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
14
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
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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
Model R R SquareAdjusted R
SquareStd. Error of the Estimate
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:
Model R R SquareAdjusted R
SquareStd. Error of the Estimate
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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