A technical approach to trend analysis practical trade timing for enhanced profits

Introduction: Defining the Trend Chapter 1 The Theory of Trends—Dow, EMH, and RMH in Context A Set of Assumptions about Short-Term Trends The Beginnings of Trend Analysis: The Dow Theory

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A Technical Approach

to Trend Analysis Practical Trade Timing for Enhanced Profits

Michael C Thomsett

Publisher: Paul Boger

Editor-in-Chief: Amy Neidlinger

Executive Editor: Jeanne Glasser Levine

Operations Specialist: Jodi Kemper

Cover Designer: Alan Clements

Managing Editor: Kristy Hart

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Introduction: Defining the Trend

Chapter 1 The Theory of Trends—Dow, EMH, and RMH in Context

A Set of Assumptions about Short-Term Trends

The Beginnings of Trend Analysis: The Dow Theory

The Dow Theory Applied

Other Price Theories: EMH

Types of EMH in Theory

The Bubble Effect

Other Price Theories: RWH

Trend Analysis as a Risk-Management Process

Chapter 2 Statistically Speaking—Trends by the Numbers

Fat Tails and Trends

Statistical Tendencies

Trends and Averages

Trends Versus Price

Strengths and Weaknesses of Trends

Pattern Cycles

Market Sentiment Expressed in the Trend

Momentum Trading

Statistical Measurements and Trend Behavior Distinguished

Spikes and How to Manage Them

After the Spike—Breakouts and Reversals

Statistical Analysis of Fundamentals

Game Theory Applied to Trend Analysis

Magical Thinking and Trends

Chapter 3 Resistance and Support—A Trend’s Moment of Truth

Tests of Breadth

The Nature of Resistance and Support

The Channeling Trading Range

Reaction High and Low Prices

The Bouncing Price in a Trend

The Flip

Wedge-Shaped Trends

Triangle-Shaped Trends

Support and Resistance Zones

Breakouts as Signals of Supply and Demand Adjustment

Chapter 4 Trendlines and Channel Lines—The Shape of Things to Come

Signal Patterns Versus Trends

Trendlines and What They Reveal

Price Increments on Charts

Trend Angles

Internal Trendlines

Validation of the Trend

Retracement Versus Reversal

Fibonacci Retracement

Channel Line Types

Chapter 5 Reversal Patterns—End of the Trend

The Dilemma: Minor or Major Reversal

Reversal Versus Consolidation

The Time Element: Momentum of Reversal

Reversal in Western Patterns

Reversal in Eastern Patterns

Divergence and Its Role in Reversal Trends

Breakouts and Proximity to Resistance or Support

Chapter 6 Continuation Patterns—A Bend in the Trend

Continuation and Its Relationship to Reversal

Western Continuation Signals

Eastern Continuation Signals

Chapter 7 Confirmation Signals—Turning the Odds in Your Favor

The Causes of Price Movement

Behavioral Psychology and the Market

The Flaw of Overconfidence

Resistance and Support as Keys to Confirmation Proximity

Strong and Weak Confirmation

Momentum and Timing of Preceding Trends

Divergence Analysis and Confirmation

Fundamental Analysis and Confirmation

Confirmation Bias

Chapter 8 Consolidation Patterns—The Sideways Pause

Consolidation and Its Meaning

Resistance and Support as Keys to Consolidation Reading

The Triangle Breakout

Volume Spikes and Gaps

Breakout Signals

Consolidation Plateaus

The Bollinger Squeeze

Chapter 9 Volume Signals—Tracking Price Trends

How Volume Confirms Trends

Confirmation Trends with Volume

Trends with Volume-Marked Breakouts

Trend Climax and Gap Patterns

On Balance Volume (OBV)

Accumulation/Distribution (A/D)

Money Flow Index (MFI)

Chaikin Money Flow (CMF)

Chaikin Oscillator

Chapter 10 Mind the Gap—When Price Jumps Signal Change

The Nature of Gaps

Gaps Filled or Unfilled

Gap Up and Gap Down

Gaps as Part of Other Signals

Gap Proximity to Resistance or Support

Chapter 11 Moving Averages—Order in the Change

Two Moving Averages

Resistance and Support

Chapter 12 Momentum Oscillators—Duration and Speed of a Trend

The Nature of Momentum

Relative Strength Index (RSI)

Moving Average Convergence Divergence (MACD)Stochastic Oscillator

Chapter 13 Volatility—Marking Risk within the Trend

Calculating Volatility

Volatility Indicator

Evolving Volatility Levels

Average True Range (ATR)

Volatility According to the VIX

Chapter 14 Fundamentals—Connecting the Two Sides

Value Versus Growth

The Concept of Fundamental Volatility

Dividend per Share and Increased Dividends

P/E Ratio

Revenue and Earnings

Debt/Equity Ratio

Comparing Fundamental Trends to Technical Trends

Chapter 15 Overview—Putting It All Together

Moving from Downtrend to Consolidation

Secondary Trend Volatility

Large Price Move Ending Primary Trend

Primary Trend with Secondary Trend

Consolidation Primary Trend with Failed BreakoutsConclusion

Endnotes

Bibliography

Index of Topics

Index of Companies

Many thanks to all of the excellent staff at Pearson Education and FT

Press, notably Executive Editor Jeanne Glasser Levine, whose long-timesupport for this and many other projects means so much; also thanks to

Editor-in-Chief Amy Neidlinger, Managing Editor Kristy Hart, and a specialnote of deep thanks to Betsy Gratner, Senior Project Editor, who workedclosely with me during production Finally, I extend my gratitude to all of thereaders who have written to me with expressions of appreciation for the

books I have written with FT Press

About the Author

Michael C Thomsett is the author of more than 80 books, including many

FT Press projects (Profiting from Technical Analysis and Candlestick

Indicators, Stock Profits: Getting to the Core, Put Option Strategies, The Options Trading Body of Knowledge, Options Trading for the Conservative Investor, Options Trading for the Institutional Investor, and Trading with Candlesticks) He also has written several other books on the topics of

technical analysis, candlesticks, and options trading Thomsett is the

cofounder of the education site ThomsettOptions.com, where he publishesarticles on the topics of fundamental and technical analysis, chart reading,and more He is a frequent speaker at investment and trading conventions andtrade shows, and he teaches several classes for Moody’s and the New YorkInstitute of Finance Thomsett lives in Nashville, Tennessee

Introduction: Defining the Trend

Efficiency or randomness? What defines the market?

Experienced professional traders realize that the market is neither efficientnor random Even the Dow Theory, the basis of traditional technical analysis,does not agree on identification of changes in primary trends The meaning oftrends is debated endlessly among technicians Is a change in direction a newprimary trend, a secondary trend, or merely a retracement? The debate isceaseless and there appears to be more disagreement than agreement on thebasic question of how trends behave

In this uncertain trading environment, how do professional traders manageeffectively? This book offers methods for trend analysis based on a few

sound principles These include the essential observation of the trading range;reversal, continuation, and consolidation; confirmation methods; gaps; andnon-price signals confirming or forecasting changes in the current trend.Every experienced trader who relies on a short list of reversal and

continuation signals, who understands how chart analysis is performed, andwho wants to recognize changes in the price pattern already understands howuncertain a trend can be and how difficult it is to quantify signals in the

moment Every trader deals with conflicting and contradictory signals andmay easily overlook the larger picture of movement in the trend

These movements may be simplified and classified as reversal,

continuation, or consolidation However, this identification is never 100

percent clear or precise Experienced traders may not be certain about thecurrent status of individual stock trends, even with an advanced level of

knowledge And those who do know also understand that the current status of

a trend is likely to change at any moment A trend in an individual stock islikely to be easier to track and predict than a trend in an index The indexcontains many different stocks, so the trend is itself the sum of net increasesand decreases in price levels for all of the components Furthermore, theindex itself, such as the Dow Jones Industrial Average—the favorite gauge ofthe market—may be weighted so that a few stocks account for a large portion

of a total trend movement This makes trends of indexes less certain Eventhough many stocks track the market closely, this book focuses on individualstock trends In these cases, it is more reliable to associate trend activity with

both fundamental and technical causes and responses.

This book is intended as a serious study of trends for experienced investorsand traders These individuals know how trends behave but also need to

solidify the analytical tools for trend analysis There are no simple answers topredicting trend direction, strength, or duration However, specific tools

technicians favor can be used in combination to anticipate trend reversal orcontinuation, and to confirm those moves

Chapter 1, “The Theory of Trends—Dow, EMH, and RMH in Context,”reviews the basic theories about trends and examines whether or not thosetheories offer reliable intelligence traders can use to time entry or exit

Chapter 2, “Statistically Speaking—Trends by the Numbers,” expands thatdiscussion by introducing statistical observations traders might use to

improve accuracy of both trend analysis and price pattern analysis Chapter 3,

“Resistance and Support—A Trend’s Moment of Truth,” provides in-depthanalysis of how resistance and support play an essential role in trend analysisand how these trading range borders may be used to test the strength of thetrend Chapter 4, “Trendlines and Channel Lines—The Shape of Things toCome,” expands on the discussion with a study of trendlines and channellines

Chapter 5, “Reversal Patterns—End of the Trend,” and Chapter 6,

“Continuation Patterns—A Bend in the Trend,” are exhaustive studies ofreversal and continuation patterns, and Chapter 7, “Confirmation Signals—Turning the Odds in Your Favor,” provides the same in-depth analysis ofconfirmation In Chapter 8, “Consolidation Patterns—The Sideways Pause,”the nature of consolidation is examined and its effect on trends Chapter 9,

“Volume Signals—Tracking Price Trends,” takes a look at volume In

Chapter 10, “Mind the Gap—When Price Jumps Signal Change,” gaps

describe how trend movement can be anticipated in the near future and howthese might be revealing or confusing Chapter 11, “Moving Averages—Order in the Change,” examines the role of loving averages and how theseimpact and anticipate changes in trends In Chapter 12, “Momentum

Oscillators—Duration and Speed of a Trend,” momentum oscillators areexamined and how they affect not only price, but also the larger trends

Chapter 13, “Volatility—Marking Risk within the Trend,” addresses the topic

of volatility in the trend, and Chapter 14, “Fundamentals—Connecting theTwo Sides,” shows how fundamental trends contribute to technical trends

Wrapping up the entire discussion, Chapter 15, “Overview—Putting It AllTogether,” puts together multiple indicators to track how trends continue andchange over time.

A distinction has to be made throughout this book between price patternsand trend attributes The study of price charts is normally focused on short-term trends and likely reversal or continuation This is based primarily onpatterns found in candlestick charts or in application of well-known technicalsignals The key here is that price analysis is short term However, beyondthose day-to-day and week-to-week analyses and swing-trading decisions, thelonger-term trend might be revealing in many more ways than the price trendcan possibly provide For example, in a short-term price trend, assumed

levels of resistance and support and, most notably, violations above

resistance or below support, often are used as the basis for timing of trades.And in fact, movement through these all-important price levels is invariablythe point at which reversal or continuation signals have the greatest meaning.However, there is a problem in basing decisions on resistance and supportthat are short term in nature

These levels may exist momentarily, but the bigger picture is found in howresistance and support provide structure for a longer-term trend In terms oftechnical trading, this can mean a matter months rather than of days or weeks.However, the reliable identification of resistance and support (as well as othertrend attributes) becomes reliable only when the chart looks at this biggerpicture So, a few standards are applied in this book with these concerns inmind First, analysis of trends is focused on individual stocks and not as

much on index or marketwide movement Second, trends are studied as

longer-term (three months or more), a departure from the swing-trading

approach based on price patterns and identification of reversal signals as aprimary signal The degree to which reversal and continuation signals areanalyzed is based not on the immediate price pattern, but on how the trendbehaves over time The concept here is that traders expect short-term pricemovement to be chaotic and fast, but longer-term trends often are far morereliable in terms of where prices are heading This is reflected in the trendand articulated by the technical analyses described in upcoming chapters.Even though nothing can ever be 100 percent certain or clear, the toolspresented in this book will help to improve confidence in timing of trades andalso in longer-term decisions to buy, hold, or sell shares of stock The

quantification of “confidence” may be described as existing between 50percent (random likelihood of a trend moving upward or downward) and 100percent (certainty of what will occur next) The study of a trend will alwaysfall somewhere in between these levels, never quite falling to a completelyrandom 50 percent, and never rising all the way to 100 percent However, inthat range, you will be able to define confidence in degrees that help manage

a portfolio of equities and to determine levels of risk For trend analysis, riskcan be defined as a level of confidence in the current policy For example, ifyou hold stock that has appreciated over several months, where does yourconfidence reside today? Is the trend continuing or leveling out? What dothese patterns mean in terms of confidence?

This theory of portfolio management—basing concepts of risk on levels ofconfidence in the current trend—might help you improve timing not only ofentry, but also of exit from a current position This can be thought of not asswing trading in the short term, but of risk management for the long-termportfolio It all relies on the trend

1 The Theory of Trends—Dow, EMH, and RMH in Context

Trends exist without doubt But what is the nature of the trend? Some

theories suggest the market is efficient, and others say it is random Some say

it is both efficient and random The debate is not settled by any means

This becomes confusing because not everyone will agree on the definition

of the trend itself A trend is the direction of movement or change in an

observed value In terms of stock charts, this usually refers to price But is theduration of the change important as well? Every trader has to decide whether

to adopt a short-term outlook, such as that of the day trader and swing traderwho rely on fast price changes, or a long-term outlook based on the study oflonger-term trends A basic statistical observation is that the longer the timeperiod studied, the more reliable the observation In other words, you cannotestablish a trend by the price action of a few days; however, with the priceaction of a few months, the trend and its properties (such as resistance andsupport) become clear

Key Point

A trend is the action of price in a specific direction, which lasts until a

change in that direction occurs

A Set of Assumptions about Short-Term Trends

When you look only at price and attempt to anticipate which direction itwill take next, you have to operate on a set of assumptions The greatest ofthese is that price acts and reacts within the current trend If you do not

recognize a trend, then the price is truly random Some stocks are both

volatile and unclear about direction, which makes any kind of trade timingboth difficult and risky However, this is often a short-term problem, whereaslonger-term trend analysis is likely to identify clear trends characterized byshort-term chaotic and random movement with overall identifiable direction.The second assumption is that price movement is a reflection of supply anddemand in the market Although this is true over the long term, short-termprice movement is likely to be characterized by reaction to any number of

information pieces, including fact and rumor, fundamental and technicalclues, and investor behavior (which, in the short term, is often irrational andcrowd-following in nature) So for short-term price analysis, relying on

supply and demand might not be reliable; it is more likely to be chaotic andirrational in nature

A third assumption is that trends tend to continue for some length of time.However, the actual time involved varies and is not predictable, so it is ill-advised to attempt to recognize a trend and settle in on the assumption thattime is on the side of the trend This is not necessarily the case This is whytrend analysis has to include the likelihood that an emerging signal can

foreshadow the end of the trend and a reversal of price movement, and thatthis reversal can be caused by a variety of emerging factors, including supplyand demand and much more Whether a change in the trend is caused by aflip in supply and demand or a less rational market belief about a company orits stock price, the change is a reality no matter what underlying

fundamentals are at play The technical aspects of the trend (price patterns,volume, moving averages, and momentum) are based on a variety of rationaland irrational influences This is why you need tools for trend analysis; ifprices were truly efficient in how they react to fundamental news, the marketwould not only be efficient, but it would also be predictable In fact,

“efficiency” as used in observation of stock prices refers to the speed of

response to information, both true and false, and not to the efficiency of price

as an accurate measure of value Markets might be quite efficient in responsetime, while making no distinction between responses to either type of

information

A fourth assumption concerns market behavior The inefficiency of

markets is easily demonstrated by a study of earnings surprises and resultingstock price behavior Stock prices fail to account for earnings’ surprises andoften overlook the effects of optimistic beliefs about stocks (especially

growth stocks) These factors distort and might even lower net returns whenearnings do not perform as expected.1

A fifth assumption, notably among contrarian investors, is that the majority

of market traders and investors overreact to surprises and uncertainties in themarket As a consequence, they tend to add greater meaning to the latestnews and assign less meaning to older (and perhaps more reliable)

information Contrarians time decisions not merely to contradict what most

market participants are doing, but more likely to act based on a different set

of criteria Most traders time and enter both buy and sell trades as a gut

reaction to surprises, and in the extreme, traders will trade based on greed(when prices have risen) or panic (when prices have fallen) The contrarian,

in comparison, tends to enter trades based on recognition of exaggeratedprice movement, especially following earnings’ surprises This is done in theknowledge of a likely correction of that overreaction within a matter of a fewdays

Key Point

Trend behavior is a reflection of market behavior; short-term price

movement often is an overreaction to today’s news

For example, on January 16, 2015, Suntrust Banks (STI) reported earnings

of $394 million, down from $426 million in the prior year’s quarter; andearnings fell from 77 cents down to 72 cents per share This negative surprisecaused the stock to drop approximately 5 percent in two days However, pricerebounded in the immediate sessions following the drop This is typical ofprice behavior and demonstrates the contrarian advantage The initial

response to a negative earnings’ surprise was a substantial drop in price, butthat was immediately corrected So a contrarian acting on knowledge of thebehavior would be likely to take bearish trade action on January 16 and thenclose the position to take profits on January 19 or 20 when the price had

corrected the overreaction This price movement is summarized in Figure 1.1

Figure 1.1 Reaction to earnings’ surprises (Chart courtesy of

StockCharts.com)The apparent price action is typical of short-term movement, especially inreaction to earnings’ surprises A positive surprise would be expected tobehave in the same manner, except with prices moving upward in an

overreaction and then correcting in following sessions

Testing for this price behavior is difficult for short-term price action,

whereas longer-term trend analysis discloses far more reliable patterns,

including reversal or continuation A basic error made by many traders is toassign too much value to the short-term trend, and this begs the questionabout the value of studying longer-term trends The assumption many tradershold is that of serial correlation, the belief that today’s price trends correlatewith or mirror previous price changes This is not the case The serial

correlation assumption has served as the basis for criticism of technical

analysis in support of a random theory about the markets However, it isapplicable as a criticism only to the degree that traders act on the assumption

of serial correlation An enlightened trader or investor, especially a

contrarian, rejects this assumption and acknowledges that a current pricepattern and activity within an existing trend are separate and apart from prior

The Beginnings of Trend Analysis: The Dow Theory

The entire science of trend analysis began with Charles Dow A reporter,

he gained attention when he published a series of articles in the Providence

Journal He moved to New York and established Dow Jones & Company

with his partner Edward Jones In 1883, they published their first daily paper,

the Customer’s Afternoon Letter Six years later, this two-page newsletter was expanded and renamed the Wall Street Journal.

The extraordinary thing about Dow was his observation that financial

information about a company could be tracked, and trends developed to

quantify financial values Dow did not imagine his trend analysis skills

applying to stock prices, as his emphasis was on the fundamentals This wasdecades in advance of the SEC requirement for public companies to publishaudited quarterly and annual statements Dow’s emphasis was on pointing outthe truth about financial trends, especially for companies that manipulatedreported profits and losses This was occurring in an era before regulation,when corporate reporting often was highly unreliable and even deceptive.Dow’s publications included quarterly and annual information about manypublicly traded companies

Key Point

Charles Dow developed the trend to track financial information His

basic theory was later applied to stock price behavior and today is thebasis for trend analysis

Dow also devised the first stock averages The first such index consisted ofnine railroads, a shipping line, Western Union, and a handful of other tradedcompanies Railroads were emphasized because they were the most activelytraded types of companies at the time Dow passed away in 1902, well before

the concept of tracking averages and the Dow Theory itself were formalized.Eventually, the first set of averages evolved and formed the basis for howmarketwide trends are set and how reversal is signaled However, Dow

himself saw the study of averages as useful in observing business trends, butnot for tracking stock prices and trends The Dow Theory, as it is knowntoday, was developed over many years by Dow’s successor as editor of the

Wall Street Journal, William P Hamilton.

A problem with any type of collective analysis, including the 30 stocks inthe Dow Jones Industrial Average (DJIA), is that movement in the indexrepresents the net of all movements of the components, both up and down Soalthough the DJIA is the most popular version of what investors consider “themarket,” it does not represent the tendencies or trends of any individual

stocks It may, in fact, cloak what truly is occurring in many stocks outside ofthe selected components of the DJIA or any other average Another flaw isfound in the weighting of the DJIA, resulting in heavy influence of a fewcompanies For example, as of March, 2015, four stocks account for nearlyone-fourth of the influence of the DJIA:2

A price-weighted index like the DJIA starts out by adding together theprice of the components and dividing by the number of stocks However, anytime a stock splits, the divisor is adjusted The net result of this is that higher-priced stocks end up having more impact on the overall index weight This iswhy four stocks account for more than one-fourth of the index value This is

a troubling reality It means that “The Dow” does not represent what is

happening in the market, but only what is happening among 30 big

companies It’s true that stock values tend to follow the DJIA, but when youconsider how these stocks are weighted, it is deceptive at best In this respect,the wishful thinking behind the tracking of such an average is a type of

“cloud cuckoo land” for investors.3

Key Point

The method of weighting indexes like the DJIA means that “the

market” is influenced by only a handful of companies This may easilydistort how DJIA movement affects an individual stock’s performance

In addition to the DJIA, three other indexes are used by the market, andthis is an essential element of the Dow Theory These are the Dow JonesTransportation Average (DJTA), consisting of 20 transportation companies(airlines, trucking companies, shipping, and railroads); the Dow Jones UtilityAverage (DJUA), including 15 utility companies; and the Dow Jones

Composite Average (DJCA), an index of all 65 stocks in the other three

Theory prominence and value among traders It is not so much the movement

of the average that matters, but application of the observed rules establishingand defining trends in general

The tenets of the Dow Theory form the core of trend analysis of stocks.The theory includes six basic tenets:

• The market contains three movements These are the primary (major)

trend, which lasts from under one year up to several years and may beeither bullish or bearish; the medium (secondary reaction) trend, lastingfrom two weeks to as long as three months and assumed to retrace from

33 percent to 66 percent of primary movement since the beginning ofthe primary trend; and the minor (swing) trend, lasting from only a fewhours up to a month or more

These three trends tend to coexist For example, a medium trend playsout within the longer-term primary trend, and the swing trend serves as

an adjustment to the medium trend

Key Point

Trends make sense when the three specific types are acknowledged

Even so, it is impossible to forecast how long a trend of any durationwill actually continue

• Market trends go through three distinct phases For bull markets, these

are the accumulation phase, characterized by the purchase of sharesamong knowledgeable investors; the public participation phase, in

which a broader cross-section of the market recognizes the popularity

of a company and also buys shares of its stock; and a distribution, orselling, phase

During the middle phase of public participation, another phenomenonoccurs Speculation levels increase as traders buy shares in the beliefthat prices are going to continue rising into the future As this occurs,knowledgeable investors (who began buying when no one else was)now begin selling against the speculative fever The phases of markettrends reveal why the contrarian approach makes sense A majority ofmarket participants (the “crowd”) invariably misses the changes intrends and tends to buy and to sell at the worst times

For bear markets, three phases also occur but in a different sequence.First is a distribution phase, in which knowledgeable investors begindisposing of shares that have appreciated to the point of being

overbought The middle phase is a bearish version of public

participation, in which the market at large recognizes that the trend hasturned Selling activity spreads as the bear market expands, and thisphase may also be characterized as a panic phase The majority of

investors want to get out of long positions before prices drop further.The third phase continues the selling activity in a widespread segment

of the market, and a slowing down of price declines During this time, agradual return to accumulation of shares occurs among knowledgeableinvestors who recognize that prices have declined to bargain levels Theoverall decline is likely to slow and even to move into a sideways

consolidation phase

The three phases do not apply in the third type of trend, a sidewaysmovement known as consolidation This period can last several months

or even years, during which prices are range bound in a narrow breadth

of trading The lack of identifiable phases does not make consolidationany less of a trend than bull or bear markets; it is, however, more

difficult to interpret

Key Point

The three distinct phases of every market define investor behavior andenable investors to track a trend’s development over time.

• The market discounts news and this is reflected in prices One

efficiency of the market is that all news is absorbed and reflected instock prices immediately However, this does not confirm the efficient

market hypothesis (EMH), which states that reaction to news is always

efficient The fact that all news is discounted immediately does notmake a distinction between true and false news It also does not meanthat price reaction is reasonable Some forms of news (such as earningssurprises) cause an immediate overreaction in price, which is then

adjusted (later on the same session or in one to two sessions that

follow)

This belief cannot be proven beyond doubt However, it is a worthwhilepart of the theory behind trend behavior It explains retracement orsudden turns in trends, whether or not the theoretical and underlyingreasons for these movements are caused by news or by other factors Italso does not explain why prices change based on rumors that have notbeen confirmed In practice, this discounting of news (broadly

speaking) may act in an inefficient manner This better explains actualprice movement in the short term, which tends to be highly chaotic

Key Point

Even though markets are efficient in the immediate discounting of

price for known information, it does not make a distinction between

fact and rumor

• Averages must confirm one another before a change in the trend is

acknowledged This is a simple idea In order for a trend to be

acknowledged as new and opposite of the previous trend, it must bewitnessed in the major average (the DJIA) and also confirmed in one ofthe others (the most popular being the Transportation Average)

However, in practice, analysts do not always agree about whether

confirmation has occurred when a turn in direction occurs Some

believe it is confirmation, but others deny this and call it a retracement

or a secondary trend

The reliance on the transportation sector made sense at the end of thenineteenth century At that time, the U.S was a leading industrial andmanufacturing country and factories depended on railroads to ship theirproducts to the market So Dow’s original belief was that activity

among railroads reflected the state of the economy This was true in

1900 and it remains true today Even with a decline in manufacturingactivity in recent years, the U.S remains a dominant manufacturingforce in the world, second only to China.4

Charles Dow believed that a bull market could occur only if both

industrials and railroads rallied together, and that by the same

argument, a bear market was not valid until a decline in the industrialswas confirmed by a decline in the rails Even with today’s global

markets, this logic may still apply, and it explains why the same

requirement for confirmation is used Even though transportation nowincludes not only rails, but trucking, shipping, and air freight

companies, the connection between industrial profits and transportationactivity is direct

Key Point

Confirmation is a key element of all changes to an existing trend Noreversal can be accepted without strong confirmation

• Trend status is confirmed by the volume of trading Another form of

confirmation is found in volume The shares traded and, more

specifically, changes in that number (either a higher or lower number ofshares traded) tend to confirm a change in the mood of the market, andthus, in the trend as well A smaller level of volume is not as significant

as a larger volume, especially when that volume spikes much higherthan a typical level of trading activity This indicates greater interest inthat stock, whether among buyers or sellers

Dow speculated that high volume represented the true sentiment of themarket, driven by one side or the other; and that increased volume

signaled the direction to follow in the trend as well As a form of

confirmation, under this theory, a sudden increase in volume mightsignal the end of a current trend and beginning of a new one When thislogic is applied to individual stocks, it clearly does confirm other

reversal signals The application of the idea to marketwide averageslike the DJIA is not as certain However, as a tenet of the Dow Theory,the role of volume has led to recognition among traders that volumeindicators should not be ignored in the analysis of trends and reversals.

Key Point

Volume is directly related to price, and often anticipates coming

changes in the current trend

• Trends continue until specific signals show that they have ended The

final tenet of the Dow Theory is logical A trend remains in effect untilreversal signals and confirmation reveal that the trend has ended andreversed This applies to averages as well as to individual stocks

This rule about trends is profound for many analysts Trends do notsuddenly end for no reason or without signals announcing their end.Those who subscribe to the random walk hypothesis (RWH) woulddisagree, claiming that all price movement is entirely random and

movement in either direction is 50/50 However, if that were true, itwould be impossible to spot specific trends, and unlikely that pricemovement would be able to continue in one direction for any duration

A 50/50 random chance occurrence would dictate that prices would riseand fall in either direction about half of the time The existence of veryreal trends disproves this idea Dow was correct Trends continue aslong as no signal arises to point to reversal and confirmation of the end

of the trend

Key Point

Trends continue until reversal signals are located Trends never simplyend for no observable reasons

The Dow Theory Applied

The tenets of the Dow Theory can be observed in the study of price charts,more in hindsight than in foresight At the moment of analysis, it is moredifficult to interpret the meaning of a reversal in price It might be a

retracement or a secondary trend, or it might be the beginning of a new major

As applied to individual stocks, all of the Dow tenets serve as importantfeatures of charts and the discipline of technical analysis To study the DowTheory in practice, a review of price charts for the industrials and

transportations is instructive Even with the imperfections of price-weightedaverages like the DJIA, the tenets of this theory provide a foundation foranalysis of trends in individual stocks

The biggest decline in the DJIA in history occurred between October, 2007and March, 2009 when the index lost 54 percent of its value From a high of14,164.53, the DJIA ended at 6,542.05 on March 9, 2009.5

After that big bear market, the DJIA bounced back to its previous five-digitlevels Tracking this history, the DJIA chart in Figure 1.2 reveals the long-term trend in effect from 2009 through 2011, a three-year period In the firsttwo years, a long-term primary bullish trend was in effect In 2011, a

secondary trend moved the index lower, only to then resume the major trend

Figure 1.2 Dow Industrials—2009 to 2011 (Chart courtesy of

average, the Dow Transportation Average tracked the industrials with

remarkable consistency Not only did the Transportation follow the DJIAdown from 2007 to 2009, but it also confirmed the return to a bull marketbetween 2009 and 2011 This is shown for the same period in Figure 1.3

Figure 1.3 Dow Transportation—2009 to 2011 (Chart courtesy of

StockCharts.com)The long-term established rising line of resistance was similar for bothaverages, and support, also rising, marked the major trend The most

significant feature of this confirming chart occurred in mid-2011, when asecondary trend in the DJIA took the index down 2,000 points over a three-month period Was this a valid trend? With confirmation by the

Transportation, it was clear that the direction had changed

Even so, the question remained: Was this a new primary bearish trend, oronly a secondary trend? The answer was revealed in the last three months of

2011, when the DJIA turned once again and rose sharply, and when this turnwas mirrored by the Transportation Average

The comparison for this three-year period makes the point that when thedirection of a primary trend changes and is confirmed, the trend itself

(primary or secondary) is real During this three-year period, an additionalnumber of swing trends was also seen, which is typical of any trend overtime

Key Point

The confirmation of DJIA trend reversal is found in similar changes in

a second average, often the Transportation Average

Moving the analysis forward, the following three years from 2012 through

2014 revealed that the established primary bull trend continued Even withthe numerous swing trends within the primary trend, the DJIA moved

upward During the year 2012, there was little movement and it appeared thatthe index was in a consolidation period However, 2013 and 2014 marked aclear resumption of the primary bull trend Much like the previous analysis,numerous swing trends interrupted the primary trend briefly, and one in late

2014 may be termed a secondary trend This is summarized in Figure 1.4

Figure 1.4 Dow Industrials—2012 to 2014 (Chart courtesy of

StockCharts.com)The action in the DJIA was once again confirmed by the TransportationAverage The 2012 consolidation was more marked in this than in the DJIA.The resulting resumption of the primary trend was again mirrored by theTransportation Average, including the secondary trend in late 2014 and thenthe resumption of the primary trend This is summarized in Figure 1.5

Figure 1.5 Dow Transportation—2012 to 2014 (Chart courtesy of

StockCharts.com)These interpretations are subjective Distinguishing between a secondarytrend and a swing trend is a matter of opinion and difficult even in hindsight.The most difficult part of this analysis is in reading the meaning of reversalsand confirmation in the moment Does a strong reversal mean the trend hasended? Or is it one of many swing trends or a new secondary trend? Analystswho study the Dow averages rarely agree universally on what current trendsmean For averages like these, confirmation beyond a second average is themost dependable form of confirmation For individual stocks, many

additional types of confirmation may be applied more effectively, becauseone stock is tracked more accurately than an index consisting of many stocks

Key Point

Is a trend a secondary or a swing trend? Because the likely duration

overlaps, it often is difficult to know However, the important thing is

to recognize when reversal has occurred

The comparison between the Industrial and Transportation Averages

establishes the validity of the concept itself Confirmation does reliably andconsistently reveal the true nature of trend movement

Beyond the Dow Theory, additional ideas about the market should be

considered as well Two of these pertain more to price patterns than to trends,

but they define concepts about how the markets work These two are theefficient market hypothesis (EMH) and the random walk hypothesis (RWH).

Other Price Theories: EMH

The markets are sometimes described as informationally efficient Thisdoesn’t mean that price movement is purely efficient, but that price

movement is efficient in the way that it responds to publicly known

information (whether true or not) The theory goes on to explain that because

of this efficiency, it is not possible to consistently beat the average returns ofthe market

The origin of EMH is traced to 1970 when Professor Eugene Fama of theUniversity of Chicago Graduate School of Business (the Booth School) wrotethat better-than-average returns are not possible based on the analysis ofhistorical price information.6

The distinction between absolute efficiency and informationally efficientmarkets is key If you assume that “information” includes both true and

untrue rumors, earnings surprises, and other announcements that are not trulyinfluential in valuation of a company’s stock, then it is quite likely that

market prices react efficiently (immediately) However, markets cannot beconsidered efficient in a real sense because of the obvious overreaction ofprice to immediate news (earnings’ surprises being primary in this

observation) Fama did not agree with this distinction He wrote:

In an efficient market, competition among the many intelligent

participants leads to a situation where, at any point in time, actual

prices of individual securities already reflect the effects of

information based both on events that have already occurred and

on events which as of now the market expects to take place in the

future.7

This belief is not universally accepted; in fact, many have challenged it.One study concluded the following:

Significant return from technical analysis, even in conjunction

with valuation methods, tends to argue against the efficient

market hypothesis Consequently, there is a close link between

the validity of technical analysis and the inefficiency of the

market.8

There can be no absolute or conclusive belief concerning EMH, because it

is a theory Studies are based on what is observed in price behavior

However, for anyone following secondary or primary trends, the concept ofefficiency in the market may be questioned and ample evidence found thatmomentum of trends changes over time and, often, reversals can be

accurately predicted with the use of strong indicators and confirmation Ifprices relied solely on efficiency in the markets, prices would reflect

information rather than the momentum of trends These are not likely to

match up

Key Point

The “efficiency” of markets refers to the speed of discounting based on

known information It does not mean that information is reliable or

even true

Unlike information, such as earnings’ surprises or merger announcements,trends are statistically likely to become established with a particular duration,level of momentum, and slope of change within the chart, and to continueuntil that momentum changes and the trend slows down, pauses, or reverses.Thus, information tends to occur without any reliable or predictable schedule,whereas the shape and duration of trends tend to act within the boundaries ofwhat can be observed statistically

Among the statistical tendencies of trends are frequently observed

characteristics These include price patterns of a specific nature that

anticipate reversal or continuation of swing trends and secondary trends, theproximity between discovered signals and the price points of resistance orsupport, and the strength of signals and confirmation These characteristicsrelate to price patterns within swing trends, but can also be observed in

secondary and primary trends

Types of EMH in Theory

Studies of the efficient market theory have led to a breakdown into threedistinct types: weak, semi-strong, and strong The weak form observes thatprices reflect all publicly known information from the past In the semi-

strong version, the belief is expanded to include both past and current

information and, further, that traded security prices change instantly so that

the current price always reflects all known information The strong form ofEMH expands to the belief that prices also reflect insider information notknown to the investing public.

These distinctions all raise a key question about the efficiency of the

market Does it include any distinction between reliable or true information

versus rumors that may end up to be unfounded? Another way to address thisissue is to speculate about whether the markets act or react efficiently

Anyone who has seen the price reaction to earnings’ surprises knows that theimmediate reaction to the surprise is likely to be an overreaction, to be

followed quickly by a correction to that overreaction So, even if markets areefficient in the reaction to information, they are not able to distinguish

between true and false information, and the reaction itself is by no meansefficient

Key Point

It is possible, even predictable, that price will react efficiently, even

when the level of reaction is inefficient This leads to immediate

correction of overreactions, especially to earnings’ surprises

For the purpose of trend analysis, this distinction is a key one Anyonerelying on the behavior of the trend will notice the statistical tendency oftrends over the long term to behave in a mathematically predictable manner,moving with a specific momentum and stopping or reversing only when thatmomentum changes This is a predictable and rational type of behavior

However, the efficiency of reaction to information may not always lead to anefficient (or rational) price movement in the short term For this reason,

reliance on technical signals and confirmation that is popularly applied toprice patterns and swing trends can also be applied to longer-term trends withequal reliance However, this reliance is not because of efficiency, but inspite of short-term inefficiency in price behavior

Another way to describe the problem with EMH is to analyze its message

in relation to market behavior EMH requires that investors and traders actwith rational expectations, an economics hypothesis stating that predictionsand expectations are equal to the expected value derived statistically.9

This efficiency standard assumes that investors tend, as a group, to behaverationally and to apply logical standards based on relevant information, and

to update their predictions based on newly revised information This

obviously inaccurate assumption recognizes the tendency among individuals

to overreact or underreact to specific information, while believing that themarket as a whole will behave efficiently

The assumption about overall markets acting efficiently does not matchwith the observed technical science of trend patterns and observations On thebasis of averages like the DJIA, the confirmation from a second average likeTransportations is consistent and demonstrates the strength of confirmationamong dissimilar types of organizations Even with the flaws of weightedaverages, the primary trend confirmation challenges EMH The extreme

primary trends like the bear market from 2007 to early 2009 that took theDJIA down 53 percent cannot be deemed as efficient The fundamental

attributes of the 30 DJIA stocks did not rationally justify a 53 percent drop inoverall index value; even so, the index dropped in spite of the known

fundamentals of the companies that made up that index

The Bubble Effect

The EMH concept is comforting in a sense It explains how markets aresupposed to work and adds an element of consistency and predictability tothe markets, even though markets do not act in accordance with those ideas.Markets are more likely to go through price bubbles, and over time,

numerous bubbles have appeared and even more readily disappeared

Bubbles are followed by sudden and violent adjustments, such as Black

Monday in 1987 and the demise of the dot.com sector following its bubble.During bubbles, “the market” as a whole may experience times of irrationalexuberance, a term first used by Chairman of the Federal Reserve Alan

Greenspan.10

Key Point

A “bubble effect” demonstrates that long-term trends are subject to

short-term distortions These self-correct rather quickly

The extreme market movements that occurred between 2007 and 2009have drawn EMH into criticism Following the 2009 decline, several

published criticisms of EMH made the point that efficiency is not necessarily

at play, especially during periods when primary trends are strong and

long-lasting It may also be the case that a reduced level of accuracy in financialdisclosures actually reduces the efficiency of markets, rather than conforming

to it.11

These periods of either irrational exuberance (bull markets) or irrationaldread and panic (bear markets) highlight a point about EMH It might be truethat markets are “informally efficient” in the sense that reaction to news isimmediate However, efficiency cannot be isolated to reaction time when, infact, the level and scope of reaction are inefficient A huge market rise or fallinvariably exceeds any fundamental reasons underlying a price move in anyform of trend, but especially in a primary trend So the largest bear trend inhistory, from 2007 to 2009 with its 53 percent drop in Dow index valuation,

is not supported by the fundamentals of the 30 stocks in that average Neither

is the bounce from 2009 through 2012 supported in any specific fundamentalimprovements among the 30 Dow stocks

Implications of calling the markets “efficient” include the assumption thatreaction to news also leads to efficient and rational price movement Historyhas shown repeatedly that markets overreact to both good and bad news, totrue and false news, and to events and news that have nothing whatsoever to

do with fundamental value So if the market is informally efficient, it doesnot reflect good judgment among investors in the ways they respond to

information In the short term, markets are chaotic and inefficient, and evenEMH proponents concede this point However, the more disturbing reality isthat longer-term trends also are inefficient in the manner in which changes inprice levels occur, both for index tracking and for individual stocks At leastfor individual stocks, an intermediate self-correcting effect grows from

supply and demand When stocks are overpriced, selling dominates; andwhen they are underpriced, buyers take over This has the effect of

maintaining an economic reality to individual stock prices

Key Point

A consistent “cause and effect” in price reaction and overreaction is

characteristic of the immediate character of supply and demand in anymarket

Adding to the long-term inefficiency of “the market” as measured by theDJIA is the fact that the Dow Jones Company replaces components

periodically What is the rationale for this? Some companies become obsoleteand should be replaced, and that makes sense However, in some cases, thereasons for removing some companies and placing others in the list of 30 isnot as clear Since its inception in 1884, the Average has been changed 53times For example, in 2013, four new companies were added to the DJIA:J.P Morgan, Nike, United Technologies, and Visa Dropped off were Alcoa,Bank of America, Hewlett-Packard, and Merck In March, 2015, Apple

(AAPL) was added, replacing AT&T (T) Without doubt, the periodic

replacement of stocks on the DJIA influences its climb in index level.12

Other Price Theories: RWH

Closely associated with EMH is the random walk hypothesis (RWH) Inthis concept of the market, all changes in stock prices are entirely random andcannot be forecast with any reliability If the EMH rationale is accepted,

current prices reflect all known information Thus, any further movement inprice is subject to evolving information, but the direction of movement isentirely random

However, if the market is truly random, trends may not develop Priceswould tend to move in a completely 50/50 manner, moving upward half ofthe time and downward the other half The examination of any stock chartover time reveals that this does not occur Trends for indexes, as well as forindividual stocks, develop, move, and continue moving until reversal signalsappear At this point, the price might level out for a period of consolidationand indecision and then either reverse or continue in the previously

established direction A truly random outcome, such as the outcome of thespin of a roulette wheel, would be black nearly half of the time and red nearlyhalf of the time The zero and double zero move the odds slightly in favor ofthe house, so that red or black occurs in 47.37 percent of spins With a total

of 38 possible numbers included (one through 36 plus zero and double zero),the random odds are the following:

18 ÷ 38 = 47.37%

The RWH premise was analyzed by professors of finance at the MIT SloanSchool of Management and the University of Pennsylvania Their conclusionwas that RWH is wrong and that trends do exist, making the markets

predictable, at least to some degree.13

Key Point

If markets were truly random, no form of analysis would have any

value The theory of a random market has been questioned many

times, and the conclusion is that markets are by no means random

Supporters of RWH point to the equilibrium of supply and demand asexplanation for this random assumption about stock prices However, thiswould require that buyers and sellers come to the table at the same time andthat no news affecting price occurs at that moment So RWH requires

efficiency in the price at all times and an equal number of buyers and sellerswho agree about the fairness of the current stock price Neither of these

occurrences is likely to occur consistently enough to support RWH as a

reasonable theory about markets

The fact that buyers and sellers are rarely available in equal numbers is one

of the factors creating trends, even ignoring fundamental realities of the

company The vacuum assumed by RWH is that markets work with extremeefficiency and balance, but this ignores the reality in another important

manner The fundamentals of companies reveal that over time, some

companies grow in terms of net profits and market share; they increase

dividends they pay; they acquire or merge with competitors; and they inventnew products and processes Other companies lose market share and profits

as their products become obsolete and as competitors outperform them Somecompanies mismanage their costs, such as General Motors, whose debt ratiorose above 200 percent before bankruptcy was inevitable This meant thatdebt accounted for more than the total valuation of the company and thatequity was nonexistent Because GM has reformed and continues to take part

in the market, the underlying problems were fundamental and far from

random The success or failure of a company (and as a result, the rise or fall

of its stock price) is inevitably traced to tangible and precise underlying

fundamentals, and not to random luck So companies like General Motors,Eastman Kodak, and others failed because of fundamental causes such asobsolescence, failure to compete, lack of control over costs, and other

problems; and successes like Wal-Mart, Microsoft, and McDonald’s also arenot random but the result of keen competition, product exceptionalism, andsmart management None of these are random influences on the

fundamentals, and they also explain why the stocks of successful companies

experience long-term bullish trends.

equilibrium It changes based on competitive pricing and quality There isnothing random about strong competition, excellent management, and quality

of products or services In a single day or week, a stock’s price moves in achaotic and possibly a random manner, but this is the reason to study trends.The momentary struggle between buyers and sellers reflects ever-changingadjustments to supply and demand, but the larger picture and the longer-termtrend clarify what really causes prices to move upward or downward overtime The numerous short-term effects on stock prices (profit-taking, bargainhunting, earnings surprises, rumors, or merger talks, to name a few) do create

a random effect on stock prices, but these reflect the swing trends only andnot the secondary or primary trends that define a stock’s price over months oreven years Those longer-term trends grow from the tangible cause and effect(supply and demand) based on fundamental analysis In this respect, the

fundamentals (competition, profit and loss, cash flow) directly affect thelong-term technical aspects (price and movement of price trends) None ofthis is random The nature of price trends is best described as the technicalreaction to the underlying fundamentals of the company

To an extent, stock prices are unpredictable, but primarily because nextyear’s fundamentals are not yet known Assuming that stock prices moverandomly without any cause is not realistic, because trends are easily

observed in prices RWH claims that it is impossible to consistently beat themarket averages, but with sound stock selection based on the fundamentalrecord, the long-term technical side consistently yields results

One aspect of RWH is a belief that technicians who rely on analysis ofprice charts respond to market and investor behavior Under this belief,

fundamentals do not matter because investors set the market mood by buying

or selling, resulting in bullish or bearish sentiment This ignores the glaringdifferences between well managed and poorly managed companies in thesame sector, and the resulting changes in stock prices over the long term.Investors are far from arbitrary in how they develop sentiment As a group,investors favor profitable companies and do not favor those companies losingmarket share and reporting net losses

Trend Analysis as a Risk-Management Process

The explanation of price movement as either efficient or random ignoresthe most important attribute of the trend: its role as a means of risk

management

By tracking stock trends and defining the differences between swing,

secondary, and primary trends, investors may develop methods for managingrisk This is accomplished through carefully timed trades based on trendbehavior Even conservative buy-and-hold traders whose portfolio is treated

as permanent are able to utilize trends to time defensive measures to avoidlosses These include closing long equity positions in anticipation of bearishturns in current trends; the purchase of put options to ensure paper profits;variations of dollar cost averaging to exploit price movements interpreted assecondary trends or retracements; and trades undertaken following

exaggerated reactions to events like earnings’ surprises

Key Point

In a real sense, trend analysis is a method for risk management By

recognizing trends as they evolve, investors and portfolio managers

can better time trade decisions

By taking steps such as these, all investors provide risk-management

attributes to trend following Understanding how trends work and

recognizing or forecasting upcoming reversals are interesting tasks by

themselves, but become meaningful when the knowledge is applied to reduceand eliminate risk

Among the risk-reducing methods investors employ is articulation of riskitself through formulations like risk-adjusted value An expected cash flowfrom an investment (whether dividend yield, option premium, or capital