efficient stock market theory

2. Introduction
Market
Efficiency
Informational
Efficiency
Operational
Efficiency

3. Definition of Efficient Markets
 An efficient capital market is a market that is efficient in
processing information.
 We are talking about an “informationally efficient” market,
as opposed to a “transactionally efficient” market. In other
words, we mean that the market quickly and correctly
adjusts to new information.
 In an informationally efficient market, the prices of
securities observed at any time are based on “correct”
evaluation of all information available at that time.
 Therefore, in an efficient market, prices immediately and
fully reflect available information.

4. Definition of Efficient Markets
(cont.)
 Professor Eugene Fama, who coined the phrase “efficient
markets”, defined market efficiency as follows:
 "In an efficient market, competition among the many intelligent
participants leads to a situation where, at any point in time, actual
prices of individual securities already reflect the effects of
information based both on events that have already occurred and on
events which, as of now, the market expects to take place in the
future. In other words, in an efficient market at any point in time the
actual price of a security will be a good estimate of its intrinsic
value."

5. Efficient Market
 Stock prices fully reflect available information
 Developed by Professor Eugene Fama
 Argues that stocks trade at their fair value
 Impossible for investors to either purchase
undervalued stocks or sell stocks for inflated prices
 Only way an investor can possibly obtain higher
returns is by purchasing riskier investments

6. Strong Form
Efficient Markets
Semi Strong Form
Efficient Markets
Weak Form
Efficient Markets
All information is
reflected on prices
All Public
information is
reflected on security
prices
All Historical
information is
reflected on security
prices
Variants of the Hypothesis

7. Weak Form of EMH
 Historical Prices used
 Current prices reflect all information found in the past
prices and trade volumes.
 Future prices cannot be predicted by analyzing prices
from past
 Technical analysis techniques do not provide excess
returns, fundamental analysis in some form may still
provide some returns
 Share prices exhibit no serial dependencies (there are no
patterns to asset prices)

8. The Random Walk Theory
 Maurice Kendall : stock prices followed a random
walk, each successive change is independent of the
previous one.
 Academic researchers : the randomness of stock prices
was the result of an efficient market.

9. Semi Strong Form of EMH
 Prices fully reflect all publicly available information
(economic news, political news, annual report) and
expectations about the future
 Prices adjust rapidly to new information
 Old information cannot be used to earn superior returns
 Neither technical analysis nor fundamental analysis
provide excess returns

10. Strong Form of EMH
 Prices fully reflect all information whether publicly
available or not.
 Private information is available with companies but is
not disclosed.
 No one can earn superior returns.

11. Testing the Form of
Market efficiency

12. Empirical Tests for Weak Form of
Market efficiency
Filter Rule
Runs Test
Serial Correlation

13. Serial Correlations
 The following chart shows the relationship (there is none)
between S&P 500 returns each month and the returns from
the previous month. Data are from Feb. 1950 to Sept. 2001.
 Note that the R2 is virtually 0 which means that knowing
last month’s return does you no good in predicting this
month’s return.
 Also, notice that the trend line is virtually flat (slope =
0.008207, t-statistic = 0.2029, not even close to significant)
 The correlation coefficient for this data set is 0.82%

14. Serial Correlations (cont.)
Unlagged vs One-month Lagged S&P 500
Returns
y = 0.008207x + 0.007451
R
2
= 0.000067
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
-30.00% -20.00% -10.00% 0.00% 10.00% 20.00%
One-month Lagged Returns
Unlagged
Returns

15. Tests of the Semi-strong Form
 Fama, Fisher, Jensen & Roll
 Event Studies
 Stock splits
 Earnings announcements
 Analysts recommendations
 Cross-Sectional Return Prediction
 Firm size
 BV/MV
 P/E

16. Empirical Evidence of strong form
of Efficiency
 Mutual Fund performances.
 Difference in return of informed buyer and naïve buy-
and-hold approach is not much.
 Not able to forecast returns properly even after getting
the information.

17. Types of market efficiency
Types of
Efficiency
Reflect What works What does not
work
Strategy to be
used
Weak Form Past Price and
Volume
Fundamental
Analysis &
Insider
Information
Technical
Analysis
Active portfolio
management
Strategy
Semi Strong
Form
Past Price,
Volume &
Publically held
information
Insider
Information
Fundamental
Analysis &
Technical
Analysis
Passive portfolio
management
Strategy
Strong Form Past Price,
Volume,
Publically and
privately held
information
Nothing Fundamental
Analysis,
Technical
Analysis & Insider
Information
Passive portfolio
management
Strategy

18. Market Inefficiencies
 Market prices of common stocks are not always
accurately priced.
 Opposes efficient market hypothesis.
 Drives asset above or below their true value.
 Example Dotcom bubble, recent market crashes.

19. Anomalies
 Anomalies are unexplained empirical results that
contradict the EMH:
 The Size effect.
 The “Incredible” January Effect.
 P/E Effect.
 Day of the Week (Monday Effect : Average closing price
of Monday is lower than average closing price of Friday).
 PEG Ratio Effect
 P/B ratio effect
 Dividend yield Effect

20. The Size Effect
 Beginning in the early 1980’s a number of studies
found that the stocks of small firms typically
outperform (on a risk-adjusted basis) the stocks of
large firms.
 This is even true among the large-capitalization stocks
within the S&P 500. The smaller (but still large) stocks
tend to outperform the really large ones.
 Greater amount of growth and opportunities.
 More volatility.

21. The “Incredible” January Effect
 Stock returns appear to be higher in January than in
other months of the year.
 This may be related to the size effect since it is mostly
small firms that outperform in January.
 It may also be related to end of year tax selling.

22. The P/E Effect
 It has been found that portfolios of “low P/E” stocks
generally outperform portfolios of “high P/E” stocks.
 This may be related to the size effect since there is a
high correlation between the stock price and the P/E.
 It may be that buying low P/E stocks is essentially the
same as buying small company stocks.
 Historical data of PE ratios helps in generating
superior returns.
 Contradicts semi strong form of efficient market
hypothesis.

23. The Day of the Week Effect
 Based on daily stock prices from 1963 to 1985 Keim found
that returns are higher on Fridays and lower on Mondays
than should be expected.
 This is partly due to the fact that Monday returns actually
reflect the entire Friday close to Monday close time period
(weekend plus Monday), rather than just one day.
 Moreover, after the stock market crash in 1987, this effect
disappeared completely and Monday became the best
performing day of the week between 1989 and 1998.

24. Review
 The Efficient Market Hypothesis
 Types of Efficiency
 Degrees of Informational Efficiency
 The Semi-Efficient Market Hypothesis
 Security Prices and Random Walks
 Anomalies
 The Low PE Effect
 Low-Priced Stocks
 The Small Firm and Neglected Firm Effects
 Market Overreaction
 The January Effect
 The Weekend Effect
 The Persistence of Technical Analysis


25.  The persistence of technical analysis: If the
EMH is true, technical analysis should be useless.
Each year however, an immense amount of
literature based in varying degrees on the subject is
printed.

26. Thank You