .10

at 90% level of confidence.

Hence we can say that the feedbacks provided by the

users on the e-commerce websites do not pose any

impact on the customer satisfaction while interacting

with the e-commerce websites.

Findings

If we don’t know the problem, we can’t find the solution.

In order to satisfy the customer after they visit and

transact from any e-commerce website, companies must

focus on the problems the customer faced. The main

problem of the customer is regarding the payment. Hence

we try to find out the various problems regarding

payment in our research work.

From the data collected, the following are some of the

problems:

42


SUMMARY OUTPUT

ANOVA

0

20

40

60

80

100

120

140

160

180

Some banks Credit/Debit

card accepted

Absence of virtual

keyboard

Different regulation regarding e signature

Server reliability

Series1

What are the problems customer face regarding payment?

It is essential for e-commerce companies to know about

the customers visit and buying pattern. Also they must

focus on the products which the customer usually buys.

In the discussion of customer satisfaction these things

should be keep in mind.

Clothing

Gadgets

Crockrey

Furniture

Electronics

Clothing

Gadgets

Crockrey

Furniture

Electronics

37%

26%

9%

8%

21%

0 44 88 132 176 220

What do you usually buy from online stores?

43


Recommendations

The customer satisfaction is required for e-commerce

business to cherish. A satisfied customer would surely

visit the e-commerce website again. To satisfy the

customer:

1) E-commerce companies must refresh their IT

infrastructure.

2) Companies must focus on improving the Transaction

process on their websites.

3) Companies must have to choose the server which is

reliable and trustable.

4) E-commerce websites must be secured. A vulnerable

website may be attacked and can be hacked easily.

The role of government can prove to be very crucial in the

success of e-commerce in India. From the research it is

clear that low network connectivity and speed or slow

accesses of internet are among the crucial factors behind

the customer satisfaction. The government should focus

on the following areas:

1) Must aim to provide access of faster internet through

broadband and wi-fi channels.

2) Improving the mobile telephony services like 3G etc.

3) Improving the network connectivity in remote areas.

4) Investment in maintaining the network towers

throughout the country.

According to the 2011 Census data, only 3.1 percent of

total houses have Internet access in India. Chandigarh

(U/T) has the highest 18.8% of total households Internet

users, followed by NCT of Delhi (U/T) 17.6% and Goa

12.7%. Bihar has below 1% of total households Internet

users which is the lowest in India. Other states like

Maharashtra have 5.8%, Uttar Pradesh has 1.9% and West

Bengal has 2.2% of total households Internet density only.

[W6]

The government must target to increase the percent of

total houses that have internet access in India. For this a

lot of investment is needed in the IT field in improving the

IT infrastructure.

Conclusion

As per the research performed, Low network

connectivity, speed or slow accesses of internet,

transaction process and security issues relates to the

satisfaction of the customer. By improving on these

factors, more customer satisfaction can be achieved.

Efforts from both the e-commerce companies and the

government are required to work on these factors. IT

infrastructure refreshing is required. The e-commerce

business which is expected to reach Rs 1, 07,800 crores

(US$ 24 billion) by the year 2015 is one of the important

contributor to the economy of the nation. The focus of the

government must be to improve internet connectivity

and availability of network. They must also focus on

improving the IT education in our country. The U.P

government free laptop scheme can be seen as a step

towards making the state IT centric state and improving

the IT infrastructure in the state. It is essential for our

country to find the impact of e-commerce on their

economies and hence try to create a policy and

environment that favors the growth and development of

e-commerce.

44


References

• Lee, I. (2008) “E-business models, services, and

communications”

• Tugrul U. Daim; Matthew Letts, Mark Krampits,

Rabah Khamis and Pranabesh Dash; Mitali Monalisa

and Jay Justice, USA (2011) IT infrastructure refresh

planning for enterprises: a business process

perspective.

• Godwin J. Udo, USA (2001) A survey study on privacy

and security concerns as major barriers for e-

commerce.

• Nir Kshetri, Barriers to e-commerce and competitive

business models in developing countries: A case

study (February) (2007).

• Voloper Creations Inc. (2008) e-commerce white

paper.

• Didar Singh (2002) Electronic Commerce: Issues Of

Policy And Strategy For India.

• [P1] Sarbapriya Ray (2011) Emerging Trend of E-

Commerce in India: Some Crucial Issues, Prospects

and Challenges.

• [P2] Shweta Sharma, Sugandha Mittal Prospects of

E-Commerce in India.

• Mustafa I. Eid (2011) Determinants of e-commerce

customer satisfaction, trust, and loyalty in Saudi

Arabia

• Arvind panagariya (1999) E-Commerce, WTO and

Developing Countries

• Elizabeth Goldsmith and Sue L.T. McGregor (2000) E-

commerce: consumer protection issues and

implications for research and education

Web References

• Associated Chambers of Commerce and Industry of

India (Assocham)

http://www.techgig.com/tech-news/editors-

pick/India-s-e-commerce-market-rose-88-in-2013-

Survey-20923

• http://www.iamai.in/PRelease_detail.aspx?nid=

3222&NMonth=11&NYear=2013

• htt p : / / w w w. a v e n d u s . co m / F i l e s / Fu n d % 2 0

Performance%20PDF/Avendus_ReportIndia%27s_

Mobile_Internet-2013.pdf

• http://en.wikipedia.org/wiki/ Information_

technology_management

• http://en.wikipedia.org/wiki/Jakob_Nielsen_%28

usability_consultant%29

• http://updateox.com/india/state-wise-internet-

users-in-india-census-2011/

45


We’re aware about organizations across the globe struggling with how best to introduce social networking tools - blogs, micro blogs like Twitter, Collaboration platforms like Facebook to internal audiences. Social networking has always been stated for distracting employees but with its proper and regulated use, it can have the obverse effect. Whether it's Facebook or Twitter or any other social networking channel, these networks help in improving communication between an employer and the employees, as well as employees and their colleagues. Social networking provides a medium of receiving immediate feedbacks in forms of appreciation and criticism which can help employees engage with fellow team-mates and even customers.

Keywords: Social Networking, Employee Engagement

Impact of Social Networking on Employee Engagement at Workplace: An Empirical Study Based on IT Industry in India

Abstract

Introduction

William A Kahn (Kahn, 1990, pg 692, Academy of

Management Journal) while explaining engagement and

disengagement at work and the psychological conditions

involved explains that “people can use varying degrees

of their selves physically, cognitively, and emotionally in

work role performances, which has implications for both

their work and performances.” Employee engagement is

harnessing the organization member selves’ to their

work roles.

The psychical, emotional dimension deals with the

credence as well as perception of employees about their

workplace, its leaders and the work culture. It also takes

into account that whether the employees have a positive

or a negative proclivity towards the organization and its

leaders. The physical dimension of employee

engagement relates to the consistency of work and

effort maintained by the employees to perform their

roles.

Although it has been acknowledged that “employee

engagement is a multi-faceted construct”, as previously

suggested by (Kahn 1990), (Truss et al 2006) define

“employee engagement simply as ‘passion for work’, a

psychological state which encompasses the three

dimensions of engagement discussed by (Kahn 1990),

and captures the common theme running through all

the definitions” (CIPD report-Working Life: Employee

Attitudes and Engagement, (Truss et al 2006)).

We’re familiar to the fact that man is a social animal and

with the coming of age, the social networking going

online and virtual instead of the traditional and actual

face to face communication, had a huge impact on the

Bhavya Mathur*Divyanhsu Ojha**

Dr. Shailendra Kumar***

*&**Student, MBA (IT), Indian Institute of Information Technology, Allahabad, UP (India). Email: [email protected] & [email protected]***Asstt. Prof., MBA (IT), MSCLIS Division, Indian Institute of Information Technology, Allahabad, UP (India). Email: [email protected]

temperament of those working and particularly of those

working under stress.

Social networking involves the use of an online platform

or website that helps people to communicate, for a social

or organizational purpose, through a variety of services,

most of which are web-based. As this is a relatively new

phenomenon and there is no common international

regulatory body, it is difficult to find an official or

universally agreed definition (Broughton, Higgins, Hicks

and Cox, 2009).

However, Boyd and Ellison’s overview (Boyd and Ellison,

2007) of the comments on the particular communication

opportunities provided by social network sites:

“A form of real-time direct text-based communication

between two or more people using personal computers

or other devices (http://en.wikipedia.org/wiki/

Instant_messaging).

We define social network sites as web-based services that

allow Individuals to:

1. construct a public or semi-public profile within a

bounded system,

2. articulate a list of other users with whom they share

a connection, and

3. View and traverse their list of connections and those

made by others within the system. (Ellison N.B. &

Boyd D, 2013 (Chapter 8: Sociality through Social

Network Sites, Pg 1))

The nomenclature of these connections may vary from

site to site (Social Network Sites: Definition, History, and

Scholarship (Boyd and Ellison, Dec 2007), Pg 211). What

makes social network sites unique is not that they allow

individuals to meet strangers, but rather that they enable

users to articulate and make visible their social networks

(Social Networking Site For Self Portfolio: N. Sampath

Kumar, Chandran, Kumar, Karnavel, March 2013, Pg 2).

While social networking has 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 (A Proof-Carrying Code Based

Framework for Social Networking, Sorren C. Hanvey). The

public display of connections is an important component

of Social networking. Beyond profiles, Social networking

varies greatly in their features and user base. Some have

photo sharing or video-sharing capabilities; others have

built-in blogging and instant messaging technology

(Hybrid-Context instructional Model. The Internet and

the Classrooms: The Way Teachers Experience It, Udeme

T Ndon, Dec 2006, Pg 27).”


Social networking establishes an open dialogue with

employees, solicits feedback and criticism, invites

interactions and discussions, and has a noticeable, active

senior leadership presence (Unleashing the Power of rdSocial Media Within Your Organization, 3 Employee

Engagement Study, Dec 2011, Page 3).

“Social media is defined as a web service that allow

individuals to construct a public or semi-public profile

within a system with definite boundaries, articulate a list

of other participants in the system whom they share a

connection and, view and explore their list of connections

and of those made by others in the system. The nature

and connection rules may vary from one service to the

other.” (Boyd DM, Ellison NB; 2007, page 2, Social

Network Sites: A De?nition)

Social networks are information-allied tools and

technologies that are used to share information and

expedite communications with internal and external

audiences. Some commonly used social networking

channels are Facebook, Twitter and LinkedIn, but social

networking can also be done in other varied forms which

may include online forums or communities, online

profiles, pictures and video uploads, E-mail. Social

networking also includes applications known as “Web

2.0,” a term encircling social mediums such as blogs,

texting, and other applications like Google Docs, Google

Blogs (BlogSpot).

Groups of interest in media that is predominantly social in

nature are found to facilitate efficient formation of

communities around a common interest such as career,

culture or politics. This involves “the interaction between

people, creating, sharing, exchanging and commenting in

virtual communities and networks (Toivonen S, 2007)”.

Various researches have advertised the benefits offered

by social networking at work place such as, effective

communication channels, efficient information and

47


channels, mediums and ways with which people virtually

connect with each other are rapidly increasing. The pace

of communication has elevated to enormous proportions

and numbers. People find social networking as an easy

and a fine medium to keep track and pace with each

other’s lives. The use of social networking at workplace

among professionals has introduced a contemporary way

of communication which has led to efficient and effective

professional as well as personal information sharing. At

the corporate level, it’s necessary for businesses to be

agile to face the competition. And since communicating

and sharing information with people is fundamental to

the success of any business, businesses now days are

taking measures to employ tools that can provide that

competitive advantage.

This research paper will analyze the effects of use of social

networking at workplace and its ability to impact the

performance of an individual at workplace. It aims at

finding the overall relationship between the average time

spend by a person on any of the social networking sites

during working hours and their performance at the

workplace.

Objectives

1. To understand the relationship between Social

Networking and Employee Engagement in IT

industry.

2. To find out the impact of Social Networking on

Employee Engagement at Workplace in IT industry.

3. To provide suggestions for improving Employee

Engagement in IT industry.

Hypothesis

I. Null Hypothesis:

H : The use of social networking at workplace does 01

not drives employee engagement.

II. Null Hypothesis:

H : There is no difference in perception regarding 02

relationship between employee engagement and

use of social networking at various levels of

management.

III. Null Hypothesis:

H : There is no difference in perception regarding 03

knowledge sharing across the hierarchy, channels for

informal learning, development and improvement of soft

skills, job performance and satisfaction.

Direct and Indirect online and Direct and Indirect offline

network groups and the ties developed in that manner

have an impact on idea sharing and strengthening the

engagement of an employee at workplace and related

works.

However it is not identified how managerial evaluation of

job performance is affected by employee’s apprehension

about

• The use of social networking.

• Social networking as a platform for exchanging

information on personal levels.

• Regulated use of social networks at workplace.

• The affect of social networking on the behavioral

aspects of an individual at workplace.

Therefore, the use of social networking at workplace

poses similar risks to the reputation of individual as well

as the organization permitting the official use of social

networking.

Development Dimensions International (DDI, 2005, Pg

27) states “that a manager must do five things to create a

highly engaged workforce. They are:

1. Align efforts with strategy

2. Empower

3. Promote and encourage teamwork and

collaboration

4. Help people grow and develop

5. Provide support and recognition where appropriate”

Adam Wootton, director of social media and games at a

New York City based global professional services company

Towers Watson, says “social media can drive engagement

and productivity. If companies want to communicate with

their employees effectively, they need to meet the

employees on the same street they’re on. If employees

use these tools and techniques to communicate, they’re

going to react well to this media.” (Social media: A tool to

boost employee engagement, productivity, Amanda

McGrory-Dixon, April 19, 2013, Pg 1)

Communicating using the various forms of social

networking is not a new concept at the personal level. The

48


relationship between employee engagement and

use of social networking at various work

specializations in an organization.


A descriptive research design is used for the study, which

is a process of collecting data from the members of a

population in order to understand the status of the

subject under study with respect to one or more variables

to determine the frequency of occurrence or the extent to

which variables are related. Questionnaire was the

instrument used for data collection. The analysis uses

random sampling and can be considered a representative

of IT professionals working across India.

The questionnaire consisted of 15 questions being close-

ended to enhance validity of response. A 1-5 type Likert

scale was used to measure respondents’ agreement with

the concepts under investigation. Given below is a brief

description of the variables considered as dependent and

independent.

Dependent variable or the Y-variable as Employee

Engagement

Employee Engagement is defined by five variables that

are: Frequency of better ideas for work (IDEA),

Knowledge-sharing and training (SHARE), Effective

communication (COMM), Effectiveness in completion of

the task assigned (TASK), improved work related decision

making (WORK_DECISION).

Employee Engagement = f (Social Networking)

Independent or the X-variable as Social Networking

Coded as OFFICIAL, Social networking is defined and

analyzed on the basis of its frequency of use for official

purpose at workplace.

To assess the degree of correctness of the results

produced by the research tool i.e. the questionnaire

Reliability Analysis is done. Cronbach's alpha is a measure

of reliability. That determines the reliability of the scale

formed by multiple Likert questions.

For testing of Null Hypothesis I, Regression Analysis is

done. Regression Analysis is done to understand the

dependency of employee engagement on use of social

networking at workplace. The results will be interpreted

through the values of R squared (Coefficient of

determination), coefficients and the p-value obtained

from regression analysis.

For testing of Null Hypothesis II and Null Hypothesis III,

analysis is done by Single factor ANOVA which signifies

that the means of several populations is equal.

Single factor ANOVA is done to understand the

perceptions of relationship between social networking

and employee engagement at different levels of hierarchy

(Top Level, Middle Level, and Lower Level).

Also Single Factor ANOVA is used to understand the

relationship between Employee Engagement and use of

Social Networking at various work specializations in an

organization (Technical , Managerial).The Administrative

position is not taken into consideration due to lack of

responses from employees related to administration.

Results & Findings

The analysis is based on the data collected by a sample of

112 IT professionals working across India. The analysis is

done to assess the impact of use of social networking at

workplace on employee engagement.

Reliability Test Results

Table (1) shows the reliability statistics of the scale.

A Cronbach’s alpha (a) value greater than 0.6 is

acceptable which means that the scale is reliable.

Therefore from the value of cronbach alpha obtained

above i.e. 0.740, it can be inferred that the scale so

formed is reliable.

Regression Analysis for Hypothesis I

R square depicts how well the regression line

approximates the data points. Higher the R squared value,

better the model fits the data. The coefficient values

depict the strength and type of relationship the

independent variable has to the dependent variable.

Table 1: Reliability Statistics

Cronbach's Alpha

.740

49


Table (2a) and Table (2b) show the regression statistics

obtained from the regression analysis.

From the results obtained in Table (2a) it can be seen that

R squared value is approximately 0.39 or 39 percent. This

explains approximately 39 percent of variation in the

dependent variable i.e. Employee Engagement.

From the results obtained in Table (2b) it can be seen that

value of coefficients is positive and p- value is 4.9956E-14

(less than 0.05) which signifies a statistically significant

relationship between independent and the dependent

variable which is positive. Therefore it can be concluded

that use of social networking at workplace drives

employee engagement in an organization.

Therefore, the null hypothesis (H ) is rejected.01

Single Factor Anova for Hypothesis II

Table (3a) shows the summary of results for single factor

anova to understand the perception of relationship

between employee engagement and use of social

networking at various levels of management. In single

factor anova if value of F is greater than the value of F-crit

(F>F-crit) the null hypothesis is rejected.

Table 2(a): Regression Statistics

Multiple R 0.630556818

R Square 0.397601901

Adjusted R Square 0.392125555

Standard Error 0.612331437

Observations 112

Table 3(b): Regression Statistics

Coefficient

Standard

Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 1.497349 0.173302 8.640101 4.9956E-14 1.1539043 1.840793 1.153904 1.840793

Official 0.461234 0.054131 8.520769 9.299E-14 0.35395963 0.568508 0.35396 0.568508

Table 3(a): Anova Statistics

Source of Variation SS df MS F P-value F crit

Between Groups 1.322666667 2 0.661333333 0.905844156 0.416134056 3.354131

Within Groups 19.712 27 0.730074074

Total 21.03466667 29

From the results obtained above,

0.905844156(F) < 3.354131(F-crit)

Hence, the null hypothesis (H ) is accepted. This confirms 02

that there is no difference in perception of relationship


50


networking across the hierarchy (Top Level, Middle Level,

and Lower Level).

Single Factor Anova for Hypothesis III

Table (3b) shows the summary of results of single factor

anova to understand the perception of relationship


networking at various work specializations in an

organization.

From the results obtained above,

0.042604 (F) < 4.042652 (F-crit)

Hence, the null hypothesis (H ) is accepted. This confirms 03

that there is no difference in perception of relationship


networking at different work specializations (Technical,

Managerial) in an organization.

Thus, the objectives of finding the impact of use social

networking on employee engagement at workplace and

understanding the relationship between them is attained

by the above analysis and its result.

Suggestions

The third objective of our research is to provide

suggestions to improve employee engagement. Social

networking has become a significant part of our lives:

with an estimated 60% of all Internet users accessing

some form of social networking. In just 10 years Facebook

has grown from a few hundred users, to over 1.15 billion

active monthly users. (http://www.cipd.co.uk/hr-

resources/research/harnessing-social-media.aspx, CIPD,

2012). Social networking is an indispensable part of

today’s communication and collaborative technologies.

They can be effectively used for increasing employee

engagement, thereby making them more productive at

their workplace. Allowing employee to use social

networking at workplace can eventually benefit an

organization.

Given below are some suggestions which can help in

improving employee engagement with effective use of

social networking at workplace.

• Having an active social media presence allows

employees to "connect with customers and let them

find out what's being said in the public domain," says

Brian Platz, chief operating officer of Silk Road

Organizations. Through an effective usage of the

social networking, the internal social groups can be

connected. This gives a platform for sharing ideas,

suggestions and getting feedbacks which can be put

to a good use thereby increasing the accuracy of

targets to be accomplished.

• Use of Contemporary social communication tools

can help a lot in staffing where there are immediate

requirements.

• Being a custodian to employee communication, the

HR department should devise strategies wherein

they can apply social networking technologies in an

integrated way to give them a real meaning. Also

embedding the concept of social networking as an

integral part of the employee engagement strategy

is the next big thing.

• For effective use of social networking, organizations

should have social networking policies. The policies

should conform to the privacy, freedom of speech

and employment laws.

• The employment contracts can be incorporated with

clauses related to confidentiality, privacy,

authentication of usage and non solicitation

covenants.

Table 3(b): Anova Statistics

Source of

Variation SS df MS F P-value F crit

Between Groups 0.0288 1 0.0288 0.042604 0.837347 4.042652

Within Groups 32.448 48 0.676

Total 32.4768 49

51


Conclusion

This research seeks to advance understanding of the

impact of social networking on employees at workplace in

IT industry across India.

Drawing from the fact, that with rapid advancement in

technology and the pace of life, the very concept of social

networking has obtained all together a different

dimension. From being offline in the past, the online form

of social networking has a relationship with employee

engagement; its impact on employee job performance is

a matter of concern.

This paper examines, explores and gains a quantitative

insight of the same in detail and establishes that

employee engagement is a function of use of social

networking at workplace. People often make use of social

networking to gain work related ideas, enhance accuracy

in decision making and as a medium of knowledge and

information sharing. It is also an efficient and useful

means of communication amongst people working in the

same place, at same or different levels of management.

References

• Adam Wootton, Director of social media and games

at Towers Watson, Social media: A tool to boost

e m p l o y e e e n g a g e m e n t , p r o d u c t i v i t y ”,

Benefitspro.com.

• “Soc ia l Media: Strategies for Employee

Engagement”, IRI Consultants.

• Kahn 1990, Frank et al 2004, “Employee

Engagement: A Literature Review”,

• Andrea Broughton, Tom Higgins, Ben Hicks and

Annette Cox, “Workplaces and Social Networking:

The Implications for Employment Relations”, (The

Institute for Employment Studies).

• Accord Management Systems. (2004), “Employee

Engagement Strategy: A Strategy of Analysis to

Move from Employee Satisfaction to Engagement”.

• Development Dimensions International (DDI):

International Journal of Business and Management

(2005), “Employee Engagement: The Key to

Improving Performance”.

• O'Reilly T (2005), “What Is Web 2.0?”

• Boyd DM, Ellison NB, J. Computer-Mediated

Communication. (2007), “Social network sites:

Definition, history, and scholarship.”

• Boyd and Ellison (2007), “Managing and Leveraging

Workplace Use of Social Media”, Journal of

Computer-Mediated Communication, SOCIETY OF

HUMAN RESOURCE MANAGEMENT.

• Toivonen S (2007), “Web on the Move Landscapes of

Mobile Social Media.” VTT Technical Research Centre

of Finland.

• MIS Quarterly, Xiaojun Zhang, “Explaining employee

job performance: The role of online and offline

workplace Communication networks”.

• Mayer Brown, “The Use of Social Media in

Workplace”.

• Workplaces and Social Networking: The Implications

for Employment Relations ,Andrea Broughton, Tom

Higgins, Ben Hicks and Annette Cox, 2009 (The

Institute for Employment Studies) Copyright © 2009

Acas.

• Psychological Conditions of Personal Engagement

and Disengagement at Work Kahn, William

A.,Academy of Management Journal; Dec 1990; 33,

4; ABI/INFORM Global.

• 3rd annual employee engagement study

UNLEASHING THE POWER OF SOCIAL MEDIA WITHIN

YOUR ORGANIZATION. By- Gagen Mac Donald, APCO

World Wide, Page 3, Engagement and Dialogue.

52


Though no one likes or wants a recession. But, in the age of globalization, no country can remains isolated from the fluctuations of world economy. Heavy losses suffered by major International Banks affect all countries of the world as they have their investment interest in almost all countries. So, what is the reality for countries like India? This study aimed to stand on the opinions of relevant stakeholders in the field of corporate financial reporting practices after economic crisis in India. A questionnaire is structured and analyzed by principal component analysis. As a result of the economic downturn, the overall impact of the global financial crisis has been felt in India in terms of the corporate financial reporting is more susceptible. In the opinion of stakeholders there is need to apply global reporting practices to smoothen the corporate’s working and for making the system more transparent.

Impact of The Economic Crisis on Corporate Financial Reporting: Stakeholders’ Perceptions

Abstract

Preamble

The current crisis is different from the Great Depression

of the 1930’s. While the earlier downturns were the

result of a slowdown in demand, the crisis of 2008 has a

different basis. It originated not in a slowdown in

demand but a financial crisis which triggered a crisis of

trust between borrowers and lenders and therefore a fall

in the asset prices. This led to massive bankruptcies in

various financial and production units. Thus unlike an

earlier ones, it is a crisis created on the supply side.

Subsequently, it has also manifested itself as a demand

side problem with unemployment and housing

foreclosures rising in the US (Kapila, 2009).

A global recession is a period of global economic

slowdown. The International Monetary Fund (IMF) takes

many factors into account when defining a global

recession, it states that global economic growth of 3% or

less is "equivalent to a global recession". Recession is a

period of general economic decline, defined usually as a

contraction in the GDP for six months (two consecutive

quarters) or longer. Subprime mortgage is a class of

mortgage used by borrowers with low credit ratings.

Borrowers who use subprime loans generally do not

qualify for loans with lower rates because they have

damaged credit or no credit history, and are thus

considered risky by lending agencies. Because the

default risk for poor credit borrowers is greater than of

other borrowers, lenders charge a higher interest rate on

subprime loans. Depression can be explained as a bad,

depressingly prolonged recession in economic activity. A

slump is where output falls by at least 10%; a depression

is an even deeper and more prolonged slump (Kumar,

2011).

Hina Agarwal*

*Lecturer, Department of Commerce, Baikunthi Devi Kanya Mahavidyalaya, Agra University, Agra.Email: [email protected]

53

Recession, in lay-man terms, is the time when there is

economic decline, leading to a slowdown in trade and

economic activity. This is generally identified by a fall in

Gross Domestic Product (GDP) in two or more

consecutive quarters. Normally, when consumers lose

confidence in the growth of the economy and start to

spend less, there is a decrease in demand for goods and

services. This, in turn, leads to a decrease in production.

On the other hand, the profit margin of companies is due

to a rise in costs and they try cost cutting measures. The

equity / stock markets react negatively to this. A lay-off

(asking people to leave) leads to a rise in the

unemployment rate and a decline in real income (Sen &

Johnson, 2011).

The US has witnessed over 11 recessions so far, since the

end of the World War II. From 1930 to 1939, the US saw

the Great Depression, which began with the Wall Street

Crash of October, 1929 and rapidly spread worldwide.

Most analysts believe the causes to be the lack of high-

growth new industries, high consumer debt and bad loans

given out by banks and investors.

In 2008, defaults on sub-prime mortgages (home loan

defaults) led to a major crisis in the US. Banks had given

out loans without researching on the payback power of

the clients. With increasing defaulters, the banks went

into bankruptcy. It was called the sub-prime crisis since it

began from high risk debt offered to people with poor

credit worthiness or unstable incomes (Sen & Johnson,

2011).


Barth and Landsman (2010) concluded that fair value

accounting played little or no role in the Financial Crisis.

They also concluded that because the objectives of bank

regulation and financial reporting differ, changes in

financial reporting needed to improve transparency of

information provided to the capital markets likely will not

be identical to changes in bank regulations needed to

strengthen the stability of the banking sector.

Pal (2010) explained the global economic crisis - due to its

unusual nature - has meant that auditors have to be very

aware of the prime importance of judging different risks

when assessing companies. This is especially true with

regards to the ‘going concern concept.’ The judgment of

these risks is a more complicated problem - and a serious

challenge for the auditor - during a period of crisis.

However, professional terms such as audit standards, the

principles of quality assurance, and methodological

recommendations are available. Therefore, any problems

can be solved though not easily.

Giannarakis and Theotokas (2011) evaluated the effect of

financial crisis in Corporate Social Responsibility (CSR)

performance. An empirical analysis is conducted, based

on companies that implement Global Report Initiatives

(GRI) reporting guidelines modifying the application level

in a point score system. Totally, 112 companies were

included in the GRI report list in 2007, pre-financial crisis,

2008, 2009 and 2010. The Wilcoxon signed rank sum test

is used in order to ascertain whether an economic

downturn affects CSR performance. Results indicate

increased CSR performance before and during the

financial crisis except for the period 2009-2010.

Companies increase their performance in order to regain

the lost trust in businesses. The study also promotes a

discussion with regards to a financial crisis and CSR

performance and reporting.

Alwan (2012) said that the accounting profession and its

standards have been affected and influenced the global

financial crisis that rocked the world, and clarify the issue

of the financial crisis and its impact on the accounting and

international accounting standards. He also

recommended that there should be sanctions on

companies that do not apply to international standards

with regard to accountability with a commitment to the

principle of reservation accounting because it helps in

minimizing the effects of the crisis. As well as the need to

adhere to the ethics of the profession of accounting and

the preparation of financial statements and reports in

accordance with the international standard for that.

Financial Crisis and Cor porate Financial Reporting

Although we have begun to emerge from the financial

crisis, there are many lessons yet to be learned from it.

The key, of course, is to draw the right lessons. And this is

no small feat. There remain marked differences in view

with respect to what went wrong during the crisis, what

problems need to be fixed and how to fix them. Indeed, as

we meet today, Legislature continues to deliberate

54



55

fundamental changes to the regulation and operation of

our financial system and markets. The stated objective of

this reform is to promote greater market resilience and

financial stability. Insofar as these reforms implicate the

quality, integrity and transparency of financial reporting,

the outcome of this debate will have potentially far-

reaching implications for the jobs that you do (Casey,

2009).

Casey (2009) has focused on three of the key lessons that

he think we can take away from the crisis, and that should

both inform policy makers' efforts at reform and caution

against legislative and regulatory responses that would

undermine the efficient functioning of our markets:

First, financial stability depends upon market confidence;

and investor confidence, in turn, depends upon the

transparency of financial statements.

Second, financial reporting and accounting standard

setting must remain focused on the needs of investors.

While there are many other important stakeholders that

rely on financial statement reporting, investors' interests

must remain paramount.

Third, financial reporting must remain relevant and

informative to investors, and should not impose

unnecessary or costly burdens that do not add to investor

understanding.

Problem of The Study

Corporate financial reporting is a means for an

organization to communicate its past actions and

proposed future plans to owners, investors or to the

society, as they are either the present or the potential

stakeholders in businesses. It is the process of

communicating both financial & non-financial

information relating to the resources & performance of a

company. The aim of corporate financial reporting is to

provide reports that are consistent and comparable, so

that the investors can take decisions in an informed

manner. In the recent past, a number of instances have

come to the force, where loopholes in the traditional

financial reporting system have been exploited to provide

misleading information to the investors, while hiding the

real financial position of the companies. There are

number of scandals take place such as Enron, Satyam

computers etc. The issue of corporate reporting for

greater transparency has come up in the wake of such

scandals & due to the process of globalization. The

inability to understand and deal with financial data is a

severe handicap in the corporate world.

Consider the international financial community to the

accounting profession as one of the causes of the global

crisis. From here we can say that the accounting

profession like other professions affected by the financial

crisis and is one of the main reasons behind this crisis,

here comes the research to study the stakeholders’ views

on corporate financial reporting after financial crisis.

There is clearly a problem of this study by answering the

following question: What is the impact of financial crisis

on stakeholders’ views about corporate financial

reporting?

Importance of The Study

The need is felt to find out the rules that are common and

global. And it gives the light to develop the new trends in

the field of corporate financial reporting. The issue of

complexity is one of the most important aspects in

financial reporting, and financial instruments are among

the most complex on which to report clearly. New

financial reporting mechanisms have been developed

with a view to providing relevant and reliable information

to the stakeholders which are not apply till now around

the world. So there is a need to develop & adopt the

standards & rules regarding corporate financial reporting.

Stakeholders in the business (whether they are internal or

external) seek information to find out three fundamental

questions. These are (i) How is the business doing? (ii)

How is the business placed at present? (iii) What are the

future prospects of the business? For outsiders, published

financial accounts are an important source of information

to enable them to answer the above questions.


Research Objective: With the consideration of the above

three lessons the study aimed to “the stakeholders’

perceptions towards corporate financial reporting

practices after economic crisis in India”.

Research Method: The Indian corporate stakeholders’

population was studied in this work. For this,

questionnaire based on mainly 5 point likert scale (of one


56

(1) to five (5) for the strongest disagree to the strongest

agree responses, respectively) questions, was structured.

The items requiring a descriptive response were avoided

simply because the respondents might not have the time

to give a response in text form. The collected data is

analyzed by using survey analysis techniques available in

software STATA 12.0 and the results are interpreted

accordingly.

The stakeholders include professionals as well as non-

professional (the owners, managers, customers,

suppliers, creditors, regulator, analysts and experts and

other members of the public). The stakeholders to Indian

quoted companies are effectively the population of the

country.

Research Sample: Primary data were collected and used

for the study and the sample was sixty six stakeholders

out of the numerous stakeholders’ population.

Research Tool: Principal Component Analysis

Principal components analysis is a quantitatively rigorous

method for achieving simplification. The method

generates a new set of variables, called principal

components. Each principal component is a linear

combination of the original variables. All the principal

components are orthogonal to each other so there is no

redundant information. The principal components as a

whole form an orthogonal basis for the space of the data.

The first principal component is a single axis in space.

When you project each observation on that axis, the

resulting values form a new variable. And the variance of

this variable is the maximum among all possible choices

of the first axis.

The second principal component is another axis in space,

perpendicular to the first. Projecting the observations on

this axis generates another new variable. The variance of

this variable is the maximum among all possible choices

of this second axis.

The full set of principal components is as large as the

original set of variables. But it is commonplace for the

sum of the variances of the first few principal components

to exceed 80% of the total variance of the original data. By

examining plots of these few new variables, researchers

often develop a deeper understanding of the driving

forces that generated the original data.

In the present section Principal Component Analysis

(PCA), a technique commonly used for data reduction

have used. It offers the solution for the problem of multi-

collinearity, the situation where the explanatory variables

are highly inter-correlated. The objective of PCA is to find

unit-length linear combination of the variables with the

greatest variance. In the analysis, first principal

component (PC) has maximal overall variance; the second

principal component has maximal variance among all unit

length linear combinations that are uncorrelated to the

first principal component; and the last principal

component has the smallest variance among all unit

length linear combinations of the variables.

These principal components represent the most

important directions of variability in a dataset. Given a

data matrix with p variables and n samples, the data are

first centered on the means of each variable. This ensures

that the cloud of data is centered on the origin of our

principal components. It neither affects the spatial

relationships of the data nor the variances along our

variables. The first principal component (Y ) is given by the 1

linear combination of the variables X , X ...X . 1 2 p

Symbolically,

The first principal component is calculated in such a way

that it accounts for the greatest possible variance in the

data set. Of course, one can make the variance of Y as 1

large as possible by choosing large values for the weights

a , a ...a . To prevent this, weights are calculated with 11 12 1p

the constraint that their sum of squares is 1. Thus,

The second principal component is calculated in the same

way, with the condition that it is uncorrelated with the

first principal component and that it accounts for the next

highest variance.

This process continues until a total of p principal

components have been calculated, where p is equals to

the original number of variables. At this point, the sum of

a112 + a12

2 + ... + a1p2 = 1

Y1 = a11X1 + a12X2 + … + a1pXp

Y2 = a21X1 + a22X2 + … + a2pXp


57

the variances of all of the principal components will be

equal to the sum of the variances of all of the variables,

that is, all of the original information has been explained

or accounted for. Collectively, all of these transformations

of the original variables to the principal components are:

The rows of matrix A are called the eigenvectors of

variance-covariance matrix of the original data. The

elements of an eigenvector are the weights a , also known ij

as loadings. The elements in the diagonal of matrix S , the y

variance-covariance matrix of the principal components,

are known as the eigen values. Eigen values are the

variance explained by each principal component and are

constrained to decrease monotonically from the first

principal component to the last (Gileva, 2010).

The full set of principal components is as large as the

original set of variables. But it is commonplace for the

sum of the variances of the first few principal components

to exceed 80% of the total variance of the original data. By

examining plots of these few new variables, researchers

often develop a deeper understanding of the driving

forces that generated the original data (MATLAB 7.10.0).

Analysis and Results

In this section of the paper we analyze the findings of the

primary research carried out as part of this project.

Summary Statistics

Descriptive statistics for the selected explanatory

variables are presented in Table 1. The number of

observations for all the variables is sixty six. Minimum and

maximum values of responses of variables under

consideration are shown in column 2 and 3 of the table.

Y = AX

Table 1: Results of Summary Statistics

Questions Observations Minimum Maximum Mean Std. Dev.

Question1 66 1 5 3.7575 1.2033

Question2 66 2 5 4.2575 0.8097

Question3 66 2 5 3.7878 0.8860

Question4 66 1 4 2.5000 0.7493

Question5 66 2 5 3.6969 1.1227

Question6 66 2 5 3.3030 1.0520

Question7 66 2 5 4.0454 .98342

Question8 66 1 5 3.3787 1.3215

Question9 66 2 5 3.9090 1.0034

Question10 66 1 5 3.8484 1.5515

Question11 66 4 5 4.5000 0.5038

Question12 66 2 5 3.5909 1.0809

Question13 66 2 5 3.5151 1.0113

Question14 66 2 5 3.8636 0.9263

Question15 66 2 5 4.1515 1.1798

Question16 66 2 5 4.1515 0.9155

Question17 66 4 5 4.1969 0.4007

Question18 66 3 5 4.0757 0.5899

Question19 66 1 5 3.1060 1.3141


58

Question20 66 2 5 3.4696 1.2180

Question21 66 1 5 3.7878 1.2590

Question22 66 2 4 3.4848 0.7492

Question23 66 1 4 2.8333 1.1446

Question24 66 2 5 4.0151 0.9363

The fourth column of the table records the arithmetic

mean value of the responses of each question, which

range between 2.5000 and 4.5000. Only two statement

i.e., Q4 (2.5000) and Q23 (2.8333) are less than the

study’s population mean of ‘3’. The means of the scores of

responses range between 2.5000 and 4.5000, only two

statement i.e., Q4 (2.5000) and Q23 (2.8333) are less than

the study’s population mean of ‘3’, which tend negative

stakeholder’s perceptions and indicate that they are

misled and not satisfied with the current pattern of

financial reporting. Stakeholders are agreed with eight

statements (Q2, Q7, Q11, Q15, Q16, Q17, Q18, and Q24)

as these mead scores ranging from 4.0151 to 4.5000. And

remaining fourteen statements have also the positive

impact on stakeholders and could be considered as

moderate when compared to a mean of there in a ‘1’ to ‘5’

range analysis.

The means of the responses concluded that stakeholders

are agreed with all the statements except Q4 and Q23,

after that, they are not satisfied with the current pattern

of the financial reporting. Variations in the responses,

expressed in terms of standard deviation is also highest

for Q10 (1.5515) and lowest for Q17 (0.4007). Results of

standard deviation also show that the responses are

highly varied, but, not negative.

Principal Component Analysis

In the present section Principal Component Analysis

(PCA), a technique commonly used for data reduction

have used. The results of ideas of Principal Component

Analysis applied on selected explanatory variables to

determine the factors that can explain the stakeholder’s

perceptions are shown in table 2.

Table 2 : Principal Component Analysis

Principal

Component

Eigenvalue Difference Proportion of

Variance

Cumulative Proportion

of Variance

1 7.1292 2.4382 0.2971 0.2971

2 4.6908 1.6134 0.1955 0.4925

3 3.0774 0.4398 0.1282 0.6207

4 2.6376 0.7949 0.1099 0.7306

5 1.8427 0.6396 0.0768 0.8074

6 1.2031 0.3891 0.0501 0.8575

7 0.8140 0.1154 0.0339 0.8915

8 0.6986 0.1188 0.0291 0.9206

9 0.5798 0.0781 0.0242 0.9447

10 0.5017 0.2207 0.0209 0.9656

11 0.2810 0.4380 0.0117 0.9773

12 0.2372 0.0563 0.0099 0.9872

The researchers have constructed each principal

component in such a way that their respective variance is

maximized. The Eigen values or variances of principal

components of the correlation matrix shown in the table

are ordered from largest to smallest. The Eigenvalues add

up to the sum of variances of the variables in the analysis

(Saxena and Bhadauriya, 2012). As the analysis is based

on correlation matrix, the variables are standardized to

have unit variance, and so the sum of eigenvalues is 24.


59

13 0.1809 0.0549 0.0075 0.9948

14 0.1260 0.1259 0.0052 1.0000

Figure 1: Scree Plot of Eigen Values after Principal Component Analysis

The scree plot is proposed to be a useful tool for

visualizing the eigenvalues relative to one another, so that

you can decide the number of components to retain

(Stata Release, 12.0). The point of interest is where the

curve start flattens. It can be seen (fig. 1) that the curve

begins to flatten between questions 6 and 7. It can also be

noticed that question 7 has an eigen value of less than 1,

so only six factors can be retained. This is consistent with

Kaiser’s Rule (only factors having eigenvalues greater than

1 are considered as common factors) (Burns and Burns,

2008).

Figure 2: Scree Plot of Eigen Values with Class Interval limits after Principal Component Analysis


60

A problem in interpreting the scree plot is that no

guidance is given with respect to its stability under

sampling. How different could the plot be with different

samples? The approximate variance of an eigenvalue ? ̂of

a covariance matrix for multivariate normal distributed

data is 2 ?^2=n. From this we can derive confidence

intervals for the eigenvalues. These scree plot confidence

intervals aid in the selection of important components

(see fig. 2). Despite our appreciation of the underlying

interpretability of the seventh component, the evidence

still points to retaining four or five principal components

(Stata Release 12).

Figure 3: Variance Explained by Principal Components

As shown in the table, Eigenvalue of first four principal

components (PC1 - 7.1292, PC2 – 4.6908, PC3 – 3.0774

and PC4 – 2.6376) is the maximum among all. These four

components individually explain 29.71 percent

(7.1292/24), 19.55 percent (4.6908/24), 12.82 percent

(3.0774/24) and 10.99 percent (2.6376/24) variance in

the total variance of all components. In total these

components explain 73.06 percent variance (29.71 +

19.55 + 12.82 + 10.99) of the total variance. This implies

that more than 70% of the variance is contained in first

four principal components (see fig. 3). These four

components are coordinated for choosing the main

variables among all 24 questions considered.

The results in Table 1 reveal the presence of four factors

with all 24 items of the stakeholder’s perceptions towards

corporate financial reporting practices. The eigenvalues

for the four factors are above 1 (given above). These four

factors explain a total of 73.06% of the variance.

Specifically, Factor 1 has fourteen significant loadings,

Factor 2 has twelve significant loadings, Factor 3 has

seventeen significant loadings and Factor 4 has fifteen

significant loadings respectively. Here same loadings lying

in two or more factors, so finally, the highlighted loadings

(see annexure) would be considered. Factor 1 has

fourteen significant loadings, Factor 2 has reduced to five

significant loadings, and Factor 3 considered four

significant loadings out of seventeen and Factor 4

considered no loading respectively. To end with, first

three factors has loaded because they cover all the 24

questions and factor 4 has eliminated.

Table 3: Factor Loadings

Var.

Principal

Component 1

Principal

Component 2

Principal

Component 3

Principal

Component 4

Unexplained

Variance

Unexplained

Variance

Q1 0.0366 -0.2765 0.0612 0.1926 0.5225 0.6203

Q2 0.0466 0.1548 -0.1896 -0.2887 0.5417 0.7615

Q3 0.0618 0.0478 0.3842 -0.1932 0.4093 0.5077


61

Turning to an interpretation of independent dimensions

as given in Table 3, one can see that the first factor

delineates a cluster of relationships among the following

attributes; ‘Reading of the financial reports of the

company before investment’ (Q1), ‘Collection of the

information about companies from other sources

(brokers, friends, colleagues etc) except the financial

reports’ (Q2), ‘Financial reports give a true and fair view of

the financial position and performance of the entity’ (Q3),

‘Companies are worried about disclosing too much

information when it comes to segment reporting’ (Q8),

‘Control over accounting scams and scandals is the reason

of emerging demand for corporate financial reporting at

international level’ (Q10), ‘Attract the investors

internationally is the reason of emerging demand for

corporate financial reporting’ (Q11), ‘Credit rating would

be suitable for investors to invest in shares’ (Q12),

‘Companies release their reports timely’ (Q13), ‘Falsified

financial reporting affects negatively on economic picture

of company as well as country’ (Q15), ‘Consideration of

accounting policies for the selection of company for

investment’ (Q18), ‘Cut throat competition among

corporate is the reason of emerging demand for

corporate financial reporting’ (Q19), ‘Declaration of

corporate financial reports at the same date and time for

control the falsified presentation’ (Q20), ‘Effectiveness of

Return on Investment of previous years on investor’s

decision’ (Q21), and ‘Current pattern of corporate

financial reporting followed by companies’ (Q23). The

nature of the highly loaded variables on this factor

Q4 -0.0209 -0.1566 0.0093 0.3622 0.5356 0.8817

Q5 -0.0132 -0.1447 0.3764 -0.0809 0.4474 0.4647

Q6 -0.3248 -0.1282 0.0645 -0.0232 0.1564 0.1578

Q7 -0.2320 -0.2107 0.1362 0.2629 0.1685 0.3508

Q8 0.2545 -0.0422 -0.1364 0.2801 0.2657 0.4725

Q9 -0.0343 0.1485 0.4789 -0.1006 0.1557 0.1823

Q10 0.2440 -0.0003 -0.1932 0.2825 0.25 0.4605

Q11 0.2813 0.1557 0.2399 0.1066 0.1151 0.1451

Q12 0.0510 -0.0572 0.0880 0.4924 0.3027 0.9423

Q13 0.3174 0.1963 0.0034 0.0934 0.0779 0.1009

Q14 -0.2091 0.2312 -0.2171 0.1526 0.2310 0.2924

Q15 0.0043 0.3771 0.1488 0.1751 0.1837 0.2646

Q16 -0.0585 0.3358 0.0117 0.2238 0.3141 0.4462

Q17 -0.2750 0.1398 0.2477 0.1378 0.1302 0.1803

Q18 0.1978 -0.3006 0.1074 0.0161 0.2611 0.2618

Q19 0.3269 0.1395 -0.0782 -0.0620 0.1177 0.1278

Q20 0.2752 0.1011 0.1488 -0.1738 0.2642 0.3439

Q21 0.3538 -0.0639 -0.0194 -0.0493 0.0808 0.0872

Q22 -0.0024 -0.1324 -0.2183 -0.1329 0.7245 0.7711

Q23 0.2314 -0.2343 0.2723 0.0834 0.1141 0.1324

Q24 -0.0373 0.4202 0.0703 0.1397 0.0950 0.1465


62

suggests that it can be named “disclosure of financial

information”. This “disclosure of financial information”

factor contributes around 30% of stakeholder’s

perceptions. Since Factor 1 has the highest eigenvalue

and variance, (eigenvalue = 7.1292, variance = 29.71%) it

necessarily represents the most important factor that has

influenced stakeholders to invest in Indian companies.

Interestingly, the results of the principal component

analysis in Table 2also reveal that the variables which

have loadings on the second factor are ‘Reporting based

on harmonized principles’ (Q9), ‘Disclosure of the

transactions and events that affect the company’s

economic position’ (Q14), ‘Effectiveness of legal structure

of the country on demand for and supply of quality of

reported financial information’ (Q16), ‘Consideration of

financial statements for the selection of company for

investment’ (Q17), and ‘Development of corporate

financial reporting practices is in right direction using IFRS

and XBRL’ (Q24). The combination of these variables can

be compositely grouped together under the proposed

heading of “appropriateness of financial information of

Indian companies”. As shown in Table 6.5, Factor 2

“appropriateness of financial information of Indian

companies” accounts for 19.55% of the total variance and

together with Factor 1 explains about 49.25% of the total

variance. All five variables are moderately correlated with

Factor 2 with factor loadings ranging from 0.0478 to

0.4202. It also suggests the appropriateness of financial

information as an instrument to strategically market the

securities of Indian companies to consumers and other

relevant stakeholders.

The third factor defining stakeholder’s perceptions

towards corporate financial reporting practices in India

relates to ‘No matters in the financial report that could be

considered to be misleading’ (Q4), ‘Relevance of

legislation and regulation related to financial reporting’

(Q5), ‘Satisfactorily resolution of noncompliance or

deficiencies in financial reporting practices by regulatory

agencies’ (Q6), ‘Necessity of reporting disclosure to meet

investor’s demand’ (Q7). For this factor, the suggested

name for it is “satisfaction with financial report” factor.

The results of the factor analysis ranked “satisfaction with

financial report” as the least important factor compared

with other variables, since it explains only 12.82% of the

total variance for the variables in the data set.

Table 4: Frequency Distribution: Factor 2 Variables – Degree of influence ofStakeholders’ Perceptions towards corporate financial reporting

Degree of Influence Value Q9 Q14 Q16 Q17 Q24

Not Important at all 1 13.23529 1.449275 1.492537 1.449275 0

Not Important 2 17.64706 13.04348 8.955224 0 10.14493

Cumulative % 17.91045 14.49275 10.44776 1.449275 10.14493

Important 4 51.47059 53.62319 41.79104 76.81159 40.57971

Very Important 5 16.17647 21.73913 40.29851 20.28986 33.33333

Cumulative % 80.59701 75.36232 82.08955 97.10145 73.91304

Neutral 3 1.470588 10.14493 7.462687 1.449275 15.94203

Mean Value 3.909091 3.863636 4.151515 4.19697 4.015152

Median Value 4 4 4 4 4

Mode Value 4 4 4 4 4


63

Overall, the principal component analysis reveals an

important result indicating that appropriateness of

financial information of Indian companies factor was

considered as one of the important factors in making a

judgement and decision whether to make investment in

Ind ian companies . The ranking pos i t ion of

appropriateness of financial information of Indian

companies’ factor as the second most important factor.

Moreover, it is also expected that the proportion of

stakeholders influenced by this factor would be relatively

high. This is confirmed by figures on Table 4, whereby high

percentages of influence are evidenced for all the five

variables constituting under appropriateness of financial

information of Indian companies factors (Q9 = 80.60%,

Q14 = 75.36%, Q16 = 82.09%, Q17 = 97.10% and Q24=

73.91%).

Concluding Remarks

The paper was aimed to provide an initial insight to the

expectations of the different groups of shareholders on

corporate financial reporting practices by considering 24

fundamental factors regarding current reporting

practices.Addressing the objective of this paper might

increase the understanding of the attitude of different

stakeholders on the idea of financial reporting and its

disclosure within the annual report. This includes primary

(investors) as well as the secondary (public at large)

stakeholders' perceptions. The perceptions of

stakeholders were focused in this paper to identify the

most demanding group of stakeholders in expecting the

companies' actions in corporate financial reporting

disclosure practices. Besides that, this study can guide the

preparers of annual reports to improve on the quantity

and quality of the corporate financial reporting practices.

The regulators also can revalue the current practices of

corporate financial reporting in India and make it

mandatory for companies to disclose the relevant

reporting issues.

Thus a recession in one country will potentially have large

scale impacts on other countries to an extent not seen in

previous recession. The paper concludes that the regional

dimension provides an important and effective

framework – not just for mitigating the impact of the

current crisis but also for reducing the chances of similar

crises in the future

References

• Alwan, B. Mohammed (2012). Financial crisis and its

impact on accounting and international accounting

standards. Interdisciplinary Journal of Contemporary

Research In Business. 3(10), 998-1017.

• Barth, Mary E. and Landsman, Wayne R., (2010). How

did Financial Reporting Contribute to the Financial

Crisis? European Accounting Review 19(3), 399-423.

Rock Center for Corporate Governance at Stanford

University Working Paper No. 79. Available at SSRN:

http://ssrn.com/abstract=1601519

• Casey, Kathleen L. (2009). Lessons from the financial

crisis for financial reporting, standard setting and

rule making. Presented in financial executives

international's 28th annual current financial

reporting issues conference, New York, retrieved from

h t t p : / / w w w. s e c . g o v / n e w s / s p e e c h / 2 0 0 9 /

spch111709klc.htm

• Giannarakis, Grigoris and Theotokas, Ioannis (2011).

The effect of financial crisis in corporate social

responsibility performance. International Journal of

Marketing Studies, 3(1), 2-10.

• Gileva, T. (2010). Econometrics of crude oil markets,

Thesis submitted to the Department of Economics,

University of Paris.

• Kapila, Uma (2009). Indian economy: performance

and policies (9th ed.). New Delhi, Academic

Foundation.

• Kumar, Nidhi Arora (2011). Impact of global recession

on Indian it industry and effectiveness of e-business in

the era of recession. Global Journal of Business

Management and Information Technology. 1 (1), 9-

25.

• MATLAB 7.10.0 Release 2010

• Pal, Tibor (2010). The impact of the economic crisis on

auditing. European Integration Studies, Miskolc, 8(1)

131-142.

• Saxena, S. P. and Bhadauriya, S. (2012). Principal

component analysis of macroeconomic determinants

of stock market dynamics: some reflections from

national stock exchange of India ltd. Transformation

and survival of business organizations: challenges

and opportunities. 97-105.

• Sen, Sohini&Johnson, Shilpa (2011). Primer:

Recession and impact on India. Retrieved from

http://www.business-standard.com/article/

markets/primer-recession-and-impact-on-india-

111080800134_1.html

Stock market is one of the most important sources for companies to raise money. It allows businesses to be publicly traded, or raise additional capital for expansion by selling shares of ownership of the company in a public market. History has shown that the price of shares and other assets is an important part of the dynamics of economic activity, and can influence or be an indicator of social mood. An economy where the stock market is on the rise is considered to be an upcoming economy.

Stock market dynamics or volatility refers to the variation in the stock price changes during a period of time. The volatility of stock market indicators goes beyond anyone’s reasonable explanations. Generally, variations in stock market are caused by the fluctuations in the performance of the economy or the macroeconomic indicators of an economy. The modeling of stock market volatility is one of the key areas of present financial research as stock market is the main determinant of economic development of a country. The present paper is intended at developing a dynamic model of macroeconomic determinants of stock market volatility via a causal loop diagramming.

Keywords: Stock market volatility, macroeconomic determinants, causal loop diagram

Causal Loop Modeling of Macroeconomic Determinants of Stock Market Volatility

Abstract

Introduction

Understanding the stock market dynamics has long been

a topic of considerable interest to both policy makers

and market practitioners. Policy makers on one hand are

interested in the main determinants of stock market

volatility and in its spillover effects on real activity, on the

other, market practitioners are interested in the direct

effects time-varying volatility exerts on the pricing and

hedging of plain vanilla options and more exotic

derivatives. In both cases, forecasting stock market

volatility constitutes a formidable challenge but also a

fundamental instrument to manage the risks faced by

these institutions (Corradi & Distaso, 2009). The

volatility of stock market indicators goes beyond

anyone’s reasonable explanations; industry

performances, economic and political changes are

among the major factors that can affect the stock market

(Goonatilake&Herath, 2007).

Stock market volatility is affected both by micro and

macro variables. Micro variables generally include

corporate results announcements, business cycles,

financial Leverage etc and the macro variables may

include the indicators of country’s economy, such as

gross domestic production, inflation rate, foreign

investment etc. Impact of macroeconomic variables is

found to be more important because the stock

performance of a particular company is influenced by

micro variables but the macro variables drop impact on

the whole stock market behavior.

Economic theory suggests that stock prices should

reveal expectations about future corporate

performance, and corporate profits generally reflect the

Sonam Bhadauriya*

*Assistant Professor (Commerce), Department of Humanities, NIMS University, Jaipur (India) Email: [email protected]

64

level of economic activities. If stock prices accurately

reflect the underlying fundamentals, then they should be

employed as leading indicators of future economic

activities, and not the other way around. Therefore, the

causal relations and dynamic interactions among

macroeconomic variables and stock prices are important

in the formulation of the nation’s macroeconomic policy.

In an economy there can be a large number of

macroeconomic variables which cause stock market

vulnerability.

The objective of the paper is to construct the system

dynamic model via causal loop diagramming for provide

assistant to stock market practitioners as well as

investors. The paper is divided in five sections. First

section gives brief description of basic framework of the

paper. Section two presents review of specific studies

conducted on relationship between macroeconomic

indicators and stock market volatility, and also on the

basics of causal loop diagramming. In section three brief

description of research objective and research

methodology is presented. Fourth section is about the

summary of macroeconomic determinants of stock

market volatility. An attempt has been made to identify

interacting causal loops for stock market returns and its

macroeconomic determinants. The paper ends with fifth

section by giving concluding remarks.

Literature Review

The relationship between macroeconomic variables and

stock market returns is, by now, well-documented in the

literature. Maysami et al. (2004) examined the long-term

equi l ibr ium re lat ionships between se lected

macroeconomic variables and the Singapore stock market

index. They concluded that the causal relations and

dynamic interactions among macroeconomic

determinants of the economy and stock prices are

important in the formulation of the nation’s

macroeconomic policy. Aksoy & Leblebicioglu (2004)

successfully implemented a rule based fuzzy logic model

to forecast the monthly return of the ISE100 Index by

combining technical analysis, financial analysis and

macroeconomic analysis. Chowdhury et al. (2006)

examined how the macroeconomic risk associated with

industrial production, inflation, and exchange rate is

related and reflected in the stock market returns in the

context of Bangladesh. They concluded that there is

relation between stock market dynamics and

macroeconomic volatility. Engle & Rangel (2006)

developed a model that allows long horizon forecasts of

volatility to depend on macroeconomic developments,

and delivers estimates of the volatility to be anticipated in

a newly opened market.

Humpe & Macmillan (2007) examined whether selected

macroeconomic variables influenced stock prices in the

US and Japan. Adam et al. (2008) in his study found that

there is co-integration between macroeconomic variable

and Stock prices in Ghana indicating long run relationship.

Kumar (2009) investigated the relationship between

macroeconomic parameters like Exchange rate and

foreign institutional investment with stock returns in

India, in particular at National Stock Exchange. By using

granger causality test he found that exchange rate and

stock returns had no causality from either of the sides

whereas stock return was found to granger cause of FII

series. Ali et al. (2010) also investigated the causal

relationship between macroeconomic indicators and

stock exchange prices. They found co-integration

between industrial production index and stock prices, and

no causal relationship between macroeconomic

indicators and the stock prices in Pakistan.

Gileva (2010) investigated the dynamics of oil prices and

their volatilities through application of different

econometrical tools as principal component analysis for

finding out the fundamental factors contributing to this

process. Haron & Maiyastri (2004), Kerby & James (2004),

Liu & Jun (2011) and Loretan (1997) used multivariate

statistical methods such as principal component analysis

and discriminant analysis for determining fundamental

factors of stock market trends. They concluded that such

analysis can be used to reduce the effective

dimensionality of other scenario specification problems.

Several authors suggested to use causal loop

diagramming as the base for dynamic modeling

technique due to its ability to cater a modeling framework

that is causal and logically structured. Binder et al. (2004)

discussed how a Causal Loop diagram can be labeled and

structured incrementally in order to finally transform it

into a Stock and Flow diagram. And, also described a

general set of possible transformation steps and offered

guidance on when to choose which step. Schaffernicht

(2007) concentrated on dealing with causal links’ polarity.

65


Then, revisited the traditional criticism of causal loop

diagrams and showed a way out; and also expressed

important information on causal loop diagramming.

Research Objective and Methodology

This paper is dedicated to the system dynamics modeling

of macroeconomic determinants of stock market

volatility via causal loop diagramming. The dynamic

system modeling is conducted by using the software

Vensim PLE v6.0. It is a visual modeling tool that allows to

conceptualize, document, simulate, analyze, and

optimize models of dynamic systems. Vensim provides a

simple and flexible way of buildings imulation models

from causal loop or stock and flow diagrams. This

software is capable of exploring thoroughly the behavior

of the model thatcan be simulated.

Macroeconomic Determinants of Stock Market Volatility

Every stock price moves for two possible reasons viz. news

about the company and news about the country. News

about the company are known as the micro variables (e.g.

results announcement, business cycle, financial leverage,

a product launch, etc.) and news about the country are

known as macro variables (e.g. a budget announcement,

nuclear bombs, inflation etc.) Impact of macroeconomic

variables is more important, because the stock

performance of a particular company is influenced by

micro variables but the macro variables drop impact on

the whole stock market behavior. On any one day, there

would be good stock-specific news for a few companies

and bad stock-specific news for others. The news that is

common to all stocks is news about macro economy.

Stock markets are barometers of the economy. It is

expected that the markets and their indicators, in the

form of indices, reflect the potential of the corporate

listed on them, and, in the process, the direction and

health of the economy. If a country’s economy is

performing well and expected to grow at a healthy rate,

the market is usually expected to reflect that.

An extensive review of literature has been conducted for

identifying the key macroeconomic variables of stock

market vulnerability and the fourteen variables are

selected for developing the causal model. Stock market

returns (SMR) are considered as the determining variable

and the selected determinants are Gross Domestic

Product (GDP), Index of Industrial Production (IIP),

Inflation (INF), Balance of Payments (BOP), Foreign

Exchange Reserves (FXRE), Foreign Exchange Rate (FXRA),

Repo Rate (RPR), Treasury Bills Rate (TBR), Prime Lending

Rate (PLR), Foreign Institutional Investments (FII), Trading

Volume (TRV), Market Capitalization (MCP), Crude Oil

Prices (CRO) and Gold Prices (GLD).

Causal Loop Diagram

Causal modeling is a form of System Dynamics (SD).

Causal modeling refers that the variables are linked by a

chain of events directly on its predecessor (Halper and

Perl, 2005). The real explanatory power of SD resides in

the shift from the linear causal chains to closed chains of

the positive and negative feedback loops. In this sense, SD

defines the so called Causal Loop Diagrams (CLDs) as

models of system that are abstract and simplified

representations of portion of reality (Cioni, 2009).

Causal Loop Diagrams are used to document the relevant

factors and the causal relationships between them. CLDs

consist two items, the first are the factors or variables and

second are the links connecting the factors. Any link has

annotations about its polarity and delay. The polarity tells

whether the dependency has positive polarity (if the

cause increases, the effect will also increase compared

with the situation where the cause did not change) or

negative polarity (if the cause increases, the effect will

decrease compared with the situation where the cause

did not change) (Binder et al., 2004). In simple words, a

“+” sign means that changes in first variable cause

changes “in the same direction” in the second variable

and a “” sign means that changes in the first variable cause

a change “in the opposite direction” in the second

variable. CLDs represent only the structure, the dynamics

of events have been abstracted away. Basically, these are

about what happens between events or variables as

cause and effect.

The sources of information used in different approaches

to manage the socio-economic and socio-technical

problems are multiple and can be broadly categorized

into three: mental, written/spoken and numerical.

Mental database present with every human being is

information rich and is the primary source of information.

The mental database contains all information on

66


conceptual and behavioral information and technical

fronts. Causal loop diagramming is pragmatic from its

outset and has always been interested in causal beliefs

that people articulate from their mental database.

Causal Loop Diagram for Stock Market and its

Macroeconomic Determinants

Researcher used mental database supported by

scrutinized review of researches on stock market volatility

and macro economy for diagraming the CLD for modeling

stock market behavior due to macroeconomic

environment. The developed causal framework is

presented in the figure 1. The figure, the polarity of the

causal loops is indicated by the blue color at the top of the

arrows. The figure shows total fifteen variables (SMR,

GDP, IIP, INF, BOP, FXRE, FXRA, RPR, TBR, PLR, FII, TRV,

MCP, CRO and GLD) in its silhouette. In the diagram, all the

variables are interconnected by the causal loops

indicating negative or positive polarity. The matrix of

polarities is shown in the table 1 for giving some clarity in

the figure. The logical causal relationships among

selected variables are as follows:

Stock Market Returns (SMR): SMR is the main

determined variable of the model. It has three positive

causal links to FII, TRV and MCP with the positive

polarities. As FII, TRV and MCP are basically, the indicators

from stock market, they directly influenced by SMR. The

rising in the stock market index results in the boost up in

the number of investors, traded volume and also in the

market capitalization.

Gross Domestic Product (GDP): The causal loop diagram

shows that gross domestic product have six positive

causal links to SMR, IIP, INF, TRV, FII and MCP. Increased

GDP results into the higher employment opportunities,

increased their disposable income, higher consumer

spending and higher corporate profits. The increased

corporate profits in turn lead to increase in the

investment and production. Boom in GDP accompanied

by increased money supply in an economy though

enhance production of goods and services; but is likely a

cause inflation rise. The increased investment

opportunities may invite FII also. Thus, higher GDP is a

benign factor for the economy which has an overall

impact on all the companies in an economy. The market

capitalization of the companies is automatically increased

with the GDP growth. Further, boom in GDP also results in

the increased inflation as increase in money supply is a

likely cause to inflation.

Index of Industrial Production (IIP): Figure shows seven

positive causal links of IIP with SMR, GDP, BOP, FII, TRV,

MCP, CRO and negative link with INF. Relationships of

industrial production with inflation, stock market and

gross domestic product are very clear as all are the

outcomes of increased money supply and higher

consumer demand. Industrial production is the outcome

of increasing corporates’ profits and the corporates’

profits are the outcomes of increased industrial

production, which in turn has direct impact on the share

prices, trading volume and also the investments. Figure

also shows that the IIP cause to CRO, It means when

industrial production increases, the demand of crude oil

also increases, which results into higher crude oil prices in

the international market, rising industrial production and

the increased inflation.

Inflation (INF): Causality of INF are identified on the GDP,

IIP, CRO, GLD and SLV with the positive polarities.

Closeness in the relationship of INF with GDP and IIP is

discussed in above paragraphs. The prices of crude oil,

gold, silver, and inflation have cause and effect

relationship. Today, commodities like crude oil, gold and

silver are renowned as an effective tool to hedge against

inflation. Hence, inflation causes an increase in demand

for these commodities and thus leads to rise in their

prices.

Balance of Payments (BOP): Figure displays the positive

causal links of BOP with the variables FXRE and FII and

negative causal link with FXRA. Increased international

trade gives rise to currency flows in the country and

improves the position of RBI to hold more foreign

currency. Further, increased trade and forex reserves also

attract investors from foreign countries which again

strengthen the forex reserves position of the country and

ultimately the value of foreign currency gets increased.

Foreign Exchange Reserves (FXRE): Forex reserves have

negative causal relationship with FXRA and positive causal

link with FII. Forex reserves are instruments to maintain or

manage the exchange rate, while enabling orderly

absorption of international money and capital flows. In

brief, official reserves are held for precautionary and

transaction motives keeping in view the aggregate of

national interests, to achieve balance between demand

67


for and supply of foreign currencies, for intervention, and

to preserve confidence in the country’s ability to carry out

external transactions. Foreign exchange reserves are

important indicators of ability to repay foreign debt and

for currency defence, and are also used to determine

credit ratings of the nations. Thus, sound foreign

exchange reserves position of the nation brings more

investments from the foreign investors.

Foreign Exchange Rate (FXRA): The causal loop diagram

displays the negative causal link of FXRA with IIP, BOP and

FXRE. It indicates that a move in exchange rate in terms of

dollar results in change in prices of imports and exports.

Further, when dollar appreciates against Indian Rupee,

the relative prices of exports to US increases as the import

prices for US consumers gets decrease. Such a rise in

exports and fall in imports reduces the current account

deficit. Increased rate of foreign exchange bound to the

domestic manufactures, who depends on the imported

raw material or necessities to reduce their imports and

the production level.

Repo Rate (RPR): The causal loop diagram shows that

Repo rate has two negative causal links to the IIP and INF.

When repo rate increases, interest rates on banks’

deposits also increase, and in turn banks raise the interest

rates on loans they offer to customers. The customers

then are dissuaded in taking credit from banks, leading to

a shortage of money in the economy and less liquidity.

Thus, on the one hand, it controls inflation is under limits

as there is less money to spend, growth suffers as

companies avoid taking new loans at high rates, leading to

a shortfall in production and expansion.

Treasury Bills Rate (TBR): TBR has also two negative

causal links to the IIP and INF. An increase in treasury bill

rate leads to higher interest rates which in turn may

reduce industrial production, performance and also the

reduction in general price level.

Prime Lending Rate (PLR): Causalities of PLR are

identified on the SMR, GDP and IIP with the negative

polarity. Economic theory says that the interest rate

channel affects the demand for goods and services.

Higher interest rates mean that the price of both financial

and real assets - shares, bonds, property, etc. - falls and

the present value of future returns drops. Further, higher

interest rates also lead to a reduction in household

consumption. When faced with dwindling wealth,

households become less willing to consume. A rise in

interest rates also makes it more expensive for firms to

finance investment. As a result, higher interest rates

normally curtail investment. If consumption and

investment fall, so does aggregate demand. Lower

aggregate demand results in lower resource utilization.

When resource utilization is low, prices and wages usually

rise at a more modest rate. However, it takes time before a

decline in resource utilization leads to a fall in inflation.

This is partly because wages do not change from month to

month but more seldom than that.

Foreign Institutional Investments (FII): FII has three

positive causal links to SMR, TRV and MCP, and a negative

causal link to BOP. FIIs usually pool large sums of money

and invest those in securities, real property and other

investment assets. As bulks of their investments are in the

stock market, the inflow and outflow of money by FIIs

affect stock market movement significantly and also the

trading volume and the market capitalization. Since, the

account of balance of payments is credited by the amount

of FII inflows, it has negative impact on the BOP.

Trading Volume (TRV): Causal loop diagram shows the

positive causal links of TRV with SMR and MCP. Trading

volume reflects the intensity of a stock, commodity or

index. Volume also provides an indication of the quality of

a price trend and the liquidity of a security or commodity.

High volume means greater reliance can be placed on the

movement in price than if there was low volume, because

heavy volume is the relative consensus of a large number

of participants. Increasing trading volume is the sign of

growth of the stock exchange and its market

capitalization.

Market Capitalization (MCP): The market capitalization

also is found to have two positive causal links with SMR

and FII. Market capitalization is the way to use the stock

price to determine the value of a company, and to know

how likely it is to grow. The investors use the figure of

market capitalization to determine the size of a company.

Normally, they are attracted with the growing trend of

market capitalization of a stock exchange.

Crude Oil Prices (CRO): Figure depicts four positive causal

links of CRO to SMR, INF, BOP and GLD. The crude oil prices

and inflation are often seen as being connected in a cause

68


and effect relationship. As oil prices move up or down,

inflation follows in the same direction. The reason why

this happens is that oil is a major input in the economy - it

is used in critical activities such as fueling transportation

and heating homes - and if input costs rise, so should the

cost of end products, which raises prices and thus

inflation. Inflation causes an increase in demand for these

commodities and thus leads to a rise in their prices.

Hence, the profit margin of the crude oil based companies

increase with ultimately influence their market

performance and resulted in the growth in the overall

stock market performance.

The impact of oil price on gold price could be established

through the export revenue channel. In order to disperse

market risk and maintain commodity value, dominant oil

exporting countries use high revenues from selling oil to

invest in gold. Since several countries including oil

producers keep gold as an asset of their international

reserve portfolios, rising oil prices (and hence oil

revenues) may have implications for the increase of gold

prices. This holds true as long as gold accounts for a

significant part in the asset portfolio of oil exporters and

oil exporters purchase gold in proportion to their rising oil

revenues. Therefore, the expansion of oil revenues

enhances the gold market investment and this causes

price volatility of oil and gold to move in the same

direction. In such a scenario, an oil price increase leads to

a rise in demand and hence prices of gold. If there is hike in

the prices of crude oil in international market, then the

import prices for India automatically raised as India is one

of the major importer for crude oil market and it will

adversely affect the account of balance of payments.

Gold Prices (GLD): GLD has two positive causal links to

SMR and SLV with positive polarities. Gold prices are

highly dominated by the changes in the international

market and fluctuate in a very intensive manner with the

variations in the international commodity market.

Investors are mostly interested in the assets with low

price and high returns. Thus, when the gold prices are on

the increasing, they generally move their investments to

the stock market. Inflation channel is the best to explain

the linkage between gold and silver markets. A rise in gold

price leads to an increase in the general price level. When

the general price level or inflation goes up, the price of

silver, which is also a good, also increase. On the other

hand, gold and silver prices sometimes fluctuates due to

changes in demand for jewelry.

Conclusion

The paper presents a logically structured framework for

interrelationship among the stock market returns and the

macroeconomic determinants, which can be helpful for

additional researches for developing non-linear modeling

techniques such as System Dynamics, Fuzzy-Neural

Networks, Fuzzy Asymmetric GARCH model, Hidden

Markov Models, Wavelet Neural Networks etc.

Investment in stock market is a science wherein an

investor should carry out a detailed enquiry before

investing. The research is aimed at developing and

following a scientific approach to understand the

behavior of stock market. The paper could work as an

investment guide with comprehensive and in-depth

knowledge on stock market investing for them.

Understanding the role of economic indicators that

determine market performance as well as analysis of their

impact on the market, are essential skills for the finance

researchers. From time to time, domestic and

international economic data are released, which impact

the financial markets. This research makes available a

broad and wide description of macroeconomic indicators

impacting stock market behavior, interpretation of the

same and also the application of various techniques for

analyzing the impact.

References

• Adam, A. M, and Tweneboah, G. (2008). Do

macroeconomic variables play any role in the stock

market movement in Ghana?,"MPRA Paper 9357,

University Library of Munich, Germany, revised 2008

Online at http://mpra.ub.uni-muenchen.de/9357/.

• Aksoy, H., and Leblebicioglu, K. (2004). Modeling

ISE100 index through fuzzy logic, Eleventh Annual

MFS Conference Proceedings, Turkey.

• Ali, I., Rehman, K. U., Yilmaz, A. K., Khan, M. A., and

Afzal, H. (2010). Causal relationship between macro-

economic indicators and stock exchange prices in

Pakistan. African Journal of Business Management.

4(3), 312-319.

• Binder, T., Vox, A., Belyazid, S., Haraldsson, H., and

Svensson, M. (2004). Developing system dynamics

model from causal loop diagrams. In proceedings of

the 22nd International Conference of the System

Dynamics Society, Oxford, UK.

69


• Chowdhury, S. S., Mollik, A. T. & Akhter, M.S. (2006).

Does predicted macroeconomic volatility influence

stock market volatility? Evidence from the

Bangladesh capital market, Working Paper,

Department of Finance and Banking, University of

Rajshahi, Bangladesh.

• Cioni, L. (2009). The good and the bad of system

dynamics. Draft paper, University of Pisa, Italy.

Retrieved from http://www.di.unipi.it/~lcioni/

listofpapers/2009.html.

• Engle, R. F. & Rangel, J. G. (2008).The Spline - GARCH

model for low frequency volatility and its global

macroeconomic causes, Review of Financial Studies,

21, 1187-1222.

• Gileva, T. (2010). Econometrics of crude oil markets,

Thesis submitted to the Department of Economics,

University of Paris.

• Goonatilake, R. & Herath, S. (2007). The volatility of

stock Market & News, International Research

Journal of Finance & Economics, 11.

• Halper, J. and Perl, J. (2005). Causes and

explanations: a structural-model approach. Part I:

Causes, British Journal of Philosophy of Science, 56,

843-887.

• Haron, K. & Maiyastri (2004).Principal component

analysis in modeling stock market returns,

Matematika, 20 (1), 31–41.

• H u m p e , A . & M a c m i l l a n , P. ( 2 0 0 7 ) .C a n

macroeconomic variables explain long term stock

market movements? A comparison of the US and

Japan, CDMA Working Paper No. 07/20.

• Kerby, A. & James, L. (2004).A multivariate statistical

analysis of stock trends, Joint Statistical Meetings,

Toronto, Ontario, Canada, August 8-12.

• Kumar, S. (2009).“Investigating Causal Relationship

Between Stock Return with Respect to Exchange Rate

And FII: Evidence from India”, Birla Institute of

Technology & Science, Pilani.

• Liu, H. & Jun, W. (2011).Integrating independent

component analysis and principal component

analysis with neural network to predict Chinese stock

market, Institute of Financial Mathematics and

Financial Engineering, College of Science, Beijing

Jiaotong University, Beijing, China.

• Loretan, M. (1997).Generating market risk scenarios

u s i n g p r i n c i p a l c o m p o n e n t s a n a l y s i s :

methodological and practical considerations,

Federal Reserve Board, March.

• Maysami, R. C., Lee, C. H. &Mohamad, A. H. (2004).

Relationship between macroeconomic variablesand

stock market indices: cointegrationevidence from

stock exchange of Singapore’s All-S sector indices,

Journal Pengurusan, 24, 47-77.

• Schaffernicht, M. (2007). Causality and diagrams for

system dynamics. Actas de la 50th International

Conference of the System Dynamics Society

Proceedings, Boston.

70


NR

TS

GD

P

IIP

WP

I

BO

P

FXR

E

FXR

A

RP

R

TBR

PLR

FII

TRV

MC

P

CR

O

GLD

SLV

NRTS + + +

GDP + + + + + +

IIP + + - + + + + +

WPI + + + + +

BOP + - +

FXRE - +

FXRA - - -

RPR - -

TBR - -

PLR - - -

FII + - + +

TRV + +

MCP + +

CRO + + + +

GLD + +

SLV

Notes: ‘+’ and ‘-’ shows the polarity of causal loops.

Appendices

Figure 1: Causal Loop Diagram of Macroeconomic Determinants of Stock Market Volatility

Table 1: Causal Loops Matrix for all variables

71


72


Guidelines for Submission of Manuscript

1. The whole manuscript is required to be in one MS-WORD file only which will start from the covering letter.

2. The author is advised to submit the complete manuscript as mere Abstract alone will not be considered for review.

3. Manuscript Title: The title of the paper should be in a 12 point Times New Roman font. It should be bold typed, centered and fully capitalized.

4. Author Name(s) & Affiliations: The Author(s) full name, designation, affiliation(s), address, mobile numbers, and e-mail address should be in italic & 11-point Calibri Font. It must be centered underneath the title.

5. Abstract: Abstract should be in fully italicized text, not exceeding 250 words. The abstract must be informative and explain the background, aims, methods, results and conclusion in a single paragraph. Abbreviations must be mentioned in full.

6. Keywords: Abstract must be followed by a list of keywords arranged in alphabetical order and separated by commas and full stops at the end.

7. Manuscript: It should be free from grammatical, spelling and punctuation errors and must be thoroughly edited. Avoid hybrid language and prefer British English. Use standard A4 paper with portrait setting. The text in 10 points Times New Roman should have single space with 1” margin set for top, bottom, left and right. The numbering of pages should be at the bottom (centre alignment).

8. Headings: All the headings should be in a 12 point Times New Roman Font. These must be bold-faced, aligned left.

9. Sub Headings: All the sub-headings should be in a 10 point Times New Roman Font. These must be bold-faced, aligned left.

10. Main Text: It should be in a 10 point Times New Roman Font, single spaced and justified. The manuscript should preferably not exceed 5,000 words. The main text should follow the following sequence:• Introduction• Review of Literature• Need / Importance of Study• Statement of the Problem• Objectives• Hypotheses• Research Methodology• Results and Discussion• Findings• Recommendations / Suggestions• Conclusions• Acknowledgements• References• Appendix / Annexure

11. Figures and Tables: These should be simple, crystal clear, centered, separately numbered & self explained, and titles must be above the table / figure. Sources of data should be mentioned below the table / figure.

12. Equations: These should be consecutively numbered in parentheses, horizontally centered with equation number placed at the right.

13. References: The authors are advised to follow APA style of referencing. The list of all references should be alphabetically arranged.

COPYRIGHT FORM

Date: ……../………./20…..…..

(dd / mm / yyyy)

I / We ………………………………………………………………………………………………………………………………………………… [Author(s) Name],

the copy right owner of article entitled …………………………………………………………………………………………………………………………….

………………………………………………………………………………………………………………………………………………….......................................

.……………..………………………………………………………………………………………………………………………………….......... (Title of Research

Article) do hereby submit the above said article and authorize you to publish the above mentioned article in your

publication viz. Prastuti - Journal of Management & Research.

I / We further state that:

• This said article authored by me/us is an original contribution and has not been plagiarized from any

source/individual. Wherever required I/We have taken permission from the copyright holder and acknowledged the

source.

• It does not infringe on the right of others and does not contain any defamatory or illicit statements.

• The work has been submitted only to ‘Prastuti – Journal of Management & Research’ and it has neither been

submitted for publication nor published elsewhere in any print/electronic form.

• I/We assign all the copyright of this article to the journal. I/We have not assigned copyright to publish this article to

any other journal.

• I/We agree that the sole and exclusive right in the whole copyright of the said paper is transferred to Prastuti –

Journal of Management & Research. This copyright transfer covers the exclusive right to reproduce and distribute

the contribution and its reproductions in offline/online form. I ensure that such rights for the above said article has

not been assigned to any other person/institute/publication.

• I/We agree to indemnify the Editors/Publisher of the journal against all claims or action arising from any breach of

warranty on my/our behalf in this agreement.

• In case of a multi-authored article, I/corresponding author have obtained permission to enter into agreement and

assign copyright from all the co-authors, in writing and all the co-authors have thoroughly read and agreed with

above warranties and authorization.

Prastuti - Journal of Management & Research(ISSN: 2320-2262)

Published bi-annually byInstitute of Business Management, GLA University, Mathura

Corresponding Author’s Name : .............................................................................................

Affiliation (Name & Address of Institute) : .............................................................................................

Corresponding Address of Author : .............................................................................................

.............................................................................................

E-mail ID : .............................................................................................

Contact Number : .............................................................................................

Signature (with Date) : .............................................................................................

Kindly fill this form and send it along with the hard copy of research article to the following address:

Dr. Ankit Saxena

Executive Editor, Prastuti

Institute of Business Management,

GLA University,

17 Km Stone, NH-2, Mathura – Delhi Road,

P.O. - Chaumuhan, Mathura (Uttar Pradesh)

PIN - 281406

Or Submit scanned copy of filled form to [email protected].

For any clarification, Feel free to Call 91-9760545367 / 9927660076.

SUBSCRIPTION FORM

Name : .................................................................................................................................................................................

Affiliation : ..........................................................................................................................................................................

..............................................................................................................................................................................................

Address for Correspondence: .............................................................................................................................................

..............................................................................................................................................................................................

..............................................................................................................................................................................................

..............................................................................................................................................................................................

E-mail id : ............................................................................................................................................................................

Contact Number(s) : ............................................................................................................................................................

Subscription Category: Institutions Teachers Scholars & Alumni Students

Other Individuals Foreign Nationals

Duration of Subscription:

1 Year 3 Years

Details of DD:

Name of Issuing Bank : ....................................................................................................

Issuing Bank Branch : ....................................................................................................

DD Number : ....................................................................................................

Amount : ....................................................................................................

Prastuti - Journal of Management & Research(ISSN: 2320-2262)

Published bi-annually byInstitute of Business Management, GLA University, Mathura

I'M A

dvt

g.9

31

24

31

40

9

volume 03 july 2014 number 01 - gla...

Documents