INDIAN STOCK MARKET EFFICIENCY AN...
Transcript of INDIAN STOCK MARKET EFFICIENCY AN...
CHAPTER V
INDIAN STOCK MARKET EFFICIENCY –
AN ANALYSIS
The Indian stock market is considered to be one of the earliest in Asia and
is regarded as the barometer of the health of the Indian economy. In line with the
global trend, reforms of the Indian stock market also started with the
establishment of Securities and Exchange Board of India (SEBI). With the
establishment of SEBI and technological advancement Indian stock market has
now reached the global standards. The major indicators of stock market
development show that significant development has taken place in the Indian
stock market during the post-reform period.
The adoption of international quality in trading and settlement
mechanisms and the reduction of transaction costs , removal of barriers to the
international equity investment, better allocation and mobilization of resources
have made the investors both domestic and foreign to be more optimistic which
in turn evidenced a considerable growth in market volume and liquidity.
Together, all these market features infer better market efficiency in Indian stock
market.
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5.1 Efficient Market Hypothesis
Efficient Market Hypothesis is an investment theory which states that it is
impossible to ‘beat the market’ because market efficiency causes exiting share
prices to always incorporate and reflect all relevant information. Stocks are
always traded at their fair value on stock exchanges and so the scope of residual
returns either by purchasing undervalued stocks or by selling stocks for inflated
prices is impossible .In an efficient market, prices fully and instantaneously
reflect all available information.
Ever since Fama (1965) propounded his famous Efficient Market
Hypothesis (EMH), a number of empirical studies have been conducted to test its
validity, both in developed markets and as well as in emerging markets. The
contradictory nature of the results and the change in the current market scenario
encouraged the researcher to conduct a research in the market efficiency of Indian
Stock Market.
Market Efficiency can be explained in three related concepts: Operational
Efficiency, Allocation Efficiency and Informational Efficiency. Operational
efficiency ensures that all transactions are completed on time, with maximum
accuracy and at least cost. Allocation efficiency talks about capital flow to the
projects with highest possible risk-adjusted returns whereas Informational
efficiency ensures that market price of a security fully reflect all information
which is affecting the pricing of security.
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Efficient Market Hypothesis mainly discusses about informational
efficiency and states that markets are efficient if the prices of securities fully
reflect all available information. Again the theory talks about three forms of
efficiency:
Weak Form Efficiency
Semi-strong Form efficiency
Strong Form
One cannot beat the market by using historical information on prices of
securities if the market is said to be weak form efficient. Semi-strong efficiency
implies that the current prices of stocks of various companies reflects not only the
information on historical prices but also reflect all publically available
information about these companies. Strong Form efficiency incorporates all types
of information in to the current pricing strategy, which is not yet proved to be
present in Indian stock market.
For the purpose of statistical analysis of weak form and semi strong form
of efficiency in Indian Stock Market the market prices of companies included in
the formation of Nifty index was collected from NSE official website. The study
was conducted with wide scope both in terms of depth of analysis and breadth of
coverage. It has taken a period of 6 years (2004-2009) and daily prices of shares
included in the formation of Nifty index.
In order to bring more validity to the result, the period in which Indian
markets were severely affected by global financial crisis was studied separately.
The period under study was 2007 October to 2008 April.
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Statistical tools like autocorrelation and run test were used to test the
weak form market efficiency. One-sample Kolmogorov-Smirnov test was used to
find out how well a data series fits a particular distribution.
Semi-strong form market efficiency was tested by taking daily returns of
companies included in the formation of Nifty Index and compared with the daily
Index returns. Beta value for the stocks was calculated to arrive at the residual
return. Residual return is the difference between the actual return and expected
return. If the difference between the actual return and expected return is zero or
near to zero the market is said to be efficient.
The formula for calculating expected return was:
Expected Return = Ri = αi + βi Rm + ei, where Rm is market index
return. The entire study period was divided in to different segments of three
months each and the process was repeated for a better result.
5.2 Market Efficiency in the Weak Form
Weak form efficiency states that current prices of stocks already reflect
all the information that is contained in the historical sequence of prices. Hence
there is no benefit in examining the historical prices as far as forecasting the
future is concerned. Weak form of market efficiency is popularly called as
random-walk theory.
If Indian Stock Market is efficient in its Weak form then it is a direct
repudiation of technical analysis. Technical analysis relies a lot on historical
prices for their future price prediction
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Weak form efficiency of Indian market during the time frame of 6 years
(2004-09) had been tested using statistical tools like Autocorrelation, and Run
test. Daily prices of shares were taken for the study. One-Sample Kolmogorov-
Smirnov Test was also used to find out how well a data series fits a particular
distribution.
Population consisted of all companies listed in NSE. Sample size was 50
companies forming NSE Nifty Index. While doing the pilot study the researcher
found that due to constant revisions by NSE, to make the shares chosen for index
construction representative of the population, data for only 29 shares were
present through out the study period of 6 years. So the Weak form efficiency is
studied in two ways; one taking only 29 shares whose data was present through
out the study period of six years and the second is taking NSE Nifty index shares
for a six year period.
5.3 Test Results of Weak Form of Market Efficiency
5.3.1 Study of 29 companies for a period of 5 years on the basis of daily
returns
The summary statistics of the returns for all the companies included in the
study are given in Table 5.1. The normality of distribution is one among the basic
assumptions of Weak-form efficient market hypothesis. Mean stock returns are
positive with majority of them having comparatively larger volatility (standard
deviation).
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Table 5.1
Descriptive Statistics For 29 companies
Company N Mean Median Minimum Maximum Std.
Deviation
ABB 1687 1401.68 974.70 286.25 4792.35 1067.68
ACC 1687 583.75 553.90 128.00 1289.80 297.00
BHEL 1687 1417.62 1437.60 199.25 2870.20 757.13
CIPLA 1688 396.23 258.70 160.10 1398.65 311.52
GAIL 1688 263.22 254.00 73.30 543.60 91.27
GRASIM 1688 1754.94 1503.33 329.15 3869.90 841.90
HCL 1688 333.63 308.63 89.70 698.00 147.75
HDFC BANK 1688 849.70 808.93 227.30 1807.10 431.96
HERO HONDA 1688 707.66 698.35 183.20 1747.75 317.47
HDFC 1688 1395.72 1310.95 299.20 3180.15 766.23
ITC 1688 532.64 202.68 115.45 1940.10 484.70
ICICI BANK 1688 566.93 533.13 120.80 1435.00 292.22
INFOSYS 1688 2482.36 2089.08 1102.30 5886.70 1140.31
JINDAL 1688 1931.98 1328.45 311.35 16490.85 2485.11
M & M 1688 546.97 541.00 99.10 1080.15 206.37
MARUTI 1600 699.04 690.48 164.30 1701.40 299.21
ONGL 1688 896.42 888.30 352.85 1484.20 223.83
PNB 1688 424.60 437.18 85.40 934.25 161.66
RANBAXY 1688 589.72 439.03 134.70 1269.35 306.08
RELCAPITEL 1688 609.14 468.05 48.60 2860.00 556.41
RELIANCE 1688 1201.24 1009.95 259.55 3220.85 747.09
SIEMENS 1688 1299.53 1043.23 187.85 6205.15 1185.84
SBIN 1688 1057.08 930.63 270.00 2470.85 559.82
SAIL 1688 94.65 74.13 8.80 287.75 62.83
SUN PHARMA 1688 843.20 855.55 267.40 1590.05 339.19
TATA
MOTORS 1480 560.29 520.45 126.20 986.25 207.32
TATA POWER 1688 624.41 519.38 112.60 1629.15 373.53
UNITECH 1685 569.54 227.15 23.15 14148.05 1555.37
WIPRO 1688 659.84 537.05 200.90 1762.30 360.97
Source: Computed from data source
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Where data are in nominal or ordinal form, or where assumptions about
the distribution of data on which a parametric test is based cannot be justified,
then non-parametric or otherwise called as distribution-free methods can be used.
But parametric tests are more rigorous than non-parametric tests. So to confirm
the distributional pattern of the returns, researcher has used Kolmogrov-Smirnov
goodness of fit test.
Kolmogorov-Smirnov Goodness-of-Fit Test tests whether or not a given
distribution is not significantly different from one hypothesised on the basis of
the assumption of a normal distribution.
This test finds out how well a data series fits a particular distribution. Test
compares the cumulative distributional function of the returns with a normal
distribution to determine if they are identical.
Table 5.2 presents the results of the Kolmogorov-Smirnov Test .It
compares an observed cumulative distribution function to a theoretical (Normal)
cumulative distribution. Low significance values (<.05) indicate that the observed
distribution does not corresponds to the Normal distribution. This confirms that
the distribution of Closing Prices is not normal. High significance values (>.05)
indicate that the observed distribution corresponds to the Normal distribution and
so the distribution of Closing Prices is normal.
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Table 5.2
One-Sample Kolmogorov-Smirnov Test for 29 companies
Company Absolute Positive Negative K-S Z p-value
ABB 0.190 0.190 -0.148 7.816 0.000
ACC 0.103 0.103 -0.076 4.243 0.000
BHEL 0.114 0.114 -0.090 4.698 0.000
CIPLA 0.303 0.303 -0.229 12.456 0.000
GAIL 0.081 0.081 -0.044 3.317 0.000
GRASIM 0.140 0.140 -0.045 5.772 0.000
HCL 0.130 0.130 -0.071 5.344 0.000
HDFC BANK 0.092 0.092 -0.076 3.788 0.000
HERO HONDA 0.151 0.151 -0.059 6.221 0.000
HDFC 0.118 0.118 -0.080 4.840 0.000
ITC 0.327 0.327 -0.195 13.416 0.000
ICICI BANK 0.091 0.091 -0.063 3.755 0.000
INFOSYS 0.203 0.203 -0.115 8.337 0.000
JINDAL 0.262 0.262 -0.258 10.759 0.000
M & M 0.034 0.028 -0.034 1.410 0.037
MARUTI 0.070 0.070 -0.058 2.813 0.000
ONGL 0.051 0.027 -0.051 2.114 0.000
PNB 0.078 0.078 -0.061 3.184 0.000
RANBAXY 0.203 0.203 -0.137 8.340 0.000
RELCAPITEL 0.157 0.146 -0.157 6.445 0.000
RELIANCE 0.138 0.138 -0.104 5.656 0.000
SIEMENS 0.205 0.205 -0.175 8.435 0.000
SBIN 0.099 0.099 -0.080 4.066 0.000
SAIL 0.158 0.158 -0.086 6.500 0.000
SUN PHARMA 0.087 0.087 -0.056 3.555 0.000
TATA MOTORS 0.077 0.077 -0.058 2.957 0.000
TATA POWER 0.154 0.154 -0.085 6.344 0.000
UNITECH 0.388 0.388 -0.363 15.914 0.000
WIPRO 0.204 0.204 -0.115 8.397 0.000
Source: Computed from data source
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Low significance values (<.05) indicate that the observed distribution
does not corresponds to the Normal distribution. Thus, the distribution of closing
prices is not normal. Majority of the values have low significance values.
5.3.2 Non Parametric Test – Run test for 29 companies
The run test can be used to examine the serial independence in share
return movements. This test has the advantage of ignoring the distribution of the
data, and does not require normality or constant variance of the data. A run can
be defined as a sequence of return changes of the same sign. e.g ++ /-- / 0 / -- /
has 4 runs. “A lower than expected number of runs indicates a market’s
overreaction to information, subsequently reversed, while a higher number of
runs reflect a lagged response to information.” Poshokwale, (1996).An
abnormally high or low number of runs indicate evidence against the null
hypothesis of a random walk.
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Table 5.3
Run Test Result for 29 companies
Company Test Value Runs Z-value p-value
ABB 974.70 9 -40.696 0.000
ACC 553.90 30 -39.673 0.000
BHEL 1437.60 34 -39.478 0.000
CIPLA 258.70 49 -38.760 0.000
GAIL 254.00 48 -38.809 0.000
GRASIM 1503.33 4 -40.951 0.000
HCL 308.63 64 -38.030 0.000
HDFC BANK 808.93 12 -40.562 0.000
HERO HONDA 698.35 68 -37.835 0.000
HDFC 1310.95 24 -39.978 0.000
ITC 202.68 37 -39.344 0.000
ICICI BANK 533.13 22 -40.075 0.000
INFOSYS 2089.07 39 -39.247 0.000
JINDAL 1328.45 15 -40.416 0.000
M & M 541.00 44 -39.004 0.000
MARUTI 690.48 22 -38.962 0.000
ONGL 888.30 44 -39.004 0.000
PNB 437.18 50 -38.711 0.000
RANBAXY 439.03 33 -39.539 0.000
RELCAPITEL 468.05 32 -39.588 0.000
RELIANCE 1009.95 8 -40.757 0.000
SIEMENS 1043.22 19 -40.221 0.000
SBIN 930.63 30 -39.685 0.000
SAIL 74.13 28 -39.783 0.000
SUN PHARMA 855.55 10 -40.659 0.000
TATA MOTORS 520.45 24 -37.288 0.000
TATA POWER 519.38 16 -40.367 0.000
UNITECH 227.15 9 -40.671 0.000
WIPRO 537.05 39 -39.247 0.000
Source: Computed from data source
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Here (Table 5.3) the p-values of all the companies are less than 0.05. So,
the null hypothesis that the price movement is not affected by the past price is
rejected at 5 percent. The significant negative Z values indicate non-randomness
of the series. The result shows that the price movements are not random in
behaviour. We can use the historical data for predicting the future prices. The
situation suggests that an opportunity to make excess returns exist in the Indian
Stock market.
5.3.3 Parametric Test – Auto Correlation Test for 29 companies
Researcher employed parametric test i.e. autocorrelation test to confirm
the findings of the non-parametric test and to measure the degree of dependency
of the series in the Weak form of efficiency during the period under study.
Autocorrelation can be defined as the cross correlation of a signal with itself. It is
the similarity between observations as a function of the time separation between
them. It is a mathematical tool for finding repeating patterns. This method is
very often used in signal processing for analysing functions or series of values.
Autocorrelation tests show whether the serial correlation coefficients are
significantly different from zero. In an efficient market, the null hypothesis of
zero autocorrelation will prevail.
In this study researcher had tested the correlation between the share price
of any period ‘t’ and ‘t +4’,between ‘t’ and ‘t + 9’and between ‘t’ and ‘t + 14’.To
analyse the results ,the three limits of correlation of coefficient have been taken.
These are ±0 to ±0.25 is low correlation, ±0.25 to ±0.75, moderate correlation
and ±0.75 to ±1 is considered to be highly correlated.
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Table 5.4
Autocorrelation Result for 29 companies
Company T+4 T+10 T +14
ABB 0.977 (H) 0.955 (H) 0.933 (H)
ACC 0.987 (H) 0.974 (H) 0.960 (H)
BHEL 0.981 (H) 0.964 (H) 0.949 (H)
CIPLA 0.974 (H) 0.948 (H) 0.923 (H)
GAIL 0.969 (H) 0.941 (H) 0.913 (H)
GRASIM 0.988 (H) 0.974 (H) 0.958 (H)
HCL 0.982 (H) 0.967 (H) 0.949 (H)
HDFC BANK 0.982 (H) 0.967 (H) 0.951 (H)
HERO HONDA 0.975 (H) 0.950 (H) 0.927 (H)
HDFC 0.983 (H) 0.971 (H) 0.958 (H)
ITC 0.978 (H) 0.958 (H) 0.939 (H)
ICICI BANK 0.983 (H) 0.969 (H) 0.954 (H)
INFOSYS 0.967 (H) 0.938 (H) 0.911 (H)
JINDAL 0.942 (H) 0.885 (H) 0.832 (H)
M & M 0.964 (H) 0.933 (H) 0.901 (H)
MARUTI 0.970 (H) 0.941 (H) 0.914 (H)
ONGL 0.959 (H) 0.921 (H) 0.883 (H)
PNB 0.964 (H) 0.929 (H) 0.895 (H)
RANBAXY 0.990 (H) 0.978 (H) 0.966 (H)
RELCAPITEL 0.985 (H) 0.972 (H) 0.957 (H)
RELIANCE 0.987 (H) 0.977 (H) 0.966 (H)
SIEMENS 0.980 (H) 0.955 (H) 0.929 (H)
SBIN 0.980 (H) 0.962 (H) 0.942 (H)
SAIL 0.981 (H) 0.966 (H) 0.951 (H)
SUN PHARMA 0.980 (H) 0.963 (H) 0.947 (H)
TATA MOTORS 0.985 (H) 0.971 (H) 0.955 (H)
TATA POWER 0.981 (H) 0.964 (H) 0.951 (H)
UNITECH 0.926 (H) 0.845 (H) 0.732 (M)
WIPRO 0.973 (H) 0.950 (H) 0.925 (H)
Source: Computed from data source
H Highly correlated (±0.75 to ±1)
M Moderate Correlation (±0.25 to ±0.75)
L Low Correlation (±0 to ±0.25)
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Table 5.4 shows the autocorrelation coefficients computed for the log of the
return series at different lags. Autocorrelation between the prices of shares has been
tested for five days, ten days and fifteen days .From the results it is very clear that
there is significant autocorrelation at 5 percent significance level among the 29
companies analysed. Results also show that the level of significance decreases by the
increase in days compared.
For example if we take the autocorrelation value of UNITEC, the value
which was 0.926 and was highly correlated at five day lag decreased to .845 when 10
days lag was considered. The same company’s auto-correlation value came to .732
when it came to 5 days lag. The presence of autocorrelation coefficients in the
market returns series suggest that there is relationship between past returns and
present returns and Indian stock market movements are predictable during the period
under study based on past information.
5.4 Study of companies involved in the construction of NSE Nifty
Index for a period of 5 years on the basis of daily returns
Since the companies involved in the construction of Nifty Index were
constantly revised the study had taken two different sample set for finding out
market efficiency of Indian stock market .One which included all those
companies which were there in the Index construction for the entire study period
i.e. six years the results of which is given in the above pages. The second
category was companies involved in the construction of index were considered in
total. The reason for this comparison was many companies which were there in
the index construction did not have their presence for the entire study period. For
example AMBUJA was there only from July 2007; similarly RPOWER was there
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only from 2008 February. Here the data was considered by the researcher in the
form of number of observations.
The summary statistics of the returns for all the companies included in the
study which were there for the entire study period are given in Table 5.5.As
mentioned above normality of distribution is one among the basic assumptions of
weak-form efficient market hypothesis. Mean stock returns are positive with
majority of them having comparatively larger volatility (standard deviation).
Table 5.5
Descriptive Statistics for Nifty shares
Company N Mean Median Minimu
m
Maximu
m
Std.
Deviation
ABB 1687 1401.68 974.70 286.25 4792.35 1067.68
ACC 1687 583.75 553.90 128.00 1289.80 297.00
AMBUJA 582 99.73 94.35 44.90 154.10 27.95
AXIS BANK 566 748.89 755.58 281.40 1268.15 203.41
BHEL 1687 1417.62 1437.60 199.25 2870.20 757.13
BHARTI 857 699.27 757.70 275.25 1125.65 190.55
CAIRN 716 199.83 204.55 100.65 327.55 50.79
CIPLA 1688 396.23 258.70 160.10 1398.65 311.52
DLF 596 503.90 434.48 132.85 1207.50 251.24
GAIL 1688 263.22 254.00 73.30 543.60 91.27
GRASIM 1688 1754.94 1503.33 329.15 3869.90 841.90
HCL 1688 333.63 308.63 89.70 698.00 147.75
HDFC BANK 1688 849.70 808.93 227.30 1807.10 431.96
HERO HONDA 1688 707.66 698.35 183.20 1747.75 317.47
HINDALCO 560 124.72 130.48 37.40 219.90 53.65
HINDUNILVA 586 237.79 237.78 184.05 299.65 23.99
HDFC 1688 1395.72 1310.95 299.20 3180.15 766.23
ITC 1688 532.64 202.68 115.45 1940.10 484.70
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ICICI BANK 1688 566.93 533.13 120.80 1435.00 292.22
IDEA 676 89.92 88.40 36.85 157.20 29.76
INFOSYS 1688 2482.36 2089.08 1102.30 5886.70 1140.31
IDFC 1067 104.61 88.65 44.80 232.50 46.29
JP ASSO 1364 368.86 244.23 53.40 2156.65 319.38
JINDAL 1688 1931.98 1328.45 311.35 16490.85 2485.11
LT 1357 1796.37 1572.80 562.05 4506.70 902.88
M & M 1688 546.97 541.00 99.10 1080.15 206.37
MARUTI 1600 699.04 690.48 164.30 1701.40 299.21
NTPC 1261 152.08 151.90 73.55 284.65 46.67
ONGL 1688 896.42 888.30 352.85 1484.20 223.83
POWER GRID 532 103.33 101.78 58.00 161.65 20.06
PNB 1688 424.60 437.18 85.40 934.25 161.66
RANBAXY 1688 589.72 439.03 134.70 1269.35 306.08
RELCAPITEL 1688 609.14 468.05 48.60 2860.00 556.41
RCOM 848 405.94 405.70 132.75 821.55 165.90
RELIANCE 1688 1201.24 1009.95 259.55 3220.85 747.09
RELINFRA 380 892.53 981.75 382.60 1462.95 288.34
RPOWER 443 181.86 158.55 89.65 450.70 93.45
SIEMENS 1688 1299.53 1043.23 187.85 6205.15 1185.84
SBIN 1688 1057.08 930.63 270.00 2470.85 559.82
SAIL 1688 94.65 74.13 8.80 287.75 62.83
STER 1375 664.66 607.90 200.40 2855.15 324.83
SUN PHARMA 1688 843.20 855.55 267.40 1590.05 339.19
SUZLON 1022 760.13 883.28 33.30 2273.05 608.78
TCS 1312 1083.71 1085.78 366.65 2043.70 387.73
TATA MOTORS 1480 560.29 520.45 126.20 986.25 207.32
TATA POWER 1688 624.41 519.38 112.60 1629.15 373.53
TATA STEEL 1024 520.20 502.23 148.80 988.90 191.37
UNITECH 1685 569.54 227.15 23.15 14148.05 1555.37
WIPRO 1688 659.84 537.05 200.90 1762.30 360.97
Source: Computed from data source
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Here also to confirm the distributional patterns of the returns, the
researcher used Kolmogrov-Smirnov goodness of fit test. The test compared the
cumulative distributional function of the returns with a normal distribution to find
out whether they are identical.
The Kolmogorov-Smirnov Test compared the observed cumulative
distribution function to a theoretical (Normal) cumulative distribution. Low
significance value (<.05) indicated that the observed distribution does not
corresponds to the Normal distribution. Thus, the distribution of closing prices is
not normal. High significance values (>.05) indicate that the observed
distribution corresponds to the Normal distribution. Thus, the distribution of
closing prices is normal. Test result shows that the distribution does not come in
the category of normal distribution as the p-values are all less than .05 at 5
percent significance level.
Table 5.6
One-Sample Kolmogorov-Smirnov Test for Nifty Shares
Company Absolute Positive Negative K-S Z p-value
ABB 0.190 0.190 -0.148 7.816 0.000
ACC 0.103 0.103 -0.076 4.243 0.000
AMBUJA 0.104 0.104 -0.079 2.500 0.000
AXIS BANK 0.078 0.055 -0.078 1.862 0.002
BHEL 0.114 0.114 -0.090 4.698 0.000
BHARTI 0.122 0.095 -0.122 3.574 0.000
CAIRN 0.115 0.115 -0.056 3.085 0.000
CIPLA 0.303 0.303 -0.229 12.456 0.000
DLF 0.110 0.110 -0.070 2.697 0.000
GAIL 0.081 0.081 -0.044 3.317 0.000
GRASIM 0.140 0.140 -0.045 5.772 0.000
HCL 0.130 0.130 -0.071 5.344 0.000
HDFC BANK 0.092 0.092 -0.076 3.788 0.000
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HERO HONDA 0.151 0.151 -0.059 6.221 0.000
HINDALCO 0.095 0.095 -0.083 2.252 0.000
HINDUNILVA 0.040 0.040 -0.028 0.958 0.318
HDFC 0.118 0.118 -0.080 4.840 0.000
ITC 0.327 0.327 -0.195 13.416 0.000
ICICI BANK 0.091 0.091 -0.063 3.755 0.000
IDEA 0.084 0.084 -0.076 2.174 0.000
INFOSYS 0.203 0.203 -0.115 8.337 0.000
IDFC 0.166 0.166 -0.103 5.425 0.000
JP ASSO 0.167 0.167 -0.162 6.177 0.000
JINDAL 0.262 0.262 -0.258 10.759 0.000
LT 0.128 0.128 -0.088 4.733 0.000
M & M 0.034 0.028 -0.034 1.410 0.037
MARUTI 0.070 0.070 -0.058 2.813 0.000
NTPC 0.061 0.061 -0.046 2.166 0.000
ONGL 0.051 0.027 -0.051 2.114 0.000
POWER GRID 0.128 0.128 -0.062 2.952 0.000
PNB 0.078 0.078 -0.061 3.184 0.000
RANBAXY 0.203 0.203 -0.137 8.340 0.000
RELCAPITEL 0.157 0.146 -0.157 6.445 0.000
RCOM 0.089 0.089 -0.055 2.589 0.000
RELIANCE 0.138 0.138 -0.104 5.656 0.000
RELINFRA 0.128 0.117 -0.128 2.487 0.000
RPOWER 0.288 0.288 -0.166 6.062 0.000
SIEMENS 0.205 0.205 -0.175 8.435 0.000
SBIN 0.099 0.099 -0.080 4.066 0.000
SAIL 0.158 0.158 -0.086 6.500 0.000
STER 0.129 0.129 -0.085 4.801 0.000
SUN PHARMA 0.087 0.087 -0.056 3.555 0.000
SUZLON 0.202 0.202 -0.116 6.459 0.000
TCS 0.059 0.059 -0.034 2.144 0.000
TATA MOTORS 0.077 0.077 -0.058 2.957 0.000
TATA POWER 0.154 0.154 -0.085 6.344 0.000
TATA STEEL 0.069 0.069 -0.053 2.202 0.000
UNITECH 0.388 0.388 -0.363 15.914 0.000
WIPRO 0.204 0.204 -0.115 8.397 0.000
Source: Computed from data source
101
5.4.1 Non Parametric Test – Run test for companies involved in the
construction of Nifty
The run test was used to examine the serial independence in share return
movements. This test has the advantage of ignoring the distribution of the data,
and does not require normality or constant variance of the data. In this test the
actual number of runs observed in a series of stock price movements is compared
with the number of runs in a randomly generated number series. If there is no
significant difference between these two, then the security price changes are
considered to be at random.
From the table (Table5.7) it can be observed that the p-values of all the
companies are less than 0.05.So, the null hypothesis that the price movement is
not effected by the past price is rejected at 5 percent significant level. The
significant negative Z values indicate non-randomness of the series. These results
were similar with the test results of 29 companies taken and showed that the price
movements were not having randomness in behaviour. So it can be inferred that
an investor can make use of historical data for predicting the future prices.
Opportunity to make excess returns exist in the Indian stock market
Many previous studies on market efficiency have employed run tests in a
similar framework such as the studies by Fama (1965), Sharma and Kennedy
(1977), Cooper (1982), Chiat and Finn (1983), Wong and Kwong (1984),
Yalawar (1988), Ko and Lee (1991), Butler and Malaikah (1992), and Thomas
(1995). These studies typically find that in most markets except in Hong Kong,
India, Kuwait and Saudi Arabia, the null hypothesis is not rejected.
102
Table 5.7
Run Test Results for Nifty shares
Company Test Value Runs Z-value p-value
ABB 974.70 9 -40.696 0.000
ACC 553.90 30 -39.673 0.000
AMBUJA 94.35 9 -23.482 0.000
AXIS BANK 755.58 42 -20.362 0.000
BHEL 1437.60 34 -39.478 0.000
BHARTI 757.70 31 -27.241 0.000
CAIRN 204.55 22 -25.206 0.000
CIPLA 258.70 49 -38.760 0.000
DLF 434.48 12 -23.532 0.000
GAIL 254.00 48 -38.809 0.000
GRASIM 1503.33 4 -40.951 0.000
HCL 308.63 64 -38.030 0.000
HDFC BANK 808.93 12 -40.562 0.000
HERO HONDA 698.35 68 -37.835 0.000
HINDALCO 130.48 15 -22.501 0.000
HINDUNILVA 237.78 46 -20.507 0.000
HDFC 1310.95 24 -39.978 0.000
ITC 202.68 37 -39.344 0.000
ICICI BANK 533.13 22 -40.075 0.000
IDEA 88.40 15 -24.942 0.000
INFOSYS 2089.07 39 -39.247 0.000
IDFC 88.65 12 -32.006 0.000
JP ASSO 244.23 23 -35.754 0.000
JINDAL 1328.45 15 -40.416 0.000
LT 1572.80 34 -35.059 0.000
M & M 541.00 44 -39.004 0.000
MARUTI 690.48 22 -38.962 0.000
NTPC 151.90 22 -34.341 0.000
ONGL 888.30 44 -39.004 0.000
POWER GRID 101.78 26 -20.917 0.000
103
PNB 437.18 50 -38.711 0.000
RANBAXY 439.03 33 -39.539 0.000
RELCAPITEL 468.05 32 -39.588 0.000
RCOM 405.70 21 -27.763 0.000
RELIANCE 1009.95 8 -40.757 0.000
RELINFRA 981.75 15 -18.081 0.000
RPOWER 158.55 24 -18.883 0.000
SIEMENS 1043.22 19 -40.221 0.000
SBIN 930.63 30 -39.685 0.000
SAIL 74.13 28 -39.783 0.000
STER 607.90 58 -34.019 0.000
SUN PHARMA 855.55 10 -40.659 0.000
SUZLON 883.28 19 -30.858 0.000
TCS 1085.78 17 -35.352 0.000
TATA MOTORS 520.45 24 -37.288 0.000
TATA POWER 519.38 16 -40.367 0.000
TATA STEEL 502.23 42 -29.452 0.000
UNITECH 227.15 9 -40.671 0.000
WIPRO 537.05 39 -39.247 0.000
Source: Computed from data source
5.4.2 Non Parametric Test – Auto Correlation Test for companies involved
in the construction of Nifty
Numerous studies on market efficiency have reported serial correlation or
autocorrelation as one of the significant tool for investigating randomness on stock
prices and stock indices. Fama (1965) investigates the behavior of the daily closing
prices of the 30 Dow Jones Industrials and finds that the first-order autocorrelation of
daily returns are positive for 23 of the 30 firms, which suggests a positive
relationship between successive daily returns. Typical recent literature on serial
correlation or autocorrelation in return movements includes LeBaron (1992), Sentana
and Wadhwani (1992), and Campbell, Grossman, and Wang (1993).
104
Table 5.8
Autocorrelation Results for Nifty Shares
Company T + 4 T+9 T+14
ABB 0.977 (H) 0.955 (H) 0.933 (H)
ACC 0.987 (H) 0.974 (H) 0.960 (H)
AMBUJA 0.977 (H) 0.952 (H) 0.927 (H)
AXIS BANK 0.942 (H) 0.900 (H) 0.853 (H)
BHEL 0.981 (H) 0.964 (H) 0.949 (H)
BHARTI 0.939 (H) 0.891 (H) 0.837 (H)
CAIRN 0.944 (H) 0.900 (H) 0.858 (H)
CIPLA 0.974 (H) 0.948 (H) 0.923 (H)
DLF 0.977 (H) 0.953 (H) 0.929 (H)
GAIL 0.969 (H) 0.941 (H) 0.913 (H)
GRASIM 0.988 (H) 0.974 (H) 0.958 (H)
HCL 0.982 (H) 0.967 (H) 0.949 (H)
HDFC BANK 0.982 (H) 0.967 (H) 0.951 (H)
HERO HONDA 0.975 (H) 0.950 (H) 0.927 (H)
HINDALCO 0.975 (H) 0.955 (H) 0.936 (H)
HINDUNILVA 0.875 (H) 0.801 (H) 0.738 (M)
HDFC 0.983 (H) 0.971 (H) 0.958 (H)
ITC 0.978 (H) 0.958 (H) 0.939 (H)
ICICI BANK 0.983 (H) 0.969 (H) 0.954 (H)
IDEA 0.970 (H) 0.942 (H) 0.917 (H)
INFOSYS 0.967 (H) 0.938 (H) 0.911 (H)
IDFC 0.975 (H) 0.955 (H) 0.932 (H)
JP ASSO 0.943 (H) 0.892 (H) 0.851 (H)
JINDAL 0.942 (H) 0.885 (H) 0.832 (H)
LT 0.977 (H) 0.955 (H) 0.933 (H)
M & M 0.964 (H) 0.933 (H) 0.901 (H)
MARUTI 0.970 (H) 0.941 (H) 0.914 (H)
NTPC 0.973 (H) 0.951 (H) 0.932 (H)
105
ONGL 0.959 (H) 0.921 (H) 0.883 (H)
POWER GRID 0.932 (H) 0.874 (H) 0.816 (H)
PNB 0.964 (H) 0.929 (H) 0.895 (H)
RANBAXY 0.990 (H) 0.978 (H) 0.966 (H)
RELCAPITEL 0.985 (H) 0.972 (H) 0.957 (H)
RCOM 0.972 (H) 0.948 (H) 0.923 (H)
RELIANCE 0.987 (H) 0.977 (H) 0.966 (H)
RELINFRA 0.929 (H) 0.864 (H) 0.797 (H)
RPOWER 0.933 (H) 0.841 (H) 0.748 (M)
SIEMENS 0.980 (H) 0.955 (H) 0.929 (H)
SBIN 0.980 (H) 0.962 (H) 0.942 (H)
SAIL 0.981 (H) 0.966 (H) 0.951 (H)
STER 0.892 (H) 0.808 (H) 0.716 (M)
SUN PHARMA 0.980 (H) 0.963 (H) 0.947 (H)
SUZLON 0.972 (H) 0.941 (H) 0.915 (H)
TCS 0.977 (H) 0.957 (H) 0.936 (H)
TATA MOTORS 0.985 (H) 0.971 (H) 0.955 (H)
TATA POWER 0.981 (H) 0.964 (H) 0.951 (H)
TATA STEEL 0.974 (H) 0.947 (H) 0.918 (H)
UNITECH 0.926 (H) 0.845 (H) 0.732 (M)
WIPRO 0.973 (H) 0.950 (H) 0.925 (H)
Source: Computed from data source
H High correlation (±0.75 to ±1)
M Moderate Correlation (±0.25 to ±0.75)
L Low Correlation (±0 to ±0.25)
The results of the tests indicate the existence of high correlation between
the share prices for majority of the companies included in the study. The
estimated serial correlation or autocorrelation is presented in Table 5.8
106
The study which is presented in this chapter seeks evidence supporting the
existence of weak-form efficiency of Indian market. The sample included the
daily closing price of all the shares included in the formation of Nifty Index. The
study period was from 2004-2009. The null hypothesis of the study was whether
the Indian Stock Market is weak form efficient. The results of both non-
parametric (Kolmogrov –Smirnov goodness of fit test and run test) and
parametric test ( Auto-correlation test )provide evidence that the share prices do
not follow random walk model and the significant autocorrelation co-efficient at
different lags reject the null hypothesis of weak-form efficiency.
The results are consistent in different sub-sample observations and for
individual securities. The issues are important to security analysts, investors and
to security exchange regulatory bodies in their policy making decisions to
improve the market condition. This study deserves a continuous research on this
area to reach an ultimate conclusion about the level of efficiency of emerging
markets like India market.
5.5 Semi-strong Market Efficiency of Indian Stock Market
Semi –strong market efficiency is part of Efficient Market Hypothesis
which implies that all publicly available information is calculated into a stock's
current share price. This means that neither fundamental nor technical analysis
can be used to achieve superior gains. In an efficient market, when a new piece of
information is added to the market, its implications for security returns are
instantaneously and un biasedly impounded in the current market price. In other
words it can be said that a capital market is efficient if the corporate event
announcements like stock split, buyback, right issue, bonus announcement,
107
merges & acquisitions, dividend etc are quickly and correctly reflected in the
security’s prices.
In the second part of this chapter researcher presents the results of the test
of Semi-strong efficiency of Indian Stock market. The study had been conducted
on 29 companies’ shares whose data were present through out the study period of
6 years.
5.6 Test Result of Semi-strong efficiency of Indian Stock Market
Semi-strong efficiency tests deal with whether or not security prices fully
reflect all publically available information. All these tests attempt to experiment
whether share prices react quickly and correctly to a new piece of information. If
the results give evidence that share prices do not react adequately and quickly to
the various information, it means that the market offers opportunities for earning
superior returns.
An investor can earn excess returns by using this publicly available
information. Some of the earlier studies conducted in testing Semi-strong form of
market efficiency have been contributed by Fama, Fisher and Jense.
Methodology followed in various studies testing Semi-strong market efficiency is
to take an economic event and measure its impact on the share price. The impact
is measured by taking the difference between actual return and expected return on
a security. This is known as the residual analysis. Excess return would be present
if there is a positive difference between the actual return and expected return. In
the present study also the researcher had used the residual analysis model
suggested by William Sharpe.
108
The formula used for calculating Expected return (Ri )
Ri = αi + βi Rm + ei
Where:
Ri = Expected Return of the i th stock
αi = Intercept
βi = Beta value of the i th stock
Rm = Return of the market index
ei = The error factor
The formula used for calculating Actual Security return =
Today’s security return Today’s price – Yesterday’s price *100
Yesterday’s Price
Today’s market return = Today’s index – Yesterday’s index *100
Yesterday’s index
Systematic risk is the variability in security returns caused by economic or
other market factors. All securities traded in the market will be affected by such
changes. But some of them exhibit greater variability while others have some
minor variations. The securities which are affected to a greater extend are said to
have higher systematic risk. Systematic risk is measured by relating the security’s
variability with the variability in the market index.
109
Beta is the statistical measure of the risk of a security. A security can have
positive, negative or zero beta value. Lager the volatility of a share, larger will be
the beta value for that share. A beta of 1.0 indicates a security of average risk. If
beta value is more than 1.0 it has above average risk. Alpha is the difference
between the actual return produced by an investment and the rate that might have
been expected, given its level of beta. Beta expresses risk in relation to the
market as a whole and its value can be positive or negative, but in practice it
tends to fall between +0.25 and +1.75.
The formula used for finding the beta and alpha co-efficient can be expressed
as:
β = n ∑ X Y – ( ∑ x) ∑ y)
n∑ X2 -
( ∑ X )
2
Where:
X = NSE Index
Y = Closing price of the security
x = Index return
y = security return
α = Y - β X
Residual Return = Actual return – Expected Return
(Residual return will be positive if the actual return is more than the estimated
return)
110
If the excess return or residual return is close to zero, it implies that the
price reaction following any of the public announcements is immediate and price
adjusts quickly to the new level. If the excess return is zero or near to zero it
would validate the presence of Semi-strong form of market efficiency.
The following tables give the test result of Semi-strong form of market
efficiency. Tests have been conducted using daily closing price of the 29
companies’ shares whose data was available for the study period of six years. The
entire study period was split in to three months each and the process was repeated
for better results. Residual mean indicate the mean of the residual returns on a
daily basis for the period under study.
111
Table 5.9
Test of Semi-strong form of market efficiency for Jan 2004 – Mar 2004
Company Residual Mean N Result
ABB 0.01612 30 Efficient
ACC 0.01031 31 Efficient
BHEL 0.01580 31 Efficient
CIPLA 0.01295 30 Efficient
GAIL 0.02289 30 Efficient
GRASIM 0.01682 29 Efficient
HCL 0.01912 32 Efficient
HDFC BANK 0.01416 28 Efficient
HERO HONDA 0.01825 30 Efficient
HDFC 0.01311 31 Efficient
ITC 0.01294 32 Efficient
ICICI BANK 0.01777 34 Efficient
INFOSYS 0.01634 28 Efficient
JINDAL 0.02139 31 Efficient
M & M 0.01588 30 Efficient
MARUTI 0.02256 25 Efficient
ONGL 0.02341 28 Efficient
PNB 0.03268 26 Efficient
RANBAXY 0.00806 36 Efficient
RELCAPITEL 0.01873 29 Efficient
RELIANCE 0.00733 31 Efficient
SIEMENS 0.01244 29 Efficient
SBIN 0.01197 27 Efficient
SAIL 0.01369 35 Efficient
SUN PHARMA 0.01527 33 Efficient
TATA MOTORS 0.01708 26 Efficient
TATA POWER 0.02085 24 Efficient
UNITECH 0.03790 24 Efficient
WIPRO 0.01099 34 Efficient
Source: Computed from data source
112
Table 5.10
Test of Semi-strong form of market efficiency for Apr 2004 – Jun 2004
Company Residual Mean N Result
ABB 0.01509 29 Efficient
ACC 0.00958 31 Efficient
BHEL 0.01600 30 Efficient
CIPLA 0.01261 30 Efficient
GAIL 0.02239 29 Efficient
GRASIM 0.01743 29 Efficient
HCL 0.01902 31 Efficient
HDFC BANK 0.01455 27 Efficient
HERO HONDA 0.01825 30 Efficient
HDFC 0.01366 30 Efficient
ITC 0.01207 30 Efficient
ICICI BANK 0.01779 33 Efficient
INFOSYS 0.01562 27 Efficient
JINDAL 0.02068 31 Efficient
M & M 0.01588 30 Efficient
MARUTI 0.02256 25 Efficient
ONGL 0.02131 27 Efficient
PNB 0.03149 27 Efficient
RANBAXY 0.00806 36 Efficient
RELCAPITEL 0.01970 28 Efficient
RELIANCE 0.00728 31 Efficient
SIEMENS 0.01244 29 Efficient
SBIN 0.01043 26 Efficient
SAIL 0.01374 36 Efficient
SUN PHARMA 0.01561 32 Efficient
TATA MOTORS 0.01708 26 Efficient
TATA POWER 0.02083 24 Efficient
UNITECH 0.03599 23 Efficient
WIPRO 0.01099 34 Efficient
Source: Computed from data source
113
Table 5.11
Test of Semi-strong form of market efficiency for Jul 2004 – Sep 2004
Company Residual Mean N Result
ABB 0.01534 30 Efficient
ACC 0.00937 30 Efficient
BHEL 0.01634 29 Efficient
CIPLA 0.01265 32 Efficient
GAIL 0.02235 30 Efficient
GRASIM 0.01608 29 Efficient
HCL 0.01985 33 Efficient
HDFC BANK 0.01455 27 Efficient
HERO HONDA 0.01923 28 Efficient
HDFC 0.01339 31 Efficient
ITC 0.01207 30 Efficient
ICICI BANK 0.01809 32 Efficient
INFOSYS 0.01659 28 Efficient
JINDAL 0.02068 31 Efficient
M & M 0.01627 29 Efficient
MARUTI 0.02249 25 Efficient
ONGL 0.02140 26 Efficient
PNB 0.03324 25 Efficient
RANBAXY 0.00845 37 Efficient
RELCAPITEL 0.01970 30 Efficient
RELIANCE 0.00705 31 Efficient
SIEMENS 0.01265 29 Efficient
SBIN 0.01084 26 Efficient
SAIL 0.01428 35 Efficient
SUN PHARMA 0.01594 32 Efficient
TATA MOTORS 0.01698 26 Efficient
TATA POWER 0.01763 24 Efficient
UNITECH 0.03769 25 Efficient
WIPRO 0.01170 35 Efficient
Source: Computed from data source
114
Table 5.12
Test of Semi-strong form of market efficiency for Oct 2004 – Dec 2004
Company Residual Mean N Result
ABB 0.01537 30 Efficient
ACC 0.00926 32 Efficient
BHEL 0.01613 29 Efficient
CIPLA 0.01201 32 Efficient
GAIL 0.02188 31 Efficient
GRASIM 0.01480 27 Efficient
HCL 0.01880 32 Efficient
HDFC BANK 0.01445 26 Efficient
HERO HONDA 0.01811 29 Efficient
HDFC 0.01381 31 Efficient
ITC 0.01135 29 Efficient
ICICI BANK 0.01825 32 Efficient
INFOSYS 0.01702 27 Efficient
JINDAL 0.02082 30 Efficient
M & M 0.01529 29 Efficient
MARUTI 0.01920 25 Efficient
ONGL 0.02284 26 Efficient
PNB 0.03232 23 Efficient
RANBAXY 0.00894 38 Efficient
RELCAPITEL 0.02037 30 Efficient
RELIANCE 0.00705 31 Efficient
SIEMENS 0.01204 30 Efficient
SBIN 0.00986 26 Efficient
SAIL 0.01329 35 Efficient
SUN PHARMA 0.01618 32 Efficient
TATA MOTORS 0.01661 27 Efficient
TATA POWER 0.01591 24 Efficient
UNITECH 0.03966 23 Efficient
WIPRO 0.01254 35 Efficient
Source: Computed from data source
115
Table 5.13
Test of Semi-strong form of market efficiency for Jan 2005 – Mar 2005
Company Residual Mean N Result
ABB 0.01514 31 Efficient
ACC 0.00929 33 Efficient
BHEL 0.01634 29 Efficient
CIPLA 0.01249 33 Efficient
GAIL 0.02221 31 Efficient
GRASIM 0.01461 27 Efficient
HCL 0.01977 32 Efficient
HDFC BANK 0.01445 26 Efficient
HERO HONDA 0.01818 28 Efficient
HDFC 0.01323 31 Efficient
ITC 0.01065 28 Efficient
ICICI BANK 0.01840 31 Efficient
INFOSYS 0.01853 27 Efficient
JINDAL 0.02028 31 Efficient
M & M 0.01498 28 Efficient
MARUTI 0.01920 25 Efficient
ONGL 0.02284 26 Efficient
PNB 0.02870 23 Efficient
RANBAXY 0.00813 36 Efficient
RELCAPITEL 0.02088 30 Efficient
RELIANCE 0.00688 29 Efficient
SIEMENS 0.01226 31 Efficient
SBIN 0.00913 27 Efficient
SAIL 0.01365 35 Efficient
SUN PHARMA 0.01663 33 Efficient
TATA MOTORS 0.01511 26 Efficient
TATA POWER 0.01661 23 Efficient
UNITECH 0.04208 23 Efficient
WIPRO 0.01497 35 Efficient
Source: Computed from data source
116
Table 5.14
Test of Semi-strong form of market efficiency for Apr 2005 – Jun 2005
Company Residual Mean N Result
ABB 0.01452 29 Efficient
ACC 0.01022 34 Efficient
BHEL 0.01634 29 Efficient
CIPLA 0.01293 35 Efficient
GAIL 0.02074 31 Efficient
GRASIM 0.01493 27 Efficient
HCL 0.02055 32 Efficient
HDFC BANK 0.01293 25 Efficient
HERO HONDA 0.01775 29 Efficient
HDFC 0.01342 31 Efficient
ITC 0.01016 27 Efficient
ICICI BANK 0.01840 31 Efficient
INFOSYS 0.01798 28 Efficient
JINDAL 0.02077 30 Efficient
M & M 0.01500 28 Efficient
MARUTI 0.02060 24 Efficient
ONGL 0.02212 26 Efficient
PNB 0.02823 24 Efficient
RANBAXY 0.00831 36 Efficient
RELCAPITEL 0.02110 29 Efficient
RELIANCE 0.00690 28 Efficient
SIEMENS 0.01253 31 Efficient
SBIN 0.00874 26 Efficient
SAIL 0.01298 36 Efficient
SUN PHARMA 0.01760 33 Efficient
TATA MOTORS 0.01507 26 Efficient
TATA POWER 0.01630 23 Efficient
UNITECH 0.03986 23 Efficient
WIPRO 0.01526 36 Efficient
Source: Computed from data source
117
Table 5.15
Test of Semi-strong form of market efficiency for Jul 2005 – Sep 2005
Company Residual Mean N Result
ABB 0.01452 30 Efficient
ACC 0.01036 33 Efficient
BHEL 0.01675 28 Efficient
CIPLA 0.01401 35 Efficient
GAIL 0.02074 31 Efficient
GRASIM 0.01493 27 Efficient
HCL 0.02023 31 Efficient
HDFC BANK 0.01311 26 Efficient
HERO HONDA 0.01706 27 Efficient
HDFC 0.01296 29 Efficient
ITC 0.01069 27 Efficient
ICICI BANK 0.01815 32 Efficient
INFOSYS 0.01752 28 Efficient
JINDAL 0.01910 31 Efficient
M & M 0.01500 28 Efficient
MARUTI 0.02100 24 Efficient
ONGL 0.02278 25 Efficient
PNB 0.02823 24 Efficient
RANBAXY 0.00910 36 Efficient
RELCAPITEL 0.02088 28 Efficient
RELIANCE 0.00613 27 Efficient
SIEMENS 0.01342 32 Efficient
SBIN 0.00881 26 Efficient
SAIL 0.01311 37 Efficient
SUN PHARMA 0.01760 33 Efficient
TATA MOTORS 0.01480 24 Efficient
TATA POWER 0.01652 24 Efficient
UNITECH 0.03927 23 Efficient
WIPRO 0.01462 36 Efficient
Source: Computed from data source
118
Table 5.16
Test of Semi-strong form of market efficiency for Oct 2005 – Dec 2005
Company Residual Mean N Result
ABB 0.01451 30 Efficient
ACC 0.01046 32 Efficient
BHEL 0.01723 27 Efficient
CIPLA 0.01497 37 Efficient
GAIL 0.02043 32 Efficient
GRASIM 0.01507 27 Efficient
HCL 0.01915 31 Efficient
HDFC BANK 0.01348 24 Efficient
HERO HONDA 0.01700 27 Efficient
HDFC 0.01296 29 Efficient
ITC 0.01101 26 Efficient
ICICI BANK 0.01870 31 Efficient
INFOSYS 0.01763 26 Efficient
JINDAL 0.01965 32 Efficient
M & M 0.01497 29 Efficient
MARUTI 0.02123 25 Efficient
ONGL 0.02258 25 Efficient
PNB 0.02823 24 Efficient
RANBAXY 0.00910 35 Efficient
RELCAPITEL 0.02055 28 Efficient
RELIANCE 0.00621 28 Efficient
SIEMENS 0.01346 31 Efficient
SBIN 0.00850 28 Efficient
SAIL 0.01422 38 Efficient
SUN PHARMA 0.01701 34 Efficient
TATA MOTORS 0.01498 26 Efficient
TATA POWER 0.01592 25 Efficient
UNITECH 0.03909 24 Efficient
WIPRO 0.01497 35 Efficient
Source: Computed from data source
119
Table 5.17
Test of Semi-strong form of market efficiency for Jan 2006 – Mar 2006
Company Residual Mean N Result
ABB 0.01451 30 Efficient
ACC 0.01064 34 Efficient
BHEL 0.01575 26 Efficient
CIPLA 0.01523 38 Efficient
GAIL 0.01935 31 Efficient
GRASIM 0.01472 28 Efficient
HCL 0.01939 30 Efficient
HDFC BANK 0.01362 24 Efficient
HERO HONDA 0.01725 26 Efficient
HDFC 0.01332 28 Efficient
ITC 0.01061 27 Efficient
ICICI BANK 0.02068 31 Efficient
INFOSYS 0.01757 25 Efficient
JINDAL 0.01732 33 Efficient
M & M 0.01333 28 Efficient
MARUTI 0.02139 24 Efficient
ONGL 0.02348 23 Efficient
PNB 0.02716 25 Efficient
RANBAXY 0.00938 35 Efficient
RELCAPITEL 0.02029 27 Efficient
RELIANCE 0.00621 28 Efficient
SIEMENS 0.01291 31 Efficient
SBIN 0.00779 29 Efficient
SAIL 0.01418 38 Efficient
SUN PHARMA 0.01792 33 Efficient
TATA MOTORS 0.01455 26 Efficient
TATA POWER 0.01387 25 Efficient
UNITECH 0.03485 25 Efficient
WIPRO 0.01457 34 Efficient
Source: Computed from data source
120
Table 5.17
Test of Semi-strong form of market efficiency for Apr 2006 – Jun 2006
Company Residual Mean N Result
ABB 0.01413 31 Efficient
ACC 0.00975 33 Efficient
BHEL 0.01584 27 Efficient
CIPLA 0.01688 38 Efficient
GAIL 0.01951 30 Efficient
GRASIM 0.01517 28 Efficient
HCL 0.01970 29 Efficient
HDFC BANK 0.01512 25 Efficient
HERO HONDA 0.01749 25 Efficient
HDFC 0.01207 28 Efficient
ITC 0.01033 28 Efficient
ICICI BANK 0.02053 32 Efficient
INFOSYS 0.01757 25 Efficient
JINDAL 0.01780 34 Efficient
M & M 0.01397 29 Efficient
MARUTI 0.01999 26 Efficient
ONGL 0.02241 25 Efficient
PNB 0.02629 26 Efficient
RANBAXY 0.00950 37 Efficient
RELCAPITEL 0.02078 25 Efficient
RELIANCE 0.00571 27 Efficient
SIEMENS 0.01259 33 Efficient
SBIN 0.00833 30 Efficient
SAIL 0.01470 38 Efficient
SUN PHARMA 0.01844 33 Efficient
TATA MOTORS 0.01196 25 Efficient
TATA POWER 0.01413 24 Efficient
UNITECH 0.03244 25 Efficient
WIPRO 0.01453 35 Efficient
Source: Computed from data source
121
Table 5.18
Test of Semi-strong form of market efficiency for Jul 2006 – Sep 2006
Company Residual Mean N Result
ABB 0.01408 31 Efficient
ACC 0.01018 33 Efficient
BHEL 0.01570 28 Efficient
CIPLA 0.01794 40 Efficient
GAIL 0.01985 28 Efficient
GRASIM 0.01478 29 Efficient
HCL 0.02034 28 Efficient
HDFC BANK 0.01478 26 Efficient
HERO HONDA 0.01605 26 Efficient
HDFC 0.01233 27 Efficient
ITC 0.01068 28 Efficient
ICICI BANK 0.01954 32 Efficient
INFOSYS 0.01814 25 Efficient
JINDAL 0.01726 35 Efficient
M & M 0.01324 27 Efficient
MARUTI 0.01934 25 Efficient
ONGL 0.02192 26 Efficient
PNB 0.02560 27 Efficient
RANBAXY 0.00979 36 Efficient
RELCAPITEL 0.02066 26 Efficient
RELIANCE 0.00572 27 Efficient
SIEMENS 0.01277 33 Efficient
SBIN 0.00868 31 Efficient
SAIL 0.01400 36 Efficient
SUN PHARMA 0.01927 34 Efficient
TATA MOTORS 0.01099 23 Efficient
TATA POWER 0.01304 25 Efficient
UNITECH 0.03239 26 Efficient
WIPRO 0.01487 36 Efficient
Source: Computed from data source
122
Table 5.19
Test of Semi-strong form of market efficiency for Oct 2006 – Dec 2006
Company Residual Mean N Result
ABB 0.01404 33 Efficient
ACC 0.01140 33 Efficient
BHEL 0.01361 28 Efficient
CIPLA 0.01835 41 Efficient
GAIL 0.01968 28 Efficient
GRASIM 0.01470 28 Efficient
HCL 0.01975 29 Efficient
HDFC BANK 0.01428 25 Efficient
HERO HONDA 0.01648 26 Efficient
HDFC 0.01331 28 Efficient
ITC 0.01068 28 Efficient
ICICI BANK 0.01903 32 Efficient
INFOSYS 0.01751 25 Efficient
JINDAL 0.01686 33 Efficient
M & M 0.01272 26 Efficient
MARUTI 0.01879 25 Efficient
ONGL 0.02213 27 Efficient
PNB 0.02665 28 Efficient
RANBAXY 0.00999 36 Efficient
RELCAPITEL 0.02129 26 Efficient
RELIANCE 0.00572 27 Efficient
SIEMENS 0.01253 33 Efficient
SBIN 0.00953 32 Efficient
SAIL 0.01398 36 Efficient
SUN PHARMA 0.01821 33 Efficient
TATA MOTORS 0.01114 22 Efficient
TATA POWER 0.01418 25 Efficient
UNITECH 0.03325 26 Efficient
WIPRO 0.01549 37 Efficient
Source: Computed from data source
123
Table 5.20
Test of Semi-strong form of market efficiency for Jan 2007 – Mar 2007
Company Residual Mean N Result
ABB 0.01435 32 Efficient
ACC 0.01132 33 Efficient
BHEL 0.01387 26 Efficient
CIPLA 0.01832 41 Efficient
GAIL 0.01937 28 Efficient
GRASIM 0.01529 30 Efficient
HCL 0.01944 30 Efficient
HDFC BANK 0.01396 24 Efficient
HERO HONDA 0.01609 27 Efficient
HDFC 0.01414 29 Efficient
ITC 0.01084 27 Efficient
ICICI BANK 0.01847 30 Efficient
INFOSYS 0.01759 23 Efficient
JINDAL 0.01596 32 Efficient
M & M 0.01321 27 Efficient
MARUTI 0.01953 24 Efficient
ONGL 0.02185 27 Efficient
PNB 0.02706 26 Efficient
RANBAXY 0.00988 36 Efficient
RELCAPITEL 0.02424 26 Efficient
RELIANCE 0.00587 27 Efficient
SIEMENS 0.01244 33 Efficient
SBIN 0.00965 32 Efficient
SAIL 0.01343 38 Efficient
SUN PHARMA 0.01843 33 Efficient
TATA MOTORS 0.01095 23 Efficient
TATA POWER 0.01350 27 Efficient
UNITECH 0.03101 28 Efficient
WIPRO 0.01483 37 Efficient
Source: Computed from data source
124
Table 5.21
Test of Semi-strong form of market efficiency for Apr 2007 – Jun 2007
Company Residual Mean N Result
ABB 0.01429 33 Efficient
ACC 0.01164 32 Efficient
BHEL 0.01611 27 Efficient
CIPLA 0.01844 41 Efficient
GAIL 0.01896 28 Efficient
GRASIM 0.01577 29 Efficient
HCL 0.01914 29 Efficient
HDFC BANK 0.01458 23 Efficient
HERO HONDA 0.01604 27 Efficient
HDFC 0.01463 29 Efficient
ITC 0.01096 26 Efficient
ICICI BANK 0.01696 30 Efficient
INFOSYS 0.01759 23 Efficient
JINDAL 0.01621 32 Efficient
M & M 0.01397 28 Efficient
MARUTI 0.01953 24 Efficient
ONGL 0.02099 29 Efficient
PNB 0.02629 26 Efficient
RANBAXY 0.00988 35 Efficient
RELCAPITEL 0.02478 26 Efficient
RELIANCE 0.00570 27 Efficient
SIEMENS 0.01282 34 Efficient
SBIN 0.00935 33 Efficient
SAIL 0.01431 39 Efficient
SUN PHARMA 0.01842 34 Efficient
TATA MOTORS 0.01055 24 Efficient
TATA POWER 0.01369 28 Efficient
UNITECH 0.03032 29 Efficient
WIPRO 0.01467 37 Efficient
Source: Computed from data source
125
Table 5.22
Test of Semi-strong form of market efficiency for Jul 2007 – Sep 2007
Company Residual Mean N Result
ABB 0.01468 32 Efficient
ACC 0.01136 33 Efficient
BHEL 0.01501 26 Efficient
CIPLA 0.01856 41 Efficient
GAIL 0.01841 29 Efficient
GRASIM 0.01521 28 Efficient
HCL 0.01915 30 Efficient
HDFC BANK 0.01620 22 Efficient
HERO HONDA 0.01483 26 Efficient
HDFC 0.01482 28 Efficient
ITC 0.01184 24 Efficient
ICICI BANK 0.01684 30 Efficient
INFOSYS 0.01738 24 Efficient
JINDAL 0.01573 33 Efficient
M & M 0.01282 28 Efficient
MARUTI 0.01955 23 Efficient
ONGL 0.02050 31 Efficient
PNB 0.02598 26 Efficient
RANBAXY 0.01033 34 Efficient
RELCAPITEL 0.02471 26 Efficient
RELIANCE 0.00580 27 Efficient
SIEMENS 0.01214 33 Efficient
SBIN 0.00988 33 Efficient
SAIL 0.01477 37 Efficient
SUN PHARMA 0.01846 35 Efficient
TATA MOTORS 0.01133 24 Efficient
TATA POWER 0.01369 28 Efficient
UNITECH 0.02959 29 Efficient
WIPRO 0.01530 38 Efficient
Source: Computed from data source
126
Table 5.23
Test of Semi-strong form of market efficiency for Oct 2007 – Dec 2007
Company Residual Mean N Result
ABB 0.01458 31 Efficient
ACC 0.01126 35 Efficient
BHEL 0.01429 26 Efficient
CIPLA 0.01827 41 Efficient
GAIL 0.01841 29 Efficient
GRASIM 0.01598 29 Efficient
HCL 0.02178 30 Efficient
HDFC BANK 0.01840 22 Efficient
HERO HONDA 0.01586 26 Efficient
HDFC 0.01535 28 Efficient
ITC 0.01145 24 Efficient
ICICI BANK 0.01684 30 Efficient
INFOSYS 0.01816 26 Efficient
JINDAL 0.01630 32 Efficient
M & M 0.01137 29 Efficient
MARUTI 0.01845 22 Efficient
ONGL 0.02050 31 Efficient
PNB 0.02598 26 Efficient
RANBAXY 0.01138 34 Efficient
RELCAPITEL 0.02596 27 Efficient
RELIANCE 0.00699 29 Efficient
SIEMENS 0.01154 33 Efficient
SBIN 0.00988 33 Efficient
SAIL 0.01389 36 Efficient
SUN PHARMA 0.01995 34 Efficient
TATA MOTORS 0.01152 25 Efficient
TATA POWER 0.01450 30 Efficient
UNITECH 0.02848 29 Efficient
WIPRO 0.01726 39 Efficient
Source: Computed from data source
127
Table 5.24
Test of Semi-strong form of market efficiency for Jan 2008 – Mar 2008
Company Residual Mean N Result
ABB 0.01466 30 Efficient
ACC 0.01132 35 Efficient
BHEL 0.01464 25 Efficient
CIPLA 0.01944 41 Efficient
GAIL 0.02250 30 Efficient
GRASIM 0.01578 29 Efficient
HCL 0.02259 30 Efficient
HDFC BANK 0.02476 22 Efficient
HERO HONDA 0.01506 27 Efficient
HDFC 0.01945 28 Efficient
ITC 0.01264 24 Efficient
ICICI BANK 0.01651 30 Efficient
INFOSYS 0.01828 26 Efficient
JINDAL 0.01776 33 Efficient
M & M 0.01154 27 Efficient
MARUTI 0.02134 23 Efficient
ONGL 0.01896 30 Efficient
PNB 0.02755 27 Efficient
RANBAXY 0.01136 34 Efficient
RELCAPITEL 0.02559 27 Efficient
RELIANCE 0.00685 27 Efficient
SIEMENS 0.01135 32 Efficient
SBIN 0.01023 34 Efficient
SAIL 0.01405 37 Efficient
SUN PHARMA 0.01818 33 Efficient
TATA MOTORS 0.01259 25 Efficient
TATA POWER 0.01450 30 Efficient
UNITECH 0.02814 29 Efficient
WIPRO 0.02037 40 Efficient
Source: Computed from data source
128
Table 5.24
Test of Semi-strong form of market efficiency for Apr 2008 – Jun 2008
Company Residual Mean N Result
ABB 0.01520 30 Efficient
ACC 0.01089 35 Efficient
BHEL 0.01783 24 Efficient
CIPLA 0.02005 42 Efficient
GAIL 0.02242 30 Efficient
GRASIM 0.01596 28 Efficient
HCL 0.02265 30 Efficient
HDFC BANK 0.02455 23 Efficient
HERO HONDA 0.01474 26 Efficient
HDFC 0.01956 27 Efficient
ITC 0.01273 22 Efficient
ICICI BANK 0.01672 30 Efficient
INFOSYS 0.01828 26 Efficient
JINDAL 0.01943 34 Efficient
M & M 0.01364 27 Efficient
MARUTI 0.02253 22 Efficient
ONGL 0.02001 30 Efficient
PNB 0.02641 29 Efficient
RANBAXY 0.01165 32 Efficient
RELCAPITEL 0.02527 28 Efficient
RELIANCE 0.00667 26 Efficient
SIEMENS 0.01129 31 Efficient
SBIN 0.01019 36 Efficient
SAIL 0.01576 37 Efficient
SUN PHARMA 0.01837 34 Efficient
TATA MOTORS 0.01224 25 Efficient
TATA POWER 0.01497 32 Efficient
UNITECH 0.02894 30 Efficient
WIPRO 0.02087 39 Efficient
Source: Computed from data source
129
Table 5.25
Test of Semi-strong form of market efficiency for Jul 2008 – Sep 2008
Company Residual Mean N Result
ABB 0.01577 29 Efficient
ACC 0.01116 34 Efficient
BHEL 0.01845 23 Efficient
CIPLA 0.02062 42 Efficient
GAIL 0.02242 30 Efficient
GRASIM 0.01616 30 Efficient
HCL 0.02407 32 Efficient
HDFC BANK 0.02486 22 Efficient
HERO HONDA 0.01324 27 Efficient
HDFC 0.01997 28 Efficient
ITC 0.01189 22 Efficient
ICICI BANK 0.01643 31 Efficient
INFOSYS 0.01940 26 Efficient
JINDAL 0.01909 35 Efficient
M & M 0.01477 27 Efficient
MARUTI 0.02253 22 Efficient
ONGL 0.02031 31 Efficient
PNB 0.02702 28 Efficient
RANBAXY 0.01175 32 Efficient
RELCAPITEL 0.02489 28 Efficient
RELIANCE 0.00667 26 Efficient
SIEMENS 0.01145 29 Efficient
SBIN 0.01005 35 Efficient
SAIL 0.01589 35 Efficient
SUN PHARMA 0.01772 35 Efficient
TATA MOTORS 0.01277 26 Efficient
TATA POWER 0.01500 32 Efficient
UNITECH 0.03016 28 Efficient
WIPRO 0.02150 40 Efficient
Source: Computed from data source
130
Table 5.26
Test of Semi-strong form of market efficiency for Oct 2008 – Dec 2008
Company Residual Mean N Result
ABB 0.01575 29 Efficient
ACC 0.01138 33 Efficient
BHEL 0.02044 23 Efficient
CIPLA 0.02009 42 Efficient
GAIL 0.02213 30 Efficient
GRASIM 0.01615 30 Efficient
HCL 0.02374 32 Efficient
HDFC BANK 0.02434 22 Efficient
HERO HONDA 0.01336 27 Efficient
HDFC 0.02073 27 Efficient
ITC 0.01137 22 Efficient
ICICI BANK 0.01667 31 Efficient
INFOSYS 0.01948 26 Efficient
JINDAL 0.01892 34 Efficient
M & M 0.01477 27 Efficient
MARUTI 0.02228 21 Efficient
ONGL 0.01975 31 Efficient
PNB 0.02698 30 Efficient
RANBAXY 0.01150 33 Efficient
RELCAPITEL 0.02433 28 Efficient
RELIANCE 0.00717 27 Efficient
SIEMENS 0.01214 29 Efficient
SBIN 0.00987 36 Efficient
SAIL 0.01600 34 Efficient
SUN PHARMA 0.01776 37 Efficient
TATA MOTORS 0.01275 25 Efficient
TATA POWER 0.01526 33 Efficient
UNITECH 0.03016 28 Efficient
WIPRO 0.02171 40 Efficient
Source: Computed from data source
131
Table 5.27
Test of Semi-strong form of market efficiency for Jan 2009 – Mar 2009
Company Residual Mean N Result
ABB 0.01593 30 Efficient
ACC 0.01098 33 Efficient
BHEL 0.02068 24 Efficient
CIPLA 0.02005 43 Efficient
GAIL 0.02139 32 Efficient
GRASIM 0.01515 30 Efficient
HCL 0.02256 31 Efficient
HDFC BANK 0.02588 23 Efficient
HERO HONDA 0.01393 27 Efficient
HDFC 0.01982 28 Efficient
ITC 0.01201 22 Efficient
ICICI BANK 0.01669 31 Efficient
INFOSYS 0.01857 25 Efficient
JINDAL 0.01837 33 Efficient
M & M 0.01515 26 Efficient
MARUTI 0.02228 21 Efficient
ONGL 0.02004 30 Efficient
PNB 0.02555 32 Efficient
RANBAXY 0.01154 32 Efficient
RELCAPITEL 0.02433 28 Efficient
RELIANCE 0.00773 27 Efficient
SIEMENS 0.01156 31 Efficient
SBIN 0.00985 37 Efficient
SAIL 0.01594 35 Efficient
SUN PHARMA 0.01796 36 Efficient
TATA MOTORS 0.01275 25 Efficient
TATA POWER 0.01491 34 Efficient
UNITECH 0.03071 27 Efficient
WIPRO 0.02245 39 Efficient
Source: Computed from data source
132
Table 5.28
Test of Semi-strong form of market efficiency for Apr 2009 – Jun 2009
Company Residual Mean N Result
ABB 0.01632 29 Efficient
ACC 0.01120 33 Efficient
BHEL 0.02131 23 Efficient
CIPLA 0.01988 43 Efficient
GAIL 0.02336 32 Efficient
GRASIM 0.01677 30 Efficient
HCL 0.02175 32 Efficient
HDFC BANK 0.02556 22 Efficient
HERO HONDA 0.01456 27 Efficient
HDFC 0.02081 28 Efficient
ITC 0.01154 23 Efficient
ICICI BANK 0.01559 31 Efficient
INFOSYS 0.01898 25 Efficient
JINDAL 0.01803 34 Efficient
M & M 0.01543 24 Efficient
MARUTI 0.02278 21 Efficient
ONGL 0.02012 29 Efficient
PNB 0.02554 32 Efficient
RANBAXY 0.01178 32 Efficient
RELCAPITEL 0.02400 27 Efficient
RELIANCE 0.00747 27 Efficient
SIEMENS 0.01019 30 Efficient
SBIN 0.00962 36 Efficient
SAIL 0.01682 35 Efficient
SUN PHARMA 0.01860 37 Efficient
TATA MOTORS 0.01453 25 Efficient
TATA POWER 0.01473 32 Efficient
UNITECH 0.03012 26 Efficient
WIPRO 0.02346 38 Efficient
Source: Computed from data source
133
From the above results given (Table 5.9 to 5.28) it is very evident that the
Indian Stock Market was efficient in the Semi-strong form during the study
period. Residual returns of all the companies during the study period had a value
near to zero.
Reforms in Indian stock market started a decade and a half ago. Enormous
and extensive changes have taken place in all the facets of the market. One can
see a radical transformation of the Indian stock market, which has become one of
the great attractions for investors outside the country also. The market has
expanded in breadth and depth. It can be perceived that Indian stock market has
become more efficient because of opening the doors for foreign players, timely
technological up gradation and also due to the prevailing economic development
in the country.
Researcher had also tried to bring more validity to the result by testing the
mean residual for the entire study period with out splitting them in to sub-samples
,the results of which is give in Table 5.29.
134
Table 5.29
Residual Mean from Jan 2004 – Jun 2009
Company Residual Mean N Result
ABB 0.01477 695 Efficient
ACC 0.01337 727 Efficient
BHEL 0.01392 720 Efficient
CIPLA 0.01483 688 Efficient
GAIL 0.01534 721 Efficient
GRASIM 0.01308 723 Efficient
HCL 0.01745 699 Efficient
HDFC BANK 0.01315 737 Efficient
HERO HONDA 0.01460 709 Efficient
HDFC 0.01612 715 Efficient
ITC 0.01329 700 Efficient
ICICI BANK 0.01533 718 Efficient
INFOSYS 0.01378 685 Efficient
JINDAL 0.01891 734 Efficient
M & M 0.01576 692 Efficient
MARUTI 0.01402 730 Efficient
ONGL 0.01243 700 Efficient
PNB 0.01544 738 Efficient
RANBAXY 0.01675 685 Efficient
RELCAPITEL 0.01763 752 Efficient
RELIANCE 0.01003 704 Efficient
SIEMENS 0.01661 713 Efficient
SBIN 0.01203 732 Efficient
SAIL 0.01737 725 Efficient
SUN PHARMA 0.01520 703 Efficient
TATA MOTORS 0.01475 716 Efficient
TATA POWER 0.01540 726 Efficient
UNITECH 0.03194 624 Efficient
WIPRO 0.01538 674 Efficient
Source: Computed from data source
135
These results also exhibited similar findings validating the existence of
Semi-strong efficiency of Indian Stock Market. (Table 5.32) The reason for such
observation could also be that most of the companies included in the present
study are large firms which are included in the construction of Nifty Index. These
firms are automatically subjected to greater attention by the investors in the
market. So the publicly available information or fundamental information gets
incorporated in to these share prices very quickly. The results could be different
for smaller or non popular companies.
The purpose of this study was to examine the opportunity for earning
abnormal profits and the potential to maximize value in the Indian Stock Market.
The results indicate that though the market is providing scope for high returns in
the recent times, the opportunity to make abnormal profits is very limited. The
results of this study are in line with many other previous studies conducted in the
Indian market. Studies conducted by Kakati and Saikia (2009), Kakati (1997),
Das, Pattanayak and Pathak (2008) all concluded stating that Indian market is
efficient in the strong form.
136
5.7 Market Efficiency during Global Financial Crisis
Market efficiency of Indian stock markets was tested for a period from
2004 to 2009 which included a time span in which Indian stock market was
severely affected by Global Financial Crisis. So the researcher had taken an effort
to test the market efficiency of Indian markets during this recession period.
Great turbulence had begun in the Indian stock market scenario with a
continual fall in stock prices on news that Lehman Brothers, Merrill Lynch, and
many other investment bankers and companies collapsed. Stock market had seen
its worst time with the global financial crisis. Almost all sectors experienced a
consistent low in their stock prices. The Sensex which had reached historical
heights in the beginning of 2008, declined to its level three years back. Similar
trend had also been observed for the S & P CNX Nifty.
137
Fig. 5.1
Nifty Index Movements
4000
4500
5000
5500
6000
6500
01
/10
/20
07
15
/10
/20
07
29
/10
/20
07
12
/11
/20
07
26
/11
/20
07
10
/12
/20
07
24
/12
/20
07
07
/01
/20
08
21
/01
/20
08
04
/02
/20
08
18
/02
/20
08
03
/03
/20
08
17
/03
/20
08
31
/03
/20
08
14
/04
/20
08
28
/04
/20
08
Date
Nif
ty In
de
x
Source: Computed from data source
The most immediate effect of financial crisis on India economy was the
outflow of foreign institutional investment (FII) from the equity market. In 2007-
08, net FII inflows into India amounted to about $20.3 billion. As compared with
this, almost $11.1 billion was pulled out during the first nine-and-a-half months
of calendar year 2008, of which $8.3 billion occurred over the first six-and-a-half
months of financial year 2008-09 (April 1 to October 16). The pullout triggered a
collapse in stock prices.
However, remarkable resistance shown by Indian stock market during
international financial crisis boosted the confidence of Investors across the world.
Now, the stock market of India has returned to its growth track but with greater
degree of volatility. In this context, it was very much essential to study the
efficiency of Indian stock market during this period to evaluate the possibility of
undervaluation or overvaluation of stocks traded in the market.
138
For the purpose of statistical analysis of weak form and semi strong form
of efficiency in Indian Stock Market during the global financial crisis, the market
prices of companies included in the formation of Nifty index was collected from
NSE official website. Period of study was from October 2007 to April 2008 i.e
the time span in which Indian markets were rigorously hit by global financial
crisis. Daily closing prices of these shares were considered for the analysis.
Weak Form efficiency of Indian market during the time frame of October
2007 to April 2008 had been tested using statistical tools like Autocorrelation,
and Run test. One-Sample Kolmogorov-Smirnov Test was also used to find out
how well a data series fits a particular distribution.
Table 5.30
Descriptive Statistics (October 2007 to April 2008)
Company N Mean Median Minimum Maximum Std. Deviation
ABB 145 1354.71 1366.00 1038.00 1650.35 183.83
ACC 145 933.09 876.55 718.50 1289.80 153.90
AMBUJA 145 133.40 138.05 112.40 154.10 14.29
AXIS BANK 145 928.20 933.85 714.45 1268.15 115.65
BHEL 145 2264.88 2236.80 1633.40 2870.20 328.47
BHARTI 145 899.75 900.10 742.60 1125.65 80.04
CAIRN 145 217.83 215.15 171.00 264.40 21.44
CIPLA 145 198.08 198.40 172.40 231.45 14.55
DLF 145 856.43 869.80 597.85 1207.50 150.16
GAIL 145 439.84 427.75 366.05 543.60 42.64
GRASIM 145 3222.53 3339.30 2404.85 3869.90 461.04
HCL 145 284.69 290.05 229.85 329.65 26.20
HDFC BANK 145 1531.48 1534.30 1226.00 1788.95 147.03
HERO HONDA 145 716.83 714.35 592.80 863.00 38.26
HINDALCO 145 186.72 188.40 149.20 219.90 16.71
HINDUNILVA 145 217.88 216.80 184.05 253.20 15.57
HDFC 145 2686.15 2670.15 2210.90 3180.15 215.29
139
ITC 145 196.36 196.25 168.80 231.30 13.38
ICICI BANK 145 1094.34 1139.70 757.75 1435.00 170.25
IDEA 145 120.95 122.15 90.85 157.20 16.11
INFOSYS 145 1632.89 1609.30 1314.60 2125.05 171.60
IDFC 145 187.90 188.50 136.85 232.50 25.40
JP ASSO 145 755.98 419.60 200.20 2156.65 614.04
JINDAL 145 7367.24 6492.45 1780.25 16490.85 5524.03
LT 145 3607.87 3641.50 2584.15 4506.70 563.91
M & M 145 713.72 711.10 578.95 865.10 70.11
MARUTI 145 921.97 909.20 722.35 1189.85 122.39
NTPC 145 220.42 217.65 184.95 284.65 26.61
ONGL 145 1108.67 1071.05 937.25 1366.25 115.67
POWER GRID 142 122.92 114.90 89.45 161.65 22.18
PNB 145 578.75 575.45 459.35 711.35 66.58
RANBAXY 145 420.40 423.30 342.10 499.40 33.49
RELCAPITEL 145 1932.71 1924.85 1060.15 2860.00 469.47
RCOM 145 651.81 675.00 481.75 821.55 93.95
RELIANCE 145 2623.75 2615.05 2151.00 3220.85 232.94
SIEMENS 145 1468.57 1672.20 571.85 2074.70 524.99
SBIN 145 2075.68 2156.75 1592.55 2464.55 275.93
SAIL 145 229.83 233.90 157.05 287.75 35.35
STER 145 875.99 836.75 682.20 1104.55 120.05
SUN PHARMA 145 1138.95 1118.60 920.85 1447.85 114.40
SUZLON 145 1147.86 1639.50 228.20 2273.05 815.68
TCS 145 956.05 961.30 779.70 1125.50 88.75
TATA MOTORS 145 715.08 712.75 609.40 830.55 56.66
TATA POWER 145 1259.71 1261.40 902.50 1629.15 144.56
TATA STEEL 145 804.99 818.05 591.10 988.90 81.49
UNITECH 145 369.13 365.65 253.15 538.25 75.54
WIPRO 145 456.05 456.30 354.10 549.50 38.89
Source: Computed from data source
The summary statistics of the returns for all the companies included in the
study are given in Table 5.30. Mean stock returns are positive with majority of
them having comparatively larger volatility (standard deviation).
140
To confirm the distributional pattern of the returns, researcher had used
Kolmogrov-Smirnov goodness of fit test. Study has also conducted the run test,
which does not require normality, to test the independence between successive
returns.
Table 5.31
One-Sample Kolmogorov-Smirnov Test
Company Absolute Positive Negative K-S Z p-value
ABB 0.139 0.139 -0.118 1.678 0.007
ACC 0.192 0.192 -0.129 2.312 0.000
AMBUJA 0.218 0.190 -0.218 2.627 0.000
AXIS BANK 0.078 0.059 -0.078 0.940 0.340
BHEL 0.116 0.116 -0.076 1.398 0.040
BHARTI 0.065 0.065 -0.027 0.787 0.566
CAIRN 0.074 0.074 -0.054 0.886 0.412
CIPLA 0.121 0.121 -0.074 1.458 0.028
DLF 0.095 0.095 -0.091 1.140 0.149
GAIL 0.185 0.185 -0.076 2.223 0.000
GRASIM 0.189 0.145 -0.189 2.274 0.000
HCL 0.125 0.065 -0.125 1.507 0.021
HDFC BANK 0.086 0.052 -0.086 1.031 0.238
HERO HONDA 0.065 0.059 -0.065 0.784 0.570
HINDALCO 0.063 0.035 -0.063 0.753 0.621
HINDUNILVA 0.057 0.057 -0.042 0.691 0.727
HDFC 0.070 0.070 -0.035 0.845 0.472
ITC 0.079 0.079 -0.042 0.953 0.324
ICICI BANK 0.135 0.107 -0.135 1.624 0.010
IDEA 0.093 0.093 -0.078 1.123 0.160
INFOSYS 0.098 0.098 -0.062 1.178 0.125
IDFC 0.082 0.082 -0.066 0.992 0.279
JP ASSO 0.298 0.298 -0.183 3.587 0.000
141
JINDAL 0.275 0.275 -0.156 3.309 0.000
LT 0.145 0.105 -0.145 1.742 0.005
M & M 0.074 0.074 -0.067 0.887 0.411
MARUTI 0.119 0.119 -0.104 1.428 0.034
NTPC 0.148 0.148 -0.091 1.780 0.004
ONGL 0.162 0.162 -0.091 1.954 0.001
POWER GRID 0.180 0.180 -0.145 2.148 0.000
PNB 0.124 0.124 -0.077 1.490 0.024
RANBAXY 0.055 0.035 -0.055 0.663 0.772
RELCAPITEL 0.076 0.076 -0.064 0.911 0.378
RCOM 0.142 0.082 -0.142 1.714 0.006
RELIANCE 0.050 0.050 -0.046 0.600 0.864
SIEMENS 0.206 0.174 -0.206 2.483 0.000
SBIN 0.129 0.103 -0.129 1.549 0.016
SAIL 0.097 0.051 -0.097 1.171 0.129
STER 0.138 0.138 -0.101 1.661 0.008
SUN PHARMA 0.116 0.116 -0.058 1.403 0.039
SUZLON 0.299 0.299 -0.230 3.594 0.000
TCS 0.113 0.113 -0.072 1.363 0.049
TATA MOTORS 0.070 0.070 -0.067 0.839 0.482
TATA POWER 0.056 0.056 -0.032 0.671 0.758
TATA STEEL 0.119 0.070 -0.119 1.428 0.034
UNITECH 0.105 0.105 -0.072 1.264 0.082
WIPRO 0.100 0.041 -0.100 1.202 0.111
Source: Computed from data source
Table 5.31 presents the results of the Kolmogorov-Smirnov Test .It compares
an observed cumulative distribution function to a theoretical (Normal) cumulative
distribution. Low significance values (<.05) indicate that the observed distribution
does not corresponds to the Normal distribution. This confirms that the distribution
of Closing Prices is not normal. High significance values (>.05) indicate that the
142
observed distribution corresponds to the Normal distribution and so the distribution
of Closing Prices is normal. Majority of the values have low significance values i.e
<.05 indicating that the stock returns are not normally distributed.
In the run test the p-values of all the companies were less than 0.05. So,
the null hypothesis that the price movements are not affected by the past price is
rejected. The result shows that the price movements are not random in behavior.
Historical data can be used for predicting the future prices which confirms that
Indian market is not efficient in the weak form during the study period the results
of which is resented in (Table 5.32)
Table 5.32
Run Test Results (October 2007 to April 2008)
Company Test Value Runs Z-value p-value
ABB 1366.00 5 -11.417 0.000
ACC 876.55 2 -11.917 0.000
AMBUJA 138.05 2 -11.917 0.000
AXIS BANK 933.85 15 -9.750 0.000
BHEL 2236.80 9 -10.750 0.000
BHARTI 900.10 12 -10.250 0.000
CAIRN 215.15 22 -8.583 0.000
CIPLA 198.40 10 -10.584 0.000
DLF 869.80 19 -9.083 0.000
GAIL 427.75 30 -7.250 0.000
GRASIM 3339.30 4 -11.584 0.000
HCL 290.05 6 -11.250 0.000
HDFC BANK 1534.30 15 -9.750 0.000
HERO HONDA 714.35 13 -10.083 0.000
HINDALCO 188.40 20 -8.917 0.000
HINDUNILVA 216.80 19 -9.083 0.000
HDFC 2670.15 18 -9.250 0.000
ITC 196.25 16 -9.583 0.000
ICICI BANK 1139.70 19 -9.083 0.000
IDEA 122.15 10 -10.584 0.000
143
INFOSYS 1609.30 15 -9.750 0.000
IDFC 188.50 11 -10.417 0.000
JP ASSO 419.60 4 -11.584 0.000
JINDAL 6492.45 3 -11.750 0.000
LT 3641.50 7 -11.084 0.000
M & M 711.10 8 -10.917 0.000
MARUTI 909.20 4 -11.584 0.000
NTPC 217.65 13 -10.083 0.000
ONGL 1071.05 9 -10.750 0.000
POWER GRID 114.90 5 -11.285 0.000
PNB 575.45 7 -11.084 0.000
RANBAXY 423.30 13 -10.083 0.000
RELCAPITEL 1924.85 17 -9.417 0.000
RCOM 675.00 9 -10.750 0.000
RELIANCE 2615.05 17 -9.417 0.000
SIEMENS 1672.20 9 -10.750 0.000
SBIN 2156.75 9 -10.750 0.000
SAIL 233.90 9 -10.750 0.000
STER 836.75 16 -9.583 0.000
SUN PHARMA 1118.60 24 -8.250 0.000
SUZLON 1639.50 5 -11.417 0.000
TCS 961.30 10 -10.584 0.000
TATA MOTORS 712.75 14 -9.917 0.000
TATA POWER 1261.40 18 -9.250 0.000
TATA STEEL 818.05 19 -9.083 0.000
UNITECH 365.65 13 -10.083 0.000
WIPRO 456.30 14 -9.917 0.000
Source: Computed from data source
Researcher employed parametric test i.e. autocorrelation test to confirm
the results of non-parametric test. i.e. is run test. It measures the degree of
dependency of the series in the Weak form of efficiency during the period of
extreme crisis in the Indian stock market, the results of which is put in
(Table.5.33)
144
Table 5.33
Autocorrelation Test Result (October 2007 to April 2008)
Company T + 4 T + 9 T + 14
ABB 0.886 (H) 0.782 (H) 0.686 (M)
ACC 0.842 (H) 0.720 (M) 0.620 (M)
AMBUJA 0.891 (H) 0.773 (H) 0.674 (M)
AXIS BANK 0.738 (M) 0.574 (M) 0.405 (M)
BHEL 0.807 (H) 0.698 (M) 0.595 (M)
BHARTI 0.705 (M) 0.531 (M) 0.357 (M)
CAIRN 0.632 (M) 0.302 (M) 0.012 (L)
CIPLA 0.713 (M) 0.504 (M) 0.362 (M)
DLF 0.846 (H) 0.701 (M) 0.564 (M)
GAIL 0.712 (M) 0.544 (M) 0.352 (M)
GRASIM 0.897 (H) 0.829 (H) 0.727 (M)
HCL 0.754 (H) 0.560 (M) 0.418 (M)
HDFC BANK 0.719 (M) 0.643 (M) 0.525 (M)
HERO HONDA 0.341 (M) 0.035 (L) 0.016 (L)
HINDALCO 0.513 (M) 0.182 (L) -0.029 (L)
HINDUNILVA 0.647 (M) 0.503 (M) 0.376 (M)
HDFC 0.560 (M) 0.421 (M) 0.383 (M)
ITC 0.640 (M) 0.403 (M) 0.252 (M)
ICICI BANK 0.817 (H) 0.695 (M) 0.555 (M)
IDEA 0.820 (H) 0.673 (M) 0.553 (M)
INFOSYS 0.750 (H) 0.505 (M) 0.328 (M)
IDFC 0.746 (M) 0.651 (M) 0.518 (M)
JP ASSO 0.849 (H) 0.711 (M) 0.600 (M)
JINDAL 0.858 (H) 0.718 (M) 0.586 (M)
LT 0.805 (H) 0.689 (M) 0.564 (M)
M & M 0.755 (H) 0.581 (M) 0.420 (M)
MARUTI 0.820 (H) 0.683 (M) 0.552 (M)
NTPC 0.748 (M) 0.578 (M) 0.479 (M)
ONGL 0.718 (M) 0.547 (M) 0.396 (M)
POWER GRID 0.866 (H) 0.756 (H) 0.654 (M)
PNB 0.804 (H) 0.663 (M) 0.555 (M)
145
RANBAXY 0.792 (H) 0.543 (M) 0.328 (M)
RELCAPITEL 0.849 (H) 0.727 (M) 0.584 (M)
RCOM 0.837 (H) 0.713 (M) 0.633 (M)
RELIANCE 0.697 (M) 0.560 (M) 0.448 (M)
SIEMENS 0.878 (H) 0.767 (H) 0.657 (M)
SBIN 0.856 (H) 0.729 (M) 0.576 (M)
SAIL 0.768 (H) 0.618 (M) 0.451 (M)
STER 0.758 (H) 0.577 (M) 0.460 (M)
SUN PHARMA 0.705 (M) 0.525 (M) 0.373 (M)
SUZLON 0.895 (H) 0.774 (H) 0.674 (M)
TCS 0.789 (H) 0.637 (M) 0.490 (M)
TATA MOTORS 0.714 (M) 0.491 (M) 0.356 (M)
TATA POWER 0.508 (M) 0.245 (L) 0.199 (L)
TATA STEEL 0.699 (M) 0.477 (M) 0.296 (M)
UNITECH 0.829 (H) 0.670 (M) 0.518 (M)
WIPRO 0.715 (M) 0.517 (M) 0.414 (M)
Source: Computed from data source
H Highly correlated (±0.75 to ±1)
M Moderate Correlation (±0.25 to ±0.75)
L Low Correlation (±0 to ±0.25)
Table 5.33 shows the autocorrelation coefficients computed for the log of
the return series at different lags. Autocorrelation between the prices of shares
had been tested for five days, ten days and fifteen days .From the results it is very
clear that there is significant autocorrelation at 5 percent significance level.
Results also show that the level of significance decreases by the increase in days
compared. For example if we take the autocorrelation value of TCS, the value
which was 0.789 and was highly correlated at five day lag decreased to 0.490
when 15 days lag was considered.
146
The presence of autocorrelation coefficients in the market returns series
suggest that there was relationship between past returns and present returns and
even during global financial crisis Indian market movements were predictable
based on past information.
The results indicate that the Indian stock market was weak form
inefficient during the study period. So the chances to earn abnormal returns by
studying the past share price behaviour existed in Indian market even during the
crisis. The global financial crisis really started to show its effects in the middle of
2007 and into 2008. Around the world stock markets have fallen, large financial
institutions have collapsed or been bought out, and governments in even the
wealthiest nations have had to come up with rescue packages to bail out their
financial systems. When compared with other markets, Indian stock market had a
faster recovery from the steep fall. This had boosted the confidence level of
investors across the world.
However, for India, year 2008 remained one of the worst years for
financial markets. During 2008, 35 IPOs (Initial Public Offerings) were cancelled
on expectation of poor response from investors. There was a sharp fall in
country’s domestic manufacturing due to the poor global demand which affected
Indian economy to a greater extended. Many companies had to postpone their
expansion projects due to lack of capital. All these were the basic reasons for
testing the Semi-strong efficiency of India markets during the recession period.
Semi-strong efficiency is developed on the foundation that all publicly available
information (both negative as well as positive ones) gets incorporated
immediately in to the share prices.
147
Tests were conducted by splitting the entire study periods in to two
phases: one from October 2007 to December 2007 and the other from January
2008-April 2008.Test results are presented in the tables given below.(Table 5.34)
Table 5.34
Test for Semi-strong form of efficiency (Oct 2007 – Dec 2007)
Company Residual Mean N Result
ABB 0.01580 29 Efficient
ACC 0.01484 34 Efficient
AMBUJA 0.01131 28 Efficient
AXIS BANK 0.01435 38 Efficient
BHEL 0.01481 35 Efficient
BHARTI 0.01986 31 Efficient
CAIRN 0.01962 31 Efficient
CIPLA 0.01335 35 Efficient
DLF 0.01722 36 Efficient
GAIL 0.01979 35 Efficient
GRASIM 0.00927 35 Efficient
HCL 0.01648 29 Efficient
HDFC BANK 0.01635 33 Efficient
HERO HONDA 0.01127 31 Efficient
HINDALCO 0.02131 30 Efficient
HINDUNILVA 0.01464 27 Efficient
HDFC 0.01464 35 Efficient
ITC 0.01505 32 Efficient
ICICI BANK 0.01685 33 Efficient
IDEA 0.02151 29 Efficient
INFOSYS 0.01567 33 Efficient
IDFC 0.01727 36 Efficient
148
JP ASSO 0.05616 27 Efficient
JINDAL 0.03099 38 Efficient
LT 0.01507 36 Efficient
M & M 0.01450 34 Efficient
MARUTI 0.01585 29 Efficient
NTPC 0.01517 32 Efficient
ONGL 0.01398 35 Efficient
POWER GRID 0.02443 31 Efficient
PNB 0.01408 38 Efficient
RANBAXY 0.01374 29 Efficient
RELCAPITEL 0.02418 36 Efficient
RCOM 0.01171 32 Efficient
RELIANCE 0.01037 33 Efficient
SIEMENS 0.01623 38 Efficient
SBIN 0.01406 34 Efficient
SAIL 0.01400 37 Efficient
STER 0.01729 39 Efficient
SUN PHARMA 0.01498 33 Efficient
SUZLON 0.01633 35 Efficient
TCS 0.01250 35 Efficient
TATA MOTORS 0.01212 31 Efficient
TATA POWER 0.02446 36 Efficient
TATA STEEL 0.01709 33 Efficient
UNITECH 0.01735 34 Efficient
WIPRO 0.01263 38 Efficient
Source: Computed from data source
149
Table 5.35
Test for Semi-strong form of efficiency (Jan 2008 – Apr 2008)
Company Residual Mean N Result
ABB 0.01519 41 Efficient
ACC 0.01680 42 Efficient
AMBUJA 0.01252 40 Efficient
AXIS BANK 0.02579 43 Efficient
BHEL 0.02049 40 Efficient
BHARTI 0.01771 44 Efficient
CAIRN 0.02302 39 Efficient
CIPLA 0.01510 42 Efficient
DLF 0.02222 39 Efficient
GAIL 0.01875 38 Efficient
GRASIM 0.01493 39 Efficient
HCL 0.02018 46 Efficient
HDFC BANK 0.01823 40 Efficient
HERO HONDA 0.01709 44 Efficient
HINDALCO 0.02021 43 Efficient
HINDUNILVA 0.01890 44 Efficient
HDFC 0.02083 45 Efficient
ITC 0.01530 42 Efficient
ICICI BANK 0.02147 41 Efficient
IDEA 0.02140 42 Efficient
INFOSYS 0.01939 43 Efficient
IDFC 0.02009 43 Efficient
JP ASSO 0.02846 41 Efficient
JINDAL 0.04972 33 Efficient
150
LT 0.01605 41 Efficient
M & M 0.01795 41 Efficient
MARUTI 0.01655 41 Efficient
NTPC 0.01263 46 Efficient
ONGL 0.01246 42 Efficient
POWER GRID 0.02823 41 Efficient
PNB 0.01715 43 Efficient
RANBAXY 0.01448 48 Efficient
RELCAPITEL 0.01864 45 Efficient
RCOM 0.01558 47 Efficient
RELIANCE 0.01150 38 Efficient
SIEMENS 0.02908 34 Efficient
SBIN 0.01485 41 Efficient
SAIL 0.02112 41 Efficient
STER 0.01948 41 Efficient
SUN PHARMA 0.02042 44 Efficient
SUZLON 0.04288 38 Efficient
TCS 0.01965 39 Efficient
TATA MOTORS 0.01273 45 Efficient
TATA POWER 0.02530 38 Efficient
TATA STEEL 0.02050 40 Efficient
UNITECH 0.02392 41 Efficient
WIPRO 0.02318 39 Efficient
Source: Computed from data source
151
Indian Stock Market is found to be efficient in the Semi-strong form
during the study period. No evidence of significant abnormal returns could be
found in both these sub-samples.
Modern portfolio theory has made enormous contribution to the
understanding of financial markets in a better way. Investors across the world
seek to earn largest possible return at a specified level of risk or the lowest level
of risk at a specified required return. Market efficiency models help the
academicians in understanding the subtleties of risk-return relationships. They
also help the investment professionals in construction of an optimal portfolio.
Even today the concept of efficiency is central to any financial market.
Predominantly, the term efficiency is used to describe a market in which relevant
information is impounded into the price of financial assets. This was the primary
focus of the study conducted here. Most of this review was concerned with the
informational efficiency of financial markets. If capital markets are sufficiently
competitive, then simple microeconomics indicates that investors cannot expect
to achieve superior profits from their investment strategies.