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that look ahead in time relative to any specified bar, are permissible Entries are taken when momentum crosses above a positive threshold (a buy signal is issued), or when momentum crosses below a negative threshold (a sell is posted) Buying or selling can take place at the open, on a limit, or on a stop The momentum calculations and generation of entries closely follow those for seasonality, but, instead of looking to previous instances of the same date in earlier years, previous instances of the moon having a phase identical to that on the target bar are examined Entries can also be generated by computing the price differences, normalizing them, and integrating the series to obtain a pseudo-price series based on previous instances of the correct lunar phase A moving average crossover model is then applied to that series.

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The repurchase rate is further complicated by the fact that Asian has many different models of cars, and each model has its own unique repurchase rate The goal for any automobile company is to have a repurchase rate of more than 100 percent How would that be possible It would occur if customers never died, if they always bought the same brand when they turned in their existing vehicle, and if many of them bought two or more cars For the automobile industry as a whole, the repurchase rate is about 35 percent The goal is to determine the repurchase rate for every Asian model and for owners of used Asian cars as well To accomplish this, it is necessary to develop some tables from the database that illustrate the situation Such tables should begin with a particular year, such as 1997.

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Because the value at any bar in the series is determined only by bars roughly 27 or more days ago, the delay in the crossovers can be compensated for by looking ahead a small number of bars Both methods are adaptive in that no specific information is required about the phase of the moon on which to place a buy or sell order The adaptive quality is important because different markets respond to the moon differently, as seen in our earlier study Both methods differ from the previous research, where buys or sells occurred a fixed number of days before or after a full or a new moon Several rules for handling confirmations and inversions are also tested to determine whether better results can be obtained over the basic models.

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You can then determine what Asian cars the owners of 1997 models owned in the summer of 2002 Next, you create the same tables for the owners of 1998, 1999, 2000, and 2001 models Finally, you have to create similar tables for Certi ed Previously Owned (CPO) Asian cars Asian cars that were bought used These data are usually available because manufacturers require their dealers to report the owners of each new and each used Asian This information is stored in Asian s database Table 4-1 shows what the 1997 Asian owners owned in the year 2002 From these data, one would assume that the repurchase rate varied from 22 to 38 percent, but that is not the case To get the real repurchase rates, you have to gure in the Asian owners who bought cars every year, every 2 years, and so on.

An example of co@mation is the following: If a buy signal occurs on a given bar and everything behaves as expected, the market should bottom around the time of the buy; if instead it tops, the buy signal might be suspect, ie, the market may not be adhering to its typical seasonal or lunar timing The crossover with cunfirmurion model implements the crossover model with an additional rule that must be satisfied before a buy or sell can be issued lf a buy signal occurs, the Slow %K on the signal bar must be less than 25%, meaning the market is near the bottom of its recent historical range.

The numbers shown in Table 4-1 mask the fact that in the 5 years between 1997 and 2002, a signi cant Table 4-1 Changes in Ownership over the Years.

Likewise, if the model generates a sell signal, a check is made to see whether Slow %K is greater than 75%, indicating that the market is near the top of its range, as would be expected if it is responding to lunar influence The con@nation and inversion model adds another element: If a buy is signalled, and the market is near the top of its range (Slow %K greater than 75%), then an inversion is assumed and, instead of issuing a buy, a sell order is posted If a sell is signalled, but the market is near the bottom of its recent range, a buy order is issued The characteristics of lunar entries are similar to seasonal entries: Both are predictive, rather than responsive, and thus lend themselves to countertrend trading And, as with any predictive entry, they may be out of tune with market behavior.

Chart A Totals A Model 17,472 14,133 4,003 980 1,729 651 396 247 23% B Model 53,259 38,950 11,666 6,221 1,040 3,047 838 521 22% C Model 26,784 19,585 7,861 1,464 565 3,924 1,267 641 29% D Model 17,646 14,111 6,619 802 382 844 4,126 466 38% E Model 452 339 146 24 20 26 57 19 32% Owned 1997 120,409 Owned 2002 91,875 Bought new 30,295 A Model 9,491 B Model 3,735 C Model 8,491 D Model 6,684 E Model 1,893 Repurchase rate 25%

As with seasonal entries, cycle or rhythm inversions may occur..

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