10 Market Efficiency

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Prediction and Timing Session 10

Copyright Jos M. Marn Portfolio Management1

Outline 10: Market Efficiency

1. The Market Efficiency Hypothesis

2. Can the Stock Market be Predicted?Different Views on Expected ReturnsRegression EvidenceMarket Newsletters EvidenceSummary of Predictability of Broad Stock Market

3. More Evidence against Market Efficiency

4. The Importance of aJointTest5. Pricing Anomalies and Superior Performance in Modern Times6. Application: Pension Funding Mispricing (Franzoni and Marn

JF06 and JPM06)

Readings: Franzoni and Marn (JF06, JPM06).

1. The Market Efficiency Hypothesis (MEH)

Suppose you learn that MSFT profits are going to increase in the future. Should you buy today?

The answer depends on whether or not the price already reflects this information (this is the idea ofmarket efficiency). The possibility of profiting from your information strongly depends on the wayinformation gets into prices. For instance:

o The price may have increased in the recent past and that information is already incorporated inthe current price level. In this case, buying today is not a good idea. Youve learned theinformation too late.

o The price may have stayed still or even gone down in the recent past. This may be justified byinnovations in other variables such as increases in ex-ante volatility or risk premium that you arenot aware of. It is not clear that buying is a good idea here either.


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There are many definitions of market efficiency. The most widely accepted notion distinguishesbetween:

o Weak formof mkt. efficiency: Prices reflect all information contained in past prices and returns(rules out technical analysis)

o Semi-strong form of mkt. efficiency: Prices reflect all public information (past prices, newspapers contents, databases, etc. )

o Strong formof mkt. efficiency: Prices reflect all available information, both public and private.

In 1970 Fama concluded that US stock prices satisfied the first two forms of market efficiency and thatit was not possible to reject the third one (due to the poor performance of management in the mutualfund industry and apparent inexistence of arbitrage opportunities in equity markets). Today we think ofasset markets in a very different way.

Market efficiency is usually analyzed by addressing two questions:

o Can the market be predicted?o Are there strategies that beat the market?

In this Chapter we analyze these two questions in the context of the first two forms of MarketEfficiency; in the next two Chapters we analyze the third one (by looking at the performance of fundmanagers)


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2. Can the Stock Market Be Predicted?

It seems predicting the market may produce handsome rewards (Remember the quiz I gave you the first day ofclass)

Stock market prediction: crucial in the development of finance and economics (Cowles; Econometrica, CowlesFoundation, )

Title: Can Stock Market Forecasters Forecast?Abstract: It is doubtfulAuthor: Alfred CowlesSource: Econometrica, July 1933 (3rdissue)

Potential reasons for predictability

o Shifts in risk or risk aversion giving rise to higher risk premium; need not provide advantages to rebalancing

o Inefficiency, such as investor sentiment; could suggest profitable rebalancing strategies (timing)

o Frictions in asset markets: illiquidity, costs, regulation, etc.

o Possibly some of the stories hedge fund managers tell

There are lotsof methods pursued.

Few, if any, are associated with strong empirical evidence.


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One common approach is a dividend-discount model

o Forecast the stream of future dividends per share,D1,D2, . . .o Construct a discount rate, k, often as a spread (premium) over an observed long-term bond yield.o Compute the implied current share value

K+

+

+

+

=2

210

)1(1 k

D

k

DV (1)

o Compare V0to the current price.

Most dividend-discount models are bullish on stocks when dividend yields are high and long-term bond rates arelow.

Different Views on Expected Returns on the Broad Market

As an illustration, look at views at the end of 2000. Key references:

o Jeremy Siegel, 1994: Stocks for the Long Run (book)

o

Brown, Goetzman and Ross, 1995: Survival (article in JoF)

o Glassman-Hassett, 1999: Dow 36.000 (book)

o R. Shiller, 2000: Irrational Exuberance (book)

The next 6 pages can help you to follow the basic arguments.


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-- Siegels and Glassman-Hassett View


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-- Brown-Goetzman-Ross Counterargument

MarketPredic

tionandTiming

3


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Market Prediction and Timing Session 10


-- Shillers View


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Market Prediction and Timing 5

Robert J. Shiller, Irrational Exuberance, Princeton University Press, 2000


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Market Prediction and Timing 6

Robert J. Shiller, Irrational Exuberance, Princeton University Press, 2000


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Regression Evidence

Suppose we estimate the regression

tt ePDbaTttR ++=+ )(),( (2)

R(t, t +T) : Value-weighted NYSE excess return from time t to t + T

(D / P)t: Dividend-yield on NYSE at time tN : Number of observations (non-overlapping returns)

T a b t(b) R2 N a b t(b) R2 N

192697 1940971 mos. -0.5 0.3 2.1 0.00 852 -0.5 0.3 2.4 0.01 6843 mos. -4.2 1.5 3.2 0.03 248 -2.0 1.0 2.5 0.03 228

12 mos. -3.2 2.8 1.6 0.03 71 -10.4 4.8 2.7 0.11 5724 mos. -12.4 6.9 2.3 0.13 35 -20.8 10.1 3.0 0.25 2836 mos. -9.4 8.2 1.9 0.14 23 -28.5 14.7 2.5 0.26 1948 mos. -21.8 13.8 1.9 0.18 17 -45.6 21.9 3.4 0.47 14


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A plot of the post-1940 experience

-0,2

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

0,02 0,03 0,04 0,05 0,06 0,07 0,08

Current dividend yield

Subsequentfour-yearreturn


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Market Efficiency Session 10


Market Newsletters Evidence

The Graham-Harvey Study


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Summary of Predictability of the Broad Stock Market

Some evidence in favor of market efficiency:

oNewsletters

Some evidence of market inefficiency:

o Shillers D/P ratio

o Others, such as the implied dividend yield (using the put-call parity) Golez (2008).

But the general idea is that predicting the market is hard, what explains the simultaneous existence ofvery different views among experts.


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3.More Evidence against Market Efficiency

Basic approach consists on testing a very weak form of the random walk condition namely that pricechanges are uncorrelated without excluding the possibility of dependency rather than the strong formof prices being i.i.d. over time. Using this criterion, the hypothesis is rejected.

It is important here to note that these tests do not use risk adjusted returns. Consequently, the rejection

of the hypothesis does not necessarily imply market inefficiency. The evidence may be due to theexistence of some latent factor that is not accounted for. See the discussion in Campbell, Lo andMacKinlay (97).

Strictly speaking, the MEH cannot be tested individually but jointly with some asset pricing model.You will see more clearly at the end of this section why this is the case.

Evidence 1: using long run returns (5 years) we have:

o For stock indexes: positive first order serial correlation (significant)

o For individual stocks: negative first order serial correlation (although not significant due tothe high idiosyncratic component)


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Table 10.2 (Marn and Rubio)

STOCK INDEXES AUTOCORRELATIONReturninterval

Samplesize

Mean(%)

StandardDev.

1 2 3 4 5

DailyreturnsIBEX

1982obs.

0,079 1,248 0,117 -0,034 -0,037 -0,004 0,002

MonthlyReturns

IGBM

402obs.

1,007 5,351 0,198 -0,043 -0,060 0,023 0,096

Monthlyreturnsequally

weightedindex

402obs.

1,319 5,817 0,287 -0,009 0,006 0,073 0,108

o In table 10.2 notice the different results for the value weighted and the equally weighted index where

small firms have a relatively larger weight.

o Notice also that the simultaneous existence of positive serial correlation (for indexes) and negative

(for individual stocks) implies the existence of positive cross serial correlationbetween individual

stocks over time. In other words, there must exist a positive correlation between an asset return and

the one period lagged return of the others.


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o This evidence is consistent with the overreaction hypothesis (according to which investors

overreact on the arrival of news) and can explain the relative success of contrarian strategies,

which involve selling winners and buying loser stocks. Why?

First, the negative auto-correlation of stocks implies that current losers (have falling prices in

the past) will become winners in the future, and vice versa.

Second, the positive cross correlation reinforces this effect. Suppose that we have two assets A

and B. If As current return is larger than the markets, then the contrarian strategy consists on

selling A and buying B. But if there is positive cross correlation, the larger As current return

would imply a larger Bs return in the future. Clearly, this effect plays in favor of the

contrarian strategy.

Evidence 2: Using short run return (say daily) we obtain positive serial correlation both for indexes andindividual stocks.

o This evidence is consistent with the under-reaction hypothesis and justifies the relative success of

momentum strategies.


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4. The Importance of aJoint Test

The previous evidence is not conclusive, as it does not take into account risk.

As a pedagogical exercise, we go over a very well known study on the bad performance of companiesdoing a public offering, PO, (both IPOs and seasonal offerings) by Loughran and Ritter (1995), JoF, 50,23-51, which illustrates the basic point:

RETURN OF NYSE STOCKS THAT REALIZED A PUBLIC OFFERING

BETWEEN 1970 AND 1990 OVER THE 5 YEARS AFTER THE OFFERING

YEARS PO(In %)

CONTROL SAMPLE(In %)

RELATIVEWEALTH

19701971

197219731974197519761977197819791980

1981198219831984198519861987198819891990

-46,3-31,6

-18,20,8234,4117,9259,4173,8217,952,6-2,1

14,976,73,8

44,09,59,36,2

80,844,422,7

-20,06,1

33,4104,4173,0127,3205,0234,0227,0193,1188,0

194,7137,667,282,258,633,414,060,325,342,7

0,670,64

0,610,491,220,961,180,820,970,520,34

0,390,740,620,790,690,820,931,131,150,86

1970-90 15,7 66,4 0,70


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From the study we could deduce that PO firms do much worse (on average a 15.7% vs. 66.4% over a 5-year period) than companies of similar size that did not do a PO.

Does this mean that there is not market efficiency (since POs do not seem to be priced correctly)?

Does this mean that we can build an arbitrage strategy (or a very successful strategy) consisting in going

long in the control sample and short in the POs portfolio?

The previous study only takes into account size (market capitalization) as an aggregate risk factor. It ispossible that this proxy undervalues the true risk of the control sample.

To address this issue, lets include also the value factor (book to market ratio). In particular, lets runthe following regression:

ititititit OPVaVMVCaVMaaR ++++= 3210 )/ln(ln ,

where OPV is the fictitious variable that takes the value 1 if the firm has done an PO and 0 otherwise.

The result is that 38.03 =a (that is, -4.5% annually) that amounts to a 5-year compounded mispricing of

25%. This means that 50% of the original mispricing (50,07%) does not come from the bad performanceof PO stocks but from a risk factor that we had not considered.


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What about FF?


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These results are consistent with the previous regression and show that the PO underpricing still existsbut it is less severe (alphas are always larger than -0.38, except in the small issuers with EW

portfolios).


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5.Pricing Anomalies (market inefficiency) AND Strategies that Beat the Market inModern Times

Previous analysis makes clear that we need to control for risk.

The current convention is to set up the study of market efficiency in the context of factorial models.

Anomalies related to the stock market are tested using the three Fama and French model (or the 4FFmodel which adds a momentum factor long past 12 month winners and short past 12 month losers)

o 3-Factor FF Model:

Ri,tRf,t= ai+ bi(RM,tRf,t) + siS M Bt+ hiH M Lt + i,t.

o 4-Factor FF Model (for checking purposes, we add momentum as this one survives the 3-factorFF model):

Ri,tRf,t= ai+ bi(RM,tRf,t) + siS M Bt+ hiH M Lt + miMOMt+ i,t.

o Corroborating evidence on the anomaly being inefficiency rather than a missing risk factor iswelcome: Surprises in analysts forecasts Surprises on returns Etc.


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To test if a strategy beats the market we require the previous test for anomalous returns PLUSpositive results in the well-known Sharpes style regression. For instance, for hedge funds, theregression takes the form:

1

K

t k kt t k

R b F u=

= + + (3)

Here, the Fs are not risk factors, but INVESTMENT STYLES. This means that the regression has a

nice interpretation:

In this regression, the managers return can only be attributed to a particular style if it is

correlated to that style, bk0. So, bkclose to zero and low R2are good news.

On the other hand, the term + ut reflects the managers skill. In particular, a positive

alpha is good news.

In applications we often use:

o Style analysis with traditional benchmarks (styles). Here the Fs are:

MSCI US equities, MSCI non-US equities, IFC emerging markets equities, JP Morgan USgovmt bonds, JP Morgan non US govmt bonds, 1-month Eurodollar deposit, gold price, FED

traded weighted dollar index


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o Style analysis with Alternative (investments) benchmarks. Here the Fs are:

Two equity-oriented benchmarks: the S&P 500 index (S&P 500) and a portfolio that capturesthe size risk, the Wilshire 17500 index minus the Wilshire 750 index (SC - LC).

Two bond market benchmarks: the month-end to month-end change in the Federal Reserve'sten-year constant maturity yield (lOY); and the month-end to month-end change in the

difference between Moody's Baa yield and the Federal Reserve's ten-year constant maturityyield (Cred Spr).

Three factors that capture the returns of trend-following managers: a portfolio of look-backstraddles on bond futures (Bd Opt); a portfolio of look-back straddles on currency futures (FXOpt); and a portfolio of look-back straddles on commodity futures (Com Opt).


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Additional Evidence (JF)

M k t Effi i S i 10


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Dynamic Trading Strategy pure alpha strategy (JPM)

o Returns


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Does the strategy still make money?

10 Market Efficiency

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