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Many kinds of algorithms are useful in cycle analysis-everything from counting the bars between tops or bottoms, to fast Fourier transforms (FITS) and maximum entropy spectral analyses (MESAS) Getting such algorithms to work well, however, can be quite a challenge; but having reliable software for cycle analysis makes it possible to build objective, cyclebased entry models and to test them on historical data using a trading simulator Whether detected visually or by some mathematical algorithm, market cycles come in many forms Some cycles are exogenous, ie, induced by external phenomena, whether natural or cultural Seasonal rhythms, anniversary effects, and cycles tied to periodic events (eg, presidential elections and earnings reports) fall into the exogenous category: these cycles are best analyzed with methods that take the timing of the driving events into account Other cycles are endogenous; ie.

bills. Disconnecting the wrong customer can translate into signi cant revenue losses, while keeping delinquent customers also affects pro tability. A wireless service provider was formed by combining the customers of 11 different companies within a short space of time. Its rst job was to create a uni ed database covering the more than 20 million customers that had been in 11 different databases. Rather than waiting years for a data warehouse, the provider created an immediate temporary marketing data mart that combined the billing data from each company. This data mart was accessed by SAS products. The data mart allowed managers in 35 different markets to analyze the data, giving each manager the ability to handle customer trends on a local level. Local managers could immediately address churn, instituting local incentives to keep customers or to rework their existing contract. Examples of the tactics used to reduce churn were initiatives for: Rewarding customers by donating to the Special Olympics Reduced rates for particular customers A special deal on a new phone Free movie rentals at Blockbuster Video Price plans developed after analyzing customer behavior Phone trade-in programs

, their external driving forces are not apparent, and nothing other than price data is needed to analyze them The 3-day cycle occasionally observed in the S&P 500 is sn example of an endogenous cycle, as is an S-minute cycle observed by the authors in S&P 500 tick data Programs based on band-pass filters (Katz and McCormick, May 1997) and maximum entropy (eg, MESA96 and TradeCycles) are good at finding endogenous cycles..

Overall, the wireless service provider wanted to know why customers were leaving, broken down by Amount of time with the company Minute use Price plan Thirty other speci c market-level and company-level trends

We have already discussed the exogenous seasonal cycles, as well as lunar and solar rhythms. In 10, endogenous cycles are explored using a sophisticated wavelet-based, band-pass filter model. Neural Networks

Armed with these objectives, the wireless service provider reorganized its IT department and hired a dozen SAS programmers to construct a decision support system. When the temporary database was built, it installed several SAS products on its Sun Enterprise servers, including Enterprise Miner and AppDev Studio.

Exposure to certain substances found in our own bodies as well as substances in our environment can have destructive effects on health. Xenobiotic detoxification helps to eliminate toxic compounds from many sources.

As discussed in 11, neural network technology is a form of artiiicial intelligence (or AI) that arose from endeavors to emulate the kind of information pmcessing and decision making that occurs in living organisms. Neural networks (or nets ) are components that learn and that are useful for pattern recognition, classification, and prediction. They can cope with probability estimates in uncertain situations and with fuzzy patterns, i.e., those recognizable by eye but difficult to dehe using pmcise rules. Nets can be used to directly detect turning points or forecast price changes, in an effort to obtain good, predictive, countertrend entry models. They can also vet entry signals generated by other models. In addition, neural network technology can help integrate information from both endogenous sources, such as past prices, and exogenous sources, such as sentiment da@ seasonal data, and intermarket variables, in a way that benefits the trader. Neural networks can even be trained to recognize visually detected patterns in charts, and then serve as pattern-recognition blocks within traditional rule-based systems (Katz and McCormick, November 1997). Genetically Evolved Entry Rules