modeling the extreme events of the top industrial returns listed in bse

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Dr.G.S.David Sam Jayakumar and A.Sulthan, “Modeling The Extreme Events of The Top Industrial

Returns Listed In BSE” – (ICAM 2016)

MODELING THE EXTREME EVENTS OF THE TOP INDUSTRIAL

RETURNS LISTED IN BSE

Dr.G.S.David Sam Jayakumar

Assistant professor,

Jamal Institute of management, Jamal Mohamed College, Trichy-20

A.Sulthan

Research scholar,

Jamal Institute of management, Jamal Mohamed College, Trichy-20

ABSTRACT

Extreme price movements in the financial markets are rare, but important the objective of

study was to evaluate the extreme events of major industries in BSE. The study was conducted

for returns of industries and shows the extreme events to which the industries are scattered for

their returns. Many models were undertaken as base for the study, to identify the extreme

events of the industries and same has been incorporated for the analysis too.

Key words: Extreme events, BSE, returns, financial markets

Cite this Article: Dr.G.S.David Sam Jayakumar and A.Sulthan. Modeling The Extreme

Events of The Top Industrial Returns Listed In BSE. International Journal of Management,

7(2), 2016, pp. 341-353.

http://www.iaeme.com/ijm/index.asp

INTRODUCTION AND RELATED WORKS

Extreme price movements in the financial markets are rare, but important. The stock market crash on

Wall Street in October 1987 and other big financial crises such as the Long Term Capital Management

and the bankruptcy of Lehman Brothers have attracted a great deal of attention among investors,

practitioners and researchers. Stock market performance of a large sample of new issues (IPOs and

SEOs) following an extreme price movement during the first three years after the offering. Strong

underperformance follows either a positive or negative one-day return event. Financial position uses

the historical returns of the instruments involved to compute On the other hand, a conditional approach

uses the historical data and explanatory variables to calculate. The overreaction (cum irrational

exuberance-excess volatility) hypothesis (De Bondt and Thaler, 1985, Shiller, 1981), the momentum

strategy as an investment style (Jegadeesh, 1990, Jegadeesh and Titman, 1993), and the extreme or tail

risk phenomenon in financial markets. The bulk of the evidence in the overreaction and momentum

literatures is based on portfolio formation and performance evaluation using typical returns over many

sequential long intervals of a month to more than a year. The extreme risk phenomenon, on the other

hand, refers to market moves that are high in severity, low in frequency and short-term in duration.

Such an episode is most dramatically illustrated by the US market crash of 1987 and the 2008-09

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financial crisis, but may also occur on days of major macroeconomic or leading company information

and geopolitical events, or simply due to technical trading (overbought/oversold).The profitability of a

contrarian strategy (long the past losers, short the past winners) in support of the overreaction

hypothesis relies upon return reversal while that of a momentum style strategy (short the past losers,

long the past winners) rests on return continuation. Whether returns reverse or continue may, however,

depend on the term structure of returns and the investment horizon (De Bondt and Thaler, 1985,

Jegadeesh and Titman, 2001, Novy-Marx, 2012, Goyal and Wahal, 2013). Short term return reversal is

reported by Brown and Harlow (1988), Lehmann (1990) and Atkins and Dyl (1990). To date, however,

there is little published research about continuation versus reversals of extreme market movements, that

is, whether an extreme fall in the market on one day is followed by an extreme fall or rise in the market

in the following days and vice versa.1 As our events are defined using the broader market movements,

microstructure effects (bid-ask bounce, volume, etc.) and group or stock specific issues (e.g., size,

earnings, book to market ratios, analyst coverage, etc.) should have minimal influence on our results.

Further, all individual stocks in this paper share the same event dates. Daniel and Moskowitz (2013)

report that the conventional static momentum strategy “crashes” during periods of high market

volatility following a bear market (cumulative negative return of the CRSP VW Portfolio over the last

24 months). Although not studied directly, this result is indicative of the implication of extreme

movements in the overall market. This paper provides additional evidence in this regard by examining

portfolios that are formed conditional upon the infrequent but extreme daily movements in the broader

market. While not dynamic in the sense of continually updated trading portfolios as in Daniel and

Moskowitz (2013), the experiment is nonetheless more targeted. Brown, Harlow and Tinic (1988)

found positive abnormal returns in the 60 days following an individual stock price change greater than

2.5% in magnitude, for both positive and negative shocks. They advocate that this supports the

Efficient Market Hypothesis (EMH) since the positiveabnormal returns simply reflect the increase in

risk following the event. The authors name this framework as the Uncertain Information Hypothesis

(UIH). Corrado and Jordan (1997) argue that the 2.5% event threshold of Brown, Harlow and Tinic

(1988) is too low, thus generating too many events. For example, assuming a Normal distribution, this

threshold means that one event is expected to occur every ten days. Accordingly, Corrado and Jordan

(1997) employed a much larger event filter of 10% price change and found that, consistent with the

Overreaction Hypothesis (OH) of De Bondt and Thaler (1985), the negative (positive) events are

followed by positive (negative) abnormal returns (AR). Similarly, Bremer and Sweeney (1991)

reported a significant price reversal (above average returns), for the individual stocks of Fortune 500, in

the days after a stock experiences a large price decline such as more than 10%. Also, they did not find

this phenomenon to be related to market movements. Further studies in different markets and for

distinct shock magnitudes led to divergent results. Lasfer, Melnik and Thomas (2003), studying

international markets, found positive (negative) shocks leading to positive (negative) abnormal returns

on a 10 day window, and attributed this result to momentum. They also found that the intensity of the

abnormal returns is proportional to the magnitude of the event, and that this effect is more pronounced

in emerging markets than in developed countries. Employing a ±20% threshold, Himmelmann,

Schiereck, Simpson and Zschoche (2012) reported positive abnormal returns on European stocks after

both negative and positive events, thus supporting Brown, Harlow and Tinic (1988). In contrast,

although adopting the same threshold, Ising, Shciereck, Simpson and Thomas (2006) found

overreaction (underreaction) to positive (negative) events in the German market. Using a qualitative

approach to define favorable and unfavorable events, Mehdian, Nas and Perry (2008) reported positive

abnormal return for both cases in the Turkish market, lending support to the UIH. Recently, Savor

(2012) used analyst reports as a proxy for information and found that the informed events are followed

by drifts (momentum) and the uninformed events are followed by reversals (overreaction). Aside from

the fact that the above studies do not consider events in terms of extreme market movements, there is

also an important methodological issue. With the exception of Corrado and Jordan (1997), most of the

studies do not control their samples for overlapping events, that is, oneor more days in the post-event

period for calculating abnormal returns where the price change is of the magnitude used to define the

event. It is thus not clear whether the reported abnormal returns support a given hypothesis

(overreaction, momentum or the UIH), or simply reflect the influence of another extreme event in the

“post-event” period. The extant evidence becomes even more confounded as many studies measure the

expected (or normal) return from the “pre-event” window that itself contains an event in the case of an

overlap.The study deals with the industrial returns of major industries listed in BSE and shows the

extreme events to which the industries are scattered for their returns. Many models were undertaken as



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base for the study, to identify the extreme events of the industries and same has been incorporated for

the analysis too.

METHODOLOGY

NEED FOR STUDY

In the finance field, it is a common knowledge that money or finance is scarce and that investors try to

maximize their returns. But when the return is higher, the risk is also higher Return and risk go together

and they have a tradeoff. The art of investment is to see that return is maximized with minimum risk. In

the above discussion we concentrated on the word “investment” and to invest we need to analysis

securities. Combination of securities with different extreme events characteristics will constitute the

portfolio of the investor.

OBJECTIVES

1. To know the industry profile of BSE.

2. To study the extreme events in stock returns of selected industries.

3. To study the extreme events of selected top industries.

4. To study the systematic extreme events involved in the selected industries stock.

5. To offer some suggestions to the investors.

INDUSTRY SELECTION

The monthly data of following industries Automobile, Health care, PSU Capital goods, Bank,

Consumer durables, FMCG, IT, Power, Metal and Oil&Gas are considered.

DATA SAMPLE

The study was conducted for log return of industries from October 2011 to June 2014. The closing

price of companies in the selected industry was collected from historical data available in BSE website.

DATA ANALYSIS

The analysis was conducted at different stages by utilizing selected time series econometric

technique. In Stage-1, the multivariate normality of the data is tested. In Stage-2 industrial returns of

top industries were identified by using multi T-square distance test. While in Stage-3 stepwise

discriminant analysis for extreme event in industries are analyzed.

PROFILE OF SELECTED INDUSTRIES

The Bombay Stock Exchange (BSE) (formerly, The Stock Exchange, Bombay) is a stock exchange

located on Dalal Street, Mumbai and is the oldest stock exchange in Asia. The equity Market

capitalization of the companies listed on the BSE was US $1 trillion as of December 2011, making it

the 6th largest stock exchange In Asia and the 14th largest in the world. The BSE has the largest

number of listed companies in the world. As of December 2011, there are over 5,112 listed Indian

companies and over 8,196 scrips on the stock exchange, The Bombay Stock Exchange has a significant

trading volume. The BSE SENSEX, also called "BSE30", is a widely used market index in India and

Asia. Though many other exchanges exist, BSE and the National Stock Exchange of India account

forth majority of the equity trading in India. While both have similar total market capitalization

(aboutUSD1.6trillion), share volume in NSE is typically two times that of BSE.

SELECTEDINDUSTRIES LISTEDINBSE

AUTOMOBILEINDUSTRYIN INDIA

The Indian automobile sector is one of its most vibrant industries. The industry accounts for 22 percent

of the country's manufacturing gross domestic product(GDP).It comprises passenger cars, two-

wheelers, three-wheelers and commercial vehicles and is currently the seventh-largest in the world with

an average annual production of 17.5million vehicles, of which 2.3millionare exported. The Indian



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auto market has the potential to dominate the global auto industry, provided a conducive environment

is created for potential innovators to come up with new pilot projects. Then ext few years are projected

to show solid but cautious growth due to improved affordability, rising incomes and untapped markets.

All these open up an opportunity for automobile manufactures in India. In addition, with the

government's backing and a special focus on exports of small cars, multi-utility vehicles (MUVs), two

and three-wheelers and auto components, the automotive sector's contribution to the GDP is expected

to double, reaching a turn over of US$145 billion in 2016, according to the Automotive Mission Plan

(AMP) 2006-2016.

INVESTMENTS

Some of the recent major investments in the automobile industry in India are as follows: BMW Group

has launched the third generation of its sports utility vehicle (SUV), the X5x Drive30d,which will be

Rs1million (US$16,635.94) cheaper than the previous version, as the model will now be assembled at the company's Chennai plant rather than being imported fully assembled. Japan's Isuzu Motors aims to

sell 50,000 pickup vehicles in India in the next few years to gain market leadership. The company,

which has a fully owned subsidiary in Chennai, has marked Rs.3,000 crore (US$499.07million) for a

120,000 units per year manufacturing facility. Mercedez-Benz India has inaugurated South India's first

AMG Performance Centre at Sundaram Motors in Bengaluru and has also launched the ML 63 AMG

for the Indian market. Mercedes-AM Gains to offer a more personalized service to its customers and

further bolster its powerful luxury SUV product portfolio in India.VE Commercial Vehicle, a joint

venture (JV) between Eicher Ltd and Volvo, is exploring the possibility of entering the small

commercial vehicle segment with arrange of mini trucks. With this move, they plant on the market with

bigger rivals such as Tata Motors, Mahindra and Mahindra and Ashok Leyland. Fiat plans to launch 12

models based on three platforms, double i ts work force to 5,000 and increase capacity by 80 percent its

Ranjanga on plant by 2018. Mahindra & Mahindra (M&M) has inaugurated a factory and a research

center for electric wheelers in Ann Arbor, Michigan, US. With an initial capacity to produce 9,000

vehicles annually, the plant will assemble its first electric two-wheeler later this year.

GOVERNMENT INITIATIVES

SIAM and the Automotive Component Manufacturers Association of India (ACMA) are two apex

bodies appointed by the Government of India to work for the development of the automobile industry

in India. India has a well-established Regulatory Framework under the Ministry of Shipping, Road

Transport and Highways in which SIAM plays an important role. Also, ACMA's active involvement in

trade promotion, upgrade in technology, quality enhancement and collection and dissemination of

information has made the body a vital catalyst forth industry's development. The Indian government

encourages foreign investment in the automobile sector and allows 100 percent FDI under the

automatic route. It is a fully delicensed industry and free import so automotive components are

allowed. Moreover, the government has not laid down any minimum investment criteria forth

automobile industry and has formulated the Automotive Mission Plan for the period 2006-2016 which

aims to accelerate and sustain growth in this sector. The plan also aims to double the contribution of the

automotive sector of the country's GDP by taking its turnover to US$145 billion and providing

additional employment to25 million people by2016.

HEALTHCARE INDUSTRY

India has been awarded a Polio Free‘status by way of an official certification presented by the World

Health Organization (WHO). India is among other countries in the South East Asian region which have

been certified as being free of the polio virus. India has been polio free since January 2011, as per

MrGhulam Nabi Azad, Minister for Health and Family Welfare, Government of India. Health care in

India today provides existing and new players with a unique opportunity to achieve innovation,

differentiation and profits. In the next decade, increasing consumer awareness and demand for better

facilities will redefine the country‘s second largest service sector employer. India's primary competitive

advantage over its peers lies in its large pool of well- trained medical professionals. Also, India's cost

advantage compared to peers in Asia and Western countries is significant cost of surgery in India is one tenth of that in the USor Western Europe. In India, the diagnostics sector has been witnessing immense

progress in innovative competencies and credibility. Technological advancements and higher efficiency

systems are taking the market on heights. The RNCOS report, 'Indian Diagnostic Market Outlook to



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2015', highlights that the IVD equipment market will grow at a compound annual growth rate (CAGR)

of around15 per cent from 2012 to 2015.Healthcare providers in India are expected to spend US $1.08

billion on IT products and services in 2014, a four per cent increaseover2013.

PUBLIC SECTOR UNDERTAKINGSINDUSTRY

Central and state Public Sector Undertakings (PSUs) play a prominent role in India‘s industrialization

and economic development. Since independence, various socio-economic problems needed to be dealt

with in a planned and systematic manner. A predominantly agrarian economy, a weak industrial base,

low savings, inadequate investments and lack of industrial facilities called or state intervention to use

the public sector as an instrument to steer the country‘s underlying potential towards self-r eliant

economic growth. The macroeconomic objectives of Central PSUs have been derived from the

Industrial Policy Resolutions and the Five Year Plans. State-level public sectors enterprises (state

PSUs) were established because of the rising need for public utilities in the states. These PSUs

operated in public utilities such as railways, post and telegraph ports, airports and power and

contributed significantly towards infrastructure development in India. Since its inception during the

First Five Year Plan, many public sector undertakings performed exceptionally well in wealth creation

for the country. Many Central PSUs, particularly the Maharatnas, are already global players matching

the best global firms in their field of operations. One of the important reasons for the excellent

performances of Central PSUs during the recent years was the empowerment of the boards of such

profit making Central PSUs by t h e Government leading to greater autonomy.

CAPITAL GOODS INDUSTRY

The development of a strong and vibrant engineering and capital goods sector has been at the core of

the industrial strategy in India since the planning process w a s initiated in 1951. The emphasis that

this sector received was primarily influenced by the rest while Soviet Union model, which made

impressive progress by rapid state-led industrialization through the development of the core

engineering and capital goods sector. The ‗Mahalanobis Model‘, which was a ‗supply oriented model

with a basic emphasis on increasing the rate of capital accumulation and saving, gave the engineering

and capital good sector a central place. Super imposed over this were the other objectives of balanced

regional development, prevention of the concentration of economic power and the development of

small-scale industries. One of the primary objectives was import substitution, which was persuades a

priority. A capital good is a durable good (one that does not quickly wear out) that is used in the

production of goods or services. Capital goods are one of the three types of producer goods, the other

two being land and labor, which are also known collectively as primary factors of production. This

classification originated during the classical economic period and has remained the dominant method

for classification.

BANKING SECTOR

India is considered among the top economies in the world, with tremendous potential for its banking

sector to flourish. The last decade witnessed a significant up surgein transactions through ATMs, as

well as internet and mobile banking. The country's banking industry looks set for greater

transformation. With the Indian Parliament passing the Banking Laws (Amendment) Bill in 2012, the

landscape of the sector has duly changed. The bill allows the Reserve Bank of India (RBI) to make

final guide lines on issuing new licenses, which could lead to a greater number of banks in the country.

The style of operation is also slowly evolving with the integration of modern technology in to the banking industry.In the next 5-10years, the sector is expected to create up to two million new jobs

driven by the efforts of the RBI and the Government of India to expand financial services into rural

areas. Two new banks have already received licenses from the government, and the RBI's new norms

will offer incentives to banks to spot bad loans and take necessary recourse to curb the practices of

rogue borrowers. The size of banking assets in India totaled US$ 1.8 trillion in FY13 and is

expected to touch US $28.5 trillion in FY 25. Bank deposits have grown at a compound annual growth

rate (CAGR) of 21.2 percent over FY06-13. In FY13, total deposits were US$1,274.3 billion. The

revenue of Indian banks in creased fromUS$11.8 billion to US$46.9 billion over the period 2001-2010. Profit after tax also reached US$12 billion from US$1.4 billion in the period.Credit to housing sector

grew at a CAGR of 11.1 percent during the period FY08-13. Total banking sector credit is anticipated

to grow at a CAGR of18.1percent (in terms of INR) to reach US$ 2.4 trillion by 2017. In FY14, private

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sector lenders experienced significant growth in credit cards and personal loan businesses. ICICI Bank

saw 141.6 per cent growth in personal loan disbursement in FY14, as per are port by Emkay Global

Financial Services. The bank also experienced healthy growth of 20.8 percent in credit card dues,

according to the report. Axis Bank's personal loan business also grew 49.8 percent, with its credit card

business expanding by 31.1 per cent.

CONSUMER DURABLES

India has been a consumption-driven economy forth last many decades. Consumer spending in the

country is expected to increase about 2.5times by 2025. Broadly categorized into urban and rural

markets, the Indian consumer segment is gaining high attention and pampering from marketers across

the globe. Global corporations view India as one of the key markets from here future growth will

emerge. The growth in India‘s consumer market will be primarily driven by a favorable population

composition and rising disposable incomes. A recent study by the McKinsey Global Institute (MGI)

suggests that if India continues to grow at the current pace, average household incomes will triple over

the next two decades and the country will be come the world‘s fifth largest consumer economy by

2025, up from 12th at present. The Government of India plays a catalytic role in the growth of Indian

consumer segments and their welfare. Itha seased key rules on foreign direct investment (FDI) in an

attempt to attract foreign firms to boost economic growth. As people are demonstrating an increasing

online shopping, future prospects pose a tremendous growth opportunity for retail and FMCG players

alike. India is likely to emerge as the world‘s largest middle class consumer market with an aggregated

consumer spend of nearly US $13 trillion by 2030, as per are port by Deloitte titled 'India matters:

Winning in growth markets'. Fuel led by rising incomes and growing affordability, the consumer

durables market is expected to expand at a compound annual growth rate (CAGR) of 14.8 percent to

US $12.5 billion in FY 2015 from US $7.3 billion in FY 2012. Urban markets account for the major

share (65percent) of total revenues in the Indian consumer durables sector. In rural markets, durables,

such as refrigerators, and consumer electronic goods are likely to witness growing demand in the

coming years.FromUS$2.1 billion in FY2010, the rural market is expected to grow at a CAGR of 25

per cent to touch US$ 6.4 billion in FY 2015. The growth of internet retail is going to complement the

growth of offline retail stores. Online retailing, both direct and through market places such as eBay,

will triple to become a Rs50,000 crore (US$8.34billion) industry by 2016, growing at a whopping 50–

55 percent per year over the next three years, according to rating agency Crisil. With growing

consumerism and disposable income, India's used goods market is likely to touch Rs.115,000 crore

(US$19.18 billion) by 2015 from Rs 80,000 crore (US$13.34billion) at present, according to a study by

an industrial body. Whether consumer goods like electronics, durables, automobiles, etc., or industrial

machinery in the capital goods sector, the options of re usage are being considered more actively than

ever before coming up at Nilakottai near Madurai. It is expected to start commercial production by the

end of 2014, according to Mr Anshu Budhraja, Chief Operating Officer, Amway India.

FAST-MOVINGCONSUMER GOODS (FMCG) INDUSTRY

Fast-Moving Consumer Goods (FMCG) or Consumer Packaged Goods (CPG) are products that are

sold quickly and at relatively low cost. Examples include non-durable goods such as soft drinks,

toiletries, Over the counter drugs, toys, processed food sand many other consumables. Though the

profit margin made on FMCG products is relatively small (more so for retailers than the

producers/suppliers), they are generally sold in large quantities; thus, the cumulative profit on such

products can be substantial. FMCG is probably the most classic case of low margin and high volume

business. Fast-moving consumer electronics are a type of FMCG and are typically low priced easily substitutable consumer electronics, including and digital cameras which are of disposable nature.

THEORETICALFRAMEWORK OFEXTREME VALUE THEORY

Extreme value theory or extreme value analysis (EVA) is a branch of statistics dealing with the

extreme deviations from the median of probability distributions. It seeks to assess, from a given

ordered sample of a given random variable, the probability of events that are more extreme than any

previously observed. Extreme value analysis is widely used in many disciplines, such as structural

engineering, finance, earth sciences, traffic prediction, and geological engineering. For example, EVA

might be used in the field of hydrology to estimate the probability of an unusually large flooding event,

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such as the 100-year flood. Similarly, for the design of a breakwater, a coastal engineer would seek to

estimate the 50-year wave and design the structure accordingly.

DATA ANALYSIS

Two approaches exist for practical extreme value analysis. The first method relies on deriving block

maxima (minima) series as a preliminary step. In many situations it is customary and convenient to

extract the annual maxima (minima), generating an "Annual Maxima Series" (AMS). The second

method relies on extracting, from a continuous record, the peak values reached for any period during

which values exceed a certain threshold (falls below a certain threshold). This method is generally

referred to as the "Peak Over Threshold" method (POT) and can lead to several or no values being

extracted in any given year.

For AMS data, the analysis may partly rely on the results of the Fisher–Tippett–Gnedenko

theorem, leading to the generalized extreme value distribution being selected for fitting. However, in

practice, various procedures are applied to select between a wider range of distributions. The theorem

here relates to the limiting distributions for the minimum or the maximum of a very large collection of

independent random variables from the same arbitrary distribution. Given that the number of relevant

random events within a year may be rather limited, it is unsurprising that analyses of observed AMS

data often lead to distributions other than the generalized extreme value distribution being selected.

For POT data, the analysis involves fitting two distributions: one for the number of events in a

basic time period and a second for the size of the exceeders. A common assumption for the first is the

Poisson distribution, with the generalized Pareto distribution being used for the exceeders. Some

further theory needs to be applied in order to derive the distribution of the most extreme value that may

be observed in a given period, which may be a target of the analysis. An alternative target may be to

estimate the expected costs associated with events occurring in a given period. For POT analyses, a

tail-fitting can be based on the Pickands–Balkemade Haan theorem.

RESULTS AND DISCUSSION

Table 1 Univariate test of normality

Industries SW statistic AD statistic p-value

Auto 0.991 1.978 <0.001*

Health care 0.992 2.12 <0.01*

PSU 0.995 1.441 <0.01*

Capital goods 0.991 2.42 <0.01*

Bank 0.984 3.491 <0.01*

Consumer durables 0.97 5.556 <0.01*

FMCG 0.982 4.206 <0.01*

IT 0.924 11.012 <0.01*

Power 0.989 2.848 <0.01*

Metal 0.989 2.295 <0.01*

Oil& gas 0.997 1.114 <0.01*



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Table 2 Multivariate test of Normality

Test name Coefficient statistic p-value

Mardia's Skewness 5.977 998.678 <0.01

Mardia'sKurtosis 207.459 60.235 <0.01

HenzeZirkler - 1.557 <0.01

Table 3 Descriptive statistics of industry returns

Industries Minimum Maximum Mean SD Variance CV Skewness Kurtosis

Auto -4.779 5.983 0.057 1.297 1.683 22.927 0.176 1.095

Health care -3.529 3.111 0.072 0.861 0.742 11.908 -0.186 0.804

PSU -4.669 4.503 -0.042 1.141 1.301 -26.947 -0.088 0.701

Capital goods -5.57 5.498 -0.021 1.555 2.418 -75.735 -0.035 0.857

Bank -5.552 9.305 0.038 1.602 2.568 42.164 0.197 1.729

Consumer durables -8.384 5.701 0.053 1.524 2.323 28.788 -0.352 2.758

FMCG -3.89 5.303 0.09 1.066 1.135 11.81 0.059 1.778

IT -11.094 9.339 0.065 1.413 1.996 21.593 -0.462 8.449

Power -4.514 4.351 -0.056 1.257 1.579 -22.64 -0.24 0.941

Metal -5.819 8.226 -0.043 1.696 2.876 -39.216 0.218 1.194

Oil& gas -4.787 3.827 -0.008 1.294 1.675 -153.481 0.029 0.284

Control Chart showing the Extreme variation of industry return at 5%and 1%significancelevel

Figure 1



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Figure 2

Table-1 to3 visualizes the result of Mardia’s Multivariate test of normality such as Mardia’s

Skewness test, Mardia’s Kurtosis test and Henze Zirkler test. The test was applied for the returns of top

securities listed in BSE. The result of the test confirms that the security returns of securities are

departed from Multivariate normality and the returns are non-normally distributed. Hence, the

researcher assumed that the returns of securities are non-normally distributed. Among the top10

industries the Mean Returns of FMCG industry is high followed by Healthcare, IT, Automobile

respectively. As for as Health Care industry is concerned standard deviation of returns are less

compared to remaining industries which are highly consistent. Finally the univariate skewness,

kurtosis, Shapiro Wilk test statistics and Anderson darling statistics confirms that the returns of FMCG

industry are departed from Univariate normality and it follows the non-normal distribution. Control

chart fig.1 and fig.2 visualize the extreme variation of industry return during the 999days’ time period.

The upper control limit for the control chart 4.1 and 5% significance level 24.56 nearly out of 999days

the mainly of 64 days are having extreme volatility in the industrial returns at 5% significance level. As

for as 1% significance level the upper T square distance is 19.59 out of 999days 113days having

extreme volatility in the industry returns the shows the T-square distance and industry distance normal

static it may have extreme volatility in the coming days.

Results of stepwise Multiple Descriptive Analysis

Table 4 Eigen value

Function Eigen value %of Variance Cumulative % Canonical Correlation

1 .043A

100.0 100.0 .204

Table 5 Wilk’s Lambda

Test of function(s) Wilks'

Lambda Chi-square df Sig.

1 .958 42.318 6 .000



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Table 6 Standardized Canonical Discriminant Function Coefficient

Industries Function

Auto -.636

Healthcare .758

Consumer Durables .487

Power .815

Metal -.533

Oil&Gas -.673

Table 7 Classification results

Out of control Points Predicted Group Membership

Total Outlier Inlier

Outlier

Count

Inlier

1 63 64

0 935 935

Based on the previous analysis, the result of multivariate outlier detection technique and control

chart shows, out of 999days, returns of the industry was extremely erratic for 64days and this

confirms the industry return has an extreme behavior. Moreover from table4 to7 describes the results

of the stepwise multiple discriminant analysis .The calculated value close to 0 the chi-square test the

also significant at 5% level. More over table 6 reviews, out of 10 industries, the returns of the 6

industries namely auto, h e a l t h c a r e , consumer durable, power, metal, oil and gas all most

dominate industry which mate industries behave extremely. Hence any events upon in the industries

will leads to extreme events return of the industries.

RESULTOFSTEPWISE RETURN ANALYSIS

Table 8 Eigen values

Table 9 Wilk’s Lambda

Test Of Function(S) Wilks' Lambda Chi- Square DF SIG.

1 .960 40.152 4 .000

Function Eigenvalue %Of Variance Cumulative

% Canonical Correlation

1 .041 100.0 100.0 .199



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Table 10 Standardized Canonical Discriminant Function Coefficient

Table 11 Classification of result

Out of control Points Predicted Group Membership

Total Outlier Inlier

Outlier

CountInlier

2

111

113

0 886 886

Based on the previous analysis, the result of multivariate outlier detection technique and control

chart shows, out of 999days, the returns of industry was extremely erratic for 64days and this

confirms the industry return have an extreme behavior. More over from table 8 to 11 describes the

result of the step wise multiple discriminant analysis. The calculated value close to 0 the chi-square is

also significant at 1% level. More over table 10 reviews out of 10 industries, the returns of the 4

industries namely auto, healthcare, consumer durable, oil and gas almost dominate industry which

behave extremely. Hence any events upon in the industries will leads to extreme events

return1oftheindustries.

SUGGESTIONS

From the study conducted on evaluation of extreme events of returns of top industries listed in BSE,

the following suggestions were given to the investors. The returns of Auto mobile industry is high in all

the years which reveals that accordingly to the Mardia’s skewness Model, if the invest or choose to

invest their funds in automobile industry, they can achieve a maximum possible returns while

compared to the other industries. Moreover, the returns of Banking, Health care and ITindustry are

greater when compared to the other industries in overall period basis. The returns earned from

Automobiles, Healthcare, OilandGas, Metal and consumer durables industries follows the above

maximum yielding industries. So, the researcher by having Mardia’s kurtosis model as the base

suggests the investors to invest their funds in automobile industry followed by banking, Healthcare and

power industries which has a higher positive returns with lower risk.

CONCLUSION

Based on the analysis, the researcher comes to a concrete conclusion. This study deals with the risk of

extreme events of returns for the selected industries listed in BSE. At first the researcher observes that

the returns of the industries are non normally distributed and it’s having a different pattern. Moreover,

the researcher emphasis the investors to look in to the average amount of returns of the security and

also the amount of risk involved before investing their funds. If, the investors observe the industries

they can see the returns of in the automobile industry plays a vital role followed by banking industry,

Power industry, Oil and gas industry, IT industry, Metal and steel industry, Health care industry and

FMCG industry so on. Finally, the selection and ARCH model of extreme events is the most

important aspect to be considered by an investor whether he or she may be an individual or

institutional investor. According to the results of the analysis the researcher recommends the investors

to invest their funds in Automobile, Banking and Power industries, and then only they can earn a

maximum return with the nominal risk. This research is very helpful to make an investment to the best

companies and also they have an idea about the extreme events of stock and market return and the

market risk. The data analysis, findings and the suitable suggestion.

Industries Function

Auto 0.985

Healthcare -0.956

Consumer Durables -0.460

Oil&Gas -0.441



352



REFERENCES

[1] Atkins, Allen B..; Dyl, Edward A., 1990, Price reversals, bid-ask spreads and market

efficiency. Journal of Financial and Quantitative Analysis, 25, 535-547.

[2] Bremer, Marc. Sweeney, Richard J., 1991, The reversal of large stock-price decreases.

Journal of Finance, 46, 747-754.

[3] Brooks, Raymond M.; Patel, Ajay; Su, Tie, 2003, How the equity market responds to

unanticipated events. Journal of Business, 76, 109-133

[4] Brown, Keith C.; Harlow, W. V.; Tinic, Seha M.., 1988, Risk aversion, uncertain

information and market efficiency. Journal of Financial Economics, 22, 355-385.

[5] Choi, Darwin; Hui, Sam K., 2014, The role of surprise: understanding overreaction and

underreaction to unanticipated events using in-play soccer betting market. Journal of

Economic Behavior & Organization, article in press.

[6] Coleman, Les, 2012, Testing equity market efficiency around terrorist attacks, Applied

Economics, 44:31, 4087-4099.

[7] Corrado, Charles; Jordan, Bradford D., 1997, Risk aversion, uncertain information, and

market efficiency. Reexamining the evidence. Review of Quantitative Finance and

Accounting, 8, 51-68.

[8] De Bont, Werner F. M.; Thaler, Richard, 1985, Does the stock market overreact? Journal

of Finance, 40, 793-805.

[9] Fabozzi, Frank J.; Fung, Chun-Yip.; Lam, Kin.; Wong, Wing-Keung, 2013, Market

overreaction and underreaction: tests of the directional and magnitude effects. Applied

Finance Economics, 23, 1469-1482.

[10] Griffin, Dale; Tversky, Amos, 1992, The weighing of evidence and the determinants of

confidence. Cognitive Psychology, 24, 411-435

[11] Himmelmann, Achim; Schiereck, Dirk. Simpson, Marc W.; Zschoche, Mortiz, 2012,

Long-term reactions to large stock price declines and increases in the European stock

market: a note on market efficiency. Journal of Economics and Finance, 36, 400-423.

[12] Ising, Jan.; Schiereck, Dirk. Simpson, Marc W.; Thomas, Thomas W., 2006, Stock

returns following large 1-month declines and jumps: evidence of overoptimism in the

German market. The Quarterly Review of Economics and Finance, 46, 598-619.

[13] Jegadeesh, Narasimhan, 1990, Evidence of predicable behavior of security returns.

Journal of Finance, 45, 881-898.

[14] Jegadeesh, Narasimhan; Titman, Sheridan, 1993, Returns to buying winners and selling

losers: implications for stock market efficiency. Journal of Finance, 48, 65-91.

[15] Jegadeesh, Narasimhan; Titman, Sheridan, 2001, Profitability of momentum strategies: an

evaluation of alternative explanations, Journal of Finance, 56, 699-720.

[16] Lasfer, M. Ameziane; Melnik, Arie; Thomas, Dylan C., 2003, Short-term reaction of

stock markets in stressful circumstances, Journal of Banking and Finance, 27, 1959-1977.



353



[17] Lehmann, Bruce, 1990, Fads, martingales, and market efficiency. Quarterly Journal of

Economics, 105, 1-28.

[18] Mehdian, Seyed; Tevfik, Nas; Perry, Marj J., 2008, An examination of investor reaction

to unexpected political and economic events in Turkey. Global Financial Journal, 18, 337-

350.

[19] Novy-Marx, Robert, 2012, Is momentum really momentum? Journal of Financial

Economics, 103, 429-453. DOI:10.1016/j.jfineco.2011.05.003

[20] Preis, Tobias; Kenett, Dror Y.; Stanley, H. Eugene; Helbing, Dirk; Ben-Jacob, Eshel,

2012, Quantifying the behavior of stock correlations under market stress. Scientific

Reports, 2 (Article 752, October), 1-5.

[21] Savor, Pavel G., 2012. Stocks Returns after major price shocks: the impact of

information. Journal of Financial Economics, 106, 645-659.

[22] Shiller, Robert, 1981, Do stock prices move too much to be justified by subsequent

changes in dividends? American Economic Review, 71, 421-436.

modeling the extreme events of the top industrial returns listed in bse

Engineering