Transcript Part 3: Market Behavior Research
Part 4: Market Behavior Research
1. Market Efficiency
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1. The Efficient Market Model
Definitions Allocationally efficient markets
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informational (external) efficiency: prices capture all information
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All investors have costless access to currently available information about the future
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All investors are capable analysts All investors pay close attention to market prices and adjust their holdings appropriately “ Fair game ”
:
where
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operational (internal) efficiency: low transactions cost
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Why worry about efficiency?
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Optimal asset allocation
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Prices are signals which determine resource allocation in a market economy
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Efficient prices are high-quality signals For allocations to be “ optimal ” the prices should be efficient
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Also, to encourage many small investors to become market participants, prices should be perceived as “ fair ”
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Competition
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Once information becomes available, market participants analyze it and trade on it
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Markets can be efficient only if a large number of people disagree with the EMH and attempt to find ways of earning speculative profits.
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While a return on a security is expected (due to risk) the long run abnormal return is zero.
• • •
There is a 50% chance of earning a positive abnormal return.
Therefore speculation is a zero-sum game.
The efficient market represents a fair game
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Role of portfolio management
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Active management
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Security analysis: Identifying mis-priced stocks
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Timing: Changing allocations between the risky and risk-free assets at the right times
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Requires information that is not known by all investors (information gathering can be expensive)
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Passive Management
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Buy and Hold: Form a well-diversified portfolio and don ’ t change the composition of the portfolio
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Index Funds: A convenient vehicle for passive portfolio management
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Even in an efficient market, a role exists for portfolio management
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Allocations to suit the desired level of risk
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Portfolios to suit various investors ’ tax considerations (e.g. capital gains as opposed to dividends)
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Portfolios tailored to age groups (e.g. short-term debt instruments for the retired and elderly)
Random Walk with Positive Trend
Security Prices
p j
,
t
1
p j
,
t
j
,
t
1
Time
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Forms of the EMH (Fama,1970)
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Weak form
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Prices reflect information contained in past prices
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Price changes (returns) should be uncorrelated Future prices cannot be predicted using information contained in past prices
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e.g. if market is not weak form efficient, profitable trading opportunities can be discovered through technical analysis
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Evidence using tests based on trading rules and return autocorrelations is largely supportive of the weak form of the EMH in U.S.
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Semi-strong form
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Prices reflect all public information
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earnings announcements
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publicly available financial information product announcements, etc.
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e.g. if market is not semi-strong form efficient, profitable trading opportunities can be discovered through fundamental analysis
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The evidence is generally supportive of the semi-strong form of the EMH in U.S.
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Strong form
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Prices reflect all information, including insider information
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e.g. if market is not strong form efficient, profitable trading opportunities can be found by trading on insider ’ s information
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The evidence clearly indicates:
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insiders do earn abnormal returns
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hence the need for insider trading regulation Implication
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In all cases the EMH is concerned with the conditions under which an investor can earn an excess profit on a security.
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By excess profit we mean earnings over and above what is expected using for example
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CAPM E(R i ) = R F + (E(R i )-R F )
i
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APT E(R i ) = R F +
1 b 1i +
2 b 2i + … .
This is called an Abnormal Return given by AR i = R i - E(R i ).
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Efficiency does not mean that investments decisions can be made mindlessly.
Three Forms of Efficiency
Strong Form Efficient Semi-Strong Form Efficient Weak Form Efficient
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2. Testing For Market Efficiency
Weak form evidence
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Test of return predictability Motivation:
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In an efficient market we should not observe a seasonal pattern. Methods:
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Market Anomalies
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Time (seasonal) patterns
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Mondays phenomenon (Gibbons and Hess,1981; Harris,1986)
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January effect (Fama,1991;Keim,1989;Reinganum,1983)
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Correlation tests
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Past return (Granger,1975)
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Equilibrium return (Fama and MacBeth,1973;Galai,1977)
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Portfolios Firm characteristics (size effects, market to book, earnings price) Market characteristics
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Run tests
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Testing some trading rule
Motivation:
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If we follow a pre-defined trading rule on when to buy and sell, can we make abnormally high returns (Fama,1991)?
Methods:
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Charts/Trading rules
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Head and shoulders
– – – – – –
Resistence and support High-low Symmetric triangle Candle Filter Moving average
Evidences:
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Roberts (1959) finds no evidence of patterns in stock price behavior
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Conrad & Kaul (1988) find positive serial correlation in weekly NYSE stock returns, but it is too weak to lead to profits after transaction costs
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Jegadeesh & Titman (1993) find that stocks exhibit a momentum property at the 3-12 month horizon, where good or bad recent performance continues
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Conrad & Kaul (1998) test 120 momentum and contrarian trading strategies and find that most do not yield positive profits. However, they do find that momentum strategies at the 3-12 month horizon are generally able to yield statistically significant profits
Weak Form Evidence
• Random Walk
Hypothesis
– where
E
( t ) = 0 and Cov( t independent
P t
P t
1
t
t-k ) = 0 so returns (price changes) are – empirical question is whether returns are
serially correlated, Cov(R
t
, R
t-1
)
• Short Horizon Correlations – Fama (1965) find zero serial correlation – Conrad and Kaul(1988), Lo and Makinlay (1988) find small positive serial correlation may be too small for trading opportunities, depends on transactions costs • Long Term Serial Correlation – evidence is mixed
Weak Form evidence (cont)
• Example 1: buy if a stock increases
x%,
sell if it decreases
y%
– some evidence that momentum and price reversal strategies may work, but it is sketchy • Example 2: buy and sell seasonally –
January effect:
evidence is strong but are there any money managers who use it?
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Monday effect:
result of settlement • Example 3: Technical Analysis – no evidence it works but it is hard to quantify – non-linear models: neural networks, fractal models are getting more popular but no evidence exists on them
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Semi-strong form experiments
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Event studies
Motivation:
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examine how rapidly do security prices adjust to unexpected new events (an earnings announcement, government policy, etc).
Evidences:
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IPOs
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There is “ underpricing ”
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initially then poor returns afterward Accounting information
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Lifo to Fifo to Lifo to evidence is strong that the market adjusts to changes
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Takeovers
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market reacts quickly and often anticipates
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13D files cause prices to jump
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Seasoned Security Issues
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new stock lowers the stock price immediately
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new debt raises the stock price immediately
Seven steps in the Event Study:
1.
2.
3.
4.
5.
6.
7.
Collect a sample of firms that had a surprise announcement (the event).
Determine the precise day of the announcement and designate this day as zero. Use daily data.
Define the period studied, e.g. 30 days (weeks, months) either side of the event.
For each firm compute the daily returns with market model approaches. [R t = a t + b t R mt + e t ] For each firm, compute the Abnormal Return for each asset. [
e t = Actual - (a t + b t R mt )]
Compute for each day the average abnormal return (AR) over all assets.
Compute the Cumulative Abnormal Return (CAR).
AR
Figure IV.8 : Abnormal Returns
0 -2 -1 Day 0 +1 +2 Time
Figure IV.8 : Cumulative Abnormal Returns
CAR Inefficient Market Efficient Market 0 -2 -1 Day 0 +1 +2 Time
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Stock splits (Fama Fisher Jensen Roll ,1969)
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Splits have no obvious effect on firm value
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Maybe splits signal impending dividend increase
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Issues in examining the results Magnitude issue Selection bias issue Lucky event issue Possible model misspecification
Strong form evidence
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Assessing performance of professional managers
Motivation:
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These test whether current publicly and/or privately available information is fully reflected in security prices and whether any type of investor (three groups: corporate insiders, security analysts and portfolio managers) can make an excess profit.
Evidences:
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Although the first group can earn abnormal profits, the results on the ability of security analysts and portfolio managers to earn abnormal returns is mixed.