il stock system). The best year wasin 2005 when the system made more than $480 000 and the worst year was in 2001 when it lost$19 000. The distribution presented above shows significant positive skewness. This suggeststhat most of the profits were made during the later part of the test during the recent run in crudeoil prices.
Before proceeding with the test design the correlation matrix between 10-day yieldsof the Amex Oil Index (XOI) and related commodities and stock indices (depicted inTable 15.1) was calculated for the preceding 15-year period. Because of high crosscorrelation between stock indices, only one stock index should be included in themodel. It is reasonable to include a broad stock index like the S&P 500 since, asthe name suggests, oil stocks are stocks and not commodities, and are inevitablyinfluenced by the general direction of the market and the economy as well as theprice of oil itself. In fact the XOI correlated better with the S&P 500 (r = 0.47) thanwith crude oil futures (r = 0.41 in Table 15.1).My final selection of related markets included NYMEX crude oil futures and theS&P 500. High correlation with the market to be predicted is not the only criterionin choosing the best model but low cross correlation between predictor variables is
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Figure 15.3 The percent drawdown is on the vertical axis and time on the horizontal axis.The drawdown was quite low and it only exceeded 4 % of equity three times during the 12-yearperiod: Once in September of 1998, once in October 2001, and the last time in September of2002.
also important. In this case there was no correlation between crude oil and the S&P500 (r = −0.02).This system was based on the successful double linear regression (LRS) slopeindicator used in Chapter 11 to trade gold. Preliminary testing showed that thissystem produced more profits with less drawdown. The linear regression lengthwas optimized and the most robust parameter set was 35 days for both intermarketsecurities and 10 days for the stocks. The following additional entry conditionsimproved on the model’s performance:• The 50-day moving average of the relative strength between the stock and theXOI was required to be above the corresponding 200-day moving average. Thiscondition was introduced in order to eliminate weak stocks with temporary stockspecific problems. This additional condition not only improved the profit factorbut also helped reduce drawdown.
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243
Figure 15.6 Graphical representation of yearly profits (gold stock system). The most profitable year was 2006 when the system made nearly $80 000 and the worst in 2000 when itbarely broke even.
involved (during the latest phase of the test) holding large positions in each stockI decided to include a second simulation with a fixed dollar amount of $10 000 pertrade.The results of the first method are depicted in the second column of Table 15.2.I also included a graphical presentation of the equity line, the yearly profits, thedrawdown and the percentage of profitable trades in Figures 15.1, 15.2, 15.3 and15.4 respectively. The system outperformed the buy and hold method by a widemargin and producing more than $1.6 million of net profits in the 12-year period ofthe test.The average drawdown (Fig. 15.3) was quite low and it only exceeded 4 % ofequity three times during the 12-year period: Once in September 1998, when the XOIdropped more than 17 % and twice during the devastating bear market of 2001–2002.It would be difficult to realize the same amount of total profits by fixing the capitalper trade to $10 000. Although most profitability metrics (profit factor, win/loss ratio,etc.) were not affected, the total net profit dropped rather dramatically but so did the
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Figure 15.5 Closed trade equity (gold stock system).
• A money flow to sell when the VFI crossed below its 100-day moving average andthe Finite Volume Element (FVE) indicator crossed below the zero line was added.This last condition helped to reduce maximum drawdown from 7.1 % to 5.4 % ofequity.
15.3
EVALUATION OF THE OIL STOCK MODEL
When specifying the test parameters in a portfolio test of a basket of stocks, the firstthing that you have to decide is the capital per trade. Different amounts or differentmethods (fixed or compounding) can produce very different results. I used a fixedpercentage (10 % of equity) capital per trade in the first simulation but as this method
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Figure 15.7 The percent drawdown is on the vertical axis and time is on the horizontal axis.The drawdown was quite low and it only exceeded 3.5 % of equity two times during the 9-yearperiod: Once in December of 2000, and once in June 2005.
maximum drawdown. This method is not comparable with the buy and hold methodas only a small percentage of equity was invested at any time.
15.4
TRADING GOLD STOCKS
This system exploited the strong correlation between gold mining stocks and goldbullion prices.A problem in testing this system is that only a few gold mining stocks tradein the major US exchanges. After excluding all industrial metal and silver miners,the portfolio on test consisted of only 55 gold producers. Some of these stocks
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241
Figure 15.4 Graphical representation of percent winning trades (top) and percent losingtrades (bottom). After some initial volatility the winning percentage settled around 72 % andthe losing percentage around 28 %. The initial volatility can be attributed to the reduced numberof oil stocks trading in 1995–1996.
• Pearson’s correlation was used as a filter to exclude stocks that temporarily decoupled from the group. The requirement was that Pearson’s correlation between thestock and XOI should be 0.5 or more.• A money flow condition was added to the entry conditions, which helped to reducedrawdown by excluding stocks in a downtrend. The Volume Flow Indicator (VFI),first introduced in my June 2004 article in Technical Analysis of STOCKS &COMMODITIES was used.The following conditions improved on the timeliness of exits:• Divergence based exit signals were triggered only when the 15-day stochastic wasbelow its five-day moving average
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245
started trading fairly recently so a shorter testing period (from 11 September 1998 to11 September 2007) than the one used in the oil stock system was used.For the same reasons mentioned in the previous test, only long positions wereconsidered.Table 15.3 Profit/loss report of the intermarket gold stock system. In the first column is theprofit of buying and holding all XAU components for the entire test duration. In the secondcolumn are profit statistics using 10 % of capital per trade and in the last column using a fixed$10 000 capital per trade. Comparison with the buy and hold is only meaningful using thefirst method, as the second method started with only one tenth the capital used by thebuy-and-hold method.
SystemIntermarket securityNumber of stocksInitial equityCapital/tradeTotal net profitProfit/loss %Annual percent gain/lossTotal number of tradesWinning tradesLosing tradesPercent profitableAvg. winning tradeAvg. losing tradeRatio avg. win/avg. lossProfitability coefficientProfit factorDrawdown %Date of max drawdownRisk/rewardStart date/testEnd dateTest period (years)Trades/year
Buy & hold
16$100 000$100 000$122 267122 %14 %110100 %$122 267$0
−40.4 %11/17/0041.9 %9/11/989/10/079.00.1
Gold stocks(% of equity)
Gold stocks(equal dollar)
Gold (spot)55$100 00010 %$243 105243 %27 %2221517168.0 %$2107−$10581.993.464.24−3.7 %6/7/054.2 %9/11/989/10/079.024.7
Gold (spot)55$100 000$10 000$123 770124 %14 %2201477366.8 %$1117−$5542.023.374.06−3.1 %11/30/993.0 %9/11/989/10/079.024.4
50020070780
50020070780
System ParametersCorrelation daysIMO – daysIMO – buyIMO – sellTime exit – bars
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Intermarket Trading Strategies
The system was based on the regression divergence model discussed in Chapter 11as it performed slightly better than the LRS method on preliminary testing. Thedouble LRS method, used for trading oil stocks, could not be used as there was onlya single intermarket security (gold) for calculating the divergence.There were no variables to optimize, except for the momentum oscillator entrythreshold. All additional entry conditions, used for the oil stock test above, weretested and included in the current system as they improved on the test performance.Comparison test results for percent of equity and equal dollar capital per trade areshown in Table 15.3, the equity line, the yearly profits and the drawdown in Figures15.5, 15.6 and 15.7 respectively.Based on profit factor, profitability, and win/loss ratio criteria this system performedslightly better than the oil stock system. However, the total net profit was considerablyless, mainly because the portfolio of the available gold stocks on test contained only55 stocks, a quarter of the stocks contained in the corresponding oil stock portfolio.One way to increase total dollar profit is to enlarge the portfolio by adding Australian,British and Canadian gold miners. This should not be a problem as it is now possible,with certain online brokers like interactive Brokers, Internaxx, Etrade or SaxoBank,to trade international stocks using a single account.The drawdown was considerably less than the oil stock system, exceeding 3 % ofequity only three times during the nine-year test: Once in December 2000 (−3.4 % ofequity), during the end of gold’s precipitous long-term decline, once in 2005 (−3.7 %of equity) because of a short-term correction and once in 2006 (−3.3 % of equity).
15.5
CONCLUSION
While the scope of this book is to demonstrate the efficacy of intermarket analysiswhen applied to market averages, the systems presented above demonstrate that it canbe applied in a similar manner and just as profitably to stock trading. The examplespresented in this chapter are by no means exhaustive of intermarket stock tradingsystems and the basic method can be used for trading stocks belonging in variousother sectors, as long as the sector index data are readily available.
16A Relative Strength AssetAllocation Trading System
The market can remain irrational longer than you can remain solvent.–John Maynard KeynesIn the first chapter I presented a static asset allocation investment strategy whereinternational stocks, commodities, bonds and forex were used to decrease the overallvolatility of a properly structured portfolio while maintaining return characteristics.Diversification obviously reduces risk, and asset allocation analysis is importantbecause it enables us to assign a value to the expected risk.The main weakness of the static model, however, is that it relies on historical returnsto calculate the risk-adjusted return of the portfolio asset allocation. Rather than relyon historical returns, a more comprehensive and dynamic approach is needed whenmaking trading decisions. This criticism is generally valid as historical returns arevery unlikely to be repeated in the future.Take, for example, the performance of the stock market from 2000 to 2007. Aninvestor who bought an S&P index fund in 2000 hasn’t made any money, whereasanother investor who bought the same index fund in 2003 has had a 70 % returnon his investment. In addition, historical returns are not the only variables that canchange over time because, as I have demonstrated in Chapter 5, correlations betweenasset classes can also change. For example, the correlation between stocks and bondshas been highly unstable during the last four years, with the correlation coefficientalternating from positive to negative.Thus developing timing strategies to dynamically allocate portfolio assets amonga choice of investments can greatly enhance portfolio performance with no additionalrisk. Even during the strongest bull markets, not every investment rises at the samerate or at the same time. In addition, only a few investments, such as money market,
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Intermarket Trading Strategies
precious metals, bonds, or certain commodities are less likely to lose money in adown market.The goal of this study was to compare the performance of the static buy andhold with a dynamic asset allocation method. The search was on for the timingtool that would trigger the move to the strongest asset classes or sectors that wouldsystematically keep the assets moving into the right instrument in approximately theright time frame. The positive benefits of this investment approach are obvious. Aminor problem, however, is that it crosses regulatory boundaries as multiple accountsare needed to trade domestic and international stocks, futures and forex. Fortunately,it is now possible to do this with certain international brokers like Interactive Brokers,Saxobank or Internaxx.The current method is based on the relative strength between an investment and abenchmark which, for the purposes of this study, was the S&P 500.
16.1
TESTING PROCEDURE
The test period covered was from 16 July 1999 to 27 November 2007, which covereda complete market cycle that included a bull and a bear market.The system was tested on two different portfolios. The securities tested in the firstsimulation are depicted in Table 16.1 and include a wide variety of investments fromall asset classes. They include one commodity index future (the GSCI), 13 international stock indices, four interest rate futures, four currencies and 15 commodityfutures: four metal, two energy and nine agricultural contracts. The available datafor international index futures did not cover the entire test period so I had to use datafor the indices themselves. Of course the indices themselves cannot be traded butwere, for testing purposes, a good enough approximation. The proliferation of exchange traded funds (ETF) gives traders a chance to profit from moves in commoditieswithout the risk of leveraged futures contracts.In the second simulation, for the benefit of investors who prefer to trade onlystocks, I tested a wide variety of ETFs which included six commodity funds, 44international country funds, 63 US sector funds, six bond funds, and four currencyETFs (see Table 16.2).The test results were compared with returns of buying and holding a static diversified portfolio consisting of the SPY ETF (40 %), an emerging market ETF (5 %),a European equity ETF (5 %), bonds (10 %), the commodities included in the CRBIndex (30 %) and the euro (10 %). The diversified portfolio composition and returnsare depicted in Table 16.3. Although returns would have been better had I used theGoldman Sachs Commodity Index (GSCI) instead of the CRB Index, I preferred touse the latter as it was not so heavily weighted in energy (for the composition ofthe GSCI and CRB see also Chapter 4). In order to demonstrate the advantages ofdiversification, the results were also compared with buying and holding only the S&P500 for the entire test duration.
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Table 16.1 Futures, currencies, US sector and international indices included in the first testare depicted in the second column and the Reuters Datalink symbols in the last column.Reuters forex symbols are different from other vendors and usually they have the =suffix orthe =R suffix (indicates Reuters platform data). Reuters also uses the @: prefix to indicatefutures contracts and the c1 suffix to indicate continuous contracts which are created bymerging different current month contracts on expiration.A/A
Security Name
Symbol (Reuters)
12345678910111213141516171819202122232425262728293031323334
Australia All OrdinariesBrazil Bovespa IndexBSE SENSEX (India)CAC 40 IndexDow Jones Euro Stoxx 50Germany DAXGreece General ShareHong Kong Hang SengKorea KOSPIMexico IPCMoscow Times IndexNikkei 225 IndexUnited Kingdom FTSE 100Amex Pharmateutical IndexUtility IndexAustralian DollarBritish PoundCanadian Dollar-US DollarJapanese Yen-US DollarSwiss FrancEuroGold (cash)GOLDMAN SACHS COMMODITY INDEXCOPPER HG ELECTRONICALUMINUM COMEX COMPOSITELIGHT CRUDE COMPOSITENATURAL GAS COMPOSITECOTTON NO 2ORANGE JUICESUGAR 11SOYBEANS COMPOSITESOYBEAN OIL E-CBOTWHEAT COMPOSITECORN E-CBOT
.AORD.BVSP.BSESN.FCHI.STOXX50E.GDAXI.ATG.HSI.KS11.MXX.MTMS.N225.FTSE.DRG.UTYAUD=GBP=CADUSD=RJPYUSD=RRD-CHF=RD-EUR=XAU=@:GIc1@:1HGc1@:ALc1@:CLc1@:NGc1@:CTc1@:OJc1@:SBc1@:Sc1@:ZLc1@:Wc1@:ZCc1(Continued)
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Table 16.1 (Continued)A/A
Security Name
Symbol (Reuters)
3536373839404142
COFFEE CLIVE CATTLE100 OZ GOLD COMPOSITEMINI SILVER E-CBOT3MTH EURO DOLLAR COMPOSITEUS 10YR T-NOTES PIT COMPOSITE10YR BOND5YR T-NOTE E-CBOT
@:KCc1@:LCc1@:GCc1@:ZZc1@:EDc1@:TYc1@:CGBc1@:ZFc1
The initial account size was $500 000 and an equal dollar amount of $30 000 pertrade was specified for both simulations. Although investors typically trade futureson low margins, no margin leverage was used in the first trading simulation model asthis would have distorted the equal allocation percentages. In actual trading the useof margin would increase the percentage return on account and percent drawdownbut it wouldn’t affect the other performance metrics.All orders were executed at the opening price on the day following a system signaland execution costs were assumed to be $10. For simplicity’s sake dividends werenot included when in the market. It was assumed that the cumulative profits wereheld in an interest bearing account when out of the market and all open positionswere closed on the last bar.To develop and optimize the system rules and parameters, I used a portfolioof securities and similar indices not included in the samples on test, identifyingparameter combinations and conditions that persisted in producing profits for themajority of the securities on test, even though they were not necessarily the optimum.The initial system consisted of a simple moving average crossover of the relativestrength indicator, buy signals were generated when the relative strength crossedover its trigger and sell signals when it crossed under. This system, however, despiteoptimizing for both moving averages – the relative strength smoothing average andthe trigger – produced less than satisfactory results. On plotting the signals on thechart it was evident that the system failed for the following reasons:• sometimes in bear markets securities were selected just because they were strongerthan the benchmark (the S&P 500) even though they were also in a downtrend.Most of these trades inevitably resulted in losses;• whipsaw around the moving average;• late signals which were triggered at the top or during highly overbought conditions.To deal with these problems I decided to add the following additional conditions:• The 25-day exponential moving average should be pointing up, to ensure that thesecurity was in an uptrend.
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251
• To avoid the problem of whipsawing the relative strength should also be pointingup.• The stochastic indicator was used to filter out buy signals in overextended markets.• The correlation coefficient was used to filter out securities that were highly correlated with the S&P 500 during bear markets as they did, sooner or later, follow theleader down.
Table 16.2 The exchange traded funds (ETF) included in the second test are depicted in thesecond column and their symbols in the last column.A/A
Security Name
Symbol
123456789101112131415161718192021222324252627282930313233
Mexico Equity & IncomeChile Fund Inc.Morgan Stan Dw Eastern EuropeTempleton Russia & East EuropeSpdr Index Dj Stoxx 50 FdMalaysia Fund Inc.Aberdeen Australia Equity FdWestern Asset Emerging marketsEmerging Market Telecom FdSingapore Fd Inc.Taiwan Greater China FundIndonesia FundThe Central Europe and RussiaTurkish Invest FundTempleton Emerging Markets FundiShares MSCI ItalyThai Fund Inc.China Fund Inc.Japan Smaller CapitalizationiShares MSCI SwitzerlandTempleton DragoniShares MSCI South AfricaiShares MSCI FranceiShares MSCI SwedenBLDRS Emerging Markets 50 ADR FdIndia FundAsia Pacific FundiShares S&P Europe 350iShares MSCI GermanyiShares MSCI SpainiShares S&P Latin America 40iShares MSCI United KingdomiShares MSCI Australia
MXECHRNETRFFEUMAYIAFEDFETFSGFTFCIFCEETKFEMFEWITTFCHNJOFEWLTDFEZAEWQEWDADREIFNAPBIEVEWGEWPILFEWUEWA(Continued)
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Table 16.2 (Continued)A/A
Security Name
Symbol
34353637383940414243444546474849505152535455565758596061626364656667686970717273747576
iShares MSCI South koreaiShares MSCI MalaysiaiShares FTSE/Xinhua China 25 IndexiShares MSCI EAFEiShares MSCI Hong KongiShares MSCI TaiwaniShares MSCI BraziliShares MSCI JapaniShares MSCI SingaporeiShares MSCI MexicoiShares MSCI CananadaiShares DJ Consumer Services FundiShares DJ Financial services FundiShares DJ Consumer Goods SectoriShares S&P SmalCap 600iShares S&P SmalCap 600 ValueiShares S&P SmalCap 600 GrowthiShares S&P MidCap 400 GrowthiShares S&P 500/Barra ValueiShares S&P 500/Barra GrowthiShares Russell 1000 ValueiShares Russell 1000 GrowthiShares Russell 2000iShares DJ Industrial SectoriShares DJ Healthcare SectoriShares DJ US Energy FundiShares DJ US Basic MaterialsiShares DJ Technology SectoriShares DJ TelecommunicationsiShares DJ Trasportation AverageiShares Nasdaq BiotechnologyiShares DJ Dividend Index FundiShares DJ US Real EstateiShares DJ Utilities Index FundiShares S&P Global UtilitiesHOLDRS UtilitiesHOLDRS Telecom ETFHOLDRS Biotech ETFHOLDRS SoftwareHOLDRS PharmaceuticalHOLDRS Regional Bank ETFHOLDRS Retail ETFHOLDRS Oil Service ETF
EWYEWMFXIEFAEWHEWTEWZEWJEWSEWWEWCIYCIYGIYKIJRIJSIJTIJKIVEIVWIWDIWFIWMIYJIYHIYEIYMIYWIYZIYTIBBDVYIYRIDUJXIUTHTTHBBHSWHPPHRKHRTHOIH
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Table 16.2 (Continued)A/A
Security Name
Symbol
7778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118
HOLDRS SemiconductorHOLDRS WirelessHOLDRS B2B Internet ETFHOLDRS Internet Architecture ETFHOLDRS Internet Infrastructure ETFHOLDRS Internet ETFHOLDRS Broadband ETFiPath S&P Goldman sachs Crude OilPowerShares DB Commodity ETFiShares G. Sachs Natural resourcesiShares Silver Trust ETFSPDR Gold SharesPowerShares DB Agriculture FundProShares UltraShort Real estateProShares UltraShort FinancialsProShares UltraShort DowProShares UltraShort Russell 2000ProShares UltraShort S&P500SPDRs Select Sector Health careSPDRs Select r Consumer StapleSPDRs Select DiscretionarySPDRs Select Sector TechnologySPDR S&P Homebuilders ETFSPDRs Select Sector UtilitiesSPDRs Select Sector IndustrialsSPDRs Select Sector Materials ETFSPDRs Select Sector EnergySPDRs Select Sector FinancialsSPDRs S&P 500 Trust ETFPowerShares QQQ TrustPowerShares Dynamic Retail PortfolioDIAMONDS Trust ETFFlaherty & Crumrine Preferred IncMorgan Stan Emerging market DebtDreyfus Stra Mun Bond FdiShares Lehman 7–10Year T-BondiShares Lehman 20+ Year T-BondAberdeen Asia-Pacific Income FdPowerShares US Dollar Bearish FdRydex CurrencyShares Australian $Rydex CurrencyShares EuroRydex CurrencyShares Yen
SMHWMHBHHIAHIIHHHHBDHOILDBCIGESLVGLDDBASRSSKFDXDTWMSDSXLVXLPXLYXLKXHBXLUXLIXLBXLEXLFSPYQQQQPMRDIAPFOMSDDSMIEFTLTFAXUDNFXAFXEFXY
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Table 16.3 Composition and performance of the static diversified portfolio from 16 July1999 to 29 November 2007. Its performance was compared with the dynamic asset allocationsystem in Table 16.4.Asset
Symbol
Allocation
Profit/Loss
SPDRs S&P ETFAsia Pacific FundS&P Europe 350BondsEuroCommodities
SPYAPBIEV
40 %5%5%10 %10 %30 %
$(5100)$49 547$13 753$15 826$32 413$83 095
100 %
$189 534
EURCRB
Total
Thus the final system was defined as follows.Buy ConditionsThe relative strength indicator was smoothed with a 150-day moving average. Theperiod was rather long, but nevertheless it was the most consistent in producingprofitable signals for the majority of the securities on test.A buy signal was generated if the relative strength crossed above its 120-daymoving average and at the same time the following conditions were also true:• The 2-day rate of change of the 25-day exponential moving average was positive.• The 20-day linear regression slope of the relative strength was positive.• The correlation coefficient between the security and the S&P was less than 0.5.• The 100-day stochastic indicator was less than 80.• The 25-day moving average of the volume flow indicator was positive.This last money flow condition was introduced to ensure that the money was flowingin and not out of the security. This indicator was tested in my July 2004 article“Volume Flow Performance” (see Bibliography) with more than satisfactory results.There was a problem, however, with this indicator, as Reuters provided no volumedata for the indices. Volume data for the futures contracts were also unreliable asthey were split in different calendar or size (mini or full size) contracts. The volumeflow indicator was therefore used only in the second (ETF) test. In the first test thiswas substituted by a different condition that did not require any volume data. Theadditional condition alleviated the whipsaw problem even further by executing longsignals only when the relative strength made a new 250-day high.Sell ConditionsA sell signal was generated if the relative strength crossed below its 90-day movingaverage and at the same time, in order to exclude false whipsaw signals, its 3-day rate
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of change was less than −0.5 %. An additional money flow condition was added, butonly in the second test, where volume data were available.16.2
DISCUSSION OF RESULTS
The test results of the relative strength system are summarized in Table 16.4, theequity line in Figure 16.3, and the monthly profits of the ETF system is depicted inTable 16.4 Test results of the relative strength system for the period from 28 August 2000to 27 November 2007. Column 2 is the return of buying and holding the SPY ETF, in Column3 is the performance of buying and holding a strategically diversified static portfolio, inColumn 4 is the test results of the relative strength test on a futures and international indexselection and in column 5 test results on an ETF portfolio. All tests were profitable andoutperformed both benchmarks by a wide margin.Test
Buy & hold
Buy & hold
Test I
Test II
SPY
Static Portfolio
Futures
ETF
S&P 500
S&P 500
Capital/tradeTotal capitalTotal profit
$500 000$500 000($15 074)
$500 000$500 000$189 534
$30 000$500