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January 2015

Study of Stock Market Efficiency and Impact of Crash on Indian and US Market

Falguni H. Pandya

Ms. Falguni H.Pandya is a faculty member with Centre for Management Studies, Dharmsinh Desai University, Nadiad, Gujarat in the Finance Area. Her areas of interest include Financial Management, Risk Management, Security Analysis and Portfolio Management and International Financial Management.  Her research papers have been published in national level referred journals and in the form of national and international conference proceedings.

Email: fhpandya@gmail.com   (m) 0 9913879766

Postal Address

B.No. 45, Gayatri Nagar Society, Nr. G E B, Maktampur Road, Bharuch-392012

 

Hardik S Shah

Mr. Hardik S Shah is a faculty member with Centre for Management Studies, Dharmsinh Desai University, Nadiad, Gujarat in the Finance Area. His areas of interest include Financial Management, Strategic Finance Management, Taxation, Accounting and Management of Financial Services.  He is pursuing research in the area of Initial Public Offering.

Email : shah_hardik07@yahoo.in, (m) 8511169690

 

Postal Address

Faculty,Centre for Management Studies (MBA Department)

Dharmsinh Desai University

College Road, Nadiad-387001, Gujarat, India

Abstract

The study attempts to find out the market efficiency of Indian and US stock market. For that two main indices of India namely BSE Sensex and CNX Nifty are studied along with US-Dow Jones for a various time brackets between 1993 to 2013. Various tests like K-S Test, Runs test, Auto correlation test, t-test and all descriptive statistics with JB test are conducted for all three indices for a time bracket of 1993-2013, 1993-1995(normal), 1996-2000 (Before DotCom Bubble), 2000-2002 (Dotcom Bubble), 2002-2007 (Before Credit Crisis), 2007-2009 (Credit Crisis) and 2009-2013 (After Credit Crisis). The research summarizes that Indian stock market is not weak form efficient in all periods however; from year 2002 onwards stock market exhibits some signs of efficiency.

Key Words: - Market Efficiency, Indices

Introduction

Emerging markets usually do not have strict standards in accounting and finance regulations and the level of market efficiency as that of advanced economies of the world. However, emerging economy like India and others have physical financial infrastructure including banks, stock exchange, money market and capital market. In addition, investment in emerging markets are sought by investors to tap the market efficiency and thus to earn higher return.  However, investments in emerging markets come with higher risk due to political instability, domestic infrastructure problems, currency volatility and limited equity opportunities.   However, despite the stage of the different market, it is impossible to ‘beat’ the market because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information. In other words, as per Efficient Market Hypothesis (EMH), stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices.  Efficient market hypothesis is stated-weak form, semi strong form and strong form of efficiency on three common forms.

 

Review of Literature

 

Malliaris and Urrutia (1992) analyzed possible causal relationship among national stock markets around the October 1987 stock market crash. The paper summarized that lead-lag relationship do not exists before and after market crash. The result provided empirical evidence of the passive role played by Tokyo but fails to confirm leading role played by New York or non-Japanese Asian markets during 1987 crash.  Schwert (1989) analyzed behavior of stock return volatility using daily data from 1985 to 1988 and found that on October, 19 there was a largest percentage change in value of the market and stock market volatility jumped during and after market crash. However, it was also observed that it returned to normal level more rapidly than any other historical crises. Research by Yüksel (2002) on Istanbul Stock Exchange during the Russian crisis in 1998 says that the comparison of the relationship during the crisis period to those during pre and post crisis period proved that there was a structural change regarding the price impact of the trading volume.  Chancharoenchai and Dibooglu (2006) examined the volatility spillovers in Southeast Asian emerging markets in the context of the mid-1997 financial crisis, and found spillover effects between various equity markets. The paper summarized that the sudden fallout in Thailand seems to have played an important role in the variation in excess returns in other Southeast Asian markets and it supports the idea of the “Asian Contagion” suggested that the crisis started in the Thailand and spread over to other financial markets. Olowe (2009) found that in the Nigerian Stock market returns show persistence in volatility and clustering and asymmetric properties.

Kazi et al (2011) investigated the contagion effect between stock market of US and sixteen OECD countries due to global financial crisis and it found existence of contagion effect between US and 16 OECD countries. Boyer et al (2006) provided empirical evidence that market crises are speeded globally. Authors categorized the stock markets of emerging economies in to two parts: those that are eligible for purchase by foreigners and those that are not. Research proved that greater co-movement during high volatility period, especially for accessible stock index returns indicates that crises spread through the asset holding of the international investors rather than through changes in fundamentals.  A novel approach was adopted by Herrero et al (2008) as they applied event study methodology to analyze whether five south East Asian countries which devalued in 1997 namely Indonesia, Korea, Malaysia, Philippines and Thailand.  The study was based on the assumption that the expectation of devaluation should help the stock of exporting firms outperform those of non-performing firms and the study could find evidence to support this hypothesis, however at different degrees depending upon the country.

Study by Salman et al (2012) attempted the short term relationship between Karachi Stock Exchange, Bombay Stock Exchange, Hong Kong Stock Exchange, and Tokyo Stock Exchange after the financial crisis of 2007. It concluded that 1. Tokyo and Karachi Stock Exchange, 2. Hong Kong and Bombay, 3. Karachi and Bombay (before and after crisis) 4. Tokyo and Bombay  have no short term relationship with each other; while Tokyo and Hong Kong stock exchange have short term relationship with each other after the financial crisis of 2007. Research by Dhal (2009) derives some crucial insights from the multivariate co integration analysis of stock prices indices for the global markets of the US, UK, Japan, Hong Kong, Singapore and India. Result says that these markets shared single co integration relationship and the Indian market played a key role and the analysis showed that global crisis could not have been associated with the breakdown of the long-run relationships among the markets. Ghosh (2006) tried to address India’s existence from the effect of financial crisis and analyzed that India’s success can be attributed to key policy decisions namely devaluation, IMF, partial liberalization of the domestic financial sector and graduation opening of the external sector. In a similar track paved by Ghosh (2006); Study by Jeyanthi et al (2012) summarized that there was no short term as well as long term negative impact of financial crisis on the Indian stock exchange.  Further, the paper found that the Indian stock market was unaffected by the global financial crisis.  Study by Patel et al (2011) confirmed the existence of weak form of efficiency in Indian market for a time period of 2004 to 2011.  Jethwani, Achuthan (2013) investigated weak form efficiency of the Indian market during, before and after financial crisis and the result shows that Indian market is not weak form efficient in all period and after 2002 it behaves in a more efficient way. This confirms with the earlier study conducted by Patel et al (2011).

Data Description

Objectives

1.      To examine the market efficiency level of selected stock markets and to test spillover effect of the financial crash for a period of 1993 to 2013 for Indian and US stock market.

2.      To study and examine the extent of crisis at various periods for the given stock markets and to derive efficiency level of Indian stock market.

To study the above mentioned objectives, two main indices of India and one of US are selected as sample for the study. The daily closing prices of BSE SENSEX,  CNX NIFTY 50 and DOW JONES of last 21 years is collected (1st January 1993-31st December 2013). The daily price values of the CNX Nifty 50 and BSE Sensex is collected from the website of NSE and BSE respectively. Dow Jones data are collected from yahoo finance.  The period was further broken in to 1993-2013 (Total study period), 1993-1995 (Normal), 1996-2000 (Before Dot com Bubble), 2000-2002 (Dot com Bubble), 2002-2007 (Before Credit Crisis), 2007-2009 (Credit Crisis) and 2009-2013 (After Credit Crisis). Before conducting any test, it is required to test whether data are stationary or not and for that Augmented Dicky Fuller Test (ADF) was conducted.

Returns are calculated using logarithmic method and it was analyzed by using time series volatility. Further, to study the variability in stock prices, standard deviation is used as a proxy.  The logarithmic method to calculate return is as follows:

Rt = In (It / It-1)* 100

Where Rt stands for return of the index and It indicate index value at time‘t’.

To compute standard deviation following standard formula was used.

Data collection and interpretation

Augmented Dicky Fuller (ADF) test indicates that data are stationary at first difference.

Normality test

Many statistical tests (e.g. t-test) require that data are normally distributed and therefore it should always be checked if this assumption is satisfied. The null hypothesis is that the data is normally distributed and the alternative hypothesis is that the data is not normally distributed. The Kolmogorov-Smirnov test is used for sample of more than 2000. If p-value is less than 0.05, null hypothesis is rejected.

 

Here as p value is 0.00 for all three indices (table 1 of appendix A B and C), null hypothesis is rejected which means that data are not normally distributed.  Hence, non-parametric tests are conducted.

 

It is a non-parametric test and is used to test weak form of efficiency of stock market. This test emphasizes on the direction of change and does not consider the change in value. The test ignores the type of distribution followed in time series and is used to check the randomness in time series under consideration. At 5% confidence level, if the observed value of z is 1.96, null hypothesis is accepted.  Following are the null hypothesis to check efficiency of the market.

1. Indian Stock Market is weak form efficient market.

2. Indian Stock market follows random walk

Analysis for Descriptive Statistics:

To study the above mentioned objectives, one of the basic requirements is that data should be normal. Usually, main two parameters namely mean and variances are used to describe the distribution.  For that skewness, kurtosis, Jarque-Bera test is applied to test the normality of the data.  Jarque-Bera test is a goodness of fit test of whether the sample data have the skewness and kurtosis matching a normal distribution.  In the data, when the skewness is zero, kurtosis is three and JB is zero, then it is perfectly normally distributed. Coefficient of variation shows volatility of the data.

SENSEX

Five (1993-2013,1993-1995, 2000-2002, 2002-2007, 2007-2009) out of seven periods under study has negative skewness, though the value is small; it indicates the tail on the left side is longer than the right side and the bulk of the values lies including median to the right side of the mean. Remaining two periods (1996-2000, 2009-2013) shows positive skewness. In case of Kurtosis, four period (1993-1995, 1996-2000, 2000-2002, 2007-2009) out of seven falls under Platykurtic distribution and other three periods (1993-2013, 2002-2007, 2009-2013) falls under Leptokurtic distribution. Therefore, it can be said that distribution of data is not normal. So on the basis of descriptive statistics, null hypothesis of random walk is rejected. For Jarque Bera Test, null hypothesis of random walk is rejected for all periods as value of JB is higher than zero. Coefficient of Variation in initial two periods (1993-2013, 1993-1995) is quite high compare to other five periods. The value of CV shows that gradually market has become less volatile. (Refer to Table No.2 of appendix A)

 

CNX NIFTY

Four (1993-2013, 2000-2002, 2002-2007, 2007-2009) out of seven periods under study has negative skewness, and it indicates the tail on the left side is longer than the right side and the bulk of the values lies including median to the right side of the mean. Remaining three periods (1993-1995, 1996-2000, 2009-2013) shows positive skewness. In case of Kurtosis, three period (1993-1995, 2000-2002, 2007-2009) out of seven falls under Platykurtic distribution and other four periods (1993-2013, 1996-2000, 2002-2007, 2009-2013) falls under Leptokurtic distribution. Hence, it can be summarized that distribution of data is not normal. So on the basis of descriptive statistics, null hypothesis of random walk is rejected. Jarque Bera test and coefficient of variation indicates same conclusion as that of Sensex. (Refer to Table No. 2 of Appendix B)

 

DOWJONES

Five (1993-2013,1993-1995, 1996-2000, 2000-2002, 2009-2013) out of seven periods under study has negative skewness, even if the value is small it indicates the tail on the left side is longer than the right side and the bulk of the values lies including median to the right side of the mean. Remaining two periods (2000-2007, 2007-2009) are having positive skewness. In case of Kurtosis, three period (1993-1995, 2000-2002, 2002-2007) out of seven falls under Platykurtic distribution and other four periods (1993-2013, 2002-2007, 2009-2013) falls under Leptokurtic distribution. Hence, we can say that distribution of data is not normal. So on the basis of descriptive statistics, null hypothesis of random walk is rejected. For Jarque Bera Test, null hypothesis of random walk is rejected for all periods as value of Jarque Bera is higher than zero. Coefficient of Variation is quite high in the overall period only. (Refer to Table No. 2 of Appendix C)

 

In summary, for Sensex except two periods (1993-1995, 2007-2009), remaining five periods are not normally distributed and which leads to rejection of the null hypothesis of random walk  and similar result is found for CNX Nifty. While for the Dow Jones in all seven periods, the value of P is significant. That means the data is not normally distributed which leads to rejection of the null hypothesis of random walk for these periods.

 

Runs Test and analysis

For both the indices namely Sensex and Nifty,  two (2007-2009, 2009-2013) out of seven periods the value of Z is insignificant at 5 % significance level and its value lies inside the interval of ±1.96, so null hypothesis of random walk is accepted. While in five (1993-2013, 1993-1995, 1996-2000, 2000-2002, and 2002-2007) out of seven periods, null hypothesis of random walk is rejected (Refer to Table No. 4 of Appendix A, B). While for the Dow Jones in four periods (1993-1995, 1996-2000, 2000-2002, 2009-2013) out of seven periods the value of Z is insignificant at 5 % significance level and its value lies inside the interval of ±1.96, so null hypothesis of random walk is accepted. While in three (1993-2013, 2002-2007, 2007-2009) out of seven periods, null hypothesis of random walk is rejected. (Table 4 of Appendix C).

 

Autocorrelation and analysis

Autocorrelation is the cross-correlation of a signal with itself as it describes the correlation between values of the process at different times as a function of the two times or of the time lag.  For null hypothesis to be true, observed serial correlation should not be statistically significant i.e. should not be greater than three times the standard error of coefficient. In case of Ljung Box Q statistics, if the value of P <0.05 then it can be said that autocorrelation exist.

 

For all three indices namely Sensex, CNX Nifty and Dow Jones in all periods the value of P is significant which means that autocorrelation exists in the series. In addition to that, there is at least one lag where correlation coefficient is > 3*Standard error for all periods. Hence, existence of autocorrelation ultimately rejects the null hypothesis of random walk in all periods. (Table No. 6 of Appendix A, B and C).

Kruskal-Wallis test

Kruskal-Wallis test is a one-way analysis of variance by ranks and a non-parametric method to test whether samples originate from the same distribution. The method is used for comparing more than two samples that are independent or not related and it does not assume a normal distribution of the residuals. The null hypothesis are.

H0: All Three Stock market indices follow the random walk for the entire period i.e. from year 1993 to 2013.

H0: Random walk follows among all three indices before the credit crisis i.e. from year 2002 to 2007.

H0: Random walk follows among all three indices during the credit crisis i.e. from year 2007 to 2009.

H0: Random walk follows among all three indices after the credit crisis i.e. from year 2009 to 2013.

 

Table A:- Kruskal Wallis For The Entire Period

 

Test Statisticsa,b

 

RETURN

Chi-Square

1.072

df

2

Asymp. Sig.

.585

a. Kruskal Wallis Test

b. Grouping Variable: INDICES

 

Table B:- Kruskal Wallis For The Period 2002-2007

 

Test Statisticsa,b

 

RETURN

Chi-Square

26.098

df

2

Asymp. Sig.

.000

a. Kruskal Wallis Test

b. Grouping Variable: INDICES

 

Table C:- Kruskal Wallis For The Period 2007-2009

 

Test Statisticsa,b

 

RETURN

Chi-Square

.049

df

2

Asymp. Sig.

.976

a. Kruskal Wallis Test

b. Grouping Variable: INDICES

 

Table D:- Kruskal Wallis For The Period 2009-2013

 

Test Statisticsa,b

 

RETURN

Chi-Square

1.357

df

2

Asymp. Sig.

.507

a. Kruskal Wallis Test

b. Grouping Variable: INDICES

 

If the P value is small, it rejects the idea that the difference is due to random sampling. For the entire period the p-value is 0.585, for the period of 2002-2007, the p-value is 0.000, for the period of 2007-2009, the p-value is 0.976 and for the period of 2009-2013, the p-value is 0.507

 

Consequently, it can be concluded that only before the period of credit crisis, the difference is not due to random sampling. Random walk follows between all three indices for entire period of 21 years as well as during the credit crisis and after the crisis period. A random walk is defined by the fact that price changes are independent of each other.

 

Conclusion

This study investigates the market efficiency of selected three stock markets. In addition to that, this research analyses that whether the crisis period alters the conclusion of efficiency of the stock market. The daily return series of SENSEX, S&P CNX Nifty and Dow Jones for a period of 1993 to 2013 is considered for the study.  The research summarizes that Indian stock market is not weak form efficient in all periods however, from year 2002 onwards stock market exhibits some signs of efficiency. Further, all three independent samples are compared and is concluded that it follows random walk during and after the crisis period but not before the crisis period.

References

1.      Boyer Brian H., Kumagai Tomomi and Yuan Kathy (2006), “How Do Crises Spread? Evidence from Accessible and Inaccessible Stock Indices”, The Journal of Finance, Vol. 61, No. 2 , pp. 957-1003

2.      Chancharoenchai Kanokwan and Dibooglu Sel (2006), “Volatility Spillovers and Contagion during the Asian Crisis: Evidence from Six South-East Asian Stock Markets” Emerging Markets Finance & Trade, Vol. 42, No. 2, pp. 4-17

3.      Dhal Sarat (2009), “ Global Crisis and the Integration of India's Stock Market”, Journal of Economic Integration, Vol. 24, No. 4, pp. 778-805

4.      Ghosh Arunabha (2006), “Pathways Through Financial Crisis: India”, Global Governance, Vol. 12, No. 4, pp. 413-429

5.      Herrero Alicia García, Gyntelberg Jacob and Tesei Andrea (2008), “The Asian crisis: what did local stock markets expect?” BIS Working Papers No 261

6.      Jethwani Kinjal, Achuthan Sarla (2013), “Stock market efficiency and crisis: evidence from India” Asia-Pacific Finance and Accounting Review ISSN 2278-1838: Volume 1, No. 2, pp. 35-43.

7.      Jeyanthi B. J. Queensly, William Albert S J, Kalavathy S. Titus (2012), The Impact Of Global Financial Crisis On Indian Stock Markets”, International Journal Of Research In Commerce And Management, Vol. 3,  No. 2, pp. 71-76.

8.      Kazi Irfan Akbar, Guesmi Khaled and Kaabia Olfa (2011), “Contagion Effect of Financial Crisis on OECD Stock Markets”, Discussion Paper No. 2011-15. Accessed from http://www.economics-ejournal.org/economics/discussionpapers/2011-15 as on 12-12-2013.

9.      Malliaris A. G. and Urrutia Jorge L. (1992), “The International Crash of October 1987: Causality Tests”, The Journal of Financial and Quantitative Analysis, Vol. 27, No. 3, pp.353-364

10.  Olowe Rufus Ayodeji (2009), “Stock Return Volatility, Global Financial Crisis And The Monthly Seasonaleffect On The Nigerian Stock Exchange” African Review of Money Finance and Banking, pp. 73-107

11.  Patel Nikunj R., Patel Bhavesh K and Ranpura Darshan (2011), “Testing Weak Form Market Efficiency Of Indian Stock Markets”, Ss International Journal Of Business And Management Research, Vol. 1, No. 3, pp. 1-28.

12.  Salman Nabeel, Abbas Muhammad, Ahmad Mushtaq, and Majid Muhammad Abdul, Khan Noheed and Raheel Awad (2012), “Impact of financial crisis on Asian stock Markets”, interdisciplinary journal of contemporary research in business Vol 3, No 11. pp. 736-741.

13.  Schwert G. William (1989), “Stock Volatility and the Crash of '87”, The Review of Financial Studies, Vol. 3, No. 1, ,pp. 77-102

14.   Yüksel Aydin (2002), “The Performance of the Istanbul Stock Exchange during the Russian Crisis”, Emerging Markets Finance & Trade, Vol. 38, No. 6, , pp. 78-99

Websites

ü  en.wikipedia.org

ü  www.investopedia.com

ü  www.moneycontrol.com

ü  www.nseindia.com

ü  www.bseindia.com

ü  finance.yahoo.com

ü  money.cnn.com

ü  www.google.com

ü  www.slideshare.net

 

Appendices

Table A:-ADF for BSE

ADF Test Statistic

-33.55698

    1%   Critical Value*

-3.4348

 

 

    5%   Critical Value

-2.8626

 

 

    10% Critical Value

-2.5674

*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(CLOSE,2)

Method: Least Squares

Date: 12/19/13   Time: 16:44

Sample(adjusted): 1/11/1993 5/04/2012

Included observations: 5040 after adjusting endpoints

Variable

Coefficient

Std. Error

t-Statistic

Prob. 

D(CLOSE(-1))

-1.009154

0.030073

-33.55698

0.0000

D(CLOSE(-1),2)

0.082824

0.026836

3.086319

0.0020

D(CLOSE(-2),2)

0.078042

0.023238

3.358451

0.0008

D(CLOSE(-3),2)

0.054351

0.019199

2.830950

0.0047

D(CLOSE(-4),2)

0.046042

0.014088

3.268078

0.0011

C

3.389471

2.206431

1.536179

0.1246

R-squared

0.464436

    Mean dependent var

-0.008262

Adjusted R-squared

0.463904

    S.D. dependent var

213.7032

S.E. of regression

156.4704

    Akaike info criterion

12.94480

Sum squared resid

1.23E+08

    Schwarz criterion

12.95257

Log likelihood

-32614.90

    F-statistic

873.0882

Durbin-Watson stat

2.004013

    Prob(F-statistic)

0.000000

 

Table B:- ADF for NSE

ADF Test Statistic

-33.34988

    1%   Critical Value*

-3.4348

 

 

    5%   Critical Value

-2.8626

 

 

    10% Critical Value

-2.5674

*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(CLOSE,2)

Method: Least Squares

Date: 12/19/13   Time: 16:50

Sample(adjusted): 1/11/1993 6/08/2012

Included observations: 5065 after adjusting endpoints

Variable

Coefficient

Std. Error

t-Statistic

Prob. 

D(CLOSE(-1))

-1.004996

0.030135

-33.34988

0.0000

D(CLOSE(-1),2)

0.067270

0.026919

2.499027

0.0125

D(CLOSE(-2),2)

0.069741

0.023318

2.990804

0.0028

D(CLOSE(-3),2)

0.047503

0.019269

2.465217

0.0137

D(CLOSE(-4),2)

0.041637

0.014057

2.962037

0.0031

C

0.998543

0.663774

1.504342

0.1326

R-squared

0.469785

    Mean dependent var

-0.004859

Adjusted R-squared

0.469261

    S.D. dependent var

64.77475

S.E. of regression

47.18961

    Akaike info criterion

10.54741

Sum squared resid

11265683

    Schwarz criterion

10.55514

Log likelihood

-26705.31

    F-statistic

896.4813

Durbin-Watson stat

2.003658

    Prob(F-statistic)

0.000000

 

Appendix A BSE Sensex

Table 1:-Test for Normality:

Tests of Normality

 

Kolmogorov-Smirnova

 

Statistic

df

Sig.

BSE

.051

5147

.000

a. Lilliefors Significance Correction

 

Table 2:-Descriptive statistics of different period

N

TIME PERIOD

MEAN

STD. DEV.

COE. OF VAR.

SKEWNESS

KURTOSIS

JORQUE BERRA

5045

1993-2013

0.040

1.638

40.689

-0.099

4.967

5194.549

676

1993-1995

0.030

1.533

51.099

-0.012

1.560

68.574

1025

1996-2000

0.052

1.767

33.964

0.032

1.869

149.383

645

2000-2002

-0.091

1.725

-19.050

-0.410

2.139

141.015

1272

2002-2007

0.150

1.398

9.339

-0.768

6.650

2468.519

346

2007-2009

-0.207

2.677

-12.960

-0.149

1.037

16.764

1243

2009-2013

0.063

1.380

21.828

1.152

15.550

12798.870

Table 3:-Descriptive statistics of each year

N

TIME

MEAN

STD. DEV.

CO. OF VAR.

SKEWNESS

KURTOSIS

JORQUE BERRA

214

1993

0.129

1.864

14.467

-0.442

1.153

18.820

231

1994

0.069

1.435

20.716

0.577

1.783

43.419

231

1995

-0.101

1.261

-12.502

0.115

0.385

1.934

238

1996

-0.003

1.522

-443.773

0.504

0.974

19.508

246

1997

0.069

1.638

23.622

-0.309

4.315

194.741

244

1998

-0.074

1.908

-25.821

-0.046

1.306

17.413

248

1999

0.199

1.815

9.118

0.053

1.492

23.121

250

2000

-0.093

2.204

-23.822

-0.243

0.946

11.787

248

2001

-0.079

1.719

-21.655

-0.462

1.679

37.976

251

2002

0.014

1.102

79.903

0.142

1.494

24.175

254

2003

0.216

1.166

5.410

-0.190

0.133

1.712

254

2004

0.048

1.610

33.266

-1.554

13.748

2102.653

251

2005

0.141

1.080

7.682

-0.442

0.405

9.890

250

2006

0.153

1.627

10.616

-0.485

3.002

103.683

249

2007

0.155

1.544

9.954

-0.210

1.455

23.786

246

2008

-0.302

2.853

-9.442

-0.069

0.904

8.567

243

2009

0.244

2.186

8.950

1.313

10.922

1277.570

252

2010

0.064

1.006

15.782

-0.254

0.650

7.149

247

2011

-0.115

1.321

-11.529

0.279

0.058

3.241

251

2012

0.091

0.919

10.082

0.088

0.623

4.389

250

2013

0.034

1.096

31.865

-0.111

1.429

21.779

 

Table 4:-Runs test

TIME PERIOD

TEST VALUE a

CASES<TEST VALUE

CASES>=TEST VALUE

TOTAL CASES

NUMBER OF RUNS

Z

P VALUE

 

1993-2013

0.0402

2454

2591

5045

2299

-6.274

0

1993-1995

0.03

339

336

675

270

-5.277

0

1996-2000

0.052

521

504

1025

480

-2.086

0.037

2000-2002

-0.0905

306

339

645

289

-2.659

0.008

2002-2007

0.1496

602

670

1272

593

-2.373

0.018

2007-2009

-0.2065

173

173

346

160

-1.507

0.132

2009-2013

0.07

621

622

1243

599

-1.334

0.182

 

Table 5:-T- Test

PERIOD

T

DF

SIGNIFICANCE

MEAN DIFF

LOWER

UPPER

1993-2013

1.746

5044

0.081

0.040

-0.005

0.085

1993-1995

0.509

674

0.611

0.030

-0.086

0.146

1996-2000

0.943

1024

0.346

0.052

-0.056

0.160

2000-2002

-1.333

644

0.183

-0.091

-0.224

0.043

2002-2007

3.819

1271

0.000

0.150

0.073

0.227

2007-2009

-1.435

345

0.152

-0.207

-0.490

0.077

2009-2013

1.615

1242

0.107

0.063

-0.014

0.140

 

Table 6:-Auto correlation test

PERIOD

LAGS

1

2

3

4

5

6

7

8

9

10

1993-2013

AC

-0.434

-0.077

0.003

0.024

-0.003

-0.046

0.023

0.009

0.003

0.024

 

STD. ERROR

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

 

Q Stat

951.428

981.670

981.722

984.716

984.757

995.237

997.999

998.447

998.495

1001.000

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

1993-1995

AC

-0.276

-0.224

-0.031

-0.007

0.064

-0.024

0.032

-0.053

-0.017

0.098

 

STD. ERROR

0.038

0.038

0.038

0.038

0.038

0.038

0.038

0.038

0.038

0.038

 

Q Stat

51.485

85.393

86.055

86.087

88.840

89.219

89.899

91.836

92.024

98.604

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

1996-2000

AC

-0.485

-0.034

0.023

0.036

-0.021

-0.076

0.064

-0.021

0.025

0.020

 

STD. ERROR

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

 

Q Stat

242.010

243.201

243.724

245.087

245.529

251.549

255.763

256.199

256.844

257.273

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2000-2002

AC

-0.419

-0.066

-0.053

0.063

-0.014

-0.026

-0.005

0.017

0.045

-0.025

 

STD. ERROR

0.039

0.039

0.039

0.039

0.039

0.039

0.039

0.039

0.039

0.039

 

Q Stat

113.744

116.556

118.387

120.937

121.061

121.508

121.525

121.710

123.009

123.429

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2002-2007

AC

-0.420

-0.139

0.031

0.067

-0.009

-0.039

-0.009

-0.010

0.010

0.073

 

STD. ERROR

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

 

Q Stat

224.359

248.858

250.069

255.753

255.853

257.822

257.924

258.065

258.181

265.090

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2007-2009

AC

-0.445

-0.055

0.060

-0.104

0.037

-0.036

-0.016

0.152

-0.082

-0.032

 

STD. ERROR

0.054

0.054

0.053

0.053

0.053

0.053

0.053

0.053

0.053

0.053

 

Q Stat

68.893

69.946

71.190

74.952

75.440

75.907

75.993

84.186

86.575

86.939

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2009-2013

AC

0.455

-0.037

-0.025

0.037

-0.021

-0.035

0.046

-0.033

0.047

-0.004

 

STD. ERROR

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

 

Q Stat

257.482

259.186

259.976

261.651

262.191

263.700

266.295

267.692

270.476

270.499

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

 

Appendix B:- CNX Nifty NSE

Table 1:- Test for Normality

Tests of Normality

 

Kolmogorov-Smirnova

 

Statistic

df

Sig.

NSE

.054

5172

.000

a. Lilliefors Significance Correction

 

Table 2:- Descriptive statistics of different periods

N

TIME PERIOD

MEAN

STD. DEV.

COF. OF VAR.

SKEWNESS

KURTOSIS

JORQUE BERRA

5070

1993-2013

0.040

1.624

40.323

-0.129

6.097

7865.758

679

1993-1995

0.029

1.469

50.243

0.043

1.078

33.095

1047

1996-2000

0.054

1.761

32.488

0.161

3.207

453.170

645

2000-2002

-0.080

1.606

19.995

-0.353

2.565

190.204

1272

2002-2007

0.143

1.464

10.227

-0.962

7.728

3361.353

346

2007-2009

-0.193

2.619

13.534

-0.345

1.785

52.800

1243

2009-2013

0.061

1.381

22.702

1.210

16.200

13895.013

 

Table 3:- Descriptive statistics of each year

N

PERIOD

MEAN

STD. DEV.

COF. OF VAR.

SKEWNESS

KURTOSIS

JORQUE BERRA

213

1993

0.158

1.744

11.046

-0.347

0.365

5.457

230

1994

0.055

1.400

25.612

0.634

1.961

52.260

236

1995

-0.112

1.242

-11.130

-0.106

0.752

5.996

250

1996

-0.004

1.526

-365.632

0.696

1.103

32.820

244

1997

0.075

1.798

24.010

0.058

7.564

581.841

250

1998

-0.080

1.777

-22.280

-0.094

1.617

27.594

254

1999

0.203

1.837

9.056

0.045

2.247

53.510

250

2000

-0.063

2.002

-31.591

-0.105

1.491

23.620

248

2001

-0.071

1.630

-22.900

-0.462

2.262

61.672

251

2002

0.013

1.061

83.184

0.078

1.457

22.463

254

2003

0.213

1.232

5.777

-0.337

0.470

7.134

254

2004

0.040

1.763

44.122

-1.802

14.397

2331.090

251

2005

0.124

1.114

9.017

-0.517

0.592

14.831

250

2006

0.134

1.650

12.305

-0.620

2.731

93.720

249

2007

0.175

1.601

9.130

-0.258

1.558

27.955

246

2008

-0.297

2.808

-9.467

-0.283

1.688

32.505

243

2009

0.232

2.143

9.233

1.508

12.621

1704.950

252

2010

0.066

1.024

15.634

-0.277

0.670

7.939

247

2011

-0.114

1.321

-11.548

0.270

0.057

3.045

251

2012

0.097

0.955

9.801

0.076

0.662

4.818

250

2013

0.026

1.138

43.529

-0.116

1.514

24.447

 

Table 4:- Runs test

TIME PERIOD

TEST VALUE a

CASES<TEST VALUE

CASES>=TEST VALUE

TOTAL CASES

NUMBER OF RUNS

Z

P VALUE

 

1993-2013

0.0403

2483

2587

5070

2293

-6.799

0

1993-1995

0.0293

346

332

678

261

-6.064

0

1996-2000

0.0542

537

510

1047

469

-3.413

0.001

2000-2002

-0.0803

308

337

645

283

-3.147

0.002

2002-2007

0.1432

593

679

1272

591

-2.429

0.015

2007-2009

-0.1935

164

182

346

168

-0.597

0.55

2009-2013

0.05

621

622

1243

607

-0.88

0.379

 

Table 5:- T-test

PERIOD

T

DF

SIDNIFICANCE

MEAN DIFF

LOWER

UPPER

1993-2013

1.766

5069

0.077

0.040

-0.004

0.085

1993-1995

0.519

677

0.604

0.029

-0.082

0.140

1996-2000

0.996

1046

0.319

0.054

-0.053

0.161

2000-2002

-1.27

644

0.204

-0.080

-0.205

0.044

2002-2007

3.487

1271

0.001

0.143

0.063

0.224

2007-2009

-1.374

345

0.170

-0.193

-0.470

0.083

2009-2013

1.553

1242

0.121

0.061

-0.016

0.138

 

Table 6:- Auto correlation test

PERIOD

LAGS

1

2

3

4

5

6

7

8

9

10

1993-2013

AC

-0.433

-0.084

0.009

0.018

0.013

-0.053

0.024

0.005

-0.004

0.042

 

STD. ERROR

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

 

Q Stat

952.337

988.317

988.750

990.450

991.253

1005.383

1008.201

1008.328

1008.416

1017.488

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

1993-1995

AC

-0.242

-0.268

-0.004

-0.006

0.018

0.003

0.057

-0.099

0.019

0.100

 

STD. ERROR

0.038

0.038

0.038

0.038

0.038

0.038

0.038

0.038

0.038

0.038

 

Q Stat

39.869

88.876

88.885

88.910

89.139

89.146

91.396

98.057

98.296

105.211

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

1996-2000

AC

-0.491

-0.030

0.026

0.003

0.039

-0.086

0.046

-0.025

0.002

0.071

 

STD. ERROR

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

 

Q Stat

253.300

254.300

254.990

255.000

256.600

264.460

266.640

267.300

267.300

272064.000

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2000-2002

AC

-0.387

-0.120

-0.030

0.043

0.035

-0.036

-0.046

0.056

0.020

-0.014

 

STD. ERROR

0.039

0.039

0.039

0.039

0.039

0.039

0.039

0.039

0.039

0.039

 

Q Stat

96.830

106.900

107.510

108.710

109.510

110.360

111.770

113.820

114.080

114.220

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2002-2007

AC

-0.406

-0.150

0.037

0.069

-0.017

-0.028

-0.012

-0.011

-0.002

0.089

 

STD. ERROR

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

 

Q Stat

210.073

241.870

243.576

249.644

250.013

250.984

251.178

251.343

251.351

261.438

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2007-2009

AC

-0.461

-0.030

0.044

-0.086

0.042

-0.062

0.007

0.140

-0.070

-0.046

 

STD. ERROR

0.054

0.054

0.053

0.053

0.053

0.053

0.053

0.053

0.053

0.053

 

Q Stat

73.867

74.260

74.936

77.515

78.135

79.491

79.510

86.436

88.182

88.945

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2009-2013

AC

-0.469

-0.020

-0.027

0.039

-0.021

-0.045

0.058

-0.039

0.047

0.001

 

STD. ERROR

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

0.028

 

Q Stat

273.528

274.037

274.950

276.821

277.361

279.904

284.134

286.079

288.879

288.880

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

 

Appendix C:- Dow Jones

Table 1:- Test for Normality

Tests of Normality

 

Kolmogorov-Smirnova

 

Statistic

df

Sig.

DOW JONES

.080

5288

.000

a. Lilliefors Significance Correction

 

Table 2:- Descriptive statistics of different periods

N

TIME PERIOD

MEAN

STD. DEV.

CV

SKEW

KURT

JB

5288

1993-2013

0.030

1.128

37.019

-0.165

8.240

14985.608

756

1993-1995

0.058

0.598

10.370

-0.361

1.758

113.743

1059

1996-2000

0.063

1.092

17.448

-0.574

4.292

871.055

647

2000-2002

-0.048

1.403

-29.325

-0.076

2.611

184.335

1274

2002-2007

0.051

0.835

16.411

0.216

2.717

401.955

355

2007-2009

-0.170

2.269

-13.318

0.262

3.526

187.931

1197

2009-2013

0.065

0.999

15.355

-0.355

3.186

531.374

 

Table 3:- Descriptive statistics of each year

N

YEAR

MEAN

STD. DEV.

CV

SKEW

KURT

JB

252

1993

0.050

0.548

10.940

-0.379

2.250

59.160

252

1994

0.008

0.688

81.942

-0.331

1.324

22.993

252

1995

0.115

0.544

4.752

-0.229

1.318

20.439

254

1996

0.091

0.755

8.298

-0.593

1.776

48.264

253

1997

0.081

1.184

14.676

-0.846

6.583

487.012

252

1998

0.059

1.256

21.203

-0.548

4.274

204.394

252

1999

0.089

1.017

11.396

0.044

-0.100

0.187

252

2000

-0.025

1.309

-51.813

-0.283

1.738

35.093

248

2001

-0.030

1.350

-45.447

-0.571

4.065

184.215

252

2002

-0.073

1.604

-22.023

0.494

1.234

26.229

252

2003

0.090

1.043

11.646

0.112

1.133

14.021

252

2004

0.012

0.683

55.536

0.010

-0.115

0.143

252

2005

-0.002

0.649

-268.501

-0.004

0.040

0.018

251

2006

0.060

0.622

10.338

-0.110

1.239

16.550

251

2007

0.025

0.918

36.960

-0.623

1.637

44.268

253

2008

-0.163

2.381

-14.583

0.227

3.822

156.163

252

2009

0.068

1.524

22.274

0.072

2.181

50.180

252

2010

0.041

1.018

24.541

-0.177

2.162

50.415

252

2011

0.021

1.328

62.201

-0.532

2.572

81.332

250

2012

0.028

0.743

26.513

0.027

0.916

8.774

252

2013

0.093

0.640

6.860

-0.202

1.362

21.176

 

Table 4:- Runs test

TIME PERIOD

TEST VALUE a

CASES<TEST VALUE

CASES>=TEST VALUE

TOTAL CASES

NUMBER OF RUNS

Z

P VALUE

1993-2013

0.05

2644

2644

5288

2758

3.108

0.002

1993-1995

0.07

378

378

756

386

0.51

0.61

1996-2000

0.07

529

530

1059

520

-0.646

0.519

2000-2002

-0.05

323

324

647

332

0.59

0.555

2002-2007

0.06

637

637

1274

691

2.971

0.003

2007-2009

-0.1

177

178

355

200

2.286

0.022

2009-2013

0.07

598

599

1197

619

1.128

0.259

 

Table 5:- T test

PERIOD

T

DF

SIGNIFICANCE

MEAN DIFF

LOWER

UPPER

1993-2013

1.964

5287

0.050

0.030

0.000

0.061

1993-1995

2.651

755

0.008

0.058

0.015

0.100

1996-2000

1.865

1058

0.062

0.063

-0.003

0.128

2000-2002

-0.867

646

0.386

-0.048

-0.156

0.060

2002-2007

2.175

1273

0.030

0.051

0.005

0.097

2007-2009

-1.415

354

0.158

-0.170

-0.407

0.067

2009-2013

2.253

1196

0.024

0.065

0.008

0.122

 

Table 6:- AUTO CORRELATION TEST

PERIOD

LAGS

1

2

3

4

5

6

7

8

9

10

 

 

 

 

 

 

 

 

 

 

 

 

1993-2013

AC

-0.507

-0.023

0.040

0.011

-0.047

0.043

-0.040

0.032

-0.027

0.050

 

STD. ERROR

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

0.014

 

Q Stat

1360.000

1360.000

1370.000

1370.000

1380.000

1390.000

1400.000

1410.000

1410.000

1420.000

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

1993-1995

AC

-0.485

0.022

-0.039

-0.017

0.028

-0.019

0.015

0.004

-0.024

0.020

 

STD. ERROR

0.036

0.036

0.036

0.036

0.036

0.036

0.036

0.036

0.036

0.036

 

Q Stat

178.266

178.632

179.794

180.013

180.605

180.877

181.057

181.067

181.520

181.830

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

1996-2000

AC

-0.473

-0.014

-0.047

0.052

-0.033

0.037

-0.037

-0.008

0.000

0.107

 

STD. ERROR

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

0.031

 

Q Stat

237.207

237.417

239.815

242.693

243.885

245.330

246.798

246.868

246.868

259.059

 

Prob

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2000-2002