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To: Toby Zidle who wrote (859)	9/29/2002 6:46:17 AM
From: globestocks	Read Replies (1) \| Respond to of 1003

yeah...i was just wondering if he was a credible name because i found this website about synergetic technical analysis...i guess its his investment method like you said. and if he is a credible name then i wanted to maybe check out the site on its trial member ship. I figure its work a try...money back guarantee anyways so...why not, right? synergytrader.com Hehe....i wish the stock market had a money back guarantee.

To: Toby Zidle who wrote (859)	9/29/2002 9:46:03 AM
From: E. Charters	Read Replies (1) \| Respond to of 1003

The one I like is Ray Tomes. His long term factor analysis and regression techniques, have been proven in predicting the 1974 and 1987 stock market crash.(before it happened -- predicting after as a test of the method is not what we are talking about) He does not sell TA techniques or advice, although at one time economic forecasting was his business. Factor analysis is useful for detecting the things you should be regressing. Linear or non linear regression does not seem to be used well in TA. I think that is because it is used as only the linear kind, not applied to enough data, and is usually not fine grained enough in trends to produce short term data reliably. A regression technique that uses a cycle-pattern repetition with variant wave recombination from fourier component breakdown is needed for more short term data. What components to use and where to start them is one of the keys. So you start with FA, which is controversial but at least has some 100 years history as an analysis tool for determining least number of effecting components. Then you do Fourier to determine dominant cycles of each factor arrived at. A trial recombination determines the phase zero point of each wave. One of the keys in factor analysis is selection of the factor possibilities for data crunching. Since some things will correlate highly with a time component, you have to name a factor as the time lag effect of say, "derivatives in gold open interest October to December Dow". And enter the data "lined-up" with this time difference or offset entered as zero time difference. This allows factor analysis to measure these time lag effects as relevant whereas if the data column is not entered with the time lag component, factor analysis will not extract it, as it does appear to be entered as a factor to test otherwise. Factor methods would ordinarily not figure all the possibilities. Thus it can be seen that trial runs must be made with factors entered that may tbe the targets, on the basis of reasonableness of their probability. This is really trial correlational analysis with principle component Factor analysis as the data reduction and testing method. You could also use multi variant correlational analysis as a method to test the hypotheses you want to test. Correlational analysis has the advantage that it is easier to program. You do end up with a dizzying array of possibilities to test however.Using a computer cluster may be an advantage here. This could take some crunching time. Once you know your factors, tbough, doing the fourier on a time period, regressing that factor and recombining with trial phases is automatic and not that time consuming. It can be done withing short term trading time lines. EC<:-}