Download Investigating Stock Market Indices of India - Empirical Analysis

Volume 10, Number 2, Fall 2015
29
Investigating Stock Market Indices of India - Empirical
Analysis
Jaspal Singh
Department of Commerce
Guru Nanak Dev University, Amritsar, India
Sidharath Seth
Department of Commerce
Guru Nanak Dev University, Amritsar, India
Abstract
Purpose: This paper examines the long run performance of all the broad market stock indexes
operational at NSE (National Stock Exchange, India) to determine the best performing index
amongst them, thereby, recommending the best index for a passive investor to invest in for long
term.
Design/methodology/approach: The performance of all broad market stock indexes has been
evaluated from 1stJanuary 2004 till 31st March 2014 using daily total return index values. The
annualized return, annualized standard deviation, Sharpe ratio, Jensen alpha and Cahart four factor
alpha have been used for performance evaluation.
Findings: CNX Nifty, the flagship index of NSE, is successful in generating superior return over
risk free rate, while, CNX Nifty Junior was found to be most profitable due to its highest
annualized return. CNX Midcap was found to be least risky. On risk adjusted basis, using CAPM
model, CNX Midcap was found to be most profitable. So, an investor planning to invest in broad
based indexes, should consider CNX Midcap Index for investing. The positive Jensen alpha has
been reduced to a negative Carhart alpha, when exposed to additional variables (i.e. size, value
and momentum), showing that the excess returns evidenced by positive Jensen alpha were
attributed to these factors and not due to superior index composition criteria.
Research implications: The results of the test of joint hypothesis using CAPM revealed that CNX
Nifty cannot be used to replicate most of the other broad market indexes namely CNX 200, CNX
500, CNX Midcap, CNX Small cap and CNX 100 Equal Weight.
Practical implications: It shall help a passive investor in choosing the best index for investment.
Originality/value: To the authors’ knowledge, this is the first study that comprehensively
investigates performance of all the broad market stock indexes at national stock exchange, India.
Keywords Index, National Stock Exchange, India, Passive Investor, Statistical Significance,
Economic Significance.
Article Classification Research paper
Introduction
Charles Dow is credited to have conceived the idea of the first stock index i.e. Dow Jones Industrial
Average in 1896. Since then, all stock exchanges across the globe have built their own indexes.
While they are widely used by the investors to know overall daily market performance, fund
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
30
managers use them as a benchmark in evaluating their periodical performance. Studies have found
that mutual fund managers fail to beat their respective benchmark index (Jensen, 1968; Malkiel,
1995; Gruber, 1996; Carhart, 1997; Daniel et al., 1997 and Chevalier and Ellison, 1999). These
findings encourage the passive mode of investment through index funds, where money is invested
in the same proportion among the securities representing the underlying index. Bogle is credited
to have founded the Vanguard 500 Index Fund as the first index fund in 1975. He popularized the
index funds as a better instrument for the long term investment over traditional mutual funds owing
to lower management costs and their perpetual growth with economy (Bogle, 2000). Due to its
attractiveness towards investment, the total worldwide assets under internal indexed management
has escalated to US$5.994 trillion as of June 30, 2011 (Olsen, 2011) while global ETF market
stood at about US$1.4 trillion at the end of 2011 (BlackRock, 2011).
At the end of September 2014, there were 43 index funds and 38 exchange-traded funds
(ETFs) in India (listed on the National Stock Exchange and the Bombay Stock Exchange), of which
24 were index-based ETFs and 14 were gold-based ETFs (ISMR, 2014). Given the variety of
indexes and index funds in India, there are numerous queries in the mind of a passive investor like
how the indexes have performed in the past and which index is the most profitable to invest with.
The present study aims at examining the long run performance of all the broad market stock
indexes operational at NSE (National Stock Exchange, India) and evaluating the best
performing index amongst them which could be recommended to the passive investor to
invest with over a long term.
The paper is organized in the following manner: Section II briefly explains about various
broad market indexes at national stock exchange, India followed by the discussion on the previous
literature in Section III, then, Section IV explains the data analyzed and the methodology adopted.
Section V deals with the discussion of the observed results and finally, Section VI concludes this
paper.
About the indexes at NSE, India
Among the recognized stock exchanges in India, NSE (National Stock Exchange) is the largest
stock exchange with maximum daily turnover in cash and derivatives segment followed by BSE
(Bombay Stock Exchange) (ISMR, 2014). As on 30 June 2014, there are 36 indexes at NSE,
maintained and managed by IISL (India Index Service Ltd, a subsidiary of NSE). They are broadly
classified by NSE into four categories, namely Broad market, Sectoral, Thematic and Strategy
indexes. This paper focuses solely on the Broad market indexes consisting of the large, liquid
stocks listed on the exchange. They serve as a benchmark for measuring the performance of the
stocks or portfolios such as mutual fund investments. Stocks are broadly classified into large cap,
mid cap and small cap categories based on their market capitalization. Based on this classification,
there are five large cap indexes (CNX Nifty, CNX Nifty Junior, CNX 100, CNX 200 and CNX
500), two mid cap indexes (CNX Midcap 50 and CNX Midcap) and one small cap index (CNX
Small cap) as listed in appendix I.
Review of Literature
A limited amount of research has been done comparing various indexes across the globe. In the
context with American markets, Statman (2000) primarily compared the Domini social index
(DSI) with S&P 500 from May 1990 to September 1998 but failed to find any risk adjusted superior
returns by the former. Later, Hakim and Rashidian (2002) examined risk-return characteristics of
Dow Jones Islamic Stock Market Indexes (DJIM) with Wilshire 5000 stock market index from
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
31
1999 to 2002 and found that they failed to generate any excess returns over three month T-bill.
Also, return and risk of Islamic index was found to be less than Wilshire 5000. Thereafter, Hussein
(2005) found that Financial Time Stock Exchange (FTSE) Global Islamic index and Dow Jones
Islamic Market Index performed in similar manner but behaved significantly different from their
common index for the period January 1996 to December 2004. Later in the same year, using Dow
Jones index and its Islamic version, Hussein and Omran (2005) concluded that the Islamic index
outperformed the non-Islamic index both over the entire period from 1995 to 2003 and the bull
period, while the vice-versa was true for the bear period. Further, Chan et. al. (2009) found
negative and statistically significant alpha for Russell 2000 growth index while assessing merits
of popular performance evaluation procedures adopted by academicians to a sample of active
money managers and passive indexes. Finally, in an extensive work on indexes and pricing models,
Cremers et. al. (2013) concluded that standard fama-french and cahart models produce
economically and statistically significant non-zero alpha for passive benchmark indexes like S&P
500 and Russell 2000.
In context to Malaysian stock market, we came across only two studies namely Ahmad and
Ibrahim (2002) and Albaity and Ahmad (2008) who compared performance of KLSI with KLCI
over different time periods and found no statistically significant difference in their mean returns.
In the global context, using the data from July 1996 till August 2003, Hussein (2004) tested
the performance of FTSE Global Islamic Index with their index counterpart (FTSE All- World
Index) and found that while Islamic index yielded statistically significant positive abnormal returns
in bull market period (July 1996 –March 2000), it underperformed in bear market period (April
2000 - August 2003). On analyzing social responsible investing across the globe, Schroeder (2007)
compared performance characteristics of SRI equity indices with benchmark indices and found no
statistical difference between them.
In Indian context, the authors came across only two studies on indices, one by Narasimhan
and Balasubramanian (1999) and the other by Dharani and Natarajan (2011). While Narasimhan
and Balasubramanian (1999) compared risk-return characteristics of only three indexes namely
Sensex, Natex and BSE 200 using mean difference test and variance difference test and found
statistically insignificant difference in the risk-return characteristics of these indices, Dharani and
Natarajan (2011) compared risk and return of only two indexes i.e. Nifty Shariah index and Nifty
index for four years from 2nd January 2007 to 31st December 2010. They tested the difference in
the mean returns of both the indices using t- test and found that there was no statistical difference
between average daily returns of both the indexes. However, there existed significant difference
between average return of both the indexes in the month of July and September.
Thus, it can be seen that very limited research is done on performance of indexes in the Indian
context. At the international level, while, majority of the studies found that there is insignificant
difference in the returns of indices, however, only two studies namely, Chan et. al. (2009) and
Cremers et. al. (2013) exclusively concluded that few indices show statistically significant
different performance from others. Compared to the above mentioned studies on Indices, our study
extends the research in two aspects. Firstly, we analyze performance characteristics of all the broad
market stock indexes operational at national stock exchange, India using total return index values,
and secondly, the study is done for a longer period i.e. from 1st January 2004 till 31st march 2014.
Database and Methodology
Out of 36 indexes operational at NSE, 10 are broad market stock indexes that have been considered
for our analysis (the other two are India VIX, a volatility index, and Nifty Dividend, a running
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
32
total of dividend points of the securities forming part of CNX Nifty Index). However, each index
has a different base date (as shown in appendix I). As a result, distinct time period is available for
calculating performance evaluation parameters, thereby rendering them to be exposed to dissimilar
market conditions. To illustrate, CNX Nifty Junior will be evaluated for 17.5 years (4th November
1996 till 31st March 2014), witnessing two bull and two bear periods but CNX Smallcap will be
evaluated for only 10.25 years (1st January 2004 till 31st March 2014), observing only one bull and
one bear period . Considering this disparity in evaluation due to varied time periods, a common
period representing one complete business cycle of stock market that includes a bear and a bull
run, has been selected from 1st January 2004 till 31st March 2014 for making meaningful
comparison of performance. Out of 10 broad based stock indexes, it was found that 9 stock indexes
have base date prior to 2nd January 2004. Hence, these are 9 stock indexes are analyzed using total
returns index values from a common date i.e. 1st January 2004 till 31st March 2014 (covering
complete cycle comprising one bull and bear period), after being exposed to similar market
conditions. The total return index values reflect the returns arising from dividend receipts and price
movement of the constituent stocks. The total return index values of all indexes is taken from
bloomberg database. The implicit yield of 91-day treasury bills of Government of India is taken
as a proxy of risk free rate (Connors and Sehgal, 2001). The data of risk free rates is taken from
the website of Reserve Bank of India (www.rbi.gov.in). CNX Nifty is taken as market proxy. The
daily data on factor portfolio excess returns for Indian stock market was obtained from the website
of IIM Ahmedabad (http://www.iimahd.ernet.in/~iffm/Indian-Fama-French-Momentum/ accessed
on 30th January 2015) (Agarwalla et al., 2013). The performance of indexes is evaluated using
annualized return, annualized standard deviation, Sharpe ratio, Jensen alpha and Cahart four factor
alpha.
The annualized return is the annual compounded return earned by an investor over a period
by investing in an asset. It is useful in a way that for comparing returns over different lengths of
time, the returns are rescaled to one year. It is calculated as follows:
R=
1 X100
(I)
Where: R= Annualized Return (expressed as percentage), Xt =Terminal Value, Xo =Initial Value,
t= Number of years.
Annualized Standard Deviation is a measure of volatility. An index with high annualized
standard deviation is considered more volatile and hence, more risky. It is calculated as follows:
X√
(II)
Where: σA = Annualized Standard Deviation, σd = Standard Deviation computed using daily
returns, T= Number of trading days in a year
The Sharpe Ratio measures the risk premium return earned per unit of total risk. It is calculated
by dividing the excess of average daily portfolio rate of return over average daily risk free rate
with the standard deviation of excess average daily portfolio returns. It is stated as follows:
(III)
Where: Si=Sharpe ratio for a portfolio, = Mean return on the portfolio,
= Mean return on
91-day RBI Treasury bills (proxy for risk-free rate of interest),
= Standard deviation
of the excess average daily portfolio returns.
The Sharpe ratio shows the excess return earned by an investor for per unit of variability, they are
exposed to by holding a riskier asset. A portfolio with highest positive Sharpe ratio is considered
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
33
best for investment while the one having negative Sharpe ratio indicates that it failed to generate
any superior return over risk free rate.
Jensen alpha is a risk-adjusted measure of fund managers’ performance that measures the excess
return on a portfolio over the expected returns as predicted by the capital asset pricing model
(CAPM). Jensen alpha is stated as follows:
(V)
= Mean Return of CNX Nifty
Where: = Jensen Alpha,
= Mean return on the portfolio,
(proxy for market),
= Mean return on 91-day RBI Treasury bills (proxy for risk-free rate of
interest), = Beta of the portfolio.
The null hypotheses tested are as follows:
H0 = An index does not generate significant excess return than CNX Nifty i.e.
α=0
H0= There is no significant difference in the relative risk of the particular index and CNX
NIFTY i.e.
β=1
H0= The particular index can be replicated by CNX Nifty, using joint hypothesis i.e.
α = 0 and β = 1
If a stock/portfolio/fund generates a better return than its beta would predict, it has a positive
Jensen Alpha, and if it returns less than the amount predicted by beta, it has a negative Jensen
Alpha. An investment manager yields a statistically significant positive Jensen alpha, if he has a
superior stock picking or market timing ability in excess of the benchmark. Similarly, a portfolio
whose beta is more than 1 is considered more volatile and hence, more risky than the market. On
the contrary, a portfolio with beta less than 1 is considered less risky than the market. Also, the
joint hypothesis H0: (α = 0 and β = 1) is tested to check if a portfolio can be replicated by the
benchmark index. If the null hypothesis is not rejected, then investing in the benchmark index, on
average, is equivalent to investing in the portfolio, without any significant difference in return or
risk.
Cahart Four Factor Alpha measures the excess return on a portfolio over the expected
returns as predicted by Cahart four factor model. It is an improvement over CAPM model as it
includes more explanatory variables. According to this model, the excess portfolio returns are
explained by controlling market-wide four risk factors i.e. market, size, value and momentum. The
alpha obtained using Cahart four factor model is stated as follows:
S
M
α β
H
= Mean return on the portfolio,
= Mean Return of CNX
Where: = Four factor Alpha,
Nifty (proxy for market),
= Mean return on 91-day RBI Treasury bills (proxy for risk-free
rate of interest),
= excess daily market premium,
= daily premium of smallcap
stocks over large cap stocks,
= daily premium of high book-to-market stocks over low bookto-market stocks,
= daily premium of one year winners vs losers.
The Cahart four factor alpha is excess return over what was predicted in the Cahart four factor
model. The Carhart model shows that alpha is attributed to investing in small and value companies
with price momentum. The positive Jensen alpha may turn into reduced Carhart alpha, when
exposed to these additional variables (i.e. size, value and momentum), showing that the excess
returns evidenced by positive Jensen alpha were attributed to these factors, not due to the
manager’s skill, whereas, the fund managers are paid for generating excess alpha.
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
34
Data Analysis
Table I reports the annualized return, annualized standard deviation and Sharpe ratio of each index,
calculated from 1st January 2004 till 31st march 2014. Among all the indexes, CNX NIFTY Junior
was most profitable by yielding highest annualized return (15.146), followed by CNX Nifty
(14.702). CNX Midcap was found to be least risky due to its lowest annualized standard deviation
(24.60) followed by CNX 500 (24.61). All the stock indexes were found to generate positive
Sharpe ratio, showing that they were successful in generating excess returns over risk free rate.
Table I: Showing Annualized Return, Annualized Standard Deviation and Sharpe ratio
of Broad Market Stock Indexes calculated using daily total return index values from 1st
January 2004 till 31st March 2014.
Indices
Annualized Return
Annualized
Standard
Deviation
Sharpe Ratio
CNX Nifty
14.70208
25.73448
0.017459
CNX Nifty Junior
15.14624
28.1019
0.016859
CNX 100
14.44888
25.67518
0.016954
CNX 200
12.93848
25.32722
0.013864
CNX 500
14.2378
24.94918
0.016978
CNX Midcap
14.56543
24.60988
0.017948
Nifty Midcap 50
10.26899
29.25636
0.006824
CNX Smallcap
13.96013
25.10716
0.016256
CNX100 Equal Weight
13.46556
25.33171
0.015026
The table II compares the performance of each broad market index with CNX NIFTY using
capital asset pricing model (CAPM). The results are estimated using ordinary least squares method.
The variance-covariance matrix of residuals is corrected for autocorrelation and heteroscedasticity
using the Newey and West (1987) approach. It was found that none of broad market indexes
generated statistically superior risk adjusted excess return over CNX Nifty. On considering
economic significance, only three indexes yielded positive Jensen Alpha. Among them, CNX
Midcap (0.00442) outperformed, followed by CNX Smallcap (0.00305) and CNX Nifty Junior
(0.00249).
Table II: Showing results of Broad Market Stock Indexes calculated using Capital Asset
Pricing Model (CAPM) from 1st January 2004 till 31st March 2014.
Indices
CNX Nifty
CNX
Nifty
Junior
Jensen
Alpha
Sig Value
Beta
Sig Value
(H0 : β =
1)
Test of Joint
Hypothesis H0 : (α =
0 and β =1)
1
0.00249
0.88
0.97
0.05**
Oxford Journal: An International Journal of Business & Economics
0.15
Volume 10, Number 2, Fall 2015
CNX 100
CNX 200
CNX 500
CNX Midcap
Nifty Midcap
50
CNX Smallcap
CNX100 Equal
Weight
35
-0.00074
-0.00543
-0.00033
0.00442
0.76
0.27
0.95
0.78
0.99
0.97
0.95
0.83
0.04**
0.00**
0.00**
0.00**
0.09*
0.00**
0.00**
0.00**
-0.01531
0.00305
0.41
0.87
0.98
0.80
0.41
0.00**
0.43
0.00**
-0.00249
0.80
0.93
0**
0.00**
The table reports the alpha coefficients and their significance value, obtained in capital asset pricing
model, computed using OLS regression. Autocorrelation and heteroscedasticity-consistent standard errors
are used to compute the significance values.
*Statistically Significant at 10% level of significance.
**Statistically Significant at 5% level of significance.
The null hypothesis, β = 1, used to test that there is no statistically significant difference in
the relative risk of each broad market index and CNX NIFTY is rejected for all indexes except
CNX Midcap 50, as the significance value of Beta for this index is more than 0.05. It shows that
these indexes are significantly lesser risky than CNX Nifty.
The sixth column of table II shows results of the test of joint hypothesis H0: (α = 0 and β =1).
The null hypothesis is rejected at 5 per cent level in case of five indexes namely CNX 200, CNX
500, CNX Midcap, CNX Small cap and CNX 100 Equal Weight. However, in the context of CNX
100, the null hypothesis is rejected at 10 per cent level. It means that CNX Nifty cannot be used to
replicate these indexes since, on average they do not have similar risk and return characteristics as
of CNX nifty.
After evaluating the performance of broad market indexes using CAPM, it was intended to
check whether the factors like size, value and momentum create any difference among the
performance of these indexes because they have been built using different sample sizes and type
of stocks. For this purpose, Cahart four factor model was used. The results of which are captured
in Table III. It shows that the alpha (captured through Cahart model) of all the indexes is negative,
meaning that superior return, if any, was due to value, size and momentum factors. While size and
value factors were found to play significant positive role in their performance, momentum
impacted negatively. The numerical value of the value factor is found highest for CNX Midcap,
CNX Midcap 50 and CNX Small cap indexes, showing that midcap and small cap stocks tend to
rise with the increase in the return of value stocks in the market. Also, the numerical value of size
factor is found highest for CNX Small cap index. However, the null hypothesis, β = 1, remains
rejected for only two indexes i.e. CNX Midcap and CNX 100 equal weight, at 5 per cent level,
meaning thereby that only these two indexes remain significantly lesser risky than CNX NIFTY.
Table III: Showing results of Broad Market Stock Indexes calculated using Cahart
Four Factor Model from 1st January 2004 till 31st March 2014
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
Indices
Alpha
36
Sig
Market
factor
Sig
Size
Sig
(H0 :
β = 1)
Value
Sig
Momentum
CNX Nifty
CNX Nifty Junior
-0.0055
0.73
1.021
0.26 0.180
0**
0.219 0**
-0.082
CNX 100
-0.0019
0.41
1.003
0.35 0.026
0**
0.032 0**
-0.011
CNX 200
-0.0094 0.04**
0.997
0.65 0.076
0**
0.072 0**
-0.009
CNX 500
-0.005
0.31
0.990
0.14 0.113
0**
0.085 0**
-0.018
CNX Midcap
-0.0093
0.49
0.923
0.00** 0.301
0**
0.286 0**
-0.069
Nifty Midcap 50
-0.0262
0.11
1.065
0.11 0.281
0**
0.346 0**
-0.156
CNX Smallcap
-0.0138
0.30
0.978
0.23 0.594
0**
0.306 0**
-0.119
CNX100 Equal
Weight
-0.0030
0.73
0.959
0**
0.107
0**
0.181 0**
-0.155
The table reports the alpha coefficients and their significance value, obtained in cahart four factor
model, computed using OLS regression. Autocorrelation and heteroscedasticity-consistent standard errors
are used to compute the significance values.
*Statistically Significant at 10% level of significance.
**Statistically Significant at 5% level of significance.
Findings and Conclusion
The study examines the long run performance of all the broad market stock indexes, currently
operational at NSE (National Stock Exchange, India) from 1st January 2004 till 31st March 2014.
It is found that CNX Nifty, the flagship index of NSE, along with all other broad market indexes,
is successful in generating superior return over risk free rate proving that Indian equity markets
tend to generate better returns as compared to 91-day treasury bills of Government of India. On
using CAPM, none of them were able to generate statistically superior risk adjusted excess return
over CNX Nifty. However, on considering economic significance, only three indexes yielded
positive Jensen Alpha, namely CNX Midcap, CNX Smallcap and CNX Nifty Junior. Thus, CNX
Midcap proves to be the best performer among all broad stock indexes after adjusting for risk. So,
a passive investor, planning to invest in broad based indexes, should consider CNX Midcap index
for investment. But the alpha (Jensen alpha) of these broad market indexes turns negative (Cahart
alpha) on being exposed to additional factors i.e. size, value and momentum in Cahart four factor
model. This shows that the excess returns evidenced by positive Jensen alpha were attributed to
these factors and not due to superior index composition criteria. Moreover, the results of the test
of joint hypothesis using CAPM revealed that CNX Nifty cannot be used to replicate most of the
other broad market indexes namely CNX 200, CNX 500, CNX Midcap, CNX Small cap and CNX
100 Equal Weight.
References
Agarwalla, S., Jacob, J. and Varma, J. (2013), “Four factor model in Indian equities market”,
Working Paper No. 2013-09-05, Indian Institute of Management, Ahmedabad (5
September).
Ahmad, Z., and Ibrahim, H. (2002), “A study of the performance of the KLSE Syari'ah index”,
Malaysian Management Journal, Vol. 6 No. 1, pp. 25–34.
Oxford Journal: An International Journal of Business & Economics
Sig
0.00**
0.01**
0.32
0.06*
0.01**
0.00**
0.00**
0.00**
Volume 10, Number 2, Fall 2015
37
Albaity, M. and Ahmad, R. (2008), “Performance of Syariah and Composite Indices: Evidence
from Bursa Malaysia”, Asian Academy of Management Journal of Accounting and
Finance. Vol. 4 No. 1, pp. 23–43.
Amenc, N., Goltz, F. and Sourd, V. (2006), Assessing the Quality of Stock Market Indices:
Requirements for Asset Allocation and Performance Measurement, EDHEC Risk & Asset
Management Research Centre, Paris.
Amenc, N., Goltz, F. and Martellini, L. (2011) “Improved Beta? Comparing Index Weighting
Methods” Journal of Indexes, Vol.14 No.1, pp.10-29.
Ang, A., Hodrick, R., Xing, Y. and Zhang, X. (2006),“The crosssection of volatility and expected
returns”, Journal of Finance, Vol. 61 No. 1, pp. 259-299.
Arnott, D., Hsu, J. and West, J. (2008), The Fundamental Index—A Better Way to Invest, Wiley,
New Jersey, NJ.
Blackrock (2011), ETF Landscape, Industry Review, BlackRock Advisor, UK.
Blitz, D. and Vliet, P. (2007), “The Volatility Effect: Lower Risk Without Lower Return.” Journal
of Portfolio Management, Vol. 34 No.1, pp. 102-113.
Bogle, J. (2000), John Bogle on Investing: The First 50 Years, McGraw-Hill Companies, New
York, NY.
Bolognesi, E., Torluccio, G. and Zuccheri, A. (2013), “A comparison between capitalizationweighted and equally weighted indexes in the European equity market”, Journal of Asset
Management, Vol. 14, No.1, pp. 14-26.
Carhart, M. (1997), “On persistence in mutual fund performance”, Journal of Finance, Vol. 52 No.
1, pp. 57-82.
Chan, K.C., Dimmock, S.G. and Lakonishok, J. (2009), “Benchmarking Money Manager
Performance: Issues and Evidence”, The Review of Financial Studies, Vol. 22 No. 11, pp.
4553-4599.
Chevalier, J. and Ellison, G. (1999), “Are some mutual fund managers better than others? Crosssectional patterns in behavior and performance”, Journal of Finance, Vol. 54 No. 3, pp.
875-899.
Chou, P., Li, W.S. and Zhou, G. (2006), “Portfolio optimization under asset pricing anomalies”,
Japan and the World Economy, Vol. 18 No. 2, pp. 121-142.
Connor, G. and Sehgal, S. (2001 ), “Tests of the Fama and French model in India” Discussion
paper, 379.Financial Markets Group, London School of Economics and Political Science,
London, UK.
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
38
Cremers, M., Petajisto, A. and Zitzewitz, E. (2013), “Should Benchmark Indices Have Alpha?
Revisiting Performance Evaluation”, Critical Finance Review, Vol. 2 No. 1, pp. 1-48.
Daniel, K., Grinblatt, M., Titman, S. and Wermers, R. (1997), “Measuring Mutual Fund
Performance with Characteristic-Based Benchmarks”, Journal of Finance, Vol. 52 No. 3,
pp. 1035-1058.
Daniel, K. and Titman, S. (1997), “Evidence of characteristics of cross sectional variation in stock
returns”, Journal of Finance, Vol. 52 No. 1, pp. 1-33.
Dharani, M. and Natarajan, P. (2011), “Seasonal Anomalies between S&P CNX Nifty Shariah
Index and S&P CNX Nifty Index in India”, Journal of Social and Development Sciences,
Vol. 1 No. 3, pp. 101-108.
Grinold, R. (1992), “Are benchmark portfolios efficient?” Journal of Portfolio Management, Vol.
19 No. 1, pp. 34-40.
Gruber, M. (1996), “Another Puzzle: the Growth in Actively Managed Mutual Funds”, Journal of
Finance, Vol. 51 No.3, pp. 783-810.
Hakim, S., & Rashidian, M. (2002), “Risk and return of Islamic stock market”, Paper presented at
the Presentation to Economic Research Forum Annual Meetings, Sharjah, UAE.
Haugen, R. and Baker, N. (1991), “The efficient market inefficiency of capitalization weighted
stock portfolios”, Journal of Portfolio Management, Vol. 17 No.3, pp. 35-40.
Hussein, K.A. (2004), “Ethical Investment: Empirical Evidence from FTSE Islamic Index”,
Islamic Economic Studies, Vol. 12 No. 1, pp. 21-40.
Hussein, K.A. (2005), “Islamic investment: Evidence from Dow Jones and FTSE indices”, Paper
presented at the International Conferences on Islamic Economics and Finance, Indonesia.
Retrieved
on
2nd
February
2015,
from
http://www.kantakji.com/media/7927/khaled_a_hussein_1.pdf.
Hussein, K.A., and Omran, M. (2005), “Ethical investment revisited: Evidence from Dow
Jones Islamic Indexes”, Journal of Investing, Vol. 14 No. 3, pp. 105–124.
ISMR (2014), Indian Securities Market A Review, National Stock Exchange of India, Mumbai.
Jensen, M. (1968), “The Performance of Mutual Funds in the Period 1945–1964.”, Journal of
Finance, Vol. 23 No.2, pp. 389–416.
Kamp, R. (2008), “Debunking 130/30 Benchmarks “Fundamentally Weighted” Long-Short
Versions of Traditional Indices Can't Work; It's Also the Wrong Approach” A Guide to
130-30 Strategies, Special Issues, Vol. 2008 No. 1, pp. 60-63.
Malkiel, G. (1995), “Returns from investing in mutual funds 1971 to 1991”, Journal of Finance,
Vol. 50 No. 2, pp. 549-572.
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
39
Narasimhan, M. S. and Balasubramanian, G. (1999), “Stock Price Indexes in India: An analysis of
Multiple Indexes and Market Performance” The ICFAI Journal of Applied Finance, Vol.
6 No. 1, pp. 63-86.
Olsen, K. (2011), “Indexed Assets Surge 25%”, Pension and Investments, available at:
http://www.pionline.com/article/20110919/PRINT/309199986/indexed-assets-surge-25
(accessed 25 November 2013).
Platen, E., R. Rendek. (2012) “Approximating the numéraire portfolio by naïve diversification.”
Journal of asset management, Vol.13 No.1 ,pp.34-50 .
Roll, R. (1977), “A critique of the asset pricing theory’s tests. Part I: On past and potential
testability of the theory”, Journal of Financial Economics, Vol. 4 No. 2, pp. 129-176.
Schroeder, M. (2007), “Is there a Difference? The Performance Characteristics of SRI Equity
Indices”, Journal of Business Finance & Accounting, Vol. 34 No. 1-2, pp. 331-348.
Sharpe, W. (1966), "Mutual Fund Performance", Journal of Business, Vol. 39, No. 1, pp. 119138.
Sharpe, W. (1975), "Adjusting for Risk in Portfolio Performance Measurement." Journal of
Portfolio Management, Vol.1, No. 2, pp. 29-34.
Sharpe, W. (1992), "Asset allocation: Management Style and Performance Measurement," Journal
of Portfolio Management, Vol. 18, No. 2, pp. 7-19.
Sharpe, W. (1994), "The Sharpe Ratio", Journal of Portfolio Management, Vol. 21 No. 1, pp. 4958.
Singh, J., & Kaur, K. (2014), “Testing Ben Graham’s Stock Selection Criteria in Indian Stock
Market”, Management and Labour Studies, Vol. 39, No.1, pp. 43-62.
Singh, J., & Kaur, K. (2014), , “Examining the profitability of value stocks in Indian Stock Market
– An empirical analysis”, Journal of Institute of Public Enterprise, Vol. 37, No. 3&4, pp.
18-31.
Statman, M. (2000), “Socially responsible mutual funds”, Financial Analysts Journal, Vol.56 No.
3, pp. 30–39.
Tamuru, H. and Shimizu, Y. (2005), “Global fundamental indices. Do they outperform marketcap weighted indices on a global basis?”, Security Analysts Journal, Vol. 43 No. 10, pp.
32-46.
Oxford Journal: An International Journal of Business & Economics
Volume 10, Number 2, Fall 2015
40
Appendix I: Different categories of Indexes at NSE and their constituents
Index Name
Base Date
Broad Market Indexes
CNX Nifty
03 Nov 1995
CNX Nifty
Junior
LX 15
04 Nov 1996
1 Jan 2009
CNX 100
1 Jan 2003
CNX 200
1 Jan 2004
CNX 500
1 Jan 1995
Nifty Midcap 50
CNX Midcap
CNX Smallcap
1 Jan 2004
1 Jan 2003
1 Jan 2004
CNX 100 Equal
Weight
1 Jan 2003
Brief Overview
It is a well diversified 50 stock index accounting for 22 sectors of the
economy.
It contains next 50 most liquid securities after CNX Nifty.
It is designed to provide exposure to 15 most liquid stocks while making
the index easily replicable and tradable.
It is a diversified 100 stock index accounting for 38 sectors of the
economy. It is a combination of the CNX Nifty and CNX Nifty Junior.
It is designed to reflect the behaviour and performance of the top 200
companies measured by free float market capitalization.
It is India’s first broad based benchmark of the Indian capital market
comprising top 500 companies.
It comprises of 50 midcap stocks.
It comprises of 100 midcap stocks.
It is designed to reflect the behaviour and performance of the small
capitalized segment of the financial market. It comprises of 100 tradable
exchange listed companies.
It comprises of same constituents as CNX 100 Index while the weightage
of each stock is fixed at 1%.
Source: Compiled from National Stock Exchange (www.nseindia.com)
Oxford Journal: An International Journal of Business & Economics