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Kinds of Breakouts l Characteristics of Breakouts . Testing Breakout Models l Channel Breakout Entries (close only channel breakouts; highest higMowest low bnxzkouts) l Volatility Breakout Entries l Volatility Breakout Variations (long positions only; currencies only; adx tremififilter) . Summary Analyses (breakout types: entry orders; interactions; restrictions andjilters; analysis by market) * Conclusion l What Have We Lamed 6 Moving Average Models 109 What is a Moving Average - Purpose of a Moving Average * The Issue of Lag l Types of Moving Averages l Types of Moving Average Entry Models l Characteristics of Moving Average Entries l Orders Used to Effect Entries * Test Methodology Tests of Trend-Following Models * Tests of Counter-Trend Models * Conclusion l What Have We Learned

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Use of Database Marketing Catalogs and the Web Use of Collaborative Filtering What Catalogers Have to Guard against When They Use the Web Lifetime Value Changes for Catalogers Building B2B Catalog Sales Summary What Works What Doesn t Work Quiz

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Dr. Steve Hecht and coworkers at the University of Minnesota Cancer Center regularly investigate the role of nitrosamines in cigarette smoke on human cancer. They have found that cruciferous vegetables such as watercress, which contains large amounts of isothiocyanates, are very effective in counteracting tobacco-specific nitrosamine-induced cancer in experimental animals. Along with this phytochemical, D-glucarate, silymarin, flavonoids from grape seed and skin extract, and green tea, rosemary extract, N-acetylcysteine, and alpha lipoic acid all help to detoxify the many cancer-causing agents in cigarette smoke. If you smoke, stop now! It is never too late to quit. The American Cancer Society has published some good news about the benefits of quitting, which begin almost immediately after the last cigarette is put out. Within twenty minutes, blood pressure, pulse, and body temperature return to normal. After eight hours, that odor known as smoker s breath

What Is an Oscillator l Kinds of Oscillators * Generating Entries with Oscillators * Characteristics of Oscillator Entries . Test Methodology l Test Results (teas of overbought/oversold models; tests of signal line models; tests of divergence models; summary analyses) - Conclusion * What Have We Learned S

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Penetration Analysis Finding Leads for Equipment Financing Managing Leads on the Web Building a Relationship with Prospects Telling Them What They Want to Hear Cluster Coding Prospect LTV Selling Timeshares Nonpro t Fund Raising Creating Referrals Summary What Works What Doesn t Work Quiz CHAPTER 14 PROFILE MARKETING

What Is Seasonality l Generating Seasonal Entries l Characteristics of Seasonal Entries . Orders Used to Effect Seasonal Entries . Test Methodology . Test Results (test of the basic crossover model; tests of the basic momentum model: tests of the crossover model with con$mtion; tests of the C~SSOV~~ model with confirmation and inversions: summary analyses) * Conclusion * What Have We Learned Chmter 9

How Modeling Can Help Getting Customers to Enter Their Own Pro les The Web Quiz What Works What Doesn t Work Quiz CHAPTER 15 A FAREWELL TO THE READER

Legitimacy or Lunacy l Lunar Cycles and Trading (generating lunar entries: lunar test methodology; lunar test results; tests of the basic cmmo~er model; tests of the basic momentum model: tests of the cnx~mer model with confirmation; test.s of the crmmver model with confirmation and inversions; summary analyses; conclusion) * Solar Activity and Trading (generazing solar entries: solar test results: conclusion) * What Have We Learned 10

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Cycle Detection Using MESA l Detecting Cycles Using Filter Banks (butterworth jilters; wavelet-basedjilters) * Generating Cycle Entries Using Filter Banks * Characteristics of Cycle-Based Entries . Test Methodology . Test Results . Conclusion l What Have We Learned 11

atabase marketing as we know it today began in the early 1980s. Its rise was chronicled and boosted by the National Center for Database Marketing, which was started by Skip Andrew in 1987 and has continued to hold conferences twice a year ever since. At rst, many companies had heard of it, but few were actually practicing it. By 1995, however, every American company knew what it was, and most had appointed a director of database marketing. The practitioners became like religious converts. They believed in it. Inside industry, some companies, including Kraft Foods, American Airlines (and many other airlines), American Express, Hallmark, JC Penney, Neiman Marcus, MCI, and scores of others, formed huge databases. The idea was that the company could reach its customers directly (instead of through mass marketing) and build their loyalty by direct communications. In most cases, it worked. Marketers soon learned how to set up control groups to prove whether what they were doing was working. They became skillful at what they were doing. At rst, these large databases were maintained on mainframes, with smaller databases kept on midrange computers. With the advent of the PC and servers, however, all large databases moved to servers, and small databases were maintained on PCs. Companies that kept their data on mainframes found that they were having considerable trouble keeping up with the twists and turns of the database marketing revolution. One twist was the need to create a relational database, which is the optimum format for database marketing. What that means is that for