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Sometimes it is more convenient to represent the Fourier transform with real sine and cosine basis functions. Then, it is referred to as the real Fourier transform (RFT). What was discussed as the Fourier transform before would then be the complex Fourier transform (CFT) [Ersoy, 1994]. For example, the analytic signal representation of nonmonochromatic wave elds can be more effectively derived using the RFT, as discussed in Section 9.4. In this and next sections, we will discuss the 1-D transforms only. The RFT of a signal u x can be de ned as

fail to detect deceit and what they can do to become better lie detectors. Such chapters were not included in the 2000 edition. The result, I believe, is a book about deception that is better structured and more comprehensive than the 2000 edition. I also realise that this book contains considerably more text than the 2000 edition, and so perhaps readers will prefer to dip into individual chapters rather than read the book from beginning to end. To facilitate this, I have ensured that the chapters can be read independently from each other.

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

As the same can be said for trend trading systems, why do I place such a strong emphasis on the point here Because these systems invariably will stop us out with relatively large losses, and it is very tempting to abandon discipline regarding stop losses when the losses are large relative to average profits Acceptance of such losses means that we will have to enjoy quite a few profitable trades before experiencing a new peak in account equity In addition, because we are fading temporarily unsustainable extremes at the trade s outset, often our stops will be triggered near the ultimate high or low of the market s move Inexperienced mean reversion traders find the temptation to abandon stop order discipline almost irresistible To help with this problem, look at the net profit column in Table 3.

1 2

12/02 Portfolio managers Research analysts Economists Trading Compliance Marketing Administration Client service Other (specify) Total* *The total number of professional staff must match with the actual number of investment staff employed by the rm. 12/01 12/00 12/99