Fundas of Commodity Mkts

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1.3 INTRODUCTION TO RISK AND RETURN

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

macro-level 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.

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How to minimize the risks?

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 commodities.

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 between a fund and its benchmark. Thus if the

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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 commodity 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 commodity increases. Income is the

cash flow paid by an 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).

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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.

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OBJECTIVE OF THE STUDY

Primary :

• To analyze the return and return pattern of selected securities in the four sectors

• To compare the performance of the stock with that of the Nifty index.

• To analyze the performance of the company securities before and after the

announcement of the budget 2006.

Secondary:

• To assess the impact of the securities return on Nifty index performance.

NEED FOR THE STUDY

In India, the MCX 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 MCX is a market capitalization –

weighted Index i.e., price change in any of the Index Commodities will lead to a change

in the index. This necessitates the need for analyzing the risk and return relationship of

the selected commodities constituting the MCX index and their impact on the MCX

index.

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SCOPE OF THE STUDY

Study could be done to analyze the performance of the selected commodities for the year

2007 .

LIMITATIONS

The limitations involved in this study are• In each sector only two good performing commodities were selected for the study.

• The performance of the company securities were studied only for a year from 1 st

January to 31 st December 2007.

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RESEARCH METHODOLOGY

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RESEARCH METHODOLOGY

RESEARCH DESIGN:

The study was carried out to compare the selected commodities with the MCX index

using their returns, and to analyze the risk involved in each comodity 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.

Four types of commodities were selected for the study and two commodities from each

sector was selected based on the recommendation given by the brokers in the firm.

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COMMODITIES TYPE SELECTED = 4

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

COMMODITIES TYPE NO OF COMMODITIES

BULLION 2BASE METALS 2

ENERGY 2

AGRI COMMODITIES 2

Total sample size 8

COLLECTION

The data collected were by means of secondary data. The data were collected from

Internet, Greenbucks Comtrade records and magazines.

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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 price – today’s priceyesterday’s price

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ANALYSIS AND INTERPRETATION

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ANALYSIS AND INTERPRETATION

ANALYSING THE IMPACT OF COMMODITIES ON MCX PERFORMANCE

The commodities selected for study are:

1)GOLD

2)SILVER

3)CRUDE

4)NATURAL GAS

5)COPPER

6)NICKEL

7)CARDAMOM

8)JEERA

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ANALYSING THE RISK AND RETURN

MONTH GOLD SILVER CRUDE

OIL

NATURAL

GAS

CARDAMOM JEERA COPPER NICKEL

JANUARY 3.45 5.79 5.93 12.03 13.94 9.12 -1.59 10.96

FEBURARY -2.01 -3.74 3.36 -4.25 1.15 20.77 9.76 12.50MARCH 2.66 -0.41 4.53 1.85 2.70 11.06 14.43 5.87APRIL -5.47 -5.77 -6.58 -0.72 -0.19 -10.61 -2.57 -0.81

MAY -3.37 -2.14 5.57 -4.17 -2.69 1.81 -3.56 -17.69JUNE -0.56 -2.76 6.10 -15.23 8.17 -22.26 3.86 -20.63

JULY 1.4 -1.97

-0.85

-1.51

2.47

-14.50

-5.05

-16.29

AUGUST 4.54 9.73 6.18 5.36 -1.13 25.28 0.95 5.66SEPTEMBER 2.12 -0.27 6.16 11.74 -1.62

3.79

2.71 3.06

OCTOBER 9.89 5.10 11.19 5.15

6.03

-0.16 -10.21 -1.58

NOVEMBER 3.07 -3.19 -2.84 -10.90 9.67 8.93 -4.12 -14.58

DECEMBER 00.00 00.00 00.00 00.00 00.00 00.00 00.00 00.00

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1)CHART SHOWING RETURN OF SELECTED COMMODITIES

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MONTHLY RETURNS

-30

-20

-10

0

10

20

30

J A N F E B M A R

A P R I L M A Y

J U N E J U L Y

A U G

S E P T O C T

N O V D E C

MONTHS

R E T U R N

GOLD

SILVER

MCX

CRUDE OIL

NATURAL G

CARDAMOM

JEERA

COPPER

NICKEL



Table 3.1.1. b RISK OF THE SELECTED COMMODITIES

THE RELATIONSHIP OF THE MCX WITH THE SELECTED COMMODITIES

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HYPOTHESIS

Ho: There is no significant relationship between the selected Commodities return and

MCXreturn.

Ha: There is a significant relationship between the selected Commodities return and

MCX return.

Table 3.1.2.a Correlation between the selected Commodities return and MCX

return.

COMMODITIES GOLD SILVER CRUDE NATURAL

GAS

COPPER NICKEL CARDAMOM JEERA

MCX 0.675 0.748 0.878 0.643 0.221 0.524 0.148 0.404SIGNIFICANT YES YES YES YES YES YES YES YESP-LEVEL .01 .01 .01 .01 .01 .01 .01 .01

INTERPRETATION:

The correlation factor was significant for gold, Silver, Crude, Natural gas and Nickel

return to MCX return. They show a positive correlation of 0.675, 0.748, 0.878, 0.643 and

0.524 respectively [refer table 3.1.2.a].

THE IMPACT OF SELECTED COMMODITIES RETURN ON THE MCX

RETURN

HYPOTHESIS

Ho : There is no significant impact of the return of the Commodities on the nifty return

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Ha: There is a significant impact of the MCX return of the selected Commodities on the

MCX return

Table 3.1.3.a Anova table for MCX return and Commodities returns

MODEL Sum of Squares df

MeanSquare F Sig.

GOLD Regression 0.038259 1 0.038259Residual 0.045741 10 0.004574 183.64 0.000Total 0.084 11

SILVER Regression 0.04693 1 0.046936Residual 0.037064 10 0.00037 126.63 0.00Total 0.084 11

CRUDE Regression 0.064731 1 0.06473Residual 0.019269 10 0.00019 133.59 0.00

Total 0.084 11NATURAL

GAS Regression 0.034712 1 0.034712Residual 0.049288 10 0.004929 170.043 0.00Total 0.084 11

CARDAMOM Regression 0.01836 1 0.01836Residual 0.082164 10 0.008216 122.3 0.00Total 0.084 11

JEERA Regression 0.013676 1 0.013676Residual 0.070324 10 0.00070 159.45 0.00Total 0.084 11

COPPER Regression 0.04089 1 0.04089Residual 0.079911 10 0.007991 150.12 0.00Total 0.084 11

NICKEL Regression 0.023104 1 0.023104Residual 0.060896 10 0.006090 137.94 0.00Total 0.084 11

Table 3.1.3.b R square table for MCX return and selected Commodities return.

Model R R SquareAdjusted R

SquareStd. Error of the Estimate

GOLD 0.675 0.455 .401 0.02138SILVER 0.748 0.559 0.525 0.01925

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CRUDE 0.878 0.771 0.748 0.01388NATURALGAS 0.643 0.413 0.355 0.02222CARDAMOM 0.148 0.022 0.76 0.02866JEERA 0.404 0.163 0.79 0.02651COPPER 0.221 0.049 0.46 0.02826



NICKEL 0.524 0.275 0.203 0.02467

Table 3.1.3.c Coefficient table for MCX return and Commodities return.

ModelUnstandardized

CoefficientsStandardizedCoefficients t

Sig.

B Std. Error BetaGOLD 0.463 0.160 0.675 2.892 0.016SILVER 0.454 0.128 0.748 3.559 .005CRUDE 0.496 0.086 0.878 5.796 .000NATURALGAS 0.218 0.082 0.643 2.654 .024CARDAMOM 0.079 0.167 0.148 0.473 0.647JEERA 0.081 0.058 0.404 1.395 .193COPPER 0.091 0.127 0.221 0.715 .491NICKEL 0.125 0.064 0.524 1.948 .080

INTERPRETATION:

ANOVA significance value of 0.000 for all the selected comodities 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 except Crude and Silver . This shows that the

returns of commodities haaving R square value less than.5 would not have affected the

MCX return very strongly as individuals [refer table 3.1.3.b]. Together they have an

impact on the MCX return .

The overall beta for the commodities were found to be 0.675, 0.748, 0.878, 0.643, 0.148,

0.404,0.221 ,0.524 for Gold,Silver,Crude,NaturalGas,Cardamom,Jeera,Copper and

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Nickel return respectively [refer table 3.1.3.c]. Among these commodities Silver and

Crude were found to be moderately risky commodities to invest in. the rest commodities

were of less risk to invest in.Gold, Silver ,Crude ,Naturalgas,Cardamom,Jeera,Copperand

Nickel returns had 67.5%, 74.8%, 87.8%, 64.3%,14.8%,40.4%,22.1%and52.4% impact

on the MCX return respectively. Beta value is a indicator of risk. When beta value is

greater than 1 then the commodities is a high risk commodities for investors.

3.1.4 DEPENDANCE OF MCX RETUN ON THE COMMODITIES RETURNS

Coefficients(a)

Model

UnstandardizedCoefficients

StandardizedCoefficients

B Std. Error Beta B Std. Error 1

(Constant) -.507 .441 -1.150 .294GOLD .078 .125 .114 2.626 .000SILVER .009 .138 .015 .067 .000CRUDE .385 .090 .682 4.291 .000NATURALGAS .114 .052 .336 2.195 .000

NICKEL .017 .028 .086 .626 .000

a Dependent Variable: MCX

Model Summary

a Predictors: (Constant), GOLD ,SILVER,CRUDE,NATUARLGAS,NICKEL

INTERPRETATION:



1 .957(a) .915 .845 1.08771

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The dependence of the MCX return to highly correlated COMMODITIES returns can be given by

the equation

MCXRETURN=

-0.507+.078(GOLD’sRETURN)+0.009(SILVER’sRETURN)+0.385(CRUDE’sRETURN)+

0.114(NATURALGAS’s RETURN)+0.017(NICKEL’s RETURN)

The R square value of 0.915 proves that the Gold, Silver,Crude,Natural gas and Nickel

commodities return have a good impact on the MCX return.

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CONCLUSION

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CONCLUSION

The risk and return plays a major role in the decision making process of the investors.

The standard deviation and beta are the true measure of risk. Investors can make

investment decisions based on the standard deviation and beta analysis. The performance

of the selected company securities before and after the announcement of budget was also

analyzed.

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BIBLIOGRAPHY

BOOKS

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

( First edition; New Delhi : Excel books, 2007)

2. Cooper R Donald and Schindler S Pamela, “Business Research Methods”,( Ninth

edition ; New Delhi : Tata McGraw-Hill ,2006)

3. Van Horne C James. And Wachowicz Jr M John, “ Fundamentals of Financial

Management “, ( Eleventh edition ; New Delhi: Prentice-Hall of India ,2006)

4. Pandey. I M, “Financial Management” , ( Ninth edition; New Delhi: Vikas

Publishing House, 2007)

WEBSITES

1. www.mcxindia.com

2. www.investmentwatch.com

3. www.moneycontrol.com

4. www.investmentcommision.in

5. www.ncdexindia.com

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http://www.mcxindia.com/

http://www.investmentwatch.com/

http://www.moneycontrol.com/

http://www.investmentcommision.in/

http://www.mcxindia.com/

http://www.investmentwatch.com/

http://www.moneycontrol.com/

http://www.investmentcommision.in/

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