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28

8 Market Models

Money itselfisnt lost or made, its simply tran rred from oneperception to another.

GordonGekko

Wall Street ihs Motion Picture

The market can be handicapped, just as a horseplayer bets on thoroughbreds. One might be su rised at just how complicated the betting at the track is-the average bettor is probably not aware ofthe potent speed sires or Diazos Center of Distribution [8]. These data provide the edge to differentiate the professional horseplayer from the amateur As with any game, the player competes for a statistical edge, and this search leads the player to a deeper exploration of diverse subjects such as mathematics, physics, and even philosophy. Trading evolves as a Glass Bead Game as the trader attempts to build the ultimate market model.

In this chapter, we construct two market models, one using data that are relatively hard to automate. First, we apply a set of the Acme trading systems to some market and sector indices. Because indices do not have a float we omit the Acme F system. The Acme M N, R, and V systems are combined to form the market model; each of the systems is applied without trade filters to eliminate many of the stock-specific requirements. This first market model is our Systems Model

Second, we develop a special version ofthe Acme M system using the market sentiment and breadth indices shown in Table 8.1. For each market index, we specify a rule based on an overbought or oversold reading; the rule interprets the reading based on the indexs correlation with the market. For example, the \TX makes a new 20-day high. Because the VIX is negatively correlated with the market, the letter "V" is displayed above the current bar. As with the Acme M system, a signal is generated when a minimum number of pattern criteria in the same direction are met. This market model is our sentiment Model

I liv.i. Ill-........11111 Al.ifiJ.i II......uii ll.i.iki., Niw i-ik. Nrw iiik

 8 Maricet Models Table 8.1. Breadth and Sentiment Indices Index Chart -fiiibol Volatili Index (\TX) Put/CaU Ratio New Highs New Lews Arms Index (TRIN) Bullish Consensus Short Sales Ratio

8.1 Systems Model

A sjstems model can be defined by combirung the fohowmg Acme sj-stems. In this model, we are taking abottoms-up approach. We simply combine ah ofthe sjstems into one strategj- and apply that strategy fo market and sector indices such as the COMPX and BTi:, as weh as ETFs such as the QQQ and SPY.

- Acme M System

- Acme N Sj-stem

- Acme R System

- Acme V Sj-stem

Figure 8.1 shows achaif ofthe Nasdaq-100 Series Trust (QQQ:Amex) with the Acme Systems Model. Each trading sj-stem has been applied unfiltered to the chart. As with any other stock, the QQQ exhibits the same characteristics with the Acme sj-stems applied to the chart-multiple entries, profit targets, and stop losses. For market and sector indices, the rectangle is a rare occurrence, so the Acme Rsignal does not trigger often; however, when it does appear, prepare for some trading action over the fohowing dajS.

Table 8.2 summarizes the performance ofthe unfiltered Systems Model for the (iThe prof it factor is consistent with the overah Acme profit factcr, so we then decided to compare the performance ofthe model for the sectorindices versus their corresponding ETFs. Sincewewantedtooptimizeforperform ance here, we applied the sjstem filters. The results arc shown in Tables 8.3 and 8.4, sorted by profit factor.

Figure 8.1. Systems Model for QQCi,

Table 8.2. QQQ Performance Report (06/10/1999 - 02/15/2002)

 Total Net Proffl \$24,318.00 Open position . \$000 Gross Prom \$5459.00 Gross Loss (\$29,741.00) Total* oftrades Percent profitable 53.45% Number winning trades Number losing trades Largest wnning trade \$5,424.00 Largest losing trade (\$2,455.00) Average winning trade \$1,743.84 Average losing trade (\$1,101.52) Ratio avg win/avg loss Avg trade (win & loss) \$419.28 tUlax consec. Winners rvlax consec. losers Avg # bars in winners Avg # bars in losers rvlax inlraday drawdown (\$6,293.00) Profit Factor 1.82 rvl ax # contracts held Account size required \$6,293.00 Raturn on account 283 24 "o

8 MaiketModds

Table 83. Maiket Indices

 ! Trader Jim loss Ealio Fn>M Fector COMPX 2.88 2.05 53-0 1.83 2.04 48°o 2.03 1.87 Table 8.4. Market ETFs ikctor htdfx # r>Wfi Jim Loss Riaio Prciit Fector 59°o 2.07 2.95 1.99 1.47 1.36 ni.A + > 1.29 1.00

Table 8.5. Sector Indices

 # Trades ". Jiin Loss Ratio Profit Fector --: 2.40 4.80 2.05 4.20 58°o 2.63 3.64 2.04 3.57 l«l 3.40 50»o 2.46 2.46 46S« 2.57 48°-o 1.96 1.76 1.84 49°o 1.89 1.80 1.64 45?!. 1.91 1.58 1.98 1.56 46-i, 1.75 39°o 2.24 1.46 44". 1.47 1.17 33".

8.1 Sjstems Model

The perforrnarice results in Tables 8.3 and 8.4 fllustrate the difference between performance derived ftom indices and their corresponding . Except for the QQQ the performance for the ETFs is mediocre at best. The problem with the other ETFs is that the spreads are higher, and more importantly they are not as volatile as the QQQ. The bottom line is that a trader wiU not be able to trade an ETF effectively imless it exhibits a combination oftight spreads, high trading volimie, and high volatility. The QQQfits these criteria, so we will take it.

Table 8.6. Sector ETFs

 ikctor Index # Trades Profitobie JiliiLossRcdio Prcfd Fector 1.70 2.73 57°o 1.83 2.44 59°o 1.65 2.35 52*. 1.72 1.87 48 . 2.01 1.85 1.80 44-0 1.84 1.43 47°o 1.57 1.40 58°o 0.97 1.42 1.16 38°o 1.77 1X19 38°o 1.74 1.06 2.05 0.92

Tables 8.5 and 8.6 compare the performance of the sector indices with sector ETFs, sorted by profit factor Again, we see how the performance ofthe ETFs is worse than the raw indices, except in those cases where the ETF is rdatively hquid and relatively volatile. Currently, the only two ETFs that we consider "trade worthy for holding periods of five days or less are the Biotechnologj HOLDRS (BBH:Amex) and the Semiconductor HOLDRS (SMH:Amex).

Notice the bottom four entries in Table 8.5; these are the four most cjdical sectors.

- Morgan Stanley C\chcal index (CYC)

- Morgan Stanley Consimier index (CMR)

- PHLX Semiconductor Sector index (SOX)

- PHLX Utility Seaor index (,UTY)

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