The stock market does not follow just a linear trend - it has some deviations from a linear function. Some cycles are well-known, such as, four-year presidential cycle or annual and quarterly fiscal reporting cycles. In addition, some cycles are defined by intrinsic characteristic properties of the system. The stock market performance curve can be considered as a sum of the cyclical functions with different periods and amplitudes. It is not easy to analyze the repetition of typical patterns in stock market performance because cycles mask themselves - sometimes they overlap to form an abnormal extremum or offset to form a flat period. It is clear that a simple chart analysis has a certain limit in identifying cycles parameters and using them for predicting.
Addaptron Software has developed Stock Market Analyzer-Predictor SMAP-2, computer program, which is able not only to extract basic cycles of the overall stock market or sectors but also to predict an optimal timing to buy or sell stocks. SMAP-2 calculation mainly based on extracting basic cyclical functions with different periods, amplitudes, and phases from historical quote curve. To detect correctly major cycles, the historical price data are transformed from time domain to frequency domain (spectrum).
How it Works
The algorithm of using SMAP-2 is simple - select input data, process data, and view results. The input data are historical quotes of the stock market indexes selected by user for any period from one year to more. than 40 years (automatic downloading). Downloaded CSV-files from the Internet are stored in INPUT subfolder and can be used for further processing
All calculations are done by clicking Processing > Performing Analysis. The calculations can take from a few minutes to an hour depending on the analyzing data period and performance of your PC.
Output results are spectrum of historical quote cycles, forecasted performance, and statistics of average prices (depending on month of year, day of month, and day of week).
* Any index as an equivalent of the overall stock market, sector, or industry can be used to find an optimal timing
* SMAP-2 forecasts performance for 1/4 of historical period. For example, if the period of historical quote data entered as input is equal 16 years, prediction will be calculated for the next 4 years.
* Using back testing helps to find an optimal time frame for better predictability.
* Annual return is calculated on a decompounded basis, i.e., for example, 20% displayed annual return means 44% total for two-year period
* SMAP-2 is packaged with an initial set of data for ^GSPC with different periods
* Historical quote data files are downloaded from publicly available data source for free (US and worldwide stock exchanges)
* The historical quote data period can be selected by users from one year to more than 40 years
* 40 MB Hard Drive Space
* Screen resolution of 800x600 or larger
* Pentium III or AMD Athlon processor (or better)
* 512MB RAM (or better)
* Internet Connection