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Maximum adverse excursion.
92
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FIGURE 7.5
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TABLE 7.4
Annual Returns for Slow Turtle on NASDAQ 100Stocks% Maximum
1997199819992000200120022003*
$ Return
% Return
DD
Exposure
128,279.75790,990.505,539,361.00–2,028,572.00433,967.50–953,636.00512,632.00
12.8370.11288.62–27.27.99–16.2610.44
–7.18–27.27–15.72–49.43–21.69–21.92–8.98
58.9297.4598.6894.9790.3586.3290.43
*Only until June 2003.
A Basket of Uncorrelated StocksLet us do a similar analysis on a basket of stocks that are largely uncorrelated: two technology stocks (MSFT and INTC), two utilities (ED and SO),two commodity stocks (NEM [gold] and PAAS [silver]), and two industrials(AA and MMM). Using 30 percent of equity per trade, we get annual returnsas shown in Figure 7.6 and Table 7.5. Using uncorrelated assets, even on abasket with only eight assets, is enough to significantly smooth out returns,particularly in bearish periods in the markets.
TABLE 7.5
Annual Returns Using Slow Turtle on Basket of Uncorrelated Stocks
Period Starting
4/22/19961/6/19971/5/19981/4/19991/3/20001/2/20011/7/20021/6/2003**Through June 2003.
$ Return
% Return
% MaximumDD
Exposure
282,569.38201,998.25468,115.38112,452.2536,308.50329,090.25–12,389.75256,371.75
28.2615.7531.535.761.7615.66–0.5110.6
–2.19–11.91–6.8–13.91–33.53–6.33–18.54–4.41
29.376.9655.7466.1567.0969.0468.7882.01
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Annual returns.
94
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FIGURE 7.6
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95
OTHER VARIATIONSThere are many different variations on the Turtle system. A few of them are:• Using a different type of moving average. The simple moving average(SMA) tends to identify trends a bit slowly. The exponential moving average or weighted moving average weights current data higher thanolder data and will signal a trend sooner than the SMA. That said, theyare also prone to more whipsawing. If you are of the rocket scientistsort, you can play with the mesa adaptive moving average developed byJohn Ehlers and discussed in his book Rocket Science for Traders(Wiley, 2001).• Pyramiding bets. The original Turtle system places some emphasis onmoney management, in particular, being able to pyramid the bet as thetrend keeps moving in the original direction. Since a trend-followingsystem is but one component of my trading, I do not feel pyramiding isnecessary. Also, the volatility in today’s markets is such that the drawdown potential is beyond immense if one is pyramiding bets. It is aguarantee that your largest loss will also be your last.• Buying on a breakout of a recent high. Rather than using a moving average crossover, you can also buy the market on a breakout of a recenthigh. For instance, buy a breakout of a 20-bar high plus some multipleof the ATR (average true range).Just as an aside, it is interesting to see what types of assets tend totrend. For instance, Figure 7.7 shows the federal funds rate from June 1987to April 2003.
CONCLUSIONI have seen variations of the turtle system printed on various Web sitesclaiming to have the original Turtle system. It is a worthwhile exercise totest this system and its variants on stocks and confirm for yourself that theydo not work. Note, however, that the Turtle system defined in this technique captured the tops and bottoms very nicely.
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16.0015.5015.0014.5014.0013.5013.0012.5012.0011.5011.0010.5010.009.509.008.508.007.507.006.506.005.505.004.504.003.503.002.502.00
1.591.501.24
Volume0.800.600.400.20
FIGURE 7.7
Federal fund rate, weekly chart from June 1987 to April 2003.
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TECHNIQUE 8
The QQQCrash System
he QQQs are my asset of choice. Representing the Nasdaq 100 index,the QQQs contain many of the stocks that tend to be valued more onfuture growth potential than on consistent earnings over the prior 20years. This method of valuation is what creates the volatility in this index—since nobody really knows how to value its components. When the QQQsmove quickly down, a buying opportunity presents itself.The crash is a close in the QQQs that is 1.5 standard deviations downfrom its 10-day moving average (use Bollinger bands [see Technique 5] withthe 10-day moving average and 1.5 standard deviations to calculate). Usingthe 10-day average guarantees that the move will be sharp. Using 1.5 standard deviations guarantees that the move is larger than 90 percent of themoves the QQQ has made in its 4-year history.Here are the rules for using the QQQ crash system:
T
• Buy the morning after the crash. The dust has settled, the panic is over.• Sell on the earlier of a close higher than the entry price of the position,or after 20 days (one month).
EXAMPLESQQQ, 1/22/03On January 21, 2003, the QQQs closed below 1.5 standard deviations off ofthe 10-day moving average (the thick line in Figure 8.1). Buying at the open97
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27.4027.2027.0026.8026.6026.4026.2026.0025.8025.6025.4025.2025.0024.8024.60
24.4924.4024.20
24.0524.00
Volume
80.00M60.00M40.00M20.00M
FIGURE 8.1
QQQ on January 22, 2003.
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on January 22 at 24.96 and holding for the first up close has the system selling on January 23 at 25.51, for a 2.20 percent profit.
QQQ, 9/18/01On September 17, 2001, of course, the market tanked, and the QQQs closedbelow the lower Bollinger band. Holding until the first day the position isprofitable (or for 20 days if it is never profitable) has the system holdinguntil October 5, where the system sells at 31.76 for a 0.75 percent profit.Even though the pressure is on that whole week, and the QQQs keepgoing down, the snapback is fierce once it happens.See the results in Table 8.1. Not bad—40 out of 40 successful trades.Whenever I see something like that I have to question the validity of thesystem. Did the system designer “curve fit,” that is, fit the variables tothe problem so as to generate a perfect success rate? The three variablesin this equation are the size of the moving average (10), the number ofstandard deviations (1.5), and the length of time I hold the trade if it is notprofitable (20).
TABLE 8.1 Results of Basic QQQ CrashAll Trades
All TradesAverage Profit/Loss %Average Bars HeldWinning TradesAverage Profit %Average Bars HeldMaximum Consecutive Winning TradesLosing TradesAverage Bars HeldMaximum Consecutive Losing Trades
402.55%1.8540 (100.00%)2.55%1.85400 (0.00%)00
Vary the Moving AverageWhen I used the 20-day moving average, the results were 39 out of 41 successful trades. When I used the 200-day moving average, the results were 30out of 33 successful trades. A few more variations and the results alwaysseemed to be successful at the 90 percent level or above. So although theresults were different than the perfect success record of the 10-day movingaverage, they were not remarkably different.
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35.00
34.7034.5034.0033.5033.0032.5032.0031.5031.0030.5030.0029.5029.0028.5028.0027.5027.44
Volume
120M100M80.00M60.00M40.00M20.00M
FIGURE 8.2
QQQ on September 18, 2001.
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Vary the Standard DeviationsIf I used 1 standard deviation instead of 1.5, the results were 71 successfultrades out of 74. Note that 1 standard deviation only implies that the movein the QQQs is larger than approximately 66 percent of its historical movesoff of its 10-day moving average. Ninety-five percent is more standard andI will stick with it.
Vary the Holding PeriodWhen I varied the holding period lower, the results were again similar. A 10day holding period resulted in 39 out of 40 successful trades. A 5-day holding period resulted in 37 out of 40 successful trades. So the basic ideaholds—when the QQQs suffer a sharp and significant loss, buying is almostalways successful.
SIMULATION OF THE BASIC QQQ CRASH SYSTEMLooking at the results a bit further, assume a 100 percent of equity for eachof the 40 trades as shown in Table 8.1. The annual returns are shown inTable 8.2 and Figure 8.3.I like to look at the maximum adverse excursion for each trade, that is,how much a trade has been unprofitable (i.e., the size of the drawndown)before returning to profitability. The results are summarized in Figure 8.4(see page 103). The figure shows that out of the 40 profitable trades, 1 wentas far as 12 percent down before returning to profitability, with the bulk ofthe trades not going more than 4 percent down before becoming profitable.For the risk-averse investor, this suggests a possible stop at the 4 or 5 percent level.Analyzing the drawdowns a bit further as shown in Figure 8.5 (see page104), we can see that only during the post-9/11 period did the drawdownsget past 8 percent. Now look at Table 8.3, page 105.TABLE 8.2
Annual Returns
Period Starting
%Return
% MaximumDD
Exposure
Entries
Exits
3/10/19991/3/20001/2/20011/2/20021/2/2003
6.8360.4610.4632.652.13
–2.99–5.69–10.17–4.32–0.11
0.185.485.543.521.18
5128132
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Annual returns.
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FIGURE 8.3
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103
Maximum adverse excursion.
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FIGURE 8.4
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Underwater equity curve (drawdown).
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FIGURE 8.5
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TABLE 8.3
Trades Using QQQ
Symbol
Entry Date
EntryPrice
Exit Date
ExitPrice
%Change
BarsHeld
MAE %
QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ
4/20/19995/25/19997/23/19998/5/19999/24/19991/7/20001/31/20004/17/20005/11/20005/24/2000
49.3151.4757.6255.0659.6282.9485.8878.0282.7575
4/20/19995/28/19997/28/19998/5/19999/24/19991/7/20001/31/20004/17/20005/11/20005/24/2000
50.6552.0358.1255.9360.089089.6989.6284.6279.5
2.721.090.871.580.778.514.4414.872.266
1331111111
–0.93–4.92–3.19–2.38–1.68–0.53–2.77–0.03–2.27–3.67
QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ
7/28/20009/12/200010/4/200011/13/200011/24/200011/30/200012/20/20002/12/20013/13/20014/3/2001
92.1993.583.570.9568.3761.7557.3156.2542.7837.2
8/7/20009/20/200010/4/200011/13/200011/24/200011/30/200012/22/20002/12/20013/13/20014/5/2001
92.2594.6285.6271.8670.4462.9860.557.0844.4537.31
0.071.22.541.283.031.995.571.483.90.3
6611112112
–9.63–5.88–1.65–3.78–0.29–2.02–5.34–1.12–1.52–9.68
QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ
6/15/20017/11/20018/22/20019/18/200112/21/20011/17/20021/23/20022/6/20022/22/20023/21/2002
41.840.4237.4531.5339.4239.4837.6536.8433.6136.15
6/15/20017/11/20018/22/200110/5/200112/21/20011/17/20021/23/20022/13/20022/22/20023/21/2002
42.64137.6931.7639.4839.6538.4336.9533.6537.02
1.911.430.640.730.150.432.070.30.122.41
11113111511
–1.24–1.36–2.43–13.73–0.68–1.19–0.8–5.08–1.55–0.69
QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ
3/26/20024/29/20025/7/20026/4/20027/24/20029/20/200212/6/200212/10/20021/22/20034/1/2003
35.4231.2429.2728.7521.8121.8325.8125.4224.9625.44
3/26/20024/30/20025/8/20026/4/20027/24/20029/25/200212/6/200212/10/20021/23/20034/1/2003
35.8931.7331.7729.3623.6221.8726.4725.625.5125.45
1.331.578.542.128.30.182.560.712.20.04
1111131111
–0.06–2.18–2.9–0.49–0.78–5.41–0.27–0.28–0.36–0.75
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THE SYSTEM APPLIED TO STOCKSIn Technique 5 on Bollinger bands we can see how a similar system, whenused on stocks, results in a high probability of successful profitable trades.However, in this section we see what happens when the QQQs hit theirlower Bollinger band, but we buy the components of the QQQs instead ofthe QQQs themselves.
QQQ-Crash-Stocks #1• Buy stock X the morning after QQQ falls 1.5 standard deviations belowits 10-day moving average.• Sell at the earlier of stock X closing above the entry price or after 20days go by.Example: AMZN, 9/18/01 We saw in the previous section what happened when QQQ fell below 1.5 standard deviations off its 10-day movingaverage on September 17, 2001. The trade, while profitable several dayslater, was not a huge success. However, if you bought the higher beta components of the QQQ on September 18 rather than the QQQs themselves, theresults would have been better (see Figure 8.6). For instance, buying AMZNon the morning of September 18 at 7.59, you would have suffered a decentsize drawdown, but 17 days later you would have been able to sell for a 3.4percent profit at 7.85 on October 11.Example: BRCM, 1/22/03: Similar to the AMZN example, buyingBRCM on January 22, 2003, at 17.05 instead of buying QQQ would have resulted in a 3.28 percent profit by selling at 17.61 the next day, January 23(see Figure 8.7, page 109).The results shown in Table 8.4 (see page 109) do not take into accountslippage, which could be huge depending on the liquidity of the stock. Butthe general principle is confirmed—when the market falls fast, buy highbeta stocks.
QQQ-Crash-Stocks #2Using 1 standard deviation instead of 1.5 standard deviations, on stocks, weget the following system and results:• Buy stock X the morning after QQQ falls 1 standard deviation below its10-day moving average.
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107
8.608.408.208.00
7.85
7.807.607.407.207.006.806.606.406.206.005.805.60
Volume
12.00M10.00M8.00M6.00M4.00M2.00M
FIGURE 8.6
AMZN on September 18, 2001.
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20.2520.0019.7519.5019.2519.0018.7518.5018.2518.0017.7517.5017.2517.0016.7516.5016.2516.0015.7515.5015.2515.0014.7514.5014.2514.0013.75
13.6113.5013.2513.0012.7512.5025.00M
Volume
20.00M15.00M10.00M5.00M
FIGURE 8.7
BRCM on January 21, 2003.
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TABLE 8.4
QQQ Crash-Stocks #1All Trades
All TradesAverage Profit/Loss %Average Bars Held
3,9052.77%3.04
Winning TradesAverage Profit %Average Bars HeldMaximum Consecutive Winning Trades
3716 (95.16%)3.70%2.13123
Losing TradesAverage Loss %Average Bars HeldMaximum Consecutive Losing Trades
189 (4.84%)–15.48%20.853
• Sell at the earlier of stock X closing above the entry price or after 20days go by.See Table 8.5 for the results of using 1 standard deviation instead of 1.5.
TABLE 8.5
QQQ Crash-Stocks #2All Trades
All TradesAverage Profit/Loss %Average Bars Held
6,9841.70%3.56
Winning TradesAverage Profit %Average Bars HeldMaximum Consecutive Winning Trades
6459 (92.48%)3.25%2.14169
Losing TradesAverage Loss %Average Bars HeldMaximum Consecutive Losing Trades
525 (7.52%)–17.47%204
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TECHNIQUE 9
The Relative FedModel (and OtherFun Things YouCan Do with Yields)
he smart investor pays close attention to what is happening in thebond arena, and in this technique we look at three examples that explain why. First, the idea that bonds compete with stocks for investment value gives rise to variations on the Fed Model, such as my ownproprietary Fed Model called the “Relative Fed Model.” Second, fast movesin yields often suggest a massive allocation trade is taking place amonghuge funds, and this allocation may suggest a possible trade in stocks. And,finally, bond investors know what they are talking about at cocktail partiesand equity investors are often clueless. So pay attention!The Fed Model is commonly used to describe a valuation techniquewhereby you look at Treasury note yields and earnings yields of the S&P 500and see which one is lower. If earnings yields are lower, then the market isovervalued (i.e., you can make more money investing in T-notes rather thanbuying the entire S&P 500 for cash and living off the earnings). If earningsyields are higher, then stocks are undervalued. There are many variations onthis model. For instance, should you use trailing earnings or forward earnings projections? Forward earnings are really more important; how many ofus know the future, let alone the analysts making these projections?You might wonder, why is this model relevant at all? Stocks are notbonds. Bonds have yields that remain static once purchased (for the mostpart). In contrast, throughout U.S. history, earnings yields have grown overall.The Fed model is useful to us only if it can be used somehow to developa trading vehicle. As it is, it cannot be used that way. If you look at trailing12-month earnings yields of the S&P 500 vis-à-vis the 10-year Treasury yield,then the number of occurrences in which the 12-month trailing earningsyield is below the 10-year is so infrequent that it is also impossible to test.
T
111
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Clearly though, there is a relationship between these two yields. Ratherthan looking at whether one yield is less than the other, look at the spreadbetween the yields. That spread is more a function of sentiment about thefuture of the economy than anything else, and it does seem to have somebearing on timing the market.I plotted the earnings yield (trailing 12-month core earnings) of the S&P500 back to 1982 and did the same for the 10-year T-note. Then I divided thebond yield by the earnings yield and bought the stock market wheneverthe ratio hit 1.5 standard deviations below its 10-day moving average. Giventhat the yields are correlated, I am simply buying the market when bondyields tank faster than earnings yields (or earnings yields go up faster thanbond yields), regardless of whether one is above the other. I am then selling the market when the ratio gets back to its 10-day moving average.In summary, 14 trades were made and 10 were profitable (71 percentsuccess rate). The average gain per trade was 11 percent. The current opentrade is down 2.1 percent. To make this examination more thorough, Iwould