market efficiency. plan for discussion efficiency and its forms misconceptions of emh anomalies...
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Market Efficiency
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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
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Efficiency : defined
An efficient capital market is a market that is
efficient in processing information…
In an efficient market, prices ‘fully reflect’
available information..
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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.
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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.
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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
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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.
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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.
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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).
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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
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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.
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Example of Efficiency
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Example of Inefficiency
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Misconceptions on EMH
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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.
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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.
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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.
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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
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Definition
A financial anomaly refers to unexplained results that deviate from those expected under finance theory
– Especially those related to the efficient market hypothesis
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Low PE Effect
Stocks with low PE ratios provide higher returns than stocks with higher PEs
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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
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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
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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
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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
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Chaos Theory
Chaos theory refers to instances in which apparently random behavior is systematic or even deterministic
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Testing Weak form of
Market Efficiency
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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.
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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
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Research Methodology
Following test are done to analyze the data : Jarque Bera Test Unit Root Test Autocorrelation test Run Test K-S Test
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Descriptive Statistics
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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.
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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.
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ADF Test
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PP Test
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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.
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Runs Test
Runs Test is for the randomness of the series.
Runs test investigates serial dependence in
share price movements
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Run Test
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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.
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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.
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Analysis
Time Series Error term is stationary
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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.
.
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K-S Test
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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
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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
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