7490.18 0.56 –244,555 65 78 614.2922 31,055 –49,075 20 87MOT/TXN168,1110.02 0.10 –416,846 58 69 389.25726,558 –49,678 22 87MSFT/INTC –419,078–0.15 –0.25 –597,389 50 68 439.37–19 31,522 –92,671 25 106PFE/MRK609,3960.14 0.36 –426,076 60 72 634.1817 29,805 –37,977 19 85VZ/SBC1,450,5170.45 1.12 –197,344 78 85 747.7325 29,028 –38,777 19 81WMT/HD828,5450.13 0.54 –284,742 62 76 528.0825 28,955 –35,605 20 84XOM/BP188,7690.04 0.12 –467,208 51 69 720.35532,697 –59,727 18 104RD/SC895,4070.59 0.88 –123,405 63 84 1381.7210 22,318 –29,360 22 74UN/UL843,9610.50 1.04 –115,600 61 85 1154.1312 19,518 –18,101 20 77Commodity XAU/GC535,6170.15 0.37 –250,630 53 70 537.6519 26,429 –27,642 30 83vs. StockXOI/CL151,7100.09 0.09 –397,009 55 76 681.63628,408 –73,957 19 119Average 191,0790.10 0.21 –541,693 56 72 681.724028,512 –60,436 22 91
0.49–0.80–0.70
© 2002 Lars Kestner - All Rights Reserved
FIGURE
9.26a
Difference from 100-day Moving Average Strategy Applied to Relative Value Markets. This strategy produces very profitabletrades.
284
285
9.26b
Breakdown Statistics (Relative Value)
1991
K-ratio–0.41–0.170.770.27–0.210.73
1992
Sharpe Ratio–0.97–0.612.101.20–0.831.85
1993
Year199619971998199920002001
K-ratio0.460.290.270.520.080.20
1994
1995
Year
1997
Sharpe Ratio1.110.351.272.15–0.301.05
1996
Net Profit by Year
Net Profit1,242,797272,8831,794,843798,438–285,4541,013,733
Performance Breakdown by Year
1999
2000
759797100102106
© 2002 Lars Kestner - All Rights Reserved
1998
144142139133127121
2001
52.08%68.31%69.78%75.19%80.31%87.60%
Num. ofNumber of Profitable PercentWindows Windows Profitable
Average Avg. Bars Avg. BarsLossWinLoss–84,8191910326109–121,011–20,3882039–71,5372591–96,17128106–42,0862185–50,79924101Profitability Windows
Length1 Month3 Months6 Months12 Months18 Months24 Months
AverageAverage Average Avg. Profit AverageAverageAverageWinK-ratio Sharpe Ratio Max DD Num Trades % Win Per Contract–691,75750–0.0170–0.24–1529,56647–0.1467–578–0.31–1,483,52230,498–181,93028,171750.50891.352,226–652,253530.0070–0.01–329,759–1,160,62427,20545–0.1862–0.45–15–317,126600.20740.461327,977–323,820540.12730.231227,419
Difference from 100 day moving average100 day averageEnter long when stat falls below –2.5, short when stat rises above 2.5, flat on crosses of zero1/1/1990-12/31/2001Breakdown by Market Sector
Difference from 100-day Moving Average Strategy Applied to Relative Value Markets. This strategy produces very profitable trades.
FIGURE
–2,000,000
–1,500,000
–1,000,000
–500,000
0
500,000
1,000,000
1,500,000
1990
Net Profit–1,435,256–416,9931,230,7221,151,305–747,5351,105,692
AverageNet Profit–458,233–843,7541,714,990–57,438–928,899595,870343,664
2,000,000
Year199019911992199319941995
MarketSectorRatesCreditVolitilityCommodityStock IndexStock PairsStock/Comm.
System Name :Parameters :Description :Run Dates :
Net Profit
PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
286
FIGURE
9.27
Difference Between 10-day and 40-day Average Strategy Applied to Five-Year Swap Spreads. This oscillatorstrategy appears to pinpoint short-term tops and bottoms.
markets. On the other hand, credit spreads and the relative performances of stockindices do seem to trend, as indicated by strong performance from channel breakout and moving average crossover strategies. Yield curve markets, commodity substitutes, and commodity/stock performance show mixed performance and nostrong propensity to trend or mean revert.By introducing new markets, we have stumbled upon a vast world of newopportunities. Unlike stock and futures markets, which have been studied ad nauseum for market inefficiencies, these new relative value markets are largelyuntapped and open to study using sound quantitative techniques. Although accessto many of these new markets is limited only to well-capitalized institutions, newproducts may soon allow entry to smaller individual investors.
Trading Strategy Evaluation (Relative Value)Difference between 10 day and 40 day moving average10 day and 40 day averagesEnter long when stat falls below –2, short when stat rises above 2, flat on crosses of zero1/1/1990-12/31/2001Avg. Avg.#Avg.Sharpe%Avg.Avg. Bars BarsofProfit Avg.Win LossLossMarketNet Profit K-ratio Ratio Max DD Trades Win hrs (000) Per Con. Win2's-10's–386,244 –0.02 –0.21 –534,470 48 69 339.29–21 33,629 –96,342 24 71YieldCurve2's-5's93,1820.06 0.06 –513,780 43 65 450.11539,867 –67,433 24 6010's-30's –696,499 –0.10 –0.43 –803,043 41 59 511.19–31 26,735 –76,261 25 632-10-30 Fly 593,941 0.27 0.42 –222,013 56 70 551.282434,244 –35,267 22 52Swap spreads –67,190 –0.03 –0.03 –612,000 52 698.79–169 30,364 –73,143 27 57CreditSpreadsHigh yield –1,478,602 –0.15 –0.59 –2,046,757 46 54 161.00 –196 41,404 –118,316 26 73VIX777,020 0.59 0.75 –143,660 38 769.592,195 33,253 –18,281 23 49VolatilityC/W–844,003 –0.10 –0.45 –1,029,945 48 65 636.43–28 29,322 –104,021 25 68CommodityS/BO–520,143 –0.13 –0.30 –931,199 51 55 1044.31 –10 36,075 –66,533 25 60S/SM272,696 0.10 0.16 –346,848 52 56 1353.83436,318 –33,970 27 54GC/SL–21,406 –0.01 –0.01 –431,533 49 65 916.24135,678 –65,813 28 67SPX/NDX –857,526 –0.05 –0.51 –934,626 47 55 1097.94 –16 36,409 –84,851 24 67StockIndicesSPX/RUT –1,289,499 –0.30 –0.68 –1,466,584 46 46 1776.70 –15 36,194 –79,393 24 67BSC/MER 587,535 0.24 0.52 –211,321 52 71 500.912228,278 –31,637 28 50StockPairsCVS/WAG 109,795 –0.01 0.08 –430,219 52 69 508.58428,195 –56,841 25 60DAL/AMR 373,636 0.05 0.24 –263,905 57 61 698.93932,109 –34,100 25 47DD/DOW138,417 0.07 0.11 –334,915 52 63 602.66431,267 –47,022 23 57FNM/FRE 469,073 0.21 0.40 –291,088 43 74 787.801429,959 –43,901 22 51GM/F–551,075 –0.17 –0.38 –667,152 48 48 605.94–17 30,348 –47,861 22 59KO/PEP790,931 0.14 0.61 –228,820 53 72 604.022535,575 –37,394 22 59MOT/TXN –186,527 –0.07 –0.13 –347,539 49 57 390.38–10 29,117 –47,705 25 55MSFT/INTC –552,185 –0.13 –0.34 –823,572 50 60 439.76–25 32,319 –75,682 28 67PFE/MRK 206,996 0.03 0.15 –392,520 51 63 633.77631,519 –42,190 21 56VZ/SBC374,054 0.22 0.35 –132,662 48 67 758.671023,130 –23,043 29 46WMT/HD415,701 004 0.28 –276,679 47 66 545.691831,731 –33,202 24 57XOM/BP –420,099 –0.07 –0.31 –688,730 46 50 716.13–13 27,423 –45,688 25 54RD/SC360,210 0.26 0.56 –207,524 36 78 1498.43720,545 –27,258 26 47UN/UL401,615 0.39 0.74 –81,30225 80 1147.001425,221 –19,265 26 45XAU/GC687,765 0.24 0.53 –231,944 59 75 543.082127,767 –35,600 25 53Commodityvs. StockXOI/CL–626,980 –0.16 –0.48 –747,108 37 41 697.32–24 41,537 –56,820 21 62Average –61,514 0.05 0.04 –545,782 47 63 684.536031,851 –54,161 25 58500,0000–500,000–1,000,000–1,500,000–2,000,000–2,500,000–3,000,000–3,500,000–4,000,000–4,500,000
–0.18–0.85–0.86
Dec-01
Portfolio Statistics–1,845,411Sharpe ratio :–4,402,173Correlation to breakout :–0.02Correlation to 10-40 MA :
Jan-01
Jan-00
Jan-98
Jan-97
Jan-96
Jan-95
Jan-99
Net Profit :Drawdown :K-ratio :
Jan-94
Jan-93
Jan-92
Jan-91
Jan-90
Equity
Strategy Name :Parameters :Description :Run Dates :
© 2002 Lars Kestner - All Rights Reserved
FIGURE
9.28a
Difference Between 10-day and 40-day Strategy Applied to Relative Value Markets. Performance of this strategy is not asexciting as other counter-trend strategies.287
Year199019911992199319941995
9.28b
1990
K-ratio–0.51–0.950.40–0.10–0.180.35
AverageNum Trades47493850474748
19911992
Sharpe Ratio–1.73–2.640.980.03–1.310.48
1993
Year199619971998199920002001
1994
1995
Year
Net Profit K-ratio414,8820.11311,4600.28100,234–0.12395,4010.32–702,768 –0.091,377,4820.34Net Profit by Year
AverageWin33,61935,88433,25334,34836,30229,11634,652
Avg. Bars Avg. BarsAverageLossWinLoss–68,8262461–95,7302665–18,2812349–67,5842662–82,122246754–40,85325–46,2102358Profitability Windows
1997
1999
2000
© 2002 Lars Kestner - All Rights Reserved
1998
2001
Num. ofNumber of Profitable PercentSharpe RatioLength Windows Windows Profitable680.5747.22%1 Month144142710.4150.00%3 Months650.0846.76%6 Months139651.3448.87%12 Months13312764–0.7650.39%18 Months121621.4951.24%24 Months
Avg. ProfitPer Contract–6–1822,195–8–165–1
1996
Average% Win66627660506558
Difference Between 10-day and 40-day Strategy Applied to Relative Value Markets. Performance of this strategy is not as exciting as other counter-trend strategies.
FIGURE
2,000,0001,500,0001,000,000500,0000–500,000–1,000,000–1,500,000–2,000,000–2,500,000
Net Profit–2,241,580–1,516,883647,69620,588–933,100286,351
AverageMax DD–518,327–1,329,379–143,660–684,881–1,200,605–358,530–489,526
Performance Breakdown by Year
AverageSharpe Ratio–0.04–0.310.75–0.15–0.600.190.03
Breakdown Statistics (Relative Value)
Difference between 10 day and 40 day moving average10 day and 40 day averagesEnter long when stat falls below –2, short when stat rises above 2, flat on crosses of zero1/1/1990-12/31/2001Breakdown by Market Sector
Average AverageMarketK-ratioNet ProfitSectorRates–98,9050.05Credit–772,896–0.09Volatility777,0200.59Commodity–278,214–0.03Stock Index –1,073,513 –0.18Stock Pairs167,8720.08Stock/Comm. 30,3930.04
System Name :Parameters :Description :Run Dates :
Net Profit
288
CHAPTER
10
Investing in the S&P 500Beating a Buy and Hold Return Using Quantitative Techniques
So far, we’ve focused on short- and medium-term trading for traders and marketprofessionals. In addition to our professional duties of trading, market professionals have savings, 401(k)s, and other investments. Much of these investments areallocated to the equity markets. In this chapter we’ll step back from complex strategies and employ quantitative techniques to analyze short- and long-term moves inthe U.S. equity market. Using macroeconomic variables such as short-term andlong-term interest rates, recent market performance, day-of-month effects, and theVolatility Index, we’ll create trading strategies that generate signals to be eitherlong the market or out of the market and invested in cash.
THE POPULARITY OF EQUITIESThere was no escaping the popularity of equities over the past five years. From thedot-com bubble to the bubbly personalities of CNBC, equities became the rageamong every walk of life. Penny stocks even had their day in the sun, sometimesrising tenfold in a matter of days. Exchanges were quick to offer products to a public that had the trading itch. Exchange traded funds (ETFs) such as the SPDRs andthe QQQs mimic popular stock indices and trade huge dollar volumes daily.The subsequent sell-off of mid-2000 to the present has left some investorsfeeling battered, bruised, and in no mood to talk about the stock market. Whetherwe want to or not, however, we must make decisions about our investments andallocate between stocks, bonds, and other asset classes. The quantitative techniques289
Copyright 2003 by Lars Kestner. Click Here for Terms of Use.
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PART 2 Harnessing the Power of Quantitative Techniques to Create a Trading Program
we have studied throughout this book can help take the emotion out of the investing process and replace it with reliable time-tested strategies.Anyone involved in the markets over the past five years was probably tooinvolved to understand the euphoria associated with the unbelievable market moves.My guess is that when the dust settles and time begins to put 1998 through 2001 inhistoric perspective, we’ll see unbelievable similarities to the end of the 1920s.The historic market rise began with the proliferation of the Internet. Popularthinking had it that new industries would be created using the Internet as a meansto reach customers. In fact, entire new industries sprouted to take advantage of thistechnological miracle. Online retailers would sell books, CDs, and even groceriesto users over the Internet. Software companies would build these sites, monitoringmillions of pieces of information, including buying habits of the consumers andinventory levels in warehouses. Networking companies became highly visible asthe speed and reach of fiberoptic infrastructures needed to cross oceans and penetrate metropolitan areas. At the same time, “old economy” stocks not involved inthe Internet suffered on perceptions that those not first to market in this brave newworld would become corporate dinosaurs.Anything involved with technology was awarded a hefty market capitalization. Internet portals, semiconductor equipment stocks, optical networking, business-to-business software, and PC stocks all saw shares rise to unbelievable heights.The key to most business models was market penetration. Losing money to achievethat market share was not an issue.Perhaps the most amazing story was that of Juno, an Internet serviceprovider. In December 1999, Juno’s share price had been mired in a trading rangebetween $15 and $20. On December 20, news from the company changed everything. Juno’s management announced that it would no longer charge for its Internetaccess. Instead, it would give away the service, in the hope of gaining marketshare. Some might see this as a sign that the company had no pricing power or thatthe move was a desperate attempt to turn around a sliding business. Investors,however, did not react the same way. The stock rallied from $17 to a high of $87over the three days following the announcement (Figure 10.1). It was an amazingtime, with extraordinary reactions from investors.Volatility in stocks during this time was also astonishing. I believe much ofthe reason for this volatility was the lack of experience with the prices wherestocks were trading.When prices trade in a range, traders have experience buying and selling.Over time, they learn that buyers step in at a price, say $40, and sellers offer stockat another price, say $60. If this pattern is repeated enough times, traders have greatconfidence to buy stock in the $40 to $45 area and offer stock around $55 to $60.This trading range eventually dampens volatility. On the other hand, consider astock that runs from $50 to $200 with very few retracements. Now, traders arereluctant to step in and buy. After all, what’s to keep a stock from falling back to
CHAPTER 10 Investing in the S&P 500
291
Juno (JWEB)75
50
12/31/99
12/29/99
12/27/99
12/25/99
12/23/99
12/21/99
12/19/99
12/17/99
12/15/99
12/13/99
12/11/99
12/9/99
12/7/99
12/5/99
12/3/99
0
12/1/99
25
Date
FIGURE
10.1
Juno (JWEB). Of all the strange things during the technology bubble, Juno’s stock price increased by400 percent when the company announced it would stop charging for its product.
old lows? Traders are equally reluctant to sell the stock at $200. If the stock ranfrom $50 to $200, what’s to keep it from trading at $400? This inexperience withprices leads to a lack of liquidity and large percentage price moves. I believe thelack of a defined trading range caused increased market volatility, especially intechnology stocks.Day traders were another phenomenon that contributed to market volatility.Most of their trading involved momentum strategies where they would buy onstrength and sell on weakness, specifically when they were able to spot large institutional orders. Day traders would buy or sell ahead of brokers executing thoseorders. This caused wild fluctuations in market prices on an intraday basis. Giventhe lack of overall liquidity in many technology names, day traders were able tomove stocks by two to four points on very light volume.Large option trades also affected daily volatility in the market. Many hedgefunds that specialized in the technology sector ballooned in size from $25 to $50million to well over $250 to $500 million due to performance and investor inflows.These hedge funds had enormous capital gains in many stocks that had risen fiveor tenfold and were looking for methods to hedge these gains. By purchasing putoptions, hedge funds received the right to sell stocks if the stock declined, whilekeeping gains if stocks rallied. Market makers on the other side of these trades
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292
dynamically hedged the option risk by buying and selling the underlying stocks.These dynamic hedges required market makers to sell when markets declined andbuy when they rallied—exacerbating large daily moves in technology namesand contributing to excess market volatility.While the popularity of equities has diminished over the past couple of yearsdue to lagging returns, the stock market is still important both as an investmentvehicle for individuals and as a means for companies to raise money from the capital markets and tweak their capital structure. In the remainder of this chapter,we’ll explore methods to outperform buy and hold investment strategies.
THE IMPORTANCE OF INTEREST RATES IN PREDICTING EQUITY PRICESThe link between interest rates and returns in equities has been well documentedand exists for a number of reasons. One of them can be summed up in the