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6 58.91 9.35 5.27 3.98 3.37 3.01 2.78 2.62 2.51 2.41 2.34 2.28 2.23 2.19 2.16 2.13 2.10 2.08 2.06 2.12 2.09 2.06 2.04 2.02 2.09 2.06 2.03 2.00 1.98 2.06 2.03 2.00 1.98 1.96 2.02 1.99 1.96 1.93 1.91 1.97 1.94 1.91 1.89 1.86 2.38 2.30 2.24 2.20 2.15 2.35 2.27 2.21 2.16 2.12 2.32 2.25 2.19 2.14 2.10 2.28 2.21 2.15 2.10 2.05 2.24 2.17 2.10 2.05 2.01 2.20 2.12 2.06 2.01 1.96 1.92 1.89 1.86 1.84 1.81 2.18 2.10 2.04 1.98 1.94 1.90 1.87 1.84 1.81 1.79 3.34 2.98 2.75 2.59 2.47 3.32 2.96 2.72 2.56 2.44 3.30 2.94 2.70 2.54 2.42 3.27 2.90 2.67 2.50 2.38 3.24 2.87 2.63 2.46 2.34 3.21 2.84 2.59 2.42 2.30 3.19 2.82 2.58 2.40 2.28 3.17 2.80 2.56 2.38 2.25 2.16 2.08 2.01 1.96 1.91 1.87 1.84 1.81 1.78 1.76 3.16 2.78 2.54 2.36 2.23 2.13 2.05 1.99 1.93 1.89 1.85 1.81 1.78 1.75 1.73 59.44 9.37 5.25 3.95 59.86 9.38 5.24 3.94 60.19 9.39 5.23 3.92 60.71 9.41 5.22 3.90 61.22 9.42 5.20 3.87 61.74 9.44 5.18 3.84 62.00 9.45 5.18 3.83 62.26 9.46 5.17 3.82 62.53 9.47 5.16 3.80 62.79 9.47 5.15 3.79 3.14 2.76 2.51 2.34 2.21 2.11 2.03 1.96 1.90 1.86 1.82 1.78 1.75 1.72 1.70 7 8 9 10 12 15 20 24 30 40 60 120 63.06 9.48 5.14 3.78 3.12 2.74 2.49 2.32 2.18 2.08 2.00 1.93 1.88 1.83 1.79 1.75 1.72 1.69 1.67 1 63.33 9.49 5.13 3.76 3.10 2.72 2.47 2.29 2.16 2.06 1.97 1.90 1.85 1.80 1.76 1.72 1.69 1.66 1.63

2 tsp (2 ml) nutmeg 3 cup (75 ml) raisins 3 cup (75 ml) chopped dried apricots 3 cup (75 ml) dried cranberries 4 cup (50 ml) wheat germ 3 cup (75 ml) psyllium fibre 4 cup (50ml) ground flaxseed

1 2

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Obviously traders can improve their system s rate of return simply by increasing the worst peak-to-valley drawdown levels allowable For example, let us assume $100,000 under management and that our trading system executes single contract positions on a given portfolio Based on this criterion, we discover that our system endured a worst peak-to-valley drawdown of $20,000 or 20 percent for our portfolio Consequently, as long as we are willing to weather a 40 percent worst peak-to-valley drawdown in equity, we could double our average annualized rate of return simply by trading two contacts instead of one Although this may sound tempting, it is important to remember that the higher the peak-to-valley drawdown we experience, the lower our system s likelihood of profitability The probability of our returning to profitability decreases exponentially as the percentage of peak-to-valley drawdowns in equity increase.

file in the C:\j2sdkee1.2.1\lib\classes folder. In addition, when you execute the preceding code, you are prompted to install and run Java Plug-in 1.2.2 from the Sun Microsystems site. You can add notes into the text area only on completion of the installation.

where (0,0), (1,1), . . . (3,3) are parameters to be estimated. The main findings from Table 14.1 are: There are small differences between unweighted and weighted gross flow estimates. There are only small differences between gross flow estimates based on householdlevel weighting and those obtained based on sample likelihoods, under the exponential models. The household-level weighted estimates use the calibrated longitudinal weights, while the sample likelihood method uses the predicted weights based on modelling. Also the calibrated weights as constructed by the Office for National Statistics are functions of auxiliary variables (age, tenure, marital status) and do not depend on the labour force frequencies. Thus these calibrated weights might be considered as ignorable because they depend only on auxiliary variables and do not depend on the labour force status. The fact that the differences between them are small implies that the estimates based on the sample likelihoods are basically just reconstructing the present weights (possibly with some smoothing) and may not reflect the full effects of informative nonresponse. Both the household-level weighting and sample likelihood procedures for estimating the labour force gross flows seem to reduce at least part of the effects of nonresponse, compared to the unweighted method. Based on their simulation study, Clarke and Tate (2002) recommend similarly that weighting should be used to produce flow estimates that offer a considerable improvement in bias over unadjusted estimates. Although the sample likelihood estimates cannot be shown to be better than the weighted estimators, their similarity to the weighted estimates indicates that they also are an improvement over the unweighted estimates.

36.84 100 47.37 100 26.67 100 57.14 100 44.44 100 42.86 100 36.36 100 34.78 100 50.00 100 61.54 100 42.85 100

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Sector Consumer Noncyclical Total CAM Weight 2.5% SCV CAM Weight Return 4.0% 10.98 3.94 SCV Allocation Selection Total Return Effect Effect Effect 6.56 3.15 0.05 0.62 0.11 0.18 0.06 0.80

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