Investing with the Trend ebook

Contents

Foreword

Preface

Acknowledgments

Chapter 1: Introduction

Believable Misinformation

Indicators and Terminology You Should Be Familiar with

Living in the Noise

Data

Part I: Market Fiction, Flaws, and Facts

Chapter 2: Fictions Told to Investors

Believable Misinformation in Investing

The Void of Accountability

Hiding behind Statistics

You Must Remain Invested or You Will Miss the 10 Best Days of the Year

Diversification Will Protect You?

Dollar Cost Averaging

Compounding Is the Eighth Wonder of the World

Relative Performance

Chapter 3: Flaws in Modern Financial Theory

What Modern Portfolio Theory Forgot or Ignored

Modern Portfolio Theory and the Bell Curve

Standard Deviation (Sigma) and Its Shortcomings

Improper Process

High Sigma Days We All Remember

Rolling Returns and Gaussian Statistics

Risk and Uncertainty

Back to the Original Question: Is Volatility Risk?

Is Linear Analysis Good Enough?

Linear Regression Must Have Correlation

The 60/40 Myth Exposed

Discounted Cash Flow Model

Chapter 4: Misuse of Statistics and Other Controversial Practices

The Deception of Average

One If by Land, Two If by Sea

Everything on Four Legs Is a Pig

Chapter 5: The Illusion of Forecasting

The Reign of Error

An Investment Professional’s Dilemma

Gurus/Experts

Masking an Intellectual Void

Earnings Season

Are Financial Advisors Worth 1 Percent of AUM (Assets under Management)?

Economists Are Good at Predicting the Market

News Is Noise

Chapter 6: The Enemy in the Mirror

Real Time versus History

Behavioral Investing

Behavioral Biases

Bias Tracks for Investors

Investor Emotions

Investors as a Whole Do Poorly

Chapter 7: Market Facts: Bull and Bear Markets

Calendar versus Market Math

Stock Exchange Holidays

Understanding the Past

Bull Markets

Bear Markets

Just How Bad Can a Bear Market Be?

Bear Markets and Withdrawals

Market Volatility

Highly Volatile Periods

Dispersion of Prices

Secular Markets

Secular Bull Markets

Secular Bear Markets

Chapter 8: Market Facts: Valuations, Returns, and Distributions

Market Valuations

Market Sectors

Sector Rotation in 3-D

Asset Classes

The Lost Decade

Market Returns

Distribution of Returns

Part II: Market Research

Chapter 9: Why Technical Analysis?

What Is Technical Analysis?

I Use Technical Analysis Because.

The Challenge of Technical Analysis

Technical Indicators

Some Things That Bother Me

Chapter 10: Market Trend Analysis

Why Markets Trend

Supply and Demand

What Do You Know about This Chart?

Trend versus Mean Reversion

Trend Analysis

Indices Analysis Summary

Trend Analysis on the S&P GICS Data

Trend Analysis in Secular Bear Markets

Chapter 11: Drawdown Analysis

What Is Drawdown?

Drawdown Terminology

The Mathematics of Drawdown and Equivalent Return

Cumulative Drawdown

S&P 500 Drawdown Analysis

S&P Total Return Analysis

Dow Jones Industrial Average Drawdown Analysis

Dow Industrials Total Return Analysis

Gold Drawdown

Japan’s Nikkei 225 Drawdown

Copper Drawdown

Drawdown Intensity Evaluator (DIE)

Part III: Rules-Based Money Management

Chapter 12: Popular Indicators and Their Uses

Moving Averages and Smoothing

Stochastics

RSI (Relative Strength Index)

Moving Average Convergence Divergence (MACD)

A Word of Caution

The Binary Indicator

How Compound Measures Work

Chapter 13: Measuring the Market

Weight of the Evidence Measures

A Note on Optimization

Indicator Evaluation Periods

Price-Based Indicators

Breadth-Based Indicators

Slope of Moving Average

World Market Climate

Cyclical Market Measure

Relative Strength

Dominant Index

Trend Capturing Measure

LTM—Long-Term Measure

Bull Market Confirmation Measure

Initial Trend Measures (ITM)

Trend Gauge

Chapter 14: Security Ranking, Selection, Rules, and Guidelines

Ranking Measures

Pullback Rally Analysis

Pair Analysis

Ranking and Selection

Rules and Guidelines

Asset Commitment Tables

Chapter 15: Putting It All Together: The “Dancing with the Trend” Model

Weight of the Evidence

Investing with the Weight of the Evidence

Ranking and Selection

Discipline

Sell Criteria

Tweaking the Model

Model in Action

Risk Statistics, Ratios, Stops, Whipsaws, and Miscellaneous

Mutual Fund Expenses

Turnover and Taxes

Watching a Tactical Strategy over the Short Term

Benchmarking

Full Cycle Analysis

Actual Results from a Rules-Based Trend-Following Strategy—Dancing with the Trend

Mean Shifting

Chapter 16: Putting Trend-Following to Work

Chapter 17: Conclusions

Financial Advice

A Compilation of Rules and Guidelines for Investors

Secular Markets and the Efficiency Ratio

The Rules-Based Trend-Following Model in October 1987

The Flash Crash of May 6, 2010

Final Observations

Appendix A: Passive versus Active Management

Appendix B: Trend Analysis Tables

Appendix C: Market Breadth

Appendix D: Recommended Reading

Bibliography

About the Author

About the Online Resources

Index

End User License Agreement

List of Tables

TABLE 2.1 Best and Worst Days

TABLE 2.2 Components of the Diversification Charts (Figures 2.3 and 2.4)

TABLE 2.3 Dollar Cost Averaging

TABLE 2.4 Compounding Example 1

TABLE 2.5 Compounding Example 2

TABLE 3.1 October 2008

TABLE 3.2 Probability of Events Occurring

TABLE 3.3 Different Results for Sigma If Not Using Correct Data Periods

TABLE 3.4 Black Monday–Tuesday, October 28–29, 1929

TABLE 3.5 Black Monday, October 19, 1987

TABLE 3.6 Full History (1885–2012) of Daily Dow Industrials

TABLE 3.7 Table Showing Data in Figure 3.3

TABLE 4.1 Long-Term Performance of Asset Classes

TABLE 6.1 Twenty-Year Returns of Various Classes and Average Investor

TABLE 6.2 Investor Performance Compared to Funds

TABLE 7.1 Market Holidays (2012)

TABLE 7.2 S&P 500 Bull Markets

TABLE 7.3 Bear Markets in Dow Jones Industrials from 2/17/1885 to 12/31/2012

TABLE 7.4 Bear Markets in the S&P 500 from 12/30/1927 to 12/31/2012

TABLE 7.5 Monthly Returns During a Bear Market While Withdrawing from Account

TABLE 7.6 Dispersion of Annual Dow Jones Industrial Prices

TABLE 7.7 Secular Bull Data

TABLE 7.8 Secular Bear Data

TABLE 8.1 Sectors Performance (1990–2012)

TABLE 8.2 Sectors Relative Performance (1990–2012)

TABLE 8.3 Sector Performance in Economic Cycle

TABLE 8.4 Asset Class Performance (1990–2012)

TABLE 8.5 Asset Class Relative Performance (1990–2012)

TABLE 8.6 Average and Median Returns for 1, 2, 3, 5, 10, and 20 Years

TABLE 8.7 Range of Returns for 1, 2, 3, 5, 10, and 20 Years

TABLE 9.1 Multicollinearity Example

TABLE 10.1 Indices Used in Trend Analysis

TABLE 10.2 Trends of 21 Days with 5 Percent Filter

TABLE 10.3 Trendiness One Explanation

TABLE 10.4 Trendiness One Table

TABLE 10.5 Trendiness Two Explanation

TABLE 10.6 Trendiness Two Table

TABLE 10.7 Trendless Analysis Explanation

TABLE 10.8 Trendless Analysis Table

TABLE 10.9 All Three Trend Analyses on All Indices

TABLE 10.10 Trend Analysis of U.S. Dollar Index

TABLE 10.11 Trend Analysis of Turkey ISE National-100

TABLE 10.12 Trend Analysis of Norway Oslo All Share Composite

TABLE 10.13 Trend Analysis of Hanoi SE Index

TABLE 10.14 Domestic Trendiness

TABLE 10.15 International Trendiness

TABLE 10.16 Commodity Trendiness

TABLE 10.17 Sector Trendiness

TABLE 10.18 Trend Analysis on Indices with Data History Prior to 2000

TABLE 10.19 Trend Analysis on Indices with Data History Prior to 1990

TABLE 10.20 Trend Analysis on Indices with Data History Prior to 1980

TABLE 10.21 GICS Sectors, Industry Groups, and Industries

TABLE 10.22 GICS Trend Analysis of Wide Variety of Trends (Partial Data)

TABLE 10.23 Summary of GICS Trend Analysis

TABLE 10.24 Summary of GICS Trend Analysis with Short Data History Issues Removed

TABLE 10.25 GICS Analysis for All Trends Up to 30-Day Duration

TABLE 10.26 Trend Analysis in Secular Bear Markets

TABLE 11.1 Drawdown Terminology Table

TABLE 11.2 S&P 500 Drawdown Decline Data

TABLE 11.3 S&P 500 Drawdown Recovery Data

TABLE 11.4 S&P 500 Drawdown Duration Data

TABLE 11.5 S&P 500 Time Spent in Drawdown

TABLE 11.6 S&P 500 Average Drawdown

TABLE 11.7 S&P 500 Drawdown Decline Data Excluding 1929 Bear Market

TABLE 11.8 S&P 500 Drawdown Recovery Data Excluding 1929 Bear Market

TABLE 11.9 S&P 500 Drawdown Duration Data Excluding 1929 Bear Market

TABLE 11.10 Bear Markets in S&P 500 from 12/30/1927 to 12/31/2012

TABLE 11.11 S&P 500 Total Return Drawdown Decline Data

TABLE 11.12 S&P 500 Total Return Drawdown Recovery Data

TABLE 13.13 S&P 500 Total Return Drawdown Duration Data

TABLE 11.14 S&P 500 Total Return Bear Markets (3/31/1936 to 12/31/2012)

TABLE 11.15 Dow Industrials Drawdown Decline Data

TABLE 11.16 Dow Industrials Drawdown Recovery Data

TABLE 11.17 Dow Industrials Drawdown Duration Data

TABLE 11.18 Dow Industrials Time Spent in Drawdown

TABLE 11.19 Dow Industrials Average Drawdown

TABLE 11.20 Dow Industrials Drawdown Decline Excluding 1929 Bear Market

TABLE 11.21 Dow Industrials Drawdown Recovery Excluding 1929 Bear Market

TABLE 11.22 Dow Industrials Drawdown Duration Excluding 1929 Bear Market

TABLE 11.23 Dow Industrials Bear Markets (2/17/1885 to 12/31/2012)

TABLE 11.24 Dow Industrials Total Return Drawdown Decline Data

TABLE 11.25 Dow Industrials Total Return Drawdown Recovery Data

TABLE 11.26 Dow Industrials Total Return Drawdown Duration Data

TABLE 11.27 Dow Industrials Total Return Bear Markets (3/31/1963 to 12/31/2012)

TABLE 13.1 Price Long-Term Performance Statistics

TABLE 13.2 Price Long-Term Performance Statistics Ranking

TABLE 14.1 Pullback Rally Analysis

TABLE 14.2 IJR/IEF Pair Performance Statistics

TABLE 14.3 Core Rotation Pairs

TABLE 14.4 Performance Comparison between Core Rotation Strategy and S&P 500

TABLE 14.5 Ranking Measures Worksheet

TABLE 14.6 Asset Commitment Table

TABLE 14.7 Alternative Asset Commitment Table

TABLE 15.1 Weight of the Evidence Levels Table

TABLE 15.2 Mutual Fund Expenses

TABLE 15.3 Peak-to-Peak (April 2000–October 2007) Analysis

TABLE 15.4 Trough-to-Trough (October 2002–February 2009) Analysis

TABLE 15.5 Since Inception Analysis: January 1996–December 2012 (Full History)

TABLE 15.6 Risk/Return Table January 1996–December 2012: Summary Statistics

TABLE 15.7 Return Analysis January 1996–December 2012 (Not Annualized If Less Than 1 Year)

TABLE 15.8 Upside/Downside Table January 1996–December 2012 (Single Computation)

TABLE 15.9 Asset Performance Comparison from 12/31/1995 to 12/31/2012

TABLE 15.10 Statistics from 1996–2012 Comparing Dance with the Trend to S&P 500

TABLE 16.1 Component Performance versus Buy and Hold

TABLE 16.2 Comparison of Two of Three and All Three to Buy and Hold

TABLE 16.3 System versus Buy and Hold Comparison

TABLE A.1 Pro and Con of Various Strategies

TABLE B.1 Trendiness One Rank

TABLE B.2 Up Trendiness Rank

TABLE B.3 Trendiness Two Up Rank

TABLE B.4 Trendiness One Rank

TABLE B.5 Up Trendiness Rank

TABLE B.6 Trendiness Two Up Rank

TABLE C.1 Daily Breadth versus Weekly Breadth

TABLE C.2 Breadth Indicators

List of Illustrations

FIGURE 1.1 Kepler’s Second Law of Planetary Motion

FIGURE 1.2 Coriolis Effect

FIGURE 2.1 Dow Industrial Annual Return Histogram

FIGURE 2.2 Missing the Best and the Worst 10 Days Each Year

FIGURE 2.3 Diversification Works

FIGURE 2.4 Diversification Does Not Work

FIGURE 2.5 Callan Periodic Table of Relative Returns

FIGURE 2.6 Morningstar Style Box

FIGURE 2.7 Trend Followers Style Box

FIGURE 3.1 Normal Distribution Versus Actual Distribution from 2/17/1885 to 12/31/2012

FIGURE 3.2 Visual for Look-Back, Look-Forward, and Data Point

FIGURE 3.3 Five-Year Look-Back and Five-Year Look-Forward Days Outside +/− 3 Sigma

FIGURE 3.4 Five-Year Look-Back and One-Year Look-Forward Days Outside +/– 3 Sigma

FIGURE 3.5 Ten-Year Look-Back and One-Year Look-Forward Days Outside +/– 3 Sigma

FIGURE 3.6 Twenty-Year Look-Back and One-Year Look-Forward Days Outside +/– 3 Sigma

FIGURE 3.7 Fifty-Year Look-Back and One-Year Look-Forward Days Outside +/– 3 Sigma

FIGURE 3.8 Nonsystematic and Systematic Risk

FIGURE 3.9 Volatility versus Risk

FIGURE 3.10 Source of Alpha and Beta from Linear Analysis

FIGURE 3.11 ABC U.S. Equity

FIGURE 3.12 XYZ U.S. Equity

FIGURE 3.13 ABC and XYZ U.S. Equity

FIGURE 3.14 Efficient Frontier (1960–2010)

FIGURE 3.15 Efficient Frontier—Each Decade from 1960 to 2010

FIGURE 4.1 Eighty-Five-Year Returns of Various Assets

FIGURE 4.2 Distribution of Returns Based on Percentage

FIGURE 4.3 Average of Rates of Change Example

FIGURE 4.4 Dow Industrials 20-Year Rolling Returns (1885–2012)

FIGURE 5.1 Buy-and-Sell Recommendations from Earnings Predictions

FIGURE 6.1 Cycle of Investor Emotions

FIGURE 7.1 Bull Markets Gain versus Duration

FIGURE 7.2 Bull Markets Gain versus Duration without 1987–2000

FIGURE 7.3 Twenty Percent Moves in Dow Industrial Average—Long Term

FIGURE 7.4 Twenty Percent Moves in Dow Industrial Average—Medium Term

FIGURE 7.5 Comparison of Mega-Bear Markets

FIGURE 7.6 Withdrawals during a Bear Market Can Be Devastating

FIGURE 7.7 S&P 500 Real Price and 20-Year Rolling Total Returns

FIGURE 7.8 Volatility Measured Three Ways

FIGURE 7.9 Dow Industrial Average Absolute Three-Month Percent Change

FIGURE 7.10 Monthly Volatility and Yearly Volatility

FIGURE 7.11 S&P 500 and VIX

FIGURE 7.12 S&P 500, VIX, and VIX Volatility

FIGURE 7.13 Dow Industrials Highly Volatile Periods since 1900

FIGURE 7.14 S&P 500, Inflation (CPI), and Price Earnings Ratio (PE)

FIGURE 7.15 Recent S&P 500, Inflation, and PE

FIGURE 7.16 Secular Bull Markets since 1900

FIGURE 7.17 Secular Bull Market Composite

FIGURE 7.18 Secular Bear Markets since 1900

FIGURE 7.19 Secular Bear Market Composite

FIGURE 7.20 Secular Bear Market (1966–1982)

FIGURE 8.1 S&P Composite and Real Shiller PE10

FIGURE 8.2 Shiller PE10 in Deciles

FIGURE 8.3 Secular Bear Market PE since 1900

FIGURE 8.4 Secular Bear Market PE Composite

FIGURE 8.5 Secular Bull Market PE since 1900

FIGURE 8.6 Secular Bull Market Composite PE

FIGURE 8.7 Sector Rotation Graphic from Sam Stovall

FIGURE 8.8 Offensive–Defensive Measure

FIGURE 8.9 Sector Rotation in 3-D

FIGURE 8.10 Sector Rotation Graphic

FIGURE 8.11 Lost Decade

FIGURE 8.12 Lost Quarter of a Century

FIGURE 8.13 Nikkei 225 (1984–2012)

FIGURE 8.14 One-Year Annualized Return of S&P 500 Price

FIGURE 8.15 Three-Year Annualized Return of S&P 500 Price

FIGURE 8.16 Five-Year Annualized Return of S&P 500 Price

FIGURE 8.17 Ten-Year Annualized Return of S&P 500 Price

FIGURE 8.18 Twenty-Year Annualized Return of S&P 500 Price

FIGURE 8.19 Twenty-Year Annualized Total Return for S&P 500

FIGURE 8.20 Twenty-Year Annualized Total Return for S&P 500 Inflation Adjusted

FIGURE 8.21 Normal Distribution

FIGURE 8.22 Distribution of Inflation-Adjusted 20-Year Returns by Quartile

FIGURE 8.23 Distribution of Inflation-Adjusted 20-Year Returns by Decile

FIGURE 8.24 Distribution of Inflation-Adjusted 20-Year Returns by Standard Deviation

FIGURE 8.25 Distribution of Inflation-Adjusted 20-Year Returns by Percentage Ranges

FIGURE 9.1 Zahorchak Method

FIGURE 9.2 Head and Shoulders Top?

FIGURE 9.3 Japanese Evening Star Pattern?

FIGURE 10.1 Chart without Price, Name, or Dates

FIGURE 10.2 Chart with Only Trends

FIGURE 10.3 Trend Length and Frequency

FIGURE 10.4 Difference between Filtered Wave Using Close and High–Low

FIGURE 10.5 Trend Analysis Example

FIGURE 10.6 Scatter Plot of Trendiness One versus Trendiness Two

FIGURE 10.7 Trendiness One versus Trendless Scatter Plot

FIGURE 10.8 Trend Analysis of U.S. Dollar Index Chart

FIGURE 10.9 Trend Analysis of Turkey ISE National-100 Chart

FIGURE 10.10 Trend Analysis of Norway Oslo All Share Composite Chart

FIGURE 10.11 Trend Analysis of Hanoi SE Index Chart

FIGURE 11.1 Volatility versus Risk

FIGURE 11.2 Drawdown Terminology Chart

FIGURE 11.3

FIGURE 11.4 Cumulative Drawdown Example

FIGURE 11.5 Dow Industrials Cumulative Drawdown (1885−2012)

FIGURE 11.6 Dow Industrials Cumulative Drawdown (1954–2012)

FIGURE 11.7 Alternative Drawdown Method for S&P 500

FIGURE 11.8 S&P 500 Distribution of Drawdowns Greater than 15 Percent

FIGURE 11.9 S&P 500 Distribution of All Drawdowns

FIGURE 11.10 S&P 500 Cumulative Drawdown Chart

FIGURE 11.11 S&P 500 Total Return Distributions Greater than 15 Percent

FIGURE 11.12 S&P 500 Total Return Distributions

FIGURE 11.13 Dow Industrials Distribution of Drawdowns Greater than 15 Percent

FIGURE 11.14 Dow Industrials Distribution of All Drawdowns

FIGURE 11.15 Dow Industrials Cumulative Drawdown

FIGURE 11.16 Dow Industrials Total Return Distribution of Drawdowns Greater than 15%

FIGURE 11.17 Dow Industrials Total Return Distribution of Drawdowns

FIGURE 11.18 Gold Cumulative Drawdown

FIGURE 11.19 Nikkei Cumulative Drawdown

FIGURE 11.20 Copper Cumulative Drawdown

FIGURE 11.21 Drawdown Intensity Evaluator (DIE) Explanation

FIGURE 11.22 Drawdown Intensity Evaluator and Normalized Version

FIGURE 12.1 Arithmetic Average Component Weighting

FIGURE 12.2 Exponential Average Component Weighting

FIGURE 12.3 Difference between Simple Average (SMA) and Exponential Average (EMA)

FIGURE 12.4 Stochastics Example

FIGURE 12.5 RSI Example

FIGURE 12.6 MACD Example

FIGURE 12.7 RSI and MACD Example

FIGURE 12.8 Binary Indicator Example

FIGURE 12.9 Compound Measures Example 1

FIGURE 12.10 Compound Measures Example 2

FIGURE 13.1 Greg Morris? Weight of the Evidence Worksheet from Mid-1980s

FIGURE 13.2 Jason Goepfert?s Equity Put/Call Ratio Analysis

FIGURE 13.3 Optimized Results Showing Good and Bad Areas

FIGURE 13.4 Indicator Evaluation Periods

FIGURE 13.5 Price Long Term (1998–2012)

FIGURE 13.6 Price Long Term (2009–2012)

FIGURE 13.7 Risk Price Trend

FIGURE 13.8 Adaptive Trend

FIGURE 13.9 Advance Decline Measure—2007

FIGURE 13.10 Advance Decline Measure

FIGURE 13.11 Up Volume/Down Volume Measure

FIGURE 13.12 New Highs/New Lows Measure

FIGURE 13.13 Breadth Combination Measure

FIGURE 13.14 Nasdaq Percent of Issues Above Their 200-Day Moving Average

FIGURE 13.15 Nasdaq Percent of Issues above Their 50-Day Moving Average

FIGURE 13.16 Slope of Moving Average

FIGURE 13.17 World Market Climate

FIGURE 13.18 Cyclical Market Measure

FIGURE 13.19 Small Cap versus Large Cap Measure

FIGURE 13.20 Growth versus Value Measure

FIGURE 13.21 Breadth versus Price Measure

FIGURE 13.22 Relative Strength Compound Measure (1999–2012)

FIGURE 13.23 Relative Strength Compound Measure (2008–2012)

FIGURE 13.24 Dominate Index Concept

FIGURE 13.25 Advance Decline Component of Trend Capturing Measure

FIGURE 13.26 Up Volume/Down Volume Component of Trend Capturing Measure

FIGURE 13.27 Price Component of Trend Capturing Measure

FIGURE 13.28 Trend Capturing Measure (2012)

FIGURE 13.29 Long-Term Measure (1994–2012)

FIGURE 13.30 Long-Term Measure (2011–2012)

FIGURE 13.31 Bull Market Confirmation Measure (1964–2012)

FIGURE 13.32 Bull Market Confirmation Measure (1999–2012)

FIGURE 13.33 Up Volume/Down Volume Initial Trend Measure

FIGURE 13.34 Advance Decline Initial Trend Measure

FIGURE 13.35 Price Initial Trend Measure

FIGURE 13.36 NYSE Price Initial Trend Measure

FIGURE 13.37 Mega Trend Plus

FIGURE 13.38 Trend Strength

FIGURE 13.39 Trend Gauge

FIGURE 13.40 Trend Gauge Over Longer Time Frame

FIGURE 14.1 Trend

FIGURE 14.2 Trend Rate of Change

FIGURE 14.3 Trend with Trend Rate of Change

FIGURE 14.4 Trend and Trend Diffusion

FIGURE 14.5 Price Momentum and Momentum Rate of Change

FIGURE 14.6 Price Performance

FIGURE 14.7 Relation to Stop

FIGURE 14.8 Relative Performance

FIGURE 14.9 PowerScore

FIGURE 14.10 Efficiency Ratio Explanation

FIGURE 14.11 Efficiency Ratio

FIGURE 14.12 Average Drawdown

FIGURE 14.13 Relative Average Drawdown

FIGURE 14.14 Price × Volume

FIGURE 14.15 Adaptive Trend

FIGURE 14.16 Weighted Performance

FIGURE 14.17 Slow Trend

FIGURE 14.18 Ulcer Index

FIGURE 14.19 Sortino Ratio

FIGURE 14.20 Beta

FIGURE 14.21 Relationship to Moving Average

FIGURE 14.22 Correlation

FIGURE 14.23 Pullback Rally Period Example

FIGURE 14.24 IJR/IEF Pair Ratio with 4 Percent Weekly Change

FIGURE 14.25 Pair and Individual Components

FIGURE 14.26 Pair Analysis versus S&P 500

FIGURE 14.27 Core Rotation Strategy and S&P 500 with Drawdowns

FIGURE 15.1 Why Breadth Is Used

FIGURE 15.2 Weight of the Evidence Explanation 1

FIGURE 15.3 Weight of the Evidence Explanation 2

FIGURE 15.4 Weight of the Evidence Explanation 3

FIGURE 15.5 Weight of the Evidence Levels

FIGURE 15.6 ETF Universe Reduced by Ranking Measures

FIGURE 15.7 Mandatory Ranking Measures

FIGURE 15.8 Mandatory and Tie-Breaker Ranking Measures

FIGURE 15.9 Weight of the Evidence (1996–2012)

FIGURE 15.10 Weight of the Evidence (2007–2012)

FIGURE 15.11 Weight of the Evidence (2011–2012)

FIGURE 15.12 Weight of the Evidence (2012)

FIGURE 15.13 Up Market Whipsaw

FIGURE 15.14 Down Market Whipsaw

FIGURE 15.15 Stop Loss Example

FIGURE 15.16 Full Cycle Analysis

FIGURE 15.17 Drawdowns Greater than 10 Percent

FIGURE 15.18 Drawdowns Greater than 20 Percent

FIGURE 15.19 Number of Months with Drawdowns Greater than 10 Percent

FIGURE 15.20 Number of Months with Drawdowns Greater than 20 Percent

FIGURE 15.21 Maximum Drawdown

FIGURE 15.22 Average Drawdown

FIGURE 15.23 Ulcer Index

FIGURE 15.24 Asset Classes from 1/1/1996 until 12/31/2012

FIGURE 15.25 Distribution of Returns for Dance with the Trend versus S&P 500

FIGURE 16.1 Adaptive Trend

FIGURE 16.2 Adaptive Trend versus Nasdaq Composite (1971–2012)

FIGURE 17.1 Secular Markets and the Efficiency Ratio

FIGURE 17.2 Weight of the Evidence in 1987

FIGURE 17.3 Flash Crash and the Weight of the Evidence

FIGURE C.1 New High New Low Validation

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Guide

Cover

Table of Contents

Begin Reading

Since 1996, Bloomberg Press has published books for financial professionals on investing, economics, and policy affecting investors. Titles are written by leading practitioners and authorities, and have been translated into more than 20 languages.

The Bloomberg Financial Series provides both core reference knowledge and actionable information for financial professionals. The books are written by experts familiar with the work flows, challenges, and demands of investment professionals who trade the markets, manage money, and analyze investments in their capacity of growing and protecting wealth, hedging risk, and generating revenue.

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INVESTING WITH THE TREND

A Rules-Based Approach to Money Management

Cover Design: Wiley

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Copyright © 2014 by Gregory L. Morris. All rights reserved.

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ISBN 9781118726976 (ePDF)

ISBN 9781118726693 (ePub)

To Laura, and our children, Dusti, Grant, Derek, and Kane.

And our grandchildren: Katy, Kinsey, Grayson, Connor, and Elaine.

Foreword

The Greek philosopher Aristotle wrote, “We are what we repeatedly do. Excellence, then, is not an act but a habit.”

The same is true of investment. Good investing is not an “event” like finding the next growth company or catching the next market turn. Rather, good investing is a disciplined process that converts research and training into a well-tested methodology, and then makes a habit of following that approach day after day.

Because profits are enjoyable, investors often believe that they are “paid” when their good ideas are comfortably working out, but that is an illusion. If you carefully study the returns of successful investors, you’ll find that their profits are more often a sort of delayed payment for actions they took much earlier: maintaining their investment discipline even when it wasn’t working in the short run; cutting losses when the evidence changed; and establishing investment positions when it was often uncomfortable to do so.

The financial markets may be efficient, but they are efficient in an interesting way. If all investors were identical and shared the same information, objectives, and temperaments, the markets might be efficient in the “academic” sense, and it would be impossible to outperform a buy-and-hold approach. But in reality, markets are full of greed, fear, uncertainty, and constant second-guessing. In that world, investors are scarcely willing to follow a well-grounded, thoroughly tested discipline once it has become uncomfortable. For those who do, their later success doesn’t emerge as “free money.” It emerges as delayed payment for the discipline to take scarce, useful actions when other investors were seeking comfort.

For example, it is difficult—particularly after a long market advance—to part with a richly priced investment position as market action begins to deteriorate. When a market advance has been rewarding, the natural inclination is to become attached to those rewards, to fall in love with the bullish “story,” and to ignore negative evidence.

It is equally difficult—particularly after a long market decline—to establish an investment position at depressed prices as market action begins to firm. When risk taking has been relentlessly punished, the natural inclination is to avoid risk. That is particularly true if investors have endured a series of whipsaws, where early purchases are followed by immediate price declines and trend-following signals to cut losses.

Discipline requires confidence, and confidence requires evidence. In any investment approach, it is critical to test that discipline against the longest history of data that you can obtain. As an investor, my greatest successes have resulted from the confidence to respond in uncertain environments, based on evidence that had proved to be effective in market cycles again and again throughout history. My single greatest disappointment was the result of a much earlier decision to ignore Depression-era data as outmoded, and then missing a large market rebound while I stress-tested my approach against that data. Test your investment approach in the most challenging conditions you can identify, because someday you will face those conditions.

Greg Morris is among the rare breed of investors who take systematic research, testing, and discipline seriously. Greg is a technical analyst. The book you are reading offers insights that he has gained from his own career as an investment manager. Greg’s investment approach is based on indicators that measure price trends, trading volume, the balance of advancing stocks versus declining stocks, and similar considerations. Aside from keeping investors generally aligned with prevailing trends, investment methods like this can be of enormous help in limiting significant market losses.

While my own investment discipline draws from some elements of technical analysis, it is also heavily weighted toward fundamental valuation, stock selection, economic measures, and other factors. Each approach has its benefits and challenges, depending on the market environment. Yet more important than these differences is what both of our approaches share in common, which is the insistence on a systematic investment process.

In addition to sharing the tools and insights of a skilled market technician, Greg shares the three key elements that distinguish an investment discipline from constant guessing:

Measuring the weight of the evidence.

Rules and guidelines to trade the weight of the evidence.

Strict discipline to follow the process.

The importance of each of those words—evidence, rules, guidelines, discipline, and, possibly most important, strict—can’t be understated.

Reliable market indicators are important, but they are not nearly enough. The essential feature of a successful investment discipline is to convert those indicators into objective guidance about when to take action, whether that action is to buy or to sell. Greg Morris brings decades of data and research together into a technically driven, rules-based approach that offers both investors and traders a solid footing to “dance with the trend.”

In nearly every long-term pursuit, the secret to success is the same. Find a set of daily actions that you expect to produce good results if you follow them consistently.

Then follow them consistently.

Preface

During tough times in the market, the individual investor is not well served by following the buy-and-hold mentality promulgated by the financial institutions, most mutual funds, and brokerage houses since the early 1950s. The “world of finance” and “Wall Street” have set the stage for investing for the long term in their effort to market themselves and sell investing ideas to an unsuspecting and financially uneducated public. With their big emphasis on diversification and its long-term protection, their confusion between buy and hold and buy and hope, the misinformation continues to pour out of the financial institutions.

Corporate pension plans are being dumped, social security is in the tank, and our financial system is in the midst of a giant restructuring because the stability of the past two decades in the last century has caused prudence, and what used to be called common sense, to be set aside for greater leverage and higher risk. Retirees as well as younger investors are learning the hard way that “investing for the long term” has periods where the performance is poor and that those periods can last a decade or much longer.

My decades of experience have taught me that there are times when one should not participate in the markets and are much better off preserving capital because bear markets can set you back for a long time, and they are especially bad when they happen in your later years. Keep in mind that the closer you get to actually needing your serious money for retirement, the worse the effect of a severe bear market can have on your assets. It is critical to understand the concept of avoiding the bad markets and participating in the good ones. It is never too late to invest intelligently for your future.

This is not a storybook. This book is a collection of almost 40 years of being involved in the markets, sharing some things I have learned and truly believe. You will soon find out that there are sections in the book that contain short concepts on various subjects, even one-liners. You will discover early that sometimes I might seem overly passionate about what I’m saying, but hopefully you will realize that is because I have well-formed opinions and just want to ensure that the message is straightforward and easily understood. It is not only a book on trend following but a source of technical analysis information learned over the past 40 years, with quotes, lists, and a host of tidbits that often seem trite, but are usually true.

Some may notice that I repeat myself in a number of places and think that I am old and slipping. While you may be correct, I often did that because it was too important at that point to provide a reference to where it was first said. I believe it is so much easier to make the point again within the context of the current discussion.

Although the book is ultimately about using trend following in a disciplined model for long-term successful investing, it will take a while to get there. I have spent a lot of time on the preparation for understanding the markets, understanding yourself, and understanding things that just don’t work, before I get into the subject of the book. You can certainly read the book from front to back, but it would be best to review the table of contents and read first the areas that catch your interest. There are two large research projects inside this book. I had originally thought about writing an academic white paper, but the formality of that exercise was unappealing, so this book is where they ended up. An effort to have all charts and data with an ending date of December 31, 2012, was purposely done, even though I have to be totally honest, I don’t know why. People who know me will understand. I also ensure that with each concept I try to show the 20-year results as I think that is a reasonable assumption as to the realistic investment horizon for most people. I have created my own footnote technique because as one gets older the superscript micro type generally used in books just doesn’t cut it, plus I prefer all the footnotes to be in a single location in the appendix instead of at the end of each chapter. Furthermore, I have divided the footnotes among academia (A) (articles, white papers, research, etc.), books (B), and websites (W). And finally, in regard to the layout of the book, I have tried to keep comments about charts and graphics such that they are on the same page or at least on opposing pages. There is nothing more frustrating for me than to read about a chart and not be able to see it.

I apologize for the occasional use of mathematics, in particular, equations. I read once that the inclusion of an equation in a book can greatly reduce its sales. That is a sad commentary, so I have tried to keep them only when I believed they were absolutely necessary. Equations are austere, appearing formal and complicated; however, most of the mathematics herein can be classified as arithmetic. If you find that annoying, again, I apologize.

On occasion I am critical of some things, and downright disdainful of others. There is much in modern finance that so blatantly is mere marketing and virtually void of investment substance that it could actually be considered a hoax. I try to be careful with criticism when the results are robust even though I do not believe in the process. When the results of some type of analysis are flawed in ways such that the rules are so complex, have an inordinate number of equations, or entirely too subjective that one could not possibly prove it wrong, I try to state as much. While I write with some certitude, I am fully aware that if I believe I am correct, it does not mean that others are not correct; it just means that I strongly believe what I’m saying. Please do not be offended from my opinions.

The goals for this book are numerous, but if I had to itemize them they would be:

Understand . . .

How markets work and how they have worked in the past.

The host of misinformation that exists in finance and investments.

The tools of modern finance and their shortcomings.

That as a human being, you have horrible natural investment tendencies.

What risk really is.

That markets trend and why.

That there are techniques to managing money that reduce risk consistently and offer hope for long-term success.

If I had to nail down a single goal for the book, it would be to provide substantial evidence that there are ways to be successful at investing that are outside the mainstream of Wall Street. Although it will appear my concern is about modern finance, it is actually directed toward the investment management world and its misuse of the tools of modern finance.

Acknowledgments

There are people without whom this book could not have been possible. Where do I start? Who do I mention first? This, quite possibly, is more difficult than the book itself.

One must never forget one’s roots. There is no doubt in my mind that my parents, Dwight and Mary Morris, are mostly responsible for all the good that I have ever accomplished. Any of the bad surely had to come from being a jet fighter pilot in the U.S. Navy for six years.

I am blessed with a truly wonderful wife, Laura. Her support during this effort was unwavering and fully appreciated. If she would just let me win at golf occasionally. . . .

It is interesting that I am working with Kevin Commins of John Wiley & Sons again. Kevin was the first editor I had when I wrote my “candlestick” book more than 21 years ago. My association with Stephen Isaacs at McGraw-Hill, and now Bloomberg Press, has been long lasting and most enjoyable.

The team at Stadion’s portfolio management department was absolutely necessary for the completion of this work (Brad Thompson, Will McGough, Rob Dailey, Clayton Fresk, Clayton Shiver, Clayton Wilkin, John Wiens, Paul Frank, Jonathan Weaver, Danny Mack, and David Pursell). They tirelessly assisted in data acquisition and analysis. Without them the research in Part II would have never been accomplished, certainly not in my lifetime. I truly hate to break out someone in that group as they all made exceptional contributions, but a special thanks to Clayton Fresk who is extremely talented with Microsoft Excel. Clayton produced most of the spreadsheets used in the research and analysis for this book and provided some unique insight into interpreting the data.

I must acknowledge and thank good friend Ted Wong for his Trend Gauge measure and volunteering (I begged) to proof the technical sections of the book. Thanks to George Schade who used his jurist mind to help me provide criticism as long as it is respectful. Doug Short (dshort.com) graciously reproduced a number of his nice graphics into grayscale. Many—in fact, most—of the charts in this book were created with Thomson Reuter’s MetaStock software, a product I have used since version one back in 1985.

What an absolute delight to wrap up a long career with my past 14 years at Stadion Money Management, LLC. Tim Chapman and Jud Doherty have been partners, leaders, and friends for all this time. Truly great people! In fact, every employee at Stadion seems to fit the same mold: sharp, hard-working, and loaded with talent. Thanks, Stadion.

As is the accepted standard, and certainly in this case the fact, whatever factual errors and omissions are sadly, but most certainly, my own.

Chapter 1Introduction

I have learned a few things over the years and probably retained even fewer. For example, I know that when dealing with the unknown such as the analysis of the stock market, you absolutely cannot speak in absolutes. I also know that random guessing about what to do in the market is a quick path to failure. One needs a process for investing. Any process is better than no process or even worse, a random or constantly changing process. Hopefully, you will find the path to a successful process with this book.

The noblest pleasure is the joy of understanding.

Leonardo da Vinci

How can you even begin to analyze the market if you are not using the correct tools to determine its present state? If you do not fully grasp the present state of the market, your analysis, whether real or anticipated, will be off by an amount equivalent to at least the error of your current analysis. And your error will be compounded based upon the timeframe of your analysis. This highlights why most forecasts are a waste of time.

Believable Misinformation

One should remember things are quite often not what they seem. It is absolutely amazing to me how much people believe that is not true (the voice of experience speaking). Below are some things that many of us learned in our formative years from our teachers and parents. Most we just accepted as fact because we heard it from people we believed.

Myth: Some believe water runs out of a bathtub faster as it gets toward the end.

Fact: Assuming the tub’s sides are cylindrical, the pressure is constant, it only appears to drain faster because you observe it starting to swirl toward the end, something you could not observe when the tub was full. The swirling action deceives one into thinking it is draining faster.

Myth: How many think that George Washington cut down a cherry tree?

Fact: George Washington did not cut down a cherry tree. That was a story told so that adults could teach their children that it was bad to tell lies—even our founding father didn’t tell lies. Parson Mason Locke Weems, the author who wrote about it shortly after Washington’s death, was trying to humanize Washington.

Myth: Did Washington throw a silver dollar across the Potomac River?

Fact: The Potomac River is almost a mile wide at Mount Vernon and silver dollars did not exist at that time.

Myth: Where was the Battle of Bunker Hill fought?

Fact: It was fought at Breed’s Hill in Charleston, Massachusetts.

Myth: Dogs sweat through their tongues.

Fact: Guess what? Dogs don’t sweat. Their tongues have large salivary glands that keep them wet.

Okay, the following two examples of believable misinformation are only for the hardy who have found this section interesting. The rest should skip them. They are only for nerds like me.

Myth: How many think that December 21 in the northern hemisphere is the shortest day of the year?

Fact: Most do. However, it is actually the longest astronomical day based on Kepler’s Second Law of Planetary Motion (planets, in their elliptical orbits, sweep out equal areas in equal time). When the Earth is closest to the sun, the northern hemisphere is tilted away and a much greater arc is swept in a day’s travel than when the Earth is the furthest distance from the sun. If the question were posed as to what is the day with the shortest period of daylight, then it would be correct.

See Figure 1.1 for an illustration of Kepler’s Second Law of Planetary Motion.

FIGURE 1.1 Kepler’s Second Law of Planetary Motion

An additional observation on the tilt of the Earth is that summers in the southern hemisphere are generally warmer than the summers in the northern hemisphere. This can be caused by significantly more ocean in the southern hemisphere but also because the southern hemisphere is tilted toward the sun when the sun is closest to the Earth.

Myth: Bath water drains counterclockwise in the northern hemisphere.

Fact: Another example of how people have believed things that are simply not true is that in the northern hemisphere many will say that water, when draining from a tub, will swirl counterclockwise. Although it very well may do so, it is not for the reason they think it will. This is an example of a little bit of scientific knowledge totally misapplied. The Coriolis Effect (see

Figure 1.2

) is caused by the earth’s rotation and generally applies to large almost frictionless bodies, such as weather systems. This is why in the northern hemisphere, hurricanes rotate counterclockwise, and in the southern hemisphere, they rotate clockwise. The rotational effect is measured in arc seconds (a unit of angular measure equal to 1/60 of an arc minute, or 1/3600 of a degree), which is an extremely small measurement of angular rotation. To apply this principle to the rotation of water draining from a tub is totally incorrect. High pressure and low pressure weather patterns are also reversed—I would love to see a weather reporter from Dallas move to Santiago and adapt to that.

FIGURE 1.2 Coriolis Effect

Hopefully, you are getting my point. In the past few years the Internet has been the source and exploitation of much hype and false information. How many times have you received an e-mail from a friend (who probably did not originate it), and believed it to be true but did not bother to check it out, but forwarded it anyhow? You should start verifying them because many of them are a hoax. Believable misinformation flourishes.

If you enjoy this type of information, I would recommend a new book by Samuel Arbesman, The Half-Life of Facts: Why Everything We Know Has an Expiration Date. Arbesman is an expert in scientometrics, which looks at how facts are made and remade in the modern world. People often cling to selected “facts” as a way to justify their beliefs about how things work. Arbesman notes, “We persist in only adding facts to our personal store of knowledge that jibe with what we already know, rather than assimilate new facts irrespective of how they fit into our views.” (B4) This is known as confirmation bias, which is dealt with in Chapter 6.

A general theme throughout this book is one of separating fact from fiction. Fiction in this case is often a well-accepted theory on finance, economics, or the market in general. If you were caught believing some of the things mentioned in the previous paragraphs, then how much from the world of investing do you believe? Just maybe you have accepted as fact some things that simply are not true. I certainly know that I did.

In this chapter a lot of basic information is provided to assist you in understanding the remainder of this book. There are definitions, mathematical formulae, explanations of anomalies, historical events that affect the data, differing methods of calculation, and a host of other important information normally found in an appendix. It is of such importance to understand this material that it belongs prior to the discussion and not in the appendix, as is usually the custom.

Indicators and Terminology You Should Be Familiar with

There are basically four different indicator types: differences, ratios, percentage, and cumulative. Differences are most common and should be adjusted for time independent scaling. As the number of issues increase over time, the scaling will get expanded and thresholds that worked in the past will need to be adjusted. One way to do this is to normalize the indicator so the scaling is always between zero and 100. The following section covers many popular indicators and concepts that will help you understand them better when discussed later in the book.

Absolute value

. In mathematical script this is denoted with | | around the value in which you want to have its absolute value. Absolute value calculations ignore the sign (positive or negative) of the number. In regard to breadth data, absolute value ignores market direction and only deals with market activity. The absolute value of +3 is 3, and the absolute value of −3 is also 3.

Accumulated/summed

(∑) (also see cumulative below). This is the term used to add up a series of numbers. For example, the advance decline line is an accumulation of the difference between the advances and the declines. That difference is summed with each new day’s difference added to the previous value. Also used with the term cumulate. In many formulae in this book it is shown either as Previous Value + Today’s Value or ∑.

Alpha

. Alpha is a benchmark relative risk adjusted measure. It is not simply excess return. If markets were truly efficient then there would be no alpha.

Arithmetic/simple moving averages

. To take an average of just about anything numerical, you add up the numbers and divide by the number of items. For example, if you have 4 + 6 + 2, the sum is 12, and the average is 12/3 = 4. A moving average does exactly this but as a new number is added, the oldest number is removed. In the example above, let’s say that 8 was the new number, so the sequence would be 6 + 2 + 8. The first 4 was removed because we are averaging only 3 numbers (3 period moving average). In this case the new average would be 16/3 = 5.33. So by adding an 8 and removing a 4, we increased the average by 1.33 in this example. For those so inclined: 8 − 4 = 4, and 4/3 = 1.33.

In technical analysis the simple or arithmetic average is used extensively. One thing that you should keep in mind is that with the simple average each component is weighted exactly the same. This tends to make the simple average stale if using it for large amounts of data. For example, the popular 200-day average means that the price 200 days ago is carrying the same weight, or having the same effect on the average as the most recent price. It, therefore, is also much slower to change direction. See exponential average.

Average true range

(ATR). Average true range is the process of measuring the price action over a particular period, usually one day. Normally this is done by just looking at the difference between the high and low price of the day. However, ATR also includes the previous days close so that if there is a gap the price action also has that movement included.

Behavioral finance

. A relative newcomer to the analysis of markets, this is the study of why investors do what they do. “Behavioral finance is the study of the influence of psychology on the behavior of financial practitioners and the subsequent effect on markets” Martin Sewell (W6). “I think of behavioral finance as simply ‘open-minded finance’” Thaler (A109).

Buy and hold

. Buy and hold is the terminology used when discussing the act of making an investment and then just holding it for a very long time. This is more common than most would believe and can be a very bad decision during secular bear periods, which can last on average 17 years.

Capitalization

. Capitalization refers to the number of shares a company has outstanding multiplied by the price of the stock. Most market indices, such as the S&P 500, NYSE Composite, and the Nasdaq Composite are capitalization weighted, which means the big companies dominate the movement of the index.

Coefficient of determination

. This measures the proportion of variability in a data set that is explained by another variable. Values can range from 0, indicating that zero percent of the variability of the data set is explained by the other variable, to 1, indicating that all of the variability in a data set is explained by the other variable. It is statistically shown as

R

2

, which is nothing more than the square of correlation.

Correlation

. A statistical measurement showing dependence between two data sets. Known in statistics and finance as R, it is used to determine the degree of correlation, noncorrelation, or inverse correlation between the two data sets (often an issue such as a mutual fund and its benchmark).

Cumulative

. Cumulative indicators can be differences, ratios, or percentage. You are adding the daily results to the previous total. The advance decline line is a good example of a cumulative indicator. It is sometimes referred to as accumulate or summed.

Detrend

. A term to denote when you subtract the price from a moving average of the price. This will amplify the price relative to its smoothed value (moving average). To visualize this, pretend you had the ability to take both ends of the moving average line and pull it taut so that the price line falls into its same relative position to the now straight moving average line. Doing this allows you to see cycles of a length greater than that of the number of periods used in the moving average.

Divergence

. This is when an indicator and price do not confirm each other. At market tops, many times the price will continue to make new highs, while an indicator will reverse and not make a new high. This is a negative divergence. A positive divergence is at market bottoms when the prices continue to make new lows while the indicator does not and makes higher lows.

Drawdown

. Drawdown is the percentage that price moves down after making a new all-time high price. Drawdowns of greater than −20 percent are known as bear markets. This book tries to convince you that real risk is drawdown and not volatility as modern finance wants you to believe.

Exponential moving averages

. This method of averaging was developed by scientists, such as Pete Haurlan, in an attempt to assist and improve the tracking of missile guidance systems. More weight is given to the most recent data and it is therefore much faster to change direction. It is sometimes represented as a percentage (trend percent) instead of by the more familiar periods. Here is a formula that will help you convert between the two:

K = 2/(N + 1) where K = the smoothing constant (trend percent) and N = periods

Algebraically solving for N: N = (2/K) − 1

For example, if you wanted to know the smoothing constant of a 19-period exponential average, you could do the math, K = 2/(19 + 1) = 2/20 = 0.10 (smoothing constant) or 10 percent (trend) as it is many times expressed.