The Handbook of Technical Analysis + Test Bank: The Practitioner's Comprehensive Guide to Technical Analysis

Foreword

I sincerely believe that this handbook is a feast for serious technical traders as well as for hardcore technical analysis practitioners. This handbook is especially meant for beginner professionals looking to improve their trading performance, and in the process, trying to avoid some of the more painful collisions with complex charting theories. I wish I had this book years ago. That said, I enjoy reading it today, finding Mark’s pearls of wisdom an aid to improve my technical trading.

Mark is one of Malaysia’s distinguished technical analysis gurus whose dazzling mind produces more fresh ideas in a book than most other experts in an entire lifetime. Since knowing him back in 2002, he has been an influential mentor and a respectable trader, becoming well known from 2002 to 2007 as being one of Malaysia’s finest traders. Most of his trading techniques and theories in the handbook are now included in most of my trading programs.

There are a lot of books on technical analysis. Most of them concentrate on very specific items, exploring a particular concept in great depth. A long and detailed handbook covering a broad range of topics with practical value such as this is much more difficult to find. Mark gives his readers diverse market indicators to identify positive investment climates, backing them up with in-depth theoretical explanations and real-world chart examples. He exposes powerful technical signals and uncovers some of the most obscure concepts in technical analysis, reducing them to a set of very clear and lucid rules.

I believe that this handbook provides an excellent starting point, as well as a comprehensive reference text for technically orientated practitioners. It outlines the primary principles of technical analysis and provides a solid foundation for moving forward into more advanced and cutting-edge concepts. For the experienced trader, this book will also serve as a reliable refresher, reinforcing good technical trading practices that are both enduring and effective. It explains technical trading in a clear and easily understandable format, examining entire concepts, from start to finish. All techniques discussed are succinctly illustrated with clear chart examples.

Mark’s handbook points the way for readers interested in the master chartist approach. He distils his vast market expertise into a simple set of technical guidelines and rules. As an example, Mark explains why he believes the markets respond in specific behavioral manner to phenomena such as volume divergence and breakaway gaps. His chapter on volume and volatility also makes it clear why market tops react in a certain manner before the ‘storm’ and why market bottoms tend to ‘storm’ before the rebound. These simple but yet profound concepts will change the way many readers approach trading and investing in the markets.

I congratulate Mark on his hard work in producing this profound handbook. It is a big achievement for the technical analysis community and we are proud of his contribution. Finally, I believe that the only thing readers need to do after reading this handbook is to make a commitment to apply his work, with the appropriate mind-set to become successful traders and investors.

I wish all readers and technical analysis fans lots of success, happy learning, and trading with technical analysis!

–Dr. Nazri Khan,

MSTA, CFTe, President, Malaysian Association of Technical Analyst (MATA); Vice President, Affin Investment Bank Malaysia

Preface

The Handbook of Technical Analysis provides a unique and comprehensive reference for serious traders, analysts, and practitioners of technical analysis. This book explains the definitions, concepts, applications, integration, and execution of many technical-based trading tools and approaches, with detailed coverage of various technical and advanced money management issues. It also exposes the many strengths and weaknesses of various popular technical approaches and offers effective solutions wherever possible. Innovative techniques for pinpointing and handling potential market breakouts and reversals are also discussed throughout the handbook. A dedicated chapter on advanced money management helps complete the trader’s education.

This handbook will prove indispensable to foreign exchange, bond, stock, commodity futures, CFD, and option traders, especially if they are looking for a fast and comprehensive route to mastering some of the most powerful tools and techniques available for analyzing price and market behavior. It is replete with hundreds of illustrations, tables, and charts, giving the trader and investor an instant visual understanding of the underlying principles and concepts discussed. Markets analyzed include bonds, commodity, equities, and foreign exchange.

With extensive content and coverage, The Handbook of Technical Analysis also provides the perfect self-contained, self-study exam preparatory guide for students intending to sit for examinations in financial technical analysis. This book helps prepare students to sit for various professional examinations in financial technical analysis, such as the International Federation of Technical Analysts CFTe Levels I and II (USA), STA Diploma (UK), Dip TA (AUS), as well as the Market Technicians Association CMT Levels I, II, and III (USA) examinations in financial technical analysis. This hand- book is organized in an accessible manner that allows the students to readily identify the topics and concepts that they will need to know for the exam. It covers the most important topics, as well as incorporating the latest technical developments in the markets so as to give the students a real-world appreciation of the topics learned. The student will find important learning outcomes at the beginning of each chapter.

The Handbook of Technical Analysis aims to be as visual as possible. Most of the charts and illustrations in this handbook were created with the objective that they would provide a rapid and efficient review of all the concepts and applications upon the second or third reading. This makes it the perfect tool for students reviewing for an examination.

OVERVIEW OF THE BOOK CONTENTS

Chapter 1 (Introduction to the Art and Science of Technical Analysis) introduces the reader to the general assumptions, approaches, and classifications associated with the application of technical analysis. It introduces the concept of the self-fulfilling prophecy and information discounting and deals with the issue of subjectivity in technical analysis.

Chapter 2 (Introduction to Dow Theory) introduces the basic concept of Dow Theory and its various tenets. It also deals with the current challenges and applicability of Dow Theory. Much of modern classical technical analysis is derived on the original assumptions of Dow Theory, and as such represents an important chapter.

Chapter 3 (Mechanics and Dynamics of Charting) describes the mechanics of chart construction and how price is quantized and filtered into OHLC data. The significance of OHLC data is dealt with in detail, including four different definitions of gaps. Charts are classified in terms of five different constant measures and how they are affected by the type of chart scaling employed. There is also a detailed discussion about how trade performance and reward to risk ratios are affected by the bid-ask spread, with respect to long and short entry and exit orders. Finally, various types of futures contracts are covered, focusing on rollover premiums and discounts, backwardation, contango, and back-adjusted and unadjusted futures charts.

Chapter 4 (Market Phase Analysis) deals specifically with market phase, describing the various phases via numerous technical approaches. It analyzes and interprets market phase in terms of volume and open interest action, chart patterns, moving averages, divergence, price momentum, sentiment, cyclic action, Elliott waves, and Sakata’s method. This helps the practitioner better anticipate and forecast potential phases in the market with more consistency.

Chapter 5 (Trend Analysis) deals with the various definitional issues associated with trend action. It also introduces the reader to the concept of wave degrees or cycles. It points out that the inability to identify wave degrees may very well result in ineffective technical analysis and trade performance. The chapter then covers the 16 important price action characteristics that will greatly improve the forecastibility of potential reversal and continuation in the markets. The bar stochastic ratio oscillator is also introduced. Price filters are discussed in detail and classified into three main categories. This is followed by the description of the various types of trade orders and their functions. The chapter also covers stoplosses and their relationship with proportional sizing. Trendlines, channel construction, fan lines, trend retracements, price gaps, trend reversal forecasts, and continuations are also covered in detail.

Chapter 6 (Volume and Open Interest) deals with volume and open interest action and defines volume divergence with respect to price-based and non-price-based volume indicators. VWAP, volume filters, volume cycles, and various volume oscillators are also discussed, pinpointing some of their weaknesses and possible solutions.

Chapter 7 (Bar Chart Analysis) covers bar chart analysis. It presents the reader various generic reversal and continuation setups with respect to single, double, triple, and multiple price bar formations. It also describes the significance of the 16 price action characteristics and how they can be employed to forecast potential price bar reversals and continuations in the market. Finally, various popular price bar formations are discussed via numerous chart examples.

Chapter 8 (Window Oscillators and Overlay Indicators) classifies indicators into window oscillators and price overlay indicators. Overlay indicators are further subdivided into numerical, geometrical, horizontal, and algorithmic indicators. The differences between static and dynamic indicators are also explained. The practitioner is then introduced to the seven main approaches to analyzing oscillators. Cycle tuned oscillators, multiple timeframe oscillator analysis, and various popular oscillators and indicators are described in detail.

Chapter 9 (Divergence Analysis) describes the application of divergence in technical analysis. Detailed coverage of the definitional issues helps clarify the confusion surrounding the topic. The practitioner is introduced to bullish, bearish, standard, and reverse divergence. Various explanations are also presented with respect to the functioning of reverse divergence. The concepts of double divergence, detrending, and signal alternation are also covered in detail. The chapter concludes with numerous chart examples illustrating the various forms of divergence in equities and commodities.

Chapter 10 (Fibonacci Number and Ratio Analysis) introduces the practitioner to Fibonacci ratio and number analysis. It covers Fibonacci retracements, extensions, expansions, and projections with numerous chart examples. All Fibonacci calculations are clearly explained and illustrated. The differences between numerically and geometrically based Fibonacci operations are also discussed. Guidelines for drawing Fibonacci retracements in single, double, and multiple leg retracements are covered in detail. Fibonacci price and time ratio analysis of Elliot waves are also explored. Various popular Fibonacci applications such as fan lines, channel expansions, and arc projections are illustrated via real-world charts.

Chapter 11 (Moving Averages) analyzes various moving averages, such as exponential, simple, and weighted moving averages. The practitioner is shown how to calculate various averages. The chapter extensively covers the seven main components and nine main applications of moving averages. Moving averages functioning as signals and triggers are also discussed.

Chapter 12 (Envelopes and Methods of Price Containment) covers price bands or envelopes and their various modes of price containment. The practitioner is introduced to the six main functions of a price envelope. The different forms of central value that may be adopted by an envelope and the construction of the upper and lower bands are also analyzed in detail. The practitioner is then shown how to tune the bands with respect to the dominant cycles in the markets. The five main forms of price containment are illustrated with suggestions for effective entry and exit of the bands.

Chapter 13 (Chart Pattern Analysis) discusses the application of chart pattern analysis. A detailed breakdown of the classification of chart patterns is presented with specific examples. There is extensive coverage of the minimum measuring objective, conditions for pattern completion, and alternative price targets. The chapter concludes with the extensive treatment of many popular reversal and continuation chart patterns.

Chapter 14 (Japanese Candlestick Analysis) introduces the practitioner to Japanese candlestick analysis. Many of the most popular Japanese candlestick formations are presented and covered in detail. Japanese candlestick formations should be read within the context of the market, and this is achieved with reference to the 16 price action characteristics discussed extensively in this chapter. The practitioner is then shown how to integrate Japanese candlestick analysis with other forms of technical analysis, such as cycles, chart patterns, oscillators, Ichimoku Kinko Hyu charting, Fibonacci levels, volume action, and moving averages.

Chapter 15 (Point-and-Figure Charting) covers Point-and-Figure charting, focusing on the minimum continuation and reversal box size, vertical and horizontal counts, box filtering, and the effects of chart scaling, as well as coverage of the most popular point and figure formations.

Chapter 16 (Ichimoku Charting and Analysis) presents a powerful set of price overlay indicators, collectively referred to as Ichimoku Kinko Hyu charting. The chapter focuses on the construction, analysis, and application of the various overlays with special attention to the time displacement and lookback periods. Methods of trend identification, potential reversals, and continuations are also discussed with respect to the various Ichimoku overlays.

Chapter 17 (Market Profile) covers market profile charting. There is detailed treatment of the value area calculation, determination of the Point of Control via Time Price Opportunity (TPO) count and volume, as well as coverage of the various popular TPO distributions.

Chapter 18 (Basic Elliott Wave Analysis) introduces Elliott wave analysis with special focus on wave construction, alternation, truncations, impulsive and corrective wave formations, as well as the application of Fibonacci ratio and number analysis to the Elliott wave structure. The significance of pattern, time, and ratio is also discussed.

Chapter 19 (Basics of Gann Analysis) covers some of the most popular Gann techniques for forecasting potential price reversals, which includes the squaring of price and range, squaring of the high and low, the square of nine time and price projections, Gann lines, Gann retracements, and Gann grids.

Chapter 20 (Cycle Analysis) covers the basic elements of cycle analysis. The principle of summation, harmonicity, proportional commonality, nominality, variation, and synchronicity are covered in detail. Cycle inversions, translations, and the tuning of oscillators to the dominant cycle are illustrated clearly on various charts. The practitioner is also presented with five basic approaches to identifying cycles.

Chapter 21 (Volatility Analysis) discusses the five measures of market and price volatility. There is also coverage of the concept of normal and standard deviation, mean deviation, skewness, kurtosis, average true range, and stock beta. Plus there is discussion of the volatility indices and their application.

Chapter 22 (Market Breadth) covers the elements and factors that affect the reliability and consistency of market breadth analysis. Market fields and components such as its nine breadth data fields and eleven data operations are discussed in detail. Various popular market breadth indicators and their applications are then illustrated via numerous equity and commodity charts.

Chapter 23 (Sentiment Indicators and Contrary Opinion) introduces the topic of sentiment analysis and analyzes the behavior and psychology of the market participants. The chapter covers contrary opinion, irrationality, and necessary conditions for the reliability of sentiment indicators. Various popular sentiment indicators are examined with the appropriate charts.

Chapter 24 (Relative Strength Analysis) is about measuring the relative strength of one market against another. The directional implications and definitions such underperformance and outperformance are explained with various examples. The application of technical analysis to RS lines is examined and illustrated via numerous charts.

Chapter 25 (Investor Psychology) covers the basic elements of investor psychology. The chapter discusses how trends, consolidations, and market reversals develop with respect to various psychological and emotional biases. It also describes the underlying forces that create chart patterns in terms of the biases of investors and traders. Topics relating to cognitive dissonance and positive feedback loops are covered in detail.

Chapter 26 (Trader Risk Profiling and Position Analysis) introduces the practitioner to trader profiling. The practitioner is exposed to the concept of risk capacity and is shown that most market participants are usually both risk averse and risk seeking at the same time, with respect to price, time, and risk size. Trade orders based on behavioral profile are also discussed in detail. The collection of bullish and bearish indications across multiple timeframes is discussed in terms of the long, medium, and shorter term trader and investor.

Chapter 27 (Integrated Technical Analysis) introduces the concept of integrated technical analysis. It shows the practitioner how to effectively combine various technical tools to achieve better forecasts and trade decisions. It stresses the importance of identifying significant bullish and bearish clustering and oscillator signal agreements in order to locate high probability trades. Multiple timeframe analysis and multicollinearity are also discussed in detail.

Chapter 28 (Money Management) covers the elements of money management for traders. It classifies money management into passive and dynamic exposures. The four stochastic exit mechanisms are introduced and explained in detail. The concept of linear and geometric expectancy, asymmetric leverage, minimum winning percentage, and win-loss distribution are discussed from the perspective of improving trade performance. Familiarity with the concepts and disciplined application of passive and dynamic components of money management are essential skills for the long-term survivability as a trader.

Chapter 29 (Technical Trading Systems) introduces the practitioner to the basic elements of constructing, testing, and optimizing technical trading systems. It covers system conceptualization, system components, and performance measurement specifications.

Appendix A (Basic Investment Decision Making Based on Chart Analysis) illustrates how charts are employed to make trading and investment decisions. The practitioner is shown how to describe both the stock and the climate or environment in which the stock is trading in bullish and bearish terms and how to identify various participatory options available in the stock with respect to the client risk capacity and expectation.

Appendix B (Official IFTA CFTe, STA Diploma (UK), and MTA CMT Exam Reading Lists) provides a list the official IFTA CFTe, STA Diploma (UK), and MTA CMT exam reading requirements.

This book also includes an overview of the companion website and test bank.

ONLINE MATERIALS

This book also includes access to a companion website (www.wiley.com/go/limta) that includes:

An online test bank based on the topics outlined in the official syllabuses for both the MTA and IFTA professional examinations

Answers to the end-of-chapter questions in the book

Excel spreadsheets that help illustrate the mathematics underlying various technical and money management concepts within the handbook

Updated charts

Additional content on new topics added to the exams

For instructions on accessing the test bank, please refer to the About the Test Bank and Website at the end of this book.

Acknowledgments

I would like to express my deepest appreciation and gratitude to Nick Wallwork, Emilie Herman, Chris Gage, and everyone at Wiley for their amazing work and inspiration, without which the creation of this book would not be at all possible.

I am especially indebted to Emilie Herman for her phenomenal contribution and expertise in helping me put this book together. I thank Emilie for her constant encouragement and guidance and for putting up with all the delays during the difficult and very challenging writing process. I would also like to convey my heartfelt appreciation to Chris Gage for his amazing work on the manuscripts.

Finally, I truly thank all my past and current graduates for their amazing participation, patience, and dedication. It is through their constant feedback, criticisms, and fervent participation that much of the technical analysis in this book have been refined and crystallized into its current form. A special word of thanks also goes out to Mr. Eric Lee at MetaQuotes (Singapore) for his very kind assistance.

The charts in this book are sourced, with kind permission, from Stockcharts.com and MetaQuotes Software Corp. Note that MetaTrader is a trademark of MetaQuotes Software Corp.

About the Author

Mark Lim graduated from King’s College London in Special Physics. He was awarded the Bronwen Wood Memorial Prize in financial technical analysis by the Society of Technical Analysis (UK) in 2007. He holds both the MSTA (UK) and the International Federation of Technical Analysts CFTe designations and is a full member of the Society of Technical Analysis (UK). Mark’s expertise includes stock, CFDs, commodity futures, and options trading. He is currently involved with mathematics and physics at the postgraduate level.

Mark is the author of The Profitable Art and Science of Vibratrading (Wiley, 2011). He is also a contributing author of The Wiley Trading Guide Volume II. He conducts a range of technical analysis and trading Masterclasses via online webinars and on-site seminars, covering intermediate to advanced profit extraction methodologies for directional and nondirectional trading.

Mark can be reached at www.tradermasterclass.com.

CHAPTER 1

Introduction to the Art and Science of Technical Analysis

LEARNING OBJECTIVES

After studying this chapter, you should be able to:

Understand the key concepts underlying technical analysis

Identify the different forms of chart analysis

Describe the objectives of technical analysis

Understand what subjectivity means in technical analysis

Recognize the strengths and weaknesses of technical analysis

Categorize market participants according to style and time in markets

Identify the various styles and approaches in technical analysis

Technical analysis is a fascinating field of study. It is as much science as it is art. Its main strength is that a lot of it is visual, giving practitioners a better feel of the underlying dynamics of the markets. We shall also be looking at the various challenges to technical analysis, their resolution, and how technical analysis affects trading in general. The classification of technical approaches, market participants, and various markets will also be discussed in detail.

1.1 MAIN OBJECTIVE OF TECHNICAL ANALYSIS

It is generally accepted that human beings are born with certain instincts, tempered and molded by evolution via the passing of time. Every human being strives and seeks to fulfill these powerful instinctive forces.

The three main motivational instincts are:

The instinct to survive

The instinct for comfort

The instinct to propagate

The instinct to survive is probably the strongest and most overpowering. Survival almost always precedes the need for comfort or to propagate the species. The instinct to survive includes:

The instinct to stay alive

The instinct to satisfy hunger

The instinct to seek safety, that is, being in a group/herd

The instinct to avoid danger (by having natural fears like the fear of fire, loud sounds, heights, etc.)

This powerful instinct to survive is the main driving force in life for striving to make a profit. But in order to make a profit to ensure continued survival, there must be a positive change in the actual or perceived value of something that we own. This change in value of some variable may be anything that will allow us to profit from change. One very popular and convenient variable of change is price. We can participate in this price change by satisfying a very simple mechanical rule that will ensure profitability every single time, which is to always buy when prices are low and sell when they are higher, popularly referred to as the buy low, sell high principle. See Figure 1.1.

Figure 1.1 The Mechanics of Profiting from a Change.

Unfortunately, in order to satisfy this simple rule of guaranteed profitability, we need to be able to do more of one thing, which is to be able to determine the direction of price ahead of time in order to know exactly when to buy low and subsequently sell higher. Hence, it is not only the mechanical action of buying low and selling high that counts, but also the timing of the action itself that is critical. This introduces an element of chance or probability into an otherwise fairly straightforward mechanical venture. Profitability therefore requires effective and efficient action in two dimensions, that is, price and time. Traders and analysts keep track of this action using a two-dimensional visualization tool, that is, a price-time chart, which tracks price on the vertical axis and time on horizontal axis.

In short, the ability to forecast or predict price or market action in a reasonably accurate fashion represents one of the skills that may be critical for longer-term success as a professional trader or analyst.

1.2 DUAL FUNCTION OF TECHNICAL ANALYSIS

Technical analysis essentially serves two main functions:

For Identification: It identifies and describes past and present price action. It serves as a historical record of what has transpired in the markets. It provides a descriptive representation of market action. This allows the market practitioner to observe how the market has performed in the past, which includes its average volatility over a specified period; its highest and lowest historical price extremes; the common areas of consolidation, average duration, and price excursion of trends; the amount of liquidity and participation in the markets; the average degree and frequency of price gapping; the impact of various monetary economic announcements on price, and so on. This information is especially critical prior to any investment or trading decision.

For Forecasting: Once a particular price or market action is identified, the practitioner may now use this information to interpret what the data actually means before inferring future price action. This inference about potential price action is wholly based on the assumption that price patterns are repetitive to some reasonable degree and therefore may be used as a basis for price predictions.

1.3 FORECASTING PRICE AND MARKET ACTION

There are three main approaches to predicting potential future price action or behavior, namely via:

Fundamental Analysis

Technical Analysis

Information Analysis

See Figure 1.2.

Figure 1.2 Three Approaches to Price Forecasting.

Forecasting Stock Prices Using Fundamental Analysis

One way to gauge the potential price of a stock is by analyzing the company’s performance via its financial statements and accounts in order to determine its intrinsic value or the worth of the security in light of all its holdings, debt, earnings, dividends, income and balance sheet activity, cash flow, and so on. This accounting information is normally represented in ratio form, as in price to earnings (P/E), price to earnings growth (PEG), price to book, price to sales, and debt to equity ratios, to name but a few.

The logic is that a strongly performing company should continue to perform well into the future and garner more demand from investors excited to participate in the expected capital gains derived from the stock’s price and appreciating dividend yields. The price of a stock is expected to rise if there are sufficient buyers, signifying a demand for it. Conversely, the price of a stock is expected to decline if there are sufficient sellers, signifying an oversupply in the stock. Demand is potentially generated if the current stock price is below its estimated intrinsic value, that is, it is currently undervalued or underpriced, whereas supply is created if the current stock price is above its estimated intrinsic value, that is, it is currently overvalued or overpriced. See Figures 1.3 and 1.4 for illustrations of using intrinsic value to forecast potential stock price movements.

Figure 1.3 Price Forecasting Based on Intrinsic Value of a Stock.

Figure 1.4 Price Forecasting Based on Intrinsic Value of a Stock.

There are various ways to determine the degree of over- or undervaluation in a stock, some of which include comparing P/E and earnings per share (EPS) ratios or investigating to what extent a stock is trading at a premium or discount in relation to its net current asset value, debt, and other fundamentals. Fundamental analysis helps provide indications as to which stocks to buy based on prior company performance, that is, over the last accounting period. Some investors resort to more active asset-allocation methods to try to time the market for a suitable stock to buy into or get out of, rather than just relying on the traditional buy-and-hold strategy. They resort to studying broad market factors and sector-rotation models in order to buy into the best fundamentally performing stocks within a strengthening industry or sector. This method is popularly termed the top-down approach to investing. A bottom-up approach relies more on a specific company’s fundamental performance. A buy-and-hold strategy in today’s volatile markets may not represent the most effective way of maximizing returns while minimizing potential risks. As a result, many fundamentalists frequently look to various asset pricing and modern portfolio models like the Capital Asset Pricing Model (CAPM) to try to achieve the best balance between risk and expected returns over a risk-free rate (along what is called the efficient frontier).

One of the problems with fundamental analysis is the credibility, reliability, and accuracy of the accounting practices and financial reporting, which is susceptible to manipulation and false or fraudulent reporting. There are various unscrupulous ways to dress up a poorly performing company or financial institution. A simple Internet search will reveal numerous past and ongoing investigations related to such practices. The other problem is the delay in the financial reporting of a company’s current financial state in the market. By the time the next audited report is completed and published, the information is already outdated. It does not furnish timely information to act upon, especially in volatile market environments, and, as a result, does not directly account or adjust for current or sudden developments in the market environment. Nevertheless, fundamental analysis does give valuable information about specific securities and their performances. Its main weakness is its inability to provide clear and specific short-term price levels for traders to act on. Therefore, fundamental information is better suited to longer-term investment decisions, as opposed to short-term market participation, where short-term price fluctuations and precise market timing may be of lesser importance.

Fundamental data, on a broader scale, accounts for the overall underlying economic performance of the markets. Supply and demand reacts to the economic data released at regular intervals, which include interest rate announcements and central bank monetary policy and intervention. One example of how supply and demand in the markets are affected by such factors is the Swiss National Bank’s (SNB) decision to maintain a 0.8333 ceiling in the foreign exchange rate of the CHFEUR, or a floor of 1.2000 in the EURCHF, with respect to the Swiss Franc. This creates a technical demand for the Euro (and a corresponding supply in the CHF) around the 1.2000 exchange-rate level. Many traders have acted and are still acting on this policy decision to their advantage, buying every time the rate approaches 1.2000, with stops placed at a reasonable distance below this threshold. The integrity of this artificial ceiling remains intact as long as the SNB stands steadfast by their policy decision to uphold the ceiling at all costs. See Figure 1.5.

Figure 1.5 SNB Policy Impacting on the Value of the CHF.

Source: MetaTrader 4

It behooves the analyst and investor to examine the actual decision-making process involved with investing in a stock based on intrinsic value. While it does provide an indication, with all else being equal, of the integrity of a certain stock relative to the universe of stocks available, there is a disruptive behavioral component that affects this process. It is not just the calculated or estimated intrinsic value that is an important element but also the general perception or future expectation of this value that plays an arguably greater and more significant role in determining the actual share price of a stock. This may explain why shares prices do not always reflect the actual value of a stock. This disagreement between price and value is the result of divergence between the actual intrinsic value and perceived or projected value.

Forecasting Stock Prices Using Information

Generally, information may be gleaned from various public sources such as newspaper reports, magazines, online bulletins, and so on, upon which market participants may then formulate an opinion about the market, making their own predictions about potential market action. Unfortunately, such publicly available information usually has little merit when used for forecasting purposes, as those more privy to non-public material information would have already moved the markets substantially, leaving only an inconsequential amount of action for latecomers to profit from, at the very most. This is where technical analysts have the unfair advantage of observing the markets moving on the charts and immediately taking action, regardless of the cause or reasons why such action exists. They are only interested in the effects such activity has on price. Technical analysts typically do not wait for news to be public knowledge prior to taking action or making a forecast based on a significant price breakout.

The use of non-public material information potentially affords insiders substantial financial gain from such knowledge, as the release of critical or highly sensitive company information may cause a substantial change in the company’s stock price. Hence it is no great feat to be able to forecast potential market direction based on such prior knowledge, especially if the non-public material information is highly significant or headline worthy. Needless to say, insider trading is illegal in the equity markets. But the possibility will always exist that it can occur and in fact has on many occasions. Unfortunately, in unregulated over-the-counter (OTC) markets, nothing stops brokers from front running large client orders, which is just another form of insider trading.

Forecasting Stock Prices Using Technical Analysis

Technical analysis is essentially the identification and forecasting of potential market behavior based largely on the action and dynamics of the market itself. The action and dynamics of the market is best captured via price, volume, and open interest action. The charts provide a visual description of what has transpired in the markets and technical analysts use this past information to infer potential future price action, based on the assumption that price patterns tend to repeat or behave in a reasonably reliable and predictable manner. Let us turn our attention to some popular definitions of technical analysis.

The following definition of technical analysis tells us that charting is the main tool used to forecast potential future price action.

Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends.

John Murphy, Technical Analysis of the Financial Markets (NYIF, 1999)

The next definition of technical analysis tells us that the charting of past information is used to forecast future price action.

Technical analysis is the science of recording, usually in graphic form, the actual history of trading . . . then deducing from that pictured history the probable future trend.

Edwards and Magee, Technical Analysis of Stock Trends (AMACOM, 2007)

Notice that the last two definitions specifically refer to the forecasting of trend action.

It is interesting at this point to draw a parallel here with information used in fundamental analysis. Technical analysis is often criticized for the use of past information as a basis for forecasting future price action, relying on the notion that certain price behaviors tend to repeat. Unfortunately virtually all forms of forecasting are based on the use of prior or past information, which certainly includes statistical-, fundamental-, and behavior-based forecasting. Companies employ accounting data from the most recent and even past quarters as a basis for gauging the current value of a stock. In statistics, regression-line analysis requires the sampling of past data in order to predict probable future values. Even in behavioral finance, the quantitative measure of the market participant’s past actions form the basis for predicting future behavior.

The following definition of technical analysis tells us that it is the study of pure market action and not the fundamentals of the instrument itself.

It refers to the study of the action of the market itself as opposed to the study of the goods in which the market deals.

Edwards and Magee, Technical Analysis of Stock Trends (AMACOM, 2007)

This next definition of technical analysis tells us that it is a form of art, and its purpose is to identify a trend reversal as early as possible.

The art of technical analysis, for it is an art, is to identify a trend reversal at a relatively early stage and ride on that trend until the weight of the evidence shows or proves that the trend has reversed.

Martin Pring, Technical Analysis Explained, 4th Edition (McGraw-Hill, 2002)

The following definition is most relevant in the formulation of trading strategies. It reminds the market participants that nothing is certain and we must weigh our risk and returns.

Technical analysis deals in probabilities, never in certainties.

Martin Pring, Technical Analysis Explained, 4th Edition (McGraw-Hill, 2002)

The next statement gives a behavioral reason as to why technical analysis works.

Technical analysis is based on the assumption that people will continue to make the same mistakes they have made in the past.

Martin Pring, Technical Analysis Explained, 4th Edition (McGraw-Hill, 2002)

This definition by Pring stresses and underscores the point that there is a real reason and explanation as to why past price patterns tend to repeat. The tendency of price to repeat past patterns is mainly attributed to market participants repeating the same behavior. Although it is not impossible with sufficient and continuous conscious effort and strength of will, human beings rarely change their basic behavior, temperament, and deep-rooted biases, especially in relation to their emotional response to fear, greed, hope, anger, and regret when participating in the markets.

The following statement about technical analysis explains its effectiveness in timing early entries and exits.

Market price tends to lead the known fundamentals. . . . Market price acts as a leading indicator of the fundamentals.

John Murphy, Technical Analysis of the Financial Markets (NYIF, 1999)

This definition by Murphy highlights a very important assumption in technical analysis, which is that price is a reflection of all known information acted upon in the markets. It is the sum of all market participants’ trading and investment actions and decisions, including current and future expectations of market action. It also reflects the overall psychology, biases, and beliefs of all market participants. Therefore, the technical analysts believe that the charts tell the whole story and that everything that can or is expected to impact price has already been discounted. This assumption forms the very basis of technical analysis, and without it, technical analysis would be rendered completely pointless.

Fundamental versus Technically Based Market Timing

Before proceeding any further, it is best to briefly explain the meaning of a few commonly used terms in trading and technical analysis:

To go long means to buy to open a new position

To liquidate means to sell to close a position previously held

To go short means to sell to open a new position

To cover means to buy to close a position previously shorted

Both fundamental and technically based market timing aim to satisfy the same basic principle of buying low and selling high. There are four basic scenarios where this may occur:

Long at a low price and liquidate at a higher price

Long at a relatively high price and liquidate at an even higher price

Short at a high price and cover at a lower price

Short at a relatively low price and cover at an even lower price

Listed below are the some of the strengths of each approach with respect to timing the markets.

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Technically Based Market Timing offers the ability to

Provide precise entry and exit prices

Provide the precise time of entry and exit

Provide real-time bullish and bearish signals

Provide real-time entry and exit price triggers

Scale in and out based on significant price levels

Time entries and exits based on volatility behavior of the underlying

Exit extended trends at technically significant price-reversal levels

Time entries and exits based on market order flow

Define percent risk in terms of significant price levels

Use volume and open interest analysis to gauge strength of an underlying move in order to time entries and exits

Use market breadth and broad market sentiment to gauge the strength of an underlying move in order to time entries and exits

Forecast potential peaks (for shorting or liquidating positions) as well as potential troughs (for getting long and covering positions) via the use of cycle and seasonality analysis

Fundamentally Based Market Timing offers the ability to:

Gauge undervalued stocks with a potential to appreciate in value, but lacking information regarding the precise price or time to get long or to cover

Gauge overvalued stocks with a risk of depreciating in value, but lacking information regarding the precise price or time to get short or to liquidate

Screen and participate in fundamentally strong stocks in a sector or industry as part of an active asset allocation or rotation strategy, but lacking information regarding the precise price or time to get long

The Fundamentalist versus Technical Analysts

Listed below are some characteristics of the fundamentalist and technical analyst:

The Fundamentalist:

Is mainly concerned with intrinsic value

Strives to understand the underlying causes for potential market moves

Is focused on which company to participate in

Can tell you which company to invest in, but cannot tell you the most advantageous moment to start participating in that stock

The Technical Analyst:

Is mainly concerned with structure and dynamics of market and price action

Is more concerned with the effects of potential market moves rather than the cause of them

Cannot usually determine what the intrinsic value of an asset is or whether it is under-or overvalued, but is able to determine precisely when to start participating, purely from the perspective of price performance

Is not concerned with the underlying factors that led to the rise in price; this is irrelevant for all practical purposes as they believe that price is a reflection of all information available in the markets and therefore that is all that really matters

In short, from what we have covered so far, we know that technical analysis:

Uses past information

Uses charts

Identifies past and current price action

Forecasts potential future price action based on historical price behavior (especially the start of a new trend)

Technical Data and Information

Technical analysts study market action. Market action itself is mainly comprised of the study of:

Price action

Volume action

Open interest action

Sentiment

Market breadth

Flow of funds

Of all the data that technical analysts employ, price is the most important, followed closely by volume action. Price itself is comprised of an opening, high, low, and closing price, normally referred to as OHLC data. OHLC data normally refers to the daily opening, high, low, and closing prices, but it may be used to denote the OHLC of any bar interval, from 1-minute bars right up to the monthly and yearly bars.

1.4 Classifying Technical Analysis

Technical analysis may be categorized into four distinct branches, that is, classical, statistical, sentiment, and behavioral analysis. Regardless of which branch is employed, all analysis is eventually interpreted via the various behavioral traits, filters, and biases unique to each analyst. Behavioral traits include both the psychological and emotional elements. See Figure 1.6.

Figure 1.6 The Four Branches of Technical Analysis.

Classical technical analysis involves the use of the conventional bar, chart, and Japanese candlestick patterns, oscillator and overlay indicators, as well as market breadth, relative strength, and cycle analysis. Statistical analysis is more quantitative, as opposed to the more qualitative nature of classical technical analysis. It studies the dispersion, central tendencies, skewness, volatility, regression analysis, hypothesis testing, correlation, covariance, and so on. Sentiment analysis is concerned with the psychology of market participants, which includes their emotions and level of optimism or pessimism in the markets. It studies professional and public opinion via polls and questionnaires, trading and investment decisions via flow of funds in the markets, as well as the positions taken by large institutions and hedgers. Finally, behavioral analysis studies the way market participants react to news, profit and losses, the actions of other market participants, and with their own psychological and emotional biases, preferences, and expectations.

Mean Reverting versus Non–Mean Reverting Approach

The type of technical studies employed also depends on the approach taken by traders and analysts with respect to their personal preferences and biases regarding the action of price in the markets. Basically, traders either adopt a contrarian or a momentum-seeking type approach. Being more contrarian in their approach implies that they do not usually expect the price to traverse large distances. In fact they are constantly on the lookout for impending reversals in the markets. In essence, they expect price to be more mean reverting, returning to an average price or balance between supply and demand. Those that adopt the mean-reverting approach prefer to employ technical studies that help pinpoint levels of overbought and oversold activity, which includes divergence analysis, regression analysis, moving average bands, and Bollinger bands. They prefer to trade consolidations rather than trend action. They normally buy at support and short at resistance. Limit entry orders are their preferred mode of order entry. Conversely, being more momentum seeking in their approach implies that they usually expect the price to traverse large distances and for trends to continue to remain intact. They are constantly on the lookout for continuation type breakouts in the markets. In short, they expect price to be more non–mean reverting, where demand creates further demand and supply creates further supply, both driven by a powerful positive-feedback cycle. Those that adopt the non–mean reverting approach prefer to employ technical studies that help pinpoint breakout or trend continuation activity, which includes chart pattern breakouts, moving average breakouts, Darvas Box breakouts, and Donchian channel breakouts. They prefer to trade trends rather than ranging action. They normally short at the breach of support and long at breach of resistance. Stop entry orders are their preferred mode of entry into the markets. See Figure 1.7.

Figure 1.7 Mean Reverting versus Non–Mean Reverting Approaches.

Advantages and Disadvantages of Technical Analysis

The advantages of applying technical analysis to the markets are:

It is applicable across all markets, instruments, and timeframes, where price patterns, oscillators, and overlay indicators are all treated in exactly the same manner. No new learning is required in order to trade new markets or timeframes, unlike in fundamental analysis where the analyst must be conversant with the specifics of each stock or market.

There is no need to study the fundamentals of the markets traded or analyzed in order to apply technical analysis, since technical analysts believe that all information that impacts or potentially may impact the stock or market is already reflected in the price on the charts.

Technical analysis provides a clear visual representation of the behavior of the markets, unlike in fundamental analysis where most of the data is in numerical form.

It provides timely and precise entry and exit price levels, preceded by technical signals indicating potential bullishness or bearishness. It has the ability to also pinpoint potential time of entry via time projection techniques not available to fundamentalists. Fundamental analysis does not provide the exact price or time of entry.

It makes the gauging of market risk much easier to visualize. Volatility is more obvious on the charts than it is in numerical form.

The concerted effort of market participants acting on significantly clear and obvious price triggers in the markets helps create the reaction required for a more reliable trade. This is the consequence of the self-fulfilling prophecy.

The disadvantages of applying technical analysis are:

It is subjective in its interpretation. A certain price pattern may be perceived in numerous ways. Since every bullish interpretation has an equal and opposite bearish interpretation, all analysis is susceptible to the possibility of interpretational ambiguity. Unfortunately, all manners of interpretation, regardless of the underlying analysis employed—be it fundamental, statistical, or behavioral—are equally subjective in content and form.

A basic assumption of technical analysis is that price behavior tends to repeat, making it possible to forecast potential future price action. Unfortunately this tendency to repeat may be disrupted by unexpected volatility in the markets caused by geopolitical, economic, or other factors. Popular price patterns may also be distorted by new forms of trade execution that may impact market action, like automated, algorithmic, or high-frequency program trading where trades are initiated in the markets based on non-classical patterns. This interferes with the repeatability of classic chart patterns.

Charts provide a historical record of price action. It takes practice and experience to be able to identify classical patterns in price. Though this skill can be mastered with enough practice, the art of inferring or forecasting future price action based on past prices is much more difficult to master. The practitioner needs to be intimately familiar with the behavior of price at various timeframes and in different markets. Although classical patterns may be applied equally across all markets and timeframes equally, there is still an element of uniqueness associated with each market action and timeframe.

It is argued that all market action is essentially a random walk process, and as such applying technical analysis is pointless as all chart patterns arise out of pure chance and are of no significance in the markets. One must remember that if this is the case, then all forms of analysis are ineffective, whether fundamental, statistical, or behavioral. Since the market is primarily driven by perception, we know that the random-walk process is not a true representation of market action, since market participants react in very specific and predictable ways. Though there is always some element of randomness in the markets caused by the uncoordinated actions of a large number of market participants, one can always observe the uncanny accuracy with which price tests and reacts at a psychologically significant barriers or prices. It is hard to believe that price action is the result of random acts of buying and selling by market participants where the participants are totally unencumbered by cost, biases, psychology, or emotion.

The strong form of the Efficient Market Hypothesis (EMH) argues that since the markets discount all information, price would have already adjusted to the new information and any attempt to profit from such information would be futile. This would render the technical analysis of price action pointless, with the only form of market participation being passive investment. But such efficiency would require that all market participants react instantaneously to all new information in a rational manner. This in itself presents an insurmountable challenge to EMH. The truth is that no system comprising disparate parts in physical reality reacts instantaneously with perfect coordination. Hence it is fairly safe to assume that although absolute market efficiency is not attainable, the market does continually adjust to new information, but at a much lower and less-efficient rate of data discounting. Therefore, technical analysis remains a valid form of market investigation until the markets attain a state of absolute and perfect efficiency.

Another argument against technical analysis is the idea of the Self-Fulfilling Prophecy (SFP). Proponents of the concept contend that prices react to technical signals not because the signals themselves are important or significant, but rather because of the concerted effort of market participants acting on those signals that make it work. This may in fact be advantageous to the market participants. The trick is in knowing which technical signals would be supported by a large concerted action. The logical answer would be to select only the most significantly clear and obvious technical signals and triggers. Of course, one can further argue that such signals, if they appear to be reliable indicators of support and resistance, would begin to attract an increasing number of traders as time passes. This would eventually lead to traders vying with each other for the best and most cost-effective fills. What seems initially like the concerted action of all market participants now turns into competition with each other. Getting late fills would be costly as well as reduce or wipe out any potential for profit. This naturally results in traders attempting to preempt each other for the best fills. Traders start vying for progressively earlier entries as price approaches the targeted entry levels, leading finally to entries that are too distant from the original entry levels, increasing risk and reducing any potential profits. This disruptive feedback cycle eventually erodes the reliability of the signals, as price fails to react at the expected technical levels. Price finally begins to react reliably again at the expected technical levels as traders stop preempting each other and abandon or disregard the strategy that produced the signals. The process repeats. Therefore, SFP may result in technical signals evolving in a kind of six-stage duty cycle, where the effects of SFP may be advantageous and desirable to traders in the early stages but eventually result in forcing traders into untenable positions. See Figure 1.8.

Figure 1.8 The Idealized Six-Stage Self-Fulfilling Prophecy Cycle.

1.5 SUBJECTIVITY IN TECHNICAL ANALYSIS

As with most forms of analysis, technical analysis has both objective and subjective aspects associated with its application. It is objective insofar as the charts represent a historical record of price and market action. But it is subjective when the technical analyst attempts to analyze the data.

Analyzing price and market action consists of three main activities, namely:

Identifying price and indicator patterns

Interpreting the data

Inferring potential future price behavior

Analyzing price and market action is ultimately subjective because all analysis is interpreted through various behavioral traits, filters, and biases unique to each analyst or observer. Behavioral traits include both the psychological and emotional elements. As a consequence, each analyst will possess a slightly different perception of the market and its possible future behavior.

Subjectivity in the Choice of Analysis and Technical Studies

The sheer number of ways to analyze an individual chart contributes to the overall level of subjectivity associated with each forecast. The problem is twofold:

What is the most appropriate form of technical analysis that should be applied to a particular chart?

What is the most appropriate choice of indicators to apply to a particular chart?

These are the usual questions that plague novices. The following charts depict the various popular forms of analysis that can be applied to a basic chart of price action. The following examples are by no means exhaustive. Figure 1.9 starts off with a plain chart devoid of any form of analysis.

Figure 1.9 A Simple Price Chart.

Source: MetaTrader 4

The next chart, Figure 1.10, shows the application of basic trendline analysis on the same chart, tracking the flow of price action in the market.

Figure 1.10 Trendline Analysis on the Same Chart.

Source: MetaTrader 4

In Figure 1.11, moving average analysis is now employed to track the same flow of price action and to provide potential points of entry as the market rises and falls.

Figure 1.11 Moving Average Analysis on the Same Chart.

Source: MetaTrader 4

Figure 1.12 depicts the application of chart pattern analysis to track and forecast the shorter-term bullish and bearish movements in price.

Figure 1.12 Chart Pattern Analysis on the Same Chart.

Source: MetaTrader 4

Figure 1.13 is an example of applying two forms of technical analysis, that is, linear regression analysis and divergence analysis to track and forecast potential market tops and bottoms. Notice that the market top coincided perfectly with the upper band of the linear regression line, with an early bearish signal seen in the form of standard bearish divergence on the commodity channel index (CCI) indicator.

Figure 1.13 Linear Regression and Divergence Analysis on the Same Chart.

Source: MetaTrader 4

Figure 1.14 is an example of applying a couple of additional forms of analysis to the basic linear regression band. In this chart, price action analysis is used in conjunction with volume analysis to forecast a potential top in the market, evidenced by the preceding parabolic move in price that is coupled by a blow-off.

Figure 1.14 Linear Regression and Volume Analysis on the Same Chart.

Source: MetaTrader 4

In Figure 1.15, volatility band, volume, and overextension analysis are all employed to seek out potential reversals in the market. We observe that price exceeds the upper volatility band, which may potentially be an early indication of price exhaustion, especially since it is accompanied by a significant volume spike. The moving average convergence-divergence (MACD) indicator is also seen to be residing at historically overbought levels, which is another potentially bearish indication.

Figure 1.15 Volatility Band, Volume, and Overextension Analysis on the Same Chart.

Source: MetaTrader 4

As we can see from just a few forms of analysis presented in the preceding charts, there are many ways to view the action of the markets, depending on the context of the analysis employed. For example, if the analyst is more interested in viewing and understanding the action of price within the context of over-reaction or price exhaustion in the markets, he or she may opt to apply technical studies that track levels or areas of potential over-reaction or price exhaustion. Technical studies that tract such behavior include linear regression bands, Bollinger bands, moving average percentage bands, Keltner and Starc bands, areas of prior support and resistance, and so on. Alternatively, if the analyst is more interested in viewing and understanding the action of price within the context of market momentum, he or she may instead opt to apply breakout analysis of chart patterns, trendlines, moving averages, and so on. As long as the reason for using a particular form of analysis is clear, there should be no confusion as to what the studies are indicating.

Contradictory, Confirmatory, and Complementary Signals

There are many instances when two oscillator signals are in clear and direct opposition with each other. This is inevitable, as each oscillator is constructed differently. The mathematics underlying each oscillator varies with the purpose it is designed for, and in most cases, it involves the manipulation of price, volume, and open interest data. A few reasons for conflicting oscillator and indicator signals are:

The mathematical construction of each oscillator or indicator is different.

Each oscillator or indicator tracks a different time horizon.

Two identical oscillators may issue inconsistent readings due to missing data on one of the charting platforms.

Two identical oscillators may also issue inconsistent readings due to variations in the accuracy, quality, and type of data available on different charting platforms.

For example, applying an oscillator that uses price, volume, and open interest as part of its calculation will yield inconsistent readings should one of the data be unavailable on the charting platform. The analysts may not be aware of the missing data and struggle to make sense of the inconsistency. The accuracy of the data is also of paramount importance for effective analysis of price and market action. Dropouts in the data as well as the inclusion or exclusion of non-trading days will cause inconsistent readings between charting platforms. There may also be variations in the oscillator readings should volume be replaced with tick volume, sometime also referred to as transaction volume. Tick volume tracks the number of transactions over a specified time interval, irrespective of the size of the transactions.

It is also important to note that conflicting signals may not always be in fact conflicting. As pointed out, the time horizons over which each signal is applied may be different. In Figure 1.16 we observe that the CCI readings over the range of prices are markedly different. The 20-period