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significant level with any of the other models The large, long-side, turning-point network with entry on limit, however, had high statistical significance when trading each of the wheats, pulling more than 40% annually from each, and more than $15,000 per trade The amazing thing is that, out-of-sample, despite the size of the net and the degree of curve-fitting seen on its portfolio performance, the model pulled in no less than 24%, with $5,000 per trade, from each of the wheats The cycle model, which worked well on hardly any market, did trade the S&P 500 profitably-returning 153%, with an average in-sample trade of $4,613, and 214% with $4,698-per-trade profit out-of-sample It should be noted that a cycle model was found to trade the S&P-500 successfully in the tests reported in our earlier study (Katz and McCormick, May 1997).

B E Y O N D B A S I C S : B R E A K T H R O U G H S U P P L E M E N T S F O R D E T O X I F I C AT I O N

New customer Retention rate Customers Average revenue Total revenue Acquisition/retention Commission Total Total cost Net revenue per customer Net revenue in total 130,000 $2,900 $377,000,000 $192 $959 $1,151 $149,630,000 $1,749 $227,370,000 Second year 54% 70,200 $2,900 $203,580,000 $ $769 $769 $99,970,000 $1,476 $103,610,000 Second year 59% 76,700 $2,900 $222,430,000 $36 $769 $805 $104,650,000 $1,536 $117,780,000

Once each market was paired with a good model-order combination, the performance data were analyzed, both in- and out-of-sample, for each of the markets An equity curve was prepared that covered both periods (see Figure C-l) Returns and statistical significance were calculated for the multiple-model portfolio, both in-sample and out-of-sample It was surprising to discover that the out-of-sample performance data revealed a return-on-account of 625% annualized! A manifestation of the Holy Grail Because model-market combinations were selected on the basis of their in-sample statistical significance, the 544% annualized in-sample return was not unexpected The probability of obtaining an in-sample profit as large as that is less than 1 in 3,000,000,000,000,000,000 (ie, 3 X lo *) Even if massive amounts of optimization, with tests of tens of thousands of combinations, took place, the results would still be extremely significant, in a statistical sense.

Using database marketing, the company would not have to commit the $3 million in the hope of getting this increased return. It could use the database to set up test and control groups to see if the communications would produce the desired results. It could select 5000 customers to receive the communications (at a cost of $180,000) to see if their retention rate could be increased by 5 percent. If the strategy worked, then the next year the company could apply the successful methods to all new acquisitions, spend the whole $3 million, and pocket the $14 million. (Table 1-1 and all the other tables in this book can be downloaded free from www.dbmarketing.com.)

Out-of-sample, the probability of finding a risk-to-reward ratio or annualized return as good as that observed is less than 1 in 40 million Again, even corrected for extensive optimization, the results would still be of extreme statistical significance In fact, no out-of-sample optimization took place In-sample, the systems were only optimized on the entire portfolio Model parameters were never adjusted for the selected markets on which the models were to be traded And only the minimal standard exit strategy was used Performance could be very substantially improved using the best of the exits found in Part III These findings demonstrate that while most systems do not work and most tests show losses, a sufficiently extensive search (as conducted in this book) can discover enough that do work to put together a portfolio trading strategy capable of producing nothing less than stellar results..

We invite all readers to visit our website at: www.wientiLic-consultants.com to e-mail us at kat.z@scientl6c-consultants.com. or

d. Targeted customers outperformed the control by 4 to 1. e. None of the above; all were results of the promotion. 7. Compute the lift in sales in the following table.

Those who wish to replicate and expand on our research may obtain afree copy of the C-Trader Toolkit (the software. required to run the code presented in this book) from our website at www.s&nGfic-eonsultants.com. A CD-ROM is also available for the nominal cost of $59.00. It contains the following: m Complete code for every method tested in this book . Commodities data from Pinnacle . Spreadsheets containing all optimization data, market-by-market analyses, equity cunw, figures, and tables m The C-Trader Toolkit, which includes the C+ + Trading Simulator, OptEvolve (the genetic optimizer), the Portfolio Simulation Shell, and related manuals NtlllE Address City C U try Phone: home (-) Fax (-1 Company state ~ ZP Country code office (-) E-mail $

Number promoted Teens Control 20s and 30s Control 40s and 50s Control Seniors Control Total 100,000 20,000 100,000 20,000 90,000 20,000 60,000 20,000 430,000 Sales rate 8.80% 7.00% 6.60% 5.80% 7.20% 6.00% 5.10% 4.30% Lift Lift in sales