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Alexander, Colin (June 1993). Trade with Moving Averages. Technical Anolysir of Stockr a n d Commodities, pp. 61-7 I. Appel, Gerald (1990). The Advanced Moving Average Convergence-Divergence Trading Method. Videotape and manual distributed by Signalert Cotportion, New York (516-829-6444). Bartie, Scott (September 1996). The COT Index. Technical Analysis of Stocks and Commodities, pp. 16-36. Barr&, Scott (October 1996). Pork Bellies and the CtX Index. Technical Analysis ofStocks und Commodities, pp. 79-92. Bernstein, Jake (1995). Trade Your Way fo Riches. MBH Commodity Advisors, Inc. (l-800-4570X25), 1995. Blau, William (January 1993). Stochastic Momentum: Technical Analysis of Stocks and Commodities. pp. 26-35. Burke, Gibbons (May 1993). Good Trading a Matter of Breeding Futures Magazine, pp. 26329. Center for Solar and Space Research, Yale University (1997). Sunspot Predictions. Release distibuted by Virtual Publishing Company. Chande. Tushar S. (March 1992). Adapting Moving Averages to Market Volatility. Technical Analysis of Stockr and Commodities, pp. 4653. Davies, D. W. (June 1993). Cyclical Channel Analysis and the Commodity Channel Index. Technical Analysis of Stocks and Commodities, pp. 3845. Davis, Lawrence (Ed.) (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold. Ehlers, John (March 1989). Moving Averages and Smoothing Filters, Technical Analysis of Stocks and Commodities, pp. 4246. Gauquelin, H., Gauquelin, R., and Eysenck, S. B. G. (1979). Personality and Position of the Planets at Birth: An Empirical Study. British Journal of Social and Clinical Psychology, Vol. 18, pp. 71-75. Goedde, Richard (March 1997). Timing a Stock Using the Regression Oscillator. Technical Analysis of Stocks and Commodities, pp. 54-60. Hannula. Hans (November 1991). The Seasonal Cycle. Technical Analysis of Stocks and Commodities, pp. 6548. Hoel, Paul G. (1966). Elemental Statiufics, 2d ed.. New York: John Wiley & Sons. 365

A manufacturer of industrial components made 40,000 different products and had $400 million in sales through 1800 independent agents. While $400 million may sound like a lot, it was less than 1 percent of the $87 billion in annual sales in this business category. The independent agents handled the products of many other manufacturers in addition to this rm s. Some agents were very good at pushing this rm s brand. Others were not so good. The company wanted to stand out from the crowd. It turned to Scott Brostoff of CSC Advanced Database Solutions to come up with a method of helping its agents succeed. Each agent had a unique territory. The rm had compiled a list of hundreds of thousands of ultimate customers based on their deliveries over 3 years. CSC decided to take this database and add Dun & Bradstreet data to it, showing for each customer the SIC code, annual sales, number of employees, and actual products that the customer had purchased. What types of data are available from Dun & Bradstreet for appending to a business-to-business le Some different types are listed in Figure 9-1. Scott decided to feed the D&B information into a model that showed which products in which quantities customers in each SIC code bought. Armed with that information, CSC decided to use the D&B data to identify prospects in each agent s territory that were not currently buying the rm s products. The model was designed to score each prospect in order to determine the 100 prospects in each agent s territory that would be

Holland, John (1975) A d a p t a t i o n i n Narural a n d Arfificial Systems Ann Arbor: The University of Michigan Press Jurik, Mark (1999) Finding the Best Data Computerized Trading Mark Jurik (Ed) New York: New York Institute of Finance/Prentice Hall, pp 355-382 Katz, Jeffrey Owen (April 1992) Developing Neural Network Forecasters for Trading Technicul Analysis of Stocks and Commodities, pp 5848 Katz, Jeffrey Owen, and McCormick, Donna L (1990) Calendar Effects Chart New York: Scientific Consultant Services Katz Jeffrey Owen, and McCormick, Donna L (March/April 1993) Vendor s Forum: The Evolution of N-TRAIN PCAI, pp 4&46 Katz, Jeffrey Owen, and McCormick, Donna L (1994) Neural Networks: Some Advice to Beginners Trader s Cat&g and Resource Guide, Vol II, No 4, p 36 Katz, Jeffrey Owen and McCormick, Donna L (July/August 1994) Neurogenetics and Its Use in Trading System Development.

The sulfur compounds in cruciferous vegetables are known for their positive effect on overall detoxification. Among these, glucosinolates are very significant in terms of their effects on Phase I and Phase II xenobiotic detoxification. Scientists have isolated more than one hundred types of glucosinolates that can be further broken down into isothiocyanates, dithiolethiones, nitriles, and thiocyanates. Dr. Steve Hecht at the University of Minnesota Cancer Center has been studying the naturally occurring aromatic isothiocyanates such as benzyl isothiocyanates and phenylethyl isothiocyanates as potent inhibitors of the induction of cancer by many cancer-causing agents, especially nitrosamines, heterocyclic amines, and PAHs. This appears to be related to their effect on both Phase I and Phase II detoxification, especially their inhibiting effect on some Phase I enzymes and a stimulation of Phase II enzymes such as GST and quinone reductase. Including sulforaphane, these isothiocyanates have been found to inhibit lung, breast, esophageal, liver, bladder, small intestine, and colon cancer. Dithiolethiones are another important class of glucosinolates that support Phase I and Phase II detoxification. Dr. Kensler at Johns Hopkins University has been studying dithiolethiones in relationship to cancers of the lung, stomach, colon, liver, breast, skin, urinary tract or bladder, and trachea. In an ongoing study in the Qidong region of China, where liver cancer and aflatoxin exposure rates are high, Dr. Kensler and coworkers have found that supplementation with oltipraz, a specific dithiolethione, showed a significant reduction in biomarkers related to liver cancer. These and other sulfur compounds can be taken as supplements in their pure form, such as sulforaphane, aromatic isothiocyanates, and