a study on testing performance of nifty …...a study on testing performance of nifty commodity...

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International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 1, January 2018, pp. 975–984, Article ID: IJCIET_09_01_096

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=1

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

A STUDY ON TESTING PERFORMANCE OF

NIFTY COMMODITY INDEX CONSTITUENT

COMPANIES IN INDIA USING CARHART FOUR

FACTOR MODEL: EMPIRICAL ANALYSIS

Jakeer Hussain Shaik

Assistant Professor, Management Department, Koneru Lakshmaiah Education Foundation

(Deemed to be University), Vaddeswaram, Green fields, Andhra Pradesh, India

Santoshi bhuma

Management Student, Management Department, Koneru Lakshmaiah Education Foundation


Vymisha Vankayala

Management Student, Management Department, Koneru Lakshmaiah Education Foundation


ABSTRACT

We differ previous studies on Testing Performance of Nifty Commodity index

constituent companies in India Using Carhart Four Factor Model in several

significant ways. We test whether empirical asset pricing models capture the value,

size and momentum patterns in average returns of domestic commodity market.

Spreads in average momentum returns, value premium, and size premium also

decrease from smaller to bigger stocks. Value premiums, size premium and momentum

premium in average stock returns of most of the commodity stocks across different

commodities does not get strong support in our tests. The present study suggests that

the investor should be careful while investing in this stock for the long term due to

huge volatility prevailing in the market.

Keywords: Return, car hart four factor model, spread, volatility.

Cite this Article: Jakeer Hussain Shaik, Santoshi bhuma and Vymisha Vankayala, A

Study on Testing Performance of Nifty Commodity index constituent companies in

India Using Carhart Four Factor Model: Empirical Analysis, International Journal of

Civil Engineering and Technology, 9(1), 2018, pp. 975–984.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=1

Jakeer Hussain Shaik, Santoshi bhuma and Vymisha Vankayala


1. INTRODUCTION:

In current scenario, all goods and products of agricultural (including plantation), mineral and

fossil origin are allowed to recognize commodity trading under the F.C.R.A. (Foreign

Contribution Regulation Act). The central government recognizes the national commodity

exchanges, permits commodities which include precious (gold and silver) and non-ferrous

metals, cereals and pulses, and oilcakes, sugar, potatoes and onions, coffee and tea, oilseeds,

oils, rubber and spices, raw jute etc. According to the positioning different phases are

described of the group of traders, their motivations, and the type of financial assets used to

take a position in commodities. Aggregating dispersed information about the strength of the

global economy among goods producers whose production has complimentarily, for guide

producers’ production decisions and commodity demand commodity prices serve as price

signals.

An index is the basket of commodities to evaluate the performance. The indexes regularly

traded in the exchanges, allow investors to add easier access to commodities without having

to enter the futures markets. Based on the essential commodities the value vary and in the

stock market the value can be traded. Trading primary or raw products in a market place for

buying and selling is called as a commodity market. There are at present about 50 major

commodity markets worldwide and 100 primary commodities.

There are two types of commodities - hard commodities and soft commodities. Hard

commodities are natural resources that must be mined or extracted such as gold, rubber and

oil. Another type of commodity is soft commodities are agricultural products or livestock

such as corn, wheat, coffee, sugar.

The commodity market have both the retail market and the wholesale market in the

country. Based on requirements it facilitates multi commodity exchange within and outside

the country. Commodity markets can consist of physical and derivatives trading using

forwards, spot prices, options on futures and futures. For centuries farmers were using

derivative trading in the commodity market in order to reduce risk for farmers.

2. REVIEW OF LITERATURE:

Many researchers, using data from before the 2000s, have found slightly negative return

correlations between commodity and stock returns (Greer, 2000; Gorton and Rouwenhorst,

2006). Return correlations among commodities in different sectors have also been found to be

small (Erb and Harvey, 2006). Commodity markets have become more integrated in

traditional markets. Return correlations between commodities and other assets such as stocks

and bonds have increased recently, as have return correlations between crude oil and other

commodities (Tang and Xiong, 2012; Silvennoinen and Thorp, 2013). As a result, time-

varying correlations in commodity markets are becoming an important issue Rotemberg

(1990).

This finding complements the finding of an increasing trend in the correlation of

commodities with crude oil and other traditional assets by Tang and Xiong (2012) and

Silvennoinen and Thorp (2013), since they examine only monotonic trends. ), who show a

larger increase in correlations for indexed commodities than for off-indexed commodities.

Dhankar and Boora (1996) found that optimal capital structure in Indian companies, both at

the macro and micro level affects the value of a company Germany being relatively less

levered. Sarkar & Goswami (2011) made an attempt to throw some light on the business risk,

financial risk, financial break-even point and total risk of Hindustan Construction Company

Ltd.

A Study on Testing Performance of Nifty Commodity index constituent companies in India Using

Carhart Four Factor Model: Empirical Analysis


It represents a significant business opportunity, economic gains are often distributed

unevenly and unwisely, providing short-term gains to few beneficiaries, at the expenses of a

more sustainable economic growth and improvement of social conditions. These strategies

could promote balanced development and attract investment, while at the same time enabling

regional and local actors to participate in the development of region-specific solutions to

development problems (Bachtler and Yuill, 2001) These strategies could promote balanced

development and attract investment, while at the same time enabling regional and local actors

to participate in the development of region-specific solutions to development problems

(Bachtler and Yuill, 2001) .She suggests that supply, global demand, exchange rate and real

interest rate are important factors when describing the co-movement. Comparing commodity

and equity markets Christoffersen et al. (2014) conclude that commodity market returns are

again segmented from equity markets since 2010, whereas commodity volatility shows a

nontrivial degree of market integration. Szymanowska et al. (2014) identify two types of risk

premia in commodity futures markets - the cross-section of spot premia related to the risk in

the underlying commodity and the term premia

The cross-section of stocks, bonds, and currency returns can be explained by only few

factors. Daskalaki et al. (2014) deviate from the standard procedure in the equity pricing

literature and use individual commodity futures instead of portfolios. Jagannathan (1985) and

De Roon & Szymanowska (2010) show different results when applying the Consumption

Capital Asset Pricing Model (CAPM). Etula (2013) and Basu & Miffre (2013) find macro

factors like the real interest rate, foreign exchange variables, and hedging pressure to affect

the pricing of commodities.

There are several ways to hedge the counterparty risk, for example, hedging of

counterparty risk with the credit charge (called a credit valuation adjustment. Gianakopulos

(1996) showed that the collateral agreement improves Pareto-efficiency in the asset market

with the default and increases the asset price. It also demonstrated that the collateral

agreement reduces the supply of the claim (Acharya and Bisin, 2011

The importance of institutions in explaining the resource curse has received wider

acceptance (e.g., Hartford and Klein, 2005). Mehlum et al. (2006), for example, show that

better institutions can avoid the resource curse, but they admit that natural resources can affect

institutional quality. Therefore, as Lederman and Maloney (2008) point out, the cross-section

econometric evidence remains weak, with results changing depending on the resource proxies

that are used. Moreover, a rare panel study by Manzano and Rigobon (2006) dismisses the

curse by controlling for fixed effects. The idea is that scarcity of resources, along with

pollution, can be overcome through technological progress.

The capital asset pricing model, and Schwert (1983) provides a survey of size-related

deviations of average returns from those predicted by the capital asset pricing model.Thus, the

capital asset pricing model (CAPM) of Sharpe (1964) and Lintner (1965) which identifies

sensitivity to the return on the market portfolio as the only common factor which determines

expected returns. The second benchmark is the three-factor model of Fama and French (1993)

which argues that portfolios constructed to mimic risk factors related to size—proxied by

market equity (ME)—and value—proxied by the ratio of book-to-market equity (BE/ME)—

should substantially add to the explanatory ability of the CAPM market beta:

The starting point for our analysis is the three-factor model of Fama and French (1993)

and its four-factor extension of Carhart (1997). (SMB, small-minus-big)—measured by the

difference between the returns on small stocks and the returns on big stocks and one related to

the value effect (HML, high-minus-low)—proxied by the difference in returns on value and

growth stocks. A momentum factor (WML, winner-minus-loser)—computed as the difference

in returns on winners and losers.



The selection of India particular for two reasons. First, India is the largest emerging

company with huge natural resources. Second, There was no specific work has been carried

out on the performance of Testing Performance of Nifty commodity index constituent

companies in India Using Carhart Four Factor Model in India. Most of the previous studies

undertook almost U.S. context. Study relating to commodity sector index is still remains

unexplored especially in Indian context.

RESEARCH OBJECTIVE:

1. Examine the risk and return characteristics of Nifty commodity index constituent

companies in India.

2. Examine the impact expected size premium on equity premium of Nifty – commodity

index constituent companies in India.

3. Examine the impact expected value premium on equity premium of Nifty –

commodity index constituent companies in India.

4. Examine the impact expected momentum factor on equity premium of Nifty –


RESEARCH HYPOTHESIS:

H0: There is no significant effect of size premium on equity premium of Nifty commodity


Ha: There is a significant effect of size premium on equity premium of Nifty commodity


H0: There is no significant effect of value premium on equity premium of Nifty


Ha: There is a significant effect of value premium on equity premium of Nifty commodity


H0: There is no significant effect of momentum factor on equity premium of Nifty


Ha: There is a significant effect of momentum factor on equity factor of Nifty commodity


H0: There is no significant effect of market premium on equity premium of Nifty


Ha: There is a significant effect of market premium on equity factor of Nifty commodity


3. RESEARCH METHODOLOGY:

Based on the extant literature, the following model was proposed to measure the impact of

carhart four factors on the expected equity return premium of Nifty – commodity index

constituent companies in India.

Ri(t) – RF(t) = α + β1 [RM(t) – RF(t)] + β2 SMB(t) + β3 HML(t) + β4 WML(t) + β5 (t),

Where

Ri(t) = The return on asset i for month t.

RF(t) = The risk free rate.

SMB = (small-minus-big) measured by the difference between the returns on small stocks

and the returns on big stocks




HML = (High-minus-low) proxied by the difference in returns on value and growth

stocks.

WML (momentum factor) = (winner-minus-loser) computed as the difference in returns

on winners and losers.

RM-RF= Market risk premium – proxied by the difference in returns on index returns and

risk free return.

4. DATA ANALYSIS:

A multiple regression was applied to check the impact of size premium, value premium, and

momentum factor on equity premium. As t-value is more than 1.96, it shows that we have

enough evidence to reject null hypothesis .for the above analysis we used turbin watson test in

order to defect the presence of auto correlation among the variables .as per the above analysis

turbin watson stastics is between one and four. It means the presence of autocorrelation not

exist among the variables. All independent variables used in the analysis are significant as t-

value is more than 1.96 variance explained by above regression model also significant as

adjusted R-square more than eighty percent for sample taken.

Table 1 Showing Results of Stocks Comprising Nifty Commodity index Obtained Using Carhart Four

Factor Model.

SMB

(BETA)

T-

VALUE

HML

(BETA)

T-

VALUE

WML

(BETA)

T-

VALUE

RM-

RF(BETA)

T-

VALVE

DW

TEST

A C C Ltd. -1.569 3.279 0.438 3.175 -1.681 3.227 2.447 3.227 3.279

Ambuja

Cements Ltd. -1.689 3.529 0.335 2.270 -1.674 2.900 1.730 2.900 3.529

Bharat

Petroleum

Corpn. Ltd.

-1.257 3.846 0.232 1.936 -1.892 2.891 2.426 2.891 3.846

C E S C Ltd. -1.369 3.435 0.129 2.937 -1.889 3.186 2.795 3.186 3.435

Coal India

Ltd. -2.135 1.967 0.025 3.809 -1.618 2.888 1.982 2.888 1.967

Grasim

Industries

Ltd.

-2.149 3.292 -0.078 4.054 -1.655 3.673 2.463 3.673 3.292

Hindalco

Industries

Ltd.

-2.129 3.837 -0.181 5.059 -1.705 4.448 2.506 4.448 3.837

Hindustan

Petroleum

Corpn. Ltd.

-2.254 3.229 -0.284 3.282 -1.696 3.256 1.674 3.256 3.229

Hindustan

Zinc Ltd. -2.378 2.194 -0.387 3.013 -1.687 2.604 2.334 2.604 2.194

Indian Oil

Corpn. Ltd. -2.502 2.767 -0.491 4.529 -1.679 3.648 2.261 3.648 3.279

J S W

Energy Ltd. -1.569 2.790 -0.594 2.819 -2.102 2.805 2.228 2.805 2.228

Jindal Steel

& Power

Ltd.

-1.689 2.780 -0.697 1.990 -2.095 2.385 2.015 2.385 2.015



N H P C Ltd. -1.257 1.969 -0.800 1.980 -2.314 1.975 1.856 1.975 2.819

N M D C

Ltd. -1.369 3.083 -0.903 2.443 -2.310 2.763 1.889 2.763 1.990

N T P C Ltd. -1.213 3.974 -1.007 3.142 -2.416 3.558 2.106 3.558 1.980

Oil &

Natural Gas

Corpn. Ltd.

-1.110 3.142 -1.110 3.974 -2.500 3.558 1.915 3.558 2.443

Oil India

Ltd. -1.007 2.443 -1.213 3.083 -2.416 2.763 1.752 2.763 3.142

Pidilite

Industries

Ltd.

-0.903 1.980 -1.369 1.969 -2.310 1.975 2.080 1.975 3.974

Ramco

Cements Ltd. -0.800 1.990 -1.257 2.780 -2.314 2.385 2.361 2.385 3.083

Reliance

Industries

Ltd.

-0.697 2.819 -1.689 2.790 -2.095 2.805 1.847 2.805 1.969

Reliance

Infrastructure

Ltd.

-0.594 4.529 -1.569 2.767 -2.102 3.648 1.863 3.648 2.780

Reliance

Power Ltd. -0.491 3.013 -2.502 2.194 -1.679 2.604 2.229 2.604 2.229

Shree

Cement Ltd. -0.387 3.282 -2.378 3.229 -1.687 3.256 2.174 3.256 2.767

Tata

Chemicals

Ltd.

-0.181 4.054 -2.129 3.292 -1.705 3.673 1.758 3.673 2.194

Tata Power

Co. Ltd. -0.078 3.809 -2.149 1.967 -1.655 2.888 2.584 2.888 3.229

Tata Steel

Ltd. 0.025 2.937 -2.135 3.435 -1.618 3.186 2.230 3.186 3.292

U P L Ltd. 0.129 1.936 -1.369 3.846 -1.889 2.891 1.889 2.891 1.967

Ultratech

Cement Ltd. 0.232 2.270 -1.257 3.529 -1.892 2.900 2.492 2.900 3.435

Vedanta Ltd. 0.335 3.175 -1.689 3.279 -1.674 3.227 2.112 3.227 3.846

Source: Data collected from C.M.I.E.- Prowess database

ACC Ltd Ambuja Cements Ltd




Bharath Petroleum Ltd Hindustan Petroleum Ltd

Indian Oil Ltd Oil India Ltd

Ramco Cements Ltd. Reliance Industries Ltd

Tata Chemicals Ltd. Tata Power Co. Ltd.



Reliance Industries Ltd. Reliance Infrastructure Ltd

Reliance Power Ltd. Tata Steel Ltd

Ultratech Cement Ltd.

5. FINDINGS:

1. By using R – statistical programming language, through linear regression analysis it is

found that the commodity index stock stocks have the highest volatility due to trading

volume of stocks above their intrinsic value and supply and demand issues.

2. According to regression analysis results, it was found that there is an inverse

association between expected size premium and equity premium of Nifty commodity






association between expected value premium and equity premium of Nifty commodity



association between expected momentum factor and equity premium of Nifty


5. According to regression analysis results, it was found that there is an association

between market premium on equity factor of Nifty commodity index constituent

companies in India.

6. It was clear that most of the commodities are very sensitive to the global macro-

economic variables. Economic, political, social, climatic conditions could influence

the prices of commodities. Carhart model clearly revels that huge uncertainty

associated with demand and supply conditions of inventory that leads to highest

volatility of commodity stocks.

6. CONCLUSION:

The paper evaluates long run performance of all 30 nifty commodity index constituent

companies from 2006 to 2016. Market risk premium, size premium, value premium and

momentum factor impact on the returns generated by the commodity stocks. It was found that

no single commodity stock was able to generate positive excess return over CNX Nifty Index.

It reveals that commodity stocks fail to generate surplus returns having low volatility as

compared with market. The year 2009 and 2010 witnessed and unprecedented commodity

inflation. This adverse economic scenario had an adverse impact on profitability market

return. Thus the present study suggests that the investor should be careful while investing in

this stock for the long term due to huge volatility prevailing in the market.

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