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Finally, landing is imminent It is possible to look inside the bright spots and see their detail, ie, the individual markets the various model-order combinations trade best The second question above can now be addressed: By identifying the consistently profitable (across samples) model-order combinations and the markets best traded by them, a good portfolio trading strategy can be developed At this time, it will become clear that out of all the studies performed during the long trip, enough has been learned to assemble a lucrative portfolio of systems and tradables By way of demonstration, such a portfolio will be compiled and run with the standard exit strategy THE BIG PICTURE For this perspective, each class of entry model (eg, all trend-following moving average models, all breakouts, all small neural networks) was examined in its entirety All the tests for each model type were averaged.

Out-of-sample and in-sample performances were separately evaluated By far the best out-of-sample performer was the genetic model: Out of all the models, it was the only one that showed a substantial profit when averaged over all the different tests The profit per trade was $3,271 Next best, in terms of out-of-sample behavior over all tests, were the small neural networks The neural network models were broken down into those for small and large nets because curve-fitting appeared to be a serious issue, especially affecting large nets The breakdown was a natural and easy one to accomplish because, in the tests, each model was tested with one small and one large network Out-of-sample, all the small neural networks taken together averaged a loss of $860 per trade.

Kmart ran into some nancial troubles in 2002, but that does not mean that we cannot learn a great deal from some of Kmart s excellent marketing programs. One of these programs focused on prospective mothers. There is a lot of money to be made from selling to this group. Mothers-to-be spend $18 billion annually, and 80 percent of that amount is spent before the baby is born. For a 2-year period families spend thousands of dollars in a category in which they have not recently been spending and in which they are unlikely to spend again until the next child arrives. Kmart and other retailers have understood the power of this segment for some time and have made major efforts to capitalize on it. Kmart developed a comprehensive strategy for targeting this segment. Using its database, it was able to reach one-third of all U.S. moms-tobe before the baby was born. The basic strategy was this:

Dr. K. Flofa at the Oregon Health Sciences University found that silymarin not only protects the liver cells against toxic damage but also helps to stimulate the liver to regenerate. In a double-blind controlled study, silymarin was found to significantly reduce the mortality of patients with liver cirrhosis. In another double-blind controlled study, silymarin was shown to decrease serum enzymes SGPT (serum glutamic pyruvate transaminase) and SGOT (serum glutamic oxalacetic transaminase), which are associated with liver damage. Many recent studies have shown that silymarin prevents cancers of the liver, skin, stomach, colon, lung, breast, and prostate. With regard to skin cancer, Dr. Mukhtar and his associates at Case Western University found that silymarin provides very effective protection against both ultraviolet light and chemical carcinogenesis. Silymarin protects against the development of cancer by increasing the detoxification activity of Phase II enzymes including glutathione-Stransferase (GST) and quinone reductase and conversely by inhibiting

This indicates a significantly better than chance entry in that random entries produced an average loss of over $2,000, with a standard deviation of slightly under $400 Going down in quality, the next best overall approach involved seasonal&y Altogether, all tests of seasonality models showed an average loss of $966 per trade Three of the moving average models (crossover, slope, and supportIre& tance) followed the performance of seasonality These models, when averaged across tests, lost around $1,500 per trade, which is close to the $2,100per-trade loss expected when using random entries In other words, the moving average models were only marginally better than random All the remaining models tested provided entries that were very close to random Cycles were actually worse than random In-sample, the genetic models ($12,533 per trade), all the neural network models (small, $8,940, and large, $13,082), and the breakouts ($1,537) traded profitably.