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risk that you won t generate the sales and pro ts at all. A perfectly safe business has a risk factor of 1. The risk can come about because customers don t pay, because of heavy competition, because of product obsolescence, etc. Years to wait is the amount of time that the insurance company has to wait before the pro ts materialize. The rst year, we are assuming that the waiting time is zero, because we are asking the policyholders to pay in advance. The chart could be made more complicated by adding administrative costs, medical examinations, etc. Leaving these out, we have a relatively clear picture of the marginal pro ts that a life insurance company can expect to realize over 5 years from 100,000 policyholders. Using the Bain & Company gures, we should expect to see this $29.5 million 5-year pro t go up by 90 percent, to $56 million, if we were to increase the retention rate by 5 percent. But that is not what happens. Table 2-6 gives the pro ts over 5 years assuming that the retention rate is somehow increased by 5 percent. The cumulative net present value does go up, but only by about 15 percent, as Table 2-7 shows. Now, 15 percent is a good solid number, but it is not 90 percent. Unfortunately, the rate is probably much less than 15 percent, because we have not gured in the costs of getting the 5 percent increase in retention. Here is where CRM really has trouble. Table 2-6 New NPV

The pattern of more profitable trading out-of-sample than in-sample is prima facie evidence that optimization played no role in the outcome; instead, in recent years, some markets have become more tradeable using such models This may be due to fewer trends and increased choppiness in many markets Tests 16 through 18: RSI Divergence Models Optimization stepped the RSI period from 5 to 25 in increments of 1, and the divergence look-back from 15 to 25 by 5 Overall, the results were poor h-sample, the stop was lest bad, with the limit close behind Out-of-sample, the stop was also best, closely followed by the open Given that the RSI has been one of the indicators traditionally favored by traders using divergence, its poor showing in these tests is noteworthy.

Year 1 Retention rate 55% Customers 100,000 Payments $1,000 Revenue $100,000,000 Commissions Claims Total costs Pro t Discount rate Net present value Cumulative NPV $20,000,000 $75,000,000 $95,000,000 $5,000,000 1.00 $5,000,000 $5,000,000 Year 2 65% 55,000 $1,000 $55,000,000 $ 275,000 $41,250,000 $41,525,000 $13,475,000 1.07 $12,640,713 $17,640,713 Year 3 70% 35,750 $1,000 $35,750,000 $ 178,750 $26,812,500 $26,991,250 $ 8,758,750 1.14 $ 7,707,752 $25,348,465 Year 4 75% 25,025 $1,000 $25,025,000 $ 125,125 $18,768,750 $18,893,875 $ 6,131,125 1.21 $ 5,061,375 $30,409,840 Year 5 80% 18,769 $1,000 $18,768,750 $ 93,844 $14,076,563 $14,170,406 $ 4,598,344 1.29 $ 3,561,005 $33,970,846

Heating Oil was profitable for all orders, Unleaded Gasoline WZY significantly profitable for the open and stop, Light Crude for the limit and stop In-sample, Soybeans traded very profitably across orders; Orange Juice, Corn, Soybean Oil, and Pork Bellies traded well with the stop Out-ofsample, the oils were not consistently profitable, while Soybeans remained profitable across orders; Orange Juice and Soybean Oil still traded profitably with the stop Tests 19 through 21: MACD Divergence Mode& The length of the shorter moving average was stepped from 3 to 15 in increments of 2; the length of the longer moving average from 10 to 40 by 5; and the divergence look-back from 15 to 25, also by 5 Only parameter sets where the longer moving average was actaally longer than the shorter one were examined..

Finally, models that appear to work, producing positive returns in both samples! With entry at open, trades were profitable across samples. In-sample, the

Present 5-year NPV New 5-year NPV Increase Percent increase $29,493,352 $33,970,846 $ 4,477,494 15%

average trade made $1,393; 45% of the trades were winners; and there was only an 8.7% (uncorrected; 99.9% corrected) probability that the results were due to chance; both longs and shorts were profitable. Despite poor statistical significance in-sample, there was profitability out-of-sample: The model pulled $140 per trade (after commissions and slippage), with 38% winning trades; only shorts were profitable. The limit performed slightly worse in-sample, but much better out-of-sample. Figure 7-2 depicts the equity curve for entry on a limit. In-sample, the average profit per trade was $1,250 with 47% winning trades (the highest so far); longs and shorts were profitable; and the probability was 13.1% (uncorrected; 99.9% corrected) that the results were due to chance. Out-of-sample, the model made $985 per trade; won 44% of the time: was profitable in long and short positions; and was only 27.7% likely due to chance. It-sample, the stop had the greatest dollars-per-trade return, but the smallest number of trades; only the shorts were profitable. Out-of-sample, the system lost $589 per trade; only short positions were profitable. Regardless of the order used, this model had relatively few trades. The market-by-market analysis (see Tables 7-1 and 7-2) confirms the potential of these models to make money. The largest number of markets were profitable in-sample. Across samples, all three orders yielded profits for Light Crude and Coffee; many other markets had profitability that held up for two of the orders, but not for the third, e.g., Heating Oil, Live Cattle, Soybeans, Soybean Meal, and Lumber. SUMMARY ANALYSES