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Add a Time and Price Filter to Reduce False Signals, Increase ReturnsFor Advisors and Investors

James H. Morgan, CFP®, IAR*340 E. 1ST Ave, Ste 250A

Broomfield, Colorado 80020303-457-9500

[email protected]

SUBMISSION FOR THE DOW AWARD

*Oak Grove, LLCA Registered Investment Advisor

CopyrightJHM-Price-Time-Filter™

by James H. Morgan

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Add a Time and Price Filter to Reduce False Signals, Increase ReturnsFor Advisors and Investors

As a money manager, it is my belief that protecting principal is just as important, if not more important, than investment returns. This is because the mathematics of losses require an investor to make twice the return as the loss just to break even, so a 40% loss requires an 80% return. Thus a method which consistently reduces downside risk, as well as keeping the investor long during up markets would be most helpful.

Some advisors and investors promote different means to reduce downside risk, including the use of multiple moving average cross over methods or a simple break above or below a longer term moving average to provide buy and sell signals. Used on a longer term basis, these methods may help to distinguish between bull and bear markets, and in the process, to provide some downside protection from bear markets or steep declines, compared to simple buy and hold.

While cross over or moving average break out methods over the long term may help to provide some downside protection and keep the investor on the right side of the market, such traditional methods are also filled with many false signals. This creates a problem for the portfolio manager, or investor with a diversified portfolio, due to the frequency of signals, as well as false signals, causing transactions in and out of many, many different securities. False signals alone can create poor returns, unnecessary costs including transaction fees and slippage, unnecessary taxes, and for the Advisor poor client satisfaction and retention.

The purpose of this paper is not so much to introduce an investing method for the SPY, but rather to provide Advisors and investors with a viable long term bull and bear market model, designed to protect capital and enhance returns. I have developed a technique which adds both a price and time requirement (JHM-Price-Time-Filter™) to the longer term moving average cross over and moving average break out methods which in back testing has resulted in a substantial reduction in the number of transactions and false signals, resulting in higher returns and lower volatility.

This paper will describe use of the JHM-Price-Time-Filter™ method with a) long term moving average cross over (10 and 40 unit crossover on weekly charts), and, b) moving average break out (60 unit moving average also on weekly charts) methods. Backtesting will initially be shown from the time period from 1994 to 2015, using the SPY for buy signals and the VFITX (Vanguard Intermediate Bond Fund) for sell signals. To demonstrate its effectiveness in other time periods, this paper will then review use of the JHM-Price-Time-Filter™ method for the 55 year time period from 1960 to 2015, as well as various 20 year plus periods between 1960 to 2015.

The JHM-Price-Time-Filter™ method will be presented from the perspective of the long term trend point of view, utilizing weekly data, as such better filters out market noise than end of day. The index used to generate buy and sell signals will be the weekly S&P 500 Index. Charts

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from 1960 to 2015 showing the JHM-Price-Time-Filter™ signals are attached, with periods of green bars indicating a buy signal period, red a sell signal period.

The Tested Methods

The first method1 tested will be a moving average cross over method. A common suggestion, for long term signals, is the use of a 50 day and 200 day cross over for end of day trading. Using weekly data from the S&P 500 index, this study will substitute a 10 unit and 40 unit moving average. During the period of 1994 to the present, for buy signals, the SPY will be purchased. When a sell signal is given, the SPY will be sold and the Vanguard Intermediate Bond Fund (VFITX) purchased. Taxes, capital gains and dividend distributions will not be taken into account unless otherwise stated2. The second method backtested is a breakout over a 200 day or 300 day moving average. Only the latter will be backtested in detail, and a 60 day moving average will used for weekly data.

1. The Basic Moving Average Crossover Method

While one purpose of simple moving average cross over method is to keep the investor in line with the major trend, such methods tend to come with a large number of transactions and sometimes many false trades.

The weekly chart of the S&P 500 from 1994 to November 2015 below presents an overview of the false signal problem with the simple 10/40 moving average method, showing both confirmed and failed buy and sell signals. Blue bars are areas where a simple moving average cross over occurred and created a buy or sell signal. Green bars, however, represent a confirmed buy signal, red a confirmed sell, but using the JHM-Price-Time-Filter™ (described below). The red arrows indicate a failed sell signal and blue arrows a failed buy signal, i.e. buy or sell signals with no continuation in trend direction. Here, there are seven failed signals (plus the last occurring in November 2015 seemingly false yet not yet resolved).

1 All actual backtesting is hypothetical. However, as the JHM-Price-Time-Filter™ method was developed in 2010, it has been used in my real time management practice since then, keeping my investment posture generally bullish and invested. Past performance is no guarantee of future performance. Data and information was obtained from sources believed to be reliable but cannot guarantee its accuracy.2 Transaction fees are $14.75 per trade.

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Reprinted with Permission from Trade Station

The chart immediately below shows the same time period but using the JHM-Price-Time-Filter™ method with 10 unit and 40 unit moving average cross over, to filter out false signals. Here, as above, the green bars indicate a buy signal, red bars indicate a sell signal, with the blue arrows showing a successful buy signal, red arrows a successful sell signal.

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Copyright Reprinted with Permission from Trade Station

In the above chart, there were a total of five signals, with three buy signals (blue arrows) and two sell signals (red arrows). The significant difference the lack of false signals, thus a significant improvement.

The JHM-Price-Time-Filter™

Let’s get into how the filter works. Essentially, the JHM-Price-Time-Filter™ requires an additional confirmation of both a weekly price and time bar after an initial moving average crossover (or a breakout in the case of the moving average price breakout). The moving average crossover or breakout from a moving average cannot by itself initiate a signal.

1. Simple Cross Over Method

Let’s begin by showing how a simple moving average cross over method works.

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The above chart shows the time period late 2009 and beginning 2010. Here a moving average cross over buy trigger occurs at the blue arrow merely because the faster moving average (red line) crosses above the slower moving average (blue line) at the first blue bar (see blue arrow).

Unfortunately, this kind of method, while tending to create better returns than a buy and hold strategy, also creates many false signals.

The JHM-Price-Time-Filter™ Method

1. The Buy Signal

The purpose in designing JHM-Price-Time-Filter™ was to reduce the effect of the well documented day to day random nature and noise of markets, which tends to create false or inaccurate signals with any method, and thus provide the Advisor or longer term investor with a method of being invested in bull trends but not so invested in bear trends.

To do so, let’s change the rules a little bit. Instead of blanket purchases or sales merely because the shorter term moving average is crosses over (or becomes greater or lesser) the longer term average, let’s require there to be both a price and time weekly bar confirmation subsequent to the crossover.

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Copyright. Reprinted with Permission from Trade Station

This is the same S&P 500 weekly chart shown immediately above. Here we are not going to buy (or sell) merely because of cross over of a moving average. Rather, we want a subsequent bar that confirms the trend direction. For buy signals, once a cross over occurs, we note, first, the highest high of the three previous bars including the cross over bar (Highest Crossover Bar). The actual cross over above occurred on June 12, 2009. The highest high for the previous three bars, in this case, happens to be the high of the initial cross over bar and is 956.23. This occurs at the first blue arrow above. Then, second, we wait for a price and time confirmation bar. This means we are looking for a subsequent weekly bar that closes higher than the Highest Crossover Bar price of 956.23. We do this because this may confirm that the cross over may introduce a new trend in the direction of the cross over. Here, the price and time confirmation bar occurs on the close of the second blue arrow because the closing price is higher (price confirmation) than 956.23 (the Highest Crossover Bar), occurring six weeks later (the time confirmation) Thus a buy signal is generated.

The chart below shows the successful result of the above signal. The buy signal occurs at the blue arrow with the price colored green to indicate a buy signal is operating.

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In contrast, view the chart immediately below using a 10 unit/40 unit cross over without the JHM-Price-Time-Filter™, noting the false signals at the red arrows. (It has yet to be seen whether the very last signal will turn out to be a false signal).


2. Sell Signal

A sell signal is generated in a similar manner (See chart immediately below).

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The above chart is the S&P 500 weekly from 10/5/2007 to 02/29/2008. Here a moving average cross over occurred where the faster moving average (red line) closed lower than the slower moving average (blue line), at the first red arrow, occurring on 12/21/2007. As with a buy signal, however, a mere cross over does not yet generate a sell signal. Rather, first, at the point of cross over, the lowest low of the previous three bars (including the cross over bar) is noted (Lowest Low Bar). The Lowest Low Bar above is 1435.65. We then, second, look for a price and time confirming bar, i.e. a subsequent weekly close below the low of the Lowest Low Bar, or below 1435.65, to generate a sell signal. This occurred on January 8, 2008 at the second red arrow.

The chart below shows how that signal played out.

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Copyright. Reprinted with Permission from Trade Station

3. Avoiding False Signals

Let’s look specifically at how a false signal may be avoided because this is a primary reason to use JHM-Price-Time-Filter™.

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The weekly S&P 500 chart covers the period from 8/5/11 to 01/27/12. There a moving average cross over occurred at the first red arrow which, without JHM-Price-Time-Filter™, triggered a sell signal. (The bars are painted red to illustrate the cross over signal, which here turns out to be false). Note our investor using this simple moving average cross over would have sold at 1178.81 and been forced to buy back in at 1316.32 on 1/27/12, for a 137.51 point loss3 at the point where the moving averages crossed again.

JHM-Price-Time-Filter™ method would have avoided the false signal. Once the initial cross over occurs, we note the Lowest Low Bar at the MA crossover is 1101.54, which happens to be the low of the cross over bar. After the initial crossover, we are looking for a subsequent weekly close below the Lowest Low Bar to confirm the moving average cross over and thus initiate a sell signal. That bar never occurred, so our portfolio manager (or investor) did not liquidate the portfolio, saving transaction fees, possible taxes and perhaps achieving higher performance.

3 Of course, selling and then buying an entire portfolio may have achieved worse results.

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Using my time and price filter method, let’s again look at the overall picture from late 1994 to the present.

Copyright Reprinted with permission from Trading Blox Software

The above chart4 represents all buy signals (blue bars) and sell signals (red bars) using 10 unit/40 unit moving average with my JHM-Price-Time-Filter™ from 1994 to the present. Thus JHM-Price-Time-Filter™ may allow the money manager or individual investor to remain invested for the major trends, and avoid longer term down markets.

The Statistical Results – 1994 to the Present

Let’s compare the results of using JHM-Price-Time-Filter™ versus simple moving average cross over and buy and hold. To generate investment statistics for the 1994 to November 2015 time period, Trading Blox software was used. For buy signals, we will purchase the S&P 500 index5, for sell signals the VFITX (Vanguard Intermediate Treasury Bond Fund).

1. The Moving Average Cross Over with the JHM-Price-Time-Filter™

Here we are using a 10 unit/40 unit moving average cross over with the JHM-Price-Time-Filter™. Starting with $100,000 in early 1994, and with the first buy signal occurring on 2/10/1995, capital hypothetically grew to $786,524 (not taking into account capital gains tax, nor however dividend or capital gain distributions and reinvestment). 4 Software routines in Trading Blox were written by James H. Morgan5 Although the S&P 500 index cannot actually be purchased, there is a high correlation between that index and the SPY. In one of my studies from late 1994 to the present, using the SPY as the purchased security yielded a result of $779,626, using the JHM-Price-Time-Filter™. When the S&P 500 was substituted for the SPY also using JHM-Price-Time-Filter™, the resulting figure was $780,467, a mere difference of $841 over a 20 year period, certainly in may opinion an insignificant amount. Thus I believe the S&P 500 may be used as a surrogate for the SPY for testing purposes.

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Transactions: 5CAGR: 9.97%Sharp Ratio: 0.86Maximum Drawdown: 18.3%6

Wins 4Losses 17

2. The Moving Average Cross Over without the JHM-Price-Time-Filter™

Starting with $100,000 in late 1994, the first buy signal occurring on 2/24/1994, capital hypothetically grew to $451,515, producing the following results:


Winning Trades 13Losing Trades 8

3. Buy and Hold

Using a Buy and Hold Strategy, with the first (and only) buy transaction on 2/25/1994, capital grew $443,994, producing the following results:


Conclusion: The higher annual return, fewer transactions and smaller drawdown illuminate the superiority of the JHM Price-Time-Filter™ Method versus simple moving average cross over.

The simplest method, buy and hold, has the advantage of having only one transaction, but inferior results v. using JHM-Price-Time-Filter™, with a much lower CAGR and capital growth, and somewhat lower CAGR than the Simple Cross over method, as well as also a worse maximum drawdown.

6 Backtesting and statistics provided by Trading Blox Software.7 Viewing the chart immediately above, the sell signals were both capital protective and accurate. The loss occurred because the VFITX was purchased and sold in 2009 for a very minor loss.8 Backtesting and statistics provided by Trading Blox Software.9 Backtesting and statistics provided by Trading Blox Software.

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The Economic Value of Avoiding Bear Markets usingJHM-Price-Time-Filter™

The following chart more better illustrates the economic value of protecting capital by avoiding bear markets and staying invested for bull markets using the JHM-Price-Time-Filter™ method. The results were calculated in Trade Station10. The SPY was hypothetically purchased at the first blue arrow and this illustration takes into account dividend and capital gains distributions reinvested into additional shares, thus creating a compounding effect. Final results did not include into account taxation.


Here there are two investors, each hypothetically investing $100,000 in late 1994. The buy and hold investor is represented by the black line, and like a good buy and hold investor buys and merely holds. That investment hypothetically grew to about $659,541 by late November, 2015. This investor’s capital would also have experienced substantial draw downs, however, from 2001 to 2003 and 2007 to early 2009.

The active investor (the blue line) also invests $100,000 in the SPY and is also charged a 1% management fee. (The management fee is the reason why the blue line lags the black line from late 1994 until mid 2000). The active investor, however, notes a sell signal generated on 01/05/2001 and moves to the Vanguard 3-7 year treasury fund (VFITX), at the first red arrow, protecting client or investor capital. The active investor next notes a buy signal 5/30/03, and repurchases the SPY. This investor then sells the SPY on 01/04/2008 with the next sell signal (second red arrow), moves into the VFITX until early 7/24/2009, when a buy signal is given. The active investor would still be long as of the end of November, 2015.

Using the JHM-Price-Time-Filter™, the active investor’s capital hypothetically grew to about $1.3 million, as compared with $659,541 of the buy and hold investor11.

10 Software routines written by James H. Morgan in Tradestation’s Easy Language.11 As mentioned previously this illustration is hypothetical.

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Longer Term Time Frames

The experienced Advisor or investor may object to the above illustrations as a limited period of time involving similar bull and bear markets, although 21 years. This is certainly a legitimate concern. So let’s backtest the JHM-Price-Time-Filter™ method starting in 1960, again using the S&P 500 index to initiate buy and sell signals, comparing JHM-Price-Time-Filter™ method to a) the simple 10 unit short term 40 unit longer term moving average crossover, b) a breakout above or below a 6012 unit moving average, and, naturally, c) simple buy and hold. (See attached charts for 1960 through November 2015, with bars painted green for periods of buy signals and red for periods of sell signals using the JHM-Price-Time-Filter™ applied with 10/40 unit moving average).

Because the VFITX did not exist until 1994, cash from sell signals will be invested in prevailing money fund rates,13 thus a sell signal closing out a buy signal will result in collecting interest at then prevailing money fund rates, not the purchase of a security14.

Also, while the SPY now provides a vehicle to purchase the S&P 500 Index, such was not available prior to 1993. Because the SPY is highly correlated with the S&P 500, all return calculations from 1960 onward for the studies below assume the S&P 500 was purchased as surrogate for SPY15.

Various time periods were tested, starting with the 55 year period 1960 to 2015. While the most important time frame may be the longest, it is also worthwhile to look at many time frames in between to see if performance is consistent throughout each time period tested. Thus, generally 20, 30, 40 year time blocks were tested, including 1960 to 1980, 1960 to 1990, 1960 to 2000, 1970 to 1990, 1970 to 2000, 1970 to 2015, 1980 to 2000, 1980 to 2010, 1980 to 2015, 1990 to 2010, and 1990 to 2015, and finally, 2000-2015. (See Tables below for backtesting results).

Last, the time period 2000 to 2015 was tested because the year 2000 was the top of the market culminating the long bull market that started in 1983. It is always instructive to see how methods test when starting near the very top of a trend going forward, rather than just the trends beginning.

12 A 200 day (40 unit for weekly data) moving average break system was also tested for the 1960 to 2015 period, but produced inferior results to the 300 day moving average system, including a lower CAGR, Sharp ratio and higher number of overall trades, as well as a worse win-loss ratio, and will thus not be discussed.13 Forex USD rates provided by Trading Blox from 1980 to the present, and 3 month treasury bill rates from 1960 up to 1980. Although the bill rates yielded somewhat less than the USD rates, because both were applied uniformly, results should generally be valid.14 Previously, when buying the SPY on long signals and the VFITX on sell signals, Trading Blox would report 2 transactions, an opening and closing buy and an opening and closing sell. Because money funds are purchased, a security is not being purchased at the closing of a buy signal, thus the software will report 1 transaction, i.e. the initial buy and closing buy.15 In one of my studies from late 1994 to the present, using the SPY as the purchased security yielded a result of $779,626, using the JHM-Price-Time-Filter™. When the S&P 500 was substituted for the SPY, the resulting figure was $780,467, a mere difference of $841 over a 20 year period, an insignificant difference.

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For the results table below, investors were assumed to have started with $100,000, incurred transaction fees of $14.95 per transaction, and neither capital gains, dividend distributions, or taxes were taken into account.

Conclusions for the 1960 to 2015 Time Frame

In all time frames, JHM-Price-Time-Filter™ used with the 10/40 unit moving average cross over or the simple 60 unit moving average break out methods, produced superior results versus the simple 10/40 moving average cross over or simple 60 unit moving average break out methods alone, including higher returns, higher Sharp Ratios, fewer number of overall transactions and a superior win-loss ratio. The JHM-Price-Time-Filter™ also bested buy and hold in all time frames tested but one (See Explanation and Caveat discussion below).

For the global period of 1960 to 2015 time period, the JHM-Price-Time-Filter™ with 10/40 unit cross over produced a CAGR of 8.08% versus 7.11% without the filter, and 8.08% v. 6.63% for Buy and Hold. JHM-Price-Time-Filter™ also had a Sharp Ratio of .74, versus .71 and .51 respectively. Significantly, the JHM-Price-Time-Filter™ produced a total of 12 signals, with 12 wins and zero losses (see Explanations and Caveats below), versus 31 total signals for the 10/40 unit cross over alone, producing 21 Wins and 10 Losses, and versus 64 signals with 26 wins, 38 losses, for the 60 unit moving average breakout.

Interestingly, the time period beginning January 8, 2000 to November 6, 2015, was tested because the year 2000 ended a substantial bull market run, and it was desired to see the results of testing beginning at the top of the bull run. Again, JHM-Price-Time-Filter™ with 10/40 unit crossover bested both mere 10/40 unit crossover and Buy and Hold, with CAGR 6.00% v. 4.47% and 2.40% respectively, as well as a higher Sharp Ratio of .60 versus .50 and .23 respectively, and a higher win/loss ratio (see last Table below).

Last, concerning the vaunted buy and hold method propounded by the random walk crowd, of the 12 time frames studied and the five methods tested, buy and hold either placed last or next to last in 8 of 12 time frames, and only placed a higher ranking than JHM-Price-Time-Filter™ for the one time period from 1980 ending in the year 2000 JHM-Price-Time-Filter™. But once the bear markets of 2001 and 2008 were included, however, JHM-Price-Time-Filter™ performed better. (See discussion in Explanations and Caveats below).

Overall, I believe both the lower overall frequency of transactions, reduction in false trades, along with high accuracy of signals and better returns, makes the use of the JHM-Price-Time-Filter™ a superior tool and method for active Advisors and Investors

Explanations and Caveats

1. Price Follow Through for Sell Signals . JHM-Price-Time-Filter™ works very well where a strong bull market occurs after buy signals, or deep bear markets occur after sell signals. While it gave excellent warning, for example, of the bear markets of 1969-1970, 1973-1975, 2001-2003 and 2008-2009, allowing the investor to protect capital by selling at higher prices and

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buying back in at lower prices, not all sell signals16 provided this opportunity. This is because not all price action after a sell signal resulted in a deep enough bear market or correction to allow repurchase at lower prices than sold. For example, on 9/4/1981, a sell was issued at 124.08 and, although the S&P 500 dropped to a low of 101.44, a subsequent buy signal on the next moving average crossover on 10/15/1982 came at 134.70. (See attached charts for all time periods). Notwithstanding, stepping aside allowed the Advisor to limit volatility and the method soon reversed to the upside.

From a different perspective, however, from the 1960 to 2015 time period, there were 11 sell signals. Of those, 8 had drops of 10% or more from the sell entry point17, one had a drop of 8.98%18 and another 6.52%19. Only one sell signal experienced an insignificant drop in price with an immediate reversal higher20. (See attached charts).

2. Topping Process. For sell signals, JHM-Price-Time-Filter™ tends to work best when prices have formed a top lasting at least several months or more. JHM-Price-Time-Filter™ and the other long term methods did not produce a timely sell signal prior to the 1987 flash crash likely due to a very minor top formed before the crash.

3. 1980 to 2000 . The period January 8, 1960 to November 6, 2000 produced the only

period where Buy and Hold did better than JHM-Price-Time-Filter™, having a CAGR of 13.22% v. 11.69% 21. This better result was likely due, first, to the persistent bull market during that period which did not experience a long lasting, deep bear market. And, second, no long term method tested protected against the flash crash that occurred in 1987. However, that being said, once subsequent bear markets are considered, looking at the time period January 8, 1980 to November 6, 2015, JHM-Price-Time-Filter™ produced a CAGR of 9.61% v. 8.65% for Buy and Hold. Or, looking at the time period of January 8, 1968 to November 8, 199022, which encompassed a bear market starting 1969 then another starting in 1973, JHM-Price-Time-Filter™ produced a CAGR of 7.05% v. 5.34% for buy and hold. And, again, for the global period of 1960 to 2015, JHM-Price-Time-Filter™ produced a CAGR of 8.08% v. 6.6% for buy and hold.

4. Wins and Losses .

Form the January 8, 1960 to November 6, 2015 period, there were 12 wins and 0 losses. A perfect method has not been created, however. This win/loss total can be misleading because this only includes gains from the purchase and sale of the S&P 500. Because idles funds were invested at money fund rates, there were no transactions during this period. Also, there were times where a sell signal occurred resulting in a gain from a previous purchase, but the 16 As noted in the time period January 8, 1960 to November 6, 2015, 12 bull market signals produced 12 wins and 0 losses.17 1962, 1969, 1973, 1977, 1981, 2001, 2008.18 1971.19 1984.20 1990.21 Of course, method variables can be optimized and a fast moving average of 22 and long moving average of 29 resulted in a CAGR of 13.85% for this time period. Notwithstanding, although producing a CAGR of 8.67% from 1960 to 2015, also resulted in 16 total transactions with 3 losses. Thus, my choice as a fund manager is still to use a 10/40 crossover with JHM-Price-Time-Filter™.22 This time period is not illustrated in the tables below, so you’ll just have to take my word on it.

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subsequent buy signal occurred at a price higher than the previous sell signal (See discussion immediately above). In the software routine as written, a win is counted where a gain occurs from a purchase and then sale of the S&P 500 index. If a buy signal, for example, occurred at 100 and then a sell signal at 140, a gain would occur. If a new buy signal occurred at 130, all the better since our investor would be buying lower than was sold. However, let’s assume that after the sale at 140, the S&P 500 dropped a few points, then rallied to 150 where a subsequent buy signal then occurred. Selling at 140 but buying back later at 150 means 10 points were forgone. This opportunity cost would not be counted as a loss, although it would certainly be factored into CAGR.

Notwithstanding, no loss occurred from a purchase and subsequent sale of the S&P 500 from 1960 to 2015 with the JHM-Price-Time-Filter™, whereas with simple 10/40 cross over, there were 31 opening and closing transactions, with 21 gains and 10 losses for buy signals, and for simple 60 unit moving average breakout, there were 64 buy transactions, with 26 gains and 38 losses.

TABLES

January 8, 1960 to November 6, 2015Method Result CAGR Sharp Ratio Total Trades Wins LossesBuy and Hold $3,601,615 6.63% .51 1 1 0JHM-Price-Time-Filter™ for 10/40 CO23 $7,664,325 8.08% .74 12 12 0

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10/40 CO24 $4,626,453 7.11% .71 31 21 10JHM-Price-Time-Filter™ for 300 MA25 $4,556,586 7.08% .68 17 15 2300 MA26 $2,610,952 6.02% .58 64 26 38

January 8, 1960 to November 6, 1980Method Result CAGR Sharp Ratio Total Trades Wins LossesBuy and Hold $222,083 3.91% .34 1 1 0JHM-Price-Time-Filter™ for 10/40 CO $322,316 5.78% .65 7 7 010/40 CO $287,350 5.20% .61 15 10 5JHM-Price-Time-Filter™ for 300 MA $242,247 4.34% .50 9 7 2300 MA $237,738 4.24% .49 26 10 16

January 8, 1960 to November 6,1990Method Result CAGR Sharp Ratio Total Trades Wins LossesBuy and Hold $544,267 5.65% .45 1 1 0JHM-Price-Time-Filter™ for 10/40 CO $776,118 6.88% .65 9 9 010/40 CO $742,636 6.72% .67 20 13 7JHM-Price-Time-Filter™ for 300 MA $586,700 5.80% .56 11 9 2300 MA $593,725 5.95% .56 32 13 19

January 8, 1960 to November 6, 2000Method Result CAGR Sharp Ratio Total Trades Wins LossesBuy and Hold $2,447,037 8.15% .63 1 1 0JHM-Price-Time-Filter™ for 10/40 CO $2,857,459 8.56% .76 10 10 010/40 CO $2,258,996 7.93% .75 24 16 8JHM-Price-Time-Filter™ for 300 MA $2,046,588 7.68% .69 13 11 2300 MA $1,527,930 6.91% .63 45 27 28

January 8, 1970 to November 6, 1990Method Result CAGR Sharp Ratio Total Trades Wins LossesBuy and Hold $336,054 6.01% .45 1 1 0JHM-Price-Time-Filter™ for 10/40 CO $477,089 7.80% .67 6 6 010/40 CO $442,133 7.40% .66 14 8 6JHM-Price-Time-Filter™ $427,227 7.23% .62 7 7 023 This is the JHM-Price-Time-Filter™ applied to the weekly S&P 500 data with a shorter term moving average = 10 and longer term moving average = 40 cross over.24 This is the shorter term moving average = 10 and longer term moving average = 40 cross over.25 This is the JHM-Price-Time-Filter™ applied to the weekly S&P 500 data 60 unit breakout method.26 This is the 60 unit moving average breakout method.

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for 300 MA300 MA $399,093 6.88% .58 23 8 15\



January 8, 1980 to November 6, 2000Method Result CAGR Sharp Ratio Total Trades Wins LossesBuy and Hold $1,322,464 13.22% .93 1 1 0JHM-Price-Time-Filter™ for 10/40 CO $999,014 11.69% .87 4 4 010/40 CO $815,766 10.61% .85 11 7 4JHM-Price-Time-Filter™ for 300 MA $893,894 11.09% .84 6 5 1300 MA $699,447 9.79% .75 22 8 14

January 8, 1980 to November 6, 2015Method Result CAGR Sharp Ratio Total Trades Wins LossesBuy and Hold $1,995,667 8.65% .63 1 1 0JHM-Price-Time-Filter™ for 10/40 CO $2,678,678 9.61% .80 6 6 010/40 CO $1,670,271 8.18% .75 18 12 6JHM-Price-

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Time-Filter™ for 300 MA

$1,989,771 8.71% .76 10 9 1300 MA $1,197,651 7.58% .63 41 17 24




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