International Bulletin of Business Administration

ISSN: 1451-243X Issue 11 (2011)

© EuroJournals, Inc. 2011

http://www.eurojournals.com

102

Evaluation of Daily Return Behavior of Tehran Stock

Exchange (TSE)

Seyed Reza Miraskari

Department of Economics, Isfahan University, Iran

E-mail: R.Miraskari@yahoo.com

Tel: +98-936-9063036

Mehrdad Zolfalizadeh

Department of Accounting, Isfahan University, Iran

E-mail: Mehrdad_Z1986@yahoo.com

Tel: +98-911-2369328

Ashraf Jafari

Department of Accounting, Islamic Azad University South Tehran

Branch, Iran

E-mail: Jafari_Ashraf@yahoo.com

Tel: +98-912-7933598

Abstract

An efficient market is a market that immediately affects securities price. Efficiency

of stock exchange can give rise to optimized allocation of resources, hence it is very

important. Objective of this survey is to study daily return behavior of Tehran Stock

Exchange in order to confirm or reject market efficiency at weak level. In literature of this

survey whenever daily return of stock exchange follows random walk theory during the

time, then efficiency of the stock exchange will be confirmed on weak form efficiency. The

studying time period of this survey is from 2005-2010. Serial correlation analysis and unit

root using the augmented Dicky-Fuller test are used in this survey. Results demonstrate that

daily return behavior of Tehran Stock Exchange does not follow random walk theory

during the above time period and this means rejection of the research hypothesis, i.e.

rejection of weak form efficiency in Tehran Stock Exchange.

Keywords: Tehran Stock Exchange, Capital Market Efficiency, Random Walk Theory,

Serial Correlation Test, Unit Root Using The Augmented Dicky-Fuller Test.

1. Introduction The main task of stock exchange is optimized allocation of scarce financial resources that being

efficient is the necessity to obtain this goal. Stock exchange efficiency has a close relationship with

existent information in the market and rapid and timely reflection of information on securities prices.

Price of securities in such market is closed to its inherent value. In fact in case of efficiency of the

capital market, prices of securities are a suitable replacement for their inherent value and these prices

are appropriate signs for resource allocation.

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Given to above issues the main case in this survey is to determine the amount and level of

efficiency by means of Tehran Stock Exchange (TSE) index. In order to study the efficiency of Tehran

Stock Exchange in this survey we have used serial correlation test and unit root using the augmented

Dicky-Fuller test which have numerous applications in researches of efficiency ground. Thus we try in

the following to represent the total framework of research by reviewing of literature, background of

previous researches and describing of research hypothesis and objectives. Afterwards data analysis is

done and conclusion and necessary recommendations will be offered.

2. Research Literature Subject of stock exchange efficiency is one of the most important subjects of the world great markets

at present. An efficient market is a market in which the existent information affects securities prices

immediately. Concept of an efficient market presumes that in dealing decisions investors consider all

the related information in stock prices. Therefore, the current stock prices include all previous

information (like the profit related to the previous season or year) and current information. Necessary

conditions to establish an efficient stock exchange have been stated as the following:

1) Easiness of progress and rapid access to information in the market in a complete form for

everyone

2) Balance between risk and return

3) Determining of the securities price based on supply and demand forces

4) Lack of severe difference between securities price and their inherent value

5) Liquidity capability of the market

6) Individual inability to one-sided change of prices (Rao, 1989)

Indeed these characteristics are the result of the whole competitive market that are appeared

because of high similarity of securities transactions to the whole competitive market. So, high degree

of efficiency is the result of high degree of closeness of securities' market transactions and whole

competitive market.

Efficiency of the capital market has various dimensions that will be referred in the following.

1- Information efficiency: by information efficiency we mean that all individuals have access

to information and it will affect securities price immediately.

2- Allocative efficiency: the capital market enjoys allocative efficiency when it could invest

the existent capitals in plans or companies that offer more return (proportional to risk).

3- Operational efficiency: markets that have higher operational efficiency enjoy more speed

and easiness in transactions and transactional costs are at the lowest level. This kind of

efficiency is leaded to increase the speed of assets' liquidity (Tehrani, 2009).

If there are essential conditions for existence of an efficient market this question is proposed

that how much of efficiency is considered. Also it should be noted that the major criterion in evaluation

of market's efficiency level is information which has grades and degrees by itself. According to this

there are three levels of information efficiency that will be stated briefly.

Weak form of an efficient market: information related to previous prices has no impact on

evaluation of future changes of prices and the current prices don't reflect the past trend. As a result it is

not possible to achieve a better prediction of future trend of prices by analyzing of previous movements

of prices. In this state, securities prices don't have historical memory and technical analyses have little

application.

Semi-strong form efficiency: the current information is a perfect reflection of published

information related to each of the securities and the investor can't obtain more return by analyzing of

this information. Applying of basic analyses won't be effective in this state too. In other words by

showing reaction towards audit reports, financial statements, accounting profit, announcing of each

share's profit, announcing of each share's distributed profit, stock analysis and etc we can't obtain a

higher investment rate of return.

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Strong form efficiency: it is the strongest form of an efficient market in which stock prices are

totally influenced by information such as public and private information. In this state that the market is

efficient in terms of qualitative information, applying of internal information won't be leaded to higher

rate of return (Shariat Panahi, 2010).

Inefficiency of the market, in general will create many problems for all beneficiaries of the

market. One of the important factors in market efficiency is existence of whole competitive conditions,

so inefficiency is leaded to creating of non-competitive conditions and consequently unoptimized

allocation of resources. Also inefficiency of the stock exchange is leaded to encountering of many

accepted companies in the stock exchange with problems and challenges in their financial supply

process. This point that the necessity to obtain an optimized level of desirability for investors is

existence of an efficient market is the motivation to perform this survey. Thus this question is proposed

that can investors of Tehran Stock Exchange achieve the optimized desirability? Or in other words

does Tehran Stock Exchange efficient?

3. Objective of Research Generally the objective of this research could be stated as studying of behavior of stock prices and

investors about communities' decisions in order to measure efficiency degree of Tehran Stock

Exchange that is stated as below:

"Evaluation of daily return of Tehran Stock Exchange to confirm or reject market efficiency at

weak level".

4. Research Background The issue of capital market efficiency has been considered since sixth decade of the twentieth century

and by appearing of "chartists" and "inherent value analysts" and representation of their views. Most

researchers paid due attention to efficiency of different goods markets in the first half of the twentieth

century and most of them showed efficiency of these markets. In sixth decade of the twentieth century

few researches were done about the efficiency of the capital market by means of serial correlation

methods and run tests. Price indexes had been studied in most of such researches, so evidences about

market efficiency were represented. Fama, one of the famous clear-sighted in this regard studied

theories related to capital market efficiency in the seventh decade of the twentieth century by offering

of three models of Fair Game, Sub Martingale and Random Flow Model. After that various methods

were offered to test market efficiency (namazi and shushtarian, 1997). At the end of the seventh decade

other methods such as moving average, quadratic predicting model and moving weighted average

played an important role in tests related to market efficiency.

Studying of securities market efficiency until twentieth century was focused on New York

stock exchange and other stocks in America and London stock exchange. Afterwards range of these

studies were extended to other stock exchanges in the world because of the importance that efficiency

of these markets had in microeconomics policies and economic growth and development. Moreover,

efficiency testing methods were improved and researchers tried to apply new statistical and

unstatistical methods, so that efficiency tests of stock exchange market entered a new phase at the end

of the twentieth century through applying of neural network techniques, resistance level, nonlinear

correlation, self-returned vector, multi-variable tests and proposing of predictability of severe and

sudden changing degree of prices. Most of the performed studies during this period showed

inefficiency of securities market. In the following a number of stock exchange efficiency tests inside

and outside of the country will be represented (namazi and shushtarian, 1997).

Guidi, Gupta et al (2010) have studied the assumptions of an efficient market in Eastern and

Central Europe's stock exchange during 1999-2009 in a research about studying of weak form

efficiency in Eastern and Central Europe markets. They used three tests of autocorrelation analysis,

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random runs test and variance ratio test in order to evaluate efficiency at weak level. Their findings

revealed that given to the autocorrelation analysis and variance ratio test Eastern and Central Europe'

stock exchange didn't follow random walk theory after being accepted in the European Union, while

random runs test showed improvement of the market's position in terms of efficiency. Also they

divided the above stock exchange in to different groups through Generalized Autoregressive

Conditional Heteroskedasticity in Mean (GARCH-M) model and studied their return and fluctuation

separately and concluded that informed investors in some groups would be able to identify bad-valued

assets and obtain abnormal return through that.

Munir and Mansour (2009) have studied stock transactions' index behavior in Kualalampour

stock exchange from Jan 1980 to Aug 2008 by means of two auto-regression models with a unit root

test in a research entitled "Is Malaysian stock exchange efficient? Evidences from unit root test" and

results showed confirmation of hypotheses related to an efficient market at weak level.

Metghalchi, Chang and Marcucci (2008) have studied productivity of some commercial

technical rules in Sweden market during the period 1986-2004 in a research entitled "Is the Swedish

stock market efficient? Evidences from some simple commercial rules". They studied the return

resulted from daily buying and selling of securities during the above period and results showed that

irregular return of daily buying and selling of securities was nonzero and as a result one of the assumed

hypotheses of an efficient market was rejected.

In 1988 Fama and French stated that an efficient stock market has random movement

characteristics of unit root which shows that it is not possible to predict stock exchange return based on

historical observations. If stock prices follow a random walk process, each shock would be durable in

the stock prices and there would be no tendency towards prices' return to precious level during the

time. Random walk characteristic means that future stock returns after that stock price is unpredictable

based on previous observations.

Ball and Brown (1968) have studied the relation between accounting profit and profit of each

share with the stock price in their research entitled "a primary evaluation about accounting profit".

According to efficient market hypothesis, announcing of accounting profit should not affect the stock

profit because prices include all of the existent information. Results of this research show that there is a

relation between accounting profit and stock prices.

Sinaee and Mahmoudi (2006) have studied sample irregular return mean during the years 1994-

2002 in a research entitled "studying the news related to stock split and reward stock on stock return in

Tehran Stock Exchange". Methodology of this research was exactly adopted from Messrs Fama,

Fisher, Jenson and Roll (F.F.J.R) who had studied the efficiency of New York Stock Exchange at semi-

strong level. Obtained results of this study showed existence of irregular return around societies and

this rejects semi-strong form of efficiency in Tehran Stock Exchange.

In 1991 a study about distribution function of stock prices' fluctuations was done in Tehran

Stock Exchange. The intended time period in this research included two periods from 1975 to 1979 and

1986 to 1990. Given to the obtained results distribution of price fluctuations in stock exchange had

projection more than normal distribution and inefficiency of the market was shown at weak level

(Emami, 1991).

A similar research was performed in 1993 about 17 companies and in time period of 1990 to

1992. In this research runs test was applied and the difference between real and expected runs for

different companies was obtained 2.5 to 6.1 which revealed inefficiency of the market at weak level

because of being higher than 1.96 (Nasrollahi, 1993).

Another research was done in 1995 through autocorrelation methods and runs test and by

means of weekly stock price of 50 companies for the time period of 1990 to 1994. Results of this

research showed inefficiency of Iran's stock exchange during the above time period (Fadaeenejad,

1995).

In 1996 another research was done through serial correlation methods, normality tests, filter

procedure and buying-maintaining method. Data under study was daily and weekly prices of 40

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companies during the years 1989-1995. Obtained results of this research revealed inefficiency of

Tehran Stock Exchange at weak level too (Namazi and Shushtarian, 1997).

As it is observed, efficiency of Tehran Stock Exchange has been studied during the two last

decades of the twentieth century by applying of different methods and all researches show that Tehran

Stock Exchange has no efficiency until 2002 even at weak level.

Various indicators of stock exchange activity had a decreasing process in the second half of the

year 2005 which were affected by different internal and external events and irregular increasing of

indexes during previous years. Anyway, policies and attempts of the government and the Stock

Exchange Organization have reduced the speed of decreasing process of stock exchange activity's

indexes since Nov 2006. While the general index was arrived to 9.459 units at the end of 2006, it

passed the limit 10000 during 2007. Also considerable reduction of stock transactions in 2006 gained

relative balance given to the above effective actions in 2007. The following are among the main actions

that have been leaded to fundamental changes in financial analyses' results after the year 2005 and are

the main reason for performing of this research:

• Approving of securities market rule of the Islamic Republic of Iran

• Constitution of supreme council of stock exchange

• Constitution of stock exchange organization

• Constitution of Tehran Stock Exchange (public corporation)

• Constitution of central deposit company of stock exchange and funds liquidation

5. Research Methodology Statistical population of this research includes accepted companies in Tehran Stock Exchange that

daily return of Tehran Stock Exchange from 13.3.2005 to 8.3.2010 has been used in order to study

efficiency at weak level. These statistics are adopted from Tehran Stock Exchange website

(www.seo.ir).

Hypothesis of this research is:

Daily return of Tehran stock exchange follows random walk theory.

Hypothesis of this research studies market efficiency at weak level, so that if daily return of

Tehran Stock Exchange has serial correlation or it has stationary process then efficiency of Tehran

Stock Exchange won't be proved at weak level. Therefore we introduce serial correlation method and

unit root using the augmented Dicky-Fuller test.

5.1. Serial Correlation Test

Serial correlation is one of the statistical tools to calculate dependency of one variable with its previous

amounts. In other words, it is possible to calculate and evaluate the relation between stock return in the

current period with previous periods by means of serial correlation that is calculated as the following:

∑

∑

=

−=

−

= ntn ktkρ1

2

1 k-tt

)r–r(

)r(r )rr( –

That Ρk is serial correlation of stock market return coefficient with k lag. N is number of

observations, rt is stock market return during period t, rt-k is the stock market return during period t-k,

is stock market return average and k is the intended lag. So

1

1

1 lnln−

−

−

−=

−

= tt

t

tt

t ppp

ppr

In which pt is equal to stock exchange market's index in t period and pt-1 is equal to stock

exchange index in period t-1. The purpose of serial correlation test is to determine whether serial

correlation among the intended coefficients is different from zero significantly or not. If stock market

return follows serial correlation statistically (ρk is different from zero significantly), then efficiency

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hypothesis related to weak form of the market will be rejected. Ljung-Box statistic (Q) has been

applied in order to test whether all correlations are simultaneously equal to zero or not. If statistic Q is

different from zero significantly, then it means that the intended sample has serial correlation. As a

result the null hypothesis which shows independency of the market return will be rejected (Guidi et al,

2010).

5.2. Unit Root Test Using the Augmented Dicky-Fuller

Stationary Concept

If sequence rt is a strict stationary, then common distribution of ƒ(rt1, rt2,…, rtk) would have no

difference with common distribution of ƒ(rt1+s , rt2+s , …, rtk+s). It means that time change factor has no

impact on common distribution function. Because of difficulty of strict stationary tests, another form of

stationary called weak stationary is usually used.

If sequence rt is a weak stationary, its average and also covariance among its lags will be

independent of time and it just depends on the studying length in the sequence:

E(rt)= µ

COV(rt , rt-1)= γƖ

Stationary Test

Applying of ordinary least squares estimation method (OLS) in experimental activities presumes that

variables of the used time series are static. On the other hand, the predominant belief is that many time

series variables are not static in economy. Thus it is necessary to be assure of their being stationary or

unstationary before using such variables.

The first step to determine stationary of a variable is observing of time series diagram of that

variable. Of course, there is this possibility that a time series variable which has time trend be static

simultaneously around such time trend. Here the topic of certain and random trends is proposed.

Variables have certain time trend that their total movement path is predictable during the time,

but if the time trend of a variable is changed randomly during the time in a way that its movement path

is not predictable it is said that this variable has random process (Johnston, 1998).

Kinds of Stationary Tests

Stationary test based on correlogram and Dicky-Fuller unit root tests and augmented Dicky-Fuller test:

Unit root test is one of the most common tests that is used to determine stationary state of a

time series process.

For the following first order autoregressive process:

yt = ρyt-1 +ut

We form the following hypothesis testing to test whether time series yt has unit root or in other

words it is static:

H0: ρ = 1

H1: ρ < 1

We can estimate parameter ρ through ordinary least squares (OLS) method as the following:

Basis of unit root test is based on the assumption that when ρ=1, first order autoregressive

process yt = ρyt-1 +ut is unstationary. Thus if coefficient ρ of the above equation is estimated through

ordinary least squares (OLS) method and its being equal to one is tested, we can prove being stationary

or unstationary of a time series process. The problem that exists in performing of such test is that

represented t statistic by ordinary least squares (OLS) method under the accuracy of hypothesis ρ=1

hasn't common distribution t even in large samples and so it is not possible to use critical quantities of t

to perform testing.

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Estimator ρ is in a way that when n is increased, probability distribution of statistic ( - ρ)

tends towards normal distribution with average amount of zero and variance 1- . Statistic ( - ρ)

does not have normal limit distribution under the assumption that H0 is correct, i.e. ρ=1 and has no

standard shape. Hence we can not use represented critical quantities by normal distribution or t in order

to do this test.

Dicky-Fuller has suggested the following statistic based on estimator in order to perform the

above test:

n(

The above statistic is a limit distribution and its critical quantities for unit root test or ρ=1 have

been obtained by help of simulation methods.

Under accuracy of the null hypothesis ρ=1, common calculated amounts of statistic t through

ordinary least squares (OLS) method are known as statistic τ and instead of t-test we should use Dicky-

Fouler τ–test. Dicky-Fuller critical amounts table regulated by McKinnon has been expanded through

simulation of Mont Carlo. If modulus of the calculated statistic is larger than the critical amount

modulus, then H0 is rejected, i.e. series is not stationary and if it is lower, H0 will be accepted and

series has random walk process and is stationary (Harris, 1992b).

Augmented Dicky-Fuller Test

In order to test stationary state first we supposed that the intended time series had a first order

autoregressive process and then we tested hypothesis ρ=1 according to that. Now if this hypothesis is

not correct and the studied time series has p-order autoregressive process, the estimating relation for ρ-

test won't have accurate dynamic definition and it will be resulted in autocorrelation of regression

disturbance terms. When disturbance terms have autocorrelation it is no longer possible to use Dicky-

Fuller test for stationary state, because limit distribution and obtained critical quantities through Dicky-

Fuller are not correct in this state. But if disturbance term ut are autocorrelated, limit distribution and

obtained critical quantities are correct in case of using of augmented Dicky-Fuller model. By assuming

that disturbance term ut related to the following relation:

∆yt = α + βt + δyt-1 + ut

has a self-distributed stationary process from p-order as below:

ut = ϴ1ut-1 + ϴ2ut-2 +…+ ϴput-p + εt

In which εt is a IID (Independent Identically Distribution) process (that is all εt are distributed

similarly and independent from each other). Now if we replace the above relation in the previous

relation we will have:

∆yt = α + βt + δyt-1 + ϴ1ut-1 + ϴ2ut-2 +…+ ϴput-p + εt

If hypothesis ρ=1 is accurate, it is concluded that ut = yt + yt-1 and so we can write the previous

relation as below:

∆yt = α + βt + δyt-1 + ϴ1(yt + yt-1) +…+ ϴp (yt-p – yt-p-1) + εt

= α + βt + δyt-1 + ϴ1∆yt-1 +…+ ϴp∆yt-p + εt ϴi

= α + βt + δyt-1 + ϴi∆yt-1 + εt

Dicky-Fuller shows that calculated statistic t has the same non-standard limit distribution τ in

order to test ρ=1 or in other words δ=0 in the above relation.

In practice number of terms along with the lag that must be considered in the above relation are

usually determined empirically because order of self-distribution process related to disturbance term is

not specified. The purpose is to add terms with lag to the relation to the extent that εt has no correlation.

The null hypothesis is ρ=1 or δ=0 i.e. existence of unit root and the process is not stationary. In order

to determine the estimation of a relation like the above one it is sufficient to consider Durbin-Watson

statistic. If existence of autocorrelation among disturbance term is established then we use ∆yt-1

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sentences to remove autocorrelation. At the same time we can express ideas based on Lagrange

coefficient test about existence of correlation among disturbance term (Noferesti, 2000; Harris, 1992a).

6. Evaluation and Analysis of Model Studying the efficiency of stock market at weak level has been done for the first time by Eigen Fama

that is one of the most known tests in the field of market efficiency at weak level. Fama test was about

daily return of 30 Dowjones industrial stock which tested distances from 1 to 10 by means of serial

correlation analysis. In this survey daily return of Tehran Stock Exchange until 25 lags are applied,

thus we have used market daily return for five years or 1153 days and subtracting of holidays that

correlation between return of each day with the previous day are studied. We determine market return

process during this time period in this survey and then we analyze it. This process is shown in figure

(1).

The first used method to test stock exchange efficiency is serial correlation test with 25 lags.

Results of this test are illustrated in table (1). Significance of autocorrelation coefficients with lags of 1

to 25 and simultaneous significance of these coefficients by Ljung-Box and Box-Pierce test

demonstrate that return of Tehran Stock Exchange has serial and predictable correlation during the

time through technical tools.

Figure 1: Return trend of Tehran stock exchange for 1153 days

Another method to study efficiency of Tehran Stock Exchange is to apply unit root test using

the augmented Dicky-Fuller method. Output of Microfit software shows that white noise of disturbance

term (εt) is occurred in the second interval based on Schwartz Bayesian Criterion (SBC) and Hannan-

Quinn Criterion (HQC), that is they have the highest amount in the second lag. Dicky-Fuller modulus

(13.8282) in this lag is larger than modulus of critical amount of this statistic (3.4161) on confidence

level 5% which shows daily return variable has been stationary and does not follow random walk

process. Return to average variable that is stationary has been explained in some of the performed

researches about the capital market behavior and being stationary does not necessarily mean

inefficiency. However, if market return variable is stationary, we can expect to obtain abnormal profit

from the market by technical analyses. It is noteworthy that augmented Dicky-Fuller test in both states

of with intercept and without trend and also with intercept and with trend has similar results, i.e. daily

return variable has stationary process in both states.

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Table 1: Results of serial correlation test until 25 lags

order Autocorrelati

on coefficient

Box-Pierce

statistic

Ljung-Box

statistic order

Autocorrelation

coefficient Box-Pierce statistic

Ljung-Box

statistic

1 0.41115 (0.000)194.7392 (0.000)195.2468 14 0.10547 (0.000)563.8746 (0.000)567.1842

2 0.25308 (0.000)268.5268 (0.000)269.2910 15 0.12166 (0.000)580.9256 (0.000)584.4901

3 0.20270 (0.000)315.8583 (0.000)316.8285 16 0.11953 (0.000)597.3840 (0.000)601.2093

4 0.17053 (0.000)349.3606 (0.000)350.5059 17 0.11563 (0.000)612.7873 (0.000)616.8704

5 0.13926 (0.000)371.7007 (0.000)372.9823 18 0.13031 (0.000)632.3484 (0.000)636.7765

6 0.13563 (0.000)392.8920 (0.000)394.3216 19 0.13162 (0.000)652.3063 (0.000)657.1043

7 0.095704 (0.000)403.4436 (0.000)404.9561 20 0.11474 (0.000)667.4714 (0.000)672.5642

8 0.12053 (0.000)420.1782 (0.000)421.8169 21 0.076587 (0.000)667.2285 (0.000)679.4587

9 0.15222 (0.000)446.8711 (0.000)448.7868 22 0.091000 (0.000)683.7683 (0.000)689.2011

10 0.17378 (0.000)481.6608 (0.000)483.9421 23 0.072527 (0.000)690.3745 (0.000)695.9536

11 0.14254 (0.000)505.0657 (0.000)507.6136 24 0.069452 (0.000)695.9513 (0.000)701.6385

12 0.15380 (0.000)532.3158 (0.000)535.1983 25 0.067129 (0.000)701.1226 (0.000)706.9541

13 0.12755 (0.000)551.586 (0.000)554.1880

7. Conclusion Daily return behavior of Tehran Stock Exchange (TSE) has been studied in this survey through serial

correlation test and unit root test using the augmented Dicky-Fuller method. Results of both tests show

that daily return behavior of the market does not follow random walk theory and has the characteristic

of return to average and it is possible to apply technical transactional principles to predict prices and

have access to abnormal profit since effect of price shocks in stationary processes are transient. We can

expect that using of such tools is effective in creating of additional return. Thus hypothesis of this

survey which says daily return of Tehran Stock Exchange follows random walk theory is rejected and

we can state that Tehran Stock Exchange has no efficiency.

References [1] Ball, R. and Brown, P. (1968). “ An Empirical Evaluation of Accounting Income Numbers”.

Journal of Financial Economics, 9(1), 3-18.

[2] Emami, A, A. (1991). "Studying of fluctuation and stock risk of accepted companies in Tehran

stock exchange", M.A thesis of business management, Tehran University, Management

Faculty.

[3] Fadaeenejad, E. (winter 1995 and spring 1996). "Weak form test of the capital market

efficiency and Tehran stock exchange", Financial Researches, No. 5&6, second year.

[4] Fama, E. and Franch, K. (1988). “ Permanent and temporary components of stock prices”.

Journal of Political Economy. 25, 383-417.

[5] Guidi, F., Gupta, R. and Maheshwari, S. (2010). “ Weak-form market efficiency and calendar

anomalies for Eastern Europe equity markets”, Munich Personal RePEc Archive(MPRA).

Online at: http://mpra.ub.unimuenchen.de/21984/.

[6] Harris, R. I. D. (1992a). “ Testing for unit roots using the augmented Dicky-Fuller tests:Some

issues relating to the size, power and the lag structure of the test, Economic Letters, 38, 381-

386.

[7] Harris, R. I. D. (1992b). “ Small sample testing for unit roots, Oxford Bulletin of Economics

and Statistics, 54, 615-625.

[8] Johnston, J. and Dinardo, J. (1997). “ Econometric Methods”. New York: McGraw-Hill.

[9] Metghalchi, M., Chang, Y.H. and Marcucci, J. (2008). “ Is the Swedish stock market efficient?

Evidence from some simple trading rules”, International Review of Financial Analysis, 17,

475-490.

[10] Munir,Q. and Mansur, K. (2009). “ Is Malaysian stock market efficient? Evidence from

threshold unit root tests”. Economics Bulletin, 29(2), 1359-1370.

111

[11] Namazi, M. and Shushtarian, Z. (spring and summer 1997), "studying of stock exchange

efficiency in Iran", Financial Researches, No. 7&8.

[12] Nasrollahi, Z. (1993). "Performce analysis of Iran's stock exchange", M.A Thesis of Economy,

Tarbiat Moddaress University, Human Arts Faculty.

[13] Noferesti, M. (2000). "Unit root and cointegration in econometrics", Rasa Cultural Services

Institute.

[14] Pesaran, M. H. and Pesaran, B. (1999). “ Microfit: Interactive Econometric analysis”. Oxford

University Press.

[15] Rao, P.R. (1989), " The resiliency of the ltigh-yield bond market: The ltv default", Journal of

Finance, vol . 44,No, 4.

[16] Shariaat Panahi, S. M. and Jafari, A. (2010), "Investment management", Etehad Publications.

[17] Sinaee, H. A. and Mahmoudi, E. (spring 2004), studying the news related to stock split and

reward stock on stock return in Tehran stock exchange ", Accounting and Audit Studies, No.

39, pp 77-96.

[18] Tashkini, A. (2005), “ Applied Econometrics by Micrifit”. Tehran Dibagaran Artistic Cultural

Institute.

[19] Tehrani, R. and Nourbakhsh, A. (2009). "Investment management", Negah Danesh

Publications.