1. Market Efficiency

2. 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

3. Efficiency : defined
An efficient capital market is a market that is efficient in processing information…
In an efficient market, prices ‘fully reflect’ available information..

4. 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.

5. 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.

6. 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

7. 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 Empirical
Work’, Journal of Finance, 25, pp

8. 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.

9. 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).

10. 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

11. 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.

12. Example of Efficiency

13. Example of Inefficiency

14. Misconceptions on EMH

15. 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.

16. 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.

17. 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

18. 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

19. Definition
A financial anomaly refers to unexplained results that deviate from those expected under finance theory
Especially those related to the efficient market hypothesis

20. Low PE Effect
Stocks with low PE ratios provide higher returns than stocks with higher PEs

21. 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

22. 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

23. 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

24. 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

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

26. Testing Weak form of
Market Efficiency

27. 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,

28. 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

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

30. Descriptive Statistics

31. 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.

32. 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.

33. ADF Test

34. PP Test

35. 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.

36. Runs Test Runs Test is for the randomness of the series.
Runs test investigates serial dependence in share price movements

37. Run Test

38. 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.

39. 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.

40. Analysis
Time Series Error term is stationary

41. 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. .

42. K-S Test

43. 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

44. 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

45. thanks