340.21 0.30 –212,57457 42 75.4314178,795 –38,904 88 28TelecomVZ–847,372 –0.07 –0.44 –932,67257 32 20.17–789 47,839 –45,356 87 35IndicesSPX112,3530.03 0.06 –401,67262 34 2246.64177,308 –37,345 85 29NDX 1,019,045 0.15 0.45 –403,30069 39 1560.51995,459 –37,758 72 228141,787 –38,903 95 34RUT 2,027,382 0.24 0.67 –234,83750 44 5289.71Average 399,1540.06 0.15 –543,19656 36 320.2111799,456 –40,176 102 3218,000,00016,000,00014,000,00012,000,00010,000,0008,000,0006,000,0004,000,0002,000,0000–2,000,000
0.510.880.77
© 2002 Lars Kestner – All Rights Reserved
FIGURE
8.33a
Saitta Support and Resistance Strategy Applied to Stocks.
AverageNet Profit–845,317–780,982–152,204614,190352,7551,239,013101,649945,894–847,3721,052,927
8.33b
19901991
K-ratio0.520.14–0.35–0.22–0.400.84
AverageAverageMax DDSharpe Ratio–1,014,739–0.45–0.40–865,929–662,869–0.070.33–438,546–419,9930.17–465,6470.440.05–506,915–437,5470.38–932,672–0.44–346,6030.39
1992
1993
1994
Year
1995
Net Profit by Year
1996
K-ratio0.030.260.20–0.14–0.06–0.37
AverageAverageNum Trades % Win68281013354334940503745345332483757326039
Performance Breakdown by YearYearNet ProfitSharpe Ratio744,65719960.962,758,88919971.411,839,7461998–0.71302,8851999–0.49–262,3782000–1.11–2,113,63620013.22
AverageK-ratio–0.24–0.15–0.020.150.070.140.010.15–0.070.14
Saitta Support and Resistance Strategy Applied to Stocks.
FIGURE
Breakdown Statistics (Stocks)
1997
Sharpe Ratio0.320.920.820.08–0.13–1.45
Avg. ProfitPer Contract–511–669–36973525964453312–7896
Alex Saitta’s support and resistance strategy20 day average of highs and lows to determine entry pointsEnter long when closes rises above last high, enter short when prices fall below last low1/1/1990– 12/31/2001Breakdown by Market Sector
Net Profit2,650,4294,088,293–936,929–578,383–1,375,5705,807,902
7,000,0006,000,0005,000,0004,000,0003,000,0002,000,0001,000,0000–1,000,000–2,000,000–3,000,000
Year199019911992199319941995
MarketSectorEnergyMaterialsIndustrialsDiscretionaryStaplesHealthcareFinancialsInfo. Tech.TelecomIndices
System Name:Parameters:Description:Run Dates:
Net Profit
2401998
2000
2001
© 2002 Lars Kestner – All Rights Reserved
1999
Average Avg. Bars Avg. BarsLossWinLoss–42,6879226–34,5946427–39,6649735–38,45010633–39,89510731–39,08712338–42,72810534–42,93911533–45,3568735–38,0028428Profitability WindowsNum. ofNumber of Profitable PercentWindows Windows ProfitableLength1448055.56%1 Month1427653.52%3 Months1398359.71%6 Months1337153.38%12 Months1277760.63%18 Months1217763.64%24 MonthsAverageWin64,23945,78369,92392,87284,718155,23598,401131,00247,839104,852
CHAPTER 8 New Ideas on Entries, Exits, and Filters
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the strategy. For example, suppose IBM makes a 40 day high and we buy stockthe next day at $100. At the time of entry, the lowest close of the past 20 daysis $90 and the average true range over the past 20 days is $2.50. In our traditional channel breakout where we buy at 40-day highs and exit longs at 20-daylows, our risk would be $10 (the difference between the entry and 20-day low).Instead of taking the $10 risk between our entry and exit points, we may chooseto exit longs if IBM falls more than twice the 20-day average true range belowour entry. Using this tighter “stop loss” strategy, we reduce our initial risk from$10 to $5.On paper, the idea of minimizing losses sounds great, but does it really addto the bottom line?If we research the channel breakout signals of INTC between 1990 and 2001,the average distance between entries of a 40-day channel breakout and exits at thetime of entry (dictated by the 20-day high or low) is 15 percent. Twice the averagetrue range at the time of each entry averages 7 percent over the life of the test. Thisconfirms that using the average true range exit will provide the strategy with aquicker exit should the trade become immediately unprofitable. Popular wisdomsuggests that cutting losses by using this tighter exit will help performance. Wewill test the channel breakout with and without the average true range exit to determine if performance actually improves.To determine the usefulness of the stop loss, I test two strategies. One strategy utilizes the 40-day/20-day channel breakout, where we enter on 40-day highsand lows and exit if an opposite 20-day extreme occurs. That is, if we’re long, wewill exit if today’s close is the lowest close of the past 20 days. The other strategy,in addition to exiting on an opposite 20-day extreme, exits longs if prices fallbelow two times the 20-day average true range from the entry price. Short positions are exited if prices rise more than two times the 20-day average true rangeabove the entry point. Which performs better?The performance numbers in Figure 8.34 speak for themselves. Coincidingwith popular belief, the strategy with a stop loss calculated from the entry pointyields a higher net profit, Sharpe ratio, and K-ratio than the strategy without the“risk minimizing” stop loss. For futures markets, the strategy utilizing the tightertwo average true range stop loss produces a net profit of $10,580,769, a Sharperatio of 0.66, and a K-ratio of 0.21, all greater than the standard channel breakout, which produces a net profit of $5,971,279, a Sharpe ratio of 0.41, and aK-ratio of 0.12.Performance tests on stocks also favor using the tighter true average truerange stop loss. The channel breakout using the stop loss produces a net profit of$9,486,543, a Sharpe ratio of 0.36, and a K-ratio of 0.15, all greater than the standard channel breakout, which produces a net profit of $3,511,542, a Sharpe ratioof 0.16, and a K-ratio of 0.06.
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Futures
Plain vanilla
With 2 ATR stop loss
Profit
$5,971,279
$10,580,769
Sharpe Ratio
0.41
0.66
K-ratio
0.12
0.21
Stocks
Plain vanilla
With 2 ATR stop loss
Profit
$3,511,542
$9,486,543
Sharpe Ratio
0.16
0.36
K-ratio
0.06
0.15
FIGURE
8.34
Profit, Sharpe Ratios, and K-ratios of Channel Breakout Strategy with Stop Loss. In fact, adding a tighterstop loss than our natural 20-day exit does improve performance.
PYRAMIDING VS. PROFIT TAKINGPyramiding and profit taking are very different concepts. Pyramiding involves addingto winning positions during the trade. For example, after buying IBM at $100, wecould pyramid the position by buying more shares if IBM rises to $105, and still moreif it reaches $110. Profit taking entails just the opposite. After buying IBM at $100,we would sell the stock at $105, regardless of whether our trading model is still flashing a long signal. Similar to the debate on the value of optimization, the argumentover whether to add to winning positions or take money off the table by taking profits leads to a heated discussion. We will look at the numbers behind the debate anddevelop our own conclusions about pyramiding versus profit taking.Using the 40-day entry/20-day exit channel breakout, we test three exit strategies:Strategy 1: Ordinary channel breakout. Enter long when today’s close is the highest of the past 40 closes. Enter short when today’s close is the lowest close ofthe past 40 closes. Exit long if today’s close is the lowest close of the past 20days. Exit short if today’s close is the highest close of the past 20 days.Strategy 2: In addition to the ordinary channel breakout entry and exit rules, wewill also exit if open profit is greater than three times the average truerange of the past 20 days. No new long entries are allowed unless five dayshave passed since the last 40-day high. No new short entries are allowedunless five days have passed since the last 40-day low.Strategy 3: In addition to the ordinary channel breakout entry and exit rules, we addanother unit long if today is a 40-day high and there have been no new 40-dayhighs over the five previous days. We add another unit short if today is a 40day low and there have been no new 40-day lows over the five previous days.Strategy 2 is an example of profit taking, while Strategy 3 pyramids extrarisk as profits accrue from a specific trade.
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Surprisingly, neither the profit taking (Strategy 2) nor the pyramiding versions (Strategy 3) of the channel breakout outperform a standard (plain vanilla)channel breakout (Strategy 1). In both futures markets and stocks (Figure 8.35),the standard channel breakout generates better reward-to-risk measure than eitherof the two versions. These results suggest that neither pyramiding as profits accruenor profit taking as trades generate profits is superior to following standard channel breakout rules.
NEW TREND FILTERSIn addition to the trend filters mentioned in earlier chapters, I will introduce threemore. We might only want to make trades if we can confirm the overall directionof the market’s trend. One possibility is to take long entries only if a 40-day highwas made more recently than a 40-day low. We might combine this filter with amoving average crossover for a dual trend trading system.Another new trend filter involves using recent high-to-low ranges. We maketrades only when recent price action is volatile enough to signal that importantinformation is entering the marketplace. This can be accomplished by filteringtrades and taking positions only when the high-to-low range of the past 10 days isgreater than the high-to-low range of the 10 days beginning 20 days ago.There is also room for another trend filter, one calculated differently than thepopular ADX and VHF filters. One method to determine if a market is trending isto gauge the overlap of today’s price action compared with previous action. Iftoday’s price range overlaps with a majority of the previous 20 days, then a market can be said to be trendless. Less overlap indicates that a trend is present.We compare today’s close to the high-to-low ranges over the past 100 days.If today’s close is contained in a prior day’s high-to-low range, we can considertoday not to be trending. As more of the past 20 days overlaps with today’s close,the more rangebound the market is. We calculate the percentage of days with over-
Futures
Plain vanilla
Pyramiding
Profit taking
Sharpe RatioK-ratio
0.410.12
0.160.06
0.240.1
Stocks
Plain vanilla
Pyramiding
Profit taking
Sharpe RatioK-ratio
0.160.06
0.150.08
–0.08–0.02
FIGURE
8.35
Sharpe Ratio and K-ratio of Pyramiding and Profit-Taking Versions of Channel Breakout Strategy. Ourplain vanilla channel breakout outperforms both the pyramiding and profit-taking versions.
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FIGURE
8.36
Channel Breakout with Trend Filter Applied to Nasdaq 100. Low values of our trend filter suggest that a major trend isunderway.
lap and plot this value as a trend statistic. Low values of the trend statistic indicatethere’s little overlap of price and the market is trending. High values of the trendstatistic indicate there’s much overlap of price and the market is congested.We can apply our new trend filter to channel breakout signals. Ideally, ourfilter will keep us on the sidelines when the market is flat and not trending, and inthe market when trends are developing. Long positions are established if today’sclose is the highest close of the past 40 days and the trend statistic is less than 0.15.Long positions are exited if today’s close is the lowest close of the past 20 days.Short positions are established if today’s close is the lowest close of the past 40days and the trend statistic is less than 0.15. Short positions are exited if today’sclose is the highest close of the past 20 days.Figure 8.36 details signals applied to the Nasdaq 100. In early November a40-day high is accompanied with a trend statistic below 0.15. This led to a profitable long entry.It appears that our trend filter aids performance on both futures and stocks(Figure 8.37). Our new channel breakout strategy improves net profit, Sharperatios, and K-ratios for both stock and futures data when compared to a standardchannel breakout strategy.
CHAPTER 8 New Ideas on Entries, Exits, and Filters
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Futures
Plain vanilla
with trend filter
Profit
$5,971,279
$7,434,945
Sharpe Ratio
0.41
0.52
K-ratio
0.12
0.18
Stocks
Plain vanilla
with trend filter
Profit
$3,511,542
$4,468,153
Sharpe Ratio
0.16
0.22
K-ratio
0.06
0.07
FIGURE
8.37
Profit, Sharpe Ratios, and K-ratios of Filtered and Nonfiltered Channel Breakout Strategies. Adding atrend filter appears to improve performance.
On futures, the trend filter channel breakout generates net profit of$7,434,945, a Sharpe ratio of 0.52, and a K-ratio of 0.18, compared to the standard (plain vanilla) channel breakout, which generates net profit of $5,971,279, aSharpe ratio of 0.41, and K-ratio of 0.12.On stocks, the trend filter channel breakout generates net profit of$4,468,153, a Sharpe ratio of 0.22, and a K-ratio of 0.07, compared to the standard channel breakout, which generates net profit of $3,511,542, a Sharpe ratio of0.16, and K-ratio of 0.06. These results suggest that adding a new and improvedtrend filter can indeed improve profitability of standard trend following strategies.
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CHAPTER
9
New Ideas of MarketsTrading Doesn’t End with Stocks and Futures
A
s investors, we think of markets as stocks, bonds, and real estate. While shorterterm traders might add futures and options to the list of tradable assets, this is usually where the list ends. But there’s an entire spectrum of other markets traded bybanks and hedge funds that many market participants may not be familiar with.While credit spreads, swap spreads, volatility swaps, and stock pairs may not bewell known to the average trader, these markets are a mainstay in the equity andfixed-income arbitrage world.Because the profitability of true arbitrage has declined over the years, hedgefunds and proprietary trading desks have begun to trade riskier strategies that ofteninvolve buying one security while selling another. Arbitrage trading in today’s marketplace involves taking risk, whether that risk is being long the 10-year note andshort the two-year note, long General Motors and short Ford, or trading productssuch as option volatility or fixed income credit spreads. Despite designations likefixed-income arbitrage, statistical arbitrage, or volatility arbitrage, trading activityin these products can better be classified as relative value trading.
THE WORLD OF RELATIVE VALUE TRADINGIn this chapter we’ll introduce readers to the wonderful world outside of CNBC. Likemany Wall Street professionals, I’m tuned into CNBC throughout the trading dayand subject to the same endless “buy, sell, or hold” discussions with the portfoliomanager du jour. Contrary to what’s shown on CNBC, there is an entire world ofmarkets outside of equities. These markets include corporate bonds, other interest247
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rate products, and derivatives based on market volatility. Although it is atypical andprobably inappropriate for the individual investor to trade most of these markets,there are hundreds of hedge funds and Wall Street trading desks actively pursuingthis relative value trading.Hedge funds and proprietary desks are the largest traders of relative valuetrading. Usually structured as limited partnerships, hedge funds are private investment vehicles used by wealthy individuals and institutions to take advantage ofmore flexible investment strategies than typical mutual funds. Unlike most mutualfunds, which are not allowed to sell short or borrow to create leveraged positions,hedge funds very commonly use leverage and derivatives to create portfoliosdesigned to profit in both rising and falling stock and bond markets.The trading desks of Wall Street’s largest firms also trade relative valuestrategies. Goldman Sachs, Morgan Stanley, and Salomon Smith Barney have hundreds of millions of dollars devoted to trading the yield curve, credit spreads,volatility, and stock pairs. This trading is instrumental in their daily business andcontributes heavily to their bottom line.While there are thousands of relative value strategies employed by proprietary trading desks and hedge funds, I believe that each strategy can be groupedinto four subsets:Pure arbitrage■ Bottom-up relative value■ Top-down relative value■ Macro trading■
Pure ArbitragePure arbitrage exists when there is a specific relationship between one or moreassets. Arbitraging the same stock trading on two exchanges or trading the underlying index versus its corresponding futures market are two examples. The expected profit in these activities has become virtually negligible, and often only lowcost trading desks even attempt to execute these strategies.The term arbitrage has been incorrectly used to describe virtually all types oftrading. Historically, arbitrage has referred to creating risk-free returns by trading twolike markets and profiting from the difference in price. For example, a stock tradermight trade local shares of cellular giant Nokia in Finland versus the AmericanDepository Receipts (ADRs) that trade in New York. Consider the following prices:Nokia trading in Finland at €14 per shareNokia ADRs trading in New York at $15 per shareUSD/€ exchange rate trading at 1.00$/€In this example, a trader buys the local shares trading in Finland at 14 eurosper share, sells ADRs trading in New York at $15, and exchanges the U.S. dollar
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proceeds, or $15, for €15. The trade then converts the local shares to ADRs inorder to zero out the position. In the process, the trader profited between the difference of the price of the local shares and the ADRs.Another example of true arbitrage is stock index arbitrage, in which tradersexploit the difference between the current prices of individual stocks traded onexchanges and the current price of stock index futures traded on the ChicagoMercantile Exchange. Because the price of stock index futures is based on a basket of the individual stocks, a strict pricing relationship must exist between thestocks and futures. The futures will be priced based on the current level of thestocks, the dividends paid until maturity, and interest rates. If the futures trade toohigh versus the underlying stocks, traders would do the following:1. Borrow $1 million at the current interest rate2. Use the borrowed money to buy $1 million of stocks based on current weightswithin the index3. Sell futures contracts of the stock index worth $1 million4. Pay daily interest on the loan5. Receive any dividend payouts from the stocks held in the position6. Unwind the stocks at futures expiration (morning of the third Friday of March,June, September, and December)The stock index arbitrage strategy above is another type of true arbitrage thatwas once practiced widely among proprietary trading desks. The profitability ofboth the local/ADR and stock index arbitrage has become virtually zero withadvances in technology and as more firms engage in the trading strategy.Bottom-Up Relative ValueBottom-up relative value strategies are less common than the other three components of the relative value umbrella. In bottom-up strategies, the trader starts witha pool of potential assets, such as stocks within the S&P 500. Based on set criteria, the trader may pick 10 stocks to buy and 10 stocks to short, subject to riskminimizing criteria.An example is the study reviewed in Chapter 1 where we bought a portfolio ofstocks that underperformed the index and shorted a portfolio of stock that outperformed. Each stock was picked so the net characteristics of the portfolio minimizeda specified risk profile. In bottom-up models, strategies could pick assets from theworld of corporate bonds, mortgages, global currencies, or technology stocks.Another possibility of a bottom-up relative value strategy would be a currencytrader focusing on one region of the world. A trader might have 10 currencies fromthe Asia-Pacific region to buy or sell, and he or she may choose two currencies tobe long, and two to be short, based on economic factors and price models. By beingboth long and short, this strategy would create a reasonably hedged portfolio.
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PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
In each example, the key to defining a bottom-up trading strategy is that thetrader begins with a pool of possible assets, and his models then select whichassets to trade.Top-Down Relative ValueTop-down relative value models are the most commonly used. In contrast to thebottom-up strategy, these top-down models preselect pairs of securities to trade,often based on factors such as similarity of industry, time to maturity of bonds, orcorrelations of currencies.Short-term stock pair trading has become widespread in the past five years.In one example, one correlated stock is traded against another when price patternsemerge. The trades are typically short-term in nature, lasting anywhere from fiveminutes to five days before positions are unwound. By comparing two similarcompanies, such as General Motors and Ford, traders might make assumptionsregarding the relative performance of the two stocks. If General Motors outperforms Ford by a large amount, traders may short General Motors and buy Ford asa hedge, expecting the performance gap to close in the future.Another example of a top-down model is yield curve trading. In addition totrading interest rates by buying and selling bonds, we can trade the relative pricing among bonds. We might make assumptions regarding the pricing of a longterm instrument, such as the 30-year Treasury bond, and a shorter-term instrument, such as the two-year Treasury note. Similar to the stock pairs, if one assetoutperforms the other, we may sell the strongest performing asset and buy theweaker asset, on the premise that performance will revert in the near future.Yet another possibility is to trade natural substitutes, such as the relative pricing of natural gas versus heating oil. Residential and commercial property is typically heated by two sources: natural gas and heating oil. If one fuel becomes moreexpensive relative to the other, the market will adjust as people switch heating choices. While this process may take some time, eventually the market prices will adjust.Another top-down relationship is the connection between the price of underlying commodities and the share prices of companies that produce, mine, or manufacture the underlying products. We might trade an index of gold stocks against themetal itself, buying one and selling the other when prices move in a specific manner. The common thread among top-down relative value models is that the securitiestraded are constant and are usually selected based on some inherent relationship.Macro TradingIn pure arbitrage, bottom-up, and top-down models, two securities are traded bycomparing their values to each other. In macro relative value trades, we take longand short positions in securities regardless of the overall portfolio. To this point,
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most of the book has been dedicated to this style of macro trading. In earlier chapters, we looked at trading futures and stocks from a long and short perspectivewithout regard for where other stocks, futures, or indices are priced. These samestrategies can be applied to more esoteric markets.
INTRODUCING RELATIVE VALUE MARKETSOur focus will now shift to applying quantitative strategies to top-down relative value trading. To trade these new strategies, we need to define certain relative value markets. We’re looking to trade the relative value of one securityagainst the other. In relative value trading, we are usually indifferent to general market moves. Our positions are structured by buying one bond andshorting another, or by buying one stock and shorting another in a similarindustry.The possibilities in relative value trading are endless, and new products arebeing created every day. Whether it is weather derivatives, pollution credits, orcheddar cheese futures, our quantitative trading methods will prepare us for theday when these new products begin trading. We’ll focus on seven general marketsfor our relative value trading strategies:Yield curve marketsCredit spreads■ Equity volatility■ Relative performance of stock indices■ Single stock pairs■ Commodity substitutes■ Stock and commodity relationships■■
Yield Curve MarketsMost individual investors are familiar with fixed income products such as bondsand mortgages. After all, many readers have home mortgages or investments ingovernment securities. When faced with investment decisions within the fixedincome world, the most important factor is often determining which maturity bondto buy. Interest rates vary over differing maturities, and these differences can bevery volatile over time.As we see in Figure 9.1, bonds with different maturities have different yields.This occurs because of a number of factors, such as future inflationary expectations, expected changes in government borrowing, and future expectations ofFederal Reserve interest rate policy. The difference in yields can lead to preferences in investments.
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US Government Yield Curve6
Yield (%)
5
9/14/01
49/16/023
8/16/02
21
29 year
27 year
25 year
23 year
21 year
19 year
17 year
15 year
13 year
11 year
9 year
7 year
5 year
3 year
1 year
3 month
0
MaturityFIGURE
9.1
U.S. Government Yield Curve. D