Lecture 6: Efficient Markets and
Excess Volatility
The Efficient Markets Hypothesis
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History of the Hypothesis
Reasons to think markets are efficient
Reasons to doubt markets are efficient
Technical analysis
Empirical evidence in literature
Homework assignment and regressions
Earliest Known Statement
“When shares become publicly known in an
open market, the value which they acquire
there may be regarded as the judgement of
the best intelligence concerning them.”
- George Gibson, The Stock Exchanges
of London Paris and New York, G. P.
Putnman & Sons, New York, 1889
Intuition of Efficiency
• Reuter’s pigeons and the telegraph
• Beepers & the internet
• Must be hard to get rich
Textbook Version Today
As one of the six most important ideas in
finance:
“Security prices accurately reflect available
information, and respond rapidly to new
information as soon as it becomes
available” Richard Brealey & Stewart
Myers, Principles of Corporate Finance,
1996
Harry Roberts, 1967
• Weak form efficiency: prices incorporate
information about past prices
• Semi-strong form: incorporate all publicly
available information
• Strong form: all information, including
inside information
Price as PDV of Expected
Dividends
• If earnings equal dividends and if dividends grow
at long-run rate g, then by growing consol model
P=E/(r-g), P/E=1/(r-g). (Gordon Model)
• So, efficient markets theory purports to explain
why P/E varies across stocks
• PEG ratio is popular indicator = g’/(P/E), where
g’ is short-run growth rate; popular rule of thumb:
buy if PEG<0.5
• PEG rule of thumb makes sense only if g’ bears a
certain relation to g; not a sensible rule.
• Efficient markets denies that any rule works
Reasons to Think Markets Ought
to Be Efficient
• Marginal investor determines prices
• Smart money dominates trading
• Survival of fittest
Reasons to Doubt these Reasons
• Marginal investor: wealth matters
• Smart money: matter of degree. Limits to
arbitrage theory
• Survival of fittest: life cycle renews
Psychological Factors
• Gambling behavior
• Overconfidence
• Slowness to make money, futility of career
trying to prove others of one’s ability
• Siegel and Peter Lynch
Popular Doubters of Efficiency
• Peter Lynch: Elementary school children
beat professionals
• Beardstown Ladies
• Robert Kiyosaki Rich Dad, Poor Dad
• Motley Fool
Raskob on the Market
“Suppose a man marries at the age of twenty-three
and begins a regular saving of fifteen dollars a
month – almost anyone who is employed can do
that if he tries. If he invests in good common
stocks and allows the dividends to accumulate, he
will have at the end of twenty years at least eighty
thousand dollars. . .I am firm in my belief that
anyone not only can be rich but ought to be rich.”
John J. Raskob, Ladies Home Journal, 1929
Raskob’s Calculation
Annuity formula (converted to terminal value)
shows that Raskob assumed 26% per year
returns:
15(10196
.
) 240
15
80,000 

.0196
.0196
Technical Analysis
• Robert D. Edwards & John Magee,
Technical Analysis of Stock Trends, 1948.
• Hand drawing of charts, judgmental
interpretation of patterns
• Difficult to test success of technical analysis
• Harry Mamaysky, SOM finds some success
in their methods.
Head & Shoulders Pattern
• Initial advance attracts traders, upward
momentum. Smart money begins to distribute
stock, trying not to kill demand.
• Eventually downturn, but smart money comes in
to support demand, manipulation. (left shoulder)
• Upward momentum resumes, ends when smart
money has distributed all shares; market drops.
• New traders try to exploit well-known tendency to
rally. New weak rally, right shoulder, then a
breakout. (Edwards & Magee)
Random Walk Hypothesis
• Karl Pearson, Nature, 72:294, July 27,
1905. Aug 10, 1905, walk of drunk
• Burton Malkiel, A Random Walk Down Wall
Street, 1973.
Random Walk & AR-1 Models
• Random Walk: xt=xt-1+t
• First-order autoregressive (AR-1) Model:
xt=100+(xt-1-100)+t. Mean reverting (to
100), 0< <1.
• Random walk as approximate implication of
unpredictability of returns
• Similarity of both random walk and AR-1 to
actual stock prices
Random Walk & AR-1(=.95)
115
110
105
x
Random Walk
AR-1
100
95
Time Period
46
41
36
31
26
21
16
11
6
1
90
Obvious Examples of
Inefficiency
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Jeremy Siegel – Nifty-fifty did well
Rebalancing
Most closed out
Polaroid and Edwin Land
Tulipmania
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Holland, 1630s.
Peter Garber, Famous First Bubbles
Mosaic virus, random-walk look
Free press began in Holland then.
Dot Com Bubble
• Toys.com: Had disadvantage relative to
bricks & mortar retailers starting web sites
• Lastminute.com: travel agency, sales in
fourth quarter of 1999 were $650,000,
market value in IPO ins March 2000 was $1
billion.
Problem Set #3: Forecast the
Market
• Step 1: Get stock price data on spreadsheet,
as from yahoo.com.
• Step 2: Create new column showing
percentage price changes
• Step 3: Create new Column(s) containing
forecasting variables
• Step 4: Test for significance and interpret
results.
Significance Test in Regression
• Use the R2 which is the fraction of the
variance of the dependent variable that is
explained by the regression.
• Compute F statistic (k, n-k-1 degrees of
freedom, and check that it is above critical
value for significance at 5% level.
• Issues of data mining, etc.
F Statistic
• F statistic with k, n-k-1 degrees of freedom,
where k = number of independent
(forecasting) variables and n = number of
observations:
R2 / k
F
1  R 2  / (n  k  1)
Regression Output - Excel
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Intercept, X Variable, X Variable
T statistic, P value
F statistic, P value
R squared