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will disappear; carbon monoxide drops and oxygen rises to normal levels in the blood. The risk of heart attack begins to decrease after only twenty-four hours. Within a couple of days, the ability to taste and smell improves significantly as does breathing capacity. Within two to three months, circulation improves and lung function increases up to 30 percent. Within one to nine months, sinus congestion, coughing, fatigue, and shortness of breath decrease. The cilia that clean debris from the lungs grow back. Within one year, excess risk of coronary heart disease is half that of a smoker. Within two years, heart attack risk drops to near normal. Within five years, lung cancer death rate and stroke risk decrease by half. Within ten years, lung cancer death rate is half that of a continuing smoker and the risk of cancer of the mouth, throat, esophagus, bladder, kidney, and pancreas decreases. Within fifteen years, the risk of coronary heart disease is the same as a person who has never smoked. So as you can see there are compelling reasons to quit smoking as soon as possible. Get your spouse or friend to quit with you because this makes it much easier. In addition, if necessary, use a nicotine substitute; several studies suggest that this also helps.

What Are Neural Networks (feed-forward neural networks) . Neural Networks in Trading l Forecasting with Neural Networks l Generating Entries with Neural Predictions . Reverse Slow %K Model (code for the reverse slow % k model: test methodology for the reverse slow % k model; training results for the reverse slow %k model) l Turning Point Models (code for the turning point models; test methodology

Figure 1-2

for the turning point models; training resulrs for the turning point models) * Trading Results for All Models (@ading results for the reverse slow %k model: frading results for the bottom ruming point model; trading results for the top turning poinf model) * Summary Analyses l Conclusion * What Have We Learned

Data quality varies from excellent to awful. Since bad data can wreak havoc with all forms of analysis, lead to misleading results, and waste precious time, only use the best data that can be found when running tests and trading simulations. Some

A marketing database gets data from the operational database, if there is one. These data consist of a summary of monthly transactions. But the marketing database also includes much more. It gets additional data from Preferences and pro les provided by the customers Promotion and response history from marketing campaigns External sources, such as Experian, Donnelly, and Claritas Lifetime value and RFM analysis, leading to creation of customer segments Modeling for churn and next best product The marketing database passes data back to the operational database. It may advise the operational database The segment that each customer has been placed in, which may lead to operational decisions. Gold customers, for example, may get different operational treatment. Expressed customer preferences, which lead to different operational treatment; e.g., smoking or nonsmoking rooms are assigned automatically.

forecasting models, including those based on neural networks, can be exceedingly sensitive to a few errant data points; in such cases, the need for clean, error-free data is extremely important Time spent finding good data, and then giving it a final scrubbing, is time well spent Data errors take many forms, some more innocuous than others In real-time trading, for example, ticks are occasionally received that have extremely deviant, if not obviously impossible, prices The S&P 500 may appear to be trading at 95200 one moment and at 25050 the next! Is this the ultimate market crash No-a few seconds later, another tick will come along, indicating the S&P 500 is again trading at 95200 or thereabouts What happened A bad tick, a noise spike, occurred in the data.

The operational database is run by IT. It is run on accounting principles, and it balances to the penny, since there are legal and tax aspects to its data. It is audited by external auditors. It contains only current data on customers. Old data are archived. There are no data on prospects until they make a purchase. The marketing database is managed by the marketing department. It is usually outsourced. It is not run on accounting principles, and it does not need to balance. There are few legal or tax considerations. It contains information on current customers, prospects, and lapsed customers and communications with them. It may retain data stretching back over a period of several years. A data warehouse combines all the data and functions of both an operational database and a marketing database (plus, in many cases, other functions such as employee data, payroll, product data, production data, etc.) in one huge database. The data warehouse is so big and complicated that it needs to be managed by a committee. It is often very expensive to build and maintain. It is seldom very exible. Marketing priorities usually get short shrift in a data warehouse, as other priorities are often given preference.