A System of Models

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A System of Models

Creating consistent portfolio returns using a system of five asset classes using models of competingindicators.

Ian NaismithSarasota Capital StrategiesFor NAAIMMarch 11, 2010

Abstract

For the vast majority of living investors, the years between 1999 and 2009 will likely go down inhistory as one of the most tumultuous periods for most asset classes, and to capture and keep total returnIt was a decade where the S&P 500 nearly fell 50% from its high of March, 2000, only to rally steadilyto its short lived new high in October, 2007only to plummet over 55% to a new decade low of March,2009. Both double dips exhibited greater decline than the 1973-1974 decline. It was a decade where theDow Jones US Real Estate Index enjoyed a bubble that was being defined by many in the financialindustry through the media that the bubble,probably wasnt a bubble. Then a drawdown of over70% occurred in the index within 25 months. The Deutsche Bank Liquid Commodity Index andcommodities in general, attracted the same bullish sentiment as real estate, and gave up over 60% from

their highexceeding the previous post-bubble decline of 1990-1993. The US Dollar Index, which hadproduced solid returns from 1995 until mid-2002, suffered over 40% of decline by March, 2008. The 30Year Bond price represented through the ETF TLT spikes up almost 50% from mid-2007 toDecember, 2008, only to pull back almost 30% by June, 2009. What made things interesting were thatthese once in a 25 year event were happening simultaneously with the major asset classes in the 4thquarter of 2008.

A smart buy and hold investor at the end of 1999, who could foresee the popularity ofalternative investments and the explosion of index based products such as ETFs and Index mutual funds,could have assembled a portfolio with $100,000 of capital allocated to a mix of: 40% in the S&P 500,25% in the 30 Year Bond, 15% in the Deutsche Bank Liquid Commodity Index, 10% in the Dow Jones

Real Estate Index, and 10% in the US Dollar Indexonly if all of the products existed. This sameinvestor would have had a run-up of over 73% without the benefit of dividends by the end of May, 2008with only one period of decline greater than 10 percent! After May, 2008, this same investor had amaximum drawdown of only -32.9% during the 2008 concert of events, and ended the decade with avalue of $132,468again, without the benefit of dividends. This points out that there is a bona fidereason for investing in various asset classes that move in and out of correlation with each other insteadof the pre-2000 standards of 60% stock / 40% bond allocations. This paper will use the same allocationsexpressed above, however, the use of technical analysis with tactical management techniques willimprove the return of the portfolio to a value of $347,251 - without the benefit of dividends, and with amaximum drawdown from 12/31/99 to 12/31/09 of -10.23%. More importantly, it presents a possibleblueprint for achieving consistent, repeatable positive results.

This paper is designed to point out 4 broad themes for active management.

It is important to use the major asset classes, and components therein to create differing streamsof return. Some streams defy performance logic such as the major peaks and valleys ofequities, real estate, and commodities during the 2000s and the almost pyramid shaped ascentand decline reaching those outliers. It is vital to have return streams that offer such geometricangles in certain times for an allocation choice.

It is important for the quantitative manager who develops or uses pre-fabricated signals tounderstand that one signal does not work all of the time. Thus, the use of several signals with the


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intent of creating markedly different return streams on a per asset class level, and the comparisonof those return streams to create a robust system for allocation use is vital for consistentperformance.

It is important for the quantitative manager to logically align competing return streams in afashion that is easily retrievable, verifiable, and actionable for trading purposes.

If the first 3 items are in place, then, psychologically it can be much easier to follow a system inthe quest for consistent, repeatable returns. A big problem with quant managers is that they canlose faith in the models they have built, and thus, deviate from the models or give up. It is

normal to experience denial or anger when the portfolios are not performing in the fashion themanager desiresand it is a managers responsibility to overcome those emotions quickly. But,it is dangerous to become apathetic, depressed, or make unusually risky decisions to get back oncourse when a manager has veered off their discipline.

This paper is also designed to accomplish a multitude of goals for the consumption of the investmentprofessional.

1. To demonstrate that disciplined use of multi asset classes through a system of one's desired singletechnical indicators (whether in their original form or modified form) and/or multi-indicator models,while making tactical allocation shifts based on rolling return look-back comparisons, can produce

consistent substantial outperformance with substantially less drawdown than each target asset classthat each model is competing against over rolling 3 year periods.

2. To present that portfolio allocation shifts based on the best rolling return of one model out of at least3 competing single indicator and/or multi-indicator models per asset class presents opportunity formarked consistency compared to the reliance of one indicator and/or one multi-indicator modeldetermining all trades.

3. To present the concept ofa capsule which contains the same indicator modified in at least 3different ways (then those modifications are left static) and applying a rolling rate of return look-back, can be added as a single dynamic indicator for consideration within a model.

4. To present that through systemizing, asset classes are converted to "return streams," however, theywill retain their asset class characteristics within the portfolio (reduced correlation, volatility

tempering) in periods where are ineffective compared to the asset class.5. Most importantly, in the spirit of open architecture, present a viable option that addresses the

personality differences of investment professionals. Specifically, the notion that a professional canpick their favorite indicators and/or multi-indicator systems and use the framework set forth in thispaper to enhance their bottom line returns for their clients. The professional should have thetechnical software for retrieving and/or creating the strategies set forth in the paper (most can beeasily created and tracked with Microsoft Excel).

Portfolio construction

This paper is designed for active managers using products that have been available for 20 yearsor less (specifically, ETFs, mutual funds). This is a demonstration of a portfolio that includes large capdomestic equity, long term US bond, commodities, the US Dollar index, and real estate. The portfolio ismandated by a system that includes models of various technical components per asset class. Withineach asset class, the models are competing with one another and the said asset class (unless specified)for allocation space.


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Definitions

return streamthe systematic conversion using technical indicators of any item that has a price from thebias of its behavioral imprint, and will fluctuate in and out of correlation to its natural path;

stream strength over a 250 day rolling period, the measurement of return stream compared to theunderlying item being priced, divided measured in increments of 10% (10% = 1), an example: if theS&P 500 has returned 8% in the past 250 days, and an indicator has returned 34% in the same timeframe, then 34% - 8% = 26% or a 2, alternatively, if the S&P 500 is -10% and an indicator is -35%

then -35% - -10% is -25% or a -2. When setting up a model, many indicators should a have aconcurrent wide range of returns, preferably non-correlating and exhibit large stream strength so that,upon look-back, the model will catch a robust trend;

an indicator - a mathematical formula presented as a tool for technical analysis, the most commonlyusedmoving averages, relative strength (RSI), MACD, etc.;

multi-indicator signaling the use of multiple indicators used independently to create one signal,whether trading based on concurrent signals (CCI and RSI give a contrarian buy simultaneously), ortrading based on sequential signal patterns (Stochastics has a ceiling, then RSI follows with a contrariansell);

hybrid indicatoran indicator which contains two or more indicators wrapped together to create one;

capsule a comparison of the performance of the same indicator, modified and used in at least 3different streams of return (example: comparing performance of a 3 day CCI oscillating between -99and +99 to a 5 day CCI oscillating between -50 and +50, and so on) for the express purpose ofcreating an indicator to compete against other indicators in a model. Most indicators should be formedfrom capsules.

modelin this case, a trading strategy that is using a rolling look-back of return comparisons betweenindicators, multi-indicators, hybrid indicators, capsules, or any combination those methods to create areturn stream per asset class;

systema comprehensive multi-asset class allocation methodology containing models

Intentional Limitations

The intentional limitations of the upcoming models are as follows:

1. The data is deliberately calculated based on end of day price and without the influence of dividends.The reason for end of day study is completely related to the time constraints of the paper. The reasonfor leaving out dividends in the calculations is to "penalize" each model (especially the bond, real estate& equity models) at a percentage rate greater than the cumulative effect of the transaction costs andslippage associated with practical trading. The rationale is: since the models work without dividends,they will work better with dividends. Secondly, it has not been calculated as to the potential decline of amodel when false sells happen because of dividend payouts and the trade remains intact, only toexperience drawdown after the dividend payout. So, strict price adherence to model is the research

method for this paper.2. Most of the trading vehicles associated with the various examples of indexes have not existed as long as

this study (12/31/99 - 12/31/09). All of the indexes chosen have highly liquid ETFs that closelyreplicate the indexes which can be traded at close with limited slippage and premium/discount pressure.Those models that have short-term study periods will produce more trades and are better suited for "notransaction fee" mutual funds. In addition, most studies will go long and short, some will go long onlyand neutral. The only use of leverage in this paper is the 30 year bond mutual fund example whereas the"long trade" is 1.2x times the 30 year bond. This was picked to illustrate the use of the long bond beforethe ETF (ticker TLT) made its debut. The strategies set forth can be used with leverage through ETFs or


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mutual funds, or done through futures contracts or FOREX. The author intentionally contained thepaper within a 10 year period, and believes that 10 year period captured 2 extraordinary drawdownperiods for each asset class, many flat periods, and 2 extraordinary run-up periods for each asset class.

3. Broad based asset classes were chosen primarily to illustrate that return streams do many times rely onperiods of associated asset class directional price strength for excess return. Since the major assetclasses rarely correlate in concert for extended periods of time, additional smoothing can occur becauseof non-correlation. However, some of the models illustrated can produce much more robust returnswhen used with sectors, industries, commodity components, individual currency pairs, or concentrated

bond types.4. There are no constant or scheduled optimizations of any indicators within the models presented. The co-

ordinates of each indicator were randomly chosen within predetermined time ranges and remained staticthroughout the back test period. The rate of return look-back period per asset class and inside capsuleswas fixed at 10 or 20 days. It should be noted, many times, longer rate of return look-back periods aremore effective than compressed periods (within 20 days) because they will keep the investor in apossible prevailing positive trend longer. Also, contraire to logic, many times the longer look-backperiod will not produce more drawdown than compressed periods. The reason for compressed look-backs is to fit this paper into a 10 year period, instead of a 9 year period or less. While the authorbelieves in regular, highly scrutinized re-optimizations, one goal of the paper is to illustrate that rollingmodel comparison using static co-ordinates could potentially replace or have a symbiotic relationship

with re-optimization methods.5. Finally, for practicality sake, simple models are expressed in this presentation. That does not mean they

are the best models, or even the best indicators were used for model construction - the author hasdeveloped much more effective and robust indicators, models, and systems. Literally, this paper is notabout producing the greatest return, but to illustrate methods for producing superior portfolio returnsthrough the implementation of technical analysis in a sound framework versus passive investmenttechniques. The math behind repeated optimization for improvement is infinite and impractical.

Intentional Strengths

The intentional strengths of the upcoming models is the use of modified indicators (most commonly in

time frame) that has produced consistent, exceptional results in real money management examples overa time-frame that exceeds the study period of this paper.

The concept behind the capsule approach

Capsuling is a method of condensing the competition between return streams created by the sameindicator to create one model. In this case, three separate CCIs with different time frames and differentcoordinates produce 3 distinct return streams which are in competition with each other to produce 1indicator to compete with the ERT indicator. The more studies of the same indicator within a capsulethat have varying degrees of time and coordinate manipulation, the more robust the capsule. In addition,

to reduce excess changing of the winning return stream within a capsule, the rate of return look-backcomparisons of the streams should be lengthened (it makes sense to have a 250 rolling day look-back orgreater). Each asset class should contain multiple models made up of multiple capsules.


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Exhibit 1: This is an example of a system that is fed by competing asset classes, each driven my models that contain capsules ofcompeting indicators. Optimal design is to have at least representation of momentum, contrarian, and meat in the middle

placement with long and short/exit points of action.


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Model A: Asset class:CommoditiesStudy index:Deutsche Bank Liquid Commodity Index Optimum Yield Diversified Total Excessreturn (daily return)Common trading representation of index:DBC (ETF)Competing indicators/index:

the index (long only)vs.

20 rolling day rate of return = (current priceprice[20] = n), calculated dailyIf n > 0% = LONG, if n < 0% = SHORT

vs.Standard 14 day slow StochasticsIf StochK > 50 = LONG, If < 10 = SHORT,If between 10 and 49.99 = OUT OF MARKET

Rate of return look-back period for competing indicators: 20 days, choosing #1 ranked

The first example is using a commodities index which has acceptable exposure to energy, metals

and agriculture. The competing indicators are common, simple, but effective methods of measurement.The rate of return method is an attempt to determine trend. It is highly effective in periods of lowervolatility and consistent price direction. The Stochastic measurement is a standard 14 day period, butcontraire to the common use as an oscillator; it makes sense to also consider its merits for catchingtrends. So, for illustration, representation of catching a meaningful trend that can sustain itself above"50," after the price behavior has been consistent enough travel from "10" to above "50," and avoiding adown trend when crossing below "50." The curious part of this example is the opportunity if shortingunder "10" which is considered oversold for this indicator. When the measurement is under "10," ifthere is a retracement up in price, quick and deep drawdown can occur. Alternatively, while shortingthis comparatively volatile asset class, in periods which are exhibiting high degrees of current volatilityrelative to their normal range can produce quick positive returns as the indicator travels from "10" to "0

This represents one example a modified "split" interpretation of this indicator.

Results:

The model significantly outperformed the index (+169.88%) with greatly reduced drawdown(+24.77%) in the 10 year period. The average rolling 3 year return outperformed the index with anaverage of 12.32% per period. The model also produced rolling 3 year positive returns over 99% of thetime. The index stream was used in the model 48.03% of the time, and an indicator was used 51.97% ofthe time.


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Table 1: Commodity asset class - comparisons of the index, different competing indicators, and the model of indicators

DBLCI Index Stochastic Split 20 Day Look-back Model

Return 165.56% 192.90% 81.57% 335.44%Daily SDEV 1.49% 1.22% 1.49% 1.44%Max Runup 425.84% 265.57% 226.95% 483.88%

Max Draw down -60.39% -36.98% -46.33% -35.62%Avg. rolling 250Day CorrelationTo Index 44% 9% 31%

Avg. ReturnRolling 3 Yr.(1,756 periods) 52.85% 35.29% 26.58% 65.17%

% Periods withPositive ReturnRolling 3 Yr. 85.9% 94.7% 68.5% 99.1%

Days used inModel (total2,515 days) 1,208 442 865

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INDEX STOCH 20D LB MODEL

10 year return

Chart 1: 10 year return for the DBLSCI, Stochastics indicator, 20 day look-back indicator, and model of indicators


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1 77 153 229 305 381 457 533 609 685 761 837 913 989 1065 1141 1217 1293 1369 1445 1521 1597 1673 17

INDEX STOCH 20D LB MODEL

Rolling 3 year returns

Chart 2: Rolling 3 year returns for the DBSLCI, Stochastics indicator, 20 Day look-back indicator, and the model of indicators.

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Model B: Asset class:

BondsStudy index:Yield of the 30Year Bond (daily return)Common trading representation of index:Multiple ETFs and mutual funds (single beta and leveraged)Competing indicators:

Exponential Moving Average:

If current 30 year yield is > 30 day EMA = LONG;If current 30 year yield is < 30 day EMA = SHORT

vs.Adaptive Moving Average:If current 30 year yield is > 10smooth, 2fast, 30slow AMA = LONG;If current 30 year yield is < 10smooth, 2fast, 30slow AMA = SHORT

vs.Weighted Moving Average:If current 30 year yield is > 9 day WMA = LONG;If current 30 year yield is < 9 day WMA = SHORT

vs.Hull Moving AverageIf current 30 year yield is > 21 day HMA = LONG;

If current 30 year yield is < 21 day HMA = SHORT


The second example is a study the 30 year yield which has an inverse correlation to the price ofthe 30 year bond. With exception of the Hull moving average (developed by Alan Hull), the competingindicators are common, simple, and effective methods of measurement. The intent is to measuredifferent periods of trend moving average types. The model of competing types of moving averagesalso has great application to other bond classes.

Results:The model significantly outperformed (+110.71%, 182.25%) with greatly reduced drawdown

(+14.76%, +35.09%) in the 10 year period compared to either side of the 30 year bond trade. Theaverage rolling 3 year return outperformed the either side of the index with an average of 15.23% and40.34% per period. The model also produced rolling 3 year positive returns 100% of the time.

Table 2: Bond asset class - comparisons of the index, different competing indicators, and the model of indicators

EMA AMA WMA HMA RYGBX RYJUX Model

Return -10.34% 6.10% 103.22% 86.45% 25.90% -45.64% 136.61%

Max Run-up 50.74% 116.72% 152.23% 133.36% 98.67% 36.61% 156.95%

Max Draw down -32.38% -35.03% -37.21% -28.29% -36.79% -57.32% -22.23%

Avg. Return

Rolling 3 Yr. -0.97% 1.63% 18.81% 14.37% 7.33% -17.78% 22.56%

(1,756 periods)

% Periods with

Positive Return 43.51% 41.51% 97.84% 93.39% 79.90% 16.17% 100.00%

Rolling 3 Yr.

Days used in

Model (total 631 528 609 747

2,515 days)


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RYGBX RYJUX MODEL

Chart 3: 10 year return without dividends for RYGBX (30 year bond 1.2x long), RYJUX (30 year bond -1x inverse) adjusting for annua

capital gains distributions, 20 exponential moving average indicator, 10-2-30 adaptive moving average indicator, 9 day weighted movingaverage indicator, 21 day Hull moving average indicator, and the model of indicators.

Chart 4: Rolling 3 year returns without dividends for RYGBX (30 year bond 1.2x long), RYJUX (30 year bond -1x inverse) adjusted for

annual capital gains distributions, 20 exponential moving average indicator, 10-2-30 adaptive moving average indicator, 9 day weightedmoving average indicator, 21 day Hull moving average indicator, and the model of indicators.

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Model C: Asset class:

Real EstateStudy index:Dow Jones US Real Estate Index (daily return)Common trading representation of index:IYR and mutual funds (single beta and leveraged)Competing indicators/index:

The Index

vs.Price Oscillator momentum:If price oscillator of 2 day minus 5 day is negative = LONGIf price oscillator of 2 day minus 5 day is positive = OUT OF MARKET

vs.Price Oscillator contrarian:If price oscillator of 2 day minus 20 day is positive = LONGIf price oscillator of 2 day minus 20 day is negative = OUT OF MARKET


The Price Oscillator indicator calculates a fast, or short, moving average and a long, or slow,moving average. The difference between these two values is then plotted. The most common approachto analyzing moving averages is to note the relative position of the 2 averages: the short moving averageabove the long moving average would yield a positive Price Oscillator value and be bullish; the shortmoving average below the long moving average would yield a negative Price Oscillator value and bebearish. However, when compressed time frames occur, it presents an opportunity for contrarian buyand sell signal. Calculating the difference between the two averages and following it as an oscillatormakes extreme positive and negative values stand out as possible overbought and oversold conditionsfor trading.

Results:

The model significantly outperformed (+50.39%) with greatly reduced drawdown (+35.79%) inthe 10 year period compared to the DJ US Real Estate Index. Additionally, the average rolling 3 yearreturn outperformed the index with an average of 3.27% per period. The model also produced rolling 3year positive returns over 86% of the time.

Table 3: Real Estate asset class - comparisons of the index, different competing indicators, and the model of indicators

DJUSRE 2-5 Price Oscillator 2-20 Price Oscillator Model

Return 44.67% 206.07% 16.78% 95.06%

Daily SDEV 2.24% 1.68% 1.46% 1.56%Max Run-up 203.55% 239.92% 148.24% 153.32%

Max Drawdown -76.92% -43.03% -66.60% -41.13%

Avg. Return Rolling 3Yr. (1716 periods) 24.80% 26.20% 16.57% 28.07%% Periods with PositiveReturn Rolling 3 Yr. 77.62% 69.70% 69.52% 86.31%Days Used In Model (total2,461 days) 1,008 855 598


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Chart 5: 10 year return without dividends for the Dow Jones US Real Estate Index, contrarian 2 and 5 day price oscillator indicator, 2and 20 day price oscillator indicator, and the model of indicators.

Chart 6: Rolling 3 year returns without dividends for the Dow Jones US Real Estate Index, contrarian 2 and 5 day price oscillatorindicator, 2 and 20 day price oscillator indicator, and the model of indicators.


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Chart 7: A demonstration of extended look-back. The 20 day look-back model in this chart is identical to Model on chart 5. Tocompare the power of extending look-back is shown by overlaying a 3 year look-back which represents reduction of noise. Normally, themore competing indicators a model has, the smoother and more successful an extended look-back.

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Model D: Asset class:

StocksStudy index:Domestic Large Cap Index - S&P 500 (daily return)Common trading representation of index:Multiple ETFs and mutual funds (single beta and leveraged)Competing capsule/indicator:

Commodity channel index (CCI) capsule (closing price) 20 day look-back:

Contents:3 day CCI, BUY if CCI < -95, SHORT if CCI > 95

vs.4 day CCI, BUY if CCI < -25, SHORT if CCI > 0

vs.6 day CCI, BUY if CCI < 0, SHORT if CCI > 25

vs.

Excess Return Trigger (ERT) (closing price):If current return is negative and less than ABSOLUTE VALUE of current return times -1 +ABSOLUTE VALUE of previous days close times -1 divided by 2, COVER total SHORTallocations, and BUY 25% of entry position. Repeat up to 4 allocations.

Formula: < (ABS of n(-1) + ABS n[1](-1)) / 2If current return is positive and greater than ABSOLUTE VALUE of current return +ABSOLUTE VALUE previous days close divided by 2, SELL total LONG allocations, andSHORT 25% of total allocation. Repeat up to 4 allocations.Formula: n > (ABS of n + ABS of n[1]) / 2


Developed by Donald Lambert, the Commodity Channel Index is used primarily to identifybeginning and ending of cycles in futures markets and is commonly used to identify buy and sellopportunities. Normally, the standard 14 day CCI is calculated so that 70-80% of all price activity fallsbetween +100 and -100 on its vertical scale. Many traders enter LONG is indicated when the CCIexceeds +100 while a SHORT position is indicated when the CCI falls below -100. Other traders alsouse this indicator in its standard form to indicate overbought and oversold markets, much like anoscillator. The standard CCI often misses the early part of a new move because of the amount of time itspends in the neutral position (between the Overbought and Oversold lines). This example is usingcompressed CCIs. The absolute range of a 3 day CCI is -100 to 100, a 4 day CCI is -133.33 to 133.33,and a 6 day CCI is -166.67 to 166.67. This study picked random numbers to create oscillators that weredivisible by 5. Thus, in this example, one could assume that the 3 day -95 and +95 represent extremeoutliers within that period. The 4 day and 6 day CCIs have a buy when the S&P 500 is far from itsoutlier bottom, and sells far from its outlier top on a per trade basis. In the past, using time compressionto force out neutrality and capture early moves for entry and exit has worked well for most asset classesand components thereof.

There are 4 steps involved in the calculation of the CCI:

a. Calculate the last period's Typical Price (TP) = (H+L+C)/3 where H = high, L = low, and C = close.b. Calculate the 20-period Simple Moving Average of the Typical Price (SMATP) .c. Calculate the Mean Deviation. First, calculate the absolute value of the difference between the last period's SMATP and the typical

price for each of the past 20 periods. Add all of these absolute values together and divide by 20 to find the Mean Deviation.d. The final step is to apply the Typical Price (TP), the Simple Moving Average of the Typical Price (SMATP), the Mean Deviation and a

Constant (.015) to the following formula:

CCI = (Typical Price - SMATP ) / ( .015 X Mean Deviation )


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The Excess Return Trigger (ERT) is a proprietary oscillator (at least to the authors knowledge)that is designed to capture outlier returns within a 2 day period relative to the absolute values of thecurrent day price near close and the previous days close. It is designed for averaging in or out of atrade, however, also works well for sequential events before entry or exit (example, one could trade afull allocation only on the third event of negative excess return and only on the third event of positiveexcess return).

Results:

The model significantly outperformed (+368.18%) with greatly reduced drawdown (+32.77%) inthe 10 year period compared to the S&P 500. The average rolling 3 year return outperformed the indexwith an average of 35.09% per period. The model also produced rolling 3 year positive returns over87% of the time.

Table 4: Large cap US stock asset class - comparisons of the index, different competing indicators, and the model of indicators

CCI Capsule FINAL

SPY 3 -95,95 4 -25,0 6 0,25 CCI Model ERT MODEL

Return -24.13% 279.04% 692.35% 383.86% 414.12% 203.95% 344.05%

Daily SDEV 1.42% 1.42% 1.42% 1.42% 1.42% 0.80% 1.14%Max Run-up 100.36% 402.16% 763.32% 438.61% 463.41% 213.05% 379.11%

Max Draw down -56.47% -32.44% -23.74% -26.27% -26.06% -15.90% -23.70%

250 day average

Correlation 30.46% 24.24% 33.37% 27.82% 25.11% 20.74%

To Index

Avg. Return

Rolling 3 Yr. 3.60% 50.35% 70.04% 45.98% 44.20% 29.70% 38.69%

(1,756 periods)

% Periods with

Positive Return 57.59% 100.00% 100.00% 98.47% 83.69% 100.00% 87.03%

Rolling 3 Yr.

Days used in

CCI Model (total 854 845 816

2,515 days)

Days used inModelof indicators 1313 1202


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Chart 8: This chart represents the 10 year return of the CCI return streams within the CCI capsule.

Chart 9: This chart represents rolling 3 year returns without dividends of the CCI return streams within the CCI capsule.


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Chart 10: This chart represents the 10 year return without dividends of the CCI capsule versus the ERT indicator and the S&P 500 ETF"SPY."

Chart 11: This chart represents rolling 3 year returns without dividends of the CCI capsule versus the ERT indicator and the S&P 500ETF "SPY."


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Model E: Asset class:

CurrencyStudy index:United States Dollar IndexCommon trading representation of index:UUP (Long), UDN (Short) and mutual funds (single beta and leveraged)Competing indicators:

15 day Simple Moving Average 1 day look-back direction compared with 15 day Hull Moving Average 1day look-back direction:

This period 15SMAlast period 15SMA < This period 15HMAlast period 15HMA = LONG USDILast period 15SMA Last period 15HMA

This period 15SMAlast period 15SMA > This period 15HMAlast period 15HMA = SHORT USDILast period 15SMA Last period 15HMA

vs.

Conjoined Bollinger Bands (a 50 day Bollinger with +2 /-2 standard deviations imbedded with a 10 dayBollinger with +2 /-2 standard deviations):

50D BOL high channel line / current price + 50D BOL high channel line / 10D BOL low channel line / 2Current price / 50D BOL Low channel line 10D BOL low channel line / 50D BOL low channel line

If the sum is less than 1, then long the US Dollar Index, if greater than 1, then short the US DollarIndex


The last example of competing indicators illustrates that plotting 15 day moving averages, onefast (the Hull) and one slow (the simple), then producing a one day look-back of the direction of eachmoving average can reveal that the fast moving average many times will confirm a move in the itembeing priced when its % of direction increases or decreasing relative to the % of direction of the slow

moving average.

The Bollinger Band was the developed by John Bollinger. The concept of a conjoined Bollingeris proprietary (to the author's knowledge) is to capture trends by confirming that the current price iscloser to the high channel line compared to the low channel line AND that the fast Bollinger low line iscloser to the slow Bollinger high line than to the slow Bollinger low line. The average of those tworatios determines the final result for trading. This method further removes the potential for price noiserelative to one channel.

Current Price 50D Boll hi 10D Boll lo 50D Boll lo

100.83 101.31 99.87 97.09

101.31 / 100.83 = 1.0064

100.83 / 97.09 = 1.0385 = .9691

101.31 / 99.87 = 1.0161

99.87 / 97.09 = 1.2086 = .9878

.9691 + .9878 = 1.957 1.957 / 2 = 0.9785 = SHORT US Dollar Index


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Results:

The model outperformed both sides of the US Dollar Index trade (+52.32%, 7.45%) withreduced drawdown (+23.77%, 0.94%) in the 10 year period compared to the USD long and USD short.The average rolling 3 year return outperformed the long index with an average of 20.70% per period, butdid not average as well as the short index with an average of -7.10% per period. The model alsoproduced rolling 3 year positive returns over 63% of the time. Also, each indicator performed well on

their own compared to the look-back model of those indicators. In the experience of the author,substantial outperformance occurs when measuring the US Dollar Index with the same indicators(different coordinates) measured weekly, rather than daily.

Table 5: Large cap US stock asset class - comparisons of the index, different competing indicators, and the model of indicators

SMA/HMA Bollinger USDI USDI -1x Model

Return 28.46% 38.53% -23.57% 21.30% 28.75%

Max Run-up 38.37% 45.08% 20.44% 62.83% 49.53%

Max Draw down -15.28% -12.16% -40.80% -17.97% -17.03%

Avg. Return

Rolling 3 Yr. 4.91% 8.00% -13.06% 13.74% 6.64%(1,756 periods)

% Periods with

Positive Return 66.44% 78.74% 1.61% 96.16% 63.44%

Rolling 3 Yr.

Days used in

Model (total 1207 1308

2,515 days)


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40,000

60,000

80,000

100,000

120,000

140,000

160,000

12/31/1999

4/30/2000

8/31/2000

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12/31/2009

BOLLINGER SMA/HULL USDI USDI Inv MODEL

10 year return comparing conjoined Bollinger, SMA/HULL Lookbacks, US Dollar index long and inverse, and the model

Chart 12: This chart represents the 10 year return without dividends of the conjoined Bollinger indicator, SMA/HMA look-backindicator, the US Dollar Index, Inverse US Dollar Index, and the model of indicators.

-40.00%

-30.00%

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

1 80 159 238 317 396 475 554 633 712 791 870 949 1028 1107 1186 1265 1344 1423 1502 1581 1660 1739

ConBoll SMA/HMA USDI USDI -1x Model

Rolling 3 year return of two competing indicators, US Dollar Index and US Dollar Index inversed, model

Chart 13: This chart represents the rolling 3 year return without dividends of the conjoined Bollinger indicator, SMA/HMA look-backindicator, the US Dollar Index, Inverse US Dollar Index, and the model of indicators.


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Putting it all together

System: Asset classes:Commodities, Bonds, Real Estate, Equities, CurrenciesParticipating indexes:Deutsche Bank Liquid Commodity Index (long, neutral, short), 30 year bond interestrates (long & short 30 year bond price), Dow Jones Real Estate Index (long, neutral)S&P 500 (long, short), US Dollar Index (long, short)

Results:

The system substantially outperformed the smart buy & hold allocator described in the abstract(+214.78%) with substantially reduced maximum drawdown (+22.73%) in the 10 year period. The system alsosubstantially outperformed and had substantially less drawdown than all asset classes that made up the system.The average rolling 3 year return outperformed the smart buy & hold allocator with an average of 24.1% perperiod. The system also produced rolling 3 year positive returns 100% of the time, with a worst rolling 3 yearreturn of 14.2%.

Table 7: The 10 year return without dividend, max run-up and drawdown, daily standard deviation, and the best and worst single day o

the system, the smart Buy & Hold allocator, S&P 500, 30 Year Bond, US Dollar Index, Dow Jones Real Estate Index, and the Deutche BankLiquid Commodity Index (all without dividends).

10 Year results DBLCI RYGBX SPY USDI DJUSRE Smart B&H System

Return (w/o dividends) 165.1% 29.4% -24.1% -23.6% 42.0% 32.5% 247.3%

Max Run-up 425.8% 98.7% 100.4% 20.4% 203.6% 73.3% 258.3%

Max Drawdown -60.4% -36.8% -56.5% -40.8% -76.9% -32.9% -10.2%

Daily Standard Deviation 1.49% 1.06% 1.42% 0.55% 2.22% 0.75% 0.62%

Best single day 9.02% 7.62% 14.52% 2.74% 18.82% 6.13% 6.49%

Worst single day -8.21% -4.28% -9.84% -3.11% -19.30% -5.47% -3.44%


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Chart 14: The 10 year returns of the system, the smart Buy & Hold allocator, S&P 500, 30 Year Bond, US Dollar Index, Dow Jones RealEstate Index, and the Deutsche Bank Liquid Commodity Index.

-75.0%

-25.0%

25.0%

75.0%

125.0%

175.0%

1 83 165 247 329 411 493 575 657 739 821 903 985 1067 1149 1231 1313 1395 1477 1559 1641 1723

DBLCI RYGBX SPY USDI

DJUSRE Smart B&H System

Chart 15: The rolling 3 years of the system, the smart Buy & Hold allocator, S&P 500, 30 Year Bond, US Dollar Index, Dow Jones RealEstate Index, and the Deutsche Bank Liquid Commodity Index (all without dividends).


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Table 7: The rolling 3 years average return, best return, worst return, and % of trailing positive returns of the system, the smart Buy &

Hold allocator, S&P 500, 30 Year Bond, US Dollar Index, Dow Jones Real Estate Index, and the Deutsche Bank Liquid Commodity Index(all without dividends).

DBLCI RYGBX SPY USDI DJUSRE Smart B&H System

Average 52.5% 8.8% 3.6% -13.2% 24.6% 13.5% 37.6%

Maximum 129.5% 45.9% 60.7% 2.6% 92.7% 48.4% 90.9%

Minimum -25.2% -19.5% -47.9% -31.2% -69.8% -24.2% 14.2%% 3 year periodsWith positiveReturns 85.9% 79.9% 57.6% 1.6% 77.6% 68.8% 100.0%

Measuring the competing indicators:

Every one of the indicators used in this paper can be expressed as a formula on Microsoft Excel and

easily monitored on a day by day basis. The easiest method of monitoring is the use software that has canexpress indicators (either factory or coded by the user) in the form of sortable columns with alerts.Tradestation definitely has these capabilities.

Conclusion:

This paper has shown how a system of models that contain earmarked capsules of competing indicatorscan be used as a potential roadmap for disciplined investing. Not only did the use of multi asset classes providemore consistent return for the smart buy and hold investor, but multi asset class use helped provide

differentiated return streams on a pre-indicator level, and enhanced returns streams with post-indicatormanipulation. Rolling return look-backs of indictor streams for allocation shifts also smooths and increasesperformance when one stream is declining while another stream is ascending in value. Also, to keep thingssimple, this paper picked out a maximum of 4 indicators in one model (model B) to present the concept of look-back as a method of aligning with the indicator which seems to have the most probable success in a currentmarket. Again, the use of competing capsules within a model increases the odds of better trading. Due to spacelimitations of the paper, 1 example of a capsule was expressed in model D.

Finally, it has been established that an advisor can pick their favorite indicator(s); manipulate thecoordinates to create different return stream patterns; align the competing indicators in an easy to followmonitoring system; assign a look-back comparison for trading purposes; and when the winning indicator issurfacing, trade within the model.


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Appendix A:

Screenshot example of the commodity model through Microsoft Excel. This information iseasily importable from Tradestation.


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Appendix B:

Screenshot example of the commodity model through Tradestation. This visual can be convertedinto a table and dropped into Microsoft Excel for further analysis.

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A System of Models

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