Weak Form of Efficient Market Hypothesis

8/9/2019 Weak Form of Efficient Market Hypothesis

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WEAK FORM OF EFFICIENT MARKET HYPOTHESIS

The Weak form of Market tests measures whether past series of share prices or returns can be

used to successfully predict future share prices or returns. . The weak form of market measures

the statistical dependence between price changes.. If no dependence is found (i.e., price changes

are random), then this provides evidence in support of the Weak form of market, which implies

that no profitable investment trading strategy can be derived based on past prices. On the other

hand, if dependence is found, for example, price increases generally followed by price increases

in the next period and vice versa; clearly indicates that this can be the basis of profitable

investment rule and violates the assumption of the Weak form of market. However, whether any

trading rule is profitable depends largely on the operating cost (such as brokerage cost, interest

cost, trading settlement procedure) and on whether transactions can be made at the exact prices

quoted in the market.

Tests applied in the Weak form of market

RUN TEST

The run test is one of the approach to test and detect statistical dependencies (randomness) which

may not be detected by the auto-correlation test. We prefer the well-known run test to prove the

random-walk model because the test ignores the properties of distribution. The null hypothesis of

the test is that the observed series is a random series. A run is defined by Siegel (1956), as a

succession of identical symbols which are followed or preceded by differentsymbols or no

symbol at all The number of runs is computed as a sequence of the price changes of the same

sign (such as; ++, _ _, 0 0). When the expected number of run is significantly different from the

observed number of runs, the test reject the null hypothesis that the daily returns are random. The

run test converts the total number of runs into a Z statistic. The Z statistics gives the probability

of difference between the actual and expected number of runs. If the Z value is greater than or

equal to _ 1.96, reject the null hypothesis at 5% level of significance.

AUTO-CORRELATION TEST

Auto-correlation test is a reliable measure for testing of either dependence or independence of

random variables in a series. It compute the price changes at different lagged 1,2,3,4, time


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periods. The serial correlation coefficient measures the relationship between the values of a

random variable at time t and its value in the previous period. It is used to test the dependence

between successive price changes serial correlation technique is used. Serial correlation or auto

correlation measures the correlation co-efficient in a series of numbers with the lagging value of

the same series. Price changes in period t+1 (or t+ any number) are correlated with the price

changes of the preceding period. Scatter diagram can be used to find out the correlation. If there

is correlation between the price of t and t+1 period, the points plotted in the graph would form a

straight line. If the price rise(or fall) in period t is followed by price rise (or fall) in period t+1

then the correlation co-efficient would be +1. If there is a positive relationship between the 2

time periods then it means that if one period that is t increases then at the same time t+1 also

increases and if t decreases then at the same time t+1 also decreases. If there is a negative

relationship between the period t and t+1 then it means that if one time period increases then at

the same time the other time period decreases and vice versa.

In this the closing price of seven power companies has been taken from the BSE listed

companies and in that two test has been applied namely, Run Test and Auto-Correlation Test.

The companies that has been taken are:-

NTPC

POWER GRID RELIANCE POWER

TATA POWER

SUZLON

ABB LTD

TORRENT POWER

ANALYSIS AND INTERPRETATION

Run test

In this the closing price of seven power companies dated from 27-1-2010 to 23-4-2010 has been

taken and in all the seven power companies that has been taken the value of the Run Test is less

than the +-1.96 at 5% level of significance which shows that the runs have occurred by chance


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and the result also suggest that the runs in the price series of stocks are not significantly different

from the runs in the series of random numbers.

Auto-Correlation

In this the returns of the seven power companies has been taken out from their respective closing

prices dated from 27-1-2010 to 26-4-2010 comprising of 60 days. In this time period of 15 days

has been taken. Time period from -

26-4-2010 to 5-4-2010 consist of t period

1-4-2010 to 11-3-2010 consist of t+1 period

10-3-2010 to 17-2-2010 consist of t+2 period

16-2-2010 to27-1-2010 consist of t+3 period

NTPC

In NTPC there is a negative correlation is found between the periods t and t+1 as well as in

between t+1 and t+2 and positive relationship has been fount between t+2 and t+3. Negative

correlation indicates that if t increases then t+1 decreases and vice versa. Positive correlation

indicates that if t+2 increases then t+3 also increases and vice versa.

Power Grid

In power grid there is a negative correlation is found between the periods t and t+1 as well as in

between t+2 and t+3 and positive relationship has been fount between t+1 and t+2. Negative



Reliance power

In Reliance Power there is a negative correlation is found between the periods t and t+1 as well

as in between t+2 and t+3 and positive relationship has been fount between t+1 and t+2.

Negative correlation indicates that if t increases then t+1 decreases and vice versa. Positive

correlation indicates that if t+1 increases then t+2 also increases and vice versa.

Tata Power

In Tata Power there is a negative correlation is found between the periods t and t+1 and positive

relationship has been fount between t+1 and t+2 as well as in between t+2 and t+3. Negative




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Suzlon Energy

In Suzlon Energy there is a negative correlation is found between the periods t+2 and t+3 and

positive relationship has been fount between t+1 and t+2 as well as in between t and t+1.

Negative correlation indicates that if t+2 increases then t+3 decreases and vice versa. Positive

correlation indicates that if t+1 increases then t+2 also increases and vice versa.

ABB Ltd

In ABB Ltd there is a negative correlation is found between the periods t+1 and t+2 and positive

relationship has been fount between t+2 and t+3 as well as in between t and t+1. Negative

correlation indicates that if t+1 increases then t+2 decreases and vice versa. Positive correlation


Torrent Power

In Torrent Power there is a negative correlation is found between the periods t+2 and t+3, t and

t+1, t+1 and t+2 Negative correlation indicates that if t increases then t+1 decreases and vice

versa.

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Weak Form of Efficient Market Hypothesis