INDIAN STOCK MARKET EFFICIENCY AN ANALYSIS

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.

85 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.

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: 86 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.

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. 87 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 = R i = α i + β i R m + e i, where R m 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

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. 88 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).

89 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

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. 90 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.

91 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

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. 92 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.

93 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

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. 94 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.

95 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)

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. 96 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

only from 2008 February. Here the data was considered by the researcher in the form of number of observations. 97 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

98 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

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. 99 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

100 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

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. 101 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 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 ACC AMBUJA AXIS BANK BHEL BHARTI CAIRN CIPLA DLF GAIL GRASIM HCL HDFC BANK HERO HONDA HINDALCO HINDUNILVA HDFC ITC ICICI BANK IDEA INFOSYS IDFC JP ASSO JINDAL LT M & M MARUTI NTPC 0.977 (H) 0.955 (H) 0.933 (H) 0.987 (H) 0.974 (H) 0.960 (H) 0.977 (H) 0.952 (H) 0.927 (H) 0.942 (H) 0.900 (H) 0.853 (H) 0.981 (H) 0.964 (H) 0.949 (H) 0.939 (H) 0.891 (H) 0.837 (H) 0.944 (H) 0.900 (H) 0.858 (H) 0.974 (H) 0.948 (H) 0.923 (H) 0.977 (H) 0.953 (H) 0.929 (H) 0.969 (H) 0.941 (H) 0.913 (H) 0.988 (H) 0.974 (H) 0.958 (H) 0.982 (H) 0.967 (H) 0.949 (H) 0.982 (H) 0.967 (H) 0.951 (H) 0.975 (H) 0.950 (H) 0.927 (H) 0.975 (H) 0.955 (H) 0.936 (H) 0.875 (H) 0.801 (H) 0.738 (M) 0.983 (H) 0.971 (H) 0.958 (H) 0.978 (H) 0.958 (H) 0.939 (H) 0.983 (H) 0.969 (H) 0.954 (H) 0.970 (H) 0.942 (H) 0.917 (H) 0.967 (H) 0.938 (H) 0.911 (H) 0.975 (H) 0.955 (H) 0.932 (H) 0.943 (H) 0.892 (H) 0.851 (H) 0.942 (H) 0.885 (H) 0.832 (H) 0.977 (H) 0.955 (H) 0.933 (H) 0.964 (H) 0.933 (H) 0.901 (H) 0.970 (H) 0.941 (H) 0.914 (H) 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) 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

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 nonparametric (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. 106 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,

merges & acquisitions, dividend etc are quickly and correctly reflected in the security s prices. 107 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 (R i ) R i = α i + β i R m + e i Where: R i = Expected Return of the i th stock α i = Intercept β i = Beta value of the i th stock R m = Return of the market index e i = 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.

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. 109 as: The formula used for finding the beta and alpha co-efficient can be expressed β = n X Y ( x) y) Where: n X 2 - ( X ) 2 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)

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. 110 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 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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