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goal, 1 low-priority Long-term capital gains 3.61 3.54 3.63 3.46 3.45
goal) and percent of Dividend income 2.04 2.30 2.46 3.36 3.39
portfolio in income
Percent of portfolio in
income securities 27% 34% 39% 57% 56%
Average number of
securities in portfolio 9.4 10.4 11.6 12.1 12.1

Source: R. Lease, W. Lewellen, and G. Schlarbaum, “Market Segmentation: Evidence on the Individual Investor,” Financial Analysts Journal (1976), 53“60.

large a weight on the most recent event. Thus, if investors overreact, a negative event would
drive a stock price too low until additional developments lead investors to revalue the price in
line with fundamentals.
In the presence of regular overreaction to changes in fundamentals, prices would tend to
reverse themselves as investors correct for the overreaction. This tendency is called mean
reversion and can be detected by negative serial correlation in asset returns. Numerous stud-
ies have uncovered negative correlation measured from weekly to five-year holding-period
returns. Moreover, while anomalies such as calendar and dividend effects do not appear
strong enough to allow abnormal profits, the degree of serial correlation in stock returns is
such that profit opportunities appear plausible. Using return histories, portfolios constructed
by buying losers and selling winners would have returned significant positive returns. Thus,
overreaction suggested by mean reversion in stock returns appears to lend credibility to be-
havioral finance; using predictions from behavioral analysis apparently can lead to profitable
portfolio strategies.

Empirical explanations from new fields of science often engender controversy and resistance
before they become conventional wisdom. Behavioral explanations of asset returns are no ex-
ception, and we provide two examples.

Closed-End Funds
The average closed-end fund sells at a discount of about 10% from its net asset value and dis-
counts of individual funds are quite volatile. Such significant discounts have long been con-
sidered a puzzle. It is no wonder that closed-end fund discounts have attracted explanations
from behavioral finance.
Stephen A. Ross (2002) illustrates that observed discounts of closed-end fund values can
easily be explained by the funds™ expenses. As a simple example, suppose a closed-end fund
invests its net asset value, NAV, in the market index (and hence adds no value from superior
management). The index has an expected return of r and pays out an annual dividend yield
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of . Using the constant growth dividend discount model, the value of the fund is given by
P NAV/(r g), where NAV is the next dollar dividend and g is the growth rate of the
fund™s assets. The growth rate of the fund will be g r because the portfolio earns a rate r
and pays out . Thus, P NAV/(r r ) NAV as expected from a fund that provides
no added value. Now suppose the fund charges the portfolio an annual fee at a rate of . In this
case, the growth rate of the fund™s portfolio is reduced to g r ( ), and the value of the
fund is reduced to: P NAV /(r r ) NAV /( ). Thus, the discount of price
from the net asset value of the portfolio will be (NAV P)/NAV /( ). With an annual
dividend yield of 2% and expense ratio of .5%, the discount will be 20% (.5/(2 .5) .2).
By this calculation, observed discounts are not extraordinary.
Obviously, closed-end funds are initiated and successfully sold when investors expect the
fund management to do better than the market. For example, if the portfolio™s expected return
is r u and its risk is similar to that of the market index, then the required rate of return is r
and the growth rate of assets will be g r u ( ). If the expected abnormal return, u,
is greater than the expense ratio, , the fund will sell at a premium.1 This explains why IPOs
(initial public offerings) of closed-end funds are at a premium; if investors do not expect u to
exceed , they won™t purchase the fund shares. The fact that in most cases the premium turns
into a discount indicates how difficult it is for management to fulfill these expectations in a
nearly efficient market.
We can expect the abnormal expected return, u, to vary quite a bit as investors ingest the
volatile actual returns of the fund, and hence the discount itself will be volatile. Also, it is not
surprising that u (hence the discount) is correlated across closed-end funds, as well as with
market returns and investor sentiment variables.
An interesting question is why the same logic does not apply to open-end funds, since they
charge similar expense ratios. The reason is that investors can always redeem open-end fund
shares at the NAV. Thus, the expense ratio is a period cost that accumulates to those who hold
on to their shares, but the stream of future expenses is not capitalized in the price of the fund
shares. Investors in closed-end funds do not have this option so the stream of future expenses
must be capitalized in the NAV, resulting in a discount from the portfolio value.
When a discount of a closed-end fund becomes large, indicating that investors expect u to
be negative, the question arises: Why don™t investors purchase the fund and liquidate the port-
folio at net asset value to instantly gain the discount? Investors can reasonably expect that
management will not readily consent to the liquidation of the fund and hence such an event
will not be likely (or inexpensive). Therefore, the possibility that the fund will be liquidated
may not severely limit the size of potential discounts.

Excessive Volatility of Stock Market Prices
Robert J. Shiller (1981) rocked the world of economists and financial practitioners when he
produced evidence suggesting that the stock market is excessively volatile. If true, this find-
ing would be an outright refutation of the efficient market hypothesis (EMH) and must result
from irrational investment behavior. Excessive volatility would be a natural outcome of over-
reaction and lead to mean reversion. It is consistent with predictions from behavioral finance
as explained earlier.
To demonstrate that stock prices are excessively volatile Shiller used a long time series of
value and annual dividends on the S&P (the market) portfolio over the period 1871“1979,

When u is too large, that is, g r u ( ) r, then the dividend growth model is not valid. In other words,
it is not plausible that u can be sustained in the long run. In that case, we can recast the model as P NAV /( )
M, where M is the present value of future abnormal returns, with the same results.
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19 Behavioral Finance and Technical Analysis

denoted by P1, P2, . . . ,PT, and D1, D2, . . . DT, respectively. For each year, starting in 1871,
Shiller used a reasonable estimate of a discount factor to compute the present value of all fu-
ture dividends plus the present value of the terminal price and arrived at a rational price for the
market portfolio for that year. This method of generating rational prices assumes that investor
expectations for future dividends and terminal price were in fact equal to the actual values.
Having calculated rational stock prices, Pt*, for all periods, Shiller compared the volatility of
the series of actual prices, P, to that of the rational prices, P*. The standard deviation of actual
prices was more than five times that of rational prices. Shiller argued that the excess volatility
of actual over rational prices could not be explained by data problems or by model assump-
tions, and several subsequent studies came to similar conclusions, even when allowing for a
time-varying discount rate.
Shiller™s model assumes that volatility of dividends represents the volatility of stock fun-
damentals. It is true that expected future dividends (knowing the appropriate discount rate) are
sufficient to compute a rational stock value, and it is plausible to take actual dividends to rep-
resent expected dividends. It is not obvious, however, that the volatility of actual dividends
represents the volatility of stock fundamentals and, therefore, it is not obvious that the vari-
ance of the series of computed rational prices represents the variance we should expect from
actual prices.
Consider an alternative model that derives the rational price from discounted future earn-
ings. If it turned out that firms actually paid out a fixed proportion of earnings, then the dis-
counted earnings model would yield the same dividends as in Shiller™s model and identical
rational prices. But in this case, the volatility of earnings will be the same as the volatility of
dividends and we would all agree that the volatility of the computed rational prices captures
the volatility of fundamentals.
Of course, in reality firms do not pay out a fixed proportion of earnings, and thus the two
models will not yield the same results. In fact, since earnings are far more volatile than divi-
dends, the volatility of rational prices will be much larger in the discounted earnings model
and will indicate that actual prices do not exhibit excess volatility. While it may appear that
the discounted dividends model is the right one because it captures the actual payout ratios,
we should ask: Why then do firms vary payout ratios? The answer is that firms tend to smooth
dividends, and herein lies the problem with Shiller™s model. The use of actual dividends to
compute rational prices and their volatility would be justified only if the dividend smoothing
were a response to transitory changes in earnings. In that case, but only in that case, the
volatility of dividends would capture the volatility of fundamentals and the resultant volatility
of the computed rational prices would still be a valid benchmark for the volatility of actual
prices. This, however, is the less likely situation. Research suggests that managers do not
quickly adjust dividends to changes in fundamentals, particularly to worsening fundamentals
(explaining why reaction of stock prices to dividend cuts is so violent). Hence, the dividend
series is likely too smooth for tests of excessive volatility. It appears, therefore, that explana-
tions from behavioral finance for “excess volatility” may have jumped the gun.
As of now, then, behavioral theories successfully demonstrate that individual behavior is
not well approximated by standard utility analysis of economic theory. But with the possible
exception of overreaction of stock prices, behavioral finance has yet to make its mark in ex-
plaining asset returns.

Technical analysis is in most instances an attempt to exploit recurring and predictable pat-
terns in stock prices to generate abnormal trading profits. In the words of one of its leading
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the technical approach to investment is essentially a reflection of the idea that the stock market
moves in trends which are determined by the changing attitudes of investors to a variety of eco-
nomic, monetary, political, and psychological forces. The art of technical analysis, for it is an art,
is to identify changes in such trends at an early stage and to maintain an investment posture until
a reversal of that trend is indicated.2

Technicians do not necessarily deny the value of fundamental information, such as we have
discussed in the three past chapters. Many technical analysts believe stock prices eventually
“close in on” their fundamental values. Technicians believe, nevertheless, that shifts in market
fundamentals can be discerned before the impact of those shifts is fully reflected in prices. As
the market adjusts to a new equilibrium, astute traders can exploit these price trends.
Technicians also believe that market fundamentals can be perturbed by irrational or behav-
ioral factors. More or less random fluctuations in price will accompany any underlying trend.
If these fluctuations dissipate slowly, they can be taken advantage of for abnormal profits.
These presumptions, of course, clash head-on with those of the EMH and with the logic of
well-functioning capital markets. According to the EMH, a shift in market fundamentals
should be reflected in prices immediately. According to technicians, though, that shift will lead
to a gradual price change that can be recognized as a trend. Such exploitable trends in stock
market prices would be damning evidence against the EMH, as they would indicate profit op-
portunities that market participants had left unexploited.
A more subtle version of technical analysis holds that there are patterns in stock prices that
can be explained, but that once investors identify and attempt to profit from these patterns,
their trading activity affects prices, thereby altering price patterns. This means the patterns that
characterize market prices will be constantly evolving, and only the best analysts who can
identify new patterns earliest will be rewarded. We call this phenomenon self-destructing pat-
terns and explore it in some depth in the chapter.
The notion of evolving patterns is consistent with almost but not-quite efficient markets. It
allows for the possibility of temporarily unexploited profit opportunities, but it also views
market participants as aggressively exploiting those opportunities once they are uncovered.
The market is continually groping toward full efficiency, but it is never quite there.
This is in some ways an appealing middle position in the ongoing debate between techni-
cians and proponents of the EMH. Ultimately, however, it is an untestable hypothesis. Tech-
nicians will always be able to identify trading rules that would have worked in the past but
need not work any longer. Is this evidence of a once viable trading rule that has now been
eliminated by competition? Perhaps. But it is far more likely the trading rule could have been
identified only after the fact.
Until technicians can offer rigorous evidence that their trading rules provide consistent
trading profits, we must doubt the viability of those rules. As you saw in the chapter on the ef-
ficient market hypothesis, the evidence on the performance of professionally managed funds
generally does not support the efficacy of technical analysis.

Technical analysts are sometimes called chartists because they study records or charts of past
stock prices and trading volume, hoping to find patterns they can exploit to make a profit. In
this section, we examine several specific charting strategies.

Martin J. Pring, Technical Analysis Explained, 2nd ed. (New York: McGraw-Hill Book Company, 1985), p. 2.
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19 Behavioral Finance and Technical Analysis

The Dow Theory
The Dow theory, named after its creator Charles Dow (who established The Wall Street Jour- Dow theory
nal), is the grandfather of most technical analysis. While most technicians today would view A technique that
the theory as dated, the approach of many more statistically sophisticated methods are essen- attempts to discern
tially variants of Dow™s approach. The aim of the Dow theory is to identify long-term trends long- and short-
term trends in stock
in stock market prices. The two indicators used are the Dow Jones Industrial Average (DJIA)
market prices.
and the Dow Jones Transportation Average (DJTA). The DJIA is the key indicator of underly-
ing trends, while the DJTA usually serves as a check to confirm or reject that signal.
The Dow theory posits three forces simultaneously affecting stock prices:
1. The primary trend is the long-term movement of prices, lasting from several months to
several years.
2. Secondary or intermediate trends are caused by short-term deviations of prices from the
underlying trend line. These deviations are eliminated via corrections when prices revert
back to trend values.
3. Tertiary or minor trends are daily fluctuations of little importance.
Figure 19.2 represents these three components of stock price movements. In this figure, the
primary trend is upward, but intermediate trends result in short-lived market declines lasting
a few weeks. The intraday minor trends have no long-run impact on price. support level
Figure 19.3 depicts the course of the DJIA during 1988. The primary trend is upward, as ev-
A price level below
idenced by the fact that each market peak is higher than the previous peak (point F versus D
which it is supposedly
versus B). Similarly, each low is higher than the previous low (E versus C versus A). This pat- unlikely for a stock or
tern of upward-moving “tops” and “bottoms” is one of the key ways to identify the underlying stock index to fall.
primary trend. Notice in Figure 19.3 that, despite the upward primary trend, intermediate trends
still can lead to short periods of declining prices (points B through C, or D through E). resistance level
The Dow theory incorporates notions of support and resistance levels in stock prices. A
A price level above
support level is a value below which the market is relatively unlikely to fall. A resistance which it is supposedly
level is a level above which it is difficult to rise. Support and resistance levels are determined unlikely for a stock or
by the recent history of prices. In Figure 19.3, the price at point D would be viewed as a stock index to rise.


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