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Bernstein s (1995) home-study course on seasonality suggests trading off significant tops and bottoms, and when there has been consistent movement in a tradable over a number of years; this approach, as well as Hannula s, may tend to involve trades lasting several weeks to several months In 1990, we first published the Calendar Effects Chart, a set of tables and a chart that show date-specific behavior in the S&P 500 cash index The chart illustrates a general up-trend from January through September, and then an up and down decline until October 24 The market then, on average, bottoms, after which time it steeply rises until the end of the year On a more detailed level, rapid gains seem to occur throughout most of January, the first half of April, and the first half of June.

insurance companies, and others; two of these are described in detail in this book. These applications are an exception to the general rule about data warehouses. NCR has succeeded where most other CRM developers have failed.

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A peak can be seen on October 8, and a very steep decline that leads to a bottom on October 24 When the tables and chart for this publication were generated, extreme movements were clipped at +2 percent to prevent them from having undue influence on the results Consequently, the steep decline in October, and the other patterns mentioned, cannot be attributed to events in specific years, for instance, the crash in 1987 For some dates, there were incredibly consistent patterns; eg, if an entry occurred on the close of April 14 and the trade was exited one day later, over 90% of the time a small profit would have been made Entry on May 6, with exit one day later, resulted in a profit 100% of the time, as did entry on July 13.

Mass marketing for certain products and services Building a customer and prospect database and using it to market to customer segments for certain products and services Having your products available in retail locations that are convenient for your customers Communicating with customers to maintain their loyalty; sending them emails, thanking them for their patronage, and getting them to ll out customer preference pro les

The market declined 90% of the time from October 18 to 19, and 89% of the time from October 16 to 17 Although the crash may have involved a much greater than normal amount of movement, the presence of a decline at the time when the crash occurred was not at all unexpected In an attempt to capture highprobability, short-term movements, the Calendar Effects Chart could have been used to enter trades that last one or two days For example, such a methodology would have caused a trader to go short at the close on October 16 and exit on October 19, thus capturing the crash The data contained in this publication could also have been used to help maintain positions during periods of steep ascent or decline There have been other studies indicating the presence of strong seasonal effects in the market that can be exploited for profitable trading.

Giant CRM multimillion-dollar data warehouses designed to produce one-to-one marketing. Million-dollar software that is designed to make the right offer to the right customer at the right time. One-to-one marketing, except in certain limited situations. Changing a company so that it is customer-focused instead of product-focused. It s a nice idea, but no one has ever done it, and no one ever will.

An investigation we conducted (Katz and McCormick, April 1997) found that short-term seasonal behavior could be used to trade the S&P 500: The system used fairly fast moving average.

1. Which is not a basic assumption of CRM a. A warehouse permits a rm to make the right offer to the right customer at the right time. b. A data warehouse is essential to marketing success.

crossovers that were computed on seasonally anticipated prices Because the anticipated prices could be computed at least one year ahead, lag in the moving average crossover was easily compensated for with a displacement that enabled the system to take trades at crossovers occurring several days after the fact The trades taken by the system typically lasted between 7 and 8 days, a fairly short-term seasonal trading model The system was profitable: It pulled an astonishing $329,900 from the S&P 500 between January 31986, and November 8, 1996 The test did not include transaction costs, but even with $15 round-turn commissions and $75 per trade slippage, a profit of $298,310 (about a 10% reduction) resulted The return on account was 732%, not annualized; assuming fixed contract trading, this amounted to over 70% per year, annualized, on a constant one-contract, no-reinvestment basis.