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in security choice. If you are in a high tax bracket, you generally will not want the same secu-
rities that low-bracket investors find favorable. At an obvious level, high-bracket investors
find it advantageous to buy tax-exempt municipal bonds despite their relatively low pretax
yields, while those same bonds are unattractive to low-bracket investors. At a more subtle
level, high-bracket investors might want to tilt or specialize their portfolios toward securities
that provide capital gains as opposed to dividend or interest income, because capital gains are
taxed less heavily, and the option to defer the realization of capital gains income is more valu-
able, the higher the investor™s current tax bracket. High tax bracket investors also will be more
attracted to investment opportunities where returns are sensitive to tax benefits, such as real
estate ventures.
A third argument for rational portfolio management relates to the particular risk profile of
the investor. For example, a General Motors executive whose annual bonus depends on GM™s
profits generally should not invest additional amounts in auto stocks. To the extent that his or
her compensation already depends on GM™s well-being, the executive is overinvested in GM
now and should not exacerbate the lack of diversification.
Investors of varying ages also might warrant different portfolio policies with regard to risk
bearing. For example, older investors who are essentially living off savings might avoid long-
term bonds, whose market values fluctuate dramatically with changes in interest rates. Be-
cause these investors rely on accumulated savings, they require conservation of principal. In
contrast, younger investors might be more inclined toward long-term inflation-indexed bonds.
The steady flow of real income over long periods that is locked in with these bonds can be
more important than preservation of principal to those with long life expectancies.
In short, there is a role for portfolio management even in an efficient market. Investors™ op-
timal positions will vary according to factors such as age, tax bracket, risk aversion, and em-
ployment. The role of the portfolio manager in an efficient market is to tailor the portfolio to
these needs, rather than to attempt to beat the market.


Resource Allocation
We™ve focused so far on the investment implications of the efficient market hypothesis. Devi-
ations from efficiency may offer profit opportunities to better-informed traders at the expense
of less-informed traders.
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Essentials of Investments, Hypothesis Companies, 2003
Fifth Edition




269
8 The Efficient Market Hypothesis


However, deviations from informational efficiency would also result in a large cost that
will be borne by all citizens, namely, inefficient resource allocation. Recall that in a capitalist
economy, investments in real assets such as plant, equipment, and know-how are guided in
large part by the prices of financial assets. For example, if the values of biotech assets as re-
flected in the stock market prices of biotech firms exceed the cost of acquiring those assets,
the managers of such firms have a strong signal that further investments in the firm will be re-
garded by the market as a positive net present value venture. In this manner, capital market
prices guide resource allocation. Security mispricing thus could entail severe social costs by
fostering inappropriate investments on the real side of the economy.
Section 7.1 demonstrates how security analysis impounds information into security prices.
To the extend that only part of this information is reflected in prices, corporations with over-
priced securities will be able to obtain capital too cheaply and corporations with undervalued
securities might forego investment opportunities because the cost of raising capital will be too
high. Therefore, inefficient capital markets will diminish one of the most potent benefits of a
market economy.

8.3 ARE MARKETS EFFICIENT?
The Issues
Not surprisingly, the efficient market hypothesis is not enthusiastically hailed by professional
portfolio managers. It implies that a great deal of the activity of portfolio managers”the
search for undervalued securities”is at best wasted effort and possibly harmful to clients be-
cause it costs money and leads to imperfectly diversified portfolios. Consequently, the EMH
has never been widely accepted on Wall Street, and debate continues today on the degree to
which security analysis can improve investment performance. Before discussing empirical
tests of the hypothesis, we want to note three factors that together imply the debate probably
never will be settled: the magnitude issue, the selection bias issue, and the lucky event issue.

The magnitude issue We noted that an investment manager overseeing a $5 billion
portfolio who can improve performance by only one-tenth of 1% per year will increase invest-
ment earnings by .001 $5 billion $5 million annually. This manager clearly would be worth
her salary! Yet we, as observers, probably cannot statistically measure her contribution. A one-
tenth of 1% contribution would be swamped by the yearly volatility of the market. Remember,
the annual standard deviation of the well-diversified S&P 500 index has been approximately
20% per year. Against these fluctuations, a small increase in performance would be hard to de-
tect. Nevertheless, $5 million remains an extremely valuable improvement in performance.
All might agree that stock prices are very close to fair values, and that only managers of
large portfolios can earn enough trading profits to make the exploitation of minor mispricing
worth the effort. According to this view, the actions of intelligent investment managers are the
driving force behind the constant evolution of market prices to fair levels. Rather than ask the
qualitative question, Are markets efficient? we ought instead to ask the quantitative question,
How efficient are markets?

The selection bias issue Suppose you discover an investment scheme that could re-
ally make money. You have two choices: Either publish your technique in The Wall Street
Journal to win fleeting fame or keep your technique secret and use it to earn millions of dol-
lars. Most investors would choose the latter option, which presents us with a conundrum. Only
investors who find that an investment scheme cannot generate abnormal returns will be will-
ing to report their findings to the whole world. Hence, opponents of the efficient market™s
view of the world always can use evidence that various techniques do not provide investment
Bodie’Kane’Marcus: II. Portfolio Theory 8. The Efficient Market © The McGraw’Hill
Essentials of Investments, Hypothesis Companies, 2003
Fifth Edition




270 Part TWO Portfolio Theory


rewards as proof that the techniques that do work simply are not being reported to the public.
This is a problem in selection bias; the outcomes we are able to observe have been preselected
in favor of failed attempts. Therefore, we cannot fairly evaluate the true ability of portfolio
managers to generate winning stock market strategies.

The lucky event issue In virtually any month, it seems we read an article in The Wall
Street Journal about some investor or investment company with a fantastic investment per-
formance over the recent past. Surely the superior records of such investors disprove the effi-
cient market hypothesis.
This conclusion is far from obvious, however. As an analogy to the “contest” among port-
folio managers, consider a contest to flip the most heads out of 50 trials using a fair coin. The
expected outcome for any person is 50% heads and 50% tails. If 10,000 people, however,
compete in this contest, it would not be surprising if at least one or two contestants flipped
more than 75% heads. In fact, elementary statistics tells us that the expected number of con-
testants flipping 75% or more heads would be two. It would be silly, though, to crown these
people the head-flipping champions of the world. They are simply the contestants who hap-
pened to get lucky on the day of the event (see the nearby box).
The analogy to efficient markets is clear. Under the hypothesis that any stock is fairly
priced given all available information, any bet on a stock is simply a coin toss. There is equal
likelihood of winning or losing the bet. Yet, if many investors using a variety of schemes make
fair bets, statistically speaking, some of those investors will be lucky and win a great majority
of bets. For every big winner, there may be many big losers, but we never hear of these man-
agers. The winners, though, turn up in The Wall Street Journal as the latest stock market gu-
rus; then they can make a fortune publishing market newsletters.
Our point is that after the fact there will have been at least one successful investment
scheme. A doubter will call the results luck; the successful investor will call it skill. The proper
test would be to see whether the successful investors can repeat their performance in another
period, yet this approach is rarely taken.
With these caveats in mind, we now turn to some of the empirical tests of the efficient mar-
ket hypothesis.


>
3. Fidelity™s Magellan Fund outperformed the S&P 500 in 11 of the 13 years that
Concept
Peter Lynch managed the fund, resulting in an average annual return for this pe-
CHECK riod more than 10% better than that of the index. Is Lynch™s performance sufficient
to cause you to doubt the efficient markets theory? If not, would any performance
record be sufficient to dissuade you?

Weak-Form Tests: Predictability in Stock Returns
Returns over short horizons Early tests of efficient markets were tests of the weak
form. Could speculators find trends in past prices that would enable them to earn abnormal
profits? This is essentially a test of the efficacy of technical analysis. The already-cited work
of Kendall and of Roberts (1959), both of whom analyzed the possible existence of patterns in
stock prices, suggests that such patterns are not to be found.
One way of discerning trends in stock prices is by measuring the serial correlation of stock
market returns. Serial correlation refers to the tendency for stock returns to be related to past
returns. Positive serial correlation means that positive returns tend to follow positive returns
(a momentum type of property). Negative serial correlation means that positive returns tend to
be followed by negative returns (a reversal or “correction” property).
Both Conrad and Kaul (1988) and Lo and MacKinlay (1988) examine weekly returns of
NYSE stocks and find positive serial correlation over short horizons. However, the correlation
Bodie’Kane’Marcus: II. Portfolio Theory 8. The Efficient Market © The McGraw’Hill
Essentials of Investments, Hypothesis Companies, 2003
Fifth Edition




How to Guarantee Successful Market Timing
Suppose you want to make your fortune publishing a After the fact, the one newsletter that was always
market newsletter. You need first to convince potential right will attract attention for your uncanny foresight
subscribers that you have talent worth paying for. But and investors will rush to pay large fees for its advice.
what if you have no market prediction talent? The solu- Your fortune is made, and you never even researched
tion is simple: Start eight market newsletters. the market!
WARNING: This scheme is illegal! The point, how-
In year one, let four of your newsletters predict an
up market and four a down market. In year two, let half ever, is that with hundreds of market newsletters, you
of the originally optimistic group of newsletters con- can find one that has stumbled onto an apparently re-
tinue to predict an up market and the other half a markable string of successful predictions without any
real degree of skill. After the fact, someone™s prediction
down market. Do the same for the originally pessimistic
group. Continue in this manner to obtain the following history can seem to imply great forecasting skill. This
person is the one we will read about in The Wall Street
pattern of predictions (U prediction of an up market,
Journal; the others will be forgotten.
D prediction of a down market).
After three years, no matter what has happened to
the market, one of the newsletters would have had a
Newsletter Predictions
perfect prediction record. This is because after three
years, there are 23 8 outcomes for the market, and Year 1 2 3 4 5 6 7 8
we™ve covered all eight possibilities with the eight let-
ters. Now, we simply slough off the seven unsuccessful 1 U U U U D D D D
newsletters and market the eighth letter based on its 2 U U D D U U D D
perfect track record. If we want to establish a letter with
3 U D U D U D U D
a perfect track record over a four-year period, we need
24 16 newsletters. A five-year period requires 32
newsletters, and so on.




coefficients of weekly returns tend to be fairly small, at least for large stocks for which price
data are the most reliably up-to-date. Thus, while these studies demonstrate price trends over
short periods, the evidence does not clearly suggest the existence of trading opportunities.
A more sophisticated version of trend analysis is a filter rule. A filter technique gives a rule filter rule
for buying or selling a stock depending on past price movements. One rule, for example, A rule for buying or
might be: “Buy if the last two trades each resulted in a stock price increase.” A more conven- selling stock
tional one might be: “Buy a security if its price increased by 1%, and hold it until its price falls according to recent
price movements.
by more than 1% from the subsequent high.” Alexander (1964) and Fama and Blume (1966)
found that such filter rules generally could not generate trading profits.
These very short-horizon studies offer the suggestion of momentum in stock market prices,
albeit of a magnitude that may be too small to exploit. However, in an investigation of inter-
mediate horizon stock price behavior (using 3- to 12-month holding periods), Jegadeesh and
Titman (1993) found that stocks exhibit a momentum property in which good or bad recent
performance continues. They conclude that while the performance of individual stocks is
highly unpredictable, portfolios of the best-performing stocks in the recent past appear to out-
perform other stocks with enough reliability to offer profit opportunities.

Returns over long horizons While studies of short-horizon returns have detected mi-
nor positive serial correlation in stock market prices, tests2 of long-horizon returns (that is, re-
turns over multiyear periods) have found suggestions of pronounced negative long-term serial

2
Eugene F. Fama and Kenneth R. French, “Permanent and Temporary Components of Stock Prices,” Journal of Po-
litical Economy 96 (April 1988), pp. 246“73; James Poterba and Lawrence Summers, “Mean Reversion in Stock
Prices: Evidence and Implications,” Journal of Financial Economics 22 (October 1988), pp. 27“59.
271
Bodie’Kane’Marcus: II. Portfolio Theory 8. The Efficient Market © The McGraw’Hill
Essentials of Investments, Hypothesis Companies, 2003
Fifth Edition




272 Part TWO Portfolio Theory


correlation. The latter result has given rise to a “fads hypothesis,” which asserts that stock
prices might overreact to relevant news. Such overreaction leads to positive serial correlation
(momentum) over short time horizons. Subsequent correction of the overreaction leads to poor
performance following good performance and vice versa. The corrections mean that a run of
positive returns eventually will tend to be followed by negative returns, leading to negative se-
rial correlation over longer horizons. These episodes of apparent overshooting followed by
correction give stock prices the appearance of fluctuating around their fair values and suggest
that market prices exhibit excessive volatility compared to intrinsic value.3
These long-horizon results are dramatic, but the studies offer far from conclusive evidence
regarding efficient markets. First, the study results need not be interpreted as evidence for
stock market fads. An alternative interpretation of these results holds that they indicate only
that market risk premiums vary over time: The response of market prices to variation in the
risk premium can lead one to incorrectly infer the presence of mean reversion and excess
volatility in prices. For example, when the risk premium and the required return on the mar-
ket rises, stock prices will fall. When the market then rises (on average) at this higher rate of
return, the data convey the impression of a stock price recovery. The impression of over-
shooting and correction is in fact no more than a rational response of market prices to changes
in discount rates.
Second, these studies suffer from statistical problems. Because they rely on returns meas-
ured over long time periods, these tests of necessity are based on few observations of long-
horizon returns.

Reversals While some of the studies just cited suggest momentum in stock market prices
over short horizons (of less than one year), other studies suggest that over longer horizons, ex-
treme stock market performance tends to reverse itself: The stocks that have performed best
in the recent past seem to underperform the rest of the market in the following periods, while
the worst past performers tend to offer above-average future performance. DeBondt and
Thaler (1985) and Chopra, Lakonishok, and Ritter (1992) find strong tendencies for poorly
performing stocks in one period to experience sizable reversals over the subsequent period,
while the best-performing stocks in a given period tend to follow with poor performance in the
following period.
For example, the DeBondt and Thaler study found that if one were to rank order the per-
formance of stocks over a five-year period and then group stocks into portfolios based on in-
vestment performance, the base-period “loser” portfolio (defined as the 35 stocks with the
worst investment performance) would outperform the “winner” portfolio (the top 35 stocks)
by an average of 25% (cumulative return) in the following three-year period. This reversal

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