Market InEfficiency

1
Market (In)Efficiency

• Introduction to Quantitative
• Portfolio Management
• Professor Matthew Rothman

2
Bill Miller at Legg Mason
LEGG MASON VALUE TRUST CONTINUES STREAK As the
Only Fund To Outperform the SP 500 For Each of
the Last 15 Calendar Years Baltimore, MD
(December 31, 2005) Legg Mason, Inc. (NYSE LM)
announces that the Legg Mason Value
Trust, managed by Bill Miller, once again
outperformed the SP 500 index for the calendar
year 2005, making it the only fund to have beaten
the SP 500 for each of the last 15 calendar
years. According to Lipper, Inc., the Value Trust
is the only fund to beat the SP 500 for any 15
straight calendar years. Average Annual Total
Returns as of 12/31/05 (dividends and capital
gain distributions reinvested) One Year Five Years
Ten Years Fifteen Years Legg Mason Value Trust
5.32 4.48 15.20 16.44 SP 500 4.91 0.54
9.08 11.53 Source Lipper, Inc. The performance
data quoted represents past performance and does
not guarantee future results. Current performance
may be lower or higher than the performance data
quoted. To obtain the most recent month-end
information please visit www.leggmasonfunds.com.
The investment return and principal value of the
fund will fluctuate so that an investors shares,
when redeemed, may be worth more or less than the
original cost. Calculations assume reinvestment
of dividends and capital gain distributions. Fund
performance would have been lower if fees had not
been waived in various periods.

• What is your belief about why Bill Miller has
  been the market in each of the past 15 years?
  Is this a violation of efficient markets?

3
Definition of Market Efficiency

• Definition
• A market is efficient if all available
  information is used in pricing securities
  (informational efficiency).
• Types of available information
• Weak form efficiency - Historical prices
• Semi-strong form efficiency Publicly available
  information
• Strong form efficiency - All available
  information (including private information)

4
The Efficient Market Hypothesis (EMH) and the
joint hypothesis problem

• The hypothesis that markets are efficient is
  called the efficient market hypothesis (EMH).
• All statements about market efficiency are
  conditioned on an asset pricing model used to
  test efficiency. That is, any test of efficiency
  is a joint test of efficiency and the
  asset-pricing model.
• Given a particular pricing model, you might find
  evidence against market efficiency. Another
  explanation, however, is that the market is
  efficient and you are using the wrong pricing
  model. This is a common dilemma in testing joint
  hypotheses.

5
The Efficient Market Hypothesis (EMH) and the
joint hypothesis problem

• For example, lets say you find that a particular
  trading strategy allows you to make profits above
  and beyond that predicted by the CAPM.
• Two possibilities
• One The market is inefficient but the CAPM is
  the right pricing model for all securities.
• Two The market is efficient but the CAPM does
  not describe the right pricing model for all
  securities

6
The Joint-Hypothesis Problem
It is a disappointing fact that, because of the
joint-hypothesis problem, precise inferences
about the degree of market efficiency are likely
to remain impossible Rationality is not
established by the existing tests and the
joint-hypothesis problem likely means that it
cannot be established.
7
Market Efficiency Making Money

• There have been (and still are) many
  misconceptions about EMH
• Market efficiency implies that you cannot make
  any money.
• Market efficiency implies that stock prices are
  random walks.
• If stock prices are random walks, then markets
  are efficient.
• Market efficiency implies that stock prices are
  not predictable.
• All these statements are wrong!

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You cannot make money if markets are
efficient.

• Not True! Counter Example
• The CAPM is a model of an efficient market
• In the CAPM, investors expect to make money by
  holding risky assets
• The expected return on a risky asset is
  determined by the risk-free return, the beta of
  the asset, and the risk premium of the market
  portfolio. Remember E(ri) rf ?i E(rm)
  - rf .

9
Correct Definition of A Random Walk

• Definition
• A series is a random walk if future changes are
  i.i.d. (that is, independently and identically
  distributed) and are unpredictable.
• Illustration
• Pt is the price of a stock at time t.
• If Pt is a random walk then Pt1 - Pt is
  unpredictable.
• If Pt is a random walk then Pt1 - Pt / Pt is
  unpredictable.
• Implications
• The future value of the price can be arbitrarily
  large
• Returns are not serially correlated.

10
Common Misconception of Random Walks

• Many practitioners use the term random walk to
  mean the lack of serial correlation in returns.
• This is not the correct usage of the term
• Random walks imply the lack of serial correlation
• But the lack of serial correlation does not imply
  random walks, if returns are not normally
  distributed.

11
Are Stock Prices Random Walks?

• Empirically
• Stock returns have little serial correlation
• This is an implication of random walks
• This was found in the early literature on market
  efficiency (Fama (1965))
• This evidence was used to prove that markets
  are efficient
• Stock returns are not normally distributed
• Lack of serial correlation does not imply random
  walk
• Stock returns have predictable volatility changes
• The recent finance literature has found ARCH,
  GARCH models fit the data (Engles Nobel Prize in
  2003).
• This means that stock prices are NOT random walks.

12
Do Random Walks Imply Efficiency?

• The following statement is true
• Even if stock prices follow random walks, that
  says nothing about market efficiency
• For example, expected returns could be
  time-varying.
• So how do you test for efficiency?!?

13
Tests of Weak form Market Efficiency

• Technical analysis refers to methods for
  detecting recurrent patterns in prices.
• Using only price histories - chartists, moving
  average, oscillators, Elliot Wave Theory.
• Sentiment indicators TRIN, Sentiment Surveys.
• Academics believe that EMH implies technical
  analysis has no merit.
• Some practioners believe technical analysis gives
  the term analysis a bad name.
• Empirical evidence
• The weak form of market efficiency is sometimes
  rejected, but the magnitudes of the
  inefficiencies are very small relative to
  transactions costs. A variety of filter rules,
  price-volume rules, moving average rules and
  other technical analysis strategies generally
  fail to find exploitable inefficiencies in the US
  stock market. (See Fama and Blume (1966), Brock,
  Lakonishok and LeBaron (1992)). However, there
  is some evidence of technical strategies working
  in foreign exchange markets, suggesting foreign
  exchange markets are weakly inefficient (See
  Arnott and Pham (1993), Chang (1996).)
• Short term momentum and long-term reversal
  results are still debated. For example,
  Short-term seasonalities like time-of-day,
  holiday and day-of-week effects, January effect,
  and momentum. We discuss these later.

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Tests of Semi-Strong form Market Efficiency

• Most Common Test is the Event Study
• Examine market reaction (abnormal reaction) when
  new news are announced.
• We need to define Normal versus abnormal
  returns.
• Collect different events where similar news is
  introduced to eliminate idiosyncratic influences.
• Place returns in event time.

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Event Study Methodology

• Define the event you want to study! Have a
  research question and a hypothesis. Define
• Examples include share splits dividend
  initiations and eliminations expirations of IPO
  lockups share repurchases company name changes
  MA activity earnings announcements etc.
• Clearly define what the announcement date is.
• Need to understand first public announcement
• Need a database of historical news reports
• Select an event window say T days
• Estimate what is the expected return of the stock
  during the announcement period. Want to
  understand ABNORMAL returns.
• Market Model approach
• a. Rt at btRmt et
• b. Excess Return (Actual - Expected)
• et Actual - (at btRmt)

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Event Study Methodology

• 3. Calculate the abnormal returns and statistical
  significance
• AR Actual Returns Expected Returns
• Abnormal returns (average excess returns
  for each date)
• Line up the returns in event time (surrounding
  announcement dates) across firms. Announcement
  date is event day 0
• Calculate average abnormal return for that date
  in the announcement window.
• This your mean return for that date.
• Standard error is the standard error of the
  announcement return.
• Then see if statistically significant.
• Also people look at CARs Cumulative Abnormal
  returns (start at first date of announcement
  window and add up abnormal returns throughout
  event study)

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Event Study Methodology Example IPO Lockup
expirations

• The company and the underwriter negotiate the
  terms of the lockup.
• Example Healtheon
• registered 5,658,184 shares.
• Insiders (pre-IPO investors such as management
  and VCs) restricted for 180 days. After this
  period float gradually increases to 52,254,368
  shares.
• Underwriters can release earlier.
• Company rules not to sell around earnings
  announcements.
• Company may conduct an SEO.

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Event Study Methodology Example IPO Lockup
expiration

• Brav and Gompers (2003, Review of Financial
  Studies)
• Collect a sample of 2,794 IPO firms conducted
  over the period 1988 to 1996
• What is the event day?
• What is the period over which we shall determine
  what normal expected return is for every firm
  in our sample?

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Histogram of Lock-Up Days
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Event Study Methodology Example IPO Lockup
expiration

• Suppose that we were to estimate the market model
  with daily data for every IPO firm over the
  period t-110 through t-10, where t denotes the
  day in which the lock up expires.
• End up with 2,794 estimates of at and bt
• What do these estimates look like?

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Resulting Abnormal Returns (averaged across
firms)
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Cumulative Abnormal Returns
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Buy and Hold Abnormal Returns
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Event Studies (Continued)

• The manner with which abnormal returns are
  calculated can make a big difference. For
  example, the choice of the CAPM or the Fama
  French 3 factor model. But it has been argued
  that in practice for short-run event studies, how
  you control for risk does not matter. Why?
• Because for a short run event study, the amount
  of systematic risk on a day or two is tiny
  relative to the size of the event. For example,
  if the market risk premium is 8 per year and
  there are 250 trading days then the daily premium
  is .00032, or a little more than 3/100 of a
  percent a day (3 basis points)
• Hence, the joint hypothesis problem is really not
  an issue and the favorable evidence form
  short-run event studies has been hailed as
  supportive of market efficiency.
• Evidence tilts toward the conclusion that
  prices adjust efficiently firm specific
  infortmation.
• Do you agree? What does Thaler say?
• For long-run event studies, however, the choice
  of the asset pricing model is crucial.

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The Adjustment Of Stock Prices To New
Information, Fama, Fisher, Jensen, Roll (1969)
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Tests of Strong Form Efficiency

• Strong form strategies assume you have
  information that the market does not have.
• Existence of Insider trading tell us that private
  information is valuable.
• Jaffee (1974) insiders can profit but so can
  outsiders from watching insiders
• Seyhun (1986) insiders can profit but not
  outsiders
• Difference is due to use of CAPM. Most of it
  occurs in small firms where we know the CAPM is
  not a good model.
• In general when an insider buys her own stock
  this is a signal of good future performance.
  Sales is really complicated though!

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Tests of Strong Form Efficiency

• Do Investment Professionals Have Private
  Information? Can Mutual Fund managers Beat The
  Market?
• Most evidence supports the view that net of
  expenses, they cannot.
• Critical though what your pricing model is CAPM
  vs FF 3 factor vs 4 factor.
• Greatest predictor of future performance is fees.
  Low fee funds outperform high fee funds
  underperform!
• Performance is not repeatable but can be highly
  profitable fund flows. In other words, investors
  think it is repeatable.
• Surprisingly, fees are quite sticky. Bad
  performing funds do not lower fees and high
  performing funds do not raise them. Indeed, funds
  close failure of the price system.

30
Caveats

• Tests of market efficiency has to consider
• The Magnitude Issue Statistical power. It is
  simply hard to detect small and economically
  important deviations from market efficiency
• The Selection Bias Issue If someone discovered a
  great money making scheme, they would not
  publicize it. We are not likely to find out about
  schemes that work. If I really thought I had
  discovered a money machine, do you think Id
  publish it or start a hedge fund myself?
• The Lucky Find Issue If you flip a fair coin 50
  times, you expect to see 50 heads. If 10,000
  people each flip a fair coin 50 times we expect
  to get at least two of them getting 75 heads.
  Now, suppose any bet can beat the market 50 of
  the time. Similarly, suppose 10,000 traders each
  make 50 bets. We expect to get at least two
  traders beat the market in 75 of their bets!

31
Bill Miller at Legg Mason

• What is your belief about why Bill Miller has
  been the market in each of the past 15 years?
• Risk return adjustment Is it enough for him to
  have simply outperformed the SP?
• Could it be luck?

32
Bill Miller at Legg Mason Risk

• Risk return adjustment Is it enough for him to
  have simply outperformed the SP? Whats the
  Beta for his fund? Is loading up on high beta
  stocks skill?
• We know CAPM doesnt fully explain returns. What
  is abnormal return relative to the 3 or 4 factor
  model?

33
Bill Miller at Legg Mason Risk

• Characteristics of His Major Holdings

34
Bill Miller at Legg Mason Risk

• Stated Investment Objective

• Portfolio Mix
• The fund, with 41 holdings, is invested
  primarily in large-capitalization stocks (93.72)
  
• 5.85 of the funds assets are invested in
  mid-capitalization stocks
• 61.77 of the funds net assets are invested in
  the Consumer Discretionary, Healthcare and
  Financials sectors
• The funds largest overweight positions relative
  to the SP 500 are in the Telecommunication
  Services and Consumer Discretionary sectors
• The funds largest underweight positions are in
  the Financials, Industrials and Consumer Staples
  sectors with no exposure to the Materials or
  Energy sectors
• Investment PhilosophyWe seek to generate excess
  returns by owning securities that have been
  priced by the market at significant discounts to
  their intrinsic value by our multi-factor
  valuation analysis. Our analytical approach is
  not based on traditional, accounting-based
  valuation measures. We focused on cash earnings
  namely, the present value of future cash flows of
  a company. Shareholder value is the result of
  cash, not accounting, earnings. In this way, we
  believe we differ from most value managers.
  Traditional valuation measures miss many
  mispriced stocks because those measures do not
  focus on the value of a business.
• Primary Risks of this Fund
• Market Risk - The risk that prices of
  securities will go down because of the interplay
  of market forces, may affect a single issuer,
  industry or sector of the economy or may affect
  the market as a whole.
• Value-Style Risk - The value approach to
  investing involves the risk that those stocks
  deemed to be undervalued by the portfolio

35
Bill Miller at Legg Mason Luck?

• There were approximately 8,044 mutual funds
  being run at the end of 2004. Of these, 4,600 are
  U.S. equity mutual funds.
• There are approximately 6,500 dead U.S. equity
  mutual funds. So in total, some 11,100 mutual
  funds have ever existed.
• There is a 50 chance of beating market as
  most people define it each year.
• Given that, we have had 11,100 mutual funds
  exist, and have had 40 of funds existing, what
  number should we expect to have beaten the market
  for 15 years in row, by pure chance? 5
• There should be more Bill Millers out there! Why
  arent there?

36
The impossibility of informationally efficient
markets Grossman and Stiglitz (1980)

• Go through the following logical steps
• If markets are perfectly informationally
  efficient,
• Then, informed investors cannot profit by
  analyzing securities fundamentals
• If it is costly to analyze then informed
  investors will stop analyzing because they lose
  money on average
• But, if they stop analyzing information there
  will be no guarantee that publicly available
  information is incorporated into prices. Thus,
  the market wont be informationally efficient
• Hence, need some expected profit to attract
  informed investors. These are normal returns to
  their investments. An efficient amount of
  inefficiency in the market!

37
The impossibility of informationally efficient
markets (continued)

• Is the GS argument consistent with passive
  investment strategies such as indexing?
• Should we all buy and hold the SP500? How many
  investors indexing is too many?
• Why do people buy stocks anyway? devices?

38
Where Do Anomalies Come From?

• Why might market inefficiencies exist and
  persist?
• Human beings are not rational information
  processors. They systematically make errors in
  judgment, use heuristics to help them in decision
  make processing, and are subject to the follies
  of greed and fear.
• Work of Kahneman and Tversky
• Vernon Smith and others
• Institutional / Structural Reasons
• Short sales constraints
• Erisa / Prudent Man Laws
• Limits of Learning / Limits of Arbitrage
• Data Mining they dont exist, rather you have
  found quirks in the data

39
Market Anomalies (Selected)

• The January effect and the Small Firm Effect.
• Predictability in Asset Returns.
• Predictability in Asset Volatility.
• Very short horizon (1 month) Price Reversal.
• Post earnings announcement drift (PEAD).
• Short horizon (3 to 12 month) price momentum.
• Long horizon (3-5 year) price reversal and B/M
  effect.
• Long term performance subsequent to a variety of
  corporate events (e.g., IPO, SEOs, Repurchases,
  dividend initiations and omissions).
• Brokerage Analysts' Earnings Estimates.
• Accounting Accruals
• Institutional Ownership
• Abnormal Trading Volume
• Measures of Normalized Earnings

40
The Small Firm Effect

• Banz (1981) found that small firms tend to
  outperform large firms in total and risk-adjusted
  basis
• Divide all NYSE stocks into 5 quintiles according
  to firm size
• The average annual return of the firms in the
  smallest-size quintile was 4.3 higher than the
  average return of the firms in the largest-size
  quintile
• This is called the Small Firm Effect.

41
Average Annual Returns By Period
42
The Small-Firm January Effect

• Keim (1983), Reinganum (1983), Blume and
  Stambaugh (1983) found that the small firm effect
  primarily occurs in January.
• Potential explanations
• Tax loss selling in December
• Small firms are neglected by large institutional
  traders
• Small firms have lower liquidity.

43
Average Stock Return By Month 1926-82
44
Average Stock Return By Month 1983-98
45
Predictability in Asset Returns

• Variables that help to predict stock returns
• dividend yield
• spread between long-term and short-term
  government bonds
• spread between Moodys Baa and Aaa corporate
  bonds
• spread between treasury bill rates and inflation
  rate
• The R² is low, typically less than 10
• Explanations
• market inefficiency
• time varying risk premium, e.g. Ferson and
  Harvey (1991)
• data mining

46
Stock Return Regression 1965-91
47
Predicting Stock Returns 1970-99

• Strategy 1 Tactical Asset Allocation
• If the SP-T-bill is predicted to be positive,
  hold the SP
• Otherwise hold T-bill.
• Strategy 2 Buy and hold SP.
• We simulate the returns of these two strategies
  starting with 100 in December 1969. Here are the
  average annual return for 1970-99

48
Predictability in Asset Volatility

• At daily and weekly intervals, stock return
  volatility is predictable
• there is volatility clustering
• high volatility days tend to be followed by high
  volatility days
• low volatility days tend to be followed by low
  volatility days
• volatility reverts back to a normal level
• Models of volatility
• Engles (Econometrica, 1982) ARCH model
• Bollerslevs (J. of Econometrics, 1986) GARCH
  model
• Hsieh (J. of Finance, 1991)
• Bollerslev, Chou, and Kroner (J. of Econometrics,
  1992)

49
SP500 Daily Returns 1928-99
50
SP500 Rolling 20-day Historical Volatility
51
SP Monthly Returns 1928-99
52
Monthly Standard Deviation of U.S. Stocks
53
Predicting Asset Volatility

• Very strong evidence of volatility clustering in
  daily returns
• Much weaker evidence of volatility clustering in
  monthly returns
• Exchange rates, commodity prices, and bond prices
  also exhibit this type of behavior
• Uses
• Short term risk management (Value-at-risk)

54
Very Short Horizon Reversals

• Jegadeesh (J. of Finance, 1990) has found that
• At the beginning of each month from 1934 to
  1987, divide all stocks into 10 deciles based on
  their previous months return
• Decile 1 has the worst performing stocks
• Decile 10 has the highest performing stocks.
• Now look at the return for the current month
• Decile 1 has the best performance!
• Decile 10 has the worst performance!
• Likely due to microstructure effects (bid-ask
  bounce) and probably hard to trade on.

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Monthly Return of Decile Portfolios Ranked On
Previous Months Performance 1934-87
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Post-Earnings-Announcement Drift (PEAD)

• Ball and Brown (1968), Foster, Olsen, Shevlin
  (1984), Bernard and Thomas (1990)
• stocks with large positive (negative) earnings
  surprises have a positive (negative) price jump
  on the announcement day and continue to increase
  (decrease) in price for 13 weeks.

57
PEAD Standard Methodology

• Bernard and Thomas (1990)
• 1974-1986 period
• Firms are assigned to one of 10 portfolios based
  on standardized unexpected earnings (SUE)
• SUE Calculation
• We need a proxy for expected earnings
• Regress current earnings on earnings four
  quarters ago with a drift term. A seasonal
  random walk model
• EPSt a b EPSt-1 et
• We can write down more elaborate time-series
  models
• Conduct the regression firm by firm.

58
PEAD Standard Methodology

• SUE Calculation
• Expected earnings are therefore
• E(EPSt) a b EPSt-1
• Then, the surprise is the realized EPS (before
  extraordinary items and discontinued operations)
  less E(EPSt).
• The last step is to scale by the estimation
  standard deviation of the forecast errors to get
  SUE
• SUE (EPS-EEPS)/(s.d.(error))
• Why is it necessary to scale by s.d.(error)?
• SUE Grouping
• Take SUE for each firm and group into deciles.
• Abnormal returns are cumulated beginning the day
  after the earnings announcement to get the
  post-earnings announcement drift.

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PEAD Standard Methodology
Source Bernard and Thomas (1989)
60
PEAD Standard Methodology
Source Bernard and Thomas (1990)
61
PEAD Results

• SUE(10) SUE(1) earns an abnormal return of 8.6
  percent.
• About a half of average abnormal return is
  concentrated in the first 60 days following the
  announcement.
• In the previous graph Bernard and Thomas sort
  firms by size into large (top 30 NYSE/AMEX),
  medium (middle 40) and small (bottom 30)
• For small and medium-sized firms, the effect is
  even greater 10
• The cumulative returns are about 2/3 as large as
  the cumulative returns during the quarter up to
  and including the earnings announcement.

62
PEAD Results

• About 25 of the effect is concentrated during
  the next four earnings announcement periods.
• Stocks held by institutions tend to have less of
  a drift (controlling for size).
• Since earnings surprises tend to include both
  permanent and temporary components, a portion of
  the initial earnings surprise (about 40)
  persists as earnings surprise a quarter later,
  with progressively smaller amounts later on.
• The anomaly has been remarkably stable over time
  and it is not explained, for example, buy either
  the size or book/market factors (Fama and French
  (1993)).
• But now it looks like SUE / PEAD has declined in
  the 1990s if not dead!

63
Brokerage Analysts' Recommendations

• Does the market incorporate the information in
  analysts recommendations?
• Womack (1996). Extreme recommendation changes
• For Buy (Sell) recommendations event-day abnormal
  returns are 3 (-4.7)
• Post recommendation drift for Buys is significant
  but short-lived with size-adjusted return of
  2.4 over the first post-event month. For Sells,
  the post-event drift lasts for 6 months and
  equals -9.1
• Barber, Lehavy, McNichols, and Trueman (1999,
  2003). Implement various trading strategies
  focusing on changes in consensus recommendations.

64
Do Analysts Recommendations Have Investment
Value? Womack (1996)
65
Womack (continued)
66
Can Investors Profit from the Prophets? Security
Analysts Recommendations and Stock Returns,
Barber, Lehavy, McNichols, and Trueman (1999,
2003)
67
Barber, Lehavy, McNichols, and Trueman (1999,
2003)
68
Bernstein Earnings Surprise Alphas
69
Analyst Earnings Revisions
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Monthly Return of Decile Portfolios Ranked On
Previous Months Performance By Cap
71
Short Horizon Price Momentum

• Jegadeesh and Titman (J. of Finance, 1993) have
  found that
• Over a 3- to 12-month horizon, stock returns
  have momentum
• Good recent performance tends to persist
• Bad recent performance tends to persist
• Each quarter from 1965 to 1989, rank stocks in
  deciles based on the previous L (3,6,9,12)
  months performance
• Go long the highest decile and hold for H
  (3,6,9,12) months
• Go short the lowest decile and hold for H months
• These long/short portfolios generate abnormal
  returns! (mainly on the short (losers) portfolio).

72
Jegadeesh Titman (1993) Average Monthly Returns
from Buys-Sells
73
Jegadeesh Titman (continued)
74
Country Momentum Strategies
75
Fama French, Long Horizon Autocorrelations
76
Long Horizon (3-5 year) Price Reversal and the
B/M effect.

• DeBondt and Thalers (1985) evidence. Past
  long-term losers earn higher future returns than
  long-term winners with most of the excess return
  earned in January
• Lakonishok, Shleifer and Vishny (1994)
• We have already discussed these findings and the
  possible interpretations

77
Long-Term Performance of Repurchasing Firms

• Ikenberry, Lakonishok, and Vermaelen, 1995,
  Market underreaction to open market share
  Repurchases, Journal of Financial Economics.
• Why would firms repurchase their stock?
• Signaling Hypothesis Asymmetric information
  firms insiders and investors. Repurchase
  announcement is a credible signal. With rational
  expectations investors should respond immediately
  in an unbiased manner.
• Underreaction Hypothesis Less than fully
  rational reaction which subsequently leads to
  positive abnormal returns.
• Data All repurchases over the period 1980-1990.
• The benchmark portfolios are i) equal-weighted
  index, ii) value weighted index, iii) size-based,
  and iv) size and book-to-market based benchmark.
• How should we calculate the test statistics?
  Cumulative abnormal returns or buy and hold
  returns?
• Key long-term results Unconditionally, investing
  in repurchasing firms leads to positive abnormal
  returns. Moreover, abnormal return is increasing
  in the firms book-to-market ratios.

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Ikenberry, Lakonishok, and Vermaelen, 1995
79
Abnormal Performance Subsequent to Dividend
Initiations and Omissions

• Price Reactions to Dividend Initiations and
  Omissions Overreaction or Drift? By Michaely,
  Thaler, and Womack (1995)
• Consistent with the prior literature find that
  short run price reactions to omissions are
  greater than for initiations (-7.0 vs. 3.4
  three day return)
• Controlling for the change in the magnitude of
  dividend yield (which is larger for omissions),
  the asymmetry shrinks or disappears, depending on
  the specification
• In the 12 months after the announcement
  (excluding the event calendar month), there is
• a significant positive market-adjusted return for
  firms initiating dividends of 7.5 and
• a significant negative market-adjusted return for
  firms omitting dividends of -11.0
• However, the post dividend omission drift is
  distinct from and more pronounced than that
  following earnings surprises
• A trading rule employing both samples (long in
  initiation stocks and short in omission stocks)
  earns positive returns in 22 out of 25 years
• Do firms that omit (initiate) dividends perform
  as expected given their characteristics?

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Summing up...

• Three definitions of market efficiency
• Markets incorporate various levels of information
  into security prices.
• Random walks and market efficiency.
• The impossibility of informationally efficient
  markets Grossman and Stiglitz (1980).
• Anomalies. There are still other anomalies we did
  not cover
• Turn of the Month Effect Stocks consistently
  show higher returns on the last day and first
  four days of the month.
• The Monday Effect
• Monday tends to be the worst day to be invested
  in stocks.
• Merger related underperformance Acquiring firms
  that complete stock mergers underperform while
  firms that complete cash tender offers do not.
  One interpretation is that acquirers who use
  their stock may use it because they believe it to
  be overvalued.

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Anomalies or Data Mining?

• Are these real anomalies?
• Are these data mining?
• Data mining
• The same data are massaged over and over again.
  It is not surprising to find something that will
  predict returns.
• Final point Most money managers do not beat the
  market
• Malkiel article
• So, if managers do not beat the market, what does
  that say about market efficiency?

82
But why it is hard to settle these issues?1)
Statistically No power

• We cannot just compare current prices to a
  reliable fundamental value model to determine the
  existence of a financial anomaly.

83
But why it is hard to settle these issues?2)
Research design Returns-Based approach

• The central theme of financial economics has been
  to assume the existence of capital market
  equilibrium under some model
• Then test whether the average returns,
  covariability, and predictability (or lack of) of
  returns is consistent with that model.
• Dont actually try to price assets.
• Just look to see if price changes are behaving
  according to posited models.

84
Financial Economists Reject Fundamental Value
Embrace Returns Based Research
Financial economists work only with hard data
and are concerned with the interrelationships
between the prices of different financial assets.
They ignore what seems to many to be the more
important question of what determines the overall
level of asset prices. There is a deep
distrust of research purporting to explore
fundamental valuations.
85
But Such Tests Have Very Low Power
This paper argues that existing evidence does
not establish that financial markets are
efficient in the sense of rationally reflecting
fundamental values. It demonstrates that the
types of statistical tests which have been used
to date have essentially no power
86
Lessons From Market Efficiency Tests
Can Time Series/Cross Sectional Tests or
Volatility Studies Establish Market Efficiency or
Inefficiency? Can Long Run Event Studies
Establish Market Efficiency or Inefficiency? Ca
n Short Run Event Studies Establish Market
Efficiency or Inefficiency?
NO
NO
MAYBE. ALWAYS ABOUT REACTION
87
Market Efficiency Tests Bottom Line

• We cannot rely on returns-based tests to tell us
  whether prices are efficient or not.
• Such tests may reject market efficiency when
  prices are efficient because we are using the
  wrong model of market equilibrium (consider FF
  three-factor model)
• Such tests may fail to reject market efficiency
  because they assume a model that attributes
  returns to rational factors (consider same)

88
Concluding Comments

• BKM, Chapter 12
• We conclude that markets are very efficient, but
  that rewards to the especially diligent,
  intelligent, or creative may in fact be waiting.
  (page 405)