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    KINDLERTHE JOURNAL OF ARMY INSTITUTE OF MANAGEMENT KOLKATA(FORMERLY NATIONAL INSTITUTE OF MANAGEMENT CALCUTTA)

    VOLUMEXIII NUMBERS1 & 2 ISSN 0973-0486 JANUARY-JUNE2013, JULY-DECEMBER2013

    Page No.

    EDITOR'S NOTE 3

    RESEARCH CONTRIBUTIONS

    India VIX and Stock Market Expectations 7

    Tamal Datta Chaudhuri & Kinjal Sheth

    Organized Retailing Potential of Consumer Electronic Industry 19

    in East India for 20122016: A Quali-Quantitative Study

    Dr. Ayan Chattopadhyay

    Does the Corporate Board Composition Create Value of the Firm? 39

    - An Empirical Study on Selected Indian Companies

    Tarak Nath Sahu & Apu Manna

    Performance of Equity Mutual Funds Based on Diversification 53

    and Selection Ability

    Subhendu Kumar Pradhan & Dr. R. Kasilingam

    STUDENTS CONTRIBUTIONS

    Article

    An Analysis of the Present Indian Financial Market 63and Three Suggested Major Areas of Reform

    Lavlesh Upadhyay, Rincal Kaur & Prashanth K

    Project Synopsis

    Credit Proposal Processing at ABC Bank Limited 69

    Debalina Chakraborty

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    Page No.

    Project Synopsis (Contd.)

    Cloud ERP Adoption in Manufacturing Segment TCS iON 77

    Sunil Gupta

    A Research on Digital Marketing Understanding the Customer and 87

    Marketers Perception Regarding its Reach, Usage and Effectiveness

    Shamik Das

    BOOK REVIEW

    Fundamentals of Financial Management 103

    Sheeba KapilCase Studies in Human Resource Management 107

    Dr R K Suri & Dr S L Gupta

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    Editor's NoteWe reap what we sow, we are the makers of our own fate. The wind is blowing;

    those vessels whose sails are unfurled, catch it, and go forward on their way, but

    those who have their sails furled do not catch the wind. Is that the fault of the

    wind? We make our own destiny. - Swami Vivekanada.

    The year 2013 commemorated the 150thbirth anniversary of this inspiring personality

    who was the spiritual Ambassador of India to the West and a youth icon, Swami

    Vivekanada. His series of lectures and inspiring messages based on the principle of

    the Atman, the doctrine of the potential divinity of the soul, taught in Vedanta, the

    ancient system of religious philosophy of India, made him famous as an orator by

    divine right and as a Messenger of Indian wisdom to the Western world. He believedthat Education is the manifestation of the perfection already in man. He advocated

    the need for that education by which character is formed, strength of mind is

    increased, the intellect is expanded, and by which one can stand on ones own feet.

    According to Vivekananda, Shri Ramakrishna used to say As long as I live, so long

    do I learn. That man or that society which has nothing to learn is already in the jaws

    of death. In keeping with this spirit of learning, the current issue of Kindler showcases

    papers in the area of Finance - India VIX and Stock Market Expectations and

    Performance of Equity Mutual Funds Based on Diversification and Selection Ability in

    addition to an analysis of the present Indian Financial Market and Credit Proposal

    Processing by our student contributors. It gives me immense pleasure to inform you

    all that the analytical paper on the present Indian Financial Market presented by our

    students won the Money Matters essay competition organized by Calcutta

    Management Association. This issue also features a Quali-Quantitative Study on the

    Organized Retailing Potential of Consumer Electronic Industry in East India; a Research

    on Digital Marketing from the Customer and Marketers Perception; an empirical study

    on select Indian companies to analyse whether the corporate board composition creates

    value of the firm and a much appreciated summer internship project synopsis on

    Cloud ERP Adoption in manufacturing segment TCS iON. Hope you enjoy reading

    the varied papers and quench your thirst for more knowledge.

    I conclude, reiterating our commitment to making Kindler a journal of international

    standards, with Swami Vivekanandas clarion call, Arise! Awake! and stop not until

    the goal is reached.

    Dr. Parveen Ahmed Alam

    Editor, Kindler

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    RESEARCH CONTRIBUTIONS

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    Kindler Vol. XIII Nos. 1 & 2 January-June 2013, July-December 2013 7

    * Professor, Calcutta Business School, Diamond Harbour Road, Bishnupur 743503, 24 Paraganas (South).Email : [email protected], [email protected].

    ** MBA 2ndyear student, Calcutta Business School, Diamond Harbour Road, Bishnupur 743503, 24 Paraganas(South). Email : [email protected], [email protected].

    # The views expressed in the paper are those of the authors and do not reflect those of Calcutta BusinessSchool. Any errors are solely ours.

    India VIX and Stock Market Expectations#

    Tamal Datta Chaudhuri* & Kinjal Sheth**

    ABSTRACT

    The purpose of this paper is three fold. First, in line with existing wisdom, it tries to provide some

    insight into the movement of the Implied Volatility Index in India (India VIX). The relationship

    between NIFTY and India VIX is explored for the period January 2008 to December 2012, and it

    is analyzed whether the volatile external and internal macro-economic environment in this period

    was reflected in the Volatility Index. Second, concepts like mean fear and intensity of fear areintroduced to understand whether the Indian stock market has become more or less volatile. We

    emphasize that it is not enough to label India VIX as an index of fear. We need to look at volatility

    of volatility to appreciate the intensity of the fear. Third, the paper provides a framework for

    forecasting volatility for volatility trading. We argue that for volatility trading, it is not enough to

    forecast volatility. We need to look at volatility of volatility also.

    INTRODUCTION

    Implied Volatility (IV) is the measure of volatility that is obtained from the Black and

    Scholes option pricing model. Volatility is the standard deviation of the returns of the

    underlying. If the underlying is NIFTY, then volatility means the standard deviation of

    NIFTY returns. The option price is a function of the spot price S, the strike price K, time

    to expiry t, volatility and the rate of interest r. For a particular option strategy if weplug in all these values, then we will get a theoretical value of the option price. At any

    point of time, S, K, t and r are observable. Only volatility has to be calculated. This

    implies that we will have to plug in a value of . The only value that we have in hand

    is the value of Historic Volatility (HV) of the underlying. However, if we plug in the actual

    option price that is quoted in the market, we will get a value of from the options

    formula; this is IV. This is the volatility as implied by the options market. Since it is

    derived by taking the help of the actual quoted options price, it is volatility of the

    underlying that is expected in the future. In the stock exchanges an index of volatility

    is available, which is constructed after taking a weighted average of IV of different kinds

    of options contracts. In India we have India VIX. In a recent paper, Whaley (2009) has

    provided the intuition behind VIX. According to him It is important to emphasize that

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    the VIX is forward looking; that is, it measures volatility that investors expect to see.

    It is not a of backward-looking index to measure volatility that has been recentlyrealized the level of VIX is implied by the current prices on options and represents

    future stock market volatility Throughout the paper we will be using IV and VIX

    interchangeably to mean the same thing. We are aware that VIX is an index, whereas

    IV can be calculated for each contract.

    IV has been well researched in the literature. To mention a few, Joseph K.W. Fung

    (2005) provides preliminary evidence to support the incorporation of estimates of implied

    volatility in an early warning system designed to provide information on potential

    financial crisis. Pierre Giot and Sebastien Laurent (2006) find that in the

    framework of encompassing regressions, the information content of implied volatility

    is very high for explaining jump/continuous components of historical volatility. Andrew

    Ang, Robert J. Hodrick, Yuhang Xing, and Xiaoyan Zhang (2006) provides a systematic

    investigation of how the stochastic volatility of the market is priced in the cross-section

    of expected stock returns. They hypothesize that if the price of aggregate volatility risk

    is negative, stocks with large, positive sensitivities to volatility risk should have low

    average returns.

    Prithviraj S. Banerjee, James S. Doran, David R. Peterson (2007) investigate the

    relationship between future returns and both current levels of implied volatility. They

    break down the study in terms of beta, size and market to book value. Maria Teresa

    Gonzalez and Alfonso Novales (2009) examine the effectiveness of information content

    of volatility indices in predicting future market returns. They find that although there is

    strong negative relationship between changes in volatility indices and current marketreturns, volatility indices best reflect current market sentiment rather than having much

    predictive value. Bagchi (2011) examines the direct and cross-sectional relationship of

    India VIX in relation to three important parameters, viz., stock beta, market to book

    value and market capitalization. His results suggest that India VIX can be regarded as

    a distinct risk factor that could assist an investor to understand the price discovery

    mechanism.

    While the above literature has looked into the predictive power of VIX for future stock

    price movements/returns, there is another literature of McMillan (2004) and Passerelli

    (2008) who examine the usefulness of IV for volatility trading. However, the concepts

    of mean fear and intensity of fear as defined in this paper, and their usefulness, has

    not been discussed in the literature. To that extent our paper adds value.

    The plan of the paper is as follows. In Section 2, in line with the existing wisdom, it tries

    to provide some insight into the movement of the Implied Volatility Index in India (India

    VIX). The relationship between NIFTY and India VIX is explored for the period January

    2008 to December 2012, and it is analyzed whether the volatile external and internal

    macro-economic environment in this period was reflected in the Volatility Index. In

    Section 3, concepts like mean fear and intensity of fear are introduced to understand

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    whether the Indian stock market has become more or less volatile. We emphasize that

    it is not enough to label India VIX as an index of fear. We need to look at volatilityof volatility to appreciate the intensity of the fear. A framework is provided in Section

    4 for forecasting volatility, and we argue that this has to be combined with the approach

    in Section 3, for volatility trading. Section 5 concludes the paper.

    RELATIONSHIP BETWEEN INDIA VIX AND NIFTY

    In this section we will examine the relationship between India VIX and NIFTY over the

    period January 2008 to December 2012. The period covers the aftermath of the world

    financial crisis (subprime crisis) and the various macro-economic adjustments that the

    economies of the world went through. In fact, we have not yet recovered much from

    the impact of the crisis. The euro region is still struggling to come to terms with the

    sovereign defaults of member countries, post the crisis.Figure 1 provides data on NIFTY and VIX over the period under consideration. Three

    observations are in order. First, there is an overall inverse relationship between VIX and

    NIFTY; second, there are occasions when VIX has spiked; and third there are times

    when VIX has fallen/risen and markets have also fallen/risen. These patterns have also

    been highlighted in the literature by McMillan (2004), Whaley (2009) and Passerelli

    (2008). VIX is an indicator of investor fear. When the underlying falls, investors who

    have exposure in the underlying, rush to protect their position by buying puts. This

    causes volatility to rise. When the underlying market rises and keeps rising, there is

    less fear in the market and activity in the options market drops. Thus volatility also

    drops.

    During the world financial crisis in 2008, there was large scale uncertainty and various

    countries reacted in different ways. The Indian financial markets reacted and we see

    huge spikes in VIX during this period (see Figure 1). VIX reached levels of 90 and

    NIFTY kept on falling. Sudden spikes indicate total chaos and lack of understanding on

    future movements in the underlying. Subsequently, India followed an expansionary

    fiscal policy and the Indian economy grew faster than many economies in the world.

    There was talk that India, along with other BRIC nations, may have to take a leading

    role in revival of the world economy. We can observe from Figure 1 that NIFTY rose

    continuously from 2009 onwards on the back of positive fiscal support from the

    government and we came to feel that India was untouched by the world financial crisis.

    As there was less fear in the market, we observe that VIX fell continuously. Not only

    that, we do not observe any major spikes. When the Government of India gradually

    withdrew fiscal support, from the middle of 2011, NIFTY started to fall. VIX increased

    to around 30 levels. After that, till date, NIFTY has had an upward trend with VIX having

    an opposite downward trend.

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    Figure 1

    Source: Metastock

    MEAN FEAR AND INTENSITY OF FEAR

    Section 2 described broad sweeps in the movement of India VIX and NIFTY, and as

    per conventional wisdom we do observe an inverse relationship between them. We

    extend this understanding further by looking at the level of mean VIX (mean fear) and

    also standard deviation of VIX (volatility of volatility or intensity of fear) in Figure 2. We

    have observed that VIX is an index of fear. Clearly its movement would indicate whether

    the fear in the market is increasing or decreasing. It is also equally important to gauge

    the intensity of the fear. If the market players can predict that the underlying is going

    to go down, then they would rush to the options market for a hedge and they would

    know the extent of the hedge required. However, if the players are not sure about the

    extent of the fall, the speed of fall, and duration of the fall, there would be panic in the

    market. That is where VIX itself tends to be volatile and we term it as volatility of

    volatility.

    In the figure we depict a 30 day rolling standard deviation (SD) of VIX and a 30 day

    rolling moving average of VIX for the years 2008, 2009, 2010, 2011 and 2012, all

    together. (Figure 4 and 5 present data for years separately). The figures shows thatafter the global crisis, in the year 2008, mean VIX and volatility of VIX, both were

    very high. As there was little clarity on the effects of the crisis at that point, fear itself

    was volatile. There was a state of panic in the market. Subsequently, as time went

    by and some clarity was seen, and also that the Indian government had taken

    proactive measures to counter the crisis, volatility of volatility went down as can be

    seen from their movement from 2009 onwards. This low level of volatility of volatility

    continued till the initial months of 2011, but then four things happened. First, government

    India VIX

    Nifty

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    of India decided to withdraw the positive stimulus measures. Second, the euro area

    moved into a crisis situation and member country defaults became imminent. Therewas again lack of clarity on the effects of such defaults on the Indian economy. Third,

    domestic rate of inflation picked up and the RBI raised the rate of interest on various

    occasions. Fourth, the economy was adversely affected by quite a few scams which

    brought about some political instability and indecision. As can be observed from the

    figure, in the later part of 2011, SD of VIX rose significantly. That is, there was

    renewed intensity of fear. Not only did mean VIX rise, its volatility also significantly

    increased. Observe that there are times when mean fear was going down, but intensity

    of fear increased.

    VIX started rising from the middle of 2011. This was accompanied by increase in

    volatility of VIX also. This fact also got repeated in the first quarter of 2012 as first, the

    Union Budget was below market expectations; second, slowdown in political decisionmaking; third, a high and persistent rate of inflation; fourth, slowdown in domestic GDP

    growth rate and also in real sector performance; and fifth, renewed crisis in the euro

    area. With the euro area reaching some consensus, towards the end of 2012, the

    external fear factor somewhat subsided leading to lower volatility of VIX. We conclude

    that the financial-economic-political scenario does get reflected in the mean and volatility

    of VIX. Although VIX and Nifty are negatively related, interestingly, Figure 3 shows that

    SD of VIX and SD of Nifty are positively related.

    Figure 2

    Source: Authors own construction

    SD Of VIX

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    Figure 3

    Source: Authors own construction

    Figure 4: SD of VIX (2008 2012)

    Source: Authors own construction

    SD Of NIFTY

    SD Of VIX

    20102009 2011

    2008

    2012

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    Figure 5: (Mean of VIX 2008 2013)

    Source: Authors own construction

    FORECASTING VOLATILITY AND VOLATILITY TRADING

    In this section we follow McMillan (2004) where Implied Volatility (IV) and Historic

    Volatility (HV) are grouped in deciles. We construct such deciles for the Indian economyfor India VIX (IV) and standard deviation of NIFTY returns (HV). The methodology that

    we adopt involves taking a 20 Day Moving Average (MA) of IV and a 10 Day, 20 Day

    and 50 Day Moving Averages of HV up to a date. Then the actual values of the

    variables are computed on a subsequent date outside the past data set and the Decile

    position of the values is marked off. The 10 Day MA of HV is the fastest and it leads

    the pack of other HVs. The longer day moving averages are the slower ones.

    In Table 1, the deciles have been constructed for one year data from January 1, 2008

    to 31st, December 2008. Then the actual data as on 1stJanuary, 2009 has been taken

    and the decile position of this actual data has been noted as given by the shaded cells.

    As the 10 day HV is to the left of the 20 day and the 50 day HV respectively, HV is

    falling. Given that IV is in the 5th

    decile and is closer in value to HV, IV set to fall. Ifwe look at Table 8, in the next year, that is January 1, 2009 to September 2009, all the

    IV and HV has moved to the 1stdecile position. Taken together, from Tables 2 and 3,

    we observe that over a two year period (2008, 2009), IV has been moving from higher

    deciles to lower deciles, and on 2ndof December, 2009 all the variables are in the first

    decile. The literature has suggested that volatility is mean reverting and hence if IV is

    in the lower deciles then it should rise and if it is in the higher deciles then it should

    fall. Accordingly, if IV is in the lower deciles, then long volatility strategies should be

    2008 2009

    2010 2011

    2012

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    undertaken and if IV is in higher deciles then sell volatility strategies should be undertaken.

    If we look at the movement in the underlying NIFTY during the period, we will observethat with decline in IV, NIFTY rose gradually during the period. Government policies

    undertaken to support the economy during the period led to upliftment in market

    sentiment.

    Table 1: Decile Composition of HV and IV for the period

    January 1, 2008 to 31.12.2008

    1st

    D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 January

    2009

    20Day MA IV 30.39 32.98 35.17 37.70 40.41 43.06 47.87 53.71 66.33 76.00 40.83

    10Day MA HV 20.80 24.82 29.89 35.52 39.40 43.80 47.75 52.81 67.95 105.00 31.279

    20Day MA HV 21.77 27.18 33.17 37.52 40.42 35.91 46.67 55.00 64.33 85.00 40.3335

    50 Day MA HV 28.96 32.56 35.30 37.24 39.18 42.50 46.64 51.88 64.56 70.00 62.0936

    Source: Authors own construction

    Table 2: Decile Composition of HV and IV for the period

    January 1, 2009 to 1.12.2009

    2nd

    D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 December

    2009

    20Day MA IV 25.90 30.51 34.06 35.57 37.75 39.85 41.58 44.34 47.57 54.00 24.748

    10Day MA HV 16.36 20.03 25.12 28.68 31.07 32.99 35.36 38.31 41.72 90.00 15.116

    20Day MA HV 17.55 25.31 28.27 30.13 32.00 34.06 36.13 38.31 47.40 72.00 17.371

    50 Day MA HV 21.39 25.24 31.93 33.25 34.60 36.56 39.52 49.20 50.60 52.00 21.314

    Source: Authors own construction

    With subsequent gradual withdrawal of government support and with a looming euro

    area crisis, the Indian economy suffered a setback and market sentiment went bearish.It may be observed from Table 2, that both HV and IV moved from lower deciles to

    higher deciles clearly supporting the mean reversal of VIX and also conducive for long

    volatility strategies. As the 10 day HV was higher than the 20 day and 50 HV respectively,

    HV was on the rise and IV would also catch up with it. Market sentiment took a beating

    and NIFTY fell continuously.

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    Table 5: Decile Composition of HV and IV for the period

    January 1, 2012 to October 31, 2012.

    16th

    D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 November

    2012

    20Day MA IV 14.99 16.73 17.40 19.86 21.22 22.64 23.47 24.19 25.31 26.23 14.86

    10Day MA HV 11.79 14.01 14.59 15.12 15.66 16.56 17.24 18.64 19.69 21.28 10.85

    20Day MA HV 12.07 13.83 14.48 15.26 15.91 16.48 17.10 18.58 19.82 20.53 9.89

    50 Day MA HV 13.04 13.88 14.64 15.78 16.04 16.30 16.92 18.00 18.89 19.32 13.32

    Source: Authors own construction

    Table 6: Decile Composition of HV and IV for the period

    January 1, 2012 to January 1, 2013.

    3rd

    D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 January

    2013

    20Day MA IV 13.71 14.33 16.25 17.15 19.13 21.29 22.92 23.75 24.82 26.17 13.37

    10Day MA HV 9.89 11.94 13.45 14.23 14.96 15.76 16.63 17.92 19.25 21.31 10.38

    20Day MA HV 10.10 12.46 13.39 14.19 14.95 15.73 16.51 17.85 19.52 21.39 8.79

    50 Day MA HV 11.733 13.00 13.28 13.56 15.06 15.71 16.32 17.27 18.63 19.40 10.03

    Source: Authors own construction

    CONCLUDING REMARKS

    While looking at the conventional relationship between India VIX and NIFTY, we also

    investigate the movement in the level of mean VIX and standard deviation of VIX. We

    feel that volatility of VIX should be tracked in order to undertake volatility trading

    strategies. Even if IV is in lower deciles and in spite of the mean reversal property of

    VIX, it is possible that IV may fall further, and an indicator of that is standard deviation

    of VIX falling. Thus long/short volatility strategies may not give desired results even if

    IV is in lower/higher deciles, if standard deviation of IV is falling/rising.

    REFERENCES

    1. Ang Andrew, Hodrick Robert J, Xing Yuhang, and Zhang Xiaoyan (2006), The Cross-Section

    of Volatility and Expected Returns, The Journal of Finance, Vol. 61, No. 1, pp. 259-299

    2. Bagchi Debasis (2011), Some Preliminary Examination of Predictive Ability of India VIX,

    NSE Research Paper, www.nseindia.com

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    3. Banerjee Prithviraj S, Doran James S, Peterson David R (2007), Implied Volatility and Future

    Portfolio Returns, Journal of Banking & Finance, Vol.31, pp. 318331994. Fung Joseph K.W. (2005), The Information Content of Option Implied Volatility Surrounding

    the 1997 Hong Kong Stock Market Crash, HKIMR Working Paper no.21/2005

    5. Giot Pierre and Laurent Sebastien (2007), The Information Content of Implied Volatility in

    Light of the Jump/Continuous Decomposition of Realized Volatility, The Journal of Futures

    Market, Vol. 27, Issue 4, pp. 337-359

    6. Gonzalez Maria Teresa and Novales Alfonso (2009), Are Volatility Indices in International

    Stock Markets Forward Looking?, The Journal of RACSAM, Volume 103, Issue 2, pp. 339-

    352

    7. McMillan, Lawrence G (2004), McMillan on Options, John Wiley & Sons, Inc., Hoboken, New

    Jersey

    8. Passarelli, D (2008), Trading Option Greeks, Bloomberg Press, New York

    9. Whaley, Robert E (2009), Understanding the VIX, The Journal of Portfolio Management,

    Spring 2009, Vol. 35, No. 3, pp. 98-105

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    Organized Retailing Potential of Consumer

    Electronic Industry in East India for 20122016:

    A Quali-Quantitative Study

    Dr. Ayan Chattopadhyay*

    ABSTRACT

    The retail industry in India is at an inflexion point with consumers finding a host of shopping

    destinations that vastly differ from the ones they have been seeing over the years. Modern or

    Organized retailing is the buzzword. The retail landscape in India is undergoing a metamorphosis

    not only in terms of emerging formats but also in terms of new age shoppers, products or services.

    Organized retailing is being considered as one of the biggest drivers of Indian Economy. Retailing

    of consumer electronic goods in India have primarily been restricted to stand alone stores with

    limited display space; however with the advent of Brand Shops, Electronic Malls, Department and

    Discount Stores in India, the shopping experience of consumers have changed dramatically. It is

    to be noted that such modern retailing formats are on a rise and many conventional retailers are

    also changing fast to fit into the competitive organized retail landscape. The present research study

    aims at estimating the organized retailing potential of Consumer Electronic industry in Eastern

    India. Both quantitative and qualitative approaches have been used. The quantitative approach

    measures the organized retailing potential in value terms using double exponential smoothing

    technique as the forecasting tool. Secondary data forms the basis of quantitative study. Thequalitative approach makes an assessment of the organized retail potential from the consumer

    preference towards various retailing formats (conventional as well as organized) and primary

    research forms the basis of this study. The results of both quantitative and qualitative studies have

    been compared to derive a conclusion. This study includes four states of East India; West Bengal,

    Bihar, Orissa & Jharkhand and primary research conducted in the state capitals covering a total

    sample base of 333 respondents.

    KEY WORDS

    Double Exponential Smoothing Technique, Mean Absolute Deviation, Chi-Square, Contingency Co-

    efficient, Association, Organized/ Modern Retail, Conventional Retail

    INTRODUCTION

    Indias consumer electronic goods market is riding the crest of the countrys economicboom. Driven by young population with access to disposable incomes and easy finance

    options, this industry has been throwing up staggering figures. The Indian consumer

    electronic industry has witnessed a sea change from the eighties to nineties and then

    in the 21stcentury. Post nineties; with the economy opening up, the Indian market saw

    the emergence of a host of international brands with LG, Sony, and Samsung starting

    * Regional Marketing Manager East, LG Electronics India (P) Ltd.; Email: [email protected]

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    their operations in India. Following them the other international brands like Panasonic,

    Sharp, Haier, Hyundai, TCL, Sansui, Akai, Canon, Nikon, and Olympus etc. startedmarketing their products in India. The market share of multinational companies is on

    a rise and exit 2011 data of ORG reflects a 65% share by the MNC on a PAN India

    level. The MNC majors, with their wide range of product portfolio are targeting almost

    all sections of the society. Also products meant specifically for Indian consumers and

    Indian conditions are being developed. India Insight products by LG are a classic

    example to this. These companies with their wide range of product portfolio, technological

    edge over the Indian manufacturers, robust distribution network and steady capital flow

    from the parent organization, already have taken majority share in India. However this

    market is characterized by low penetration levels, especially at rural level. Today, the

    Consumer Electronics industry is dominated largely by LG & Samsung in most of the

    product categories followed by companies like Sony, Canon, Nikon, Panasonic andVideocon.

    India with its huge consuming class and rapid economic growth is one of the largest

    spenders in consumer electronics in Asia. Double income families, rising income levels,

    availability of credit, changing lifestyle, introduction of new products, and increasing

    consumer awareness has led to the surging demand of consumer electronic goods in

    India. The digital technology revolution has enabled the industry to earn profits from

    growing interaction of digital applications, such as LCD TV, Mobile phones etc. Smart

    TV is a unique example of such an integration of digital applications. The single device

    acts a Flat Panel TV that has the option of being used as a Computer monitor, a device

    where Internet browsing is an experience on a larger screen, a device where movies

    and other videos can run without any DVD player, a device where information can beaccessed and shared through Smart phones (mobile phones) and so on. Most of the

    MNC majors are differentiating themselves from each other which also is providing the

    profit margins to them. Profits from other generic products are on a decline with increase

    in the input costs. Another important factor that has contributed to the expanding

    consumer electronic goods market is the phenomenal growth in Indian media. Even

    consumers in remote areas are equally aware of the latest gadgets due to high media

    penetration.

    Even consumers in remote areas are equally aware of the latest gadgets due to high

    media penetration. This is further fuelled by aggressive marketing initiatives and the

    new age communication via internet. Government of India has left no stone unturned

    in their policy initiatives to boost the growing consumer electronic industry. Foreign

    investment up to 100% is possible in the Indian Consumer Electronic industry to set up

    units exclusively for exports. It is now possible to import duty free all components and

    raw materials, manufacture products and export it. Electronic Hardware Technology

    Park (EHTP) is another initiative to provide benefits to companies that are replacing

    certain imports with local manufacturing. EHTP benefits include export credits, no

    duties on imported components or capital equipments, business tax incentives, and

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    expedited export import process. The government, in an attempt to encourage

    manufacture of electronics in India has changed the tariff structure significantly.

    The consumer electronics segments comprise the audio video and the home appliance

    products. Since the 90s the market dynamics have been changing at a fast pace.

    Product offerings from various companies have changed over times with a special

    focus on service. Products typically suited for the Indian sub-continent have been

    introduced. Gradually the concept of sellers market started shifting towards a buyers

    market. To start this revolution, the conventional televisions were replaced by the flat

    televisions followed by the introduction of slim fit and ultra slim fit TVs. The audio visual

    segment also saw the launch of LCD television by Samsung, initially targeted to a niche

    segment. Sony and LG joined the race shortly followed by the other Indian companies.

    Over time, wide size variants were introduced with advanced features. LCD TVs which

    was earlier a product for high end customers only was then introduced for mass market

    in small screen sizes. Sony, Samsung, LG initiated the process with other companies

    joining the league subsequently. Further consumer excitement has been created through

    new product launches in the form of LED TVs, a television that is 1.16 inches in width,

    a razor thin television launched for the first time in India by Samsung. The digital media

    segment has probably been the fastest to change their product offerings. Sonys

    Handycam has become a generic name in the movie camera segment. The still camera

    segment with a big list of competition from Sony, Canon, Kodak, Nikon, Samsung,

    Olympus, Panasonic etc are constantly offering new varieties to the Indian consumers.

    The still cameras with features of movie camera have also been introduced. The Home

    Appliance segment had refrigerators and air conditioners as the only two categories for

    the consumers saw the launching of washing machines and micro wave ovens. From

    conventional direct cool refrigerators; frost free refrigerators, side by side refrigerators,

    low mount refrigerators were introduced by the MNCs. Samsung, LG, Whirlpool, Godrej,

    Onida, Voltas, Videocon, Hitachi, Panasonic are the key players in the segment. The

    air conditioner segment also witnessed a radical change from the window acs to the

    split acs, cassette acs and tower acs. Washing machines have added a new flavour

    to the Indian household with the semi automatic, fully automatic machines in top

    loading and front loading variety. With increase in the number of working Indian couples,

    Micro wave ovens have brought a big relief. The solo, grill and convection variants have

    their own benefit. The consumer electronic industry has not only witnessed a paradigm

    shift in terms of the product offerings but also in terms of the channels that sell theseproducts. From the conventional retailing system in the form of standalone stores with

    limited display space, this industry has witnessed the emergence of bigger stores,

    brand shops, chain stores and off late the modern retailing system in the form of

    department stores, electronic malls, discount stores. MNCs like Sony, Samsung, Philips,

    Panasonic, LG and Videocon have their brand shops (modern/ organized retailing

    formats), the most successful among them being Sony and Samsung. Both these

    companies have over 200 stores each in the county in the form of large format brand

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    shops and experience stores, smaller format exclusives stores. These brand shops and

    exclusives have their presence even in smaller cities and towns. The shift from metrosto mini metros and smaller towns are being efficiently managed by both these companies.

    The All India Modern Retail Consumer Electronics (AI MR CE) Sales have been steadily

    growing. 2006 recorded a sales growth of 86% over the previous year while in 2008 the

    growth was 74% over the previous year. It is to be noted that the AI MR CE sales

    growth between 2005 and 2011 has been at a staggering 858% which indicates that

    modern retailing of CE Goods is fast picking up in India with organized retailers and

    manufacturers investing more in the MR formats and consumers accepting the same.

    Thus it appears that the demand and supply of CE Goods retailing through MR formats

    is catching up fast in India. The researcher has made an attempt to make a forecast

    of the All India MR CE Sales and East India to be specific. Quantitative (Double

    Exponential Smoothing) Technique has been used to evaluate the same and secondary

    data forms the basis of the quantitative assessment. Since such quantitative technique

    do not take into consideration the consumer acceptance factor, primary research was

    conducted in East India to cross check the consumer acceptance factor and compared

    with the findings of the quantitative study to arrive at a final conclusion.

    REVIEW OF LITERATURE

    Research Studies on Consumer Electronics have primarily been done by research

    institutions, business bodies and individual research works related to Indian market is

    not found in abundance. The limited research work by individuals on the said subject

    makes it increasingly difficult to establish a sound theoretical base though it also

    creates opportunity for further research. Some of the research studies found relevantto the present study are elaborated.

    Jagwinder Singh (2011) in his study, A Comparison Of Rural And Urban Buying Of

    Consumer Durables aims at differentiating the buying behaviour of rural households

    from that of urban households for three different product categories; Television,

    Refrigerator, and Automobiles. The sample includes households (50% each from urban

    & rural areas) across the Punjab state of India using convenience sampling. The study

    concludes that no significant differences could be observed between rural and urban

    consumers in terms of their timing of purchase, buying the same brand of other durable,

    number of items, and duration of planning before buying. Habitat (rural or urban) has

    a relation with income for the timing of buying a television, refrigerator, and automobile

    except in case of buying of an automobile on festive / special occasion, where the

    income had no relation with habitat. There is a relation between habitat and income in

    terms of duration of planning for different time periods before the buying of a television

    and refrigerator. The habitat also reveals association with income in terms of planning

    for months before buying an automobile. No association has been observed between

    habitat and income in case of planning for few days, few weeks and years before

    buying an automobile. Bhagaban Das (2008) made a study on Categorizing Consumers

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    Buying Behaviour: A Factor Analysis in Consumer Durable Market which aims to find

    consumers perception on buying colour television in the Balasore town of Orissa.Fifteen variables were listed for the purpose of the study. The factor analysis reveals

    that the consumers consider various aspects of Television which include Physical

    structure, Technical aspects, Quality, Price etc. Six factors were extracted and the study

    concludes that the consumers perception on buying colour television is mostly affected

    by these six factors that are labeled as Structural Add-Ons, Word of Mouth, Durability,

    Technical Features, Ground Reality, and Recommendation of Dealers. Behavioural

    Changes on Indian Organized retailing was studied by K. Venugopal & N. Santosh

    Ranganath, (2012). The study highlights that organized retail has so far remained

    confined to urban areas and saturation effect is yet to be felt, hence movement to other

    parts has not happened in a big way. Organized retailing has come to be identified with

    lifestyles, particularly the affluent ones. Appeal to other classes of society needs to be

    created.

    Another study by Stanley George (2012) on Occasion Based Promotional Strategies of

    Consumer Durable Segment in Kerala aims to understand the importance of Occasion

    Based Marketing; which as an approach connects when and why consumers use the

    product with how they shop for the product. The study analyses the promotional strategies

    of consumer durable companies and retailers during festival season in Kerala using

    Content Analysis of print-based advertisements. The study however does not consider

    releases in media other than newspapers (especially TV) and the offers that are not

    publicized through mass media (like some in-store promotions) have not been considered.

    Also the size and colour of the advertisement, types of promotions and its differentialimpact has not taken into consideration for this study. The study concludes that retailer

    promotions are used more than manufacturer promotions in the print advertisements

    announcing promotional offers in the market during festival seasons with Sales promotions

    being the thrust area. Mridula S. Mishra, (2007), made an independent study on The

    Consumption Pattern of Indian Consumers: Choice between Traditional and Organized

    Retail. The study highlights that organized retail has started to spread its roots in the

    Indian market since past one decade and is gradually making mark among all sections

    of the society. This paper tries to explore the way organized retail has dramatically

    changed not only in terms of the retailing structures in India but also with respect to

    the consumption behavior. Consumption behavior was examined using a structured

    questionnaire. The results show that, for consumers, shopping mall and similar organizedretail format is the preferred type of retail store, due to convenience and variety.

    Vijay Gabale, et al. (2008) has made a Demand Forecasting in the Indian Retail

    Industry. The study reveals that the retail sector is growing at a good pace and increasing

    competition is forcing retailing to use demand forecasting tools. This affects the

    manufacturing scheme. Small scale retailers can employ qualitative technique and

    historical data to predict demand. Demand forecasting remains a critical tool that plays

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    key roles in manufacturing, advertising, placement and allocation of resources. The

    study on Booming Electronics Market in India by RNCOS, (2011), highlights thatConsumer Electronics market will grow at a CAGR of around 18% during 2011 2014.

    Also large number of companies will foray into the Indian market with their diversified

    portfolio. The research highlights price sensitivity, distribution channel analysis and rural

    marketing will play a crucial role in Consumer Electronics growth.

    Most of the researches conducted on organized retailing concentrate either on the

    opportunities and challenges or on the consumption pattern from conventional as well

    as modern retailing or on the future formats of organized retailing etc. Studies on Sales

    Forecast of Consumer Electronics Industry from the organized channel are found to be

    scarce and studies specific to East India was not found. Modern Retail Consumer

    Electronics Sales forecast in East India for the period 2012 2016 was thus identified

    as the research gap and estimation done using both quantitative and qualitative study.

    RESEARCH OBJECTIVE

    Two basic research objectives have been framed.

    1. The first objective deals with the Modern Retail Sales Forecast of Consumer

    Electronics Industry (in value terms) at an All India and East India level for

    the period 2012 2016 using quantitative techniques. Similar forecast for

    overall retail industry in India has also been made for the same time period.

    2. The second objective deals with the qualitative assessment of modern

    retail sales potential of consumer electronics industry. The same has been

    made from the consumer preference towards different formats for their final

    purchases (including modern retail formats). Also the level of association

    between the different state locations in East India and preferred formats in

    retailing has been studied to arrive at a conclusion.

    METHODOLOGY

    RESEARCH OBJECTIVE 1: Modern Retail Sales Forecast of Consumer Electronics

    Industry for the period 2012 2016 using Quantitative Approach

    The quantitative assessment uses Normative Method. Using this process or method,

    an attempt is made to forecast the Modern Retail Sales (in value terms) for Consumer

    Electronics industry. The forecasting method uses Secondary data captured for the

    period 2004 to 2011 and forecast made for the period 2012 to 2016.

    Exponential Smoothing Technique is used as the forecasting methodology in the present

    study. Exponential Smoothing is another Averaging technique that inherently assigns

    weight to the observations. Exponential smoothing methods are recursive, that is, they

    rely on all observations in the time series. It is a procedure for continually revising a

    forecast in the light of more recent experience. Exponential Smoothing assigns

    exponentially decreasing weights as the observation get older. In other words, recent

    observations are given relatively more weight in forecasting than the older observations.

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    When the data shows a trend, Double Exponential Smoothing1 is the most effective

    method of forecasting which single exponential smoothing method does not anticipate.Single exponential smoothing uses the formula:

    t+1 = Y

    t + (1-) Y

    twhere:

    Yt+1

    represents the forecast value for period t + 1

    Yt is the actual value of the current period, t

    Yt is the forecast value for the current period, t &

    is the smoothing constant, or alpha, 0 1

    To account for a trend component in the time series, double exponential smoothing

    incorporates a second smoothing constant, beta (). Now, three equations must be

    used to create a forecast: one to smooth the time series, one to smooth the trend, andone to combine the two equations to arrive at the forecast:

    Ct = Y

    t + (1-)(C

    t-1 + T

    t-1).....(i)

    Tt = (C

    t - C

    t-1) + (1 )T

    t-1.....(ii)

    Yt+1

    = Ct + T

    t.....(iii)

    All symbols appearing in the single exponential smoothing equation represent the same

    in the double exponential smoothing equation, and is the trend-smoothing constant

    (whereas is the smoothing constant for a stationary constant process); Ct is the

    smoothed constant process value for period t; and Tt is the smoothed trend value for

    period t.

    As with single exponential smoothing, one has to select the starting values for Ctand T

    t, as well as values for and . These processes are judgmental, and

    constants closer to a value of 1.0 are chosen when less smoothing is desired (and

    more weight placed on recent values) and constants closer to 0.0 when more

    smoothing is desired (and less weight placed on recent values). Determination of

    and values is critical in the correctness of the forecast. Since there are no

    strict rules about selecting these parameters, one has to experiment with the

    smoothing constants to find the most accurate forecast at the lowest possible MAD

    (Mean Absolute Deviation). The absolute deviation is the absolute value of the

    difference between Yt and Y

    t.

    From the secondary data sources, the All India Modern Retail Sales and All India

    Modern Retail Consumer Electronics Sales (both in value) were captured and

    analysed. The sales values from 2004 till 2011 shows a trend (as shown below)

    which is why Double Exponential Smoothing Methodology is chosen as the

    forecasting tool for the present study that accounts for the trend component in the

    time series.

    1 Hamdy A. Taha, 1999. Operations Research An Introduction, PHI, 6th Edition, Chapter 13,pp 510