Efficient Capital Markets and Anomalies

1
Efficient Capital Markets and Anomalies

• Chapter 8

2
Background

• If investors ignore information, market prices of
  securities will not react to news announcements
• Security prices that do not fully reflect public
  information are said to be weakly efficient
  prices
• A weakly efficient price drifts further away from
  the securitys value than a semi-strongly
  efficient price
• Good investors can use these inefficiencies to
  earn trading profits

3
Background

• A perfectly efficient price reflect all knowable
  information about the security
• Always equal to the securitys value
• Value can change continuously to reflect arrival
  of new information
• Good financial analysts that are active traders
  will be unable to earn returns sufficient to
  compensate them for their costs and still yield
  an economic profit in a perfectly efficient
  market
• All securities are priced correctly

4
Background

• This chapter examines security price behavior and
  pricing inefficiencies
• Pricing inefficiencies provide profit
  opportunities
• Levels of pricing efficiency
• Weakly Efficient Market Hypothesis
• Market prices reflect all historical information
• Semi-strong Efficient Market Hypothesis
• Market prices reflect all public information
  (including all historical information mentioned
  above)
• Perfectly (or Strong) Efficient Market Hypothesis
• Market prices reflect everything that is
  knowable, including inside information

5
Background

• Security prices should not move along smoothly
• Rapid price movements due to new information
  should cause randomness in successive price
  changes, not a smooth continuity
• Randomness means that a trend-like series of
  small upward or downward prices moves is not
  likely to occur
• If a price change is to happen, it should happen
  all at once, not in a series of small movements

6
Background
Good News
Bad News
New information arrives in the market on day t.
7
Background

• This chapter offers both evidence supporting the
  hypotheses and anomalies
• You should reach a conclusion about whether you
  believe each of the three hypotheses
• Your conclusions will help determine the way you
  invest
• If, for example, you believe all three
  hypotheses, you should be a passive investor who
  trades infrequently

8
Evidence SupportingWeakly Efficient Hypothesis

• Is it possible that security prices do not
  reflect all historical information?
• Which is easy to obtain and cheap
• Technicians focus on past security prices
• Look for meaningful trends in historical security
  prices
• Attempt to extract predictions from whatever
  patterns they find

9
Filter Rules

• An X filter is a mechanical trading rule
• If a securitys price rises by at least X, buy
  and hold until the price peaks and falls by at
  least X
• When price decreases from a peak level by X,
  liquidate long position and sell short
• Hold short position until price reaches a low
  point and then begins to rise
• If (when) the price rises above X, cover the
  short position and go long

10
Filter Rules

• Different filter rules can be testing by changing
  the X value
• If stock prices fluctuate randomly, filter rules
  should not outperform randomly chosen stocks

11
Figure 8-2Using a 10 Filter Rule to Trade a
Security
12
Filter Rules

• Filters ranging from .05 to 50 have been tested
• In general, filter rules generate large
  commissions (especially those with small X
  values)
• After deducting for commissions, filter rules do
  not outperform naïve buy-and-hold strategy
• Some filters result in large net losses after
  deducting commissions

13
Serial Correlation Tests

• Serial correlation (autocorrelation) tests should
  be able to determine if security prices move in
  trends or reversals
• Measures the correlation coefficient in a series
  of numbers with lagged values in the same series
• Lags of any length can be used
• Stock prices exhibit a long-run upward trend of
  about 6.6 a year in the U.S.
• Thus, some positive serial correlation is found
• But, technical analysts focus on short-term trends

14
Serial Correlation Tests

• So, do daily or weekly price change trends exist
  and, if so, can they be used to earning a trading
  profit after commission?
• Many studies have failed to detect statistically
  significant serial correlations on a daily,
  weekly or monthly basis
• Scientific evidence supporting weak form
  efficiency

15
Serial Correlation Tests

• Some conflicting evidence exists
• DeBondt Thaler (1985) find evidence of
  long-term stock price overreaction and negative
  serial correlation for individual stocks
• Lo MacKinlay (1988) found positive serial
  correlation for a diversified portfolio of stocks
• Conrad Kaul (1993) suggest that the above
  results are due to statistical measurement errors

16
Runs Tests

• Perhaps security prices change randomly most of
  the time but occasionally exhibit trends that the
  serial correlations tests cannot detect
• A runs test can be performed to determine if
  irregular trends occur in price changes
• A run occurs when the changes between consecutive
  numbers switch direction

17
Runs Tests

• A series of random numbers is expected to
  generate a certain amount of positive, negative
  or zero runs
• By comparing the actual number of runs to the
  expected number, we can determine if a non-random
  number of runs occurred
• Results suggest that actual number of runs do not
  differ statistically from the number of expected
  runs

18
Anomalies in Weakly Efficient Hypothesis

• While most of the scientific evidence supports
  the weakly efficient hypothesis, some anomalous
  evidence exists
• Day-of-the-Week Effectsthe stock market tends to
  fall on Mondays and rise the rest of the week
• Mondays returns are calculated from Fridays
  closing price to Mondays closing price thus
  this is also known as the weekend effect
• Most of Mondays negative returns occur in the
  first hour of trading
• This day-of-the-week pattern is observed in stock
  markets around the world

19
Anomalies in Weakly Efficient Hypothesis

• Holiday effect
• Returns on the day before holiday weekends are 9
  13 times higher than the average daily return
• About 1/3 of the average stocks annual return
  was earned in pre-holiday trading days
• Friday to Monday
• Negative (positive) returns on a Friday are
  usually followed by large negative (positive)
  returns on Monday
• The large commissions paid (relative to the small
  positive daily returns) will more than offset the
  potential benefit of this knowledge

20
Anomalies in Weakly Efficient Hypothesis

• January Effectaverage stocks return in January
  is more than 5 times the mean monthly return
• A large part of the typical stocks annual return
  is generated during January
• This is a larger anomaly than the day-of-the-week
  effects
• Can yield net trading profits after deducting
  transaction costs
• Buy stocks before Christmas and sell at the end
  of January

21
Anomalies in Weakly Efficient Hypothesis

• Reasons for the January Effect
• Perhaps investors are selling stocks in December
  to establish tax losses
• Reinvesting in the market in January is fueling
  the effect
• January Effect is even stronger internationally
  than within the U.S.

22
Figure 8-5Monthly Average Returns from Stock
Markets Around the World for January and the
Other 11 Months
23
Tests of Semi-Strong Efficiency

• Most semi-strong tests utilize an event study
• Event studies focus on a specific news event
• Observe security prices to determine if they
  react rationally when the event becomes public
  knowledge
• If prices react quickly and efficiently, this
  supports semi-strong hypothesis

24
Reactions to Federal Announcements

• Public announcements to macroeconomic statistics
  typically occur at scheduled times
• U.S. government keeps statistics secret until the
  precise moment of the public announcement
• Many of the announcements are made when the
  financial markets are closed
• If the statistics contain valuable new
  information the markets react immediately and
  continuously until the new information is
  impacted into security prices

25
Reactions to Federal Announcements

• Gwilym, Buckle, Clarke and Thomas (GBCT) analyzed
  Financial Times Stock Exchange 100 stocks price
  index and the Short Sterling 3-Month Interest
  rate futures contracts on a transaction-by-transac
  tion basis
• Examine announcement windows beginning two
  minutes prior to a public announcement and ending
  10 minutes after the announcement
• Both the stock market and the bond market remain
  at normal levels during the two minutes prior to
  announcement
• Suggests no information leakage
• Within the first 15 seconds after the
  announcement, volatility increases but drops
  within six minutes after the announcement
• Larger price changes initially but the price
  changes become gradually smaller

26
About Information and Market Prices

• Stock price reactions to corporate-specific
  announcements are slower than federal government
  announcements
• Fewer investors focus on corporate-specific
  announcements
• Corporations do not announce information at
  precise pre-scheduled times
• Studies show that stock prices take about 14
  minutes to begin adjusting to corporate earnings
  announcements and there are no opportunities for
  profitable trading after 30 minutes
• Results indicate that the financial markets
  adjust to new information quickly
• In minutes and seconds (for industrialized
  nations) rather than days or weeks

27
Stock Splits and Stock Dividends

• Neither of these events change the total value of
  the firm or investors wealth
• Offer a nice way to test the semi-strong
  efficient market hypothesis
• A two-for-one stock split (or a 100 stock
  dividend) will leave the firm with twice as many
  shares of stock with each share being worth half
  as much
• If security markets are efficient, the firms
  market capitalization should not be impacted by a
  stock split or stock dividend

28
Stock Splits and Stock Dividends

• Why do companies offer stock dividends and
  splits?
• Information signaling hypothesis
• Companies can conserve cash while sending a
  signal to the public about future earnings growth
• Liquidity hypothesis
• Reduces the stocks market price making it more
  affordable to small investors

29
Stock Splits and Stock Dividends

• Fama, Fisher, Jensen and Roll (1969) studied 940
  stock splits and stock dividends
• Calculated a characteristic line for each stock
  analyzed and examined the residual errors
• If residual error at time of event was zero, the
  securitys actual rate of return equaled the
  predicted rate of return, and the event had no
  impact on the return
• If residual error were positive (negative) the
  assets return was greater (lower) than expected
• Residual errors were averaged over the 940 stocks
• Reduces the influence of other effects (events)

30
Stock Splits and Stock Dividends

• FFJR find that the monthly residual error tended
  to be increasingly positive in the 30 months
  preceding the split or stock dividend
• After the event the average residuals fluctuate
  around zero for the next 30 months
• They evaluated the cumulative average monthly
  residuals when companies subsequently
• Increased their cash dividend payments
• Have small positive residuals in the months after
  the event
• Decreased their cash dividend payments
• Have larger negative residuals in the months
  after the event

31
Stock Splits and Stock Dividends

• In the long-run, stock splits and stock dividends
  do not seem to impact
• The liquidity of the split stocks
• The market value of the firm
• Investors returns
• If an investor can correctly predict which
  companies are going to split, it may be possible
  to earn excess returns
• Studies involving stock splits and stock
  dividends appear to support the semi-strong
  efficient market hypothesis

32
Anomaly Size Effect

• Banz (1981) Reinganum (1981) show that small
  company stocks earned higher rates of return than
  large company stocks, on average
• Size based on market capitalization
• Found that small cap stocks were also riskier,
  but even after adjusting for risk the size effect
  remained
• Even after adjusting for the impact of infrequent
  price changes the size effect remained

33
Inter-Relationship Between January and Size
Effects

• Keim (1983) found that abnormal returns in
  February through December tend to be similar
• But, small firms experience a positive January
  effect while large firms experience a negative
  January effect
• Why this occurs is unknown
• This appears to be a worldwide phenomenon

34
Growth-Value Anomaly

• Semi-strong form of EMH suggests that money
  managers who use a particular management style
  should not consistently outperform managers using
  another management style
• Value managers
• Seek undervalued stocks which they purchase, hold
  and sell when the price reaches the stocks value
• Typically buy stocks with low P-E ratios, below
  average earnings growth rates, high cash dividend
  yields and low price-to-book values
• Growth manager
• Seek stocks enjoying a high rate of earnings
  growth which is expected to continue
• Typically buy stocks with high P-E ratios, high
  Price-to-book values and low cash dividend yields

35
Growth-Value Anomaly

• Both of these management styles are popular and
  are frequently compared
• Value stock investors have historically
  outperformed growth stock investors on a
  risk-adjusted basis over extended periods of time
• Growth managers seem to over-estimate growth
  rates and therefore receive lower returns

36
Growth-Value Anomaly

• Investors can analyze three different ratios to
  make quantitative distinctions between growth and
  value stocks
• Current yield cash dividend per share ? market
  price per share
• Growth rates in earnings
• Price-earnings ratios

37
Price-to-Book Ratio

• Fama and French (1992) analyzed only the
  Price-to-Book ratio to distinguish between value
  and growth stocks
• Price-to-book ratio market price of stock ?
  book value of stock
• Examined ? 2,000 firms and divided the sample
  into deciles
• Lowest P/B decile contains value stocks while
  highest P/B decile contains growth stocks
• Lakonishok, Schleifer and Vishny (1994) find the
  P/B anomaly persists even after adjusting for
  firm size
• Loughran (1997) finds the P/B ratio does not have
  predictive power for large firms and that growth
  firms outperform value firms when considering
  value-weighted return by P/B quintiles

38
Figure 8-11Companies Average Returns for Each
P/B Decile, 1962-89
Earned more than twice the average return of the
portfolio in Decile One.
39
SP/BARRA Growth and Value Index Funds

• Vanguard started two new funds in 1992 based on
  P/B ratios
• SP/BARRA Growth Fund contains those firms in the
  SP500 with high P/B ratios
• SP/BARRA Value Fund contains those firms in the
  SP500 with low P/B ratios
• Funds are rebalanced semi-annually
• The SP/BARRA Value Index has offered superior
  long-run performance over the SP/BARRA Growth
  Index
• However, there are periods when the Growth Index
  outperformed the Value Index

40
Growth vs. Value Investing

• Capaul, Rowley and Sharpe (1993) analyzed a
  similar investing strategy on an international
  level
• Find that value investing seems to outperform
  growth investing
• Constitutes an anomaly to the semi-strong form of
  efficient market hypothesis

41
Tests of Strong Form Efficiency

• Mutual funds are managed by professional money
  managers
• Do these funds offer superior performance?
• Findings
• Large funds perform no better than small funds
• Funds with high turnover perform slightly worse
  than funds with low turnover
• Funds charging a load fee perform slightly worse
  than no-load funds
• Funds with high management fees perform slightly
  worse than funds with low management fees
• Majority of equity mutual funds in U.S. are
  unable to outperform SP500
• Burton Malkiel concludes that most investors
  would be better off selecting an index fund with
  low fees rather than trying to select a hot
  active fund manager

42
Tests of Strong Form Efficiency

• Inside information
• An insider is defined as
• Any corporate director
• Any one owning 10 of the firms equity shares
• Any executive in corporation with access to
  non-public information about the corporation
• Any insider or outsider using material non-public
  information (obtained in a breach of fiduciary
  trust) to trade a corporations securities
• Within the U.S., the SEC does not allow insiders
  to keep profits earned from trading corporate
  stock held less than six months
• Also does not allow insider to sell stock short
• Also requires full disclosure of dealings which
  is then released to the public
• Weekly Insider Report (Vickers Stock Research
  Corp.)
• Value Line Investment Survey
• www.InsiderScores.com (free)
• www.InsiderTrader.com (free)

43
Insider Trading

• Even outsiders receiving tips from insiders can
  be prosecuted for insider trading
• Insider Trading Sanctions Act of 1984 and
  Securities Fraud Enforcement Act of 1988 provide
  for
• Penalties of three times any damages that might
  have been caused
• Fines up to 1,000,000
• Up to 10 years imprisonment

44
Tests of Strong Form Efficiency

• Do insiders earn statistically significant
  trading profits?
• Jaffe (1974) performed an event study
• If a security had three or more net insiders as
  buyers (sellers) Jaffe assumed a balance of
  favorable (unfavorable) inside information
  existed for that corporation
• Results indicate that the average insider did not
  earn enough after one month to pay their
  commission costs
• After 8 months the average insiders gained 5.07

45
Tests of Strong Form Efficiency

• Seyhun (1986) finds that Jaffes estimates of
  insider profits may have been too high
• Also examines outsiders trading on purchased
  information about insider trades
• Unable to earn net profits after commissions
• Meulbroek (1992) analyzed 229 episodes of insider
  trading
• Finds abnormal returns of 3.06 on average, on
  the day of the trade
• Provides support for claim that insider trading
  helps stock prices reflect all knowable
  information because on the inside trading day,
  the stock prices moved to incorporate the new
  information
• Many financial economists argue that insider
  trading should be legalized

46
The Bottom Line

• Your informed opinion on market pricing
  efficiency will determine the way you manage your
  investments
• If you believe market anomalies are
  insignificant, you will not search for over- or
  under-valued securities
• You may decide that some anomalies exist but
  cannot be used in any practical sense
• For instance, if you bought small stocks before
  the Christmas holiday and held them through the
  end of January, you could expect to benefit from
  the holiday effect, the small stock effect and
  the January effect
• However, this is likely to amount to only very
  small abnormal returns and the effects you hope
  to capitalize upon may not occur this year

47
The Bottom Line

• Several well-documented anomalies force us to
  conclude that the market is not perfectly
  efficient
• How should investors manage money given that,
  while securities markets are not perfectly
  efficient, they are highly efficient?
• Millions of investors select passive investing
  because they believe the anomalies are small
• Million of investors select active investing
  because they think the anomalies offer the
  opportunity for profitable trading
• Still others combine the above methods