in Investor Decision Making
Adopted from Chapter 10 of Contrarian Investment Strategies: The
By: David Dreman
Reprinted with permission by the Author
In Investor Decision Making
Investors, even professionals, fall prey to important logical fallacies
and psychological failings. Some of the latter are relatively new;
others have been known for decades. These psychological pressures
impact our decisions under conditions of uncertainty in a very predictable
manner, not only in the marketplace, but in virtually every aspect
of our lives. The bottom line is that these powerful forces lead most
people to make the same mistakes time and again. Understanding them
is your best protection against stampeding with the crowd, and may
help you to profit from their mistakes instead. But as you read on
you'll see it's much easier said than done.
Your Market Odds
Despite what many economists and financial theorists assume, people
are not good intuitive statisticians, particularly under difficult
conditions. They do not calculate odds properly when making investment
decisions, which causes consistent errors. First, we must learn why
such mistakes occur so frequently. Once their nature is understood,
we can develop a set of rules to help monitor our decisions and to
provide a shield against serious mishap. We will then see how the
contrarian strategies are anchored upon these intuitive statistical
Simplification We Believe
Let's quickly review the limitations of man's information-processing
capabilities, a sort of black hole that is constantly exerting great
force on his decisions. According to Nobel laureate Herbert Simon,
people are swamped with information and react consciously to only
a small part of it. Simon also stated that when overwhelmed with facts,
we select a small part of them and usually reach a different conclusion
from what the entire data set would suggest.
have found that people react to this avalanche of data by adopting
shortcuts or rules of thumb rather than formally calculating the actual
odds of a given outcome. Known to psychologists as judgmental heuristics
in technical jargon, these shortcuts are learning and simplifying
strategies we use for managing large amounts of information. Backed
by the experience of a lifetime, most of these judgmental shortcuts
work exceptionally well, and allow us to cope with data that would
otherwise overwhelm us.
a car down a superhighway, for example, you concentrate only on operating
the vehicle, on other traffic, and on traffic signs, screening out
thousands of other distracting and disruptive bits of information.
The rule of thumb is to focus solely on what directly affects our
driving, and the rule is obviously a good one.
use selective processes in dealing with probabilities: in many of
our decisions and judgments, we tend to be intuitive statisticians.
We apply mental shortcuts that work well most of the time. We think
our odds of survival are better when driving at 55 miles an hour that
at 90 miles an hour, although few of us have ever bothered to check
the actual numbers. A professional basketball team is likely to beat
an amateur one, a discount computer store will probably sell personal
computers more cheaply than Macy's or Bloomingdale's. And we might
expect to get to a city 300 miles away faster by air than by ground
(if it is not a United Express flight to a Colorado ski resort). There
are dozens of examples that such procedures are valuable and immensely
an intuitive statistician has limitations as well as blessings. The
very simplifying processes that are normally efficient time-savers
lead to systematic mistakes in investment decisions. They can make
you believe the odds are dramatically different from what they actually
are. As a result, they consistently shortchange the investor.
produced by the subjectively calculated probabilities are large, systematic,
and difficult to eliminate, even after people have been made fully
aware of them, as we'll see next.
Bio's of "Representativeness"
Let us first look at one of the most common of the cognitive biases
that Daniel Kahneman of Princeton and the late Amos Tversky of Stanford
call "representativeness." The two professors show it's a natural
human tendency to draw analogies and see identical situations where
in fact there are important differences.
market, this means labeling two companies, or two market environments,
as the same when the actual resemblance is superficial. Give people
a little information and, click!, they pull out a picture they're
familiar with, though it may only remotely represent the current situation.
the aftermath of the 1987 crash. In five trading days the Dow fell
742 points, culminating with the 508 point decline on Black Monday,
October 19. This wiped out almost $1 trillion of value. "Is this 1929?"
asked the media in bold headlines. Many investors taking this heuristical
shortcut cowered in cash. They were caught up in the false parallel
between 1987 and 1929.
At the time, the situations seemed eerily similar. We had not had
a stock market crash for 58 years. Generations grew up believing that
because a Depression followed the 1929 Crash it would always happen
this way. A large part of Wall Street's experts, the media, and the
investing public agreed.
was that the two crashes had only the remotest similarity. In the
first place, 1929 was a special case. The nation has had numerous
panics and crashes in the nineteenth and early twentieth centuries
without a depression. Crashes or no, the thriving American economy
always bounded back in short order.
how often they occurred, Victor Niederhoffer, in his insightful book,
The Education of a Speculator; notes that Henry Clews wrote after
the panic of 1837 that "Prices dropped to zero." The same observer
casually stated a few pages later, "The panic of 1857 was much more
sever." Clews doesn't say whether in the latter panic sellers actually
had to pay buyers to cart away their stocks or bonds. In neither case
was there a depression. So crash and depression were not synonymous.
important, it was apparent even then that the economic and investment
climate was entirely different. My Forbes column of May 2, 1988, noted
some of the differences clearly visible at the time. The column stated
that although market savants and publications were presenting charts
showing the breathtaking similarity between the market post-crash
in 1988 with that of 1929, there was far less to it than met the eye.
Back then the market rallied smartly after the debacle before beginning
a free-fall in the spring of 1930, and many experts believed history
would repeat itself 58 years later.
were obvious. The major averages had moved up 20% from the October
19, 1987, bottom and then skidded lower, again in a manner similar
to early 1930. But as I warned readers; a chart, unlike a picture,
is not always worth a thousand words; sometimes it is just downright
and investment fundamentals of 1988 were worlds apart from those of
1930. At that time economic and financial conditions were beginning
to blow apart, as the worst depression in the nation's history rapidly
hard for even the most fervent gloom-and-doomer to argue that a parallel
situation existed after the 1987 crash. The economy was rolling along
at a rate above most estimates pre-crash and sharply above the recession
levels projected in the weeks following the October 19 debacle.
fundamentals were encouraging. The P/E of the S& P 500 was a little
over 13 times earnings, down sharply from the 20 times earnings just
prior to the 1987 crash, and below the long-term average of 15 to
16. The underlying fundamentals of the two periods were dramatically
different. 1929 it wasn't. Investors who followed the representativeness
bias missed an enormous buying opportunity: by July, 1997, the market
quadrupled from that time.
heuristic covers a number of common decision-making errors. Kahneman
and Tversky defined this heuristic as a subjective judgment of the
extent to which the event in question "is similar in essential properties
to its parent population" or "reflects the salient features of the
process by which it is generated." People often judge probabilities
"by the degree to which A is representative of B, that is, by the
degree to which A resembles B."
are A and B? It depends. If you are estimating the probability that
A came from B, A might be a person and B might be a group, say of
doctors. The judgment you want to make in this case is the probability
A is also a doctor. Or A might be an event and B might be a potential
cause. Again you are judging the probability that A comes from B.
A, for example, would be the similarity or representativeness in people's
minds of the 1987 crash to B which, in this case, would be the 1929
crash and depression.
the definition of representativeness is abstract and a little hard
to understand, let's look at some more concrete examples of how this
heuristic works, and how it can lead to major mistakes in many situations.
it may give too much emphasis to the similarities between events (or
samples), but not to the probability that they will occur. Again looking
at the 1987 crash, it appeared similar to 1929 in its stunning decline,
but this by itself did not mean that a Great Depression would follow.
In fact, as we have seen, there have been many crashes, but only one
Great Depression. Still, the dramatic event of the 1929 Crash followed
by the Great Depression was an overpowering image. After the 1987
crash, people did not step back and try to logically assess what the
probabilities were that the next event would occur in an identical
manner. Rather, by using the representativeness heuristic, or mental
shortcut, they assumed this would be the outcome.
representativeness may reduce the importance of variables that are
critical in determining the event's probability. Again using the crash
as an illustration, the major differences between the situations in
1987 and 1929, outlined in the Forbes article, were downplayed, with
the focus solely on the market's plunge.
type of representativeness bias occurs time and again in the marketplace.
During the Gulf Crisis in the last half of 1990, for example, the
stock market fell dramatically on the fears of a worldwide shortage
of oil. The seizing of the Kuwaiti oil fields by Iraq, and the subsequent
embargo on Iraqi oil, triggered the bias for both investors and the
media. The surface similarities to the past indicated an oil shortage,
followed by a skyrocketing increase in price, culminating in runaway
inflation, as was the case in 1981, or a severe recession as in 1973
Ã� 1974. Markets plunged, as investors fearfully recalled the battered
sales of large cars and other gas-guzzlers including yachts (whose
prices dropped sharply) as well as other economic horrors.
bias worked in an identical manner to the way it had after the 1987
crash. Yet 1990 was dramatically different from 1973 or 1981.
about the dangers of false parallels in a column written at the time.
While it was impossible to predict the outcome of the Persian Gulf
Crisis, the world was not facing a major protracted increase in oil
market pundits immediately compared this oil crisis with those of
1973/1974 and 1979/1980. Back in 1980, for example, oil experts stated
that crude would reach $100 a barrel by the end of the decade, at
the latest. Then, too, leading dailies ran front-page series for months
on how higher oil prices would permanently damage the economy. Some
of the statistics conjured up to back the predictions were terrifying.
One showed that at the then-current price of oil, almost the entire
capital of the Western world would flow into the coffers of OPEC (Organization
of Petroleum Exporting Countries) members. Another demonstrated that
Saudi Arabia would accumulate more capital in six or seven years than
the value of all stocks on the NYSE.
actually happened? By the late 1980s oil had dropped to as low as
$12 a barrel. Fear sells newspapers and keeps people glued to the
tube, but fear does not make money in the stock market.
of this was forgotten as the crisis developed in the late summer of
1990. In fact, the differences between the Gulf Crisis and the two
previous oil crises were remarkable. In 1990 the world was facing
an oil glut, not the shortages of the two earlier occasions. Oil prices,
rather than tripling as they did in the seventies, were up only about
30% in the 1990 crisis. Too, this time around the OPEC members had
not banded together to increase prices. Instead they had mostly condemned
the Iraqi aggression and felt threatened by it. The OPEC cartel indicated
it would make up the Iraq-Kuwait difference to keep prices from rising
further. With the 50-year-plus supply the Saudis and some of the other
producers had, and their pressing need for hard cash, economic considerations
ranked up there with altruism. Finally, there was a unanimity among
the major powers in response to the crisis that had not occurred in
well over a century.
strongly indicated that oil prices were not destined to move higher
for long, if at all. The panic that gripped many investors had created
the finest buying opportunity of the decade.
see the representativeness bias resulted in a near identical investor
reaction to the Gulf Crisis as it did to the 1987 crash. First, people
put undue weight on the surface similarities between the potential
oil crisis of 1990 and those of 1973/1974 and 1980. Secondly, investors
again downplayed the critical differences between the two periods
the article outlined, which were far more important than the casual
resemblances. Again, the bias contributed to major investor errors
sure you have guessed, the representativeness heuristic can apply
just as forcefully to a company or an industry as to the market as
a whole. Here is one such an example:
Dell Computer collapsed on Wall Street, losing 50% of its value in
months. One day it had a market capitalization of $4.6 billion; six
months later, it was just over $2.2 billion. Same company but worth
less than half as much, and trading at only 4.6 times the previous
year's earnings. What caused the drop? Earnings were weak, as the
company took some major charges while repositioning its personal computer
lines and restructuring its marketing.
probably happened was this: two other industry leaders, IBM and Digital
Equipment Corporation (DEC), were weak, and investors lumped the three
companies together. IBM was in temporary trouble, while DEC's was
more serious. Dell was not. It was a very different kind of company
with different products. Its repositioning was fabulously successful
and it went on to become a major player in the personal computer (PC)
industry. If you had bought it at it 1993 low, you would have increased
your money more than 59-fold by late 1997. The representativeness
bias worked the same way as in the two previous examples.
and Tversky's findings, which have been repeatedly confirmed, are
particularly important to our understanding of stock market errors.
Law of Small Numbers
The representativeness bias is responsible at least in part for a
number of other major and oft-repeated errors. All mutual fund organizations
work from the principle that investors flock to better-performing
mutual funds Ã� even though financial researchers have shown that the
"hot" funds in one time period are often the poorest performers in
another. The final verdict on the sizzling funds in the 1982/1983
market was disastrous. Ditto for the aggressive-growth funds of 1991
to 1997. Investors lost billions of dollars in these funds. Many,
although far more risky, could not hold a candle to the long-term
records of many conservative, blue-chip mutual funds.
people are continually enticed by such "hot" performance, even if
it lasts for brief periods. Because of this susceptibility, brokers
or analysts who have had one or two stocks move up sharply, or technicians
who call one turn correctly, are believed to have established a credible
record and can readily find market followings.
an advisory service that is right for a brief time can beat its drums
loudly. One market-letter writer was featured prominently in the Sunday
New York Times for being bearish in July of 1996, as the market dropped
rapidly. He was right for three weeks but missed the enormous rally
of the prior 18 months, as well as the subsequent rise for the balance
of 1996, which kept him out of the market as it spiked 80%.
it doesn't matter if the advisor is wrong repeatedly; the name of
the game is to get a dramatic prediction out there. A well-timed call
can bring huge rewards to a popular newsletter writer. Eugene Lerner,
a former finance professor who heads Disciplined Investment Advisors,
a market-letter writer, speaking of what making a bearish call in
a declining market can do, said, "If the market goes down for the
next three years you'll be as rich as CroesusÃ‰The next time around,
everyone will listen to you." With hundreds and hundreds of advisory
letters out there someone has to be right. Again, it's just the odds.
Garzarelli gained near immortality when she purportedly "called" the
1987 crash. Although, as the market strategist for Shearson Lehman,
her forecast was never published in a research report, nor indeed
communicated to its clients, she still received widespread recognition
and publicity for this call, which was made in a short TV interview
this "brilliant call," her record, according to a fellow strategist,
"has been somewhat mixed, like most of us." Still, her remark on CNBC
that the Dow could drop sharply from its then 5300 level rocked an
already nervous market on July 23, 1996. What had been a 40-point
gain for the Dow turned into a 40-point loss, a good deal of which
was attributed to her comments. Only a few days earlier, Ms. Garzarelli
had predicted The Dow would rise to 6400 from its then value of 5400.
Even so, people widely followed her because of "the great call in
Weinstein, editor of The Professional Tape Reader, an advisory letter
headquartered in Hollywood, Florida, advertises week after week that
the market is heading south. He naturally tells potential subscribers
that following his advice will make them mega-bucks. Mr. Weinstein's
track record leaves much to be desired. According to the Hulbert Financial
Digest, his advice has significantly under performed the market.
is, market-letter writers have been wrong in their judgments far more
often than they would like to remember. However, advisors understand
that the public considers short-term results meaningful when they
are, more often than not, simply chance. Those in the public eye usually
gain large numbers of new subscribers for being right by random luck.
brings us to another important probability error that falls under
the broad rubric of representativeness. Amos Tversky and Daniel Kahneman
call this one the "law of small numbers." Examining journals in psychology
and education, they found that researchers systematically overstated
the importance of findings taken from small samples. The statistically
valid "law of large numbers" states that large samples will usually
be highly representative of the population from which they are drawn;
for example, public opinion polls are fairly accurate because they
draw on large and representative groups. The smaller the sample used,
however (or the shorter the record), the more likely the findings
are chance rather than meaningful.
Tversky and Kahneman study showed that typical psychological or educational
experimenters gamble their research theories on samples so small that
the results have a very high probability of being chance. This is
the same as gambling on the single good call of an investment advisor.
The psychologists and educators are far too confident in the significance
of results based on a few observations or a short period of time,
even though they are trained in statistical techniques and are aware
of the dangers.
how readily people over generalize the meaning of a small number of
supporting facts. Limited statistical evidence seems to satisfy our
intuition no matter how inadequate the depiction of reality. Sometimes
the evidence we accept runs to the absurd. A good example of the major
overemphasis on small numbers is the almost blind faith investors
place in governmental economic releases on employment, industrial
production, the consumer price index, the money supply, the leading
economic indicators, et cetera.
statistics frequently trigger major stock- and bond-market reactions,
particularly if the news is bad. For example, investors are concerned
about the possibility of rising prices. If the unemployment rate drops
two-tenths of one percent in a month when it was expected to be unchanged,
or if industrial production climbs slightly more than the experts
expected, stock prices can fall, at times sharply.
this happen? No. Flash statistics, more times than not, are near worthless.
Initial economic and Fed figures are revised significantly for weeks
or months after their release, as new and "better" information flows
in. Thus, an increase in the money supply can turn into a decrease,
or a large drop in the leading indicators can change to a moderate
increase. These revisions occur with such regularity you would think
that investors, particularly pros, would treat them with the skepticism
the real world refuses to follow the textbooks. Experience notwithstanding,
investors treat as gospel all authoritative-sounding releases that
they think pinpoint the development of important trends.
of how instant news threw investors into a tailspin occurred in July
of 1996. Preliminary statistics indicated the economy was beginning
to gain steam. The flash figures showed that GDP (gross domestic product)
would rise at a 3% rate in the next several quarters, a rate higher
than expected. Many people, convinced by these statistics that rising
interest rates were imminent, bailed out of the stock market that
month. By the end of that year, the GDP growth figures had been revised
down significantly (unofficially, a minimum of a dozen times, and
officially at least twice). The market rocketed ahead to new highs
to August 1997, but a lot of investors had retreated to the sidelines
on the preliminary bad news
as irrational is the overreaction to every utterance by a Greenspan
or other senior Fed or government official, no matter how offhanded.
Like ancient priests examining chicken entrails to foretell events,
many pros scrutinize every remark and act upon it immediately, even
though they are not sure what it is they are acting on. Remember here
the advice of a world champion chess player when asked how to avoid
making a bad move. His answer: "Sit on your hands."
investors don't sit on their hands; they dance on tiptoe, ready to
flit after the least particle of information as if it were a strongly
documented trend. The law of small numbers, in such cases, results
in decisions sometimes bordering on the inane.
and Kahneman's findings, which have been repeatedly confirmed, are
particularly important to our understanding of some stock market errors.
of averages indicates that many experts will have excellent record
Ã� usually playing popular trends Ã� often for months and sometimes
for several years, only to stumble disastrously later. If you buy
the record just after a period of spectacular performance, chances
are the letter writer or manager will not sustain it.
is the sad lesson to be learned from the records of the market-letter
writers above and from the turbo-charged, aggressive growth managers
of mutual funds in the mid-eighties, many of whom forlornly traded
their hot hands in for a bartender's apron or UPS uniform after decimating
their clients' portfolios It is the same lesson that investors over
the centuries have had to relearn with each new supposedly unbeatable
Rate vs Base Rate
A third flaw, in many ways parallel to the second, also indicates
man's shortcomings as an intuitive statistician. In making decisions,
we become overly immersed in the details of a particular situation
and neglect the outcome of similar situations in our experience. These
past outcomes are called prior probabilities, and logically should
help to guide similar choices in the present.
tend not to. This shows up clearly in an experiment made with a group
of advanced psychology students. The group was given a brief personality
analysis of a graduate student, said to have been written by a psychologist
who had conducted some tests several years earlier. The analysis was
not only outdated but contained no indication of the subject's academic
preference. Psychology students are taught that profiles of this sort
can be enormously inaccurate. The study, which follows, was intended
to provide them with nothing of practical value.
Tom W. is of high intelligence, although lacking in true creativity.
He has a need for order and clarity and for neat and tidy systems
in which every detail finds its appropriate place. His writing is
dull and rather mechanical, occasionally enlivened by somewhat corny
puns and flashes of imagination of the sci-fi type. He was a strong
drive for competence. He seems to have little feeling and little sympathy
for other people, and does not enjoy interacting with others. Self-centered,
he nevertheless has a deep moral sense.
is currently a graduate student. Please rank the following nine fields
of graduate specialization in order of the likelihood that Tom W.
is now a student in that field. Let rank one be the most probable
Humanities and Education
Physical and Life Sciences
Social Science and Social Work
the lack of substantive content, the graduate students should have
ignored the analysis entirely, and made choices by the percentage
of graduate students in each field Ã� information that had been provided
for them. It was assumed they would act upon the real data. At least,
according to the laws of normative probability, this was what was
expected of them. According to these laws, the more unreliable the
available information in a specific situation (called the case rate
Ã� in this example the profile of Tom W.), the more one should rely
on established percentages (called the base rate Ã� in this instance
the percentage of students enrolled in each field).
group look at the base rate percentages? No. This experiment and others
like it demonstrated that the students relied entirely upon the profile
and decided that computer sciences and engineering were the two most
probable fields for Tom W. to enter, even though each had relatively
few people in them. In spite of their training to the contrary, the
psychology students based their decisions on unreliable information,
ignoring the more pertinent data. Nonetheless they were confident
their decisions were made on the facts.
example in the stock market is the emphasis people put on the outlook
for each exciting initial public offering or concept stock (the case
rate), even thought the substantiating data is usually flimsy at best.
Still, investors rarely examine the high probability of loss in such
issues (the base rate). Instead, most buyers of hot IPOs in the 1980s
and 1990s focused on the individual story and forgot that over 80%
of these issues had dropped in price after the 1962 and 1968 market
breaks. Here again, the prior probabilities, although essential, were
the complexity and uncertainty in the investment situation, the less
emphasis you should place on your current appraisal, and the more
you should look to the rate of success or failure of similar situations
in the past for guidance.
way, rather than attempting to obtain every fact and sliver of information
about a difficult investment situation (much of which is contradictory,
irrelevant, and difficult to evaluate correctly), you should, if possible,
gauge the long-term record of success or failure of a particular course
rule could be applied to a broad number of investment situations.
For example, if you like a concept stock, you might take a cross-section
of favorites of other periods and see how they worked out; or if you
decide to try your hand at market timing, examine how well the system
you selected has worked over time.
instance, the information in the particular case being examined should,
where possible, be supplemented by evidence of the long-term record
of similar situations Ã� the base rate Ã� before making your decision.
to the Mean
The three previous cognitive biases, stemming from representativeness,
buttress one of the most important and consistent sources of investment
error. As intuitive statisticians, we do not comprehend the principle
of regression to the mean. Although the terminology sounds formidable,
the concept is simple. This statistical phenomenon was noted over
100 years ago by Sir Francis Galton, a pioneer in eugenics.
the height of men, Galton found that the tallest men usually had shorter
sons, while the shortest men usually had taller sons. Since many tall
men come from families of average height, they are likely to have
children shorter than they are, and vice versa. In both cases, the
height of the children was less extreme than the height of the fathers.
of this phenomenon gave rise to the term regression, which has since
been documented in many areas. The effects of regression are all around
us. In our experience, most outstanding fathers have somewhat disappointing
sons, brilliant wives have duller husbands, people who seem to be
ill adjusted often improve, and those considered extra-ordinarily
fortunate eventually have a run of bad luck.
to the mean, although alien to us intuitively, occurs frequently.
Take the reaction we have to a baseball player's batting average.
Although a player may be hitting .300 for the season, his batting
will be uneven. He will not get three hits in every ten times at bat.
Sometimes he will bat .500 or more, well above his average (or mean),
and other times he will be lucky to hit .125. Over 162 games, whether
the batter hits .125 or .500 in any dozen or so games makes little
difference to the average. But rather than realizing that the player's
performance over a week or a month can deviate widely from his season's
average, we tend to focus only on the immediate past record. The player
is believed to be in a "hitting streak" or a "slump." Fans, sportscasters,
and, unfortunately, the players themselves place too much emphasis
on brief periods and forget the long-term average, to which the players
will likely regress.
occurs in many instances where it is not expected and yet is bound
to happen. Israeli Air Force flight instructors were chagrined after
they praised a student for a successful execution of a complex maneuver,
because it was normally followed by a poorer one the next time. Conversely,
when they criticized a bad maneuver, a better one usually followed.
What they did not understand was that at the level of training of
these student pilots, there was no more consistency in their maneuvers
than in the daily batting figures of baseball players. Bad exercises
would be followed by well-executed ones and vice versa. Their flying
regressed to the mean. Correlating the maneuver quality to their remarks,
the instructors erroneously concluded that criticism was helpful to
learning and praise detrimental, a conclusion universally rejected
by learning theory researchers.
this work in the stock market? According to the classic work on stock
returns of Ibbotson and Sinquefield, then at the University of Chicago,
stocks have returned 10.5% annually (price appreciation and dividends)
over the last 70 years, against a return of about 5.6% for bonds.
An earlier study by the Cowles Commission showed much the same return
for stocks going back to the 1880s.
10-1 shows, however, the return has been anything but consistent Ã�
not unlike the number of hits a .300 career hitter will get in individual
games over a few weeks. There have been long periods when stocks have
returned more than the 10.5% mean. Within each of these periods, there
have been times when stocks performed sensationally, rising sometimes
50% or more in a year. At other times, they have seemed to free-fall.
Stocks, then, although they have a consistent average, also have "streaks"
the long-term rate of return of common stocks, like the batting average
of a ballplayer, is the important thing to remember. However, as intuitive
statisticians, we find it very hard to do so. Market history provides
a continuous example of our adherence to the belief that deviations
from the norm are, in fact, the new norm.
of 1927 and 1928 or 1996 and 1997 thought that returns of 30 to 40%
were in order from that time on, although they diverged far from the
mean. In 1932 and 1974, he believed huge losses were inevitable, although
they, too, deviated sharply from the long-term mean. The investor
of mid-1982, observing the insipid performance of the Dow Jones Industrial
Average (which was lower at the time than in 1965) believed stocks
were no longer a viable investment instrument.
Week ran a cover story, just before the Great Bull Market began in
July 1982, entitled "The Death of Equities." In 1987, after the Dow
had nearly quadrupled its level of 1982, I attended a dinner of money
managers just prior to the crash. The almost universal opinion at
the table was that stocks would go much higher. The table was right
Ã� for another ten days.
scenarios have been enacted at every major market peak and trough.
Studies of investment advisor buying and selling indicate that most
experts are closely tied, if not pilloried, to the current market's
movement. The prevalent belief is always that extreme returns Ã� whether
positive or negative Ã� will persist. The far more likely probability
is that they are the outliers on a chart plotting returns, and that
succeeding patterns will regress towards the mean.
mask the relevance of these long-term returns by detailed study of
a specific trend and by intense involvement in it. Even those who
are aware of these long-term standards cannot always see them clearly
because of preoccupation with short-term conditions.
that are extremely high or low should be treated as deviations from
long-term norms. The long-term return of the market might be viewed
like the average height of men. Just as it is unlikely that abnormally
tall men will beget even taller men, it is unlikely that abnormally
high returns will follow already high returns. In both cases, the
principle of regression to the mean will most probably apply, and
the next series of returns will be less extreme.
experts in the stock market are no more aware of the principles of
regression than anyone else, each sharp price deviation from past
norms is explained by a new, spurious theory. This, together with
other cognitive biases we will examine, leaves the investor vulnerable
to the fashions of the marketplace, however far removed prices may
be from intrinsic worth.
it Looks Good, It Must Taste Good
There is yet another powerful heuristic bias stemming from representativeness.
This is the intuitive belief that inputs and outputs should be closely
correlated. We believe, in other world, that consistent inputs allow
greater predictability than inconsistent ones. Tests have shown, for
example, that people are far more confident that a student will regularly
have B average if he has two B's rather than an A and a C, although
the belief is not statistically valid. Or if the description of a
company is very favorable, "a very high profit is most representative
of that description," and vice-versa. This fallacy usually leads to
consistent errors in the market.
application of this finding is the manner in which investors equate
a good stock with a rising price and a poor stock with a falling one.
One of the most common questions analysts, money managers, or brokers
are asked is, "if the stock is do good, why doesn't it go up?" or,
"if contrarian strategies are so successful why aren't they working
now?" The answer, of course, is that the value (the input) is often
not recognized in the price (the output) for quite some time. Yet
investors demand such immediate, though incorrect, feedback Ã� and
can make serious mistakes as a consequence.
interesting aspect of this phenomenon is that investors mistakenly
tend to place high confidence in extreme inputs or outputs. As we
have seen, Internet stocks in the mid Ã� 1990s were believed to have
sensational prospects (the input), which was confirmed by prices that
moved up astronomically Ã� as much as 10- or even 20-fold (the output).
The seemingly strong fundamentals went hand-in-hand with sharply rising
prices for HMO stocks in the mid-1980s or for the computer leasing
and medical technology stocks of 1968 and 1973. Extreme correlations
look good and people are willing to accept them as reliable auguries,
but as generations of investors have learned the hard way, they aren't.
thinking is applied to each crash and panic. Here the earnings estimates
and outlooks (the inputs) erode as prices drop. Graham and Dodd, astute
market clinicians that they were, saw the input-output relationship
clearly. They wrote "an evitable rule of the market is that the prevalent
theory of common stock valuations has developed in rather close conjunction
with the change in the level of prices."
immediate success invariable leads to playing the fads or fashions
currently performing well rather than investing on a solid basis.
A course of investment, once charted, should be given time to work.
Patience is a crucial but rare investment commodity. The problem is
not as simple as it may appear; studies have shown that businessmen
and other investors abhor uncertainty. To most people in the market
place, quick input-output matching is an expected condition of successful
Shark Attacks and Falling Airplane Parts
What is more likely cause of death in the U.S.: being killed by a
shark or by pieces falling from an airplane? Most people will answer
that shark attacks are more probable Shark attacks receive far more
publicity that deaths from falling plane parts, and they are certainly
far more graphic to imagine, especially if you've seen Jaws. Yet dying
from falling airplane parts is thirty times more likely than being
killed by a shark attack.
is an example of availability, a heuristic which causes major investor
errors. According to Tversky and Kahneman, this is a mental rule of
thumb by which people "assess the frequency of a class or the probability
of an event by the ease with which instances or occurrences can be
brought to mind".
most heuristic, or mental shortcuts, availability usually works quite
well. By relying on availability to estimate the frequency or probability
of an event, decision-makers are able to simplify what might otherwise
be very difficult judgments.
judgmental shortcut is accurate most of the time because we normally
recall events more easily that have occurred frequently. Unfortunately
our recall is influenced by other factors besides frequency, such
as how recently the events have occurred, or how salient or emotionally
charged they are. People recall good or bad events our of proportion
to their actual frequency. The chances of being mauled by a grizzly
bear at a national park are only one or two per million visitors,
and the death rate is lower. Casualties from shark attacks are probably
an even smaller percentage of swimmers in coastal waters. But because
of the emotionally charged nature of the dangers, we think such attacks
happen much oftener that they really do.
the occurrence of disaster, rather than their probabilities of happening,
that has an important impact on our buying of casualty insurance.
The purchase of earthquake and airline insurance goes up sharply after
a calamity, as does flood insurance.
result, the availability rule of thumb breaks down, leading to systematic
biases. The bottom line is that availability, like most heuristics,
causes us to frequently misread probabilities, and get into investment
difficulties as a result.
saliency, and emotionally charged events often dominate decision-making
in the stock market. Statements by experts, crowd participation, and
recent experience strongly incline the investor to follow the prevailing
1990's small-capitalization growth stocks rocketed ahead of other
equities. By early July 1996 this was almost the only game in town.
The experience is repeated and salient to the investor, while the
disastrous aftermath of the earlier speculation in aggressive growth
issues in the sixties, seventies, and eighties has receded far back
of recent and salient events to move people away from the base-rate
or long-term probabilities cannot be exaggerated. Time and again,
we toss aside our long-term valuation guidelines because of the spectacular
performance of seemingly sure winners. As psychologists have pointed
out, this bias is tenacious.
reflection shows that this judgmental bias reinforces the others.
Recent and salient events, whether positive or negative, strongly
influence judgments of the future. People, it appears, become prisoners
of such experience and view the future as an extension of the immediate
past. The more memorable the circumstances, the more they are expected
to persist, no matter how out-of-line with prior norms.
here is to keep your eye on the long-term. While there is certainly
no assured way to put recent or memorable experiences into absolute
perspective, it might be helpful during periods of extreme pessimism
or optimism to wander back to your library. If the market is tanking,
reread the financial periodicals from the last major break. If you
can, pick up The Wall Street Journal, turn to the market section,
and read the wailing and sighing of expert after expert in August
and September of 1990, just before the market began one of its sharpest
recoveries. Similarly, when we have another speculative market, it
would not be a bad idea to check the Journal again and read the comments
made during the 1979 to 1983 or 1991 to 1998 bubbles. While rereading
the daily press is not an elixir, I think it will help.
and Hindsight Biases
We might briefly look a two other systematic biases that are relevant
to the investment scene and tend to fix investment errors firmly in
place. They are also difficult to correct, since they reinforce the
others. The first is known as anchoring, another simplifying heuristic.
In a complex situation, such as the marketplace, we will choose some
natural starting point, such as a stock's current price, as a first
cut at its value, and will make adjustments from here. The adjustments
are typically insufficient. Thus, an investor in 1997 might have thought
a price of $91 was too high for Cascade Communications, a leader in
PC networking, and that $80 was more appropriate. But Cascade Communications
was grossly overvalued at $91 and dropped to $22 before recovering
bias is interesting. In looking back at past mistakes, researchers
have found, people believe that each error could have been seen much
more clearly, If only they hadn't been wearing dark or rose-colored
glasses. The inevitability of what happened seems obvious in retrospect.
Hindsight bias seriously impairs proper assessment of past errors
and significantly limits what can be learned from experience.
lunching with a number of money managers in 1991. They were bullish
on the market, which was moving up strongly at the time. One manager,
looking at the upsurge of financial stocks from the depressed levels
of 1990, asked rhetorically, "How could we not have bought the financial
stocks then?" In 1988, he asked the same question about other ultra
cheap companies after the much more damaging 1987 crash. He'll likely
ask it again after the next major surge.
bias too is difficult to handle. That walk to the library may be as
good a solution as any. I think you will see that the mistakes were
far less obvious than they appear today.
Biases and Market Fashions
Now, with some knowledge of decisional biases, we can understand why
the tug of fashion has always been so persistent and so influential
on both the market population and the expert opinion of the day.
the fashion, the experts could demonstrate that the performance of
a given investment was statistically superior to the other less-favored
ones in the immediate past, and sometimes stayed that way for fairly
bulbs appreciated sharply for seven years until 1637. A Dutch expert
in that year could easily show that for more than a decade tulips
had returned considerably more than buildings, shops, or farms. The
recent record was exciting, and rising prices seemed to justify more
of the same.
continually repeats itself. A buyer of canal bonds in the 1830s or
blue-chip stocks in 1929 could argue that though the instruments were
dear, each had been a vastly superior holding in the recent past.
Along with the 1929 Crash and the Depression came a decade-and-a-half
passion for government bonds at near-zero interest rates.
in good-grade common stocks again came into vogue in the 1959s and
1960s. By the end of the decade, the superior record of stocks through
the postwar era had put investing in bonds in disrepute. Institutional
Investor a magazine exceptionally adept at catching the prevailing
trends, presented a dinosaur on the cover of its February 1969 issue
with the caption, "Can The Bond Market Survive?" The article continued:
"In the long run, the public market for straight dept might become
of stocks occurred just as their rates of return were beginning to
decrease. Bonds immediately went on to provide better returns than
stocks. Of course, as we know, it happened all over again. In 1982,
the greatest bull market of the century for stocks began Ã� naturally
at the time institutional funds were stampeding out of equities. As
always after a major miscalculation, perceptions shifted radically.
Money managers once again tilted sharply towards stocks, with enormous
flows of new moneys pouring into equities in the past decade and a
the statistics on expert failure, we saw that the professionals tended
to play the fashions of the day, whatever they were. One fund manager,
at the height of the two-tier market in 1972/1973, noting the skyrocketing
prices of large growth stocks at the time, said that their performance
stood out "like a beacon in the night." Both the growth stocks and
the concept stocks were clobbered shortly thereafter.
market history provides convincing testimony about the ephemeral nature
of fashion, it has captivated generation after generation of investors.
Each fashion has its supporting statistics, the law of small numbers.
The fashions are salient and easy to recall and are, of course, confirmed
by rising prices Ã� the inputs and outputs again. These biases, all
of which interact, make it natural to project the prevailing trend
well into the future. The common error each time is that, although
the trend may have lasted for months, even for years, it was not representative
and was often far removed from the performance of equities or bonds
over longer periods (regression to the mean). In hindsight, we can
readily see the errors and wonder why, if they were so obvious, we
did not see them earlier.
biases, which are all interactive, seem to flourish particularly well
in the stock market and to result in a high rate of investor error.
We are too apt to look at insufficient information in order to confirm
a course of action, we are too inclined to put great emphasis on recent
or emotionally compelling events, and we expect our decisions to be
met with quick market confirmation. The more we discuss a course of
actions and identify with it, the less we believe prior standards
are valid. So each trend and fashion looks unique, is identified as
such, and inevitably takes its toll. Knowledge that no fashion prevails
for long is dismissed.
We find, then, that the information-processing shortcuts Ã� heuristics
Ã� which are highly efficient and timesaving in day-to-say situations,
work systematically against us in the market place.
in recent years has it been recognized that people simply don't follow
probability theory under conditions of uncertainty. The implications
of these cognitive biases are enormous, not only in economics, management,
and investments, but in virtually every area of decision-making. The
tendency to underestimate or ignore prior probabilities in making
a decision is undoubtedly the most significant problem of intuitive
prediction in fields as diverse as financial analysis, accounting,
geography, engineering, and military intelligence.
biases, which affect each of us to a greater or lesser extent, are
locked more firmly into place by the group pressures described earlier.
When our own cognitive biases are reinforced by the powerful influence
of experts and peer groups we respect, and who interpret information
in the same way, the pressure to follow becomes compelling.