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


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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


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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)


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.


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


99

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


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


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


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


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)


H High correlation (±0.75 to ±1)



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


112

Table 5.10

Test of Semi-strong form of market efficiency for Apr 2004 – Jun 2004
































113

Table 5.11

Test of Semi-strong form of market efficiency for Jul 2004 – Sep 2004
































114

Table 5.12

Test of Semi-strong form of market efficiency for Oct 2004 – Dec 2004
































115

Table 5.13

































116

Table 5.14

































117

Table 5.15

































118

Table 5.16

































119

Table 5.17

































120

Table 5.17

































121

Table 5.18

































122

Table 5.19

































123

Table 5.20

































124

Table 5.21

































125

Table 5.22

































126

Table 5.23

































127

Table 5.24

































128

Table 5.24

































129

Table 5.25

































130

Table 5.26

































131

Table 5.27

































132

Table 5.28

































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
































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

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/20

07

15

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07

29

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07

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07

26

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07

10

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07

24

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/20

07

07

/01

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08

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08

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08

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Date

Nif

ty In

de

x


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



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


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


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)


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


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)


H Highly correlated (±0.75 to ±1)



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)




AMBUJA 0.01131 28 Efficient

AXIS BANK 0.01435 38 Efficient


BHARTI 0.01986 31 Efficient

CAIRN 0.01962 31 Efficient


DLF 0.01722 36 Efficient






HINDALCO 0.02131 30 Efficient

HINDUNILVA 0.01464 27 Efficient




IDEA 0.02151 29 Efficient


IDFC 0.01727 36 Efficient

148

JP ASSO 0.05616 27 Efficient


LT 0.01507 36 Efficient



NTPC 0.01517 32 Efficient


POWER GRID 0.02443 31 Efficient




RCOM 0.01171 32 Efficient





STER 0.01729 39 Efficient


SUZLON 0.01633 35 Efficient

TCS 0.01250 35 Efficient



TATA STEEL 0.01709 33 Efficient




149

Table 5.35

Test for Semi-strong form of efficiency (Jan 2008 – Apr 2008)




AMBUJA 0.01252 40 Efficient

AXIS BANK 0.02579 43 Efficient


BHARTI 0.01771 44 Efficient

CAIRN 0.02302 39 Efficient


DLF 0.02222 39 Efficient






HINDALCO 0.02021 43 Efficient

HINDUNILVA 0.01890 44 Efficient




IDEA 0.02140 42 Efficient


IDFC 0.02009 43 Efficient

JP ASSO 0.02846 41 Efficient


150

LT 0.01605 41 Efficient



NTPC 0.01263 46 Efficient


POWER GRID 0.02823 41 Efficient




RCOM 0.01558 47 Efficient





STER 0.01948 41 Efficient


SUZLON 0.04288 38 Efficient

TCS 0.01965 39 Efficient



TATA STEEL 0.02050 40 Efficient




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.

indian stock market efficiency an...

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