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7/31/2019 Project Report Kevin
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A Summer Project Proposal for
PGDM
By
Kevin Dinesh GalaBatch 2011-2013
PGDM B - 119
Under the guidance of
Date27st June, 2012
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Abstract
By
Kevin Dinesh Gala
This project has been a great learning experience for me; it has helped me to learn about
portfolio risk measurement using multi factor model and the impact of intra industry and inter
industry correlation.
The projects included computing intra industry correlations and inter industry
correlations which are used for computing factor loading parameter. The project was to select a
representative set of listed companies which represent the industries correctly in model.
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Contents
Abstract............................................................................................................................................ 2
Introduction.................................................................................................................................. 4
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Introduction
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Multi factor model for portfolio risk index caculation
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Introduction of the company
ICICI Bank is India's second-largest bank with total assets of Rs. 4,062.34 billion
(US$ 91 billion) at March 31, 2011 and profit after tax Rs. 51.51 billion (US$ 1,155
million) for the year ended March 31, 2011. The Bank has a network of 2,752 branches and
9,225 ATMs in India, and has a presence in 19 countries, including India.
ICICI Bank offers a wide range of banking products and financial services to
corporate and retail customers through a variety of delivery channels and through its
specialised subsidiaries in the areas of investment banking, life and non-life insurance,venture capital and asset management.
The Bank currently has subsidiaries in the United Kingdom, Russia and Canada,
branches in United States, Singapore, Bahrain, Hong Kong, Sri Lanka, Qatar and Dubai
International Finance Centre and representative offices in United Arab Emirates, China,
South Africa, Bangladesh, Thailand, Malaysia and Indonesia. Our UK subsidiary has
established branches in Belgium and Germany.
ICICI Bank's equity shares are listed in India on Bombay Stock Exchange and the
National Stock Exchange of India Limited and its American Depositary Receipts (ADRs)
are listed on the New York Stock Exchange (NYSE).
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Profile of the company
Name of the company: ICICI Bank
Year of Establishment:
Headquarter :
Nature of Business: Banking Service
Services: Banking products, financial service, investment banking, life and non-life insurance, venture capitaland asset management
Number of Employees:
Website: www.icicibank.com
Slogan: Khayaal aapka.
Vision:
.
Mission:
.
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Equity Liquidity
The model classifies Indian companies under nineteen different industries. Each industry is
represented by group of companies. Selection of companies for representing a that particular
industry is based on its liquidity. Here liquidity refers to equity liquidity and not financial ratios
like current ratio and quick ratio. Equity liquidity is measured on following parameters:-
1. As liquidity for an asset is determined by how easily it can be converted to cash. In similar
manner liquidity for equity is determined by how easily shares of stock can be converted to cash.
The market for a stock is said to be liquid if the shares can be rapidly sold and the act of selling
has little impact on the stock's price. Generally, this translates to where the shares are traded and
the level of interest that investors have in the company. Company stock traded on the major
exchanges can usually be considered liquid. Often, approximately 1% of the float trades hands
daily, indicating a high degree of interest in the stock.
2. Equity liquidity can also be determined by the bid ask spread for a companys stock. Bid price
is the price that buyer of stock quotes and ask price is the price at which a seller is willing to sell
the stock of company. For liquid stocks the bid ask spread is much less than 1% of the price. For
illiquid stocks, the spread can be much larger amounting to a few percent of the trading price.
The model uses equity correlation as a proxy for asset correlation
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Exercise 1
An analysis was done for each industry present in model and for each industry present in portfolio of
ICICI bank that was used to determine the risk index. The findings of the analysis were as follows:-
1. The portfolio included listed and non listed companies under each industry. So first part was to
find the listed companies under each industry in the portfolio and flag them. This was done in order to
find the characteristic of each industry as to find the number of listed and non listed companies for each
industry in the portfolio.
2. The companies present in the portfolio were compared with the companies present in the model
under each industry to compute correlations. This was to find out the number of same companies
present in the portfolio and also present in the model for each industry. Using this information it was
found the number of listed companies present in the portfolio and not present in the model for each
industry. Whether the listed companies present in the portfolio should be used in the model to represent
that particular industry to compute correlations.
This exercise helped in finding the mapping between companies present in the portfolio and the
companies present in the model. The excel sheet for this exercise is attached below.
Exercise 2
For each industry present in model a list of listed companies along with their market capital was
collected using Bloomberg as data source and following were the findings:-
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1. For each industry the total market capital was calculated and for each company under that
industry percentage market capital of total capital was calculated.
2. The companies present in the model under a particular industry were flagged and the total
percentage of market capital was calculated. This was done was all industries.
3. For each industry, total percentage of market capital for top 10 companies based on market
capital was computed.
This exercise was used to compare the total market capital of top 10 companies with that of companies
present in the model for each industry. As a conservative approach the percentage of market capital of
companies under a industry should be greater that top 10 companies for each industry. With the help of
this exercise it was found that for some of the industries the model did not include the top 10
companies. The excel sheet for this exercise is attached below.
Exercise 3
To find the criteria for selecting the companies to represent a particular industry used in model a
exercise was conducted for Utilities, Chemical and Fertilizers, Pharmaceuticals and Biotechnology
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industries. Following were the finding:-
1. The model included companies having market capital greater than 0.1% of total market capital
of that industry.
2. Among the companies selected from above criteria only those companies were selected having
average bid ask spread less than 0.5.
From the above findings it was concluded that for an industry, companies having large market capital
and highly liquid in terms of equity liquidity (as explained in equity liquidity section ) are affected by
changes in economics and market characteristics and are useful to represent the behavior of a particular
industry to changes of economics and market characteristics.
Exercise 4
Some companies have a diversified profile and have presence in various industries. Classifying those
companies under a particular industry was done based on method as stated below:-
1. The company description was collected for such companies from Bloomberg data source. From
this description it was identified the various industries the company has its presence. For some of thecompanies based on its description it was identified to which industry they should be classified.
2. For remaining companies, from which business segment major part of revenue is generated was
found and the company was classified under that industry.
This method helped in classifying companies having presence in various industries under particular
industry. The sheet having examples of such companies is attached below.
Exercise 5
The model did not include NBFC (Non banking financial companies) industry for computing
correlations. So list of companies use to represent NBFC industry was to be formed to use in model to
compute the correlations. Following method was use:-
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1. A list of companies under NBFC was collected from Bloomberg used as data source. Based on
method explained in exercise 3 a list of companies was selected for representing NBFC industry.
2. NBFC as industry includes companies such as rating companies, exchanges, holding
companies, Gold ETF companies. After discussion with industry analysis team of ICICI bank such
companies were not included in NBFC industry as these companies does not offer financial services.
So such companies were excluded from the list.
Using above method a list of companies representing NBFC (Non banking financial companies)
industry was formed.
Exercise 6
The model did not include Retail and Gems/Jewelry industry for computing correlations. So list of
companies use to represent Retail and Gems/Jewelry industry was to be formed to use in model to
compute the correlations. Following method was use:-
1. According to ICICI bank their definition of retail industry includes companies doing business
in apparel retail only. Based on this definition four companies were selected from retail industry. Thelist of these companies is attached below.
2. For Gems/Jewelry industry ICICI bank defines it as including those companies involved in
retail business of gems and jewelry. Based on this definition of gems/jewelry industry five companies
were selected under this industry. The list of these companies is attached below.
Using industry definition for retail and gems/jewelry industry a list of companies under this industry
was formed having nine companies in all.
Exercise 7
After forming list of companies to represent NBFC and Retail and Gems/Jewelry industry equity data
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(stock price) was collected for those companies from Nov 2010 to March 2012 from Bloomberg. Using
these equity data correlations was computed. The model converted equity data (stock price) to returns
data using price2ret function of matlab. The working of this function is, current day closing price of
stock is converted to returns by diving current day closing price of stock by previous day closing of
stock price and taking natural log of the ratio. For companies under NBFC and Retail and
Gems/jewelry industries the equity data (stock price) was converted to returns data as done by price2ret
function and correlations were computed. Following was the method for computation of intra industry
and inter industry:-
Intra industry correlation
Intra industry correlation were computed by combining each company present in the NBFC industry
with other companies present in that industry. The correlations were computed using moving average
of one month with three month look back period. There are 32 companies present in NBFC industry so
in all there were 496 (31*16=496) combinations for computing correlations using moving average for
one month with three months look back period. The average of these combinations was the intra
industry correlation for NBFC industry.
Similar steps were followed to compute intra industry correlation was Retail and Gems/jewelry
industry. The intra industry correlation was computed for following periods:-
Period
One month movingaverage period
Three month lookback period
Mar-12 1st March 2012 to 31st March 201231st March 2012 to 1st Dec2011
Dec-11 1st Dec 2011 to 31st Dec 2011 31st Dec 2011 to 1st Sep 2011
Sep-11 1st Sep 2011 to 3oth Sep 2011 3oth Sep 2011 to 1st June 2011
Jun-11 1st June 2011 to 3oth June 20113oth June 2011 to 1st March2011
Mar-11 1st March 2011 to 31st March 201131st March 2011 to 1st Dec2010
Following table shows the value of intra industry correlation for NBFC and Retail and Gems/Jewelry
industry for above mentioned periods:-
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Inter industry correlation
For inter industry correlation NBFC and Tech Hardware and Equipment was used. The equity data was
collected for companies under NBFC and Tech Hardware and Equipment and were converted to
returns data as done by price2ret function of matlab. Inter industry correlation were computed by
combining each company present in the NBFC industry with each company under Tech Hardware and
Equipment industry. The correlations were computed using moving average of one month with three
month look back period. There are 32 companies present in NBFC industry and 16 companies presentunder Tech Hardware and Equipment industry so in all there were 512(32*16=512) combinations for
computing correlations using moving average for one month with three months look back period. The
average of these combinations was the inter industry correlation for NBFC and Tech Hardware and
Equipment industry.
Similar steps were followed to compute inter industry correlation between Retail and Gems/jewelry
industry and Tech Hardware and Equipment industry. The inter industry correlation was computed for
periods as stated in above table. Following table shows the value of inter industry correlations for
NBFC and Retail and Gems/Jewelry industry with Tech Hardware and Equipment industry for above
mentioned periods:-
After this exercise intra industry correlation for NBFC and Retail and Gems/Jewelry industry and inter
industry correlations between NBFC and Retail and Gems/Jewelry were computed and this was
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