Time Cycle Background

Origins - The stock market crash of 1929

The origin of the theory of business cycles can be traced back to the 18th century, when economists such as Juglar, Kitchin and Kondratief became famous for finding specific cycles in business activities. The big breakthrough in modern cycle analysis came when ER Dewey first became interested in cycles while Chief Economic Analyst of the Department of Commerce in the early 1930's. With the huge event of the 1929 market crash, President Hoover wanted to know why there were not more warning signs; especially why there were no signs signaling such a cataclysmic economic event. He tasked Dewey, among others, with finding a way to forecast events like the crash of 1929.

Once Dewey became involved in this journey of discovery, his research led him to the little known study of cycles. His efforts were just getting started when Roosevelt replaced Hoover in the White House. As with most succeeding administrations, the easiest course of action when faced with major economic problems is to blame the previous administration. This is exactly what Roosevelt did. Of course, there would be little interest in supporting anything that Hoover had done. To do so would be tantamount to admitting the crash was not Hoover’s fault. So Roosevelt did not endorse or support Dewey's research and, as such, did not continue the project. This did not stop Dewey, however.

Further studies

Dewey rapidly became a cycle analysis zealot, eventually believing that all significant events in human history can be reasonably accurately predicted through cycle analysis. Dewey devoted his life to the study of cycles, claiming that "everything that has been studied has been found to have cycles present." He carried out extensive studies of cyclicity in economic, geological, biological, sociology, physical sciences and other disciplines. In 1941 he formed the Foundation for the Study of Cycles with a board that included distinguished scientists and industrialists to act as a central clearing house of cycles studies from diverse areas.

As a result of his research, Dewey asserted that seemingly unrelated time series often had similar cycle periods present and that when they did, the phase of these cycles was most nearly the same (cycle synchrony). He also asserted that there were many cycles with periods that were related by powers or products of 2 and 3. Practical cycle theory assumes that some cycles consistently persist in data and are repetitive, and these cycles will continue to behave similarly in the future. Cycles that have performed well in the past should continue to perform well into the future, if they are genuine. Certainly, it is postulated, these are the types of cycles that can be used for forecasting.

What cycles are

In short, a cycle is a rhythmic fluctuation that repeats over time with reasonable regularity. When it is sufficiently regular and persists over a long enough span of time, it cannot reasonably be the result of chance. And the longer a non-chance rhythm continues, the more predictable it becomes.

There are some interesting characteristics of cycles:

• Cycles persist without change of period for as far back as there are data. After distortion (exogenous events), cycles will revert to the pre-distortion pattern.
• Cycles of any period tend to have counterparts in other phenomena, and even in other disciplines.
• Cycles of the same period tend to synchronize, or crest at the same calendar time, regardless of phenomena.

How cycles are used in CycleProphet

We believe that the technical study of cycles is a valuable tool in the projection of financial markets, stocks, commodities, futures, etc.

We are heavily involved in the extension of Dewey’s work. We have a research project underway that is taking an in-depth, scientific approach to definitively provide the parameters associated with time cycles, where we actually “map” historical data to highly correlated cycles of time. Having mapped historical cycles to immutable and unchanging cycles of time, we have developed a highly reliable process of mapping the future data. The assumption is, if the time cycles are proven to be less than 10% randomized, the odds become very high that a future map of the data can be produced by extending those same time cycles into the future.

Below is an example of the November 28, 2008 forecast showing how this technology accurately predicted the March 9 low of 2009 and the subsequent dramatic move higher in the market.

In the example above, no future data was provided to the CycleProphet algorithms. The blue line is the CycleProphet forecast, base purely on our patent-pending time mapping algorithms. The green line represents the actual daily closing prices of the S&P 500.

As you can see, this technology absolutely nailed the March 9 low, to the day, a full 3 months prior to the event.