Description
The sectoral analysis quantifies the key parameters of the economy. The analysis of different sectors of economy facilitates the Government to use it as the reference guide for the formulation of economic policy.
Electronic copy available at: http://ssrn.com/abstract=1940886
ANALYSIS OF SECTORAL MARKET EFFICIENCY- A STUDY ON BANKING
SECTOR
R.Rajesh Ramkumar
1
, Dr.M.Selvam
2
and Dr.G.Indhumathi
3
Abstract
The sectoral analysis quantifies the key parameters of the economy. The analysis of
different sectors of economy facilitates the Government to use it as the reference guide for the
formulation of economic policy. The sectoral analysis is a summary that explains the economic
growth report covering different industries. The sectoral analysis of our economy focuses on the
key points of the latest reforms initiated by the Government of India. In addition, the study of
this nature, analyses the efficiency of corporate sectors in the stock market. The banking industry
is the core sector of the economy and therefore this paper tests the market efficiency across the
companies under banking sector, listed at the BSE, by using the daily closing share prices from
J an 2006 to Dec 2009. The parametric and non-parametric tests were used for analysing the
sectoral efficiency of sample banking companies and it is found that the banking sector was
efficient during the study period.
Keywords: Market Efficiency, Autocorrelation, Runs Test and Sectoral Analysis
1. Introduction
The sectoral analysis is employed by investors who are to select better stocks to invest in.
The investors identify most promising sectors and review the performance of companies within
the sector to determine which individual stock would provide better returns and ultimately, be
purchased. There are three aspects that would generally affect the performance of a company's
stock in the stock market. The first aspect is the performance of the individual company. The
second is the performance of the market as a whole. The third is the performances of the sector
1
Ph.D Research Scholar, Dept of Commerce and Financial Studies, Bharathidasan University,
Tiruchirappalli-620 024, Tamil Nadu, India, Email: [email protected] No:
+919789277094
2
Associate Professor and Head, Dept of Commerce and Financial Studies, Bharathidasan
University, Tiruchirappalli-620 024, Tamil Nadu, India, Email: [email protected], Contact
No: +919443025112
3
Assistant Professor, Dept of Commerce, Mother Teresa Women’s University, Kodaikanal-
624101, Email: [email protected], Contact No: +919884434722
Electronic copy available at: http://ssrn.com/abstract=1940886
to which the company belongs. It is a known fact that the sectors are groups of companies which
perform similar functions in the economy. The sector analysis involves the process of dividing
the total market into sectors and then studying the market performance of each sector
individually, so that each sector can be compared to other sector or to the market as a whole.
The sectoral analysis covers the market efficiency of different sectors of the economy. It
focuses on the key points of the latest reforms of economy as initiated by the Government of
India. The banking industry is one of the core sectors of the economy. Therefore, this paper tests
the market efficiency across the companies under banking sector, listed at the BSE, using daily
closing share prices during the study period.
2. Review of Literature
The following are the few existing research studies available on the sector analysis and
they are reviewed.
Mufeed Rawashdeh and J ay Squalli (2004), in their article entitled, “A Sectoral
Efficiency Analysis of the Amman Stock Exchange”, tested the market efficiency across the
four sectors, namely, banking, industrial, insurances and services in the Amman Stock Exchange
(ASE). This study used daily sectoral indexes between 1992 and 2004 using variance ratio and
runs tests. It is found from the analysis that the random walk and weak form efficiency
hypotheses were rejected for all sample sectors.
A study entitled, “A Sectoral Efficiency Analysis of Malaysian Stock Exchange under
Structural Bank”, by Chin Wen Cheog (2008), investigated the weak form market efficiency
using daily returns of nine sectoral indices in Malaysian stock market between 1996 and 2006.
The study found that the sectoral indices of Malaysian stock markets were inefficient weak-form
(except the property index).
The study entitled, “The Monetary Transmission Mechanism in Pakistan: A Sectoral
Analysis”, by Tasneem Alamand Muhammad Waheed, investigated the monetary transmission
mechanism in Pakistan at a sectoral level. Taking the structural transformation of the economy
and the monetary and financial reforms during 1990s, the researchers assessed whether the
reform process exercised notable impact on the monetary transmission mechanism. The study
found evidence to support sector-specific variation in the real effects of monetary policy. The
study also suggested significant changes in the transmission of monetary shock to real sector of
the economy during the post-reform period.
The above literature provides an overview of different models used to study the sectoral
efficiency around the world. However, there was no comprehensive study carried out in Indian
stock markets. Thus an attempt has been made in this study to evaluate market efficiency in the
Indian context by taking the models used in the above studies.
3. Statement of the Problem
The Capital Market is a vital institution as it facilitates economic development. It is true
that so many parties are interested in knowing the efficiency of the Capital Market. The retail
investors can be motivated to save and invest their savings in the Capital Market only if their
securities in the market are appropriately priced. The Random Walk Hypothesis of stock prices is
concerned with the question of whether one can predict future price from past prices. Many
studies tested the efficiency in global stock market and also tested the random walk hypotheses
for various popular indices. But in India, few studies examined the returns of thestock market
especially with reference to stock indices like S&P CNX Nifty, BSE 100 Index, Nifty J unior, etc.
It is important to note that there were no comprehensive studies carried out to test the sectoral
efficiency in the Indian context. The individual investors are not fully informed of the sectoral
efficiency in the Indian stock market. Therefore, the present study aims to investigate the
efficiency of Indian Stock Market for different sectors which were actively traded in the Bombay
Stock Exchange (BSE). This study analyses the market efficiency among the sample companies
to under banking sectors listed in the BSE.
4. Objectives of the Study
The present study was carried out to examine the market efficiency of the banking
companies listed in the BSE- Bankex.
5. Hypotheses of the Study
The present study tested the following null hypotheses
NH1: There is no normal distribution in the returns of the shares of sample banks.
NH2: There is no significant difference in the share price behavior of sample stocks.
6. Methodology of the Study
6.1 Sample Selection
For the purpose of this study, stocks of all 18 banks stocks listed in BSE Bankex were
taken as the total sample population. In the banking sector, there are totally 18 companies which
were listed on 2
nd
J anuary 2010 and these companies were taken for this study. The details of
sample companies are given in Table-1.
6.2 Sources and Collection of Data
The present study was mainly based on secondary data (banking stock daily returns
prices) which were collected from the Prowess Corporate Database. Further, the available
secondary data were collected from the Annual Reports, published research reports by banking
industry etc. In addition, other related information was collected from various books, periodicals
and websites like www.bseindia.comand www.yahoofinance.com.
6.3 Period of the Study
The present study was mainly intended to examine the sectoral efficiency (market) of
stocks of banking companies listed in BSE Bankex from 1
st
J anuary 2006 to 31
st
December 2009.
6.4 Tools Used for Analysis
In order to evaluate the sectoral efficiency, tools like Runs Test and Autocorrelation were
used.
(a) Runs Test
It is a non-parametric test used for measuring market performance. It does not require
specification of the probability distribution. It depends only on the price. They are essentially
concerned with direction of changes in price.
N
n ) 1 N ( N
M
3
1 i
2
i
?
?
? ?
?
Where,
M =Expected number of runs
n
i
=Number of price changes of each sign (i=1,2,3)
N =Total number of price changes.
(b) Autocorrelation
It is the statistical tool used for measuring the company’s successive terms in a given
time series and dependence of the successive share price changes.
?
?
?
?
?
?
?
? ?
?
n
1 t
2
t
k n
1 t
k t t
k
) R R (
) R R )( R R (
p
Where,
K =Number of lags
Rt =Real rate of returns
n =Total number of observations
P
k
=Sample autocorrelation function for the lag K
R =Mean returns
6.5 Limitations of the Study
The study suffers from thefollowing limitations
1. The study was based on secondary data, and hence it is riddled with certain limitations
which are bound to be connected with the use of secondary data.
2. This study focused only on the banking sector as it is one of the core sectors of the
economy.
3. All the limitations, associated with Runs Test and Autocorrelation Tests, are applicable to
this study also.
7. Analysis of Market Efficiency of Banking Sector’s Stocks
The analysis of market efficiency of banking stocks is arranged as follows:
7.1 Market Efficiency - Runs Test
7.2 Market Efficiency- Autocorrelation
7.1 Market Efficiency- Runs Test
Table-2 shows the analysis of Runs Test by having mean value as the base for sample
banking stocks. From the above Table, it is understood that out of 18 stocks, nine stocks in the
banking sector, namely, stocks of Allahabad Bank, Bank of India, Canara Bank, Federal Bank,
HDFC Bank, ICICI Bank, Kodak Mahindra Bank, Oriental Bank of Commerce and Yes Bank
followed the normal distribution. The Z values of these nine banks were significant under normal
distribution at 5% level. Therefore, the null hypothesis (NH1), “There is no normal distribution
in the returns of the shares of sample companies”, is not fully accepted. The remaining sample
stocks (the stocks of AXIS Bank, BOB, IDBI, IOB, Indusind Bank, Karnataka Bank, PNB, SBI,
and UBI) did not follow normal distributions as its mean values were not significant.
The results of Runs Test by having median value as the base for sample banking stocks
are given in Table-3. It is clear that 11 stocks out of 18 stocks in the banking sector followed
normal distribution. Those stocks belong to Allahabad Bank, Bank of Baroda, Bank of India,
Canara Bank, Federal Bank, HDFC Bank, ICICI Bank, Indian Overseas Bank, Karnataka Bank,
Kodak Mahindra, and Oriental Bank of Commerce. Besides, the Z values for these 11 banks
were significant under normal distribution at 5% level. According to the analysis, it is to be
noted that majority of sample banks followed normal distribution. Hence the null hypothesis
(NH1), “There is no normal distribution in the returns of the shares of sample companies”, is
rejected under median base analysis. The stocks of other banks AXIS Bank, IDBI Bank, IOB,
Indusind Bank, PNB, SBI, UBI and Yes Bank did not follow normal distribution as their values
were not significant.
7.2 Market Efficiency – Autocorrelation
Table-4 reveals the results of autocorrelation of sample banking stocks during the study
period. It is understood from the above Table that out of 18 sample banks taken for this study,
only ten banks earned significant value in all the 10 lags. Those banks are BOB, Canara Bank,
HDFC Bank, ICICI Bank, IDBI Bank, IOB, Karnataka Bank, Oriental Bank of Commerce, SBI
and Yes Bank. Further, it is to be noted that the values of these ten banks are significant at 5%.
The analysis of autocorrelation reveals the fact that there are three banks (namely AXIS Bank,
PNB and UBI) that did not earn significant value at 5% level in all the 10 lags. The analysis of
stocks of Allahabad Bank reveals that its value was significant in first 5 lags but not significant
in the subsequent 5 lags. In the case of Federal Bank, its value was significant in the first 3 lags
but not significant in the last 7 lags. The value of BOI and Kodak Mahindra banks showed
zigzagsign in its value. Finally, the remaining one stock (Indusind Bank) reached the significant
level at 1
st
lag only, but not the remaining 9 lags. Chart-1 explains the autocorrelation results for
the banking sector. From the above chart, it is found that the banks residuals exhibit a distinct
behaviour. Some of the banks were initially positive, then became negative and after that again
turned positive. This shows that the returns were not randomly distributed.
8. Findings of the Study
The following are the important findings of the study.
1. The market efficiency of banking sector was tested by Runs Test which indicated that
there was no randomness in the stock market, because the returns for all sample stocks
were not normally distributed.
2. It is found that nine banks, namely, Allahabad Bank, BOI, Canara Bank, Federal Bank,
HDFC Bank, ICICI Bank, Kodak Mahindra Bank, Oriental Bank of Commerce, and Yes
Bank followed normal distribution based on their mean values in the Runs Test.
3. Based on the median values, only 11 banks (Allahabad Bank, BOB, BOI, Canara Bank,
Federal Bank, HDFC Bank, ICICI Bank, IOB, Karnataka Bank, Kotak Mahindra Bank,
Oriental Bank of commerce) followed the normal distribution.
4. The results of autocorrelation for few sample banks revealed significant returns at 5%
level.
5. The returns of the sample banks were not distributed randomly under the Autocorrelation
Test during the study period.
9. Conclusion
The study examined the returns of 18 sample companies for market efficiency by using
Runs Test and Autocorrelation Function (ACF). The study reveals that the results of both tests
(Runs and Autocorrelation) for Allahabad Bank, BOI, Canara Bank, Federal Bank, HDFC Bank,
ICICI Bank, Kotak Mahindra Bank, Oriental Bank of commerce support normal distribution.
This shows that the above eight banks were in a good position during the study period and
investors of those banks earned maximum returns in the stock market operations. This depicts
the growth of banking sector and their efficiency in the Indian Capital market.
10. Scope for Further Research
The followings are pointers towards further research.
? The study with similar objectives could be made with reference to other sectors.
? BSE Sector Indices, Midcap Indices and Small Cap Indices could be taken up for further
study.
? There could be further study to examine the information content relating to economy,
political, legal procedure etc.
? The study to determine the market factors which affect the share price movements of the
companies could be taken up.
? The NSE market could be researched upon with different sectors.
References
1. Anand Pandey (2003), “Efficiency of Indian Stock Market”, Indian Economic J ournal,
Vol.36, No.4 (April – J une), 68-121.
2. Civleek, M.A.(1991), “Stock Market Efficiency Revisited: Evidence from the Amman
Stock Exchange”, The Amman Stock Exchange, The Middle East Business and
Economic Review, 3,27-31.
3. Chin Wen Cheog (2008), “ A Sectoral Efficiency Analysis of Malaysian Stock
Exchange under Structural Bank”, American J ournal of Applied Science Vol.5, No.10,
1291-1295
4. Damodar Gujarati (1999), “Essential of Econometrics”, Mc-Graw Hill International
Publication, New Delhi
5. David Edwarrd (2005), “Finance Statistics an Introduction”, Springer Publication,
Mumbai.
6. Gupta S.P. (2008), “Statistical Methods”, Sultan Chand and Sons Publications, New
Delhi.
7. Kulkarni S.N. (1978) “Share Price Behaviour in India: A Spectral Analysis of
Random Walk Hypothesis”, Sankhya Vol. 40, series D, II 135-162.
8. Mufeed Rawashdeh J ay Squalli (2005), “A Sectoral Efficiency Analysis of the Amman
Stock Exchange”, Working Paper No. 05-04, December 2005.
9. Pandey I.M. (2005), “Financial Management Theory and Practice”, Tata McGraw
Hill Publication, New Delhi.
10. Peijie wang (2003), “Financial Econometrics Methods and Models”, Vikas Publishing
House (Pvt) Ltd, New Delhi.
11. Peter Mulder and Henri L.F.de Groot “ International Comparisons of Sectoral Energy
and Labour Productivity Performance”,
12. Sharma J .L and Robert E. Kennedy (1977) “A Comparative Analysis of Stock Price
Behaviour on the Bombay, London and New York Stock Exchanges”, J ournal of
Financial and Quantitative Analysis Sep 1977.
13. Tasneem Alam and muhammed Waheed “The Monetary Transmission Mechanism in
Pakistan: A Sectoral Anaysis” http://ssrn.com/abstract=971318.
Table 1- Details of Sample Banking Sector Companies listed on 02.01.2010
Sl. No Name of the Bank Sl. No Name of the Bank
1 Allahabad Bank 10 Indusind Bank
2 AXIS Bank 11 Indian OverseasBank
3 Bank of India 12 Karnataka Bank
4 Bank of Baroda 13 Kotak Mahindra Bank
5 Canara Bank 14 Oriental Bank of Commerce
6 Federal Bank 15 Punjab National Bank
7 HDFC Bank 16 State Bank of India
8 ICICI Bank 17 Union Bank of India
9 IDBI Bank 18 Yes Bank
Source: www.bseindia.com
Table 2 - Results of Runs Test with Mean Base for sample Banking Stocks
Bank Name N Significance level Z
Allahabad Bank 442 .004 -2.881*
AXIS Bank 466 .171 -1.370
Bank of Baroda 464 .166 -1.385
Bank of India 453 .025 -2.240*
Canara Bank 439 .002 -3.055*
Federal Bank 452 .021 -2.302*
HDFC Bank 456 .048 -1.981*
ICICI Bank 444 .005 -2.813*
IDBI Bank 458 .054 -1.923
Indian Overseas Bank 465 .144 -1.460
Indusind Bank 480 .704 -.380
Karnataka Bank 459 .086 -1.716
Kotak Bank 446 .007 -2.693*
Oriental Bank of Commerce 447 .009 -2.626*
Punjab National Bank 496 .587 .543
State Bank of India 487 .950 -.063
Union Bank of India 459 .076 -1.775
YES Bank 439 .003 -2.987*
Source: Computed from Prowess
*
Significance at 5% level
Table 3 – Results of Run Test with Median Base for sample banking stocks
Bank Name N Significance level Z
Allahabad Bank 434 .001 -3.460*
AXIS Bank 462 .096 -1.667
Bank of Baroda 456 .040 -2.052*
Bank of India 457 .047 -1.988*
Canara Bank 439 .002 -3.141*
Federal Bank 454 .029 -2.179*
HDFC Bank 456 .040 -2.052*
ICICI Bank 442 .003 -2.949*
IDBI Bank 458 .054 -1.923
Indian Overseas Bank 457 .047 -1.987
Indusind Bank 482 .701 -.385
Karnataka Bank 451 .018 -2.372*
Kotak Mahindra Bank 446 .007 -2.693*
Oriental Bank of Commerce 447 009 -2.629*
Punjab National Bank 486 .898 -.128
State Bank of India 487 .949 -.064
Union Bank of India 465 .140 -1.475
YES Bank 461 .083 -1.731
Source: Computed from Prowess
*
Significance at 5% level
Table 4 - Results of Autocorrelation of Sample Banking Stocks during the Study Period
Name of
the Bank
ACF&
Probability
Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Lag 10
Allahabad
ACF .088 .054 .012 -.028 -.014 -.030 .016 .036 -.004 -.005
Probability .006* .006* .015* .023* .043* .054 .081 .080 .119 .168
Axis
ACF .019 -.040 -.022 .007 -.056 -.035 -.002 .035 .041 .046
Probability .548 .381 .497 .657 .361 .351 .462 .446 .386 .312
BOB
ACF .084 -.002 .010 -.032 -.080 -.022 .021 -.029 .018 .022
Probability .009* .033* .035* .044* .014* .023* .034* .043* .031* .038*
BOI
ACF .076 .012 -.026 -.022 -.072 -.060 -.006 -.025 .002 .042
Probability .014* .046* .079 .122 .030* .014* .025* .035* .055 .049*
Canara
ACF .081 .012 .004 -.074 -.036 -.026 -.015 .006 -.016 -.012
Probability .011* .037* .045* .018* .021* .031* .049* .028* .016* .022*
Federal
ACF .088 -.019 -.006 .022 -.033 -.038 -.036 -.053 -.003 .024
Probability .006* .019* .046* .090 .129 .142 .139 .088 .130 .158
HDFC
ACF .067 -.055 -.056 -.084 -.034 -.036 -.031 .073 .002 .034
Probability .037* .026* .016* .002* .002* .003* .004* .001* .002* .003*
ICICI
ACF .123 -.037 -.015 -.024 -.058 -.108 -.010 .067 .022 .020
Probability .000* .000* .001* .002* .001* .000* .000* .000* .000* .000*
IDBI
ACF .032 -.046 .023 -.058 -.024 -.057 .035 .029 .003 .003
Probability .009* .012* .024* .013* .021* .012* .014* .018* .030* .047*
IOB
ACF .142 .007 .004 .007 -.011 -.055 -.020 .023 .034 -.028
Probability .000* .000* .000* .001* .001* .001* .001* .002* .003* .004*
Indusind
ACF .069 -.033 -.038 -.017 -.034 -.014 .003 .074 .015 .015
Probability .031* .057 .069 .117 .130 .190 .272 .078 .110 .148
Karnataka
ACF .100 .034 .034 .013 -.003 -.068 -.038 -.001 .027 .036
Probability .002* .004* .007* .016* .031* .010* .011* .020* .026* .027*
Kodak
ACF .094 -.007 -.013 .017 -.035 -.059 -.031 -.013 .055 -.038
Probability .003* .014* .033* .060 .068 .034* .0418 .064 .038* .038*
Oriental
ACF .122 .001 .063 -.009 -.023 -.058 .015 -.004 .022 -.008
Probability .000* .001* .000* .001* .002* .001* .002* .004* .006* .010*
PNB
ACF .025 -.013 .002 -.018 -.034 -.001 .037 -.042 -.022 -.016
Probability .435 .677 .854 .893 .818 .898 .828 .723 .761 .812
SBI
ACF .086 -.028 -.013 -.024 -.079 -.064 .020 .076 .022 -.029
Probability .007* .018* .042* .037* .011* .005* .008* .002* .003* .003*
UBI
ACF .048 -.033 -.031 -.043 -.013 -.065 -.023 -.034 .045 .000
Probability .134 .189 .232 .194 .284 .111 .144 .151 .123 .173
Yes bank
ACF .172 -.059 .028 -.033 -.062 -.077 .006 .047 .044 .029
Probability .000* .000* .000* .000* .000* .000* .000* .000* .000* .000*
Source: www.bseindia.com,
*
Significance at 5% level
Chart 1 - The Chart showing the Result of Autocorrelation for Banking Sector
Source: Computed from Table-4
doc_987067928.pdf
The sectoral analysis quantifies the key parameters of the economy. The analysis of different sectors of economy facilitates the Government to use it as the reference guide for the formulation of economic policy.
Electronic copy available at: http://ssrn.com/abstract=1940886
ANALYSIS OF SECTORAL MARKET EFFICIENCY- A STUDY ON BANKING
SECTOR
R.Rajesh Ramkumar
1
, Dr.M.Selvam
2
and Dr.G.Indhumathi
3
Abstract
The sectoral analysis quantifies the key parameters of the economy. The analysis of
different sectors of economy facilitates the Government to use it as the reference guide for the
formulation of economic policy. The sectoral analysis is a summary that explains the economic
growth report covering different industries. The sectoral analysis of our economy focuses on the
key points of the latest reforms initiated by the Government of India. In addition, the study of
this nature, analyses the efficiency of corporate sectors in the stock market. The banking industry
is the core sector of the economy and therefore this paper tests the market efficiency across the
companies under banking sector, listed at the BSE, by using the daily closing share prices from
J an 2006 to Dec 2009. The parametric and non-parametric tests were used for analysing the
sectoral efficiency of sample banking companies and it is found that the banking sector was
efficient during the study period.
Keywords: Market Efficiency, Autocorrelation, Runs Test and Sectoral Analysis
1. Introduction
The sectoral analysis is employed by investors who are to select better stocks to invest in.
The investors identify most promising sectors and review the performance of companies within
the sector to determine which individual stock would provide better returns and ultimately, be
purchased. There are three aspects that would generally affect the performance of a company's
stock in the stock market. The first aspect is the performance of the individual company. The
second is the performance of the market as a whole. The third is the performances of the sector
1
Ph.D Research Scholar, Dept of Commerce and Financial Studies, Bharathidasan University,
Tiruchirappalli-620 024, Tamil Nadu, India, Email: [email protected] No:
+919789277094
2
Associate Professor and Head, Dept of Commerce and Financial Studies, Bharathidasan
University, Tiruchirappalli-620 024, Tamil Nadu, India, Email: [email protected], Contact
No: +919443025112
3
Assistant Professor, Dept of Commerce, Mother Teresa Women’s University, Kodaikanal-
624101, Email: [email protected], Contact No: +919884434722
Electronic copy available at: http://ssrn.com/abstract=1940886
to which the company belongs. It is a known fact that the sectors are groups of companies which
perform similar functions in the economy. The sector analysis involves the process of dividing
the total market into sectors and then studying the market performance of each sector
individually, so that each sector can be compared to other sector or to the market as a whole.
The sectoral analysis covers the market efficiency of different sectors of the economy. It
focuses on the key points of the latest reforms of economy as initiated by the Government of
India. The banking industry is one of the core sectors of the economy. Therefore, this paper tests
the market efficiency across the companies under banking sector, listed at the BSE, using daily
closing share prices during the study period.
2. Review of Literature
The following are the few existing research studies available on the sector analysis and
they are reviewed.
Mufeed Rawashdeh and J ay Squalli (2004), in their article entitled, “A Sectoral
Efficiency Analysis of the Amman Stock Exchange”, tested the market efficiency across the
four sectors, namely, banking, industrial, insurances and services in the Amman Stock Exchange
(ASE). This study used daily sectoral indexes between 1992 and 2004 using variance ratio and
runs tests. It is found from the analysis that the random walk and weak form efficiency
hypotheses were rejected for all sample sectors.
A study entitled, “A Sectoral Efficiency Analysis of Malaysian Stock Exchange under
Structural Bank”, by Chin Wen Cheog (2008), investigated the weak form market efficiency
using daily returns of nine sectoral indices in Malaysian stock market between 1996 and 2006.
The study found that the sectoral indices of Malaysian stock markets were inefficient weak-form
(except the property index).
The study entitled, “The Monetary Transmission Mechanism in Pakistan: A Sectoral
Analysis”, by Tasneem Alamand Muhammad Waheed, investigated the monetary transmission
mechanism in Pakistan at a sectoral level. Taking the structural transformation of the economy
and the monetary and financial reforms during 1990s, the researchers assessed whether the
reform process exercised notable impact on the monetary transmission mechanism. The study
found evidence to support sector-specific variation in the real effects of monetary policy. The
study also suggested significant changes in the transmission of monetary shock to real sector of
the economy during the post-reform period.
The above literature provides an overview of different models used to study the sectoral
efficiency around the world. However, there was no comprehensive study carried out in Indian
stock markets. Thus an attempt has been made in this study to evaluate market efficiency in the
Indian context by taking the models used in the above studies.
3. Statement of the Problem
The Capital Market is a vital institution as it facilitates economic development. It is true
that so many parties are interested in knowing the efficiency of the Capital Market. The retail
investors can be motivated to save and invest their savings in the Capital Market only if their
securities in the market are appropriately priced. The Random Walk Hypothesis of stock prices is
concerned with the question of whether one can predict future price from past prices. Many
studies tested the efficiency in global stock market and also tested the random walk hypotheses
for various popular indices. But in India, few studies examined the returns of thestock market
especially with reference to stock indices like S&P CNX Nifty, BSE 100 Index, Nifty J unior, etc.
It is important to note that there were no comprehensive studies carried out to test the sectoral
efficiency in the Indian context. The individual investors are not fully informed of the sectoral
efficiency in the Indian stock market. Therefore, the present study aims to investigate the
efficiency of Indian Stock Market for different sectors which were actively traded in the Bombay
Stock Exchange (BSE). This study analyses the market efficiency among the sample companies
to under banking sectors listed in the BSE.
4. Objectives of the Study
The present study was carried out to examine the market efficiency of the banking
companies listed in the BSE- Bankex.
5. Hypotheses of the Study
The present study tested the following null hypotheses
NH1: There is no normal distribution in the returns of the shares of sample banks.
NH2: There is no significant difference in the share price behavior of sample stocks.
6. Methodology of the Study
6.1 Sample Selection
For the purpose of this study, stocks of all 18 banks stocks listed in BSE Bankex were
taken as the total sample population. In the banking sector, there are totally 18 companies which
were listed on 2
nd
J anuary 2010 and these companies were taken for this study. The details of
sample companies are given in Table-1.
6.2 Sources and Collection of Data
The present study was mainly based on secondary data (banking stock daily returns
prices) which were collected from the Prowess Corporate Database. Further, the available
secondary data were collected from the Annual Reports, published research reports by banking
industry etc. In addition, other related information was collected from various books, periodicals
and websites like www.bseindia.comand www.yahoofinance.com.
6.3 Period of the Study
The present study was mainly intended to examine the sectoral efficiency (market) of
stocks of banking companies listed in BSE Bankex from 1
st
J anuary 2006 to 31
st
December 2009.
6.4 Tools Used for Analysis
In order to evaluate the sectoral efficiency, tools like Runs Test and Autocorrelation were
used.
(a) Runs Test
It is a non-parametric test used for measuring market performance. It does not require
specification of the probability distribution. It depends only on the price. They are essentially
concerned with direction of changes in price.
N
n ) 1 N ( N
M
3
1 i
2
i
?
?
? ?
?
Where,
M =Expected number of runs
n
i
=Number of price changes of each sign (i=1,2,3)
N =Total number of price changes.
(b) Autocorrelation
It is the statistical tool used for measuring the company’s successive terms in a given
time series and dependence of the successive share price changes.
?
?
?
?
?
?
?
? ?
?
n
1 t
2
t
k n
1 t
k t t
k
) R R (
) R R )( R R (
p
Where,
K =Number of lags
Rt =Real rate of returns
n =Total number of observations
P
k
=Sample autocorrelation function for the lag K
R =Mean returns
6.5 Limitations of the Study
The study suffers from thefollowing limitations
1. The study was based on secondary data, and hence it is riddled with certain limitations
which are bound to be connected with the use of secondary data.
2. This study focused only on the banking sector as it is one of the core sectors of the
economy.
3. All the limitations, associated with Runs Test and Autocorrelation Tests, are applicable to
this study also.
7. Analysis of Market Efficiency of Banking Sector’s Stocks
The analysis of market efficiency of banking stocks is arranged as follows:
7.1 Market Efficiency - Runs Test
7.2 Market Efficiency- Autocorrelation
7.1 Market Efficiency- Runs Test
Table-2 shows the analysis of Runs Test by having mean value as the base for sample
banking stocks. From the above Table, it is understood that out of 18 stocks, nine stocks in the
banking sector, namely, stocks of Allahabad Bank, Bank of India, Canara Bank, Federal Bank,
HDFC Bank, ICICI Bank, Kodak Mahindra Bank, Oriental Bank of Commerce and Yes Bank
followed the normal distribution. The Z values of these nine banks were significant under normal
distribution at 5% level. Therefore, the null hypothesis (NH1), “There is no normal distribution
in the returns of the shares of sample companies”, is not fully accepted. The remaining sample
stocks (the stocks of AXIS Bank, BOB, IDBI, IOB, Indusind Bank, Karnataka Bank, PNB, SBI,
and UBI) did not follow normal distributions as its mean values were not significant.
The results of Runs Test by having median value as the base for sample banking stocks
are given in Table-3. It is clear that 11 stocks out of 18 stocks in the banking sector followed
normal distribution. Those stocks belong to Allahabad Bank, Bank of Baroda, Bank of India,
Canara Bank, Federal Bank, HDFC Bank, ICICI Bank, Indian Overseas Bank, Karnataka Bank,
Kodak Mahindra, and Oriental Bank of Commerce. Besides, the Z values for these 11 banks
were significant under normal distribution at 5% level. According to the analysis, it is to be
noted that majority of sample banks followed normal distribution. Hence the null hypothesis
(NH1), “There is no normal distribution in the returns of the shares of sample companies”, is
rejected under median base analysis. The stocks of other banks AXIS Bank, IDBI Bank, IOB,
Indusind Bank, PNB, SBI, UBI and Yes Bank did not follow normal distribution as their values
were not significant.
7.2 Market Efficiency – Autocorrelation
Table-4 reveals the results of autocorrelation of sample banking stocks during the study
period. It is understood from the above Table that out of 18 sample banks taken for this study,
only ten banks earned significant value in all the 10 lags. Those banks are BOB, Canara Bank,
HDFC Bank, ICICI Bank, IDBI Bank, IOB, Karnataka Bank, Oriental Bank of Commerce, SBI
and Yes Bank. Further, it is to be noted that the values of these ten banks are significant at 5%.
The analysis of autocorrelation reveals the fact that there are three banks (namely AXIS Bank,
PNB and UBI) that did not earn significant value at 5% level in all the 10 lags. The analysis of
stocks of Allahabad Bank reveals that its value was significant in first 5 lags but not significant
in the subsequent 5 lags. In the case of Federal Bank, its value was significant in the first 3 lags
but not significant in the last 7 lags. The value of BOI and Kodak Mahindra banks showed
zigzagsign in its value. Finally, the remaining one stock (Indusind Bank) reached the significant
level at 1
st
lag only, but not the remaining 9 lags. Chart-1 explains the autocorrelation results for
the banking sector. From the above chart, it is found that the banks residuals exhibit a distinct
behaviour. Some of the banks were initially positive, then became negative and after that again
turned positive. This shows that the returns were not randomly distributed.
8. Findings of the Study
The following are the important findings of the study.
1. The market efficiency of banking sector was tested by Runs Test which indicated that
there was no randomness in the stock market, because the returns for all sample stocks
were not normally distributed.
2. It is found that nine banks, namely, Allahabad Bank, BOI, Canara Bank, Federal Bank,
HDFC Bank, ICICI Bank, Kodak Mahindra Bank, Oriental Bank of Commerce, and Yes
Bank followed normal distribution based on their mean values in the Runs Test.
3. Based on the median values, only 11 banks (Allahabad Bank, BOB, BOI, Canara Bank,
Federal Bank, HDFC Bank, ICICI Bank, IOB, Karnataka Bank, Kotak Mahindra Bank,
Oriental Bank of commerce) followed the normal distribution.
4. The results of autocorrelation for few sample banks revealed significant returns at 5%
level.
5. The returns of the sample banks were not distributed randomly under the Autocorrelation
Test during the study period.
9. Conclusion
The study examined the returns of 18 sample companies for market efficiency by using
Runs Test and Autocorrelation Function (ACF). The study reveals that the results of both tests
(Runs and Autocorrelation) for Allahabad Bank, BOI, Canara Bank, Federal Bank, HDFC Bank,
ICICI Bank, Kotak Mahindra Bank, Oriental Bank of commerce support normal distribution.
This shows that the above eight banks were in a good position during the study period and
investors of those banks earned maximum returns in the stock market operations. This depicts
the growth of banking sector and their efficiency in the Indian Capital market.
10. Scope for Further Research
The followings are pointers towards further research.
? The study with similar objectives could be made with reference to other sectors.
? BSE Sector Indices, Midcap Indices and Small Cap Indices could be taken up for further
study.
? There could be further study to examine the information content relating to economy,
political, legal procedure etc.
? The study to determine the market factors which affect the share price movements of the
companies could be taken up.
? The NSE market could be researched upon with different sectors.
References
1. Anand Pandey (2003), “Efficiency of Indian Stock Market”, Indian Economic J ournal,
Vol.36, No.4 (April – J une), 68-121.
2. Civleek, M.A.(1991), “Stock Market Efficiency Revisited: Evidence from the Amman
Stock Exchange”, The Amman Stock Exchange, The Middle East Business and
Economic Review, 3,27-31.
3. Chin Wen Cheog (2008), “ A Sectoral Efficiency Analysis of Malaysian Stock
Exchange under Structural Bank”, American J ournal of Applied Science Vol.5, No.10,
1291-1295
4. Damodar Gujarati (1999), “Essential of Econometrics”, Mc-Graw Hill International
Publication, New Delhi
5. David Edwarrd (2005), “Finance Statistics an Introduction”, Springer Publication,
Mumbai.
6. Gupta S.P. (2008), “Statistical Methods”, Sultan Chand and Sons Publications, New
Delhi.
7. Kulkarni S.N. (1978) “Share Price Behaviour in India: A Spectral Analysis of
Random Walk Hypothesis”, Sankhya Vol. 40, series D, II 135-162.
8. Mufeed Rawashdeh J ay Squalli (2005), “A Sectoral Efficiency Analysis of the Amman
Stock Exchange”, Working Paper No. 05-04, December 2005.
9. Pandey I.M. (2005), “Financial Management Theory and Practice”, Tata McGraw
Hill Publication, New Delhi.
10. Peijie wang (2003), “Financial Econometrics Methods and Models”, Vikas Publishing
House (Pvt) Ltd, New Delhi.
11. Peter Mulder and Henri L.F.de Groot “ International Comparisons of Sectoral Energy
and Labour Productivity Performance”,
12. Sharma J .L and Robert E. Kennedy (1977) “A Comparative Analysis of Stock Price
Behaviour on the Bombay, London and New York Stock Exchanges”, J ournal of
Financial and Quantitative Analysis Sep 1977.
13. Tasneem Alam and muhammed Waheed “The Monetary Transmission Mechanism in
Pakistan: A Sectoral Anaysis” http://ssrn.com/abstract=971318.
Table 1- Details of Sample Banking Sector Companies listed on 02.01.2010
Sl. No Name of the Bank Sl. No Name of the Bank
1 Allahabad Bank 10 Indusind Bank
2 AXIS Bank 11 Indian OverseasBank
3 Bank of India 12 Karnataka Bank
4 Bank of Baroda 13 Kotak Mahindra Bank
5 Canara Bank 14 Oriental Bank of Commerce
6 Federal Bank 15 Punjab National Bank
7 HDFC Bank 16 State Bank of India
8 ICICI Bank 17 Union Bank of India
9 IDBI Bank 18 Yes Bank
Source: www.bseindia.com
Table 2 - Results of Runs Test with Mean Base for sample Banking Stocks
Bank Name N Significance level Z
Allahabad Bank 442 .004 -2.881*
AXIS Bank 466 .171 -1.370
Bank of Baroda 464 .166 -1.385
Bank of India 453 .025 -2.240*
Canara Bank 439 .002 -3.055*
Federal Bank 452 .021 -2.302*
HDFC Bank 456 .048 -1.981*
ICICI Bank 444 .005 -2.813*
IDBI Bank 458 .054 -1.923
Indian Overseas Bank 465 .144 -1.460
Indusind Bank 480 .704 -.380
Karnataka Bank 459 .086 -1.716
Kotak Bank 446 .007 -2.693*
Oriental Bank of Commerce 447 .009 -2.626*
Punjab National Bank 496 .587 .543
State Bank of India 487 .950 -.063
Union Bank of India 459 .076 -1.775
YES Bank 439 .003 -2.987*
Source: Computed from Prowess
*
Significance at 5% level
Table 3 – Results of Run Test with Median Base for sample banking stocks
Bank Name N Significance level Z
Allahabad Bank 434 .001 -3.460*
AXIS Bank 462 .096 -1.667
Bank of Baroda 456 .040 -2.052*
Bank of India 457 .047 -1.988*
Canara Bank 439 .002 -3.141*
Federal Bank 454 .029 -2.179*
HDFC Bank 456 .040 -2.052*
ICICI Bank 442 .003 -2.949*
IDBI Bank 458 .054 -1.923
Indian Overseas Bank 457 .047 -1.987
Indusind Bank 482 .701 -.385
Karnataka Bank 451 .018 -2.372*
Kotak Mahindra Bank 446 .007 -2.693*
Oriental Bank of Commerce 447 009 -2.629*
Punjab National Bank 486 .898 -.128
State Bank of India 487 .949 -.064
Union Bank of India 465 .140 -1.475
YES Bank 461 .083 -1.731
Source: Computed from Prowess
*
Significance at 5% level
Table 4 - Results of Autocorrelation of Sample Banking Stocks during the Study Period
Name of
the Bank
ACF&
Probability
Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Lag 10
Allahabad
ACF .088 .054 .012 -.028 -.014 -.030 .016 .036 -.004 -.005
Probability .006* .006* .015* .023* .043* .054 .081 .080 .119 .168
Axis
ACF .019 -.040 -.022 .007 -.056 -.035 -.002 .035 .041 .046
Probability .548 .381 .497 .657 .361 .351 .462 .446 .386 .312
BOB
ACF .084 -.002 .010 -.032 -.080 -.022 .021 -.029 .018 .022
Probability .009* .033* .035* .044* .014* .023* .034* .043* .031* .038*
BOI
ACF .076 .012 -.026 -.022 -.072 -.060 -.006 -.025 .002 .042
Probability .014* .046* .079 .122 .030* .014* .025* .035* .055 .049*
Canara
ACF .081 .012 .004 -.074 -.036 -.026 -.015 .006 -.016 -.012
Probability .011* .037* .045* .018* .021* .031* .049* .028* .016* .022*
Federal
ACF .088 -.019 -.006 .022 -.033 -.038 -.036 -.053 -.003 .024
Probability .006* .019* .046* .090 .129 .142 .139 .088 .130 .158
HDFC
ACF .067 -.055 -.056 -.084 -.034 -.036 -.031 .073 .002 .034
Probability .037* .026* .016* .002* .002* .003* .004* .001* .002* .003*
ICICI
ACF .123 -.037 -.015 -.024 -.058 -.108 -.010 .067 .022 .020
Probability .000* .000* .001* .002* .001* .000* .000* .000* .000* .000*
IDBI
ACF .032 -.046 .023 -.058 -.024 -.057 .035 .029 .003 .003
Probability .009* .012* .024* .013* .021* .012* .014* .018* .030* .047*
IOB
ACF .142 .007 .004 .007 -.011 -.055 -.020 .023 .034 -.028
Probability .000* .000* .000* .001* .001* .001* .001* .002* .003* .004*
Indusind
ACF .069 -.033 -.038 -.017 -.034 -.014 .003 .074 .015 .015
Probability .031* .057 .069 .117 .130 .190 .272 .078 .110 .148
Karnataka
ACF .100 .034 .034 .013 -.003 -.068 -.038 -.001 .027 .036
Probability .002* .004* .007* .016* .031* .010* .011* .020* .026* .027*
Kodak
ACF .094 -.007 -.013 .017 -.035 -.059 -.031 -.013 .055 -.038
Probability .003* .014* .033* .060 .068 .034* .0418 .064 .038* .038*
Oriental
ACF .122 .001 .063 -.009 -.023 -.058 .015 -.004 .022 -.008
Probability .000* .001* .000* .001* .002* .001* .002* .004* .006* .010*
PNB
ACF .025 -.013 .002 -.018 -.034 -.001 .037 -.042 -.022 -.016
Probability .435 .677 .854 .893 .818 .898 .828 .723 .761 .812
SBI
ACF .086 -.028 -.013 -.024 -.079 -.064 .020 .076 .022 -.029
Probability .007* .018* .042* .037* .011* .005* .008* .002* .003* .003*
UBI
ACF .048 -.033 -.031 -.043 -.013 -.065 -.023 -.034 .045 .000
Probability .134 .189 .232 .194 .284 .111 .144 .151 .123 .173
Yes bank
ACF .172 -.059 .028 -.033 -.062 -.077 .006 .047 .044 .029
Probability .000* .000* .000* .000* .000* .000* .000* .000* .000* .000*
Source: www.bseindia.com,
*
Significance at 5% level
Chart 1 - The Chart showing the Result of Autocorrelation for Banking Sector
Source: Computed from Table-4
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