Lecture 6: Efficient Markets and Excess Volatility

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Lecture 6 Efficient Markets and Excess Volatility
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The Efficient Markets Hypothesis

• 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

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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

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Intuition of Efficiency

• Reuters pigeons and the telegraph
• Beepers the internet
• Must be hard to get rich

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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

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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

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Price as PDV of Expected Dividends

• If earnings equal dividends and if dividends grow
  at long-run rate g, then by growing consol model
  PE/(r-g), P/E1/(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 PEGlt0.5
• 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

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Reasons to Think Markets Ought to Be Efficient

• Marginal investor determines prices
• Smart money dominates trading
• Survival of fittest

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Reasons to Doubt these Reasons

• Marginal investor wealth matters
• Smart money matter of degree. Limits to
  arbitrage theory
• Survival of fittest life cycle renews

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Psychological Factors

• Gambling behavior
• Overconfidence
• Slowness to make money, futility of career trying
  to prove others of ones ability
• Siegel and Peter Lynch

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Popular Doubters of Efficiency

• Peter Lynch Elementary school children beat
  professionals
• Beardstown Ladies
• Robert Kiyosaki Rich Dad, Poor Dad
• Motley Fool

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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

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Raskobs Calculation

• Annuity formula (converted to terminal value)
  shows that Raskob assumed 26 per year returns

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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.

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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)

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Random Walk Hypothesis

• Karl Pearson, Nature, 72294, July 27, 1905. Aug
  10, 1905, walk of drunk
• Burton Malkiel, A Random Walk Down Wall Street,
  1973.

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Random Walk AR-1 Models

• Random Walk xtxt-1?t
• First-order autoregressive (AR-1) Model
  xt100?(xt-1-100)?t. Mean reverting (to 100),
  0lt ?lt1.
• Random walk as approximate implication of
  unpredictability of returns
• Similarity of both random walk and AR-1 to actual
  stock prices

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Random Walk AR-1(?.95)
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Obvious Examples of Inefficiency

• Jeremy Siegel Nifty-fifty did well
• Rebalancing
• Most closed out
• Polaroid and Edwin Land

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Tulipmania

• Holland, 1630s.
• Peter Garber, Famous First Bubbles
• Mosaic virus, random-walk look
• Free press began in Holland then.

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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.

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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.

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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.

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F Statistic

• F statistic with k, n-k-1 degrees of freedom,
  where k number of independent (forecasting)
  variables and n number of observations

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Regression Output - Excel

• Intercept, X Variable, X Variable
• T statistic, P value
• F statistic, P value
• R squared