Market Efficiency. Plan for Discussion Efficiency and its Forms Misconceptions of EMH Anomalies Testing Weak form of Market Efficiency Case Study of selected.

Market Efficiency

Plan for Discussion

Efficiency and its Forms Misconceptions of EMH Anomalies Testing Weak form of Market Efficiency Case Study of selected NSE indices

– S&P CNX Nifty

– CNX Nifty Junior

Efficiency : defined

An efficient capital market is a market that is

efficient in processing information…

In an efficient market, prices ‘fully reflect’

available information..

Efficient Market

In an efficient market,

– Market price is an unbiased estimate of the

true value of the investment.

Market Efficiency does not require that the

market price be equal to true value at every

point in time.

Efficient Market

Errors in the market price be unbiased

implying that prices can be greater than or

less than true value, as long as these

deviations are random.

Randomness implies that there is an equal

chance that stocks are under or over valued

at any point in time.

In 1960s and early 1970s

Fama (1965) concluded that

Most of the evidences are consistent with Efficient

Market Hypothesis

Stock prices showed Random walk

Predictable variations in equity return were

statistically insignificant

Reference:

Fama EF (1965) “The behaviour of stock market prices”. Journal of Business. 38:34–105

Forms of Market Efficiency

Fama (1970) defined three form of market

efficiency :

Weak Form

Semi-Strong Form

Strong Form

Reference : Fama, E F (1970): ‘Efficient Capital Markets: A Review of Theory and EmpiricalWork’, Journal of Finance, 25, pp 383-417.

Weak Form

Weak form of efficiency implies that :

The current price reflects the past information

or the history of prices.

Suggesting that charts and technical analyses

that use past prices alone would not be useful

in finding valuable stocks.

Semi-Strong Form

Semi-strong form of efficiency implies that

the current price reflects the information

contained not only in past prices but all

publically available information (financial

statements/reports).

Semi-Strong Form

Academic research supports the semi-strong form of the EMH by investigating various corporate announcements, such as:

– Stock splits

– Cash dividends

– Stock dividends

Strong Form

Strong form of efficiency implies that:

the current price reflects all information, public

as well as private, and

no investors will be able to consistently find

under valued stocks.

Example of Efficiency

Example of Inefficiency

Misconceptions on EMH

Misconceptions of EMH

No group of investors will beat the market in the

long term.

Given the number of investors in markets, the

laws of probability suggests that a fairly large

number can beat the market consistently over

long periods,

– not because of their investment strategies but

because they are lucky.

Misconceptions of EMH

An efficient market does not imply that stock

prices cannot deviate from true value;

there can be large deviations from true value.

The deviations do have to be random.

Fama’s new View

Fama (1998) suggests that apparent anomalies require:

– new behavioural based theories of the stock market and

– the need to continue the search for better models of asset pricing.

Reference: Fama, E F (1998): ‘Market Efficiency, Long-term Returns, and Behavioural Finance’, Journal

of Financial Economics, 49, pp 283-306.

Anomalies Definition Low PE Effect Low-Priced Stocks Small Firm and Neglected Firm Effect Market Overreaction January Effect Day-of-the-Week Effect Chaos Theory

Definition

A financial anomaly refers to unexplained results that deviate from those expected under finance theory

– Especially those related to the efficient market hypothesis

Low PE Effect

Stocks with low PE ratios provide higher returns than stocks with higher PEs

Low-Priced Stocks

Stocks with a “low” stock price earn higher returns than stocks with a “high” stock price

There is an optimum trading range

Market Overreaction

The tendency for the market to overreact to extreme news

– Investors may be able to predict systematic price reversals

Results because people often rely too heavily on recent data at the expense of the more extensive set of prior data

January Effect

Stock returns are inexplicably high in January

Small firms do better than large firms early in the year

Especially pronounced for the first five trading days in January

Day-of-the-Week Effect

Mondays are historically bad days for the stock market

Wednesday and Fridays are consistently good

Tuesdays and Thursdays are a mixed bag

Chaos Theory

Chaos theory refers to instances in which apparently random behavior is systematic or even deterministic

Testing Weak form of

Market Efficiency

Random walk hypothesis

Ko and Lee (1991),

If the random walk hypothesis holds, the weak form of the efficient market hypothesis must hold,

Thus, evidence supporting the random walk model is the evidence of market efficiency.

Reference : Ko, K.S. and Lee, S.B. (1991) A comparative analysis of the daily behavior of stock returns: Japan,

the U.S and the Asian NICs. Journal of Business Finance and Accounting, 18, 219-234.

Case Study- NSE

This study attempts, to seek evidence for the

weak form efficient market hypothesis using

the daily data for stock indices of the

National Stock Exchange for the period of

1 January 2000 to 31 Oct 2008

Research Methodology

Following test are done to analyze the data : Jarque Bera Test Unit Root Test Autocorrelation test Run Test K-S Test

Descriptive Statistics

Analysis

Stock returns are not normally distributed,

Also verified with the Jarque-Bera statistic, which is a test statistic for testing whether the series is normally distributed.

The hypothesis of normal distribution is rejected at the conventional 5% level.

Unit Root Test

A test to determine whether a time series is stationary or not,

whether the null hypothesis of a unit root can be rejected.

ADF Test

PP Test

Analysis The null hypothesis that there is a unit root

cannot be rejected for both Nifty and Nifty Junior , in the level form.

For the first differences of both , the null hypothesis of a unit root is strongly rejected.

Both indexes contain a unit root, that is, non-stationary in their level forms, but stationary in their first differenced forms.

Runs Test

Runs Test is for the randomness of the series.

Runs test investigates serial dependence in

share price movements

Run Test

Analysis

It can be seen that the total number of runs are 8 and 15 for S&P CNX Nifty and CNX Nifty Junior respectively.

Therefore, the hypothesis of randomness for both the series is rejected.

Autocorrelations

Autocorrelation is the correlation of a series with itself .The autocorrelation function (ACF) test is examined to identify the degree of

autocorrelation in a time series.

Analysis

Time Series Error term is stationary

Kolmogorov Smirnov Test

KS is used to determine how well a random sample of data fits a particular distribution (uniform, normal, poisson).

It is based on comparison of the sample’s cumulative distribution against the standard

cumulative function for each distribution.

.

K-S Test

Analysis

The Kolmogorov Smirnov Goodness of Fit

Test (KS) shows 0.00 significance for the Z

at the 5 percent level.

Null hypothesis of normal distribution for

both is rejected

Conclusion

Jarque Bera : No Normality

K-S Test : Does not fit in Normal Distribution

Run Test : No Random Walk

Autocorrelation : Time series error : Stationary

Unit Root Test : Random Walk