Comparative Analysis Of Power And Infrastructure Sector Companies Stock Price For The Fy2007-2008 Vis A Vis Nifty

PROJECT REPORT ON

COMPARATIVE ANALYSIS OF SHARE PRICE OF POWER AND INFRASTRUCTURE SECTOR COMPANIES FOR THE FY 2007 - 2008 VIZ A VIZ NIFTY

AT ANANDRATHI FINANCIAL SERVICES LTD, PUNE

BY APURV GOURAV UNDER THE GUIDENCE OF MR. S K VAZE

FOR THE PARTIAL FULFILLMENT OF THE COURSE PGDM-BA

MITCON INSTITUTE OF MANAGEMENT BALEWADI , PUNE-45 2007-2009

1

Chapter

Particulars

Page No.

No. Executive Summary

i-ii

Acknowledgements

iii-iv

Company profile

v-x

Industry Profile

xi-xvii

Table of Contents: 1

Research Problem and its background

1-2

2

Objectives and Scope

3

3

Research Methodology

4-6

4

Review of Literature

5

Data Presentation and Analysis

17-33

6

Findings and conclusions

34-35

7

Suggestions or Recommendations

7-16

36-37

Annexure : Limitations and scope for further studies References List of Figures

i ii iii

EXECUTIVE SUMMARY The study which is conducted is with pure intention to understand the risks arising in the securities market sector, Or in share trading in specially Power 2

and Infrastructure Sector Company. For any stock broking firm to strengthen and increase their client base, the client of the brokerage firm must be satisfied with the returns they gain by investing in the share market. The investors must understand the association between the risk and the return. The investors must know how the market performed in the past and how it may perform in the future. The investors must also know how the companies in different sectors performed in the past. Two good performing sectors (Power and Infrastructure) were first selected for the study. Then six companies per sector were selected in all the two sectors and their performance was compared with the Nifty index. The performance of the companies were analyzed by comparing the returns of the companies to that of the Nifty index return. The monthly average of all the selected company securities was studied for the year 2007-2008. The return of the companies’ security for every day was calculated and was compared with the returns of the Nifty index return. The relationship between the company return and the Nifty return was found through correlation. Regression was run between the companies’ returns and Nifty return, from the ANOVAs significance value the feasibility of the model taken for study was checked. From the R square value dependence of the Nifty return on the company return were found out. Also from the regression the beta factor was calculated for each company security. The dependence of the Nifty on the highly correlated company returns in each sectors were separately calculated. To help the investors understand the market and its fluctuations better the performance of the company securities were analyzed before and after the announcement of Budget. For this Paired sample t test was used which compares the mean of the returns before and after the announcement of Budget. Also the standard deviations for the returns of all the companies were found out before and after the

3

announcement of budget. From this we can analyze if the Budget had any effect or influence on the companies returns. The performances of the selected two sectors were analyzed for the last financial year 20072008. The most risky sector was found out. The month in which the risk was high in most of the sectors were found out. The risk involved in investing in the companies in the future was studied.

CERTIFICATE OF MIMA

4

CERTIFICATE OF COMPANY

5

ACKNOWLEDGEMENT

6

Success is the manifestation of diligence, perseverance, inspiration, motivation & innovation. The completion of any interdisciplinary project depends on cooperation, coordination & combined efforts of several sources of knowledge, energy & time. Hence, I approach this matter of acknowledgment through these lines & trying my best to give full credit wherever it is due.

I would like to give special thanks to my Project guide subject Mr. S.K VAZE, without his help it would difficult to tack the intricacies of the project. He guided me at every step during the development of the project with valuable suggestions & solution

I would also be thankful to the company ANANDRATHI FINANCIAL SERVICES LTD.PUNE, from where I have gaining the training & collected the data related to the company, under the guidance of Mr. YOGENDRA SINGH PAWAR & thus completed this project I would also be thankful to Dean Mr. SANJU DAVIS and the Director Dr.VIDYADHAR VEDAK and the Registrar Mr. Santosh Mahajan who provide me the opportunity of the project and the value able suggestion.

APURV GOURAV

Company Profile ANAND RATHI FINANCIAL SERVICES LTD.

7

NSE SEBI Registration Number: INB 230676935. BSE SEBI Registration Number: INB 011121754. SEBI registration no. of Navratan Capital for F & O INF 230676935. Anand Rathi is a leading full service securities firm providing the entire gamut of financial services. The firm, founded in 1994 by Mr. Anand Rathi, today has a pan India presence as well as an international presence through offices in Dubai and Bangkok. AR provides a breadth of financial and advisory services including wealth management, investment banking, corporate advisory, brokerage & distribution of equities, commodities, mutual funds and insurance, structured products - all of which are supported by powerful research teams. The firm's philosophy is entirely client centric, with a clear focus on providing long term value addition to clients, while maintaining the highest standards of excellence, ethics and professionalism. The entire firm activities are divided across distinct client groups: Individuals, Private Clients, Corporate and Institutions and was recently ranked by Asia Money 2006 poll amongst South Asia's top 5 wealth managers for the ultra-rich. In year 2007 Citigroup Venture Capital International joined the group as a financial partner. Management Team Our senior Management comprises a diverse talent pool that brings together rich experience from across industry as well as financial services. Mr. Anand Rathi - Group Chairman Chartered Accountant Past President, BSE Held several Senior Management positions with one of India's largest industrial groups

8

Mr. Pradeep Gupta - Vice Chairman 17 years of experience in Financial Services Mr. Amit Rathi - Managing Director Chartered Accountant & MBA Plus 11 years of experience in Financial Services

Indian Products •

Equity & Derivatives

•

Mutual Funds

•

Depository Services

•

Commodities

•

Insurance

•

IPO’s

Global Products •

Structuring of trusts / investment companies

•

Offshore Mutual Funds

•

Structured Products / Deposits including capital-guaranteed notes on

•

Trading in global markets (Equities, Bonds, Commodities)

•

Real Estate investments

•

Alternative investments (including hedge funds and fund-of-hedge funds)

9

INSTITUTIONAL EQUITY The Institutional sales and trading team provides cutting edge market information and investment advice to clients, coupled with excellent execution capabilities. A highly experienced and reputed team of equity analysts support the sales team. There is an extensive focus on research on companies, sectors and macro-economy. The institutional equity team tracks nearly 250 large and mid-sized companies to give clients an unparalleled breadth of ideas. We also provide Investment Advisory Services for institutional clients in India and overseas for investment in the Indian equity markets

MANAGED INVESTMENT SERVICES Portfolio Management Services (PMS) AR Portfolio Management Service is a discretionary investment service created to meet the demand for more targeted investment styles and opportunities. It offers a range of specialized investment strategies designed to capture opportunities across the market spectrum. The range of products varies from the highly defensive, capital-protected to the most aggressive strategies in the equities and derivatives markets. Our investment process ensures that your strategy and portfolio are built on solid foundations. Together you and your relationship manager select the strategy in line with your individual goals. AR investment specialists then construct and manage your portfolio in accordance with the chosen investment strategy.

10

Real Estate Opportunities Fund AR Real Estate Opportunities Fund is a private equity fund for high net-worth individuals, corporate and institutions, to invest in equity-linked instruments in the Indian real estate and infrastructure sectors.

As part of the structural reforms to further boost India's economic growth, the government has recognized the need for institutional finance in the real estate sector. In early 2005, the government has relaxed the FDI guidelines in real estate and also allowed the setting up of real estate investment funds under SEBI guidelines. These developments are expected to provide much needed capital to provide for the increasing demand for quality real estate in major urban centers across the country.

To capture this opportunity, AR has brought together a team of specialists and advisors to guide the fund's investments who bring together expertise in the areas of real estate consulting, development, legal and financial structuring.

INDUSTRY PROFILE STOCK BROKING INDUSTRY

11

Stock market occupies an important position in the national economy of a country. It facilitates the mobilization of the saving of individual and pools them in reservoir of capital, which can be deployed of the economic development of a Country. Efficient stock market is the key to the raising of capital by the corporate sector of the economy and the protection of the interest of the Investors. In the last decade far reaching developments have taken place in the Working of the stock market. Present stock market is significantly different from is used to be in eighties and before .there appears to be new Opportunity what Challenges and threats in stock market. A stock broking firm is a firm which buys and sells shares and other securities for a person in behalf of them. The service provided by a stock broking firm is also a financial service like banking and insurance. The stock broking firm buys and sells shares and other securities for its clients and they charge a brokerage for every buying and selling based on the capital involved in the transaction. The stock broking firms of India are regulated and controlled by SEBI (Security Exchange Board of India). Indian stock broking firms are on an expansion drive to increase their network into more cities and towns to lure clients into stock investments. The major players in the industry are Kotak securities, Karvy, Motilal Oswal, Anagram and ICICI direct. The top ten players’ corners one forth of the total equity markets turn over. Due to booming stock markets and growing retail interest in equity and equity-related investments. Having witnessed a high level of consolidation in the last 3-4 years, the domestic stock-broking houses could tap the IPO market to raise funds for their expansion, besides mergers and acquisitions. The broking business will continue the good run for at least two more years, due to the growing economy and good performance by the Indian corporate sector. For the sector the best is yet to come. Only about 1.1 to 2 per cent of the household income is routed into the stock market while the stock markets have risen by 45 per cent in the year 2005. There is lot of good opportunity. During 2005, broking firms including IL&FS Investsmart, India infoline and Indiabulls tapped the markets through IPOs to raise fund for expansion. Among the stock broking firms we can 12

see large and small players raising funds through IPO. Stock exchange listed brokerages – JM Financial Securities, Geojit Financial Services, Fortis securities Prime Securities and the recently listed IL&FS Investsmart, India Infoline and Indiabulls – have seen big rise in their stock prices, mainly due to increased business and rising stock markets. Sensing the opportunity the public sector banks are also entering this segment. With the entry of several corporate players, the industry has attained size and scale.

INTRODUCTION TO TOPIC

13

RISK Risk is an important consideration in holding any portfolio. The risk in holding securities is generally associated with the possibility that realized returns will be less than the returns expected Risks can be classified as Systematic risks and Unsystematic risks. •

Unsystematic risks: These are risks that are unique to a firm or industry. Factors such as management capability, consumer preferences, labor, etc. contribute to unsystematic risks. Unsystematic risks are controllable by nature and can be considerably reduced by sufficiently diversifying one's portfolio.

•

Systematic risks: These are risks associated with the economic, political, sociological and other macrolevel changes. They affect the entire market as a whole and cannot be controlled or eliminated merely by diversifying one's portfolio.

The three main risk associated with investing in a share are 1. The value of your investment could fall. 2. The amount of income you receive can fall, or stop altogether. 3. Your investment may increase at a lower rate than the rate of inflation, thus eroding the purchasing power of your investment. How to minimize the risks?

14

The company specific risks (unsystematic risks) can be reduced by diversifying into a few companies belonging to various industry groups, asset groups or different types of instruments like equity shares, bonds, debentures etc. thus,

asset classes are bank deposits, company

deposits, gold, silver, land real estate, equity share, computer software etc. Each of them has different risk-return characteristics and investments are to be made, based on individual’s risk preferences. The second category of risk (systematic risk) is managed by the use of beta of different company shares.

METHODS TO CALCULATE THE RISK Standard Deviation: Volatility is a direct indicator of the risk of the fund. The standard deviation of a fund measures this risk by measuring the degree to which the fund fluctuates in relation to its average return of a fund over a period of time. A security that is volatile is also considered higher risk because its performance may change quickly in either direction at any moment.

Beta Beta is a measure of the volatility, or systematic risk, of a security or a portfolio in comparison to the market as a whole. Beta is fairly a commonly used measure of risk. It basically indicates the level of volatility associated with the fund as compared to the benchmark and is also known as "beta coefficient". So quite naturally the success of Beta is heavily dependent on the correlation

15

between a fund and its benchmark.

Thus if the fund’s portfolio doesn’t have a relevant

benchmark index then a beta would be grossly inadequate.

Beta can be calculated using regression analysis, and beta is the tendency of a security's returns to respond to swings in the market. A beta that is greater than one means that the fund is more volatile than the benchmark, while a beta of less than one means that the fund is less volatile than the index. A fund with beta very close to 1 means the fund’s performance closely matches the index or benchmark.

RETURN The gain or loss of a security in a particular period is called return. The return consists of the income and the capital gains relative on an investment. It is usually quoted as a percentage. The general rule is that the more risk you take, the greater the potential for higher return - and loss. Return can come from two sources, capital growth and income. Capital growth occurs when the market value of the share increases. Income is the cash flow paid by a share such as dividends. VOLATILITY Volatility is the degree to which an asset's value rises and falls. Typically, higher volatility equals higher risk. Generally, growth assets (such as shares and property) have a higher risk than defensive assets (such as government bonds and cash).

16

RELATIONSHIP BETWEEN RISK AND RETURN: Risk-Return Tradeoff The principle that potential return rises with an increase in risk is called risk return trade off. Low levels of uncertainty (low risk) are associated with low potential returns, whereas high levels of uncertainty (high risk) are associated with high potential returns. In other words, the risk-return tradeoff says that invested money can render higher profits only if it is subject to the possibility of being lost. Because of the risk-return tradeoff, you must be aware of your personal risk tolerance when choosing investments for your portfolio. Taking on some risk is the price of achieving returns; therefore, if you want to make money, you can't cut out all risk. The goal instead is to find an appropriate balance - one that generates some profit.

17

OBJECTIVE, SCOPE AND NEED OF THE STUDY

18

NEED FOR THE STUDY

In India, the S&P CNX Nifty is the most scientific Index that was constructed keeping in mind Index funds and Index derivatives. All companies to be Included in the Index have a market capitalization of Rs.5 billion or more. The S&P CNX Nifty is a market capitalization – weighted Index i.e., price change in Any of the Index securities will lead to a change in the index. This necessitates the need for analyzing the risk and return relationship of the selected stocks of Power and Infrastructure sector Constituting the Nifty index and their impact on the Nifty index .

19

RESEARCH METHODOLOGY

3. RESEARCH METHEDOLOGY

20

RESEARCH DESIGN: The study was carried out to compare the selected industrial securities with the Nifty index using their returns, and to analyze the risk involved in each company in the sectors and risk involved in the sector for investment. Thus the study undertaken was Descriptive study.

SAMPLING DESIGN SAMPLING METHOD Judgmental sampling was used as sampling method. The sector and the companies in the sector were selected based on the recommendation given by the brokers in the firm.

SAMPLE SIZE SIZE: it refers to the number of elements included in the study. Two sectors were selected for the study and six companies from each sector were selected based on the recommendation given by the brokers in the firm.

SECTORS SELECTED

=

2

The sample size of the project can be known by the following table

21

SECTOR

NO OF COMPANIES

Power

6

Infrastructure

6

Total sample size

12

COLLECTION The data Collected were by means of Secondary data. The data were collected from Internet, ARG securities records and magazines. TOOLS USED FOR ANALYSIS  Beta  Correlation  Regression  Paired sample t test  Descriptive statistics o

Mean

o

standard deviation

 Return Return was calculated using the formula Return = yesterday’s share price – Today’s share price Yesterday’s share price

22

DATA PRESENTATION, ANALYSIS AND INTERPRETATION

23

ANALYSIS AND INTERPRETATION 3.1 ANALYSING THE IMPACT OF POWER SECTOR ON NIFTY PERFORMANCE The companies selected for study are 1. BIL Power 2. JP Hydro 3. NTPC 4. Reliance energy ltd 5. Suzlon Energy 6. TATA Power DESCRIPTION

Overview of Power Sector in India' is the industry's annual reference source offering a holistic view of the developments taking place in the power sector. It analyses the prevailing scenario in the Indian power sector along with the existing policy & regulatory framework. It tracks the growth and performance of the Indian power sector, the ongoing reform initiatives and offers statistical updates on generation, transmission & distribution areas. It also lists the organizations in 'Players in the Power Sector' section.

India had been traditionally depending on thermal power as a major source of power generation, which constitutes about 65% of current capacity. Balance is contributed by Hydro power ( 26% ) , Nuclear ( 3 %) and Renewable energy ( 6%).Over 87% of the current installed capacity in the country is by the government, with the state governments 24

having lion’s a share of over 52% and the balance by central (federal ) government. Due to the initiative of government of India to encourage Public Private Partnerships in power sector, share of private companies has gone up to steadily to MW, about 13 % of the installed capacity. This report covers the overall industry scenario, demand & supply, growth drivers, critical success factors for the industry. It also deals with investment opportunities in the sector and an outlook.

Indian Power Sector Performance This section covers the performance of Indian power sector in generating electricity. In Indian power sector, state government dominates the electricity generation followed by central government. The private sector has a small but growing presence in distribution and is making an entry into transmission. With the economic growth industrial sector consumption is rising to match with household sector. Key Players Analyzed This section covers the key facts about major players currently operating in the Indian power sector such as National Thermal Power Corporation Limited, Nuclear Power Corporation of India Limited, North Eastern Electric Power Corporation Limited, Damodar Valley Corporation, Power Grid Corporation of India, Reliance Energy Limited and Tata Power.

- The generating capacity has grown manifolds from 1,362MW in 1947 to more than 112,058MW. However, India still has a huge demand supply gap. The Ministry of Power 25

has set an ambitious goal of adding 100,000MW of capacity by 2012 to bridge this demand supply gap. This offers a US$90bn opportunity in the next 8 years.

- Indian power sector is plagued by high T&D losses, which is one of the main issues for the deteriorating state of SEBs. However, stress is now being given on improving the T&D segment in contrast to the earlier focus on generation. It is estimated that the T&D sector also requires around US$80bn investments.

- This sector has been through number of reforms in the past 10 years, which have not been very successful. In fact, there have been disasters such as Enron and Orissa’s attempt at reforms. Hence, it will be interesting to observe the impact of the current reform initiatives namely, the APDRP, The Electricity Act, 2003. - As per the Indian government regulation in Power sector 100% FDI (Foreign Direct Investment) is allowed.

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3.1.1 ANALYSING THE RISK AND RETURN Table 3.1.1.a Monthly Return of the selected securities in the power sector MONTH

NTPC

REL

Suzlon

0.18342 2

0.063561

0.031465

0.185721

0.162085

0.022462

0.10432 2

-0.00814

0.055714

0.085516

-0.01031

JUNE 2007

-0.0064

-0.04858

-0.03596

0.14011

0.159963

0.144942

JULY 2007

0.027708

0.17305

0.085079

0.293782

-0.14994

0.095728

-0.1024

0.22974 6

0.045537

-0.01814

-0.00409

-0.06818

0.060376

0.49164 2

0.118835

0.546703

0.146344

0.247609

0.01402

0.10876 7

0.231761

0.54774

0.360037

0.420695

NOVEMBER 2007

0.178612

0.35552 9

-0.00942

-0.07061

-0.03881

-0.03716

DECEMBER 2007

0.618865

0.20438 6

0.060216

0.231481

0.021007

0.257958

JANUARY 2008

-0.36261

-0.44465

-0.21861

-0.07368

-0.84051

-0.13068

FEBUARY 2008

-0.00534

-0.04

0.030349

-0.20661

-0.08903

0.095426

APRIL 2007

MAY 2007

AUGUST2007 SEPTEMBER 2007 OCTOBER 2007

BIL Power

JP Hydro

0.148596

TATA power

27

MARCH 2008

-0.21903

-0.2623

-0.02673

-0.20298

-0.06254

-0.16315

CHART 3.1.1 a POWER SECTOR COMPANY RETURNS

28

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3.1.2 THE RELATIONSHIP OF THE NIFTY WITH THE SELECTED SECURITIES IN THE POWER SECTOR HYPOTHESIS Ho: There is no significant relationship between the selected securities return and Nifty return. Ha: There is a significant relationship between the selected company securities return and Nifty return. Table 3.1.2.a Correlation between the selected company securities return in the power sector and Nifty return.

COMPANY

BIL Power

JP Hydro

Nifty

0.5196 44

0.632 0.764168 0.744981 35

NTPC

REL

Suzlon 0.558288

TATA Power 0.620669

Significant

YES

YES

YES

YES

YES

YES

P level

0.00

0.00

0.00

0.00

0.00

0.00

3.1.3 THE IMPACT OF SELECTED SECURITIES RETURN IN THE POWER SECTOR ON THE NIFTY RETURN HYPOTHESIS Ho : There is no significant impact of the return of the selected securities on the nifty return Ha: There is a significant impact of the nifty return of the selected securities on the Nifty return

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Sum of Squares

Model

Mean Square

df

F

Sig.

0.0275 08

Table 3.1.3.a Anova table

Regressi on

0.02750 8

Residual

0.07436 3

BIL Power Total

0.10187 2

Nifty return 1

and Power

250

0.04073 5

1 0.040735

Residual

0.06113 7

249 0.000246

0.10187 2

250

Regressi on

0.059488

1

0.059488

Residual

0.042383

249

0.00017

0.101872

250

Regressi on

0.056538

1

0.056538

Residual

0.045333

249

0.000182

0.101872

250

0.031752

1

0.031752 0.000282

NTPC Total

REL Total Regressi on

92.110 1

0.000

165.90 77

0.000

companies returns

349.4908

310.5473

Residual Suzlon Energy Total

0.07012

249

0.101872

250

Regressi on

0.039244

1

0.039244

0.062628

249

0.000252

0.101872

250

Residual TATA Power Total

sector

249 0.000299

Regressi on

JP Hydro Total

for

112.7532

31 156.0297

Model BIL Power

R

R Square

Adjusted R Square

Std. Error of the Estimate

0.519644

0.27003

0.267099

0.017281

0.632350 03

0.399866 57

0.39745639

0.01566934

NTPC

0.764168

0.583953

0.582283

0.013047

REL

0.744981

0.554997

0.55321

0.013493

Suzlon

0.558288

0.311685

0.308921

0.016781

TATA Power

0.620669

0.38523

0.382761

0.015859

JP Hydro

Table 3.1.3 b Rsquare table of NIFTY return

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Table 3.1.3.c

Coefficient table for Nifty return and power sector companies return.

Unstandardized Coefficients

model

Standardize d Coefficients

t

Sig.

0.923003

9.59740 1

0.000 0.000

B

Std. Error

BIL Power

0.2913927 56

0.0303616 33

JP Hydro

0.2527454 2

0.019622

1.5758

12.8805 2

0.49691

0.02658

1.170497

18.69467

0.000

REL

0.331342

0.018802

1.668337

17.62235

0.000

Suzlon

0.177822

0.016746

1.745821

10.61853

0.000

TATA Power

0.320383

0.025649

1.197626

12.49119

0.000

NTPC

Beta

INTERPRETATION: The correlation factor was significant for BIL Power, JP Hydro, NTPC, REL, Suzlon Energy and TATA Power return to Nifty return. They show a positive correlation of 0.519, 0.632, 0.764, 0.744, 0.558 and 0.620 respectively [refer table 3.1.2.a]. ANOVA significance value of 0.000 for all the selected companies in the sector proves that the model taken for study was fit at a 95% level of confidence [refer table 3.1.3.a]. The R square value was less than 0.5 for all the selected companies in the sector. This shows that the returns of companies would not have affected the 33

Nifty return very strongly as individuals [refer table 3.1.3.b]. Together they have an impact on the Nifty return.

The overall beta for the companies were found to be 0.923, 1.575, 1.170, 1.668,1.745 and 1.197 for BIL Power, JP Hydro, NTPC, REL, Suzlon energy and TATA Power return respectively [refer table 3.1.3.c]. Among these companies NTPC power industry and REL were found to be moderately risky securities to invest in. the rest companies were of less risk to invest in. The BIL Power, JP Hydro, NTPC, REL, Suzlon and TATA Power returns had 92%, 157%, 117%, 166%, 174% and 119% impact on the Nifty return respectively. Beta value is a indicator of risk. When beta value is greater than 1 then the company is a high risk company for investors. For the BIL Power, The months of May, June, November and February were moderate risky period for investors as the beta value were higher in those months. Considering the beta value for the months we can see that the highest risk was in the month of March 2008. We can also see that the SD was also high during the months of May and October 2007 this shows that the returns where highly volatile in those months. SD for the BIL Power in the month of May and October are critically higher than nifty returns overall SD, this shows that the investors cannot expect the same return as of nifty return. The volatility was very high for the BIL Power securities in those months; also the securities had a high beta value in the month of March 2008. Day trading would be suitable in the months other than May, September, February. The volatility was high in the months of October. Also the company declared 5% dividend and released their Quarterly results in the month of September. The overall beta value of the BIL Power share return for the year is at a moderate risk level.

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For the JP Hydro, The overall beta was 1.5758 which shows that the company Jayprakash hydropower industries was a highly risky company for investors to invest. As beta value increases risk increases. The beta values show a moderate risk in the months of May, July, September. In these months of the year the beta value is above 0.7 near to 1 where beta value of one declares high risk position. This shows at those months the volatility of the shares was high compared to Nifty. High beta value is also due to volatility. Volatility in share price occurs due to news or results. The main reason for the share value to have high volatility was the news about conversion of their FCCB’s (Foreign Currency Convertible Bonds) to equity shares. The volatility in the month of September may have been due to the release of second quarterly result. In the month of August JP Hydro declares the interim dividend at 7.50%.The volatility may be due to the result, there was a decrease in the EPS. Taking the overall beta value of the company the company was a highly risky company for the investors to invest.

For the NTPC (National Thermal Power Corporation), the overall beta was 1.170 which shows that the company was a highly risky company for investors to invest. The overall beta shows that the NTPC involves high risk than JP Hydro, REL and TATA Power. The SD for all the months was not too higher than the overall SD of the Nifty return. The value of the returns varies as the Nifty return varies for the year. The beta value was high in the months of October, November, December, January and February. In those months the beta value is higher than 1.1 it would have been of highly risk to invest in the months of October, November, December, January and Feburary. The volatility would have been higher than the volatility of the Nifty. The volatility in

35

the share price in the month of September may be due to the MOU with BHEL. The fluctuation in the share prices in the month of December due to government decision and quarterly result of the company. In third quarterly result of the company net profit is 1,779.90 crore .It is around 200 crore higher than previous result.

For the REL (Reliance Energy Ltd), The overall beta value shows that the company was a Highly risk company for investors to invest. The SD for every month was very high than the overall SD of nifty return. The SD was high only in the months of October and January. The beta value is high in the months of October, and January. Those are the months with high risk for the year. Months of April had a low beta value so those months should have been of low risk. For the Suzlon Energy, The overall beta was 1.74, this makes Suzlon Energy a high risk company for investors to invest in. The SD for every month was not too high than the SD of overall nifty returns for the year Except in the month of January. The SD was very high in the month of January. It is not a good month for day trading as SD is very high and the volatility is also very high. The beta values were higher than 3.0 in the months of January, those months are considered as highly risky months for investment point of view. The risk of investment was very low in the months of July and September because the beta values are very low in those months. In The month of October the company had change the split value the old face value of the company stock was Rs.10 but on 23rd of October the new Face value is Rs 2. Suzlon energy had issued zero coupon foreign currency convertible bonds worth o million to fund its organic growth.

36

For TATA Power, The overall beta was 1.19; this makes TATA Power a high risk company for investors to invest in. The SD for every month was too high than the SD of overall nifty returns for the year. The SD was very high in the month of October. It is not a good month for day trading as SD is very high and the volatility is also very high. The beta values were higher than 1.0 in the months of May, October, November and January, those months are considered as highly risky months for investment point of view. The risk of investment was very low in the months of July and August because the beta values are very low in those months. In the month of May TATA Power had allowanced final Dividend of 95% to the shareholder, but its effect on 17th July.

3.1.4 DEPENDANCE OF NIFTY RETUN ON THE POWER SECTOR COMPANY RETURNS Table 3.1.4.a Regression between nifty return and highly correlated company returns in power sector Coefficient s

37

Standardi zed

Model

Unstandardized Coefficients

Coefficien ts

B

Beta

(Consta 1 nt)

Std. Error -0.0000

NTPC

0.497334

REL

0.330172

t

-1.194

0.23 3

1.7104 18.77031

0.00 0

1.6683 17.67863

0.00 0

0.000 0.026496

0.018676

Sig.

a Dependent Variable: NIFTY

Table 3.1.4.b R square table for highly correlated companies with Nifty return Model Summary M odel

A

R

R Square

Adjusted R Square

Std. Error of the Estimate

1 0.764815 0.584941

0.580941

0.013023

2 0.745374 0.555582

0.551582

0.013476

Predictors: (Constant), NTPC, REL

INTERPRETATION: The dependence of the Nifty return to highly correlated company returns can be given by the equation 38

Nifty return= 0.2388 + 0.497334 (NTPC return) + 0.330172 (REL return) The R square value of 0.58 and 0.55 proves that the NTPC and REL companies return have a good impact on the Nifty return.

3.1.5 ANALYSIS OF THE PERFORMANCE OF THE POWER COMPANIES BEFORE AND AFTER THE ANNOUNCEMENT OF BUDGET: Paired sample t test Hypothesis Ho: there is no significant impact of the announcement of budget on the returns of BIL Power, JP Hydro, NTPC, REL, Suzlon Energy and TATA Power before and after the budget. Ha: there is a significant impact of the announcement of budget on the returns of BIL Power, JP Hydro, NTPC, REL, Suzlon Energy and TATA Power before and after the budget.

Table 3.1.5.a

SD and mean before and after the budget for the power sector

companies return

39

BEFORE COMPANY

AFTER

MEAN

SD

MEAN

SD

BIL Power

0.0012

0.35 45

-0.01 24

0.05 12

JP Hydro

-0.000 01

0.04 92

-0.01 49

0.06 23

NTPC

0.0030

0.03 49

-0.00 09

0.03 19

REL

-0.009 2

0.05 45

-0.01 05

0.06 75

Suzlon Energy

-0.001 1

0.03 49

-0.00 19

0.05 38

0.0050

0.03 86

-0.00 90

0.04 01

TATA Power

40

Table 3.1.5.b paired sample t test result for Power companies return. Paired Samples Test

Paired Differences

Mean

Std. Deviatio n

t Std. Error Mean

df

Sig. (2taile d)

95% Confidence Interval of the Difference Lower

Upper

P air 1

BILBEBILAF

-0.00 5

0.043 0

0.080 2

0.2385 15

0.563 84

4.9972 2

38

0.00

P air 2

JPHBE JPHAF

-0.00 7

0.054 8

0.047 5

0.3068 1

0.499 54

8.4774 5

38

0.00

P air 3

NTPCB ENTPCA F

0.001

0.0337

0.0522

0.638004

0.84954

14.2479

38

0.00

P air 4

RELBE RELAF

-0.010

0.0576

0.0465

0.283539

0.47208

8.12019

38

0.00

P air 5

SUZBE SUZAF

-0.003

0.0469

0.0709

0.243554

0.5309

5.46097

38

0.00

P air 6

TATAB ETATAA F

-0.002

0.0399

0.0845

0.276719

0.61929

5.29956

38

0.00

INTERPRETATION In all the cases the SD before the budget is lower than the SD after the budget. This was due to the investor’s perception towards the budget. Due to the perception towards the tax reforms and other aspects of the budget the buying and selling of shares increases increasing volatility, thereby increasing the SD of the returns. Later after the budget there is no as much volatility as

41

before budget. In power sector securities NTPC and REL had effected through budget. This shows that those two securities had impact due to the budget. The mean is positive for NTPC before the budget, but the mean after budget is negative. Through the budget there is no any significant on BIL Power, NTPC and JP Hydro. The Paired sample T test result shows there was no significant impact of budget on the returns of the securities of the companies BIL Power, JP Hydro, NTPC, REL, Suzlon Energy and TATA Power. But there are some changes after and before budget.

CHART 3.1.5.a NIFTY RETURN AND POWER SECTOR AVERAGE RETURN

42

3.2 ANALYSING THE IMPACT OF INFRASTRUCTURE SECTOR ON NIFTY PERFORMANCE The companies selected for study are 1. Ansal properties and infra (Ansal infra) 2. GMR Infra 3. GTL Infra 4. Lanco infratech ltd (LITL Infra) 5. Reliance Infra (RIIL) 6. Unitech ABOUT INFRASTRUCTURE SECTOR For four consecutive years, India’s GDP has grown at over 8% and is expected to maintain a healthy growth trajectory. Following several years of robust economic growth, the need to improvement the countries existing INFRASTRUCTURE has increased tremendously. To sustain growth, we must not only remove the existing infrastructure bottlenecks but also lay a healthy foundation for the future. India clearly has a long way to go before it catches up with other developed and developing countries and this gap provides a very attractive long term investment opportunity.

43

INDIA

US

UK

CHINA

618

14,240

6,756

1,684

2,983

21,443

6,467

1,471

people(Km) Rail route per million

56

755

276

57

people (Km) Cargo handled at ports per

572

7,953

9,733

4,265

capita (Kg) No of passenger handled

71

4,780

3,517

151

Electric consumption per capita (kwH) Roads per million

at airport per 1000 people

Source: Economic times 07-01-2008 The most important constraint in achieving a faster growth of manufacturing and effectively India’s GDP is the fact that the infrastructure consisting of Roads, Railway, Ports, Airports, Communication and Electric Power is not up to the standard of competitor countries. Recognizing the need of continued socio-economic progress, the government is keenly pursuing investment across the board and has outlined an ambitious investment program in Eleventh Plan.

INFRASTRUCTURE SPENDING

44

SECTOR

10th Plan (Anticipated

11th Plan (Projected

Electricity(incl. NEC) Roads & Bridge Telecommunication Railways Irrigation Water supply & Sanitation Ports Airports Storage Gas TOTAL Rs Crore at 2006-2007 Prices

Investment) 2002-2007 Investment) 2008-2012 291,850 616,526 144,892 311,816 123,411 267,001 119,658 258,001 111,503 223,131 64,803 199,127 4,096 73,941 6,771 34,784 4,819 22,378 8,713 20,500 880,515 2,027,169 Source: Planning commission

3.2.1 ANALYSING THE RISK AND RETURN Table 3.2.1.a Monthly Return of the selected securities in the Infrastructure sector MONTH APRIL 2007

MAY 2007 JUNE 2007

Ansal Infra

-0.45685 0.14827 -0.19028

GMR Infra

GTL Infra

LITL

RIIL

0.17527

-0.0230 9

0.0044 0.188047 14

0.091625

0.17475

0.2481 54

-0.022 0.030012 6

0.360444

0.50548

0.1656

0.3217

-0.02323

Unitech

-0.12235

45

8 JULY 2007

AUGUST 2007 SEPTEMBER 2007 OCTOBER 2007

0.013834 -0.07782 0.127774 -0.11365

NOVEMBER 2007

-0.0056

DECEMBER 2007

0.713539

JANUARY 2008

FEBUARY 2008

MARCH 2008

-0.42944 -0.11296 -0.28184

0.12024

-0.3512 7

-0.0375

73 0.1639 0.135032 94

0.106535

0.0594 68

0.2151 95

-0.10541

-0.57462

-0.7846

0.1935 01

0.1930 1.920544 61

0.293858

0.01955

0.3289 47

0.2961 1.089672 42

0.248903

0.4384

0.0603 96

0.1806 06

-0.35293

-0.00625

-0.0274

0.7647 06

0.5883 0.159876 9

0.281766

-0.3098

-0.3756 6

-0.437 54

-0.24815

-0.21604

0.02073

-0.0652 5

0.0032 65

-0.16855

-0.06166

-0.1482

-0.1559 4

-0.184 32

-0.33288

-0.23256

CHART 3.2.1 a INFRASTRUCTURE SECTOR COMPANY RETURNS

46

47

3.2.2 THE RELATIONSHIP OF THE NIFTY WITH THE SELECTED INFRASTRUCTURE SECURITIES

48

HYPOTHESIS Ho: There is no significant relationship between the selected securities return and Nifty return. Ha: There is a significant relationship between the selected company securities return and Nifty return.

Table 3.2.2.a Correlation between the selected company securities return in the Infrastructure and Nifty return.

COMPANY

Ansal Infra

GMR Infra

GTL Infra

LITL Infra

Reliance infra

Unitech

Nifty

0.5158 78

0.427511 44

0.2583 34

0.635 20

0.356451

Significant

YES

YES

YES

YES

YES

YES

P level

0.00

0.00

0.00

0.00

0.00

0.00

0.54804

3.2.3 THE IMPACT OF SELECTED SECURITIES RETURN IN THE INFRASTRUCTURE SECTOR ON THE NIFTY RETURN HYPOTHESIS Ho : There is no significant impact of the return of the selected securities on the nifty return Ha: There is a significant impact of the nifty return of the selected securities on the Nifty return Table 3.2.3.a Anova table for Nifty return and Infrastructure sector companies returns Model

Sum of

df

Mean

F

S 49

Squares Regressi on Ansal Infra

GMR Infra

GTL Infra

LITL Infra

Reliance infra

Square

0.027111

1

Residual

0.07476

24 9

Total

0.101872

25 0

Regressi on

0.018619

1

0.018619

Residual

0.083253

24 9

0.000334

Total

0.101872

25 0

Regressi on

0.006799

1

Residual

0.095073

24 9

Total

0.101872

25 0

Regressi on

0.041103

1

0.041103

Residual

0.060768

24 9

0.000244

Total

0.101872

25 0

Regressi on

0.012943

1

Residual

0.088928

24 9

Total

0.101872

25 0

ig.

0.027111 0.0003

90.29 7

0 .00

55.68 63

0 .00

17.80 57

0 .00

168.4 23

0 .00

36.242

0 .00

0.006799 0.000382

0.012943

0.000357

50

Regressi on Unitech

Table 3.2.3.b

0.030597

1

Residual

0.071275

24 9

Total

0.101872

25 0

0.030597

0.000286

106.891

0 .00

R square table for Nifty return and Infrastructure sector companies

return.

Model

R

R Square

Ansal Infra

0.515877 58

0.266129 68

0.26318241 2

0.01732752

GMR Infra

0.427511

0.182766

0.179484

0.018285

GTL Infra

0.258334

0.066737

0.062989

0.01954

LITL Infra

0.635203

0.403483

0.401087

0.015622

Reliance Infra

0.356451

0.127057

0.123551

0.018898

Unitech

0.54804

0.300348

0.297538

0.016919

Table 3.2.3.c

Adjusted R Square

Std. Error of the Estimate

Coefficient table for Nifty return and Infrastructure sector companies

return.

Unstandardized model

Coefficients B

Std. Error

Standardize d Coefficients

t

Sig.

Beta 51

Ansal Infra GMR Infra GTL Infra

9.5024 7

0.176738

0.018599

1.499797

0.132066

0.017698

1.378395

0.118201

0.028012

0.562357

4.2196 8

7.4623 26

0.243459

0.01876

1.6507

12.977 79

Reliance Infra

0.171272

0.02845

0.738895

6.020133

Unitech

0.2009

0.019432

1.48907

10.33882

LITL Infra

INTERPRETATION: The correlation factor was significant for Ansal properties and infra, GMR Infra, GTL Infra, Lanco infratech, Reliance Infra and Unitech return to Nifty return. They show a positive correlation of 0.515878, 0.42751144, 0.258334, 0.63520, 0.356451, and 0.54804 respectively [refer table 3.2.2.a]. ANOVA significance value of 0.000 for all the selected companies in the sector proves that the model taken for study was fit at a 95% level of confidence [refer table 3.2.3.a]. The R square value was less than 0.5 for all the selected companies in the sector. This shows that the returns of companies would not have affected the Nifty return very strongly as individuals [refer table 3.2.3.b]. Together they have an impact on the Nifty return.

52

The overall beta for the companies were found to 1.4997, 1.3783,0.5623, 1.6507, 0.7388 and 1.4890 for Ansal properties, GMR Infra, GTL Infra, LITL Infra, Reliance Infra and Unitech return respectively [refer table 3.2.3.c]. Among these companies Ansal Properties, GMR Infra, GTL Infra, LITL Infra, Reliance Infra and Unitech was found to be risky securities to invest in. Except Reliance Infra the rest companies were of moderately high risk to invest in. The Ansal properties, GMR Infra, GTL Infra, LITL Infra, Reliance Infra and Unitech returns had 149%, 137%, 56%, 165%, 73% and 148% impact on the Nifty return respectively. Beta value is an indicator of risk. When beta value is greater than 1 then the company is a high risk company for investors. For Ansal Properties and Infra, The overall beta was 1.4997, which shows that Ansal Infra was a highly risky company to invest in. The beta values were in the range of moderate risk in the months of April, July, August, September, and October. And the beta values were in the range of high risk in the months of May2007 and January 2008. As beta increases towards one the volatility of the security is correlating to the volatility of the market which is very risky for an investor. The SD was high in the months of April 2007and January 2008, where in the month of May 2007 and January 2008 the beta was also high. As SD increases the volatility of the return increases, which is the cause of volatility in the share price. In the months of April 2007 and January 2008 the volatility would have been higher than any other month of the year. The reasons for volatility in the market are news and results. The volatility in the months of April should have been the effect of the news about Ansal Properties gives Final dividend @ 10% for the year. There was no significant news other than regular AGM proceedings submission. Still the volatility was high in January.

53

For GMR Infra, The overall beta was 1.378, That shows that GMR Infra has risk of investment. The SD of all the months of the year are not significantly higher than the SD of overall nifty return for the year, this also supports the fact that GMR Infra was of medium risk for investors to invest. The beta value was higher than 1.0 in the months of May, June, July, August, September, October, January, February, and March . Those months were of highly risk. The reasons for volatility in the market are news and results. The high volatility in the month of January should have been due to the announcement of approval for allotment of equity shares to the employees of the GMR Infra under the Employees Stock Option Scheme (ESOS)". Also in the month of July the company declared split, through split the companies’ equity face value is changed by Rs 2. In the month of October the SEBI had announce to ban on P Notes, due to announcement the market was gone down.

For GTL Infra, The overall beta was 0.5623, which shows that GTL Infra was a moderately risky security to invest in. The beta values were higher than 0.6 in the months of May, June, July, January and March. Those months are considered as moderately risked for investment. The volatility and down fall would have been high in those months and in the month of June the security return would have been almost equal to nifty return. The SD for all the months was not significantly higher than the overall SD of the nifty return. The SD was quite high in the months of July. The volatility would have been high in those months. The high volatility in the month of December should have been due to the news of the company received FDI of Rs15 Crore. In the month of January, February, April, May and June there was only news about buying and selling shares and allotment of equity shares for ESOS.

54

For Lanco Infratech LTD, The overall beta was 1.650, which shows that LITL Infra was a highly risky security to invest in. For most of the months in the year 2007-2008 the beta value was above 0.6.In the month of September and January the beta value was 2.180 and 2.355 respectively. Only in the months of May it would have been safe to invest, in all the other months the risk was moderate. In the month of December the risk was high compared to other moderate risk months. The returns in the month of December would have been almost equal to the returns of the market. And the market is volatile due to the company decision; the company had undergone the MOU with the Gulftaner company ltd UAE to cooperate the variety of port and transportation Project.

For Reliance Infra, The overall beta was 0.7388, which shows that Reliance Infra was a moderately risky security to invest in. The beta value was high only in the months of July, the beta was 1.5478. In July months would have been of highly risk to invest in. As risk decreases the returns earned also decreases. The returns earned by an investor in Reliance Infra would have been less than the returns he could have earned if invested in any of the other securities we have selected for the study in the Infrastructure sector. The SD was significantly high in the month of September. In the month of April the company had announced final Dividend of 53%.

For Unitech, the overall beta was 1.4890 which shows that Unitech was a risky security to invest in. The beta value was high in the months of June, January, February and March the beta was 1.7391, 1.846, 1.779 and 1.428 respectively. In January months would have been of highly risk to 55

invest in. As risk decreases the returns earned also decreases. The returns earned by an investor in Unitech would have been less than the returns he could have earned if invested in any of the other securities we have selected for the study in the Infrastructure sector. The SD was significantly high in the month of August.

3.2.4 DEPENDANCE OF NIFTY RETUN ON THE INFRASTRUCTURE SECTOR COMPANY RETURNS Table 3.2.4.a Regression between nifty return and highly correlated company returns in infrastructure sector Coefficie nts Unstandardized Coefficients Model (Consta 1 nt) LITL

t

Sig.

0.000

-0.593

0.553

0.01864

13.046 7

0.000

Std. Error

B -0.000 0.24319 5

Standardize d Coefficients Beta

1.650

56

Unitech A

0.201044

0.019424

1.4890 10.35036

0.000

Dependent Variable: NIFTY

Table 3.2.4.b R square table for highly correlated companies with Nifty return

Model 1. 2. A

R

R Square

Adj. R Square

Std. Error

0.63645

0.405069

0.401069

0.015591

0.547699

0.299975

0.295975

0.016913

Predictors: (Constant), LITL, Unitech

INTERPRETATION: The dependence of the Nifty return to highly correlated company returns can be given by the equation Nifty return= -0.0003 + 0.401069 (LITL) + 0.2995975 (Unitech).The R square value of 0.4010 proves that the LITL and Unitech return have a good impact on the Nifty return.

3.2.5 ANALYSIS OF THE PERFORMANCE OF THE INFRASTRUCTURE COMPANIES BEFORE AND AFTER THE ANNOUNCEMENT OF BUDGET: Paired sample t test

57

Hypothesis Ho: there is no significant impact of the announcement of budget on the returns of Ansal properties, GMR Infra, GTL Infra, LITL infra, Reliance Infra and Unitech before and after the budget. Ha: there is a significant impact of the announcement of budget on the returns of Ansal properties, GMR Infra, GTL Infra, LITL Infra, Reliance Infra and Unitech before and after the budget.

Table 3.2.5.a SD and mean before and after the budget for the Infrastructure sector companies return BEFORE COMPANY

MEAN

SD

AFTER MEAN

SD

-0.004 6

0.056 7

-0.016 7 0.0538

GMR Infra

0.0026

0.048 3

-0.007 3 0.0567

GTL Infra

-0.002 1

0.036 9

-0.008 6 0.0398

0.0024

0.065 8

-0.010 2 0.0483

Reliance Infra

-0.008

0.035 9

-0.021 5 0.0418

Unitech

0.0010

0.058

-0.013 0.0601

Ansal Infra

LITL Infra

58

9

0

Table 3.2.5.b paired sample t test result for Infrastructure sector companies return. Paired Samples Test

Paired Differences

Mean

Std. Deviati on

t

Std. Error Mean

df

Sig. (2taile d)

95% Confidence Interval of the Difference Lower

Upper

P air 1

ANSBE ANSAF

-0.01 1

0.054 3

0.048 34

0.3077 1

0.5036 2

8.3911 4

3 8

0.00

P

GMRB

0.00

0.048

0.049

0.3334

0.5357

8.7045

3

0.00

59

air 2

EGMRA F

3

3

92

1

2

43

8

P air 3

GTLBE GTLAF

-0.00 5

0.037 4

0.106 86

0.1070 4

0.5400 9

3.0278 74

3 8

0.00

P air 4

LITLBE LITLAF

-0.00 3

0.056 9

0.052 76

0.2467 6

0.4605 9

6.7026 81

3 8

0.00

P air 5

RIILBE RIILAF

-0.014

0.0378

0.11003

0.04022

0.48613

2.391719

3 8

0.00

P air 6

UNIBE -UNIAF

-0.007

0.0577

0.04781

0.27354

0.46732

7.746545

3 8

0.00

INTERPRETATION: In all the cases the SD before budget is greater than SD after budget. This shows there was high selling and buying before budget. That would have created the volatility which in turn would have increased the SD increasing risk. The high amount of volatility would have been due to the investors’ perception towards the budget. The SBI bank return did not have significant change in SD before and 60

after the budget. But there was a change in mean, that shows that the volatility before and after the budget was same and the budget had no significant effect on SBI share. But there was a decrease in return for all the share after budget other than Canara bank. ICICI shares did well before and after budget. ICICI had the highest mean return before budget and also the SD did not change a lot after budget. The Paired sample t test result shows there was no significant impact of budget on the returns of the securities of Canara bank, HDFC bank, ICICI bank, SBI bank and UTI bank. But there are some changes after and before budget.

CHART 3.4.5.a NIFTY RETURN AND INFRASTRUCTURE SECTOR AVERAGE RETURN

61

62

FINDINGS AND INFERENCE

6. FINDININGS AND INFERENCE

63

 From studying the performance of the 12 companies in the two sectors for the year

2007-2008. The Power sector was found to be the most risky sector to invest in. As the risk was high the returns gained by the investors would have been huge.  The Infrastructure sector was found to be the less risky sector than Power to invest in.

Most of the companies selected for the study in the Infrastructure sector had a beta in the risky range, a beta above 1.0. Some companies have their beta very low making the investment in the company a un risky one.  The effect of announcement of Budget on the company securities was studied by comparing the means of the returns of the company securities before and after the announcement of Budget using Paired sample t test.  From comparing the mean and standard deviation for the twelve companies in two

sectors before and after the announcement of budget in the financial year 2007-2008, it was found that the announcement of Budget had no immediate effect on the company securities in all the two sectors. The data used to analyze the effect of Budget was the share price of the companies every day for one month before the Budget and one month after the budget. The budget would have had an effect for sure on the share price. But the analysis proves that there was no effect. The study considered only the fluctuation in the share price for one month after the Budget. The effect on share price due to the announcement of the Budget was not influence at that stage. It would have taken some time to have a significant impact on the share price of the companies return.  From the analysis we can see that some companies had increase in the standard deviation of their returns, some companies had decrease in the standard deviation of

64

their returns. But most of the companies did not have significant decrease or increase in their standard deviation. this supports the results of the paired sample t test results.  From the study we can see that the beta value is high for most of the companies in

Power sector, month of January and it’s also happen in the Infrastructure sector. The study was not able to find out the specific reason for the high beta in that month.  The standard deviation of the returns to the company securities in the Power sector

was high mostly in the month of January. Two companies had their highest standard deviation in the month of October.  In the Infrastructure sector the standard deviation was high for most of the companies

in the month of January. Day traders could have made good profit due to the fluctuation in the price in the month of January in the infrastructure sector. But the standard deviation was highest in the month of September for GMR Infra. The SD was 0.1800.  TATA Power was the company under study which had the highest beta of 1.197.And

in Infrastructure sector LITL (Lanco Infratech) had the highest beta of 1.650. This makes both company the most risky company to invest in, among the companies which were taken for the study.  We can infer from the study that the Nifty index was dependent more on the Power

sector compared to Infrastructure sectors like.

The performance of the selected companies in the two sectors were studied and compared with the performance of Nifty index. The returns for the selected companies were analyzed. The risk involved in investing in the sectors was studied. The risk factor beta was calculated for all the 65

companies selected for study and the calculated beta was used to differentiate risky and non risky companies. The impact of the company securities on the Nifty Index was assessed. The effect of price fluctuation on the returns of the company securities was also studied. The performance of the selected company securities before and after the announcement of budget was also analyzed.

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RECOMMENDATIONS

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7. RECOMMENDATIONS

 Assuming that the market and the companies’ securities will perform in the same way as

they performed in the year 2007-2008. Risk averse investors must not invest in the Power sectors. These risk averse investors can safely invest in the Infrastructure sector and also the Power sector is performing well in the market.  Risk willing investors can invest in the Infrastructure sector and as the risk involved is

high the returns earned will also be high.  June, July and December will be the best months for the day traders to earn more returns. Because in these months the fluctuation in the share price of the most of the companies are high.

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ANNEXTURE

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LIMITATIONS The limitations involved in this study are •

In each sector only six good performing companies were selected for the study.

The performance of the company securities were studied only for a year from 1st April 2007 to 31st March 2008.

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REFERENCES BOOKS 1. R.Nandagopal, K.Arul rajan and N. Vivek , “Research Methods in Business”, ( First

edition; New Delhi : Excel books, 2007) 2. SP Gupta, “Statistical Methods”(34th edition; Sultan chand and sons)

3. Donald R Cooper and Pamela S Schindler, “Business Research Methods”,( Ninth edition ; New Delhi : Tata McGraw-Hill ,2006) 4. James C. Van Horne and John M. Wachowicz Jr, “ Fundamentals of Financial Management “, ( Eleventh edition ; New Delhi: Prentice-Hall of India ,2006) 5. I M Pandey, “Financial Management” , ( Ninth edition; New Delhi: Vikas Publishing House, 2007) 6. A K Vashisht & RK Gupta,”Investment Management and Stock Market”(Deep and Deep Publication Pvt Ltd,Delhi) WEBSITES 1. www.nseindia.com 2. www.bseindia.com 3. www.investmentwatch.com 4. www.moneycontrol.com 5. www.investmentcommision.in 6. www.powermin.gov.in 7. www.iloveindia.com/indianeconomy 8. www.cea.nic.in 71

9. www.yahoofinance.com 10. www.investopedia.com 11. www.bilpower.com 12. www.jhpl.com 13. www.ntpc.co.in 14. www.suzlon.com 15. www.tatapower.com 16. www.ansalpi.com 17. www.gmrgroup.in 18. www.gtlinfra.com 19. www.lancogroup.com 20. www.unitechgroup.com

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