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Investment Analysis and Portfolio Management

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Investment Analysis & Portfolio Management (FIN630)
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Lesson # 10
TECHNICAL ANALYSIS Contd...
Dow Theory:
Charles Dow was one of the founders of Dow Jones & Co. (DJ, NYSE), publisher of The
Wall Street Journal. He is sometimes credited with being the inventor of the point and
figure chart. The Dow Theory holds that there are three components in the movement of
stock prices. The primary trend is the long-term direction of the market and is the most
important. The terms bull and bear originated with the direction of Dow Theory primary
trend. The secondary trend refers to a temporary reversal in the primary trend, one that does
not persist long enough to become the primary trend. Finally, daily fluctuations in the stock
price are meaningless and contain no useful information.
The Dow Theory is often illustrated by an ocean analogy. The tide is either is coming in or
going out ­ the primary trend. Even when the tide is going out, waves still wash ashore ­
the secondary. And as everyone who has ever spread a blanket on the beach knows,
sometimes for no apparent reason ripples from the waves reach far up the sand and soak
your belongings.
The Dow Theory is based on the price movement of the Dow Jones Industrial Average
(DJIA). Changes in the primary trend of the DJIA are confirmed by the Dow Jones
Transportation Average. The logic is that industrial firms make products, and transportation
companies ship them. When both averages are advancing, the economy is in good shape. An
explanation of the technical points of this famous market technique is available in the most
public libraries.
Surprising, Charles Dow had little to do with the development of this theory. The Wall
Street Journal, in fact, suggests that the entire field of technical analysis may have
originated from "the distortion and selective editing of Mr. Dow's ideas." While Dow were
believed highly correlated with the business cycle.
In Charles H. Dow and the Dow Theory, George W. Bishop, a financial historian, states,
"there is no evidence that Dow looked upon the averages as containing anything more than
an indication of statistical nature of the trend." There is also no evidence Dow ever
suggested prices would be predicted by interpreting charts. The term "Dow Theory" appears
to have first been used in a 1902 book by Samuel Armstrong Nelson entitled the ABC of
stock speculation.
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OLD PUZZLES & NEW DEVELOPMENT:
Fibonacci Numbers:
Fibonacci Numbers have intrigued mathematics and scientist for hundreds of years.
Leonard Fibonacci (1170-1240) was a medieval mathematician who discovered the series of
numbers while studying the reproductive behavior of rabbits. The beginning of the
Fibonacci series is shown below.
1,1,2,3,5,8,5,13,21,34,55,89,144,233,......
After the initial pairs of ones, each succeeding number is simply the sum of the previous
two.
The remarkable thing about these numbers is the frequency with which they appear in the
environment. Sunflowers have seeds spirals around the center of the plant. Some spirals
contain seeds leaning counterclockwise, with other spirals going the other way. On most
sunflowers, the number of clockwise spirals and the number of counter clockwise spirals are
adjacent Fibonacci numbers. A blossom might have 34 counterclockwise spirals and 55
clockwise spirals. The structure of pine cones, the number of chambers in a nautilus
seashell, the topology of spiraling galaxies, and the ancestry of bees all reveal Fibonacci
numbers. Even a professional journal, the Fibonacci quarterly, is devoted to the study of this
series.
Technical Analyst who follows Fibonacci numbers usually makes use of the number 1.618.
This number is called the golden mean and appears in ancient writings and architecture.
(The golden mean features prominently in the dimensions of the Parthenon). After the first
10 or so numbers in the series, each Fibonacci number divided by its immediate predecessor
equals 1.618. For example, 89/55=1.618; 134/89= 1.618 and so on. This magic number is
used to calculate Fibonacci ratios.
Many Fibonacci advocates in the investment business use the first two ratios, 0.382 and
0.618, to "compute retrenchment levels of a previous move." For instance, a stock that falls
from $50 to $35(a 30 percent drop) will encounter resistance to further advances after it
recoups 38.2 percent of its loss (that is, after it rises to $40.73).
Some technical analysts keep close tabs on resistance and support levels as predicted by the
Fibonacci ratios. Even people who do not subscribe to this business know that many other
people do, and that when stock prices approach important Fibonacci levels, unusual things
can occur.
Fibonacci numbers occur frequently and inexplicably in nature.
Kondratev Wave Theory:
Nikolay Kondratev was a Russian economist and statistician born in 1892. He helped
develop the first soviet five-year plan. From 1920 to1928 he was Director of the study of
business activity at the Timiriazev Agriculture Academy. While there he devoted his
attention to the study of Western capitalist economies. In the economies of Great Bertain
and United states, he identified long term business cycles with a period of 50 to 60 years.
He became well known after the U.S. crash of 1870. His hypothesis of a long-term business
cycle is the called Kondratev wave theory.
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Note that the market crashof 1987 occurred 58 years after the crash of 1929, a period
consistent with Kondratev's theory. Some modern economists believe that significant
macroeconomics changes, such as floating exchange rates, the elimination of the gold
standard, and the reduction of barriers to free trade, make the business cycle less
predictable. Still, many market analysts consider Kondratev's work in their assessment of
the stock market and its risks.
Nikolay Kondratev made the mistake of criticizing Stalin openly. For his crimes, he was
executed in 1928. He was posthumously cleared of all charges in 1987, the year of the most
recent market crash.
Kondratev wave theory states there is a 50- to 60-year business cycle.
Chaos Theory:
At recent finance conferences, a few researchers have presented papers on chaos theory
and its application to the stock market. In physics, chaos theory is a growing field of study
examining instances in which apparently random behavior is, in fact, quite systematic or
even deterministic. Scientists apply this theory to weather prediction, population growth
estimates, and fisheries biology.
As an example of the later application, a given volume of ocean water, left free from human
interference, will not necessarily reach an equilibrium population of the various species that
inhibit in it. As fish grow, they consume the smaller fry (of their own or different species) in
increasing numbers. Fewer young fish are left to mature; this couple with the natural death
of the older fish, eventually results in a sudden drastic reduction in fish population, causing
dismay to fisherman and excitement in the local media. At the same time, it results in
reduced predation and food competition by the surviving fry, so the population begins to
grow dramatically, and the cycle continues. Interactions between species add complexity to
the process.
Investment analysts have sought a pattern in stock market behavior since the origin of the
exchanges. Many remains unknown about how security prices are determined and chaos
theory may eventually provide some partial answers. If the apparent randomness of security
price changes can be shown to be nonrandom, much of the theory of finance would need
revision.
Chaos theory sees systematic behavior amidst apparent randomness.
Neural Networks:
A neural network is a trading system in which a forecasting model is trained to find a
desired output from past trading data. By repeatedly cycling through the data, the neural net
work eventually learns the pattern that produces the desired output. If the desired output
remains elusive, more data is included until a pattern is found. Neural networks may also
include a feedback mechanism whereby experience is gained from past errors.
This topic is a hot one in the investment community. National conferences have been
organized dealing exclusively with the topic, and the trade literature publishes many articles
on the topic. A problem with the concept of a neural network is that the stock market is
seldom deterministic. Situations constantly change, and what may have been true a few
years ago will not necessarily prevail tomorrow. Financial academics are especially leery of
backtests, or research that tests a hypothesis using past data. Mining the data will almost
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always result in some apparent cause and effect between past events and stock market
performance. Research that tests a hypothesis using a subsequent data is much more useful.
An article in the popular press describes Wall Street's response to this criticism:
One way to get around this hazard is to build something called a genetic algorithm into your
neural network. A sexy term that currently causes Wall Street rocket scientists to swoon,
genetic algorithms enable neural nets to adapt to the future by spawning schools of baby
nets, each of which is sent to swim against the changing flow of data, Where only the fittest
survive to take over the role of the mother.
No matter what someone's field of study, they are interested in the search for a better
mousetrap. Essentially, what all security analysts seek to do is find improvements in their
methodology for security selection.
Indicators of the Witchcraft Variety:
Even in this era of political correctness, some indicators are less worthy than others. If there
is no logical connection between what an indicator measures and what it purports to show,
the indicator probably should not receive much study time. A few such indicators are well
established in market folklore, and while they may have no logical place in the investment
decision-making process, an awareness of them is helpful.
The Super Bowl Indicator:
This well-known market statistic will bring a smile to the face of many investment
professionals when asked about it. The super bowl indicator states that the stock market
will advance the following year if the super bowl football game is won by a team from the
original National Football League (NFL). This indicator was correct 28 of 33 times over the
period 1967 through 1999. Such a percentage might seem unlikely to have occurred by
chance.
There is a statistical problem with this indicator, however. For one thing, there are more
original NFL teams that there are teams in the other conference, the American Football
Conference (AFC). The Indianapolis Clots, Pittsburgh Steelers, and Cleveland Browns (all
AFC teams) are original NFL teams. Couple this information with the fact that the stock
market rises more often than it falls and the odds favor the indicator.
Few people admit to being persuaded by the super bowl indicator; most will agree it is
unlikely that any true cause-and ­effect relationship exist between the game and the market.
Still, many professional investment managers and individual investors alike subconsciously
root for the NFL team, just in case.
Sunspots:
The public began to associate sunspots with the stock market through five works of William
Stanley Jevons published between 1862 and 1897. While the notion of using the eleven-year
solar cycle as a forecasting device has few advocates today, it was the focus of much
discussion 100 years ago.
Jevons found that rainfall and temperature appeared to be related to solar activity:
The success of the harvest in any year certainly depends upon the weather, especially that of
the summer and autumn months. Now if this weather depends upon the solar period, it
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follows that the harvest and the price of grain will depend more or less the solar period, and
will go through periodic fluctuations in periods of time equal to those of the sun spots.
The essence of his history is that increased sunspot activity leads to warmer temperatures
and more rain, leading to an improved harvest and a stronger economy, and finally to higher
stock prices. He tested this theory on English grain prices between 1259 and 1400. Jevons
observed a ten-to eleven-year cycle in the money market and believed this might be, at least
in part, because of the solar influence on crops and the economy.
Hemline Indicator:
Like the super bowl indicator, the hemline indicator is market folklore that few people take
seriously, but many like to talk about it. The essence of the hemline indicator is this:
As shorter dresses for women become the fashion, the market advances, and vice versa.
Simultaneously plotting skirt lengths and market levels reveals a remarkable correlation. In
the 1920s the market rose and so did hemlines. During the Great Depression, dresses
touched the ground. There was gradual rise in the market and in hemlines through World
War II; the rest of the forties and the fifties peaked in the go-go days of 1960s with
miniskirts. The 1970s saw peasant dresses and mixiskirts and an economic recession.
During the prosperity of the 1980s things moved back up. During one stretch in the early
1990s the market was nearly flat for over a year. What was the dress fashion? Slits on the
side of skirts presumably the marked did not know what to make of them.
All these "indicators," of course, are likely to be purely spurious correlations. What
economic cause and effect could possibly be at work? The lack of an economic
underpinning is the reason technical indicators of this type are called witchcraft.
Breadth Indicators:
The Advance-Decline Line (Breadth of the Market):
The advance-decline line measures, on a cumulative daily basis, the net difference between
the number of stocks advancing in price and those declining in price for group of stocks
such as those on the NYSE. Subtracting the number of declines from the number of
advances produces the net advance for a given day (which, of course, can be negative). This
measure may include thousands of stocks.
The advance-decline line, often referred to as the breadth of the market, results from
plotting a running of these numbers across time. The line can be based on daily or weekly
figures, which are readily available from daily newspaper such as The Wall Street Journal.
The advance-decline line is compared to a stock average, in particular in DJIA in order to
analyze any divergence that is, to determine whether movements in the market indicator
have also occurred in the market as a whole. Technicians believe that divergence can signal
that the trend is about to change.
The advance-decline line and the market averages normally move together. If both are
rising (declining), the overall market is said to be technically strong (weak). If the advance-
decline line is rising while the market average is declining, the decline in the market
average should reverse itself. Particular attention is paid to a divergence between the two
during a bull market. If the market rises while the line weakens or declines to reverse itself
and start declining.
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New High and Lows:
Part of the information reported for the NYSE and other stocks is the 52-week high and low
prices for each stock. Technicians regard the market as bullish when a significant number of
stocks each day hit 52-week highs. On the other hand, technicians see rising market indexes
and few stocks hitting new highs as a troublesome sign.
Volume:
Volume is an accepted part of technical analysis. High trading volume, other things being
equal, is generally regarded as a bullish sign. Heavy volume combined with rising prices is
even more bullish.
Sentiment Indicators:
Short-Interest Ratio:
The short interest for a security is the number of shares that have been sold short but not yet
bought back. The short interest ratio can be defined relative to shares outstanding or average
daily volume, as in;
Short interest ratio = Total shares sold short / Average daily trading volume
The NYSE, Amex and NASDAQ report the short interest monthly for each stock. The
NYSE and Amex indicate those securities where arbitrage or hedging may be important, but
the significant of these activities cannot be determined. For investors interested in th short
interest, each month. The Wall Street Journal reports NYSE and Amex issues for which a
short interest position of at least 100,000 shares existed or for which a short position change
of 50,000 shares occurred from the previous month. A list of stocks with the largest short
interest ratios broken down by exchange can be found at www.trading-ideas.com
In effect, the ratio indicates the number of days necessary to "work off" the current short
interest. It is considered to be a measure of investor sentiment, and many investors continue
to refer to it.
Investors sell short when they expect prices to decline; therefore, it would appear the higher
the short interest, the more investors are expecting a decline. A large short interest position
for an individual stock should indicate strong negative sentiments against a stock.
Many technical analysts interpret this ratio in the opposite manner as a contrarian indicator
a high short interest ratio is taken as bullish sign, because the large number of shares sold
short represents a large number o shares that must be repurchased in order to close out the
short sales. In effect, the short seller must repurchase regardless of whether or not his or her
expectations were correct. The larger the short interest ratio, the larger the potential demand
that is indicated. Therefore, an increase in the ratio indicates more "pent-up" demand for the
shares that have been shorted.
The short interest ratio for a given method should be interrupted in relation to historical
boundaries, which historically were in the range of 1 to 2 for the NYSE. The problem is that
the boundaries keep changing. In the 1960s, 1970s and 1980s, a ratio of 2 was bullish. More
recently, the ratio has been in the 3 to 6 range regardless of the market.
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Mutual Fund Liquidity:
Several indicators are based on the theory of contrary investing. The idea is to trade
contrary to most investors, who supposedly almost always lose. This is an old idea on Wall
Street, and over the year technicians have developed several measures designed to capitalize
on this concept. As mentioned above, the short interest is often used as a contrarian
indicator, with high short levels in a stock viewed as being overly pessimistic.
Mutual fund liquidity can be used as a contrary opinion technique. Under this scenario,
mutual funds are viewed I a manner similar to odd - lotters, that is, they are presumed to act
incorrectly before a market turning point. Therefore, when mutual fund liquidity is low
because the funds are fully invested, contrarian believes that the market is at, or near a peak.
The funds should be building up cash (liquidity); instead, they are extremely bullish and are
fully invested. Conversely, when funds hold large liquid reserves it suggests that they are
bearish. Contrarians would consider this a good time to buy, because the market may be at,
or near, its low point.
The Opinions of Investment Advisory Newsletter:
Investors' intelligence an investment advisory service, samples weekly the opinions of
about 150 investments advisory services and calculates an index of investments service
opinions. It has found that on average, these services are most bearish at the market bottom
and last bearish at the market top. This index, published since 1963, is now available
weekly and is widely quoted in the investing community.
The "bearish sentiment index" is calculated as the ratio of advisory services that are bearish
to the total number with an opinion. When this index approaches 55 or 60 percent, this
would indicate a bearish indicate a bearish attitude on the part of investment advisory
services. As this ratio approaches 20 percent, the opposite occurs. Thus, a contrarian should
react in the opposite direction of the sentiment this ratio is exhibiting. As the ratio nears 60
percent, the contrarian becomes bullish, because a majority of the investment advisory
services are bearish, and around 20 percent the contrarian becomes bearish, because most of
the investment advisory services are not bearish.
The reason for this seeming contradiction to logic that investment advisory services are
wrong at the extremes is attributed to the fact these services tend to follow trends rather than
forecast them. Thus, they are reporting and reacting to what has happened rather than
concentrating on anticipating what is likely to happen.
The Future of Technical Analysis:
Although there is much in finance that we do not completely understand, technical analysis
has persisted from more than 100 years, and it is not likely to disappear from the investment
scene anytime soon. Improved quantitative methods coupled with improved behaviorist
Werner De Bondt, for instance, recently reported substantial evidence that the public
expects the continuation of past price trends. That is, they are bullish in bull markets and
pessimistic in bear markets.
Technical analysis is a controversial topic. While it currently has little standing in the
academic literature, a great deal about price movements has yet to be discovered.
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Technical analysts like to use charts. They believe that supply and demand determines
security prices, that changes in supply and demand cause prices to change, and that charts
can be used to predict changes in supply and demand and in investor behavior. Popular
types of charts are the line chart, bar chart point and figure chart, and the candlestick chart.
Technical indicators are measure of economic and non-economic activity that purportedly
have a relationship to subsequent market behavior. Some of these indicators, such as the
mutual fund cash position or short interest ratio, have economic underpinnings, while others
(the super bowl or hemline indicators) do not. All are part of market folklore.
Fibonacci numbers are inexplicably common in nature. Some people believe that the
Fibonacci series is helpful in predicting changes in security trading patterns. Popular areas
of research today among technical analysts include chaos theory (the search for patterns in
randomness) and neural networks (the notion that computer algorithms can be taught to look
for optimum patterns).
Conclusions about Technical Analysis:
Technical analysis often appeals to those who are beginning a study of investments, because
it is easy to believe tat stock prices form repeatable patterns over time or that certain
indicators should be related to future market price movements. Most people who look at a
chart of a particular stock will immediately see what they believe to be patterns in the price
changes and clear evidence of trends that should have been obvious to anyone studying it.
On the one hand, academicians are highly skeptical of technical analysis, to say the least.
Most academics discussions at the college level dismiss, or seriously disparage, this
concept. A primary reason is that thorough tests of technical analysis techniques typically
fail to confirm their value, given all costs and considering an alternative, such as a buy-and-
hold strategy.
In addition to these reasons, other troubling features of technical analysis remain. First,
several interpretations of each technical tool and chart pattern are not only possible but
usual. One or more of the interpreters will be correct (more or less), but it is virtually
impossible to know beforehand who these will be. After the fact, we will know which
indicator or chart or whose interpretations was correct, but only those investors who used
that particular information will benefit. Tools such as the Dow Theory are well known for
their multiple interpretations by various observers who disagree over how the theory is to be
interpreted.
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