Financial Markets Efficiency

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major index calculated by stock exchange authority for (Morocco, Tunisia, Egypt, ... The Casablanca stock exchange held its first trading session in November ...

International Research Journal of Finance and Economics ISSN 1450-2887 Issue 49 (2010) © EuroJournals Publishing, Inc. 2010 http://www.eurojournals.com/finance.htm

Financial Markets Efficiency: Empirical Evidence form some Middle East & North Africa Countries (MENA) Khaled Al-Zaubia Associate Professor, Department of Banking and Finance The Hashemite University Marwan Al-Nahlehb Assistant Professor, Department of Financial Management Jerash Private University – Jordan Abstract Many empirical studies examine financial market efficiency; the concept of market efficiency had been anticipated at the beginning of the 20th century by Bachlier (1900) in his PhD in mathematics from the Sorbonne. In his opening paragraph, Bachlier recognized that "past, present and even discounted future events are reflected in market price, but often show no apparent relation to price changes". Bachlier contribution in this field was overlooked until it was circulated to economists by Paul Samuelson in the late 1950s. In the early 1950s, researchers were, for the first time, able to use electronic computers to study the behavior of price series in order to extract from it a long-term movement, or trend, and then use the residual portion for short-term movements and random fluctuations. By applying RWH (Random Walk Hypothesis) on the time series that consist of the major index calculated by stock exchange authority for (Morocco, Tunisia, Egypt, Jordan, and Turkey) financial markets, the time series starts from 1 January 2004 to 31 December 2006 which if it works (RWH) then the expected returns of speculative strategies should be zero. Some other statistical tests will be performed such as testing the normality of returns.

Introduction There is a little evidence on efficiency in emerging markets because these markets are considered to be in a transition period from emerged to developed markets, the legislation environment and the structure of markets under analysis are still immature so they considered to be as emerged markets. According to Efficient Market Hypotheses (EMH) developed by Professor Eugene Fama in 1960, it says that there are three common forms or levels of market’s efficiency in these forms are; weak form efficiency, semi-strong form efficiency and strong form efficiency, each of which have different implications for how markets work. Weak form efficiency implies that the information set is that the market index reflects only the history of prices or returns themselves Weak form efficiency advocates assert that fundamental analysis can be used to identify stocks that are undervalued and overvalued. Therefore, keen investors looking for profitable companies can earn profits by researching financial statements. Weak form efficiency advocates assert that fundamental analysis can be used to identify stocks that are undervalued and overvalued. Therefore, keen investors looking for profitable companies can earn profits by researching financial statements. Semi-strong form efficiency implies that share prices do not adjust to publicly available new information very rapidly and in a biased fashion, such that excess returns can be earned by trading on

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that information and that might creates an excess returns on short run . This implies that neither fundamental analysis nor technical analysis techniques will be able to reliably produce excess returns. Strong-form efficiency implies that Share prices reflect all information, public and private, and no one can earn excess returns. While according to the random walk hypothesis which has been presented by Burton G. Malkiel in 1973 which says that stock market prices evolve according to a random walk and thus the prices of the stock market cannot be predicted ; based on this the expected returns of speculative strategies should be zero. So some other statistical tests will be performed such as testing the normality of returns. Paul A. Volcker, (1975), “Inflation, Recession, Oil, and International Financial Markets, examined the relationships between Inflation, Recession, Oil, and international financial markets. His focus was on explaining the interactions between oil markets and international financial markets and the channels of connection between these markets. Asma Mobarek & Keavin Keasey, in their study on Dhaka Stock Market of Bangladesh, they said that in the emerging markets speculations are common; large investors can easily speculate in the markets. Since the emerging markets are less organized market compare to mature markets and most of the time these markets working without market makers and timely available information, there is always a possibility to make profit by large investors and insiders. The ability to predict stock price changes based on a given set of information lies behind the notion of stock market efficiency. So the lower the market efficiency; the greater predictability of stock price changes. A study by Christopher Gan and others in (2006) they examined the relationships between New Zealand Stock Index and a set of seven macroeconomic variables from January 1990 to 2003 using co integration tests to determine whether the New Zealand Stock Index is leading indicator for macroeconomic variables like Inflation rate, Exchange rate, Gross Domestic Product, Money Supply, Long Term Interest rate, Short Term Interest rate, and Domestic Retail Oil Price. Their major finding is that the NZSE40 (New Zealand Stock Index) is consistently determined by the interest rate, money supply and real GDP during 1990-2003. Their results suggest that investment perception of New Zealand is a mixture of other mature stock markets. Thus, investors who are interested in investing in New Zealand should pay more attention to the above mentioned macroeconomic variables rather than the exchange rate and inflation rate index (CPI). From the last study we can notice the complexity in relationships between the three major categories of research (Oil Prices, Macroeconomic Variables, and Stock Markets). Bernd Hayo, Ali M. Kutan, (2005), "The Impact of News, Oil Prices, and Global Market Developments on Russian Financial Markets", they analyzed the impact of news, Oil Prices, and Global market Development on Russian financial Markets. Using daily returns on stock and bond markets over the period September 1995 to November 2001, the major result was that the sensitivity of Russian stock market to oil prices and US stock market changes. Background of Sample Financial Markets Casablanca Stock Exchange (CSE) Brief History The Casablanca stock exchange held its first trading session in November 1929, throughout its 77 yearold existence it has undergone several reforms. The first, in 1948, established the Casablanca stock exchange as a legal entity. Then in 1967, a second reform entailed a legal and technical reorganization, defining the exchange as a public institution. In 1993 a number of reforming legal bills where enacted, providing the CSE with a regulatory and technical framework it needed for its development.

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Structure The CSE is a regulated market where stocks are traded publicly. The management of the exchange is entrusted to a business corporation management company (private sector). The capital of the management company is owned entirely by certified brokerage firms. The directors of the management company are appointed by the minister of finance; the management company is responsible for the admission of stocks for listing on the CSE (as well as delisting) and for insuring that trading is in complaisance with laws and regulations of the exchange. Main Indices • MASI Float • MADEX Float The MASI float is a broad stock index, calculated using the floating-weighted capitalization method, it comprises all shares listed on CSE and reflects the developments of the market as a whole. The MADEX Float is a blue chip index, also calculated using the floating-weighted capitalization method, it comprises the most actively traded shares, in terms of liquidity, measured on the basis of the previous half-year including the shares traded in a continues basis on the Casablanca market. It has proved to be an instrument particularly adapted to portfolio management. During 2005, the MASI float and MADEX float posted solid gains to close at (5539.13) points and (4358.87) points respectively at 31/12/2005, the index that will be taken into consideration for analysis purpose is the MASI float because it express all the market and considered to be the main indicator for the market which it reach up to 9479.61 points by the end of 2006 Figure (1). Figure 1: MASI price path 2004-2006 Casablanca Stock Exchange 11000 Index

9000 7000 5000

12/06/06

30/08/2006

25/05/2006

16/02/2006

11/07/05

08/03/05

05/02/05

24/01/2005

15/10/2004

07/12/04

04/07/04

01/02/04

3000

Date

Source: www.casablanca-bourse.com

Market Performance The performance of Casablanca stock market in the last three years was unusual, the capitalization of market reaches to 417 billions in local currency (end of 2006) while it was only 115 billions (end of 2003), MASI is the major index calculated by stock exchange authority and consist of all traded stocks. MASI multiplied three times within three years (figure 1), the reason why we took major index, market capitalization and trading volume as an indicators for activity of the markets that these parameters are considered to be as headlines for profitability, liquidity, and safe investment criteria for any investor, also most of literature took these indicators as a proxy for financial markets activity with a great attention of financial indices.

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Figure 2: Market Capitalization and Trading Volume 2003-2006 Casablanca Stock Exchange 417.09

450 400 Billion MAD

350 300

252.33

250 200 150 100

Market Capitalization

206.52 148.52

166.42

Trading Volume

115.374 53.55

71.76

50 0 2003

2004

2005

2006

Source: Casablanca bourse Bulletins

Cairo and Alexandria Stock Exchange (CASE) Brief History and Structure The Alexandria stock exchange was officially established in 1883 followed by the Cairo stock exchange in 1903, as early as of 1906 their were 328 joint stock companies traded with a total capital of (91 millions EP). Activity on the Cairo and Alexandria exchanges was quiet brisk until 1958 when trading reached a peak of (110.7 million EP), with Nasser's nationalization program in the early 1960,s trading volumes fell to 3-7 million EP a year and just 32 companies remained listed. The exchanges were never closed, however, and as the market shrank, the government subsidized the remaining brokers whose commissions had reached to almost nothing. In the med 1970,s the exchanges receive support as part of Sadat's open-door economic policy and the laws encouraging foreign investment and private sector development. Both the exchanges are governed by the same board of directors, and both are electronically linked for real-time trading. Misr Clearing, settlement and depositary (MCSD) is a private company that handles clearing and settlement operation in Egypt. Main Index • CASE30 Index CASE30 Index includes the top 30 companies in terms of liquidity and activity, the CASE 30 price index is weighted by market capitalization adjusted by free float, during 2005 the CASE30 recorded the highest ever annual growth rate of 146%, the market capitalization and trading volume grew with 94.87% and 283.33% respectively. Market Performance In figure (3) CASE30 that consisting of top 30 companies in accordance to liquidity and profitability, with a base year of 1998 up to the end of 2003 the index was around base value of 1000 points, while by the beginning of year 2004 the index starts to go up and reaches top of its levels by the beginning of 2006 to around 8000 points.

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International Research Journal of Finance and Economics - Issue 49 (2010) Figure 3: Case 30 price path 2004-2006

11/10/2006

27/07/2006

14/05/2006

21/02/2006

1/12/2005

14/09/2005

3/7/2005

12/4/2005

26/01/2005

8/11/2004

24/08/2004

22/03/2004

9/6/2004

9000 8000 7000 6000 5000 4000 3000 2000 1000 0 4/1/2004

Index

Egypt Stock Market Index

Date

Source: http://www.egyptse.com

Market capitalization and trading volume also took the same trend of general index (CASE30) trend, from figure (4) the market capitalization of both Cairo and Alexandria stock exchange went to up normal levels, market capitalization growth rate for the period 2003-2006 was around 210%, and 942% for traded value of financial instruments for the same time period. Figure 4: Market Capitalization and Trading Volume 2003-2006 Egypt Stock Exchange 600

534 456

Billions EGP

500 400

271

300 200 100

234 172

Trading Volume

161

28

Market Capitalization

42

0 2003

2004

2005

2006

Source: Egypt Stock Exchange Bulletins

Amman Stock Exchange Brief History and Structure The Amman Financial Market (AFM), (renamed the Amman Stock Exchange (ASE) in 1999) opened in 1978 following an initiative by the Jordanian central bank and the IFC. The move was seen as a major push towards the development of the capital market in Jordan. Jordan's stock exchange is regarded as one of the most developed and sophisticated markets in the region and is included in the IFC index, the exchange is a non-profit making, private sector organization with legal and financial autonomy.

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Main Index • ASE share price index ASE share price index has had a share-price index since 1980, in 1992 the index was revised and updated in cooperation with the International Finance Corporation of the World Bank, the index comprises the 70 most liquid companies, the weight of each company in the index is determined by its relative percentage of the total market capitalization of the 70 companies, the ASE share price index had a base value of 100, which was changed to 1000 at the beginning of 2004, and a base date of 31/12/1991. The index is divided into four sectors: banking and financial, Insurance, Services, and Industrial. From figure (5) Amman Stock Exchange main index had made extraordinary growth rate for the period from end of 2003 up to the beginning of 2006 with approximately 250% growth rate. Figure 5: ASE Index price path 2004-2006 Amman Stock Exchange 10000

Index

8000

6000

4000

10/04/06

07/04/06

04/04/06

01/04/06

10/04/05

07/04/05

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07/04/04

04/04/04

01/04/04

2000

Date

Source: www.ase.com.jo Up normal rates where in all parameters of this market especially the market capitalization where it reaches 27 Billion of local Jordanian currency by the end of 2005 with 245% growth rate from that of the end 2003. Figure 6: Market Capitalization and trading Volume 2003-2006 Amman Stock Exchange 30000

26667.1

Million JD

25000

21078.2 16871

20000 15000 10000

14209.9

13033.8

Trading Volume

7772.8 3793.2

5000

1855.2

0 2003

Source: ASE Bulletins

Market Capitalization

2004

2005

2006

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Tunis Stock Exchange Brief History and Structure The Tunis Stock Exchange creation goes back to 1969; the role of the market was relatively insignificant in Tunisian economy till the end of last century, while this importance increased when a major reform was adopted at the end of 1994 with the declaration of the 1994 law. Main Indices • BVMT index • Tunindex The BVMT index is a non-weighted index and is composed of 30 stocks listed on the official quotation, the base value of the index was reset at 465.77 at 31/3/1998. The Tunindex is a price index based on capitalization; it's composed of 39 stocks and has a base value of 1000 at 31/12/1997 Performance of Tunis Stock Exchange Performance of Tunis Stock Exchange was relatively normal in comparison of other markets, never the less it had achieved extraordinary growth rate within the last three years when considering its performance in the last decade, Tunindex (main index of Tunis Stock Exchange) was looping around its base value for a long time. From Figure (7) Tunindex toke upward tendency by med 2005 and crossing boarder of 1600 points by the end of 2006. Figure 7: Tunindex price path 2004-2006 Tunis Stock Market Index

Index

1800 1600 1400 1200

10/2/2006

7/2/2006

4/2/2006

1/2/2006

10/2/2005

7/2/2005

4/2/2005

1/2/2005

10/2/2004

7/2/2004

4/2/2004

1/2/2004

1000 800

Date

Source: www.bvmt.com.tn

Other indicators of the market toke upward tendency by med of 2005, market capitalization of the market almost doubled within the last three years as its obvious from the next figure, while trading volume growth rate for the same period was approximately 384%. Figure 8: Market Capitalization and trading Volume 2003-2006 Tunis Stock Exchange 5490

6000

4606

5000 Millions TD

3840

4000 2976

3085

Market Capitalization

3000

Trading Volume 1661

2000 948

1000

690

0 2003

Source: Tunis Stock Exchange Bulletins

2004

2005

2006

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Istanbul Stock Exchange Brief History and Structure The concept and operations of an organized securities market in turkey have their roots in the second half of the 19th century, following the proclamation of the Turkish republic, a law was enacted in 1929 to reorganize the fledging capital markets under the new name of the Istanbul Securities and Foreign Exchange Bourse. Soon, the bourse became very active and contributed substantially to the funding requirements of new enterprises across the country. The early 1980s so a marked improvement in both the legislative and institutional framework of the Turkish capital markets. In 1981, the capital markets law was enacted and one year later the main regulatory body responsible for the supervision and regulation of the Turkish securities markets, the capital markets board (CMB) based in Ankara, was established. Main National Indices • ISE National-all shares index • ISE national-30 index • ISE national-50 index • ISE national-100 index The ISE National-all shares index constituents include all shares traded on the national market except investment trusts, ISE National-30 index includes 30 national market companies which have high market values and liquidity and represent the sectors, in which they operate, ISE National-50 index includes 50 national market companies which have high market values and liquidity and represent the sectors, in which they operate, ISE National-100 index comprises 100 stocks and doesn’t include investment trusts. The companies are selected according to pre- determined criteria and all national-30 companies are included automatically. The selected companies must also represent the sectors in which they operate; the try-based ISE national-100 index has a base value of 1 and a base date January 1986. Performance of the ISE Performance of Turkish financial markets was wobbly in the last decade maybe due to unstable exchange rate of Turkish Lira, never the less that performance took an obvious trend in the last three years as it's cleared by figure (9) when ISE NATIONAL-100 price Index took upward trend. Figure 9: ISE national-100 index price path 2004-2006 Istanbul Stock Exchange (ISE) 50000

Index

40000 30000 20000

Date

Source: www.ise.com

02/09/06

02/05/06

02/01/06

02/09/05

02/05/05

02/01/05

02/09/04

02/05/04

02/01/04

10000

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Market capitalization and trading volumes reached high levels with sharp growth rates in the last three years as figure (10) shows that. Figure 10:

32 51 31 .3 4

Istanbul Stock Exchange

26 99 31 .2 9

350000 20 84 22 .9 5

250000 200000

218317.84

230037.68

14 66 44 .9 7

Millions YTL

300000

Market Capitalization Trading Volume

132555.53

150000 96072.77

100000 50000 0 2003

2004

2005

2006

Source: ISE Bulletins

The Applied Methodology Normality in returns is very important and will be tested in two major ways. Histogram, if the residuals are normally distributed the histogram should be bell-shaped. The second Normality Test will be performed by calculating the Jarque-Bera statistic, if it is not statistically significant, then normality in returns exists. At this level of analysis the Q-Statistics test will be performed which often used as a test of whether the series is white noise, the Q-statistic at lag k is a test statistic for the null hypothesis that there is no autocorrelation up to order k and is computed as

Where Tj is the j-th autocorrelation and T is the number of observations. There remains the practical problem of choosing the order of lag to use for the test. If I choose too small a lag, the test may not detect serial correlation at high-order lags. However, choosing too large a lag; the test may have low power since the significant correlation at one lag may be diluted by insignificant correlations at other lags; this issue is standardized by choosing different time lags at 5, 10, 20, and 50. A third statistical test will be performed in order to clarify the statistical features of the time series, the Augmented Dickey-fuller (1979) and Phillips-Peron (1988) tests to determine whether or not the series are stationary. The Augmented Dickey-fuller test H0: a unit root exists in market K index time series. ∆ FMAKt = σ +

ρ

FMAKt-1 + ∑m γ i =1

i



FMA

+

ε

t

K t −1

H0: a unit root exists in international oil market price time series. m ∆ IOPt = σ + Φ IOPt-1 + ∑ θ ∆ IOP + t j

j =1

If a unit root exists, then the coefficients

t −1

ω

ρ and Φ are not significantly different from zero.

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The Phillips-Peron test The Phillips-Peron test is different from the previous approach by proposing a nonparametric method of controlling for higher-order serial correlation in a series. Econometric Results Data Base Tests Results (The Characteristics of Market) Descriptive Statistics This section considers the empirical characteristics of returns in the capital markets under analysis which are international oil market and some financial markets at MENA area which are Morocco, Tunisia, Egypt, Jordan, and Turkey financial markets. Table 1:

The main statistics of the data

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability Observations

Oil 0.00117418 0.00087655 0.12152309 -0.06986821 0.02123935 0.30526074 4.79687733 47.11275 0.000000 741

Casablanca 0.00128427 0.00132538 0.05721414 -0.04892967 0.00902663 -0.12221322 8.59658848 92.21483 0.000000 741

Egypt 0.00256861 0.00229638 0.07072783 -0.07617046 0.01795082 -0.22669678 5.07939562 86.45676 0.000000 741

Jordan 0.00117389 0.00096817 0.08721377 -0.07205074 0.01509983 0.01195026 5.54385006 76.49738 0.000000 741

Tunis 0.00071537 0.00050627 0.02743527 -0.01385887 0.00528298 0.50571429 4.98833786 88.71273 0.000000 741

Turkey 0.00113078 0.00225732 0.05869820 -0.08305536 0.01730020 -0.27769700 3.85401746 51.46588 0.000000 741

Concerning the main descriptive statistics, I observe that the returns in international oil market were characterized by the highest standard deviation, so the volatility of oil prices was the biggest one among all the series of returns under consideration. The value of the Skewness in Turkey, Egypt, and Casablanca is negative which shows that the returns are left-skewed and the kurtosis in the case of the returns is much smaller than in the case of the series of levels which indicates thin tails in the distribution. Those are the common stylized facts observed in the series of returns on capital markets. For each of the series of returns we have performed the Jarque-Bera test for the null hypothesis at 5% level of significance which allows me to reject the null hypothesis about normality. The JarqueBera test shows that the returns do not have the normal distribution. Following we have checked the autocorrelation of the series of returns. Table 2:

The Ljung - Box statistics at the lag 5, 10, 20 and 50 with p-values in parenthesis

OIL Casablanca Egypt Jordan Tunis Turkey

Q (5) 3.6079 (0.607) 96.5216 (0.000) 22.9570 (0.000) 29.2502 (0.000) 37.5257 (0.000) 1.4173 (0.922)

Q (10) 10.3985 (0.406) 102.5417 (0.000) 31.0693 (0.001) 31.9638 (0.000) 40.3653 (0.000) 14.5280 (0.150)

Q (20) 17.6429 (0.611) 108.2714 (0.000) 42.4529 (0.002) 38.0798 (0.009) 48.4684 (0.000) 23.1968 (0.279)

Q (50) 39.7130 (0.851) 158.8936 (0.000) 95.9310 (0.000) 76.8086 (0.009) 61.7244 (0.000) 78.1280 (0.007)

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For Casablanca, Egypt, Jordan, and Tunis the p-values are very small (0.000) which conclude the presence of autocorrelation in all time series of returns for these countries financial markets, while p-values for international oil market and turkey financial market are significantly different from zero which indicate the absence of autocorrelation at all levels of lags at these two markets. Augmented Dickey-fuller (1979) and Phillips-Peron (1988) tests for all time series of returns are presented for each market at three levels of critical values, level and first difference Table 3:

Augmented Dickey-fuller and Phillips-Peron test for unit root and their critical values for Oil returns

Oil critical values 1% critical values 5% critical values 10%

Augmented Dickey-fuller Level -12.55365 -3.44180476127 -2.86583542753 -2.56906212269

Phillips-Peron Level -28.69874

From Table(3) both Augmented Dickey-fuller and Phillips-Peron tests indicate the rejection of null hypothesis of a unit root for level in time series of International Oil returns. Table 4:

Augmented Dickey-fuller and Phillips-Peron test for unit root and their critical values for Casablanca stock exchange returns

Casablanca Stock exchange critical values 1% critical values 5% critical values 10%

Augmented Dickey-fuller Level -9.75313 -3.44180476127 -2.86583542753 -2.56906212269

Phillips-Peron Level -19.36469

From Table(4) both Augmented Dickey-fuller and Phillips-Peron tests indicate the rejection of null hypothesis of a unit root for level and the rejection of null hypothesis of a unit root for the first difference in time series of Casablanca stock exchange returns. Table 5:

Augmented Dickey-fuller and Phillips-Peron test for unit root and their critical values for Egypt stock exchange returns

Egypt Stock exchange critical values 1% critical values 5% critical values 10%

Augmented Dickey-fuller Level -10.59307 -3.44180476127 -2.86583542753 -2.56906212269

Phillips-Peron Level -24.21720

From table(5) both Augmented Dickey-fuller and Phillips-Peron tests indicate the rejection of null hypothesis of a unit root for level and the rejection of null hypothesis of a unit root for the first difference in time series of Egypt stock exchange returns. Table 6:

Augmented Dickey-fuller and Phillips-Peron test for unit root and their critical values for Jordan stock exchange returns

Jordan Stock exchange critical values 1% critical values 5% critical values 10%

Augmented Dickey-fuller Level -11.73218 -3.44180476127 -2.86583542753 -2.56906212269

Phillips-Peron Level -24.15340

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From Table(6) both Augmented Dickey-fuller and Phillips-Peron tests indicate the rejection of null hypothesis of a unit root for level and the rejection of null hypothesis of a unit root for the first difference in time series of Jordan stock exchange returns. Table 7:

Augmented Dickey-fuller and Phillips-Peron test for unit root and their critical values for Tunis stock exchange returns

Tunis Stock exchange critical values 1% critical values 5% critical values 10%

Augmented Dickey-fuller Level -11.19279 -3.44180476127 -2.86583542753 -2.56906212269

Phillips-Peron Level -22.02139

From Table(7) both Augmented Dickey-fuller and Phillips-Peron tests indicate the rejection of null hypothesis of a unit root for level and the rejection of null hypothesis of a unit root for the first difference in time series of Tunis stock exchange returns. Table 8:

Augmented Dickey-fuller and Phillips-Peron test for unit root and their critical values for Turkey stock exchange returns

Turkey Stock exchange critical values 1% critical values 5% critical values 10%

Augmented Dickey-fuller Level -12.24351 -3.44180476127 -2.86583542753 -2.56906212269

Phillips-Peron Level -26.24755

From Table(8) both Augmented Dickey-fuller and Phillips-Peron tests indicate the rejection of null hypothesis of a unit root for level and the rejection of null hypothesis of a unit root for the first difference in time series of Turkey stock exchange returns.

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