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reversal effect
effect, in which losers rebound and winners fade back, seems to suggest that the stock market
The tendency of
overreacts to relevant news. After the overreaction is recognized, extreme investment per-
poorly performing
formance is reversed. This phenomenon would imply that a contrarian investment strategy”
stocks and well-
performing stocks investing in recent losers and avoiding recent winners”should be profitable. Moreover, these
in one period to returns seem pronounced enough to be exploited profitably.
experience reversals
The reversal effect also seems to depend on the time horizon of the investment. While
in the following
DeBondt and Thaler (1992) found reversals over long (multiyear) horizons, and studies by
Jegadeesh (1990) and Lehmann (1990) documented reversals over short horizons of a month

The fads debate started as a controversy over whether stock prices exhibit excess volatility. See Robert J. Shiller, “Do
Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?” American Economic Review 71
(June 1971), pp. 421“36. However, it is now apparent that excess volatility and fads are essentially different ways of
describing the same phenomenon.
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8 The Efficient Market Hypothesis

or less, we note above that an investigation of intermediate-term stock price behavior (using
3- to 12-month holding periods) by Jegadeesh and Titman (1993) found that stocks exhibit a
momentum property in which good or bad recent performance continues. This of course is the
opposite of a reversal phenomenon.
Thus it appears that there may be short-run momentum but long-run reversal patterns in
price behavior. One interpretation of these patterns is that short-run overreaction (which
causes momentum in prices) may lead to long-term reversals (when the market recognizes and
corrects its past errors). This interpretation is emphasized by Haugen (1995).

Predictors of Broad Market Movements
Several studies have documented the ability of easily observed variables to predict market re-
turns. For example, Fama and French (1988) show that the return on the aggregate stock mar-
ket tends to be higher when the dividend/price ratio, or the dividend yield, is high. Campbell
and Shiller (1988) find that the earnings yield can predict market returns. Keim and Stam-
baugh (1986) show that bond market data such as the spread between yields on high- and low-
grade corporate bonds also help predict broad market returns.
Again, the interpretation of these results is difficult. On the one hand, they may imply that
stock returns can be predicted, in violation of the efficient market hypothesis. More probably,
however, these variables are proxying for variation in the market risk premium. For example,
given a level of dividends or earnings, stock prices will be lower and dividend and earnings
yields will be higher when the risk premium (and therefore the expected market return) is
larger. Thus, a high dividend or earnings yield will be associated with higher market returns.
This does not indicate a violation of market efficiency. The predictability of market returns is
due to predictability in the risk premium, not in risk-adjusted abnormal returns.
Fama and French (1989) show that the yield spread between high- and low-grade bonds has
greater predictive power for returns on low-grade bonds than for returns on high-grade bonds,
and greater predictive power for stock returns than for bond returns, suggesting that the pre-
dictability in returns is in fact a risk premium rather than evidence of market inefficiency. Sim-
ilarly, the fact that the dividend yield on stocks helps to predict bond market returns suggests
that the yield captures a risk premium common to both markets rather than mispricing in the
equity market.

Semistrong-Form Tests: Market Anomalies
Fundamental analysis uses a much wider range of information to create portfolios than does
technical analysis. Investigations of the efficacy of fundamental analysis ask whether publicly
available information beyond the trading history of a security can be used to improve invest-
ment performance and, therefore, are tests of semistrong-form market efficiency. Surprisingly,
several easily accessible statistics, for example a stock™s price“earnings ratio or its market cap-
italization, seem to predict abnormal risk-adjusted returns. Findings such as these, which we
will review in the following pages, are inconsistent with the efficient market hypothesis and,
therefore, are often referred to as market anomalies.
A difficulty in interpreting these tests is that we usually need to adjust for portfolio risk be-
fore evaluating the success of an investment strategy. For example, many tests use the CAPM
to adjust for risk. However, we know that even if beta is a relevant descriptor of stock risk, the
empirically measured quantitative trade-off between risk as measured by beta and expected re-
turn differs from the predictions of the CAPM. If we use the CAPM to adjust portfolio returns
for risk, inappropriate adjustments might lead to the incorrect conclusion that various portfo-
lio strategies can generate superior returns.
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274 Part TWO Portfolio Theory

Tests of risk-adjusted returns are joint tests of the efficient market hypothesis and the risk
adjustment procedure. If it appears that a portfolio strategy can generate superior returns, we
then must choose between rejecting the EMH or rejecting the risk adjustment technique. Usu-
ally, the risk adjustment technique is based on more questionable assumptions than the EMH;
if we reject the procedure, we are left with no conclusion about market efficiency.
An example of this problem is the discovery by Basu (1977, 1983) that portfolios of low
price/earnings ratio stocks have higher average returns than high P/E portfolios. The P/E
P/E effect
effect holds up even if returns are adjusted for portfolio beta. Is this a confirmation that the
Portfolios of low P/E
market systematically misprices stocks according to the P/E ratio?
stocks have exhibited
This would be a surprising and, to us, disturbing conclusion, because analysis of P/E ratios
higher average risk-
adjusted returns than is such a simple procedure. While it may be possible to earn superior returns using hard work
high P/E stocks. and much insight, it hardly seems likely that following such a basic technique is enough to
generate abnormal returns.
One possible interpretation of these results is that the model of capital market equilibrium
is at fault in that the returns are not properly adjusted for risk. This makes sense, since if two
firms have the same expected earnings, then the riskier stock will sell at a lower price and
lower P/E ratio. Because of its higher risk, the low P/E stock also will have higher expected
returns. Therefore, unless the CAPM beta fully adjusts for risk, P/E will act as a useful addi-
tional descriptor of risk and will be associated with abnormal returns if the CAPM is used to
establish benchmark performance.

The small-firm-in-January effect One of the most frequently cited anomalies with
respect to the efficient market hypothesis is the so-called size or small-firm effect, originally
small-firm effect
documented by Banz (1981). Figure 8.3 illustrates the size effect. It shows the historical per-
Stocks of small firms
formance of portfolios formed by dividing the NYSE stocks into 10 portfolios each year ac-
have earned abnormal
cording to firm size (i.e., the total value of outstanding equity). Average annual returns are
returns, primarily in
the month of January. consistently higher on the small-firm portfolios. The difference in average annual return be-
tween portfolio 10 (with the largest firms) and portfolio 1 (with the smallest firms) is 8.59%.
Of course, the smaller-firm portfolios tend to be riskier. But even when returns are adjusted
for risk by using the CAPM, there is still a consistent premium for the smaller-sized portfo-
lios. Even on a risk-adjusted basis, the smallest-size portfolio outperforms the largest-firm
portfolio by an average of 4.3% annually.
This is a huge premium; imagine earning an extra return of this amount on a billion-dollar
portfolio. Yet it is remarkable that following a simple (even simplistic) rule such as “invest in

F I G U R E 8.3 16
Average return in excess
Returns in excess of 14
of risk-free rate
risk-free rate and in
Excess returns (%)

12 Return in excess of CAPM
excess of the Security
Market Line for 10
size-based portfolios
Source: Stocks, Bonds, Bills,
and Inflation 2000 Yearbook,
Ibbotson Associates, 2000. 2
1 2 3 4 5 6 7 8 9 10
(Small firms) Portfolio decile (Large firms)
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8 The Efficient Market Hypothesis

low capitalization stocks” should enable an investor to earn excess returns. After all, any in-
vestor can measure firm size costlessly. One would not expect such minimal effort to yield
such large rewards.
Later studies (Keim, 1983; Reinganum, 1983; and Blume and Stambaugh, 1983) showed
that the small-firm effect occurs virtually entirely in the first two weeks of January. The size
effect is in fact a small-firm-in-January effect.
Some researchers believe the January effect is tied to tax-loss selling at the end of the year.
The hypothesis is that many people sell stocks that have declined in price during the previous
months to realize their capital losses before the end of the tax year. Such investors do not put
the proceeds from these sales back into the stock market until after the turn of the year. At that
point, the rush of demand for stock places an upward pressure on prices that results in the Jan-
uary effect. Finally, the January effect is said to show up most dramatically for the smallest
firms because the small-firm group includes, as an empirical matter, stocks with the greatest
variability of prices during the year. The group, therefore, includes a relatively large number
of firms that have declined sufficiently to induce tax-loss selling.
Some empirical evidence supports the belief that the January effect is connected to tax-loss
selling. For example, Ritter (1988) shows that the ratio of stock purchases to sales by individ-
ual investors is below normal in late December and above normal in early January. This is
consistent with tax-loss rebalancing.
The fundamental question is why market participants do not exploit the January effect and
thereby ultimately eliminate it by bidding stock prices to appropriate levels. One possible ex-
planation lies in segmentation of the market into two groups: institutional investors who in-
vest primarily in large firms and individual investors who invest disproportionately in smaller
firms. According to this view, managers of large institutional portfolios are the moving force
behind efficient markets. It is professionals who seek out profit opportunities and bid prices to
their appropriate levels. Institutional investors do not seem to buy at the small-size end of the
market, perhaps because of limits on allowed portfolio positions, so the small-firm anomaly
persists without the force of their participation.

4. Does this market segmentation theory get the efficient market hypothesis off the Concept
hook, or are there still market mechanisms that, in theory, ought to eliminate the
small-firm anomaly?

The neglected-firm effect and liquidity effects Arbel and Strebel (1983) give
another interpretation of the small-firm effect. Because small firms tend to be neglected by
large institutional traders, information about such firms is less available. This information de-
ficiency makes smaller firms riskier investments that command higher returns. “Brand-name”
firms, after all, are subject to considerable monitoring from institutional investors that assures
high-quality information, and presumably investors do not purchase “generic” stocks without
the prospect of greater returns. An article by Merton (1987) shows that neglected firms might
be expected to earn higher equilibrium returns as compensation for the risk associated with
limited information. In this sense the neglected firm premium is not strictly a market ineffi-
ciency, but is a type of risk premium.
As evidence for the neglected-firm effect, Arbel (1985) divided firms into highly re- neglected-
searched, moderately researched, and neglected groups based on the number of institutions firm effect
holding their stock. The January effect was largest for the neglected firms. The tendency of
Research by Amihud and Mendelson (1991) on the effect of liquidity on stock returns investments in stock
might be related to both the small-firm and neglected-firm effects. They argue that investors of less well-known
firms to generate
will demand a rate-of-return premium to invest in less liquid stocks that entail higher trading
abnormal returns.
costs. Indeed, spreads for the least liquid stocks easily can be more than 5% of stock value. In
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276 Part TWO Portfolio Theory

F I G U R E 8.4 1.8

Average monthly return (%)
Average rate of return 1.6
as a function of the 1.4
book-to-market ratio
Source: Eugene Fama and
Kenneth R. French, “The
Cross Section of Expected
Stock Returns,” Journal of 0.6
Finance 47 (1992), pp.
1 2 3 4 5 6 7 8 9 10
Book-to-market decile

accord with their hypothesis, Amihud and Mendelson show that these stocks show a strong
tendency to exhibit abnormally high risk-adjusted rates of return. Because small and less-
analyzed stocks as a rule are less liquid, the liquidity effect might be a partial explanation of
their abnormal returns. However, this theory does not explain why the abnormal returns of


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