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2

Introduction

This Action Section is a supplement to the book Jackass

Investing. In this section I present specific trading strategies

that exploit many of the behaviors engaged in by people who

gamble their money by believing in – and placing their money

on – the myths exposed in the book. Then in the Action Section

for the final myth, “Myth #20 – There is No Free Lunch,” I

show how you can combine these trading strategies into a “Free

Lunch” portfolio. As you learned in the book, a Free Lunch

portfolio is one that produces both greater returns and less risk

than conventional portfolios.

The trading strategies presented in this section vary in

complexity and the commitment required by you to capture

their returns. While the returns from some of them can be

captured simply by investing in mutual funds or ETFs that are

managed pursuant to the trading strategies, others require you

to actively monitor and potentially trade them on a day-to-day

basis.

Because of this varying complexity and my interest in making

the book and this Action Section beneficial to all investors, I

present two categories of Free Lunch portfolios. These are the

“Regular” Free Lunch portfolio that incorporates all of the

trading strategies, and the “Simplified” Free Lunch portfolio

that eliminates the trading strategies that require day-to-day

monitoring.

The trading strategies presented in this section are by no

means intended to be comprehensive. But they do represent a

good cross-section of strategies that can be employed to better

diversify your portfolio across multiple return drivers. This

process results in true portfolio diversification (in contrast with

the “illusory” diversification preached by conventional financial

Action – Introduction 3

wisdom), and is the basis for the creation of your Free Lunch

portfolio.

Despite the fact that I present the historical performance of

many of these trading strategies, either in this section or in the

myth, that performance is often hypothetical, in that no single

account may have traded pursuant to each strategy or during

the period for which its performance is displayed. While many

of the trading strategies were developed prior to the start of

their displayed performance, others were developed more

recently and their historical performance is solely the result of

back-testing. Because of this the disclaimer that PAST

PERFORMANCE IS NOT INDICATIVE OF FUTURE

PERFORMANCE, which you see displayed on virtually all

investment marketing materials, holds true for each of these

trading strategies, as it does for any investment.

Contents

Introduction ........................................................................................................ 2

Action – Myth #1 Stocks Provide an Intrinsic Return ................................. 5

Action – Myth #3 You Can’t Time the Market .......................................... 19

Action – Myth #4 “Passive” Investing Beats “Active” Investing .............. 27

Action – Myth #6 Buy Low, Sell High ......................................................... 34

Action – Myth #7 It’s Bad to Chase Performance ...................................... 45

Action – Myth #8 Trading is Gambling – Investing is Safer ..................... 50

Action – Myth #10 Short Selling is Destabilizing and Risky ...................... 53

Action – Myth #11 Commodity Trading is Risky........................................ 57

Action – Myth #12 Futures Trading is Risky .............................................. 59

Action – Myth #14 Government Regulations Protect Investors ................ 64

Action – Myth #15 The Largest Investors Hold All the Cards................... 67

Action – Myth #16 Allocate a Small Amount to Foreign Stocks ................ 70

Action – Myth #20 There is No Free Lunch ................................................ 76

5

Action – Myth #1

We saw in Myth #1 how people’s sentiment towards buying and

selling stocks, rather than actual corporate performance,

dominates short-term stock performance. The trading strategy

I present here is designed to capitalize on this return driver.

The strategy buys the stocks of good companies when they are

being shunned by others and attempts to ride prices higher as

sentiment improves. Best yet, the strategy is time-tested with

more than a decade of actual market performance. Because this

trading strategy generally produces portfolios that contain less

than 10 stocks, it is suitable for both small and large portfolios.

Discovering Piotroski

In 2001 I launched a market neutral equity fund that held a

portfolio of about 100 U.S. exchange traded stocks each long

and short and also utilized a variety of trading strategies. One

of the strategies we employed was an adaptation of a trading

strategy developed by Joseph Piotroski, at the time an

accounting professor at the University of Chicago Graduate

School of Business. Piotroski’s strategy combined value

measures, such as price to book ratio, with financial

performance, such as profitability and cash flow. Not only did

Piotroski’s trading strategy perform well when tested against

historical stock market data, it has continue to perform well in

real-time since its release to the public more than ten years

ago. Let’s take a look.

Piotroski Performance

Piotroski tested his strategy across 21 years of historical data

(1976 – 1996). In that back-testing Piotroski’s strategy

outperformed the market by an average of 13.4% per year.

Those results, of course, had the benefit of hindsight. Meaning

that the strategy had been developed to perform well on that

6 Jackass Investing – Action for Myth #1

specific data. (No one develops and publishes a trading strategy

that shows losing results on its back-tested data set!). When

developing a trading strategy there is always an element of

“curve-fitting” that strategy to the data set upon which it is

back-tested, either intentionally or unknowingly. Because of

this, backtested performance alone is an insufficient indicator

of the efficacy of a strategy. Whether the strategy is valid or

not, and can continue to perform after the date of its initial

release, is a function of the validity of its return drivers. When

we incorporated Piotroski’s trading strategy into our fund, it

was because we agreed with the premise behind its return

driver. It actually had very little in-market performance at that

time. But we were confident that the strategy could capture

and profit from shifts in peoples’ preferences.

Piotroski first published his strategy in a paper he released in

June 2000. (See: Piotroski, Joseph D. "Value Investing: The

Use of Historical Financial Statement Information to Separate

Winners from Losers." June 2000). That means that it now has

ten years of in-the-market performance without the benefit of

hindsight.

Any concerns about the strategy having been fitted to past data

dissolved with its in-market performance over the past ten

years. One organization that has tracked the Piotroski strategy

is the American Association of Individual Investors (AAII). The

AAII is a nonprofit organization whose mission is to provide

individual investors with the education and tools they need to

build wealth. For a small annual subscription fee AAII provides

access to financial data on thousands of stocks, tracks the

performance of dozens of stock trading strategies and publishes

top-quality articles on investing and personal financial

management. They have compiled the monthly performance of

a trading strategy based on Piotroski’s method that continues

to support the validity of his original research.

Action – Myth #1 7

Impressively, from January 2001 through year-end 2009 the

strategy produced a 29% average annualized return. As I

prepared to publish this book its performance virtually

exploded, bringing the average annual return to 48% by the

end of 2010. The strategy produced these returns at a time

when most people were merely trying to avoid substantial

losses. This performance helps to confirm the research that

shows that sentiment is a key driver of short-term stock

performance.

Figure 1 and Figure 2 display the performance of this strategy

beginning January 2001 (subsequent to its publication). The

returns include a cost of 4% each year to account for the

transaction costs that could have been incurred in actual

trading.

Note 1-1: As of January 2011, transaction costs are assumed to

be 0.50% per transaction.


Growth in $1,000

Allocated to Piotroski Trading Strategy On January 1, 2001

Piotroski-based Trading Strategy Annual Returns

Figure 1. Source: AAII.com

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

Dec

-00

Dec

-01

Dec

-02

Dec

-03

Dec

-04

Dec

-05

Dec

-06

Dec

-07

Dec

-08

Dec

-09

Dec

-10

2001 92.65% 2006 -19.14%

2002 -19.25% 2007 -5.24%

2003 145.20% 2008 27.61%

2004 75.60% 2009 71.62%

2005 -12.16% 2010 413.02%



Piotroski Method - Concept

The Piotroski trading strategy first identifies stocks that are

trading at a low price relative to their book value. This often

occurs because these companies have recently had poor

historical performance and suffer from analyst neglect.

Analysts typically prefer to track “glamour” companies with

strong positive momentum. As a result, there is very little

information regarding these companies’ future prospects being

released in the marketplace.

Piotroski’s strategy then looks at the fundamentals of each

company to determine its future earnings potential. This

information is readily available simply by looking at its balance

sheet and income statement. He assigns points to each

fundamental measure to create what he calls an F_SCORE for

each stock. He chose nine fundamental signals that attempt to

measure various dimensions of each firm's financial condition.

Based on each signal's realization, he assigned a "1" for "good"

signals about firm performance or a "0" for a "bad" signal. The

sum of those signals, ranging from 0 to 9, is the firm's overall

fundamental signal. These nine signals measured three areas

of a firm's financial condition: profitability, financial liquidity,

and operating efficiency. The higher the score, the greater the

likelihood the firm will earn positive future returns.

The tested results, plus the results earned after the strategy

was published in 2000, support Piotroski’s initial intuition that

certain stocks could be mispriced. This may seem obvious to

you. But it wasn’t obvious to academics, who for years were

taught that markets were efficient and that simple trading

strategies were incapable of earning returns in excess of

market averages. What may have begun as academic research


for Piotroski resulted in a trading strategy that you can use to

make money trading stocks.

What Piotroski has done, by starting with low price-to-book

value stocks, is identify those that have been given up on by

people He then identifies companies with fundamentally solid

financial metrics. These are his signals that indicate a healthy

company. As a result, once people also start to realize these

downtrodden companies are healthy, they begin to buy the

stock at depressed prices. In this way Piotroski has “stacked

the deck” to capture the primary return driver that powers

stock returns in the short-term, which is peoples’ enthusiasm

for stocks. In this case their enthusiasm for particular stocks.

Piotroski Method –Specifics

The following steps outline the specifics of the Piotroski trading

strategy. The nine signals he defines are used to screen the

initial universe of stocks to result in a portfolio of those stocks

most likely to rise in price as people gain confidence in each

company’s financial health. Only stocks that pass all nine

screens are included in the portfolio. This will yield an average

of approximately four stocks each month. If you loosen up the

constraints and require a stock to pass eight of the nine screens

to be accepted, the portfolio will include an average of 25 stocks

per month.

While Piotroski began his research by looking at corporate

financial data contained in the COMPUSTAT-Standard &

Poor's database of financial information on publicly traded

companies, we will rely heavily on data provided by the AAII.

AAII's Stock Investor Pro is used to identify the stocks to be

included in the Piotroski trading strategy and to update its

performance. Stock Investor Pro covers a universe of over 9,000

NYSE, Amex, Nasdaq National Market, Nasdaq Small Cap,

and over-the-counter stocks. To help ensure minimum liquidity


and financial reporting standards over-the-counter bulletin

board stocks and ADRs were excluded.

You can learn more about the American Association of

Individual Investors at www.aaii.com. The following

description of the Piotroski trading strategy is derived from the

description published by AAII. AAII runs the screens for the

end of each month and generally publishes the results in the

middle of the following month.

Piotroski first limited his universe to the bottom 20% of stocks

according to their price-to-book value ratio. Price-to-book value

was a favorite measure of value investors such as Benjamin

Graham and his disciples who sought companies with a share

price below their book value per share. In the short-run the

market can overreact to information and push prices away

from their true value. Because of this, measures such as price-

to-book-value ratio help to identify which stocks may be truly

undervalued.

Valuation levels of stocks vary over time, often dramatically

from bear market bottoms to bull market tops. During the

depths of a bear market, many firms can be found selling for a

price-to-book ratio less than one. In the latter stages of a bull

market, few companies other than troubled firms sell for less

than book value per share.

Piotroski found that most stocks trading with an extremely low

price-to-book-value were either neglected or financially

troubled firms. Small, thinly traded stocks are rarely followed

by analysts. The flow of information is limited for these stocks

and can lead to mispriced stocks. Analysts typically ignore

these stocks and tend to focus on stocks with general interest.

This points out another “feature” of the Piotroski trading

strategy. Most of the stocks selected by the initial price-to-book

http://www.aaii.com/


value screen are low capitalization stocks. This does not pose a

problem for most individuals, but may limit investment by the

larger mutual funds and hedge funds, as they may not be able

to invest large amounts in many of the selected stocks. As

described in Myth #15 – The Largest Investors Hold All the

Cards, this ability to buy and sell small capitalization stocks is

an investment advantage held by smaller individuals over

large institutional investors.

Once the price-to-book screen has identified the 20% of stocks

that are most undervalued, Piotroski’s 9-point ranking system

comes into play. Profitability, financial leverage, liquidity, and

operating efficiency are examined using popular ratios and

basic financial elements that are easy to use and interpret.

Stocks that pass all nine screens are included in the portfolio.

Minimum Profitability

Piotroski awarded up to four points for profitability: one for

positive return on assets, one for an improvement in return on

assets over the last year, one for positive cash flow from

operations, and one if cash flow from operations exceeds net

income. These are simple test that are easy to measure.

Because the requirements are minimal, there is no need to

worry about industry, market, or time specific comparisons.

1. Return on Assets. Piotroski defined return on assets

(ROA) as net income before extraordinary items for the

fiscal year preceding the analysis divided by total assets

at the beginning of the fiscal year. AAII’s Stock Investor

Pro deviates by using net income after extraordinary

items in its calculation. ROA examines the return

generated by the assets of the firm. Piotroski did not

look for high levels of ROA, only a positive figure. While

the screen may not seem to be very restrictive, he found

that over 40% of the low price-to-book-value stocks had


experienced a loss in the prior two fiscal years. Positive

income is a significant event for these firms.

2. Improving Profitability. The next variable Piotroski

considered looked for improving profitability. Piotroski

awarded one point if the current year's ROA was greater

than the prior year's ROA.

3. Operating Cash Flow. Piotroski awarded one point if a

firm had positive operating cash flow. Operating cash

flow is reported on the statement of cash flows and is

designed to measure a company's ability to generate

cash from day-to-day operations as it provides goods and

services to its customers. It considers factors such as

cash from the collection of accounts receivable, the cash

incurred to produce any goods or services, payments

made to suppliers, labor costs, taxes, and interest

payments. A positive cash flow from operations implies

that a firm was able to generate enough cash from

continuing operations without the need for additional

funds. A negative cash flow from operations indicates

that additional cash inflows were required for day-to-

day operations of the firm.

4. Accrual Accounting Check. This examines the

relationship between the earnings and cash flow. A

point is awarded if cash from operations exceeded net

income before extraordinary items. The measure tries to

avoid firms making account adjustment to earnings in

the short run that may weaken long-term profitability.

Piotroski feels that this element may be especially

important for value firms, which may have a strong

incentive to manage earnings to avoid triggering

problems such as violations to debt covenants.


In AAII’s Stock Investor Pro, Income after taxes

represents income before extraordinary items so the

screen looks for firms with cash from operations greater

than income after taxes for the most recent fiscal year.

5. Financial Leverage. Piotroski awards one point if a

company’s ratio of debt to total assets declined in the

past year, He defined debt to total assets as total long-

term debt plus the current portion of long-term debt

divided by average total assets. The higher the figure

the greater the financial risk. Judicious use of debt

allows a company to expand operations and leverage the

investment of shareholders provided that the firm can

earn a higher return than the cost of debt. Normally,

the more stable the cash flow of a firm, the greater

financial risk a company can assume. However, a

company must meet the rules (covenants) along with

the interest payments of its debt or risk bankruptcy and

complete loss of control of the firm. By raising

additional external capital, a financially distressed firm

is signaling that it is unable to generate sufficient

internal cash flow. An increase in long-term debt will

place additional constraints on the financial flexibility of

a firm, and will likely come at great cost.

6. Liquidity. To judge liquidity, a company earns one point

if its current ratio at the end of its most recent fiscal

year increased compared to the prior fiscal year.

Liquidity ratios examine how easily the firm could meet

its short-term obligations, while financial risk ratios

examine a company's ability to meet all liability

obligations and the impact of these liabilities on the

balance sheet structure.

The current ratio compares the level of the most liquid

assets (current assets) against that of the shortest


maturity liabilities (current liabilities). It is computed

by dividing current assets by current liabilities. A high

current ratio indicates high level of liquidity and less

risk of financial trouble. Too high a ratio may point to

unnecessary investment in current assets or failure to

collect receivables or a bloated inventory, all negatively

affecting earnings. Too low a ratio implies illiquidity

and the potential for being unable to meet current

liabilities and random shocks that may temporarily

reduce the inflow of cash.

Piotroski assumed that an improvement in the current

ratio is good signal regarding a company's ability to

service its current debt obligations. He also indicated in

a footnote that the decline in current ratio was only

significant if the current ratio is near one.

7. Equity. The final capital structure element awards one

point if the firm did not issue common stock over the

last year. Similar in concept to an increase in long-term

debt, financially distressed companies that raise

external capital could be indicating that they are unable

to generate sufficient internal cash flow to meet their

obligations. Additionally, issuing stock while its stock

price is depressed (low price-to-book) highlights the

weak financial condition of the company.

We screen for stocks that have maintained or reduced

the number of outstanding shares during their last

fiscal year.

8. Gross Profit Margin. Companies gain one point for

showing an increase in their gross margin. Long-term

investors buy shares of a company with the expectation

that the company will produce a growing future stream

of cash. Profits point to the company's long-term growth


and staying power. Gross profit margin reflects the

firm's basic pricing decisions and its material costs.

Computed by dividing gross income (sales minus cost of

goods sold) by sales for the same time period. The

greater the margin and the more stable the margin over

time, the greater the company's expected profitability.

Trends should be closely followed because they

generally signal changes in market competition.

Piotroski zeroed in on improving gross profit margin

because it serves as an immediate signal of an

improvement in production costs, inventory costs, or

increase in the sale's price of the company's product or

service.

9. Asset Turnover. The final element in Piotroski's

financial scoring system adds a point if asset turnover

for the latest fiscal year is greater than the prior year's

turnover.

Asset turnover (total sales divided by beginning period

total assets) measures how well the company's assets

have generated sales. Industries differ dramatically in

asset turnover, so comparison to firms in similar

industries is crucial. Too high a ratio relative to other

firms may indicate insufficient assets for future growth

and sales generation, while too low an asset turnover

figure points to redundant or low productivity assets.

An increase in the asset turnover signifies greater

productivity from the asset base and possibly greater

sales levels.


Summary of the Stock Screens used in the Piotroski Trading

Strategy

At the start of each month run the following stock screens. The

stocks that make it through all of the screens constitute the

Piotroski portfolio for that month. Select stocks for which:

First, The price-to-book ratio ranks in the lowest 20% of the

entire AAII Stock Investor Pro database, then:

The return on assets for the last fiscal year (Y1) is

positive

Cash from operations for the last fiscal year (Y1) is

positive

The return on assets ratio for the last fiscal year (Y1) is

greater than the return on assets ratio for the fiscal

year two years ago (Y2)

Cash from operations for the last fiscal year (Y1) is

greater than income after taxes for the last fiscal year

(Y1)

The long-term debt to assets ratio for the last fiscal year

(Y1) is less than the long-term debt to assets ratio for

the fiscal year two years ago (Y2)

The current ratio for the last fiscal year (Y1) is greater

than the current ratio for the fiscal year two years ago

(Y2)

The average shares outstanding for the last fiscal year

(Y1) is less than or equal to the average number of

shares outstanding for the fiscal year two years ago (Y2)


The gross margin for the last fiscal year (Y1) is greater

than the gross margin for the fiscal year two years ago

(Y2)

The asset turnover for the last fiscal year (Y1) is greater

than the asset turnover for the fiscal year two years ago

(Y2)

You can track the portfolio and performance of the Piotroski

trading strategy by becoming a member of AAII, which I highly

recommend, at www.AAII.com.

19

Action – Myth #3

In addition to providing you with a usable trading strategy,

this section shows how:

1. a hypothesis/concept (fading the sentiment of the most

aggressive retail investors) leads to

2. collection of the data relevant to identifying the

sentiment extremes, which leads to

3. a study that indicates the valid basis for a trading

strategy, that leads to

4. development of the actual trading strategy to exploit the

initial hypothesis

Once this process is understood, it can be expanded to produce

dozens of trading strategies that exploit numerous, unrelated

return drivers. This is the approach my firm, Brandywine

Asset Management, first developed almost 30 years ago and

continues to employ today.

The majority of people do not employ a systematic process

when they invest their money. As a result, they react

emotionally to every market move or geopolitical event. The

result, as shown in the DALBAR studies, is that they

dramatically underperform the average performances of the

mutual funds and ETFs in which they invest. This provides us

with a great opportunity to outperform those same funds.

Because the majority of people are so consistent at mis-timing

the market, we can use them as our George Costanza indicator

and do the opposite.

There are numerous measures of market sentiment. Some are

quite direct. Others esoteric. In 1996 my trading was

highlighted in an article in the financial weekly Barron’s after I

captured sizable profits from the grain market rally that


spring. In the same article was the following comment

regarding the timing indicator used by another trader. “[Trader

name] has developed his own set of indicators such as the

number of dental operations performed on pets. They were up

100% last year, indicating a jump in the spending of disposable

income. Ergo, inflation.”

This trader clearly did not employ the K-I-S-S (Keep It Simple

Stupid) method! There were certainly more direct ways to

measure investor sentiment than dental operations performed

on pets. Unfortunately, a couple of years after this article was

published the trader lost substantial money in the Asian

markets, resulting in a total loss for him and the people who

entrusted their money to him.

I prefer to employ more direct measures of investor sentiment.

Some of these investor sentiment measures are based on direct

surveys of market participants. These include the surveys

conducted by Investors Intelligence (II) and the American

Association of Individual Investors (AAII). II surveys over one

hundred independent market newsletters and records each

advisor’s stance on the U.S. stock market as bullish, bearish or

correction. They have applied a consistent approach to their

record-keeping since they began the survey in 1963.

Interestingly, when the founder of Investors Intelligence, AW

Cohen, first developed the survey he expected that the best

time to be long the market would be when most advisors were

bullish. As we all know now, this proved to be exactly opposite

the truth. The majority of advisors are almost always wrong at

market turning points. The Investors Intelligence website is

located at: http://www.investorsintelligence.com/.

I already introduced you to the AAII. The AAII states that they

“arm individual investors with the education and tools they

need to build wealth.” One of the tools they provide is a weekly

measure of the percentage of individuals who are bullish,

http://www.investorsintelligence.com/


bearish or neutral on the stock market in the short-term. The

AAII also surveys people on the percentage of their assets they

have allocated to stocks, bonds and cash.

One of the more recent measures has been developed by

TrimTabs Investment Research. TrimTabs is an investment

research firm focused on equity market liquidity. They develop

quantitative trading models that incorporate supply and

demand factors, rather than the conventional price or earnings

data.

TrimTab’s research confirms what we learned in Myth #3,

people are poor market timers and in particular, people who

place money in leveraged stock market ETFs are “impressively

wrong in both directions.”

According to TrimTabs’ model (developed by Vincent Deluard),

in the week following inflows into leveraged U.S. equity ETFs,

the market falls by an annualized 13.7% and in the week

following outflows the market rises by an annualized 10.3%.

This creates an opportunity to develop a trading strategy based

specifically on the inability of people to successfully time the

market.

The Sentiment Strategy - Concept

Based on the TrimTabs study, I developed a sentiment-based

trading strategy that takes both long and short directional

positions in the U.S. stock market, depending on whether

people are aggressively selling or buying stocks (through

ETFs), respectively. In keeping with what we learned in Myth

#3, the strategy does the opposite of what most people are

doing. It buys the SPDR S&P 500 ETF (SPY) when people are

bearish and buys the ProShares Short S&P 500 ETF (SH)

(which attempts to produce the inverse of the S&P 500 index

each day) when people are bullish.


The Sentiment Strategy - Performance

Because most inverse and leveraged ETFs began trading in the

mid 2000s, flows in these ETFs started being meaningful in

2006. Because (as you’ll see in the strategy specifics section)

the strategy requires one year of data before it can start

trading, I started the track record in January 2007. This gives

us four full years of performance data, including the financial

crisis. I did not include interest income for the 74% of the time

that the strategy is out of the market. Figure 3 and Figure 4

display the performance of the sentiment strategy compared

with a long position held in the S&P 500 Index.

The sentiment strategy beat the S&P 500 by about 95% in the

four years ending in 2010. What is more impressive is that the

sentiment strategy achieved this performance by being

invested only 26% of the time. In other words, the sentiment

strategy trades very little, but it trades very well. The

sentiment strategy achieved a positive rate of return in 75% of

the months. As you can see in the chart in Figure 4, the

sentiment strategy achieves most of its gains during sharp

market sell-offs - when investors are most emotional and more

prone to mistakes. As a result, the sentiment strategy is a

great edge against the passive long positions of your portfolio.

All you need to trade this strategy are the guts to go against

the crowds in times of intense market stress.


Performance of the Sentiment Strategy

Relative to SPY (Spiders)

Performance of the Sentiment Strategy Relative to SPY (Spiders)

SPY SENTIMENT

(SPIDERS) STRATEGY

YEARS 4.0 4.0

AVERAGE ANNUAL RETURN -0.96% 17.11%

ANNUALIZED VOLATILITY 19.60% 17.52%

MAXIMUM DRAWDOWN -55.19% -10.82%

% PROFITABLE MONTHS 56% 75%

% PROFITABLE ROLLING 12 MONTHS 43% 100%

% PROFITABLE YEARS 75% 100%

TIME IN MARKET 100.00% 26.22%

Figure 3

Figure 4


Sentiment Strategy ETF Listing

Figure 5


Sentiment Strategy – Specifics:

Here are the specific trading rules of the strategy:

1. Determine level of bullishness or bearishness of

investors in leveraged ETFs.

Figure 5 shows the current ETF Listing for the

Sentiment Strategy.

Bloomberg and other data providers report ETF share

data on a daily basis. For Bloomberg users, just type:

FASSO Index HP <Enter> to access the historical

shares outstanding of FAS (Direxion Financial Bull 3X).

Yahoo! Finance also reports the latest available data by

ticker.

Flow is measured as change in shares multiplied by

price. Of course, flows need to be adjusted by the

leverage used in the ETF. For example, inflows and

outflows to/from an ETF that attempts to return two

times the daily return of the S&P 500 (such as the

ProShares Ultra S&P 500 ETF or “SSO”) will get two

times the weighting of those that flow into or out of a

single leveraged ETF (such as SPY). Inflows into inverse

ETFs are counted as outflows (from stocks). The

resultant total we will call the “Daily Money Flow”

(DMF).

2. Each day calculate the average of the past ten (10) days

DMF. This is the “10dDMF.” Convert the 10dDMF into

a normalized score using its rolling 100-day mean and

standard deviation. Normalized score is calculated by

subtracting the mean from an observation and dividing


the result by the historical standard deviation. A

normalized score of 2.0 would imply that flows are two

standard deviations greater than the historical mean

(note that for normally distributed variables, such

occurrences happen about 2.5% of the time).

If the normalized score of 10dDMF is below -1.75 (which

indicates very negative sentiment), buy SPY. If the

normalized score of 10dDMF is above 1.75 (which

indicates ebullient sentiment), buy SH (ProShares Short

S&P 500)

3. Hold the position for 5 days. If the buying/selling

criteria remain in effect, the 5-day hold period resets

with each day the normalized score exceeds the

threshold. Also, if the normalized score falls within the

threshold and then exceeds the threshold again during

the hold period, a new entry signal is issued, and the 5-

day hold period begins again.

Note 3-1: Results shown in this Action Section are slightly

different than the results shown in the book due to

adjustments in the strategy algorithm and the ETF list.

Note 3-2: Transaction costs are assumed to be 0.10% per

transaction.

27

Action – Myth #4

In Myth #4 I discussed that fact that the major stock market

indexes were not created with the goal of maximizing returns,

but with the goal of being representative. In addition, they

allocate to their constituent stocks in a way that creates quirky

biases in their portfolios. Because of this there are better

alternatives for you to get broad stock market exposure than to

place your money into the most popular stock index funds, such

as those that track the S&P 500 Index. Prior to 2005 your only

option would have been to create your own “actively passive”

index fund, a significant undertaking. But today you can put

money into one of the many new index funds that have been

created with the intent of “performing,” rather than simply

being “representative.”

Index Innovation

There have been numerous studies conducted that show that

by focusing on some basic attributes; certain stocks may

outperform others over time. For example, in Myth #1 we saw

that the primary driver of stock returns long-term was

corporate earnings growth. And in Myth #4 I referenced a

study that established that higher dividend payout ratios (the

percentage of a company’s earnings that are paid to

shareholders as dividends) leads to higher earnings growth.

While you can develop your own stock selection methods to

attempt to identify such stocks for inclusion in your portfolio,

only the largest accounts will be able to diversify across the

hundreds of stocks necessary to obtain true index-like

diversification. Fortunately others have done this for us. Since

2005, many new indexes have been created with the goal of

systematically maximizing returns by following a clearly-

defined set of rules, while still following a (reasonably) low-

turnover approach. We now have a selection of “performance”

(my term) index funds based on these new indexes that can


serve as an alternative to the “representative” index funds such

as those based on the S&P 500.

Performance index funds can be classified into three primary

categories. These are Equal-Weighted, Fundamental and

Efficient. Equal-Weighted indexes are described by their

classification. The stocks in these indexes are held in equal

allocations, so that a 1% move in any stock has the same

impact on the index as a 1% move in any of the other stocks.

Fundamental indexes allocate to each stock in the index based

on factors such as each company’s earnings, dividend growth,

positive changes in analyst recommendations, buying of shares

by company insiders or book value. Efficient indexes weight

each stock in the index based on quantitative information such

as expected returns, correlations and volatilities.

The major index providers, such as S&P and FTSE, have

jumped on the Performance index bandwagon. In 2002 S&P

launched an equal-weighted version of its S&P 500 Index. In

2005 FTSE teamed with Research Affiliates LLC to create a

series of fundamental indexes and later with EDHEC to create

a series of efficient indexes. As the FTSE EDHEC indexes were

only developed in 2010, there are not yet any ETFs trading

pursuant to their methodologies. However, there are a number

of ETFs trading pursuant to equal-weighted and fundamental

indexes. The equal-weighted indexes are based on the

traditional Representative indexes such as the S&P 500 and

the Russell indexes. They allocate to the same stocks as those

in the indexes but on an equal-weighted basis. The

fundamental indexes may start out by looking at the stocks

that make up one of the Representative indexes, but they

ultimately select only the subset of those stocks that exhibit

the characteristics that have been found to be predictive of

future share price appreciation.


In this Action I present the performance of four Performance

ETFs; one that is equal-weighted, the Rydex S&P 500 Equal

Weight ETF (RSP); and three based on fundamental indexes;

the PowerShares FTSE RAFI U.S. 1000 ETF (PRF), the

Vanguard Dividend Appreciation ETF (VIG), and the

Guggenheim Insider ETF (NFO). These funds can serve as

replacements for the typical index fund(s) that people

incorporate in their portfolios.

Rydex S&P 500 Equal Weight ETF (RSP)

The Rydex S&P 500 Equal Weight ETF tracks the performance

of the equal-weighted S&P 500 Index. While this index

includes the same stocks that comprise the S&P 500, and

therefore is only intended to be representative, rather than

seek out stocks that will outperform others, by allocating

evenly to each of those stocks it does correct the flaw (in the

S&P 500) whereby the biggest companies get the biggest

allocation.

Performance of the Rydex S&P 500 Equal Weight ETF (RSP)

Relative to the S&P 500 Total Return Index

Figure 6

$0

$500

$1,000

$1,500

$2,000

$2,500

Dec

-10

Dec

-09

Dec

-08

Dec

-07

Dec

-06

Dec

-05

Dec

-04

Dec

-03

Gro

wth

in

In

itia

l $

1,0

00

RSP

S&P 500 TR


PowerShares FTSE RAFI US 1000 ETF (PRF)

The PowerShares FTSE RAFI US 1000 Index was developed by

FTSE, in partnership with Research Affiliates. FTSE, which is

owned by The Financial Times and London Stock Exchange, is

a company that develops market indexes. Research Affiliates is

the firm founded by Robert Arnott. It was Mr. Arnott who

kicked off the fundamental indexing wave with research he

presented in the early 2000s. (The “RAFI” acronym stands for

“Research Affiliates Fundamental Index”.)

The RAFI US 1000 Index consists of 1,000 US-listed companies

with the largest RAFI fundamental values, selected from the

common stocks traded on the New York Stock Exchange, the

American Stock Exchange and the NASDAQ National Market.

Performance of the PowerShares FTSE RAFI US 1000 ETF (PRF) Relative to the S&P 500 Total Return Index

Figure 7

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Dec

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Jun-

06

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08

Dec

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Jun-

09

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-09

Jun-

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PRF

S&P 500 TR


Vanguard Dividend Appreciation ETF (VIG)

This Vanguard ETF is managed by Vanguard and tracks the

Mergent Dividend Achievers Index. The Mergent Dividend

Achievers Index follows U.S. listed companies that have

consistently increased their annual regular dividends for at

least the past ten consecutive years and have met certain

liquidity screens. (Mergent is a provider of financial data that

also develops and licenses equity and fixed income indexes

based on its proprietary investment methodologies.)

Performance of the Vanguard Dividend Appreciation ETF (VIG) Relative to the S&P 500 Total Return Index

Figure 8

$0

$200

$400

$600

$800

$1,000

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VIG

S&P 500 TR


Guggenheim Insider ETF (NFO)

The Guggenheim Insider ETF tracks the performance of the

Sabrient Insider Sentiment Index.1 This is an index of

approximately 100 stocks that are selected (out of

approximately 6,000 eligible securities) to be purchased

because company insiders have been active buyers of the stock

and earnings expectations have increased. Stocks selected

pursuant to these factors have shown to produce above-average

returns in more than a decade of back-testing. Although there

are other funds based on fundamental factors that could also be

selected to serve as Performance indexes in your portfolio, I am

presenting NFO for two primary reasons. First, I believe in the

power of legal “insider information” as a predictor of a stock’s

price, and second, the fund now has more than four years of

actual in-market performance. Many of the other

fundamentally-based ETFs are more recently formed.

Performance of the Guggenheim Insider ETF (NFO)


Figure 9

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

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Dec-

10

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09

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08

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07

Dec-

06

NFO

S&P 500 TR


1Joshua Anderson, “The Sabrient Insider Sentiment Index,”

Sabrient (October 20, 2006).

http://www.sabrient.com/pdfs/whitepaper-InsiderSentimentIndex.pdf

http://www.sabrient.com/pdfs/whitepaper-InsiderSentimentIndex.pdf

34

Action – Myth #6

Buy low, sell high is a great concept that fails in its

implementation. As I showed in both Myth #6 – Buy Low, Sell

High and Myth #7 – It’s Bad to Chase Performance, the

opposite approach is what actually works.

One such “opposite” stock trading strategy was developed by

William O’Neil, the founder of Investor’s Business Daily. Mr.

O’Neil developed his strategy by looking at the characteristics

exhibited by the best performing stocks prior to them posting

their large gains. He identified seven key indicators for

inclusion in this trading strategy. These indicators form the

mnemonic – CAN SLIM – that spells out the name of the

trading strategy. Because this trading strategy generally

produces portfolios that contain less than 10 stocks, it is

suitable for small portfolios.

The CAN SLIM concepts were first published by Mr. O’Neil in

1988 in the first edition of his best-selling book, How to Make

Money in Stocks. The book is now in its fourth printing. (See:

O’Neil, William J. "How to make Money in Stocks: A Winning

System in Good Times or Bad." 1988.) The AAII developed and

tracks a version of the CAN SLIM strategy and ranks it among

the leading stock trading strategies over the past ten years.

Over their entire test period, from January 1998 through

December 2010, the CAN SLIM trading strategy produced a

23% average annualized return. I have adjusted the returns to

include a cost of 4% each year to account for the transaction

costs that could have been incurred in actual trading. Figure 10

and Figure 11 present this performance.




Growth in $1,000 Allocated to CAN SLIM Trading Strategy

on January 1, 1998

CAN SLIM Trading Strategy Annual Returns

The CAN SLIM Strategy

The AAII has developed a systematic process for trading CAN

SLIM based on their interpretation of the strategy as described

by William O’Neil. The following description, which details the

criteria underlying each letter in the CAN SLIM mnemonic, is

derived from this interpretation. A description of each letter in


Figure 11. Source AAII.com

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

$18,000

$20,000

Dec

-97

Dec

-98

Dec

-99

Dec

-00

Dec

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Dec

-02

Dec

-03

Dec

-04

Dec

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Dec

-06

Dec

-07

Dec

-08

Dec

-09

Dec

-10

1998 23.15% 2005 19.28%

1999 31.23% 2006 24.53%

2000 32.73% 2007 25.50%

2001 48.74% 2008 -14.07%

2002 15.80% 2009 89.81%

2003 72.29% 2010 -27.31%

2004 -7.64%


the acronym is followed at the end of this Action by a list of the

specific criteria underlying the AAII interpretation of the CAN

SLIM trading strategy.

C = Current Quarterly Earnings per Share: How Much Is

Enough?

The CAN SLIM approach focuses on companies with proven

records of earnings growth but that are still in a stage of

earnings acceleration. O'Neil's study of winning stocks

highlights the strong quarterly earnings per share of the

securities prior to their significant price run-ups.

When screening for quarterly earning increases, it is

important to compare a quarter to its equivalent quarter

last year – i.e., this year's second quarter compared to last

year's second quarter. Many firms have seasonal earnings

patterns and comparing similar quarters eliminates any

bias arising from this seasonality.

O'Neil recommends looking for stocks with a minimum

increase in quarterly earnings of 18% to 20% over the same

quarterly period one year ago. When examining a

percentage change it is not only important to check the

figures for unusually small base numbers that may distort

the percentage change figures, but it is also important to

check if any of the numbers in the calculation are negative.

A change in sign, as in a negative to a positive, requires

special consideration and may result in misleading

screening results. When screening on user-defined fields

such as custom growth rates, you may find it useful to

include some secondary or qualifying criterion to help

ensure proper screening results. In the CAN SLIM screen,

positive earnings for the current quarter are required to

help make the results of the growth rate calculation more

meaningful.


Whenever you are working with earnings, the issue of how

to handle extraordinary earnings comes into play. One-time

events can distort the actual trend in earnings and make

company performance look better or worse than a

comparison against a firm without special events. O'Neil

recommends excluding these non-recurring items from the

analysis. The AAII screen examines growth in earnings

from continuing operations only.

So the first two screening filters that make up the trading

strategy are:

a. quarterly growth rate greater than or equal to

20%; and

b. positive earnings per share from continuing

operations for the current quarter.

A = Annual Earnings Increases: Look for Meaningful

Growth

Beyond looking for strong quarterly growth, O'Neil likes to

see an increasing rate of growth. An increasing growth rate

in quarterly earnings per share is so important in the CAN

SLIM system that O'Neil warns shareholders to consider

selling holdings of those companies that show a slowing

rate of growth two quarters in row. This screen specifies

that the growth rate from the quarter one year ago to the

latest quarter be higher than the previous quarter's

increase from its counterpart one year prior. Basically, the

current quarter's growth over the past 12-month period

must be better than the previous quarter's growth over the

past 12-month period.

In addition, the winning stocks in O'Neil's study had a

steady and significant record of annual earnings in addition


to a strong record of current earnings. The CAN SLIM

system tries to identify the strong companies leading the

current market cycle.

The primary screen for annual earning increases that

O'Neil uses is increasing earnings per share in each of the

last five years. In applying this screen, we specify that

earnings per share from continuing operations be higher for

each year when compared against the previous year. To

help guard against any recent reversal in trend, a criterion

is included requiring that the earnings over the last 12

months be greater than or equal to earnings from the latest

fiscal year. This group of criteria proved to be the most

stringent independent filter.

O'Neil also recommends screening for companies showing a

strong annual growth rate of 25% or 50% over the last five

years. The winning companies in O'Neil's study had a

median growth rate of 21%. O'Neil specifies a minimum

annual growth rate of 25% in earnings per share from

continuing operations over the last five years. This criterion

proved to be the second most restrictive screen when used

independently.

N = New Products, New Management, New Highs: Buying

at the Right Time

O'Neil feels that a stock needs a catalyst to start a strong

price advance. In his study of winning stocks, he found that

95% of the winning stocks had some sort of fundamental

spark to push the company ahead of the pack. This catalyst

can be a new product or service, a new management team

after a period of lackluster performance or even a structural

change in a company's industry, such as a new technology.


These are very few qualitative factors that lend themselves

to inclusion in a fully systematic trading strategy. However,

the existence of a catalyst is often reflected as a jump in the

stock price. O’Neill recognizes this as well and emphasizes

that investors should pursue stocks showing strong upward

price movements. O'Neil says that stocks that seem too

high-priced and risky most often go even higher, while

stocks that seem cheap often go even lower. (I agree. In

Myth #7 – It’s Bad to Chase Performance” I discuss the

existence and cause of market trends and in the next Action

present a trading strategy that you can use to profit from

them.)

O'Neil's newspaper, Investor's Business Daily, highlights

stocks within 10% of their 52-week high and this is the

criterion AAII established for their screen. Used

independently the screen allows about one-third of the

companies to pass the filter. The number of companies

passing will vary over the course of the market cycle. One

would expect many companies to pass during a strong

market expansion, while a smaller number of companies

would pass during the early stage of a bear market.

S = Supply and Demand: Small Capitalization Plus Volume

Demand

As the catalyst starts pushing the price of a company's

stock up, those firms with a smaller number of shares

outstanding should increase more quickly than those with a

large number of outstanding shares. In his study of winning

stocks, O'Neil found that 95% of the winning stocks had

fewer than 25 million shares outstanding, while the median

for the group was 4.6 million.

In a separate study of stock market winners by Marc

Reinganum, published in the September 1989 issue of the


AAII Journal, the findings resulted in a similar conclusion

and established the cut-off at 20 million outstanding

shares. The Reinganum study examined the characteristics

of stocks prior to their big price increase and found that the

median figure of the stocks in the study was 5.7 million

shares, which doubled during the two years that each

winning stock was examined. This probably indicates that

firms split their shares during their big price increase.

What this also indicates is that the CAN SLIM trading

strategy lets smaller investors take advantage of their size.

Because of the small size of the companies selected by the

CAN SLIM trading strategy, the largest mutual funds are

effectively unable to trade in these stocks.

O'Neil also feels that share buybacks, which reduce the

public float of company's stock, is positive because it

reduces the supply of the company's stock while boosting

per share earnings.

O'Neil suggests that investors consider looking at the actual

"float" of the stock. The float is the number of shares in the

hands of the public – determined by subtracting the

number of shares held by management from the number of

shares outstanding. Thus AAII requires a stock to have

fewer than 20 million shares available through the float in

order to be considered. When examined independently, this

criterion proves to be the least restrictive screen.

L = Leader or Laggard: Which is Your Stock?

O'Neil is not like the patient value investor – looking for

out-of-favor companies and willing to wait for the market to

come around to his viewpoint. Rather, he prefers to scan for

rapidly growing companies that are market leaders in

rapidly expanding industries. O'Neil advocates buying

among the best two or three stocks in a group. You should


be compensated for any premium you pay for these leaders

with significantly higher rates of return.

After identifying a strong industry, O'Neil warns against

avoiding the market leaders by purchasing "sympathy"

stocks that are similar but significantly cheaper when

examined by factors such as price-earnings ratios and

weaker price performance. These stocks often continue to

languish while the market leaders continue their strong

rise.

O'Neil suggests using relative strength to identify market

leaders. Relative strength compares the performance of a

stock relative to the market as a whole. Relative strength is

typically reported with a base level of zero or one – in which

case the base level represents stock performance equal to

the market index. Numbers above the base level reflect

performance above the market index, while below-market

performance can be seen with figures below the base. AAII

uses a base level of zero.

Companies are often ranked by their relative strength

performance and their percentage ranking among all stocks

is calculated to show the relative position against other

securities. Investor's Business Daily presents the

percentage ranking of stocks and O'Neil recommends only

looking for stocks with a percentage rank of 70% or better –

stocks that have performed better than 70% of all stocks.

AAII requires the 52-week relative strength to equal or

exceed 70%.

I = Institutional Sponsorship: A Little Goes a Long Way

O'Neil warns against selecting low-priced stocks with small

capitalization and no institutional ownership, because these


stocks have poor liquidity and often carry a lower-grade

rating.

O'Neil feels that a stock needs a few institutional sponsors

for it to show above-market performance. Three to 10

institutional owners are suggested as a reasonable

minimum number. This number refers to actual

institutional owners of the common stock, not institutional

analysts tracking and providing earnings estimates on

stocks.

Beyond looking for a minimum number of institutional

owners, O'Neil suggests that investors look at the past

record of the institutions. The analysis of the holdings of

successful mutual funds represents a good resource for the

investor because of the widely distributed information on

mutual funds.

AAII established a screen for stocks to have at least five

institutional owners.

It is difficult to strike a balance between looking for stocks

with room to expand further and stocks that may be over-

owned. O'Neil warns that while some institutional

sponsorship is required, once everyone has jumped on the

stock it may be too late to buy into it.

M = Market Direction: How to Determine It?

The final aspect of the CAN SLIM system looks at the

overall market direction. While it does not impact the

selection of specific stocks and is not included in the version

of the CAN SLIM strategy I present here, the trend of the

overall stock market has a tremendous impact on the

performance of the individual stocks you select. O'Neil

focuses on technical measures when determining the


overall direction of the marketplace and recommends that

you sell 25% of your stocks when the market peaks and

begins a major reversal.

Summary of the stock screens used in the CAN SLIM Trading

Strategy

At the start of each month run the following stock screens. The

stocks that make it through all of the screens constitute the

CAN SLIM portfolio for that month. Select stocks for which:

The growth of earnings per share from continuing

operations, as of the latest fiscal quarter (Q1) over the

same quarter one year prior (Q5), is greater than or

equal to 20%

The growth of earnings per share from continuing

operations, as of the latest fiscal quarter (Q1) over the

same quarter one year prior (Q5) is greater than the

growth of earnings per share from continuing

operations, as of the previous fiscal quarter (Q2) over

the same quarter one year prior (Q6)

Earnings per share from continuing operations for the

two most recent fiscal quarters (Q1 and Q2) is positive

Growth in earnings per share from continuing

operations over the last five years is 25% or more

Earnings per share from continuing operations has

increased over each of the last five fiscal years as well as

over the last 12 months

The current stock price is within 10% of its 52-week

high


The stock's float is less than 20 million shares

The 52-week relative strength is in the top 30% of the

entire database (percent rank greater than 70)

There are at least five institutional shareholders

You can track the portfolio and performance of the CAN SLIM

trading strategy by becoming a member of the AAII, which I

highly recommend, at www.AAII.com.

45

Action – Myth #7

In Myth #7 we saw that buying-and-holding the S&P

Diversified Trends Indicator outperformed, on a risk-adjusted

basis, a buy-and-hold position in the S&P 500 TR Index. The

reason for this is two-fold. One, the S&P DTI, despite trading

in just 24 markets, is more diversified than the S&P 500 TR,

which trades in 500. That is because the markets in the S&P

DTI include interest rate markets, currencies, precious metals,

grains, livestock and other commodities. They are not all

subject to the same event risk and therefore their prices move

more independent of each other than do the 500 stocks in the

S&P 500 TR. Two, the S&P DTI is adaptive. It follows trends.

When the trend in any given market is up it is long. When the

trend is down it is short. The S&P 500 TR in contrast, naively

holds on to long positions even when the trend is clearly down.

As good as the S&P DTI is however, it does have its flaws, as

were discussed in the myth. The primary flaw is that it applies

different rules to the energy markets than it does to other

markets. This appears to be a case of “curve-fitting” the

strategy to the observed results. But there’s an easy solution to

that problem. Simply apply the strategy consistently to all

markets.

In this Action I will present what is perhaps the simplest

possible trend following strategy, even simpler than underlying

the S&P DTI. We will apply this strategy equally to all markets

in the portfolio. Because this trading strategy is applied across

30 futures markets it requires $1 million for full diversification

and is therefore suitable only for the largest accounts. Small

accounts can however, invest in the mutual funds and ETFs

presented in the Action Section for Myth #20.


The “Month-end Trend Strategy”

The name of this trading strategy describes its main

characteristic: it takes long and short positions in each market

in its portfolio based on the direction of the trend indicated by

the month-end closing price of each market. Specifically, there

are two components of the ETM Diversified Trend Strategy:

1. the specifics of the trend following system to be

employed

2. the selection of, and allocation among, the markets to be

included in the portfolio

Specifics of the Trend Following System

The trend following system underlying the Month-end Trend

Strategy incorporates one of the simplest possible algorithms,

yet it is effective in capturing longer-term trends. The system

will always be long or short in each market. Here’s how it

works: At month-end we will compare the price of each market

to that market’s 250-day moving average. A moving average is

simply the average of the closing prices of that market over the

250 trading days ending at month-end. If the market price at

month-end is below the moving average we will either

maintain our short position (if already short) or reverse our

long position to a short position in that market at the close of

trading on the first trading day of the following month. If the

market price at month-end is above the 250-day moving

average we will place an order (if not already long) to go long

that market at the close of trading on the first trading day of

the following month. We apply this system to a portfolio of 30

futures markets diversified across stock indexes, interest rate

contracts, currencies, energy, metals and agricultural markets.


Market Selection and Allocation

It is important to avoid the market selection bias exemplified

by the inconsistent trading of energy markets in the S&P

Diversified Trends Indicator. To maintain consistency, the

Month-end Trend Strategy will trade a diversified portfolio of

futures markets that bases its market allocations simply on the

goal of producing consistent returns across a range of market

conditions. As a result, we allocate to each market with the

intent of creating a portfolio that is balanced across all

markets, so that no single market will, over time, dominate

performance. The following markets displayed in Figure 12

have been selected to provide broad portfolio diversification:

The allocation to each market is designed to ensure that over

time, each market will have a balanced impact on the volatility

of the entire portfolio. To achieve this we allocate to each

market based on the standard deviation of the daily move in

the price of each market over the prior 250 days. For example,

if over the past 250 trading days the Euro futures contract has

Figure 12

Commodities

Currencies

Metals

Energy

Stock

IndexesInterest

Rates

U.S. 30-yr. Bond

German Bund

Long Gilt (UK)

Japanese Government Bond

Eurodollar

S&P 500

DAX 30 (Germany)

FTSE 100 (UK)

Volatility index

Crude oil

Heating Oil

RBOB Gasoline

Natural Gas

Gold

Silver

Copper

Corn Sugar

Wheat Orange Juice

Soybeans Cotton

Coffee Live Cattle

Cocoa Lean Hogs

Euro

British Pound

Japanese Yen

Aussie Dollar


averaged a move of $800 from one day to the next and corn has

averaged $400, then we will trade one Euro for every two corn

contracts. That gives us the relative allocation among all

markets. Now we need to calculate the specific number of

contracts to be traded in each market. This is a function of the

amount of portfolio volatility we are willing to accept. As a

rough rule of thumb, a million dollar account trading a total of

50 contracts across all markets will produce an average annual

standard deviation of returns approximately equal to that of

the S&P 500. For the purpose of calculating the performance

displayed in Figure 13 and Figure 14, I targeted a return over

the 20 year period 1991 – 2010 that was equal to the return

earned by the S&P 500 during that period. Because the Month-

end Trend Strategy is more diversified than the S&P 500 the

back-tests show it can achieve the same annualized return of

the S&P 500 but with only 52% of the S&P 500s annualized

volatility. The diversification benefit has an even greater

impact on the maximum drawdown. While the S&P 500 lost

more than 50% of its value during the financial crisis, the Trend

Strategy (which profited during the financial crisis) suffered a

maximum drawdown over its entire 20 year history of less than

12%, while targeting the same annualized return as the S&P 500.

This is the same trend following strategy that produced the

results displayed in Figure 36 in Myth #12. While that

performance, trading just five stock index futures contracts,

outperformed the S&P 500 on a risk-adjusted basis (the return

matched that of the S&P 500 Total Return Index while the

maximum drawdown was 47% less), risk is reduced even more

when the strategy is employed across 30 markets rather than

just five.

This simple trend-following strategy produced the following

back-tested returns relative to the S&P 500 Total Return Index

over the past 20 years.


Performance Comparison: Trend Strategy vs. S&P 500 Total Return Index

Figure 13

Performance Comparison: Month-end Trend Strategy vs.

S&P 500 Total Return Index

Trend

Following

Years 20 20

Average Annual Return 8.93% 9.11%

Annualized Volatility 14.98% 7.81%

Maximum Drawdown -51% -12%

% Profitable months 63% 65%

% Profitable Rolling 12-Months 78% 86%

% Profitable years 80% 84%

S&P 500 TR

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Figure 14

50

Action – Myth #8

In Myth #8 I showed that simply buying-and-holding stocks in

multiple industry sectors provided very little diversification

value, especially in bear markets, as the sector performances

are highly correlated with each other. But they are not

perfectly correlated and this opens up the opportunity to trade

pursuant to a disciplined strategy that attempts to identify and

allocate to the strongest sectors and avoid the weakest.

Schreiner Capital Management (SCM) is a registered

investment advisor that provides investors with active

management solutions. They have been trading client assets

pursuant to sector allocation strategies since 1989. The

strategy and performance presented here is an original sector

allocation strategy developed by SCM and differs from the

specific strategy they currently employ for their clients. Before

I present the strategy rules, let’s take a look at the

performance of the strategy relative to that of the S&P 500

Total Return Index (Figure 15 and Figure 16).

Performance Comparison: Sector Allocation Strategy vs. S&P 500 Total Return Index

Figure 15

S&P 500 Total

Return

Sector Allocation Strategy

Years 12 12 Average Annual Return 0.21% 4.60% Annualized Volatility 16.12% 13.35% Maximum Drawdown -51% -41% % Profitable Months 56% 56% % Profitable Rolling 12-Mos. 61% 68% % Profitable Years 67% 67%


Performance Comparison: Sector Allocation Strategy vs. S&P 500 Total Return Index

Figure 16

The performance shows that this simple momentum-based

strategy outperformed a buy-and-hold position in the S&P 500

Total Return Index while suffering both lower volatility and

drawdowns.


The SCM sector allocation strategy is a straightforward

momentum-based strategy that attempts to take long positions

in the strongest-performing industry sectors and avoid

positions in the weakest. The strategy has been tested using

Rydex mutual funds for its sector exposure, but could also

likely be adapted to use other sector funds or ETFs. The

portfolio as defined by SCM includes 20 separate mutual funds.

While 17 of these are U.S. sector funds, the portfolio does also

include two country funds and one managed futures fund.

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S&P 500 Total Return Index


These 20 Rydex mutual funds are:

Symbol Name Symbol Name

RYBIX Basic Materials RYOIX Biotechnology

RYCIX Consumer Products RYPIX Transportation

RYEIX Energy RYRIX Retailing

RYFIX Financial Services RYSIX Electronics

RYHIX Health Care RYTIX Technology

RYHRX Real Estate RYUIX Utilities

RYIIX Internet RYVIX Energy Services

RYKIX Banking RYEUX Europe 1.25x Strategy

RYLIX Leisure RYJHX Japan 2x Strategy

RYMIX Telecommunications RYMBX Commodities Strategy

Figure 17

The basic underlying rule is to buy each mutual fund, selected

from the list, when it shows a positive rate of change (ROC) in

price over the prior 42 trading days. Each fund gets a 5%

allocation of the cash allocated to this strategy. Once a position

is entered into, it is held in the strategy for a minimum of 21

trading days. Following 21 trading days, a position will then

exit if and when the ROC is negative.

Note 8-1: The strategy algorithm was adjusted for results

beginning January 2011, so results in the book may differ

slightly from the results included in the Performance Reports

on the interactive website.



53

Action – Myth #10

As I showed in Myth #10, the ability of investors to be able to

sell stocks short provides value to the overall financial

markets. But short-selling also provides tremendous

opportunities to the short-sellers as well. First, holding short

positions in a stock portfolio can serve as a “hedge” to reduce

portfolio losses when there are sizable market sell-offs. But

short positions can also serve as a source of additional returns.

By selecting stocks of poorly run companies to sell short, the

short-seller will profit as other investors begin to recognize the

underperformance of those companies as well and sell the

stock. This collective selling will cause the stock price to fall,

enabling the original short seller to buy those stocks back at a

lower price. For decades short-selling was exclusively the

domain of hedge funds, which reduced the risk in their long

stock portfolios by also holding short stock positions. But you

can now participate as well. In recent years various mutual

funds have been formed that exploit the benefits of short-

selling.

Long-Short & Market Neutral Funds

There are two categories of funds that take both long and short

positions in stocks. One is the “long-short” fund. These funds

take short positions but may retain a long market bias. An

example is a fund that holds long stock positions valued at

100% of the portfolio’s value and short positions equal to 30% of

the portfolio’s value. This fund is 70% net long. A true “market-

neutral” fund attempts to more closely balance both long and

short positions by dollar value, as well as other sector,

capitalization and style “factors.”

There are two primary ways to participate in a long-short or

market-neutral fund. The first is to create your own. You can

do this by following a trading strategy that ranks stocks in


order of their attractiveness, but instead of simply buying those

deemed most attractive; also sell short those deemed least

attractive. The Piotroski trading strategy, described in the

Action for Myth #1, is one method that can be used for this

purpose since it ranks stocks on a scale of 0 (great to short) to 9

(great to hold long). Each month rebalance the account to hold

an equal dollar amount of good stocks (such as those ranked 8

& 9) long and bad stocks (those ranked 0 & 1) short. While this

approach does result in a dollar balanced long-short fund, it

requires dozens or hundreds of positions each long and short

and sophisticated formulas in order to effectively reduce the

factor exposures. As a result, the creation of a true market

neutral fund is generally suitable only for larger portfolios.

The second way to participate is to put your money into a fund

that takes both long and short positions. When I launched my

first market neutral hedge fund in 1996 there were no mutual

funds that employed market neutral strategies. At that time

there were restrictions on short selling that essentially

prevented a mutual fund from being market neutral. That is no

longer the case today. A great source of mutual fund data is

www.morningstar.com and using their free mutual fund

screener results in a list of more than a dozen market neutral

funds. One that stands out is the JP Morgan Research Market

Neutral Fund (JMNAX). JMNAX uses fundamental analysis to

create a portfolio that is both long and short approximately 150

stocks. The fund also attempts to eliminate any factor biases by

being market-, dollar-, sector-, and style-neutral. This

neutrality is evidenced by the fund’s low 0.23 correlation to the

S&P 500. Figure 18 shows the performance of JMNAX relative

to the S&P 500 Total Return Index (since it can serve as an

alternative to a buy-and-hold position in stocks).

http://www.morningstar.com/


Performance of the JP Morgan Research Market Neutral Fund (JMNAX)


There are other long-short funds that even without being truly

market neutral, can add value and minimize losses during

stock bear markets because of their short-selling component.

The TFS Market Neutral Fund (TFSMX), which uses a purely

quantitative approach to long-short stock selection, has

demonstrated this ability since its inception in 2004 (Figure

19). As evidenced by its 0.43 correlation to the S&P 500, it does

have slightly more market exposure than does JMNAX, but its

short positions have reduced losses during losing periods for

stocks.

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Figure 18


Performance of the TFS Market Neutral Fund (TFSMX)


Note: 10-1: Due to limitations on availability to new investors,

we have removed TFSMX from the portfolio. We have replaced

this market neutral constituent with TMNFX.

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Figure 19

57

Action – Myth #11

In this myth I introduced you to the term structure of

commodity futures contracts. The term structure is simply a

phrase that refers to the relationship in prices between futures

contracts that will be expiring soon and those that will not

expire for months or years to come. Because each of these

futures contract months trades at a price that is potentially

different from the other contract months, opportunities arise to

profit from these differences. One popular strategy that is

employed to capture this return driver, specifically in the

currency markets, is the “carry trade.”

The carry trade opportunity is created by the fact that interest

rates are at different levels in different countries. For example,

while the Australian one-year interest rate (how much you

would earn over the next twelve months if you purchased a

one-year Australian government note today) is at 5.75%, the

one-year Japanese interest rate is at 0.20%. This means that

an investor who borrows the equivalent of $1,000,000 in Japan

and buys a treasury note of the same value in Australia will

make $55,500 over the course of the year (person pays $2,000

in interest in Japan and earns $57,500 in interest in

Australia). The currency futures markets are priced to account

for this differential in interest rates between countries. The

futures contract for the Australian dollar one year in the future

is priced approximately 5.75% below today’s value of the

Australian dollar. This ensures that a person can’t simply buy

a one-year Australian note today, sell the Australian Dollar

futures contract (one-year forward) to hedge the currency risk

over the next year, and pocket the 5.75% risk-free. But by being

priced this way it enables a person to buy the one-year futures

contract, and, if the Australian Dollar remains at the same

level, profit from the fact that it will rise in price by 5.75% over

the next year. This is the basis for the carry trade.


Many hedge funds and institutional investors frequently

engage in this carry trade. Because they have credit lines and

other means of borrowing, they may only post collateral of a

few percent of the face value of the borrowed money (certainly

less than $50,000), therefore earning more than 100% on their

invested funds.

There is of course a risk. Currency values fluctuate. If the

Japanese Yen increases in value by more than 5.55% over the

course of the year relative to the Australian Dollar, then the

entire profit is wiped out. That is because when the investor

pays back the $1,000,000 value of Japanese Yen that he

borrowed, it will actually cost 5.55% more, or $1,055,500. But

despite that risk of occasional loss, over time the carry trade

has produced positive returns.

Until recently, the use of this trading strategy has been

confined primarily to professional traders. But over the past

few years ETFs have been launched that provide people with

access to this strategy. One of those is the PowerShares DB

G10 Currency Harvest ETF (DBV). DBV seeks to track the

performance of the Deutsche Bank G10 Currency Future

Harvest Index – Excess Return, plus the U.S. short-term

interest rate. The fund compares the three-month interest

rates for each of the G10 currencies and then takes long

positions in the highest-yielding currencies and short positions

in the lowest-yielding currencies. Because this fund earns

returns from return drivers that are distinct from those that

drive the performance of most people’s portfolios, DBV can

provide portfolio diversification value.

Figure 20 shows the performance of the Deutsche Bank G10

Currency Future Harvest Index – Excess Return, starting in

1993, the first full year of its back-test, and extending through

September 2006 (reduced by 81 basis points per year to account

for the fees being charged to the ETF); and DBV since its first


full month of trading in October 2006 through year-end 2010.

The losses suffered in 2009 occurred as the carry trade

“unwound” during the financial crisis. As a result, this

strategy, while being uncorrelated to stocks for much of its

history, did not provide diversification value during the 2008

financial crisis for portfolios with long stock exposure.

Performance of the PowerShares DB G10 Currency Harvest ETF (DBV)

Figure 20

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60

Action – Myth #12

We saw in Myth #12 how a diversified managed futures

portfolio, which I also refer to as “global trading,” can provide

more consistent returns with less risk than a portfolio

comprised of long positions in stocks, bonds and real estate.

This is because the commodity trading advisors (CTAs) that

trade these portfolios employ a broad range of trading

strategies to trade across hundreds of global stock index,

interest rate, energy, metals and agricultural markets. This

diversification, and the ability of CTAs to capture both up and

down market trends, is also why CTAs, in the aggregate,

produced profits during the 2008 financial crisis.

CTAs employ a wide variety of trading strategies to capture

profits from global trading. Some of the most popular methods

include:

Momentum (also called “trend following”): I discussed this in

Myth #7 – It’s Bad to Chase Performance. The propensity for

markets to trend is a key trading strategy underlying many

CTAs’ trading programs. It was the use of trend following

strategies in the currency, interest rate, stock index, energy,

metals and agricultural markets that contributed to the

substantial profits earned by CTAs during the financial crisis

of 2008.

Fundamentals: These trading strategies incorporate

fundamental market data, such as the cost-of-production data

used in the Marginal Cost of Production strategy described in

Myth #9. While this term is also used to refer to traders who

make discretionary decisions (do not follow a fully-systematic

investment process), I use it to describe systematic trading

strategies that base their trading decisions on data other than

market prices. Fundamental futures trading strategies are


similar in concept to value or growth strategies used to select

stocks; with the difference being that fundamentally-based

futures strategies can be applied to raw commodities in

addition to stock indexes. This greatly expands the

diversification possibilities.

Relative Value, Yield Curves and Cross-rates: These strategies

all look at the relationship between or among two or more

markets to signal trading opportunities. A relative value

strategy may look at the prices of different futures contract

months for example, to determine that one contract should be

sold while another be purchased. Yield curve strategies may

buy long-term interest rate contracts and sell short short-term

interest rate contracts in the expectation that the yield curve

will steepen (short-term rates will rise in relation to long-term

rates). Cross-rates refer to the relationship among various

currencies. A trading strategy may buy the Euro while selling

the Japanese Yen (the currency carry trade described in Action

#11 utilizes cross-rate trading, but I’ve defined that as a

separate trading strategy). Relative value strategies are

similar in concept to market neutral strategies trading in

stocks, where some stocks are held long while other stocks are

sold short.

A key benefit of futures trading is that it allows a trader to

develop and incorporate dozens, or even hundreds, of different

trading strategies that can be applied across a hundred or more

global markets. While some CTAs specialize in a specific style

of trading (trend following being the most popular), there are

many CTAs that also diversify across both multiple trading

strategies and global markets. As a result, in creating the “Free

Lunch” portfolios that will be presented in the Action section

for Myth #20, I recommend a significant allocation to these

global trading strategies in any properly diversified portfolio.

(DISCLOSURE: although my firm, Brandywine Asset

Management, has experience trading in equities, private

http://www.brandywine.com/



equity, mutual funds and ETFs, today our focus is solely on

futures trading. While we made this decision precisely because

we believe in the value futures trading can bring to a portfolio,

this focus may also create a bias in our belief in the necessity of

including managed futures in any truly diversified portfolio).

You can place money with a CTA either in an individually

managed account or fund. A managed account often requires at

least $100,000, and in many cases, more than $1,000,000 to

open. The high minimum investment level of the managed

account is required in order for the CTA to have enough assets

to fully diversify the account across a wide range of futures

markets. In contrast, managed futures funds, also called

“commodity pools,” combine peoples’ money into a single pooled

account. Unfortunately, most commodity pools are private and

limited to “Accredited Investors” only. This also means that the

funds cannot be publicly marketed; people need to “find” the

commodity pools on their own or be introduced to their

managers through a direct relationship. Most of these “private”

commodity pools also require relatively high initial

investments of $100,000 or more. However, in recent years a

number of commodity pools have publicly registered, enabling

people to make initial investments of as little as $1,000. A few

of the public funds available include:

Altegris Managed Futures Strategy O (MFTOX)

MutualHedge Futures Strategy C (MHFCX)

TFS Hedged Futures Fund (TFSHX)

Grant Park Managed Futures Strategy C (GPFCX)

Note 12-1: Due to limitations on availability to individual

investors, we have replaced MFTCX with MFTOX.


In the Action section for Myth #20, I recommend a significant

allocation to these four funds or, alternatively, privately

managed accounts or funds that you can find through the

resources listed below, to provide your portfolio with valuable

global trading diversification.

Additional information on CTAs providing managed accounts

and private pools can be found at the following sources:

www.albourne.com: Albourne is a consultant, online

community and database provider to professional and private

investors.

www.autumngold.com: is a database provider specializing

in CTAs. The web site provides free access to CTA profiles and

performance information.

www.barclayhedge.com: BarclayHedge is one of the oldest

providers of CTA data, having been founded in 1985, and is the

developer of the BTOP 50 CTA index I presented in Myth #12.

In addition to its database services BarclayHedge provides

consultation to individuals and professional investors.

www.iasg.com: Institutional Advisory Services Group

assists clients in selecting CTAs that best match each client’s

risk tolerance and expected rates of return. The IASG website

provides access to a comprehensive database of CTAs and

hedge funds.

www.managedfutures.com: This is part of Altegris Group,

the manager of the Altegris Managed Futures Strategy Fund

mentioned previously. Altegris specializes in helping investors

discover and access managed futures programs and other

alternative investments. The web site contains access to a

database of CTAs.

http://www.albourne.com/

http://www.autumngold.com/

http://www.barclayhedge.com/

http://www.iasg.com/

http://www.managedfutures.com/

64

Action – Myth #14

Myth #14 stresses that investors should not expect government

regulations to protect them from bad investments. I also

mention Alfred Winslow Jones and how the term “hedge fund”

was derived from the fact that he created the first fund that

“hedged” its long stock positions with offsetting short positions,

thereby creating a “hedged fund.” In this Action I will present a

description of what due diligence is and provide you with some

guidance that you should follow when evaluating an

investment opportunity.

Investment Due Diligence

Holly Miller, founder of Stone House Consulting and co-author

of the book The Top Ten Operational Risks: A Survival Guide

for Investment Management Firms and Hedge Funds, is an

expert on the practice of investment manager due diligence.

Due diligence is the process of asking about, and

understanding, the risks inherent in investing with an

investment manager or even a single investment. She has

provided the following advice regarding due diligence.

In general, the industry refers to two types of due diligence

that can be performed: investment due diligence and

operational due diligence, with the latter essentially including

everything that is not directly related to investment decision-

making. Many investors erroneously believe that each type of

due diligence can be performed independent of the other. Yet

without knowing about a manager’s approach toward

investing, it is impossible to assess whether the manager has

sufficient operational, compliance and IT infrastructure to

support the investment process. Likewise, without an

understanding of the manager’s operational processes,

investors cannot evaluate whether there are adequate controls

on the manager’s investment and trading activities.


There is no single checklist that an investor can follow to

ensure he or she is asking an investment manager all the most

important questions. Depending on the answers received,

follow-up questions are often warranted. For example, if a

manager indicates they will not invest more than a specified

percentage of a portfolio in a given sector or industry, the

prudent investor will follow up by asking, “How do you monitor

that? What tools to you use? How often do you check? Do you

have a tolerance? Have you ever violated this restriction? Was

it intentional or inadvertent? How did it happen? How did you

find out you had made the error?”

Due diligence should always be performed before making an

investment. Yet ongoing due diligence is necessary to ensure

appropriate controls remain in place and that the manager has

adapted operational processes, systems and compliance

oversight based on regulatory changes, new product

introductions, staff turnover and market dynamics. At a

minimum, investors should revisit all due diligence reviews of

their managers on an annual basis and in some instances more

frequent reviews will be appropriate.

The devil is in the details and there is no easy short-cut to the

process. It is possible, though, to conduct due diligence in a way

that seeks to readily identify ‘red flags’ that would discourage

the manager from investing. Why continue to do your

homework on a manager you would avoid? Once enough red

flags are encountered, there is no need to complete an

exhaustive due diligence review. Instead, these flags signal

that it is time to move on to another potential opportunity. So

ask critical, ‘deal-breaker’ questions early in the process.

For this reason, most investors begin the due diligence process

on the investment side. If the manager’s investment approach,

philosophy and decision-making process – as well as the

manager’s track record – are insufficiently attractive, there is


no point conducting any sort of operational due diligence

review.

Once a person has made the decision to pursue any investment,

they can then follow some basic Q & A guidelines. In the

United States, professional investment managers are generally

required to register either with the SEC or the CFTC

(Commodity Futures Trading Commission). These

organizations provide investors with some guidelines to follow.

You can see them at http://investor.gov/ask-questions/ and

http://www.nfa.futures.org/nfa-investor-information/index.html

http://investor.gov/ask-questions/

http://www.nfa.futures.org/nfa-investor-information/index.html

67

Action – Myth #15

In June 2007, Roger Ibbotson, Zhiwu Chen and Wendy Hu

published a research paper titled “Liquidity as an Investment

Style.” In this paper they showed that less liquid stocks, those

that trade few shares relative to their earnings or shares

outstanding, outperform more liquid stocks. While you can

create your own portfolio of less liquid stocks by screening

stock databases to find those that have low trading volume

relative to their earnings and capitalization, you can now also

invest in mutual funds that do this for you.

Zebra Capital Management was founded in 2001 by Roger

Ibbotson and Zhiwu Chen and they launched a series of

products based on their Liquidity Return Strategy in 2009. In

2010 Zebra partnered with American Beacon Advisors to

launch two mutual funds based on this strategy. Those funds,

the American Beacon Large Cap Equity Fund (AZLPX) and the

American Beacon Zebra Small Cap Equity Fund (AZSPX),

began trading in June 2010. Although these two funds have

short performance histories, the research results displayed in

Myth #15, coupled with the back-tested results shown in

Figure 20 for the strategy (combining both small and large

capitalization stocks) prior to the start of trading in the mutual

funds, indicate that they could outperform the market averages

by a few percentage points per year. Because these are new

funds they also have only a few million dollars each in assets.

But if their research results is any guide, these two funds could

grow in size quite rapidly as their performance takes hold.


Back-tested Performance of the Liquidity Strategy Used in AZLPX and AZXPX

Figure 22 and Figure 23 display the actual performance of each

fund (beginning in June 2010).

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Performance of the American Beacon Zebra Large Cap Equity Fund (AZLPX)

Performance of the American Beacon Zebra Small Cap Equity Fund (AZSPX)

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70

Action – Myth #16

In Myth #16 I showed that simply buying-and-holding stocks

that are traded on exchanges or whose headquarters are based

in different countries provided very little diversification value,

especially in bear markets, as the country performances are

highly correlated with each other. But they are not perfectly

correlated and this opens up the opportunity to trade pursuant

to a disciplined strategy that attempts to identify and allocate

to the stocks domiciled in the strongest countries and avoid the

weakest. In Action #8 I presented a trading strategy developed

by Schreiner Capital Management (SCM), a registered

investment advisor, which followed a momentum-based

strategy of buying sector mutual funds that showed positive

price momentum. In this action I present a second strategy

developed by SCM to trade country-specific ETFs. Before I

present the strategy rules, let’s take a look at the performance

of the strategy relative to that of the MSCI All-World ex-USA

Index (Figure 24 and Figure 25). This performance has been

adjusted to reflect commissions of $0.01/share.


Performance Comparison: International Allocation Strategy vs.

MSCI All-World ex-USA Index

Performance Comparison: International Allocation Strategy vs.

MSCI All-World ex-USA Index

MSCI All-World ex-USA

International Allocation Strategy

Years 11 11 Average Annual Return 1.39% 8.91% Annualized Volatility 19.21% 15.74% Maximum Drawdown -59% -33% % Profitable Months 56% 52% % Profitable Rolling 12-Mos. 62% 66% % Profitable Years 64% 64%

Figure 24. For the period January 2000 through December 2010

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Figure 25. For the period January 2000 through December 2010


The performance shows that this simple momentum-based

strategy outperformed a buy-and-hold position in the MSCI All-

World ex-USA Index while suffering both lower volatility and

drawdowns.


The SCM International allocation strategy is a straightforward

momentum-based strategy that attempts to take long positions

in the strongest-performing country ETFs and avoid positions

in the weakest. The portfolio as defined by SCM includes 25

separate ETFs.


Symbols

ECH iShares MSCI Chile Investable Mkt Index

EPI WisdomTree India Earnings

EWA iShares MSCI Australia Index

EWC iShares MSCI Canada Index

EWD iShares MSCI Sweden Index

EWG iShares MSCI Germany Index

EWH iShares MSCI Hong Kong Index

EWJ iShares MSCI Japan Index

EWL iShares MSCI Switzerland Index

EWN iShares MSCI Netherlands Invstbl Mkt Index

EWO iShares MSCI Austria Investable Mkt Index

EWP iShares MSCI Spain Index

EWQ iShares MSCI France Index

EWS iShares MSCI Singapore Index

EWT iShares MSCI Taiwan Index

EWU iShares MSCI United Kingdom Index

EWW iShares MSCI Mexico Investable Mkt Index

EWY iShares MSCI South Korea Index

EWZ iShares MSCI Brazil Index

EZA iShares MSCI South Africa Index

FXI iShares FTSE China 25 Index Fund

ILF iShares S&P Latin America 40 Index

RSX Market Vectors Russia ETF

THD iShares MSCI Thailand Invest Mkt Index

TUR iShares MSCI Turkey Invest Mkt Index

These 25 ETFs are:

Figure 26


The basic underlying rule is to buy each ETF, selected from the

list, when it shows a positive rate of change (ROC) in price over

the prior 42 trading days. Each fund gets a 4% allocation of the

cash allocated to this strategy. Once a position is entered into it

is held in the strategy for a minimum of 21 trading days or

until the ROC turns negative.

Frontier Markets

In addition to employing a country allocation strategy, there is

one other area where a person can obtain some portfolio

diversification by placing money in stocks of other countries.

There are three levels of classification commonly used by

investment professionals to describe the status of the markets

of various countries. “Developed” countries, those such as the

United States, Germany and Japan, are well established and

very liquid. “Emerging” markets, such as Brazil, Russia and

Mexico, have lower trading activity and less well established

regulations than do the developed market countries. The final

classification, which refers to the countries with the smallest

stock market capitalizations and liquidity, are referred to as

“Frontier” markets. These include countries such as Vietnam,

Pakistan, and Jordan. Although the stock markets of frontier

markets are subject to the same emotional price moves as those

of both developed and emerging markets, they do tend to be

slightly less correlated to those markets. This is evidenced by

the 0.64 correlation of the frontier markets to the developed

markets and the 0.59 correlation of the frontier markets to the

emerging markets. In contrast, the developed markets and

emerging markets are 0.92 correlated with each other over the

same period.2 Because of this lower correlation we are

including a frontier markets mutual fund in our portfolio. This

is the Guggenheim Frontier Markets ETF (FRN). FRN has

approximately a 95% overlap with the MSCI EAFE Markets

Index.


Note 16-1: The strategy algorithm was adjusted for results

beginning January 2011, so results in the book may differ

slightly from the results included in the Performance Reports

on the interactive website.

Note 16-2: As of January 2011, transaction costs are assumed

to be 0.10% per transaction.

2 Indexes and period used in the correlation calculations are

the monthly returns of the MSCI Frontier Markets Index,

MSCI Emerging Markets Index and MSCI All-World ex-USA

Index for the period June 2002 through December 2010.

76

Action – Myth #20

There is a free lunch in investing. It is the use of true portfolio

diversification. In this final (and most important) Action I will

show the specific trading strategies that combined to create the

performance of the Free Lunch portfolios presented in Myth

#20. These trading strategies, many of which have been

described in detail throughout this Action section, are based on

– and provide exposure to – more than two dozen separate

return drivers. As a result, unlike the composition of the

Conventional portfolio displayed in Myth #20, the Free Lunch

portfolios provide you with true portfolio diversification. In

order to provide model portfolios that can be used by a wide

range of investors, I have developed two types of Free Lunch

portfolios for you to select from; simplified and regular. The

only difference between the two is:

the “simplified” portfolio invests only in mutual funds

and ETFs, and does not trade them on an active basis.

the “regular” portfolio also includes individual stocks

and trades those as well as some of the ETFs.

In addition, each of the two portfolio types has the ability to

employ one of three levels of volatility:

Conservative: (this is the “regular” Free Lunch portfolio)

Moderate: “Free Lunch MR” (“MR” stands for “moderate

risk.” This portfolio matches the historical volatility of

the Conventional portfolio)

Aggressive: “Free Lunch AR” (“AR” stands for

“aggressive risk.” This portfolio matches the historical

volatility of the S&P 500)


Before I describe the Free Lunch portfolios in detail, let’s take

a look at the constituents of what conventional financial

wisdom considers being portfolio diversification (Figure 27).

Conventional Portfolio Constituents

Knowing what we do now, after the facts presented in Myth

#16 – Allocate a Small Amount to Foreign Stocks,” Myth #17 –

Lower Risk by Diversifying Across Asset Classes, Myth #18 –

Diversification Failed in the 2008 Financial Crisis, and of

course Myth #20 – There is No Free Lunch, we can see that the

Conventional portfolio is a highly concentrated, non-diversified,

risky portfolio. As described in Myth #20, virtually all of its

returns are dependent on just three return drivers. In stark

contrast, the Free Lunch portfolios are much more properly

diversified, as illustrated in Figure 28.

Figure 27. The composition of the Conventional portfolio was

partially determined by looking at the allocations made by target-date

funds with approximately 20 years until retirement.

Large Cap U.S. Stocks

Mid Cap U.S. Stocks

Small Cap U.S. Stocks

Developed World Stocks

Emerging Markets Stocks

Aggregate U.S. Bonds

High Yield Bonds

Real Estate -U.S.

Cash


Free Lunch Portfolio Diversification

A listing of each constituent and the percentage allocated to it

in the Free Lunch portfolios is provided in Figure 29. This table

also includes a listing of the actual investments you can make

to replicate the performance of the Free Lunch Portfolios, as

well as, if applicable, the number for the myth in which the

trading strategy underlying the investment was introduced.

You will notice that the last 18 listed trading strategies are

related to Myth #12. These are the “global trading” strategies

that can be accessed through investments in managed futures

accounts and funds. Although 18 separate return drivers are

identified, gaining exposure to these can be as simple as

investing in managed futures mutual funds (for smaller

portfolios) or private accounts (for larger portfolios). I identify

these later in this Action.

Figure 28. Use controls at bottom of frame to enlarge pie chart.

Large Cap - Static Long

Mid Cap - Static Long

Small Cap - Static Long

Developed World

Emerging Markets

Aggregate U.S. Bonds

High Yield

Real Estate - U.S.

Equal-Weighted Large Cap

Ind Corp Fundamentals

Dividend Increases

Insider Sentiment

Selective Small-cap Stocks

Selective Growth Stocks

Large Cap Liquidity

Small Cap Liquidity

Sector Timing

Market Timing - Country

Frontier Markets

Long International Bonds

Long-ShortMarket NeutralMarket Timing - Inv Sent

Credit & Equity Arbitrage

Currencies - Carry Trade

Currency - Momentum

Currency - Fundamentals

Currency - Cross-rates

Int Rates - Momentum

Int Rates - Fundamentals

Int Rates - Yield Curves

Stock Ind - Momentum

Stock Ind - Fundamentals

Stock Ind - Relative Value

Energy - Momentum

Energy - Fundamentals

Energy - Relative Value

Metals - Momentum

Metals - Fundamentals

Metals - Relative Value

Ags - Momentum

Ags - Fundamentals

Ags - Relative Value


Exposure to Each Return Driver/Constituent of the Free Lunch Portfolio

Constituent Allocation Investment Myth

Cash 0.00%

Large Cap - Static Long 1.00% SPY

Mid Cap - Static Long 1.00% MDY

Small Cap - Static Long 1.00% IJR

Developed World 1.00% CWI

Emerging Markets 3.00% VWO

Aggregate U.S. Bonds 4.00% AGG

High Yield 4.00% JNK

Real Estate - U.S. 10.00% VNQ

Equal-Weighted Large Cap 1.00% RSP 4

Ind Corp Fundamentals 1.00% PRF 4

Dividend Increases 1.00% VIG 4

Insider Sentiment 1.00% NFO 4

Selective Small-cap Stocks 2.00% Piotroski Strategy 1

Selective Growth Stocks 2.00% CAN SLIM Strategy 6

Large Cap Liquidity 2.00% AZLPX 15

Small Cap Liquidity 2.00% AZSPX 15

Sector Timing 2.00% Trade Funds 8

Market Timing - International 2.00% Int'l ETF Strategy 16

Frontier Markets 3.00% FRN 16

Long International Bonds 4.00% BWX

Long-Short 5.00% TMNFX 10

Market Neutral 5.00% JMNAX.lw 10

Market Timing - Inv Sent 7.00% Trade SPY & SH 3

Credit & Equity Arbitrage 0.00% 14

Currencies - Carry Trade 3.00% DBV 11

Currency - Momentum 3.00% 12

Currency - Fundamentals 0.90% 12

Currency - Cross-rates 2.10% 12

Int Rates - Momentum 2.75% 12

Int Rates - Fundamentals 1.65% 12

Int Rates - Yield Curves 1.10% 12

Stock Ind - Momentum 2.75% 12

Stock Ind - Fundamentals 1.83% 12

Stock Ind - Relative Value 0.92% 12

Energy - Momentum 1.75% 12

Energy - Fundamentals 1.05% 12

Energy - Relative Value 0.70% 12

Metals - Momentum 2.00% 12

Metals - Fundamentals 1.20% 12

Metals - Relative Value 0.80% 12

Ags - Momentum 3.75% 12

Ags - Fundamentals 2.25% 12

Ags - Relative Value 1.50% 12

Als

o in

clu

de

d in

Co

nve

ntio

na

l p

ort

folio

Global Trading

Allocations can be made

to managed futures

accounts, private funds

or mutual funds, as

described in the

following section.

Figure 29


Brief Description of the Constituents of the Free Lunch Portfolios That Are Not Separately Described in an Action

Large Cap – Static Long: SPY is an ETF that tracks the

mainstay of most people’s portfolios, a long position in the

stocks that make up the S&P 500.

Mid Cap – Static Long: MDY is an ETF that tracks the

performance of the S&P 400 Mid-cap Index.

Small Cap – Static Long: IJR is an ETF that tracks the

performance of the S&P 600 Small-cap Index.

Developed World: CWI is an ETF that tracks the performance

of the MSCI All-World (ex-USA) Index. This fund provides long

exposure to approximately 800 stocks in 22 developed countries

(such as Germany, Japan, and the U.K, but not the USA).

Emerging Markets: VWO is an ETF that tracks the

performance of the MSCI Emerging Markets Index. This fund

provides long exposure to approximately 700 stocks in 21

emerging countries (such as China, Brazil and Russia).

Aggregate U.S. Bonds: AGG is an ETF that tracks the

performance of the Barclay Aggregate Bond Index. This fund

provides exposure to more than 600 bonds.

High Yield: JNK is an ETF that tracks the performance of the

Barclay High Yield Very Liquid Index. This fund provides

exposure to approximately 200 bonds.

Real Estate – U.S.: VNQ is an ETF that tracks the performance

of the MSCI U.S. REIT Index. This fund provides exposure to

approximately 100 REITs.


Description of the Constituents of the Global Trading Strategies Included in the Free Lunch Portfolios

In Action #12, I provided a brief description of the different

global trading strategies listed in the preceding table. There

are many more trading strategies that may fall outside of the

classifications listed in the table. There are also other methods

of obtaining the global trading diversification in the Free

Lunch portfolios in addition to investing with CTAs (which I

described in Action #12). But my experience with global trading

is through the use of futures contracts, which is why I

recommend managed futures as the source of global trading

diversification in the Free Lunch portfolios. The key is to make

sure that your portfolio contains a significant allocation to

trading strategies that produce returns that are independent of

each other. Managed futures (CTAs) provide this

diversification value. (DISCLOSURE: As I disclosed in Action

#12, although my firm, Brandywine Asset Management, has

experience trading in equities, private equity, mutual funds

and ETFs, today our focus is solely on managed futures

trading. While we made this decision precisely because we

believe in the value futures can bring to a portfolio, this focus

may also create a bias in our belief in the necessity of including

managed futures in any truly diversified portfolio).

Exposure to the global trading of currencies, interest rates,

stock indexes, energy, metals, and agricultural markets

described in this Action can be obtained in three different

ways. One is to identify individual funds or managers that

trade pursuant to each of the various trading strategies

represented in the global trading portion of the Free Lunch

portfolios and invest with them. The Free Lunch portfolio does

contain one such fund, DBV, which utilizes the currency carry

trading strategy described in Action #11. But there are few

other funds available to smaller investors that pursue



individual global trading strategies, rendering this approach

problematic.

A second approach to gain exposure to the global trading

strategies is to establish the necessary brokerage accounts,

research and develop the strategies, and then trade them

yourself. I outlined one such “marginal cost of production”

trading strategy in Myth #9, and a simple momentum (also

called “trend following”) trading strategy in Action #7. But this

second approach is completely unrealistic for all but the most

well-capitalized and experienced investors. That is because

global trading in more than 100 markets (which is what is

represented in the Free Lunch portfolios) employing dozens of

trading strategies, is a professional pursuit. While today’s

technology allows some strategies, such as the momentum

strategy, to be executed by experienced traders on a part-time

basis, the full deployment of a diversified mix of global trading

strategies requires a full-time commitment and millions of

dollars in capital. It’s not as simple as tracking a single trading

strategy in stocks, such as the Piotroski trading strategy

described in Action #1.

A third approach, and the one I recommend for the vast

majority of investors, is to invest with one or more CTAs who

trade a broad range of global markets pursuant to a wide

variety of trading strategies. (Of course, as disclosed

previously, you must consider my bias, as this is the focus of

my firm.) There are hundreds of CTAs that in the aggregate

provide significant global trading diversification. I provide a

short list of services that provide information on these CTAs at

the end of Action #12. You can invest directly with these CTAs

in either managed accounts (generally requiring up to a million

dollars or more to obtain full diversification) or funds (which

allow substantially smaller minimum investments as they pool

your money with other investors). While most futures funds are

private (meaning they cannot be publicly advertised, but if you


find them, they are often open to new investors), another option

has recently become available. As I mentioned in Myth #12,

there are mutual funds that employ the services of multiple

CTAs. For purposes of estimating the allocations to the various

global trading strategies in the Free Lunch portfolio, I assume

that the investor makes an equal allocation of 8% of their

portfolio to each of four managed futures mutual funds. These

are the MFTOX, MHFCX, TFSHX and GPFCX funds.

While this approach enables you to invest pursuant to the Free

Lunch portfolio, it is unable to be used in the Free Lunch MR

and Free Lunch AR portfolios. I will explain why, and how you

can do so, in the following section.

The Leveraged Free Lunch Portfolios

The Free Lunch MR and the Free Lunch AR portfolios hold the

same constituents as the regular Free Lunch portfolio, but with

an additional allocation to the global trading – managed

futures component. This additional allocation is made possible

without the use or cost of borrowed money. That is because

managed futures investors are able to use “notional” funds to

increase their exposure. Here’s how it works.

The typical managed futures account is required to hold cash

as a security deposit to maintain its positions. Although this is

also referred to as a “margin” requirement, it is different than

using margin to leverage a stock position. There is no

borrowing involved, nor interest required to be paid. A gold

futures contract, for example, obligates the buyer of that

contract to purchase 100 ounces of gold at a specified future

date (this is why it’s called a “futures” contract). The buyer can

sell that contract prior to that future date, thereby eliminating

the obligation to actually purchase the gold. In fact, this is

what occurs in the majority of futures transactions. Because

the buyer never actually owns the gold, only an obligation to


purchase the gold at a future date, there is no requirement to

pay for the gold (or borrow money to pay for the gold). Instead,

the futures exchange upon which the gold contract is traded

requires the buyer to post money as a “good faith” security

deposit, or “margin.” The amount of the margin deposit varies,

but in a managed futures account with approximately 10%

annualized volatility, a reasonable rule of thumb is that the

total margin requirement to maintain all the positions in the

account is approximately 10% of the account value. As a result,

if the account value is $1,000,000 an investor may actually

deposit only $250,000, an amount that is sufficient to cover the

$100,000 margin, plus potential losses of 15%. The remaining

$750,000 is considered to be “notional” funds.

Understanding this, it is easy to see how a person who invested

$1,000,000 could simply instruct the futures manager to trade

the $1,000,000 as if it were a higher level. In the case of the

Leveraged Free Lunch portfolios, all allocations would remain

the same to each of the other portfolio constituents, with the

exception of adding notional funding to the managed futures

accounts that execute the global trading strategies. Figure 30

shows the increased allocation from the portfolio to the global

trading – managed futures accounts, that is required for each

of the two Leveraged Free Lunch portfolios.

Allocations Required to be made to Global Trading in Each of the Three Free Lunch Portfolios

Portfolio Allocation to Global Trading Increase

Free Lunch 32% 0%

Free Lunch MR 48% 16%

Free Lunch AR 66% 34%

Figure 30


The investor would still maintain the same 68% allocation to

all other portfolio constituents. While investors in the Free

Lunch portfolio can use the four managed mutual funds

referenced in this Action to get their Global Trading exposure,

those funds do not allow a person to use notional funding.

Therefore, investors desiring to create one of the Leveraged

Free Lunch portfolios must do so by opening an individual

managed account, or by investing in a private managed futures

fund, that allows notional funding. Because the minimum

investment necessary for a manager to properly diversify a

managed account can exceed $1,000,000, this option is not

available to smaller investors. There are, however, a number of

managed futures funds that do provide investors with the

ability to invest substantially smaller amounts in a fund class

that is traded at a higher leverage level, thereby making those

funds suitable for inclusion in one of the Leveraged Free Lunch

portfolios.

The chart and table below illustrate the historical, back-tested

performance of the Free Lunch, Free Lunch MR and Free

Lunch AR portfolios compared with the Conventional portfolio.

The results shown in the chart and table below are based

on the portfolio compositions (constituents and weights)

outlined in the book. The historical back-tested results

of the current Free Lunch portfolio and current

Simplified Free Lunch portfolio will vary due to the

changes in the composition (constituents and weights) of

the portfolios as shown in Figure 29 and Figure 33.


Performance Comparison: Free Lunch Portfolios & Conventional Portfolio

Performance Comparison of Free Lunch Portfolios to

Conventional Portfolio and S&P 500 TR Index

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Va

lue

of

Init

ial

$1

,00

0 In

ve

stm

en

t

Free Lunch AR

Free Lunch MR

Free Lunch Portfolio

Conventional Portfolio

S&P 500

TR Index

Convent-

ional

Portfolio

Free

Lunch

Portfolio

Free

Lunch

MR

Portfolio

Free

Lunch

AR

Portfolio

Years 30 30 30 30 30

Average Annual Return 10.54% 9.91% 11.62% 12.66% 14.77%

Annualized Volatility 15.48% 11.49% 9.07% 11.49% 15.48%

Maximum Drawdown -51% -44% -22% -21% -20%

% Profitable Months 62% 66% 65% 60% 57%

% Profitable Rolling 12-Mos. 78% 80% 96% 93% 92%

% Profitable Years 80% 77% 93% 93% 93%

Figure 31

Figure 32


The Simplified Free Lunch Portfolios

Several constituents, including the five constituents of the Free

Lunch portfolios that require active trading, have been

removed from the Simplified Free Lunch portfolios in order to,

well, simplify them. The allocation made to these constituents

has been reallocated across trading strategies that provide the

most portfolio diversification value. A listing of each

constituent and the percentage allocated to it in the Simplified

Free Lunch portfolios is provided in Figure 33. Similar to the

composition of the Free Lunch portfolios displayed in Figure

29, this table also includes a listing of the actual investments

you can make to replicate the performance of the Free Lunch

Portfolios, as well as, if applicable, the number for the myth in

which the trading strategy underlying the investment was

introduced. Exposure to the 36% allocated to the global trading

can be achieved with equal 9% investments in each of four

managed futures mutual funds. These are the MFTOX,

MHFCX, TFSHX and GPFCX funds.


Exposure to Each Return Driver/Constituent of the Simplified Free Lunch Portfolio

Constituent Allocation Investment Myth

Cash 0.00%

Developed World 5.00% CWI

Emerging Markets 6.00% VWO

Aggregate U.S. Bonds 7.00% AGG

Real Estate - U.S. 12.00% VNQ

Equal-Weighted Large Cap 5.00% RSP 4

Frontier Markets 6.00% FRN 16

Long International Bonds 7.00% BWX

Long-Short 6.00% TMNFX 10

Market Neutral 6.00% JMNAX.lw 10

Currencies - Carry Trade 4.00% DBV 11

Currency - Momentum 3.38% 12

Currency - Fundamentals 1.01% 12

Currency - Cross-rates 2.36% 12

Int Rates - Momentum 3.09% 12

Int Rates - Fundamentals 1.86% 12

Int Rates - Yield Curves 1.24% 12

Stock Ind - Momentum 3.09% 12

Stock Ind - Fundamentals 2.06% 12

Stock Ind - Relative Value 1.03% 12

Energy - Momentum 1.97% 12

Energy - Fundamentals 1.18% 12

Energy - Relative Value 0.79% 12

Metals - Momentum 2.25% 12

Metals - Fundamentals 1.35% 12

Metals - Relative Value 0.90% 12

Ags - Momentum 4.22% 12

Ags - Fundamentals 2.53% 12

Ags - Relative Value 1.69% 12

Als

o incl in

Conv p

ort

Global Trading

Allocations can be made

to managed futures

accounts, private funds

or mutual funds, as

previously described.

Figure 33


The Leveraged Simplified Free Lunch Portfolios

To create the Simplified Free Lunch MR and the Simplified

Free Lunch AR portfolios, all allocations would remain the

same to each of the other portfolio constituents, with the

exception of adding notional funding to the managed futures

accounts that execute the global trading strategies. This is

identical to how the additional performance is obtained in the

“regular” Free Lunch portfolios. Figure 34 shows the increased

allocation from the portfolio to the global trading – managed

futures accounts, that is required for each of the two Leveraged

Simplified Free Lunch portfolios.

Allocations Required to be made to Global Trading in Each of the Three Simplified Free Lunch Portfolios

The investor would still maintain the same 64% allocation to

all other portfolio constituents. The process necessary to obtain

the additional managed futures exposure is the same as that

described for the Leveraged Free Lunch portfolios.

The table on the following page summarizes the allocations to

be made to each portfolio constituent for each of the six Free

Lunch portfolios.

Portfolio Allocation to Global Trading Increase

Simplified Free Lunch 36% 0%

Simplified Free Lunch MR 48% 12%

Simplified Free Lunch AR 66% 30%

Figure 34


Summary of the Allocations to be Made to Each Constituent of Each Portfolio

Note to Global Trading / Managed Futures exposure in the

“MR” and “AR” portfolios:

(1) The additional exposure to global trading / managed futures

is obtained by placing an amount of cash up to that

available (the "Cash Level") in a leveraged managed futures

account or fund that provides the exposure equal to the

"Trading Level." Target returns on the Trading Level

should be 10% - 12%.

Constituent Myth Investment

Cash 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Large Cap - Static Long SPY 1.00% 1.00% 1.00% 0.00% 0.00% 0.00%

Mid Cap - Static Long MDY 1.00% 1.00% 1.00% 0.00% 0.00% 0.00%

Small Cap - Static Long IJR 1.00% 1.00% 1.00% 0.00% 0.00% 0.00%

Developed World CWI 1.00% 1.00% 1.00% 5.00% 5.00% 5.00%

Emerging Markets VWO 3.00% 3.00% 3.00% 6.00% 6.00% 6.00%

Aggregate U.S. Bonds AGG 4.00% 4.00% 4.00% 7.00% 7.00% 7.00%

High Yield JNK 4.00% 4.00% 4.00% 0.00% 0.00% 0.00%

Real Estate - U.S. VNQ 10.00% 10.00% 10.00% 12.00% 12.00% 12.00%

Equal-Weighted Large Cap 4 RSP 1.00% 1.00% 1.00% 5.00% 5.00% 5.00%

Ind Corp Fundamentals 4 PRF 1.00% 1.00% 1.00% 0.00% 0.00% 0.00%

Dividend Increases 4 VIG 1.00% 1.00% 1.00% 0.00% 0.00% 0.00%

Insider Sentiment 4 NFO 1.00% 1.00% 1.00% 0.00% 0.00% 0.00%

Selective Small-cap Stocks 1 Piotroski Strategy 2.00% 2.00% 2.00% 0.00% 0.00% 0.00%

Selective Growth Stocks 6 CAN SLIM Strategy 2.00% 2.00% 2.00% 0.00% 0.00% 0.00%

Large Cap Liquidity 15 AZLPX 2.00% 2.00% 2.00% 0.00% 0.00% 0.00%

Small Cap Liquidity 15 AZSPX 2.00% 2.00% 2.00% 0.00% 0.00% 0.00%

Sector Timing 8 Trade Funds 2.00% 2.00% 2.00% 0.00% 0.00% 0.00%

Market Timing - International 16 Int'l ETF Strategy 2.00% 2.00% 2.00% 0.00% 0.00% 0.00%

Frontier Markets 16 FRN 3.00% 3.00% 3.00% 6.00% 6.00% 6.00%

Long International Bonds BWX 4.00% 4.00% 4.00% 7.00% 7.00% 7.00%

Long-Short 10 TMNFX 5.00% 5.00% 5.00% 6.00% 6.00% 6.00%

Market Neutral 10 JMNAX.lw 5.00% 5.00% 5.00% 6.00% 6.00% 6.00%

Market Timing - Inv Sent 3 Trade SPY & SH 7.00% 7.00% 7.00% 0.00% 0.00% 0.00%

Credit & Equity Arbitrage 14 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Currencies - Carry Trade 11 DBV 3.00% 3.00% 3.00% 4.00% 4.00% 4.00%

12 MFTOX 8.00% 9.00%

12 MHFCX 8.00% 9.00%

12 TFSHX 8.00% 9.00%

12 GPFCX 8.00% 9.00%

Global Trading / Managed

Futures (1)

Simplified

Free

Lunch

Simplified

Free

Lunch MR

Als

o inclu

ded in

Conventional port

folio

Free

Lunch

Simplified

Free

Lunch AR

(1) Cash

Level: 32%

Trading

Level: 48%

(1) Cash

Level: 32%

Trading

Level: 66%

(1) Cash

Level: 36%

Trading

Level: 48%

(1) Cash

Level: 36%

Trading

Level: 66%

Free

Lunch MR

Free

Lunch AR

Figure 35.

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specific trading strategies

action section beneficial

regular free lunch portfolio

displayed performance

final myth

historical performance

past performance