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Table 15-5 presents the performance data for best of the top 10 solutions (longs and shorts) for the MSES with the addition of genetically evolved, rule-based signal exits. Trades were entered randomly. The table is broken down into results for long positions and results for short positions. It is further broken down by sample and test. Sample (IN or OUT) and test may be BSLN (when the rules were not used) or RULE (when the mles were. used). On the long side, in-sample, the addition of the genetically evolved rule-based exit substantially reduced the loss on the average trade from a baseline of $688 to $324. The percentage of winning trades increased from 41 to 43%. The annualized risk-to-

given incentives to gather pieces of pro le information every time they spoke to their customers. The bankers typed the collected data into a customer needs record in the database, using a terminal on their desk. The way it worked was this: A customer might tell a banker that he was currently renting, but that he planned to purchase a home in about 9 to 12 months. The banker would enter this information into the system. Eight months later, the banker would receive a message generated by the database to recontact this customer to discuss home loan options.

Performance of the Modified Standard Exit Strategy with an Added Genetically Evolved Rule-Based Signal Exit When Trades Are Entered Randomly

During the rst year, more than 1 million leads and $4 billion in growth opportunities were sent to National bankers. During the next 6 months, 570,000 new leads were sent, which resulted in the closing of $4.4 billion worth of new loans. During the second year of the system, premium sales of banking products increased by 25 percent over the previous year, while sales of wealth management products increased by 40 percent. The close rate for leads increased by ve times over the close rate before the new system began. The bank achieved a $391 million return on investment on one campaign.

reward ratio improved from -0.35 to -0.17. Out-of-sample, the benefit of the genetically evolved mle-based signal exit was maintained, but not quite as dramatically. The loss on the average trade was cut from $1,135 to $990. The percentage of winning trades increased from 39 to 41%. The risk-to-reward ratio improved slightly from -0.61 to -0.60. Overall, adding the genetically evolved rule-based element to the exit strategy worked. In contrast to the neural exit, the benefit was maintained out-of-mm ple, suggesting that curve-fitting and over-optimization were not major issues. On the short side, similar benefits were observed in both samples. In-sample, the addition of the genetically evolved rule-based element reduced the loss on the average trade from a baseline of $2,084 to $1,645. The percentage of winning trades remained unchanged. Paradoxically, the annualized risk-to-reward ratio worsened somewhat, going from - 1.09 to - 1.15. Out-of-sample, the loss on the average trade dropped substantially, from $1,890 in the baseline test to $1,058, when the rule element was active. The percentage of winning trades rose from 38 to 40%, and the annualized risk-to-reward ratio improved, from - 1.02 to -0.73. Again, the addition of the genetically evolved rule-based signal exit to the MSES worked and continued to work out-of-sample.

One valuable method of segmentation is by quadrants. The idea in quadrant marketing is to take two variables, such as spending and income, and divide all customers into four groups based on these variables. For example, let s take a bank. Most banks and nancial institutions have learned to use a variety of inducements to capture the names and transactions of all their customers. What they may not have is their customers incomes and ages. These can be obtained as appended information from Experian or other sources. Armed with these data, they can develop four quadrants based on income and amount borrowed. The resulting quadrants might look like Table 8-1.

Table 15-6 contains information regarding the market-by-market performance of the MSES for the long side, with the added rule-based signal exit. Several markets showed profitability in both samples: the NYFE, Light Crude, Unleaded Gasoline, and Live Hogs. Other markets were profitable in-sample, but lost heavily out-ofsample (and vice versa). The consistency between in-sample and out-of-sample results was not high.

Q2 high income, low asset Population % of total Income Marital Average age Average asset Total assets % of assets Q3 low income, low asset Population % of total Income Marital Average age Average asset Total assets % of assets 377,221 44% $55,669 53% 29 $766 $288,951,286 5% 356,331 41% $138,665 79% 38 $855 $304,663,005 5% Q1 high income, high asset Population % of total Income Marital Average age Average asset Total assets % of assets 69,778 8% $136,778 78% 52 $44,556 $3,109,028,568 51%

Table 15-7 shows the same market-by-market breakdown as in Table 15-6, but only the short side is represented. More consistency was evident between in-sample and out-of-sample performances for the short side than for the long. Most notably profitable in both samples was the Japanese Yen. Light Crude, Unleaded Gasoline, Feeder Cattle, Live Hogs, Soybean Meal, and Coffee were also profitable in both samples.