Overview Of The Indian Commodity Market Finance Essay

In India market for hereafters are from a really long clip back, it was at that place in early 1800s. After Independence, the Forward Contracts ( Regulation ) Act, 1952 ( FCRA, 1952 ) was passed to advance and modulate this market with Forward Markets Commission ( FMC ) being set up in 1953 in Mumbai as the regulator. “ Commodity derived functions were banned in the late ’60s, but were revived once more in the ’80s.After the successful equity market reforms of the ’90s, the Government of India tried to retroflex similar reforms for the trade good derivatives markets and in 1999 suggested that the Minimum Support Price ( MSP ) as a price-hedging instrument could be replaced with derived functions markets. National-level multi-commodity exchanges were permitted to be set up on conditions of being backed by internationally predominating best patterns of trading, uncluttering and colony. The national trade good exchanges follow electronic, crystalline trading and uncluttering with novation, similar to the equity market [ See Box 2 ] . At present, 105 trade goods have been approved for merchandising out of which 95 trade goods are actively traded. The development of the trade good derivatives market in India like many other states has been hindered by policy reversals on concerns sing its consequence on monetary values and supplies of indispensable trade goods. ” This apart, integrating of topographic point and hereafters market is cited as a critical factor for farther growing of trade good hereafters in India. Harmonizing to Nair ( 2004 ) , the major stumbling block for the development of trade good hereafters markets in India is the disconnected physical/spot market with authorities Torahs and assorted revenue enhancements that hinder the free motion of trade goods critique draws attending to the prevalence of bilateral trades in local exchanges, the deficiency of monetary value transparence both in the ( fragmented ) hereafters and topographic point markets for many trade goods and the absence of certified warehouses.

At present 22 Exchanges are recognised/registered for forward/futures merchandising in trade goods. Most of the trade good exchanges in India are individual trade good platforms and cater chiefly to the regional demands. However, three national-level multi-commodity exchanges have been set up in the state to get the better of the job of atomization. These exchanges are:

1. National Multi Commodity Exchange of India ( NMCE )

2. Multi Commodity Exchange of India ( MCX )

3. National Commodity & A ; Derivatives Exchange of India ( NCDEX )

NMCE ( National Multi Commodity Exchange of India ) – It is the first province of the demutualized multi-commodity Exchange, National Multi Commodity Exchange of India Ltd. ( NMCE ) was promoted by commodity-relevant public establishments, viz. , Central Warehousing Corporation ( CWC ) , National Agricultural Cooperative Marketing Federation of India ( NAFED ) , Gujarat Agro-Industries Corporation Limited ( GAICL ) , Gujarat State Agricultural Marketing Board ( GSAMB ) , National Institute of Agricultural Marketing ( NIAM ) , and Neptune Overseas Limited ( NOL ) . While assorted built-in facets of trade good economic system, viz. , warehousing, co-ops, private and public sector selling of agricultural trade goods, research and preparation were adequately addressed in structuring the Exchange, finance was still a critical missing nexus. Punjab National Bank ( PNB ) took equity of the Exchange to set up that linkage. Even today, NMCE is the lone Exchange in India to hold such investing and proficient support from the trade good relevant establishments. These establishments are represented on the Board of Directors of the Exchange and besides on assorted commissions set up by the Exchange to guarantee good corporate governance.. NMCE commenced hereafters merchandising in 24 trade goods on 26th November, 2002 on a national graduated table and the basket of trade goods has grown well since so to include hard currency harvests, nutrient grains, plantations, spices, oil seeds, metals & A ; bullion among others..

MCX ( Multi Commodity Exchange of India ) : – Headquartered in Mumbai, Multi Commodity Exchange of India Ltd ( MCX ) is a state-of-the-art electronic trade good hereafters exchange. The demutualised Exchange has lasting acknowledgment from the Government of India to ease on-line trading, and glade and colony operations for trade good hereafters across the state. Having started operations in November 2003, today, MCX holds a market portion of over 85 % * ( as on March 31, 2012 MCX had a market portion of 86 % ) of the Indian trade good hereafters market. The Exchange has more than 2,170 registered members runing through over 3,46,000 including CTCL trading terminuss spread over 1,577 metropoliss and towns across India. MCX was the 3rd largest trade good hereafters exchange in the universe, in footings of the figure of contracts traded in CY2011

4.2 PURPOSE OF THE STUDY

In India, as derived functions merely hereafters merchandising in trade goods is allowed. These trading are delineated into agribusiness and non agribusiness trade goods. There has to be a systematic attack to harvest benefits from trade good hereafters. These markets are driven by intelligence, rumours, informations release facets and many more.These all things sometimes consequences in market volatility so measuring the hazard extenuation possibility through usage of hereafters is necessary and besides the usage of other derivative instruments like options. These complicated facets make the research desk inevitable in securities firm house. Hence the survey is utile to understand the hereafter market tendencies, analyze the fudging effectivity of trade good hereafters and the demand of options in trade good market.

4.3 SIGNIFICANCE OF THE STUDY

To acquire to cognize the fudging effectivity of the hereafters as it is many times talked that the hereafters increases bad trading instead than fudging tool and to happen out wheather monetary value find mechanism is at that place and how efficient is it in fudging monetary value hazard, It besides covers the demand of trade good option in the market and its usage as an fudging tool.

4.4 LIMITATION OF STUDY

The survey is limited to Kolkata and Bangalore securities firm houses merely.

Limited figure of respondents i.e.50 as it was non easy to acquire informations from the corporate.

Time restraint was a major restriction.

Not covered the industrialist and husbandmans who trade in trade good market to fudge the production risk..

5.1 Introduction

This portion of the research paper shows the elaborate analysis and related reading of the collected informations in a elaborate mode. This analysis and reading is based on the research methodological analysis mentioned in Chapter 3 and the micro analysis of the Indian trade good market.In this Chapter ab initio the analysis of the respondents is done and so it moves on to analyze the the primary informations and farther to reason a secondary research is besides done from the information beginnings as before mentioned in Chapter 3.

5.2 RESPONDENTS PROFILE

The figure of respondents taken in for this research is 50. Basically the respondents are those people who trade in trade good market. Majority of respondents are from Kolkata and Bangalore, respondents are from all the age groups. Out of 50, 20 % of respondents are professional people like Chartered Accountants, Company Secretary, and MBA who trade in trade good and other fiscal instruments and remainder are the people who trade in trade good market, respondents besides includes the people orking in for securities firm houses who deals in trade good.

5.3 ANALYSIS OF DATA

The analysis is done on both primary informations and Secondary Data.

Exchange in which respondents trade.

Chart 5.1: Preference for Commodity Exchange

Beginning: Primary Data

60 % of the respondents prefer MCX as the trade good exchange for their investing, whereas NCDEX is preferred by 29 % of the sample, and remainder of the respondents prefer options other than MCX and NCDEX.

Preference for the type of Commodity among Investors.

Chart 5.2: Type of Commodity trade

Beginning: Primary Data

Bullion, Metals, Agricultural Products are the most preferable trade good types by 49 % , 18 % , and 33 % of the respondents severally.

Opinion about Indian Market Commodity market volatility.

Chart 5.3: Opinion about Indian trade good market volatility

48 % of the respondents are of the sentiment that Commodity Market in India is extremely volatile, and 43 % per centum of respondents feel that the volatility of the Indian Commodity Market is moderate, whereas it is thought to be low volatile by 9 % of the respondents.

Degree of engagement in trade good market.

Chart 5.4: Degree of engagement in trade good market

Most of the respondents are really active at trading in Commodity Market, whereas 31 % of the respondents are found to be less active in the Commodity Market.

5.3.4 Relationship between Mitigation of hazard and degree of engagement by an investor

Table 5.1: – Cross tabular matter of degree of investement engagement and extenuation of hazard.

How_Active * Mittigation of hazard Crosstabulation

Count

Mittigation of hazard

Entire

Least Priority

Less Priority

Impersonal

High Precedence

Highest Precedence

How_Active

Very Active

1

1

0

4

10

16

Moderate

1

4

8

3

5

21

Less Active

2

2

7

2

1

14

Entire

4

7

15

9

16

51

Chart 5.5: Cross tabular matter of degree of investing engagement and extenuation of hazard

Interpretation: – Cross tabular matter analysis shows that action of a bargainer in a market have a of import function in taking up extenuation of hazard. Active bargainer gives highest precedence to Mitigation of hazard on the other manus moderate and less active bargainers are non motivated towards hazard extenuation.

5.3.5 Cross tabular matter Relationship between investors degree of engagement and

Pure investing Purpose

Table 5.2: Cross tabular matter relationship between investement degree of engagement and

investement intent

How_Active * Pure_Investement_Purpose Crosstabulation

Count

Pure_Investement_Purpose

Entire

Least Priority

Less Priority

Impersonal

High Precedence

Highest Precedence

How_Active

Very Active

3

3

6

3

1

16

Moderate

3

5

5

5

3

21

Less Active

1

2

1

3

7

14

Entire

7

10

12

11

11

51

Chart 5.6: Cross tabular matter relationship between investement degree of engagement and

investement intent

Interpretation: – Through this cross tabular matter we get to cognize that investors who are less active they make investement in hereafters with pure investement intent. On the other manus really active and moderate investors are more impersonal towards pure investement intent.

Factor analysis:

Factors taken into history are Risk extenuation, Parking of extra hard currency, Pure investement intent and To countervail monetary value volatility.

Table 5.3: Factor analysis of Risk extenuation, Parking of extra hard currency, Pure investement

intent

KMO and Bartlett ‘s Trial

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.615

Bartlett ‘s Test of Sphericity

Approx. Chi-Square

14.924

Df

6

Sig.

.41

Interpretation: -Kaiser Meyer Olkin Values range between 0 and 1. A value near to 1 indicates a high correlativity and suggests that the information is dependable whereas a value near 0 indicates that correlativity is less than partial correlativity.

KMO value should be greater than 0.5, otherwise we need to roll up more informations or some of the factors should be eliminated. There are Four classs of value on the footing of KMO value. ( 0.5-0.7 is second-rate, 0.7-0.8 is good, 0.8-0.9 is great, greater than 0.9 is superb )

In this instance, the KMO value is.615, so the information falls in the class of ‘mediocre ‘ . It means that the informations are just plenty to transport on the factor analysis.

Further, there should be some relationship between the variables, if we need to transport out Factor analysis. So the significance value should be less than 0.5.

In this instance the sig value is 0.04 which shows that the informations are related to each other, so we can transport out Factor Analysis

Communalities

Initial

Extraction

Mittigation of hazard

1.000

.432

Pure_Investement_Purpose

1.000

.687

Parking_Excess_Cash

1.000

.774

To_offset monetary value volatility

1.000

.710

Extraction Method: Chief Component Analysis.

In this instance we assume that the initial discrepancy is common, so the communalities ab initio are 1.But in the extraction column, the discrepancy has changed for every factor. For Example, discrepancy of.710 signifies that variable 1 is 71 % common to discrepancy of other factor

Component Matrixa

Component

1

2

Mittigation of hazard

.187

.630

Pure_Investement_Purpose

.818

.135

Parking_Excess_Cash

-.863

.170

To_offset monetary value volatility

.098

-.837

Extraction Method: Chief Component Analysis.

a. 2 constituents extracted.

Testing of Hypothesis

5.4.1 Arrested development Analysis

On the primary informations collected for finding the impact of assorted independent variables like Easy money, Speculators, Demand, Supply, Expiry day of the month on dependent variable i.e. monetary value.

Table 5.4: Arrested development analysis of independent variables like Easy money, Speculators Demand, Supply, Expiry day of the month on dependent variable i.e. monetary value.

Variables Entered/Removed

Model

Variables Entered

Variables Removed

Method

1

Easy Money, Speculators, Demand, Supply, Expiry_Datea

.

Enter

a. All requested variables entered.

Model Summary

Model

Roentgen

R Square

Adjusted R Square

Std. Mistake of the Estimate

1

.761a

.679

.531

.74737

a. Forecasters: ( Constant ) , Easy_Money, Speculators, Demand, Supply, Expiry_Date

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Arrested development

33.743

5

6.749

12.082

.000a

Residual

24.577

44

.559

Entire

58.320

49

a. Forecasters: ( Constant ) , Easy_Money, Speculators, Demand, Supply, Expiry_Date

B. Dependent Variable: Monetary value

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

T

Sig.

Bacillus

Std. Mistake

Beta

1

( Constant )

-.980

.791

-1.238

.222

Demand

.511

.122

.436

4.202

.000

Speculators

.487

.100

.496

4.872

.000

Supply

.037

.150

.027

.248

.805

Expiry_Date

.146

.151

.107

.966

.339

Easy money

.170

.123

.151

1.380

.175

a. Dependent Variable: Monetary value

Interpretation: The 2nd tabular array of involvement is the Model Summary tabular array. This tabular array provides the R and R2 value. The R2 value is 0.679, which represents the simple correlativity and, hence, indicates a grade of correlativity. The R2 value indicates how much of the dependant variable, Price of the trade good, can be explained by the independent variables, Demand, speculator, Supply, Easy money, Expiry day of the month. In this instance, 67.9 % is the consequence which is just plenty.

3. The following tabular array is the ANOVA tabular array. This tabular array indicates that the arrested development theoretical account predicts the result variable significantly good. In the “ Regression ” row go to the Sig. column. This indicates the statistical significance of the arrested development theoretical account that was applied. Here, P & lt ; 0.000 which is less than 0.05 and indicates that, overall, the theoretical account applied is significantly good plenty in foretelling the result variable.

4. The following tabular array is Coefficients which provides us with information on each forecaster variable. This provides us with the information necessary to foretell Price from, Demand, speculator, Supply, Easy money, Expiry day of the month. It is seen that Demand and Speculators contribute significantly to the theoretical account ( by looking at the Sig. column ) .

5. By looking at the B column under the Unstandardized Coefficients column we can show the arrested development equation as:

Price = -.980 + 0.511 ( Demand ) + .487 ( Speculators )

5.4.2 Arrested development Analysis

An analysis of investors perception about Indian trade good market and there engagement in the market as independent variable. And its impact on hazard extenuation scheme as a dependant

Table 5.5: Arrested development analysis on factors finding extenuation of hazard

Interpretation: –

The 2nd table Model Summary tabular array. This tabular array provides the R and R2 value. The R2 value is 0.669, which represents the simple correlativity and, hence, indicates a grade of correlativity. The R2 value indicates how much of the dependant variable, Price of the trade good, can be explained by the independent variables, How active an investor is at that place in the trade good market and sentiment about volatility in the trade good market. In this instance, 67.9 % is the consequence which is just plenty.

3. The following tabular array is the ANOVA tabular array. This tabular array indicates that the arrested development theoretical account predicts the result variable significantly good. In the “ Regression ” row go to the Sig. column. This indicates the statistical significance of the arrested development theoretical account that was applied. Here, P & lt ; 0.000 which is less than 0.05 and indicates that, overall, the theoretical account applied is significantly good plenty in foretelling the result variable.

4. The following tabular array is Coefficients which provides us with information on each forecaster variable. This provides us with the information necessary to foretell Price from, How Active an investor participate and his sentiment of Indian trade good market. It is seen that contribute significantly to the theoretical account ( by looking at the Sig. column ) .

5. By looking at the B column under the Unstandardized Coefficients column we can show the arrested development equation as:

Price = 6.128 + 0.767 ( How Active ) + .548 ( Volatility )

5.4.4 About monetary value find mechanism in future market

Chart 5.6: Future market monetary value find procedure

Interpretation: – As the Price find mechanism is concerned 28 % of the respondents say that it depends on the hereafter and topographic point relationship, 25 % says its reflected foremost on topographic point, 23 % on hereafters, 24 % says market is efficient. So with this decision we can state that it is non really clear which manner the monetary value is discovered.

5.4.5 Factor Analysis: – Of the factors which can assist in finding wheather monetary value find mechanism exist in the trade good market factors taken into consideration are efficient market, monetary value reflects foremost on the topographic point market, information foremost reflects foremost on the hereafters market and their exist a relationship between hereafters and topographic point monetary values

KMO and Bartlett ‘s Trial

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.557

Bartlett ‘s Test of Sphericity

Approx. Chi-Square

19.164

df

6

Sig.

.004

Interpretation: -Kaiser Meyer Olkin Values range between 0 and 1. A value near to 1 indicates a high correlativity and suggests that the information is dependable whereas a value near 0 indicates that correlativity is less than partial correlativity.

KMO value should be greater than 0.5, otherwise we need to roll up more informations or some of the factors should be eliminated. There are Four classs of value on the footing of KMO value. ( 0.5-0.7 is second-rate, 0.7-0.8 is good, 0.8-0.9 is great, greater than 0.9 is superb )

In this instance, the KMO value is.557, so the information falls in the class of ‘mediocre ‘ . It means that the informations are just plenty to transport on the factor analysis.

Further, there should be some relationship between the variables, if we need to transport out Factor analysis. So the significance value should be less than 0.5.

In this instance the sig value is 0.04 which shows that the informations are related to each other, so we can transport out Factor Analysis

Correlation Matrix

Efficient

Futures exchange

Spot market

Closely_Related

Sig. ( 1-tailed )

Efficient

.003

.147

.014

Futures exchange

.003

.009

.218

Spot market

.147

.009

.074

Closely_Related

.014

.218

.074

5.4.6 Cross Tabulation

To look into the demand of trade good options and to happen out peculiarly in which trade good the fiscal instrument Idahos much needed.

Importance * Agriculture Products Crosstabulation

Count

Agribusiness Merchandises

Entire

No

Yes

Importance

Very Important

9

14

23

Not Important

9

2

11

Indifferent

3

8

11

Entire

21

24

45

Importance * Bullion Cross tabular matter

Count

Bullion

Entire

No

Yes

Importance

Very Important

17

6

23

Not Important

3

8

11

Indifferent

6

5

11

Entire

26

19

45

Importance * Metals Cross tabular matter

Count

Metallic elements

Entire

No

Yes

Importance

Very Important

20

3

23

Not Important

10

1

11

Indifferent

10

1

11

Entire

40

5

45

Importance * Fossil/Energy Crosstabulation

Count

Fossil/Energy

Entire

No

Yes

Importance

Very Important

21

2

23

Not Important

9

2

11

Indifferent

11

0

11

Entire

41

4

45

Interpretation: –

This cross tabular matter analysis is done to find the demand of trade good options in the market and besides to cognize in which trade good merchandise option is needed. So as per the information collected it shows that the trade good option is needed in the market it is much need derivative tool in the agribusiness trade good.

5.4.7 Purpose for which trade good option can be used in the market

Chart 5.6: Purpose for which trade good option can be used in the market

Interpretation: – As the chart shows that the investing in trade good option will be more for devising net income through motion in the monetary value and besides it will be used as a hazard extenuation tool.

5.4.8 Research based on Secondary Research Comdex index hereafters and topographic point

Correlation: – This analysis is done in order to look into the motion of hereafters and topographic point. As per the analysis correlativity shows negative correlativity.31. Which denotes motion in monetary value of one consequence the other reciprocally.

Decision: – This shows that the motion in the monetary value of one consequence the other so there exist a monetary value find mechanism between these two monetary values.

6.1 Findingss

From the research we got to cognize that on the footing of an single engagement degree in the trade good hereafters market determines his demands for investing. It is found that an active investor invest in hereafters market to extenuate the hazard so it can be considered that the hereafters are being considered as a fudging tool.

On the other manus non active investors in the trade good hereafters chief motivation is either doing a pure investing or countervailing the monetary value volatility in the market.

Factors like extenuation of hazard, parking of extra hard currency, countervailing monetary value volatility and pure investing are the chief grounds for trading in trade good hereafters.

It is found that the factors like Supply, Demand, Speculators, Easy Money, Expiry day of the month all have a direct consequence on the monetary value of the trade good and there exist a correlativity among them.

It is besides found that there exist a relationship between single perceptual experience of trade good market and there investing determination. As per the research done it is found that investor who view trade good market as volatile and are besides active in market they in trade good hereafters for hazard extenuation i.e. they consider hereafters as a fudging tool.

On other manus who feels trade good market is non that volatile it is moderate on less volatile and they are excessively active in market there investing intent is more as pure investing or offsetting monetary value volatility.

It is found that factors which help in monetary value find mechanism like market efficiency, contemplation of market information foremost on the topographic point market, consequence of market information foremost on the hereafters market and that topographic point market and hereafters market are interrelated and helps in finding.

As per the secondary research done on the information ‘s collected of 3 index traded fund on MCX of India both their day-to-day topographic point and future monetary values of past one twelvemonth daily historic monetary value it shows a coo relation, which means that they are closely interrelated so it can be said that market information are reflected on both the monetary values so hereafters can be said to detect monetary value because of co orelation.

As the research country besides analyse the demand of trade good option in the market it comes out with a determination that trade good option is much needed point particularly in the agri trade good sector

6.2 Decision: –

To reason soon the Indian trade good market is acquiring really popular the volume of trade has increased so this has resulted in increasing the efficiency degree of the market. But as India being a agribusiness driven state and agri market chiefly depends on the monsoon which is really unsure so fudging against the monetary value hazard through trade good hereafters can be good thought and it has proved effectual excessively in past and in other trade good excessively similar energy, bullion, metals trade good hereafters can be a effectual tool. Apart from hereafters in order to promote trade good market trade good option excessively is about to be introduced in the market and as per the research investors are truly looking frontward for it and that excessively particularly it will be great aid in the agricultural sector.

6.3 Suggestion

Commodity market is a good topographic point excessively invest in it can assist in extenuation of hazard like monetary value in hazard in today ‘s rising prices effected scenario. Commodity market can assist in procuring a certain monetary value degree through the usage of hereafters. Through the usage of relation between hereafters and topographic point monetary value find can be done so this can be used for arbitraging and theorizing future tendencies in monetary values. With debut of trade good option possibility in close future investement in trade good market will be emcouraged as it will supply a better hedge mechanism. So it can be said that it is worthwhile doing an investement in trade good market now in order to fudge against the monetary value hazard, rising prices hazard and production hazard.

6.4 SUGGESTION FOR FURTHER STUDY

As my research is limited to countries like Kolkata and Bangalore soa farther research can be conducted covering major metropoliss.

Sample size can be increased for acquiring a more clesr consequence. Respondents can include husbandmans and industrialist who trade in trade good market to fudge against their production and monetary value hazard

More variables can be added which affect the trade good hereafters.