Addaptron Software is pleased to announce the release of a new 2013.05 version of Stock Market Forecast Tools SMFT-1. It is an integrated system that includes three major programs: the most popular software program SMAP-3 for stock market cycles analysis and forecast, NNSTP – Neural Network Stock Trend Predictor, and FTA-2 – a modified version of InvAn-4 that is a comprehensive tool used by serious investors for years. The new version includes several improvements, such as, models optimization, ability to read more different input file formats, and optional feature to enable a free Downloader.
SMFT-1 consists of the tools that employ fundamental ratios rating model, technical analysis, chart pattern analysis, Elliott Wave theory, cycle analysis, candlesticks model, trend lines analysis, regression models, etc. The calculations are empowered by Neural Network. The implemented methods are statistically proven and widely used.
Most methods are provided with back-test calculation to estimate the accuracy of forecast within the recent performance period. The back-testing computations also may play an important role if more than one method is used. It allows estimating a weight of each method in a composed result; the weights that are proportional to the ability of the methods to predict the price.
The purpose of this research was to compare two statistical methods: one that based on Cycle Analysis, another – on a simple Neural Network. Price and volume data were used to train this particular Neural Network. These statistical forecasts were built using historical data of S&P-500 index for six months (from June 2009 to January 2010).
The charts below shows how actual 5-day performance (yellow line) differ from predicted performances by these two methods. The top half is the comparison of Neural Network prediction, bottom half – Cycle Analysis. Green bars mean buy signals, red – sell*.
Three major conclusions for this particular historical period:
- Cycle Analysis prediction gives signals too early, Neural Network prediction – too late.
- In average, the prediction by Cycle Analysis showed slightly better accuracy than the one by Neural Network.
- It seems logical to combine these two methods to improve the accuracy.
* ) The calculations have been performed by an integrated experimental system that combined two applications: Cycle Analysis predictor SMAP-3 and Neural Network predictor NNSTP-2
Charts represent S&P-500 forecast for February 22-26, 2010. The calculations have been performed using Neural Network Stock Trend Predictor NNSTP-2 and Stock Market Predictor SMAP-3 (cycle analysis). A summarized prediction could be a moderate uptrend (1-2%) with flat or downtrend ending. Back-testing fails can be explained by increasing news factor or a possible reversal driven by fundamental changes.