Dow Theory

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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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