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Having customers ll out preferences pro les does more for you than just provide information. It will change the behavior of your prospects and customers. Once they have invested their time in lling out your pro le and have thought about the types of services they would like to receive from you, they will think differently about your company. They will be more responsive to your next offer, whether it is a direct-mail, email, print, TV, or radio offer. You have moved them one step closer to being pro table customers. You can prove that to your satisfaction by comparing two groups of similar customers. To one group, you send

enough frequency to enter on a limit at a better price without missing too many good trends. The same three periods, evident when average equity was broken down by breakout type, appeared when entry orders were analyzed. For the limit and stop entries, equity surged strongly from August 1985 to June 1988. Equity increased, but to a lesser extent, with a stop order. For entry at the open and on a stop, equity was choppy and down-trending from June 1988 to July 1994, when the limit order modestly gained. From July 1994 to December 1998, equity for entry at the open mildly declined, the stop evidenced a serious decline, and the limit had no consistent movement. The stop did better than average during the first period and much worse than average during the third, more decay in performance over time occurred with a stop order than with the other orders. In all periods, the limit order performed best. When equity was analyzed for all models and order types combined, most of its gains were in the first period, which covered less than the first third of the insample period. By the end of this period, more than 70% of the peak equity had

Figure 14-7

already accumulated. In the second period, equity drifted up a little. In the third period, equity declined, at first gradually and then, after July 1997, at a faster pace.

an email offer to complete their pro le. To the other group, you do not. Table 14-4 shows an example of the results. If you look at this table, it is clear that those who completed your pro le turned out to be much better spenders than those who did not. This does not necessarily prove that the pro les were worth it. Maybe the people who lled out the pro le were better customers to begin with. But why did the nonresponders who were asked to ll out a pro le buy more than the people who were not solicited at all Because you communicated with them! Communicating with customers always improves relations and sales sometimes not as much as you would hope, but always more than noncommunication. So if you want to determine the true result of your pro ling operation, don t just look at the Table 14-4 Results of a Survey

Interactions seemed strongest between breakout types and time. The most notable was between volatility breakouts (versus the others) and time (in-sanple versus out-of-sample). Volatility breakouts performed best early on, but later became the worst. The volatility breakout with stop entry deteriorated more in recent years than it did with entry on a limit, perhaps due to the common use of stops for entry in trend-following models. Finally, the highesthigMowest-low breakout sometimes favored a stop, while the volatility model never did.

In a large lidded pot, cook the ground beef over medium-high heat until browned, breaking the meat up with a fork. Remove the meat from the pot and set aside. Heat olive oil in the pot. Add carrots, onion, celery, and garlic; cook about five minutes or until the vegetables are tender-crisp. Add the cooked beef, cooked pearl barley, broth, tomatoes, red wine, thyme, and bay leaf. Season with salt and pepper. Bring soup to a boil. Reduce heat, cover, and cook twenty minutes. Stir in parsley and serve. Garnish each serving with toasted croutons or a dollop of sour cream, if desired.

Test Customers Not completed Annual sales Sales per person Completed Annual sales Sales per person 200,000 186,000 $4,836,000 $26 14,000 $1,512,000 $108 Control 200,000 200,000 $3,200,000 $16

Restricting trades to long positions greatly improved the performance of the volatility breakout in-sample, and improved it to some extent out-of-sample. Breakout models do better on the long side than on the short one. The ADX trend filter had a smaller benefit in-sample and provided no benefit out-ofsample. Restricting trading to currencies produced lessened in-sample performance, but dramatic improvements out-of-sample. The gain was so great that the model actually profited out-of-sample, which cannot be said for any of the other combinations tested! The currencies were not affected by the rising efficiency other markets had to simple breakout systems, perhaps because the currency markets are huge and driven by powerful fundamental forces. The poorer in-sample performance can be explained by the reduced number of markets traded.