Evidence Regarding Market Efficiency From Studies.

Evidence Regarding Market Efficiency From Studies

Background Information

Early 1970’s, Fama & MacBeth did a famous study testing the CAPM.

They found weak evidence that portfolios of stocks with higher betas had higher returns, and found an intercept slightly higher than zero. (CAPM Assumes Alpha = 0)

Beta & Return of Portfolios

Beta

Return

Early Evidence

Early evidence basically supported the weak and the semi-strong form EMH.

Early Weak Form EMH Tests

(+) Serial Correlation: + returns follow + returns for a given

stock or - returns follow - returns for a given stock

Called “momentum” or “inertia”


(-) Serial Correlation: + returns follow - returns for a given

stock or - returns follow + returns for a given stock.

Called “reversals”

Tie to a Random Walk

If we find (+) or (-) serial correlation, this is evidence against the weak-form EMH as it implies that past prices can be used to predict future prices.

(Technical analysis)


In 1960s Fama showed that:

1. Stock Prices followed a random walk

2. No evidence of serial correlation. The price of a stock is just as likely to rise after a previous day’s increase as after a previous day’s decline.

Early Semi-Strong Form EMH Tests Event studies in the 1960s & 1970s

looked at stock prices around the release of new information to the public.

(Fundamental analysis)

Graph of a Typical Study Keown and Pinkerton (1981): CARs for

target firms around takeover attempt.

See graph on p. 371 in text

Challenges to the EMH

1980s & 1990s: Empirical evidence began to

accumulate that provided evidence first against the semi-strong EMH and later against the weak form EMH

Initially any evidence against EMH called an anomaly.

More Recent Tests of the Semi-Strong Form EMH Are abnormal risk-adjusted returns

possible if you trade after information is made public? (fundamental analysts)

General Equation for Abn. Returns: Actual Rit – Predicted Ri,t

Abn. Returns:Use Historic Data

Without a risk adjustment:

Actual Rit – Actual Rm,t

With a risk adjustment:

Actual Rit – [ai + Bi[Actual Rm,t]

Or,

Actual Rit – [Actual Rmatch,t]

Challenges to Testing

Difficult to measure risk-adjusted returns

a) Is beta the proper measure of risk?

b) CAPM is forward looking and you are using historic data.

c) Is your matched firm the best match?

Quarterly Earnings Surprises

(Quarterly EPS Released –

Forecasted Quarterly EPS)

Measure the abnormal risk-adjusted return after an earnings surprise.

Measure CAR: Actual Rit – Predicted Ri,t

(Used CAPM)

Quarterly Earnings Surprises Rank from highest to lowest by magnitude of

earnings surprises and place stocks into decile portfolios.

See if trading on earnings surprises results in subsequent abnormal returns.

(Cumulative Abnormal Returns (CARs) are the daily abnormal returns summed up over time)

Evidence: Quarterly Earnings Surprises

For positive earnings surprises: The larger the earnings surprise the

higher the positive abnormal return.

The upward drift in the stock price continues a couple of months after the earning announcement!

Evidence: Quarterly Earnings Surprises

For negative earnings surprises: The larger the negative earnings

surprise the larger the loss as measured by the abnormal return.

The downward drift in the stock price continues a couple of months after the earning announcement!

Interpretation: Mkts Efficient Measurement Errors

Markets are efficient. The evidence of abn. risk-adjusted returns is due to various Measurement Errors when using the CAPM.

(1) Benchmark Error: Beta & SML wrong

(2) CAPM is a forward looking model & are

testing it with historic or ex-post data.

Interpretation:CAPM Not Valid

Markets are efficient. The evidence of abnormal risk-adjusted returns (evidence against market inefficiency) is inconclusive as the CAPM may not be the proper risk adjustment model.

[Joint or Dual Hypothesis Problem!]

If the CAPM is wrong, then abnormal risk-adjusted returns using this model are wrong.

Interpretation: Mkts Not Efficient

Behavioral Finance: Psychological and behavioral elements lead to predictable biases.

Arbitrage: 1. Not always possible to execute

arbitrage trades.2. Arbitrage is risky and therefore limited

Evidence of Abn Risk Adj. Returns …. After share repurchase announcements

(Ikenberry (1995)) After dividend initiations and omissions

(Michaely (1995)) After stock splits

(Ikenberry (1995)) After seasoned equity offerings & after IPOs

(Loughran and Ritter (1995))

Size Effect

Portfolios of small cap stocks earn positive abnormal risk-adjusted returns (+ alphas):

Size Effect

January Anomaly: Most of the abnormal returns occur in January! (tax loss selling??)

Grossman/Stiglitz: Professionals move prices to efficiency. Don’t buy at the small cap end of the market much due to limits on portfolio positions.

Problem With CAPM?

Possible sources of risk for small caps Neglected by analysts and institutional

investors, so is less information, which implies higher risk.

Less Liquidity: Higher trading costs as bid-ask spreads are wider, and broker commissions are larger.

Background Information

Back to Early 1970’s, Fama & MacBeth test of CAPM.

Fama MacBeth CAPM Test Early 1970’s

Beta

Return

Relationship Between Beta and Returns Fama & French re-examined the earlier

tests of the CAPM forming size decile portfolios.

Fama-French 1992

Beta & Return of Portfolios

Beta

Return Small cap stocks

Large cap stocks

Fama-French Interpretation

See that small cap stocks have higher betas than large cap stocks. Fama and French concluded that size is driving the relationship between beta and return not beta!

Previous Slide (cont)

Also see that within the small cap groupings, portfolios of stocks with lower betas have higher returns! The same is true within the large cap groupings.

Interesting Fact

Fama, once a strong proponent of the CAPM now claimed that beta was dead. Beta was a rough proxy for size in his earlier tests!!

The Cross Section of Expected Stock Returns Table 1 Panel A

Average Monthly Returns (in Percent)

All Low-β β-2 β-3 β-4 β-5 β-6 β-7 β-8 β-9 High-β

All 1.25 1.34 1.29 1.36 1.31 1.33 1.28 1.24 1.21 1.25 1.14

Small - ME 1.52 1.71 1.57 1.79 1.61 1.50 1.50 1.37 1.63 1.50 1.42

ME-2 1.29 1.25 1.42 1.36 1.39 1.65 1.61 1.37 1.31 1.34 1.11

ME-3 1.24 1.12 1.31 1.17 1.70 1.29 1.10 1.31 1.36 1.26 0.76

ME-4 1.25 1.27 1.13 1.54 1.06 1.34 1.06 1.41 1.17 1.35 0.98

ME-5 1.29 1.34 1.42 1.39 1.48 1.42 1.18 1.13 1.27 1.18 1.08

ME-6 1.17 1.08 1.53 1.27 1.15 1.20 1.21 1.18 1.04 1.07 1.02

ME-7 1.07 0.95 1.21 1.26 1.09 1.18 1.11 1.24 0.62 1.32 0.76

ME-8 1.10 1.09 1.05 1.37 1.20 1.27 0.98 1.18 1.02 1.01 0.94

ME-9 0.95 0.98 0.88 1.02 1.14 1.07 1.23 0.94 0.82 0.88 0.59

Large - ME 0.89 1.01 0.93 1.10 0.94 0.93 0.89 1.03 0.71 0.74 0.56

Interesting Result

Within each size group, the higher the beta the lower the return.

The Cross Section of Expected Stock ReturnsTable 5

Book-to-Market Portfolios

All Low 2 3 4 5 6 7 8 9 High

All 1.23 0.64 0.98 1.06 1.17 1.24 1.26 1.39 1.40 1.50 1.63

Small - ME 1.47 0.70 1.14 1.20 1.43 1.56 1.51 1.70 1.72 1.82 1.92

ME-2 1.22 0.43 1.05 0.96 1.19 1.33 1.19 1.58 1.28 1.43 1.79

ME-3 1.22 0.56 0.88 1.23 0.95 1.36 1.30 1.30 1.40 1.54 1.60

ME-4 1.19 0.39 0.72 1.06 1.36 1.13 1.21 1.34 1.59 1.51 1.47

ME-5 1.24 0.88 0.65 1.08 1.47 1.13 1.43 1.44 1.26 1.52 1.49

ME-6 1.15 0.70 0.98 1.14 1.23 0.94 1.27 1.19 1.19 1.24 1.50

ME-7 1.07 0.95 1.00 0.99 0.83 0.99 1.13 0.99 1.16 1.10 1.47

ME-8 1.08 0.66 1.13 0.91 0.95 0.99 1.01 1.15 1.05 1.29 1.55

ME-9 0.95 0.44 0.89 0.92 1.00 1.05 0.93 0.82 1.11 1.04 1.22

Large - ME 0.89 0.93 0.88 0.84 0.71 0.79 0.83 0.81 0.96 0.97 1.18

Value Puzzle It is not evident why value stocks should

be riskier than growth stocks. Value stocks have lower standard deviations than growth stocks after controlling for size.

Value Puzzle

Value Puzzle:

Value stocks have lower standard deviations and higher returns!

Fama-French Findings

Beta does not explain returns.

Small cap stocks have higher returns. Small cap stocks have higher betas, but it is size not beta driving higher returns.

Low P/E or high Book-to-Market of equity stocks have higher returns.

Explanations for Fama-French Results

Alternative Explanations for their results?

Market Semi-Strong Efficient:

Small cap stocks and low P/E (high B/M) stocks generate higher returns because they are riskier. However, this risk is not captured by Beta!

Problem

Lack of a theoretical model to explain why size and style (value vs growth) are important risk factors. The CAPM had an elegant, logical theory underlying it, this has none!


Market Semi-Strong Efficient: Abnormal risk-adjusted returns for small cap stocks or for stocks with low P/E (or high B/M) are due to various measurement errors when using the CAPM.

(1) Benchmark Error: Beta & SML wrong

(2) CAPM is a forward looking model & we are testing it with a historic or ex-post data.


Market Semi-Strong Efficient. Abnormal risk-adjusted returns (evidence against market inefficiency) are inconclusive as the CAPM may not be the proper risk adjustment model.

[Joint or Dual Hypothesis Problem!]

If the CAPM is wrong, then abnormal risk-adjusted returns using this model are wrong.


Market Not Semi-Strong Form Efficient:

Can make abnormal returns using public information regarding market capitalization and P/E or B/M ratio.

How can this persist?

Behavioral Finance

Decisions people make deviate from the maxims of economic rationality in predictable ways:

1. Attitudes towards Risk

2. Non Bayesian Expectation

Formation

3. Framing Effects of Decisions

Attitudes Toward Risk: Example

90% chance of $1 million; 10% chance of $0. I offer to buy you out for $900,000. Will you take my offer?

Attitudes Toward Risk: Example

90% chance to lose $1 million; 10% chance of $0. I will take the bet if you pay me $900,000. Will you take my offer?

Behavioral Finance

Attitudes Towards Risk:

People look at gains and losses relative to some reference point rather than the levels of final wealth.

Display Loss Aversion! Outcome Typically Doesn’t follow standard von Neumann-Morgenstern rationality.

Behavioral FinanceNon-Bayesian Expectation Formation

Representativeness: Predict the future taking a short history of data and determine the model driving the data. (Too small a weight on chance.)

Conservatism: Slow updating to new information as have extrapolated a short earnings history too far into the future.

Non-Bayesian Expectations

1st 2 winters here mild. Assumed they were always like that.

Investors may extrapolate short histories of rapid earnings growth too far in the future and may overprice “glamour” stocks.

Behavioral Finance

Framing Effects

How data is presented can affect the decisions people make.

Framing Effects: Example

Investors will allocate more money to stocks rather than bonds when they see long-term cumulative wealth graphs than they will if you only show them volatile short-term stock returns.

Behavioral Finance Explanation for Quarterly Earnings Surprise In this case, would argue that initially

there is slow updating or “conservatism” as a reaction to the news released by the earnings surprise. [Short run under-reaction]

Eventually keep seeing good news so “representativeness” sets in [then get over-reaction].

Mkt Not Efficient?

(Lakonishok, Shleifer and Vishney) These professors offer a different interpretation.

Markets are inefficient. People overreact with a lag. Overprice firms with good recent returns (growth) and underprice firms with poor recent returns (value).

Long-Term Horizons: Test of Weak-Form EMH

DeBondt and Thaler (1985):

Create Loser and Winner portfolios based on past 36 months of CARs. Top decile are Winners, bottom decile are Losers.

Examine CAR’s for next 36 months.

“Loser’s outperform “winners” Is an overreaction followed by a correction.

Efficient Market Believers Say....

Evidence is due to market risk premiums varying over time. Is not overshooting & correction but instead a rational response to changes in the discount rate.

Short Horizons(Tests of Weak Form EMH) Lo and MacKinlay (1988) test to see if

there is serial correlation of weekly stock returns for NYSE stocks.

Lo & MacKinlayStock Price

Period

1 2

+ momentum

- momentum

reversal

reversal

Lo & MacKinlay

If momentum is present, the variance of returns should increase as the number of periods used is increased.

If there is no momentum, gains or losses will tend to reverse, keeping the variance of returns from becoming wider.

Evidence: Lo & MacKinlay

Lo and Mackinlay (1988) find serial correlation of weekly stock returns for NYSE stocks as the variance of returns increases as the return interval is lengthened. Implies there is inertia in the short run.

Evidence: Lo & MacKinlay

The effect is the strongest in the small cap stocks.

Not clear if abnormal returns are

possible by exploiting this information.

Intermediate Horizons:Test of Weak-Form EMH

Study by Jegadeesh and Titman.

Intermediate Horizons

1.Measure stock rates of return over the past 6 months.

2.Rank the stocks from highest to lowest past 6 month return and then divide the sample into deciles. “Losers” are the bottom decile and “winners” are the top decile

Jegadeesh and Titman

3. For the next 36 months, every time one of the winners or losers reports quarterly earnings, record 3-day returns starting 2 days before the earnings announcement and ending the day of the announcement.

4.Observe the difference in 3-day returns between the winners and losers reporting earnings in each month.

Evidence: Jegadeesh and Titman

For the 1st 7 months, the market is pleasantly surprised by the earnings announcements of the winners and disappointed by the earnings announcements of the losers.

(momentum in the short run)


From months 9 - 36, the market is pleasantly surprised by the earnings announcements of the losers and disappointed by the earnings announcements of the winners.

(Reversals in the intermediate term) If the stock market is efficient, it should be

able to anticipate the good or bad reports in advance.


Abnormal profit opportunities. Reversion to the mean. The market overreacts with a lag.

Consistent with “Representativeness and Conservatism.”

Short Run: Inertia Intermediate Run: Reversals

Technical Analysts

Technical analysts claim to exploit these trends or patterns.

Mutual Fund Performance

If the stock market is not weak or semi-strong form efficient, then professional portfolio managers should be able to achieve abnormal risk-adjusted returns!

Evidence: Mutual Funds

Malkiel (1995) examined the alphas of mutual funds.

Recall Regression Model

(Ri,t – RFRt) = i + i(Rm,t - RFRt) + ei,t

If market is efficient what should we find regarding the multiple-period alpha?


WSJ Article, “Stock Funds Just Don’t Measure Up”. Oct. 5, 1999

After adjusting for size and survivorship bias, funds trailed the S&P 500 by 1.4% per year which is on average what they charge for annual expenses.


Other studies: 1970’s – 1990’s: After expenses & commissions, only 1/3 beat the market on a risk-adjusted basis.

STRONG FORM EMH TESTS

Are abnormal risk-adjusted returns possible if you trade using private information?

Evidence on Insiders

Corporate insiders are required to report their transactions to the SEC.

They are not supposed to trade when in the possession of “material” information.

Even with regulation, they achieve positive risk-adjusted abnormal returns.

Market Crash of Oct. 1987

23% Drop in One Day??

No large release of news

Efficient Market explanation: Due to chance. Are outliers in the distribution. Just an outlier observation in a random process.

Panic & Crowd Psychology (behavioral

finance explanation)

Internet Bubble

Some companies saw their stock price go up just by adding dotcom to their names

When 3-Com spun off Palm Pilot, but kept 95% of the shares, The 95% of Palm owned by 3-Com were worth more than the market cap of 3-Com. Implies negative value for the rest of 3-Com!

Internet Bubble

It is obvious now that the 1998-March 2000 tech run-up was a bubble, but was this market inefficiency, or merely poor valuations?

How do you know a bubble when you are in it?

Should you try to short a bubble if you don’t know when it will burst?

Limits of Arbitrage

Just because you know something is overvalued or undervalued, doesn’t necessarily mean you can make money off it

Classic Example: We know that someday the sun will explode, but you can’t short the Earth

Shleifer and Vishny (1997) Paper

Most arbitrage is not carried out by small investors, but by large money managers.

They usually manage OPM (other people’s money)

Most arbitrage in the real world is actually “risk arbitrage” and requires capital

If money managers observe a price discrepancy and commit capital to an arbitrage position based on convergence, the initial movement may be away from convergence, but that merely means there is a greater opportunity for profit, and more capital should be committed.

But that is exactly when investors are most likely to pull out.

Investors invest based on PBA (Performance Based Arbitrage) rather than expected returns

This lack of capital prevents arbitrage from taking place

This is often given as an explanation for the collapse of LTCM (Long-Term Capital Management).

Amazingly, the Shleifer and Vishny paper came out about a year prior to the LTCM collapse

Evidence Regarding Market Efficiency From Studies.

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