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The antioxidant compound lycopene, which is abundant in tomatoes, has extraordinary ability to protect against cancers of the prostate and breast.

Another important consideration is the optimization of model parameters When running tests, it is often necessary to adjust the parameters of some component (eg, an entry model, an exit model, or some piece thereof) to discover the best set of parameters and/or to see how the behavior of the model changes as its parameters change Several kinds of model parameter optimizations may be con-.

If the response rate for direct mail is 1.8 percent, any direct-mail offer would be a loser. This is proof of the assertion that database marketing does not work with packaged goods. But with email, pro table database marketing becomes a possibility. The response rate will be lower, at 1 percent, but the marketing costs are far lower. The value of a prospect s name is 14 cents. A marketer might be willing to offer half that for a name. The lifetime value sets an upper boundary for the offering price. Anyone who offers to pay the full lifetime value for a prospect s name will never make a pro t. As a rule of thumb, we can assume that buyers will never offer more than half the projected lifetime value to acquire a name. So the offering price of a name with a projected LTV of $0.14 could not be higher than $0.07. So, if the offering price of a prospect name to an e-commerce consumer name buyer is $0.07, what is the name worth to the seller Here we have to consider marketing costs. Buyers do not materialize from nowhere once names are acquired. To sell names, most companies need the services of list managers and brokers who must advertise and get fees. The list owner will be lucky to get 75 percent of the offering price from any one buyer. With luck, the names can be sold several times per year. So we end up with the value of a name looking something like Table 5-5. So we have an answer for the CEO of the new dot com. His names are worth $0.16 each if he nds a good list manager and promotes the names, and if everything goes well.

xxi ducted. In manual optimization, the user of the simulator specifies a parameter that is to be manipulated and the range through which that parameter is to be stepped; the user may wish to simultaneously manipulate two or more parameters in this manner, generating output in the form of a table that shows how the parameters interact to affect the outcome. Another method is brute force optimization, which comes in several varieties: The most common form is stepping every parameter through every possible value. If there are many parameters, each having many possible values, running this kind of optimization may take years. Brute force optimization can, however, be a workable approach if the number of parameters, and values through which they must be stepped, is small. Other forms of brute force optimization are not as complete, or as likely to find the global optimum, but can be run much more quickly. Finally, for heavy-duty optimization (and, if naively applied, truly impressive curve-fitting) there are genetic algorithms. An appropriate genetic algorithm (GA) can quickly tind a good solution, if not a global optimum, even when large numbers of parameters are involved, each having large numbers of values through which it must be stepped. A genetic optimizer is an important tool in the arsenal of any trading system developer, but it must be used cautiously, with an ever-present eye to the danger of curve-fitting. In the investigations presented in this book, the statistical assessment techniques, out-ofsample tests, and such other aspects of the analyses as the focus on entire portfolios provide protection against the curve-fitting demon, regardless of the optimization method employed. Jeffrey Owen Katz, Ph.D., and Donna E McCormick

Amounts Offering price Marketing costs Net revenue Sales per year Value of a name $0.07 25% $0.053 3 $0.158