Part 4: Market Behavior

Research

1. Market Efficiency

1. The Efficient Market Model1. The Efficient Market Model• Definitions

Allocationally efficient markets– informational (external) efficiency: prices capture all

information • All investors have costless access to currently available information

about the future• All investors are capable analysts• All investors pay close attention to market prices and adjust their

holdings appropriately“Fair game”:

where

– operational (internal) efficiency: low transactions cost

• Why worry about efficiency?– Optimal asset allocation

• Prices are signals which determine resource allocation in a market economy

• Efficient prices are high-quality signals• For allocations to be “optimal” the prices should be efficient• Also, to encourage many small investors to become market

participants, prices should be perceived as “fair” – Competition

• Once information becomes available, market participants analyze it and trade on it

• Markets can be efficient only if a large number of people disagree with the EMH and attempt to find ways of earning speculative profits.

• 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

– Role of portfolio management• Active management

– Security analysis: Identifying mis-priced stocks– Timing: Changing allocations between the risky and risk-free

assets at the right times– Requires information that is not known by all investors

(information gathering can be expensive)• Passive Management

– Buy and Hold: Form a well-diversified portfolio and don’t change the composition of the portfolio

– Index Funds: A convenient vehicle for passive portfolio management

• Even in an efficient market, a role exists for portfolio management

– Allocations to suit the desired level of risk– Portfolios to suit various investors’ tax considerations (e.g.

capital gains as opposed to dividends)– Portfolios tailored to age groups (e.g. short-term debt

instruments for the retired and elderly)

Random Walk with Positive Trend

Random Walk with Positive Trend

Security Prices

Time

1,,1, tjtjtj pp

• Forms of the EMH (Fama,1970)– Weak form

• Prices reflect information contained in past prices• Price changes (returns) should be uncorrelated• Future prices cannot be predicted using information

contained in past prices • e.g. if market is not weak form efficient, profitable trading

opportunities can be discovered through technical analysis• Evidence using tests based on trading rules and return

autocorrelations is largely supportive of the weak form of the EMH in U.S.

– Semi-strong form• Prices reflect all public information

– earnings announcements– publicly available financial information– product announcements, etc.

• e.g. if market is not semi-strong form efficient, profitable trading opportunities can be discovered through fundamental analysis

• The evidence is generally supportive of the semi-strong form of the EMH in U.S.

– Strong form• Prices reflect all information, including insider information• e.g. if market is not strong form efficient, profitable trading

opportunities can be found by trading on insider’s information

• The evidence clearly indicates:– insiders do earn abnormal returns– hence the need for insider trading regulation

– Implication• In all cases the EMH is concerned with the conditions under

which an investor can earn an excess profit on a security.• By excess profit we mean earnings over and above what is

expected using for example– CAPM E(Ri) = RF + (E(Ri)-RF )i

– APT E(Ri) = RF +1b1i + 2b2i +….

This is called an Abnormal Return given by ARi = Ri - E(Ri). • Efficiency does not mean that investments decisions can be

made mindlessly.

Three Forms of Efficiency

Three Forms of Efficiency

Semi-StrongForm

Efficient

Strong FormEfficient

Weak FormEfficient

2. Testing For Market Efficiency2. Testing For Market Efficiency• Weak form evidence

– Test of return predictability Motivation:

• In an efficient market we should not observe a seasonal pattern.

Methods:• Market Anomalies

– Time (seasonal) patterns• Mondays phenomenon (Gibbons and Hess,1981; Harris,1986)• January effect (Fama,1991;Keim,1989;Reinganum,1983)

– Correlation tests• Past return (Granger,1975)• Equilibrium return (Fama and MacBeth,1973;Galai,1977)• Portfolios• Firm characteristics (size effects, market to book, earnings

price)• Market characteristics

– Run tests

– Testing some trading rule Motivation:

• If we follow a pre-defined trading rule on when to buy and sell, can we make abnormally high returns (Fama,1991)?

Methods:• Charts/Trading rules

– Head and shoulders– Resistence and support– High-low– Symmetric triangle– Candle– Filter– Moving average

Evidences:• Roberts (1959) finds no evidence of patterns in stock price

behavior• Conrad & Kaul (1988) find positive serial correlation in

weekly NYSE stock returns, but it is too weak to lead to profits after transaction costs

• Jegadeesh & Titman (1993) find that stocks exhibit a momentum property at the 3-12 month horizon, where good or bad recent performance continues

• 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 t-k) = 0 so returns (price changes) are

independent

– empirical question is whether returns are serially correlated, Cov(Rt , Rt-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

P Pt t t 1

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?

– 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

• Semi-strong form experiments– Event studies Motivation:

• examine how rapidly do security prices adjust to unexpected new events (an earnings announcement, government policy, etc).

Evidences:• IPOs

– There is “underpricing” initially then poor returns afterward• Accounting information

– Lifo to Fifo to Lifo to evidence is strong that the market adjusts to changes

• Takeovers– market reacts quickly and often anticipates– 13D files cause prices to jump

• Seasoned Security Issues– new stock lowers the stock price immediately– new debt raises the stock price immediately

Seven steps in the Event Study:1. Collect a sample of firms that had a surprise

announcement (the event).2. Determine the precise day of the announcement and

designate this day as zero. Use daily data.3. Define the period studied, e.g. 30 days (weeks,

months) either side of the event.4. For each firm compute the daily returns with market

model approaches. [Rt = at + btRmt + et]

5. For each firm, compute the Abnormal Return for each asset. [et = Actual - (at + btRmt)]

6. Compute for each day the average abnormal return (AR) over all assets.

7. Compute the Cumulative Abnormal Return (CAR).

Figure IV.8 : Abnormal ReturnsFigure IV.8 : Abnormal Returns

Time

AR

Day 0-1 +1 -2 +2 0

Figure IV.8 : Cumulative Abnormal ReturnsFigure IV.8 : Cumulative Abnormal Returns

Time

CAR

Day 0-1 +1 -2 +2

Efficient MarketInefficient Market

0

• Stock splits (Fama Fisher Jensen Roll ,1969) – Splits have no obvious effect on firm value – Maybe splits signal impending dividend increase

Issues in examining the results• Magnitude issue• Selection bias issue• Lucky event issue• Possible model misspecification

• Strong form evidence– Assessing performance of professional managers Motivation:

• 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:• 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.

4. Market Microstructure