Download Investments: Analysis and Behavior

Investments: Analysis
and Behavior
Chapter 6- Efficient-Market
Hypothesis
Learning Objectives
 Understand the role of randomness and luck investment
in performance.
 Identify the levels of market efficiency.
 Characterize the time series of stock returns.
 Avoid gamblers fallacy and data snooping problems.
 Recognize that price bubbles challenge market
efficiency.
6-2
Short-term Speculation:
Good, or Lucky?
 A coin-flipping contest
6 billion people pay $1 each to join
Heads you stay in, tails you are out
 After one round, 3 billion are still in
 After ten rounds, about 6 million are still in
 Imagine, flipping 10 heads in a row.
 People begin to believe they are good at flipping, not lucky.
 After 20 rounds, around 6,000 people left
 Locals become heroes!
 But half of these falter in the next round
 After 25 rounds, 180 flippers are remain
 If the game stopped now, each would receive $33.3 million
 These people write books about their technique and strategy
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 After the 30th round, only 6 remain
 Each would get $1 billion of the game stopped
 It would probably take 32 rounds to end with a single winner
 The odds of flipping heads 32 times in a row is roughly one in six
billion.
 Is the winner good at flipping? Lucky?
There are millions of investors, analysts, portfolio
managers, advisors, etc. participating in the investment
process.
 Many will appear to be top performers (at least for the shortterm)
 Who is lucky and who is good? How can you tell?
6-4
Market Efficiency
 The price for any given stock is effectively “fair”
 = the expected net present value of all future profits
 Discounted using a fair risk-adjusted return
 Need
 Large number of buyers and sellers
 Free and readily available information
 Essentially identical securities
 Uninhibited trading
 If there are bargains available, investors would bid up
the price buying those stocks until the stock is no
longer a bargain.
 If markets are efficient, then it would be difficult for an
investor to consistently beat the market.
6-5
Efficient Market Hypothesis
 EMH
 Security prices fully reflect all available information.
 New information arrives in an independent and random fashion
 Current stock prices reflect all relevant risk and return information
 Investors rapidly adjust stock prices to reflect new information
 Levels of Efficiency (based on Information)
 Not efficient
 Weak-form: prices reflect all stock market information
 Prices, volume, patterns, trading rules, etc.
 Semistrong-form: current prices reflect all public information
 Accounting statements, economic activity, old news stories
 Strong-form: current prices reflect all public and private info
6-6
Figure 6.1 Stock Prices Reaction to IBM’s Announcement
Prices do react quickly!
 International Business Machines Corp. raised its 2008 earnings forecast by
five cents a share due to what the technology giant said would be the
positive impact of a $15 billion stock-repurchase authorization
6-7
Table 6.3 Short-Term Price Changes in the Market Are Random and
Unpredictable
Are Daily Returns Predictable?
Date
Open
High
Low
Close
Change
Prior
Following
%
Day
Day
A. Dow Jones Industrial Average Big Up Days
24-Jul-02
7698.5
8243.1
7489.5
8191.3
489.0
6.35%
-1.06%
-0.06%
29-Jul-02
8268.0
8749.1
8268.0
8711.9
447.5
5.41%
0.95%
-0.37%
8-Sep-98
7964.9
8103.7
7779.0
8020.8
380.5
4.98%
-0.55%
-1.94%
16-Mar-00
10139.6
10716.2
10139.6
10630.6
499.2
4.93%
3.26%
-0.33%
15-Oct-02
7883.2
8304.6
7883.2
8255.7
151.4
4.80%
0.35%
-2.66%
1-Oct-02
7593.0
7964.2
7558.4
7938.8
346.9
4.57%
-1.42%
-2.31%
24-Sep-01
8242.3
8733.4
8242.3
8603.9
368.1
4.47%
-1.68%
0.65%
5-Apr-01
9527.2
9969.9
9527.2
9918.1
402.6
4.23%
0.31%
-1.28%
11-Oct-02
7540.7
7919.6
7540.7
7850.3
316.3
4.20%
3.40%
0.35%
15-Oct-98
7953.1
8375.6
7885.6
8299.4
330.6
4.15%
0.39%
1.41%
4.81%
0.40%
-0.65%
Averages
6-8
Table 6.3 Short-Term Price Changes in the Market Are Random and
Unpredictable (cont)
B. Dow Jones Industrial Average Big Down Days
17-Sep-01
9294.6
9294.6
8755.5
8920.7
-684.8
-7.13%
0.00%
-0.19%
31-Aug-98
8079.0
8149.0
7517.7
7539.1
-512.6
-6.37%
-1.40%
3.82%
14-Apr-00
10922.9
10922.9
10173.9
10305.8
-617.8
-5.66%
-1.81%
2.69%
19-Jul-02
8356.7
8356.7
7940.8
8019.3
-390.2
-4.64%
-1.56%
-2.93%
20-Sep-01
8375.7
8711.4
8304.5
8376.2
-382.8
-4.37%
-1.62%
-1.68%
27-Aug-98
8377.9
8448.7
8062.2
8166.0
-357.4
-4.19%
-0.92%
-1.40%
12-Mar-01
10638.5
10638.6
10138.9
10208.3
-436.4
-4.10%
-1.97%
0.81%
3-Sep-02
8659.3
8659.3
8282.9
8308.1
-355.4
-4.10%
-0.09%
1.41%
27-Sep-00
7996.0
7997.1
7664.9
7701.5
-295.7
-3.70%
1.98%
-1.42%
12-Oct-00
10424.1
10462.3
9873.7
10034.6
-379.2
-3.64%
-1.05%
1.57%
-4.79%
-0.84%
0.27%
Note: Data are for the ten-year period from 6/21/98 to 6/20/08.
6-9
Figure 6.3 Daily Returns Are Noisy and Random Around A Mean of Zero,
from 6/21/98 to 6/20/08.
A. DJIA Daily Returns
15.00%
10.00%
5.00%
0.00%
C. Nasdaq Daily Returns
15.00%
-5.00%
-10.00%
Days
10.00%
5.00%
0.00%
-5.00%
-10.00%
Days
6-10
Random Walks and Prediction
 Random Walk Theory
Stock prices movements do not follow any patterns or
trends
 Past price action cannot be used to predict future price
movements.
 Subsequent price changes represent arbitrary departures from
previous prices.
Random walk with drift
 Stock prices do tend to increase, on average, over time.
6-11
Millions Examine Chart Patterns and Trends
 The Human Brain is well suited to seeing patterns
 Even when the data is random and no pattern exists!
 Gambler’s fallacy
 Popular, but erroneous, belief that some self-correcting process
impacts random events.
 After 5 fair coin flips of “heads” in a row, many people act as if they
believe the probability of a heads in the next flip is different from 50%
 Lottery players examine previous numbers picked. So do Keno
players.
 Data-Snooping
 Patterns may appear in random data if enough different tests are
examined.
 Much data is available and computers can quickly crunch it.
 Back testing and out of sample tests can help determine whether a
pattern may repeat in the future or is simply an artifact of the data.
6-12
From Dogs to Fools
 Dogs of the Dow
 Strategy identifies the 10 highest-dividend paying firms in the DJIA
 1992 book, Beating the Dow by O’Higgins and Downes
 Buying the Dogs at the beginning of each year is shown to beat the buyand-hold strategy of just owning the 30 stocks in the Dow by over 4%
per year.
 Value-oriented strategy
 Dow Five
 Buy the 5 lowest priced dogs
 Beats the Dow by 8% per year
 No basis in theory
 Motley Fool Adaptation
 Foolish Four: Of the Dow Five, throw-out the lowest price stock and doubleup on the second lowest price stock.
 Purported to beat the Dow by 12% per year
 Motley Fool eventually abandoned strategy when it failed.
6-13
Investing in an Efficient Market
 Two investors are walking down the street when one
spots a $100 bill on the sidewalk. He points it out. The
companion says, “It must not be a real $100 bill or
someone would have already picked it up.”
 If markets are efficient, we should not bother to look for
bargains. However, if no one is looking for bargains, how can
markets be efficient?
 Studies show that, on average, mutual funds and
investment newsletter writers do not beat their
benchmark.
 Evidence for EMH
 What should investors do if markets are efficient?
6-14
Challenges to EMH
 Excessive Volatility
Why is the market so volatile?
Dividends are not as volatile as stock
prices.
 Bubbles
Japanese stock bubble.
Nasdaq bubble.
Housing bubbles.
Credit bubbles.
6-15
 From January 2, 1985, at 11,543, the Nikkei 225 soared
to a closing high of 38,916 on December 29, 1989.
 This represents a gain of 237.1% in the Nikkei over a 5-year
period, and a stunning 27.5% compound annual rate of return.
 Then, the bubble burst and the bottom fell out of the Japanese
equity market.
 Fifteen years after the Japanese market peak, in December,
2004, the Nikkei stood at 10,796. That’s 72.3% below the
December, 1989 peak.
 From a (split-adjusted) level of 125 on January 31, 1985,
the Nasdaq 100 soared to 4,816.35 on March 24, 2000.
 This represents a 15 1/4-year return of 3,753.3%, and an
amazing compound return of 27.1% per year.
 Then the Nasdaq plunged, losing over 80% of its value by 2002.
6-16
5,000
4,500
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0
Nasdaq 100
Nikkei 225
?
50,000
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
Nikkei 225
Nasdaq 100
Figure 6.6 Will Post-crash Nasdaq 100 Valuations Languish For A Decade or
More?
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
6-17
Causes of Market Imperfections
 Investors make decisions influenced by
emotions and psychological biases.
 If large groups of investors become too
optimistic or pessimistic, they may move
prices
Investor mood
 Investor mood can impact expectations
6-18
If prices reflect a dividend discount model:
PV = D1 / (k – g)
But expectations become biased:
E(P) = D1 / (k – E(g))
So prices can deviate from true value by:
E ( P)
kg

PV
k  E(g )
6-19
Figure 6.7 A Cycle of Investor Emotions Throughout a Price Bubble
Euphoria
Thrill
Anxiety
Denial
Excitement
Optimism
Optimism
Price
Peaks
Fear
Desperation
Relief
Price
Bottoms
Hope
Panic
Capitulation
Depression
Despondency
6-20
Can investment fraud occur in an efficient market?
 Microcap Fraud
Microcap stocks, or penny stocks
Low liquidity means the price can be susceptible to false
press releases of exaggerations or lies, and “pump and
dump” schemes.
 Fraud on the Internet
 Easy place to spread false “news” and “unbiased” opinion.
6-21
Red Flags to Watch For!
 Assets Are Large But Revenues Are Small
 Unusual Accounting Issues
 Thin Public Float (company’s shares available to the
public)
 SEC Trading Suspensions
 High Pressure Sales Tactics
6-22
EMH
 The EMH is still hotly debated.
In Support:
 Short-term prices are unpredictable
 Price adjust quickly and pretty accurately
 Professional investors don’t seem to beat the market, on
average.
Against:
 Market is too volatile
 Stock market bubbles exist
 Investor mood may drive prices away from fair value
 Investment fraud
6-23