7
Stock Price Behavior
and Market Efficiency
7-1
Learning Objectives
1. The foundations of market efficiency
2. The implications of the forms of market
efficiency
3. Market efficiency and the performance of
professional money managers
4. What stock market anomalies, bubbles, and
crashes mean for market efficiency
7-2
Market Efficiency
• Can you consistently “beat the market?”
• Earn a positive excess return
• Excess return = return above that earned by
other investments with the same risk
• Informational Efficiency
• Efficient relative to a specific information set
• Driving force = competition for profit
7-3
Economic Forces Driving Market
Efficiency
• Investor rationality
• No systematic over- or under-valuation
• Independent deviation from rationality
• Irrational investors have dissimilar beliefs
• Arbitrage
• If inefficiencies exist, arbitrage will bid it away
• “Market efficiency doesn’t require that everybody be
rational, just that somebody is.”
These conditions are so powerful that any one of them leads to efficiency.
7-4
Market Efficiency
The Efficient Market Hypothesis (EMH):
Major financial markets reflect all relevant
information at any given time
• Bachelier (1900); Fama (1960, 1972)
Market efficiency research:
•
•
Examines the relationship between stock prices
and available information
Prediction of EMH theory: if a market is
efficient, it is not possible to “beat the market”
7-5
Forms of Market Efficiency
• Weak-form Efficient Market:
• Information = past prices and volume data
• Technical analysis is of little use
• Semistrong-form Efficient Market:
• Information = publicly available information
Fundamental analysis is of little use
• Strong-form Efficient Market:
• Information = Public or private
• “Inside information” is of little use
7-6
Efficient Market Hypotheses
STRONG
Public & Private
Information
WEAK
SEMISTRONG
Public Information
Security Market
Information
7-7
Why Would a Market be Efficient?
• The driving force toward market efficiency is simply
competition and the profit motive.
• Even a relatively small performance enhancement
can be worth a tremendous amount of money
(when multiplied by the dollar amount involved).
• This creates incentives to unearth relevant
information and use it.
7-8
Implications of Market Efficiency
• Does Old Information Help Predict Future Stock
Prices?
• Sophisticated research techniques employed
• Some results find future returns partly predictable by
past returns
• Not enough predictability to earn an excess return
• Trading costs defeat trading systems built on past
returns
• Result: Extremely difficult to outperform a buy-and-hold
strategies involving broad market indexes
Technical Analysis implication:
No matter how often a particular stock price path has related to
subsequent stock price changes in the past, there is no assurance that
this relationship will occur again in the future.
7-9
Implications of Market Efficiency
• Random Walks and Stock Prices
• Many people would say stock prices are predictable
• Truth: it is very difficult to predict stock market prices.
• Considerable research has shown that stock prices
change through time as if they are random.
• Price increases about as likely as price decreases
• Random Walk  no discernable pattern to the
path that a stock price follows
Yt+1 = Yt + ε
7-10
Random Walks and Stock Prices
7-11
How New Information Gets into Stock
Prices
• Stock prices change when traders buy and sell shares based on
their view of the future prospects for the stock.
• Future prospects are influenced by unexpected news
announcements:
• News changes traders’ opinions
• Prices adjust to unexpected news in three basic ways:
• Efficient Market Reaction: The price instantaneously adjusts to
the new information.
• Delayed Reaction: The price partially adjusts to the new
information.
• Overreaction and Correction: The price over-adjusts to the new
information, but eventually falls to the appropriate price.
7-12
How New Information Gets into Stock Prices
7-13
Event Studies
• A technique to test for the effects of new
information on stock prices
• Two areas of focus:
• The adjustment process itself
• The size of the stock price reaction to a news
announcement
7-14
Event Studies
• Isolate effect of stock specific news from overall market
reactions:
Abnormal return = Observed return – Expected return
• Expected return calculation:
• Market Model using an index (Nasdaq 100 or S&P 500)
• Long-term average return on the stock
• Align the abnormal return on a stock to the days relative
to the news announcement.
• Day 0 = the day a news announcement is made.
•
•
One day after news announcement = +1
One day before news announcement = -1
7-15
Event Studies
• According to the EMH:
• The abnormal return on any day should relate only
to information released on that day
• Cumulative Average Return (CAR):
• CAR = abnormal returns accumulated over a 60 or
80-day period (day 0 +/- 30 or 40 days)
• The first cumulative abnormal return, or CAR, is just
equal to the abnormal return on day -40.
• The CAR on day -39 is the sum of the first two abnormal
returns.
• Examining CARs indicates any over- or underreaction to an announcement
7-16
Event Studies
• Abnormal return = Observed Return–Expected Return
• “Cumulative Abnormal Return”
Cumulative Abnormal Return
7.0%
6.0%
5.0%
CAR
4.0%
3.0%
2.0%
1.0%
0.0%
-1.0%
-40
-36
-32
-28
-24
-20
-16
-12
-8
-4
0
4
8
12
16
20
24
28
32
36
40
-2.0%
Day Relative to Event
7-17
Event Study Example
• Friday, May 25, 2007:
• Advanced Medical Optics, Inc. (EYE), recalled a contact lens
solution called Complete MoisturePlus Multi Purpose
Solution.
• EYE took this voluntary action after the Centers for Disease
Control and Prevention (CDC) found a link between the
solution and a rare cornea infection called acanthamoeba
keratitis (AK).
• EYE chose to recall their product even though no evidence
was found that their manufacturing process introduced the
parasite that can lead to AK.
• Further, company officials believed that the occurrences of
AK were most likely the result of end users who failed to
follow safe procedures when installing contact lenses.
7-18
EYE Event Study
• Tuesday, May 29, 2007
• EYE shares opened at $34.37, down $5.83 from the
Friday closing price.
7-19
EYE Event Study
Advanced Medical Optics,
Inc.’s cumulative abnormal
return hovered around zero
before the announcement.
After the news was
released, there was a
large, sharp downward
movement in the CAR.
Overall CAR pattern = essentially what EMH would predict:
• A band of cumulative abnormal returns
• A sharp break in cumulative abnormal returns, and
• Another band of cumulative abnormal returns.
7-20
Informed Traders and Insider Trading
• If a market is strong-form efficient, no
information of any kind, public or private, is
useful in beating the market.
• But, it is clear that significant inside
information would enable you to earn
substantial excess returns.
• Should any of us be able to earn returns
based on information that is not known
to the public?
7-21
Informed Traders and Insider Trading
• In the U.S. it is illegal to make profits on nonpublic information:
• Necessary to instill trust in U.S. stock markets
• Enforced by SEC
• Distinguish between:
• Informed trading (vs. “noise” or “liquidity” trading)
• Legal insider trading
• Illegal insider trading
7-22
Informed Trading
• Informed Trader:
• One who makes a decision to buy or sell a
stock based on publicly available
information and analysis
• Information sources:
•
•
•
•
The Wall Street Journal
Quarterly reports issued by a company
Financial information from the Internet
Talking to other investors
7-23
Legal Insider Trading
• Some informed traders are also insider
traders.
• Trading by company “insiders” in the stock of
their own company:
•
•
•
•
Perfectly legal trades
Must comply with SEC reporting rules
SEC reports trades to the public
Corporate insiders must declare that trades made were
based on public information about the company, rather than
“inside” information.
7-24
Who is an “Insider”?
• Someone with material non-public
information
• Information not known to the public
• if known, would impact the stock price
• Illegal insider trading = acting on such
information in an attempt to make a profit
7-25
Illegal Insider Trading
• “Tipper” = person who purposely divulged material
non-public information
• “Tippee” = person who knowingly used such
information in an attempt to profit
• Enforcement Problems:
• Difficult to prove that a trader is truly a tippee
• Difficult to track insider information flows and trades
• People claim that they just “overheard” someone
talking
• When you take possession of material non-public
information, you become an insider, and are bound to
obey insider trading laws.
7-26
It’s Not a Good Thing: What did Martha do?
• The SEC believed that Ms. Stewart was told by her friend and
ImClone founder, Sam Waksal, that a cancer drug being
developed by ImClone had been rejected by the FDA.
• December 27, 2001: Martha Stewart sold her 3,928 shares in
ImClone
• ImClone traded below $60 per share, a level that Ms.
Stewart claimed triggered an existing stop-loss order.
• December 28, 2001: FDA rejection announced after the
market closed
• December 31, 2001: ImClone shares closed at about $46 per
share on the next trading day
7-27
It’s Not a Good Thing: What did Martha do?
• June 2003: Ms. Stewart and her stock broker, Peter
Bacanovic, were indicted and plead not guilty to nine federal
counts.
• January 2004: Ms. Stewart’s trial began
• Securities fraud charge dismissed
• Ms. Stewart was convicted on all four counts of obstructing
justice:
•
•
•
•
Fined $30,000 – maximum allowed
Sentenced to five months in prison
Two years of probation
Five months of home confinement.
Minimum permitted
• Peter Bacanovic:
•
•
•
Fined $4,000
Sentenced to five months in prison
Two years of probation
Martha Stewart was accused, but not convicted, of insider trading.
Martha Stewart was accused, and convicted, of obstructing justice.
7-28
Are Financial Markets Efficient?
• Market efficiency difficult to test
• Four basic reasons:
•
•
•
•
Risk-adjustment problem
Relevant information problem
Dumb luck problem
Data snooping problem
7-29
Are Financial Markets Efficient?
• Short-term stock price and market movements are
difficult to predict with any accuracy.
• The market reacts quickly and sharply to new
information.
• Studies find little or no evidence that such reactions can be
profitably exploited
• If the stock market can be beaten, the manner is not
obvious.
7-30
Implications: If Markets are Efficient
• Security selection becomes less
important, because securities will be
fairly priced.
• Small role for professional money
managers.
• Timing the market makes little sense.
7-31
Market Efficiency and the Performance
of Professional Money Managers
•Hard to predict how
many professional
money managers will
beat the Vanguard
500 Index Fund.
•The dashed red line
 the percentage of
professional money
managers who can
beat the Vanguard
500 Index Fund over
a 10-year investment
period is low and
stable
7-32
Percentage of Managed Equity Funds Beating
the Vanguard 500 Index Fund, One-Year
Returns
– In only six of
the 21 years
(1986—2006)
did more than
half beat the
Vanguard 500
Index Fund.
7-33
Percentage of Managed Equity Funds Beating
the Vanguard 500 Index Fund, Ten-Year
Returns
In only two of
these 21
investment
periods, did
more than half
the professional
money
managers beat
the Vanguard
500 Index Fund
7-34
Market Efficiency and the Performance
of Professional Money Managers
Time periods: 1-year, rolling 3-, 5- and 10-year investment periods
Two questions:
- What % of the time did 1/2 professionally managed funds beat
the Vanguard 500 Index Fund?
- What % of the time did 3/4 of them beat the Vanguard 500 Index
Fund?
7-35
Market Efficiency and the Performance
of Professional Money Managers
• If:
• Markets are inefficient … and
• Fundamental analysis is valuable
• Then:
• Why can’t mutual fund managers beat a broad market
index?
•
•
Enormous resources
Substantial survivorship bias
•
•
Poorly performing managers and funds disappear
Should lead to survivors who can beat the market
• Professional money managers’ inability to
outperform a broad market index is consistent with
equity market efficiency.
7-36
What is the Role for Portfolio
Managers in an Efficient Market?
• To build a portfolio to the specific needs of
individual investors:
• Basic principle of investing: hold a well-diversified
portfolio
• Exactly which diversified portfolio is optimal varies
by investor
• Factors that influence portfolio choice:
•
•
•
•
Investor’s age
Tax bracket
Risk aversion
Employer
7-37
Market Anomalies
•
Stock price behavior that is both baffling
and hard to reconcile with market efficiency
•
Three facts to keep in mind about market
anomalies:
1. Not many $$ involved
2. Fleeting, tending to disappear when discovered
3. Transaction costs render trading systems
unprofitable
7-38
Market Anomalies
•
•
•
•
Day-of-the-week Effect
January Effect
Turn-of-the-year Effect
Turn-of-the-month Effect
7-39
The Day-of-the-Week Effect
• Tendency for Monday to have a negative, statistically
significant average
• The effect is much stronger in the 1950-1979 time
period than in the 1980-2006 time period.
7-40
The Amazing January Effect
• Tendency for small-cap stocks to have large returns
in January
• Does the January effect exist for the S&P 500?
S&P500 
7-41
The Amazing January Effect
• But, look at returns on small-cap stocks
Small-cap 
7-42
The Turn-of-the-Year Effect
• “Turn of the Year Days:”
• The last week in a calendar year and the first two weeks in the
next calendar year
• “Rest of the Days:”
• Any daily return that does not fall into this three-week period
• “Turn of the Year” returns are higher than “Rest of the Days”
returns
• Largest difference in the 1962-1983 period
7-43
The Turn-of-the-Month Effect.
•
“Turn of the Month Days:”
•
Daily returns from the last day of any month or the following three days of
the following month
•
“Rest of the Days:” Any other daily returns
•
“Turn of the Month” returns exceed “Rest of the Days” returns.
•
•
The turn-of-the-month effect is apparent in all three time periods.
Interestingly, the effect appears to be stronger in the 1984-2006 period than
in the 1962-1983 period.
The fact that this effect exists puzzles EMH proponents.
•
7-44
Bubbles and Crashes
• Bubble:
• Market prices soar far in excess of what normal and
rational analysis would suggest.
•
•
•
Investment bubbles eventually pop
When bubble pops, asset values plummet
Can form over weeks, months, or even years
• Crash:
• Significant and sudden drop in market values.
•
•
•
Crashes are generally associated with a bubble
Crashes are sudden, generally lasting less than a week.
The financial aftermath of a crash can last for years
7-45
The Crash of 1929
October 1929
M
T
W
R
F
M
T
W
R
7-46
The Crash of 1929—The Aftermath
1929 - 1932
7-47
The Crash of 1987 & the Aftermath
Friday, October 16th:
The DJIA fell 108
points to close at
2,246.73.
Monday, October 19:
DJIA lost about 22.6%
of its value on a new
record volume of about
600 million shares
DJIA plummeted
508.32 points to
close at 1,738.74;
intraday low of
1,616.21.
Tuesday, October 20th:
Market rallied, closing at 1,841.01, up 102 points.
Market recovered relatively quickly.
7-48
NYSE Circuit Breakers
• Triggered if DJIA drops by 10, 20, or 30 %:
• Designed to “break the circuit” with futures markets in
Chicago.
• Reset quarterly based on DJIA at end of quarter
% Drop
10%
20%
30%
Trading Halt
Maximum 1 hour
Maximum 2 hours
Remainder of day
7-49
The Asian Crash
• “The Bubble”
• The Asian Crash started with a booming bull
market in the 1980s
• Japan and emerging Asian economies seemed to be
forming a powerful economic force.
• The “Asian economy” = investor outlet for those wary of
the U.S. market after the Crash of 1987
• “The Crash”
• The Nikkei Index crash began in 1990
• Lengthened into a particularly long bear market
7-50
The Asian Crash—Aftermath
Bubble
Crash
7-51
The “Dot-Com” Bubble and Crash
• Mid-1990s:
• Rise in Internet usage and growth potential fueled
widespread excitement over the “new economy.”
• Investor euphoria led to a surge in Internet
IPOs
• “DotComs”
• Lacked solid business models
• Many suffered huge losses
7-52
The “Dot-Com” Bubble and Crash
7-53
The “Dot.com” Bubble & Crash
AMEX Internet Index
Date
Index Level
% Change
October 1998
114.60
March 2000
688.52
500 %
October 2002
58.59
-91 %
7-54
7
Stock Price Behavior
and Market Efficiency
7-55