Stock Forecast Methods

Stock Market Trading and Investing

Stock Market Forecast for August 16-27 by Pattern Similarity

A pattern recognition system is to classify data based on statistical information excerpted from the data. Normally, a pattern recognition system consists of data input, converter, an extraction processor to classify accordingly to descriptive scheme, and output grouped data. The statistical classification of patterns assumes that the patterns are generated by a probabilistic system.

Some patterns can be repeatable in the future, therefore, we can use selected present patterns to predict the future pattern. Investment Analyzer InvAn-4 (IA) by Addaptron Software has an extra feature to predict stock or index prices using pattern similarity. The prediction period can be chosen within a range 1..60 trading days; the period of historical data that used for matching recommended in 4..16 times longer than prediction period. IA searches for the best matches by scanning all historical data from the internal database.

IA ranks all possible matches on the basis of maximum correlation and minimum deviation within given historical period. IA performs pattern matching using open, high, low, and close prices and volume data. When scanning is completed, IA composes forecast using several best matched patterns (top ranked). Since the statistical regularities of the patterns help to create more stable picture, IA allows adding up many top-rated patterns. The composite result is built as a weighted average with weights proportionally pattern ranks.

The chart below shows S&P-500 Index forecast for August 16-27, 2010 by Investment Analyzer. The forecast is a small uptrend and then a bigger downtrend:

Stock Market Forecast for August 16-27 by Pattern Similarity

Nothing in this piece or blog should be construed as investment advice in any way. Always do our own research or/and consult a qualified investment advisor. It is wise to analyze data from multiple sources and draw your own conclusions based on the soundest principles. Be aware of the risks involved in stock investments

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August 14, 2010 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , , , , | Leave a comment

Pattern Similarity S&P-500 Forecast for the Last Two Weeks of July 2010

Pattern similarity method predicts stock market downtrend-uptrend reverse during the last two weeks of July 2010. The chart below was calculated and plotted using Investment Analyzer InvAn-4. 80 historical days are used to predict within next 10 trading days. With settings for 10 best matches, the Analyzer scans among 153990 cases. The chart shows that S&P-500 index may continue a downtrend with fluctuation then gradually reverse to a moderate uptrend. The reverse point is expected on July 21 with a minimum value around 1050.

Pattern Similarity S&P-500 Forecast for the Last Two Weeks of July 2010

InvAn-4 searches for the best match from internal database by scanning all historical data. It ranks all possible matches on the basis of minimum deviation and maximum correlation within given historical period. Pattern matching is performed using open, high, low, and close prices and volume data. When scanning is completed, it composes forecast using several best matched patterns (top ranked). The composite result is built as a weighted average with weights proportionally patterns’ ranks. The number of patterns that form composite forecast can be adjusted.

July 17, 2010 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , , | Leave a comment

Predicting Stock Market Using Cycle Analysis and Synthesis

Investors could benefit from a fluctuating nature of the stock market. A semi-cyclical nature of the market is a bad surprise for some investors but others know how to take advantage of the cycles. To discover cyclical patterns in the market movement, investors use different software tools.

Stock market cycles may help to maximize ROI.
One of the stock market characters is that it has powerful and pretty consistent cycles. Its performance curve can be considered as a sum of the cyclical functions with different periods and amplitudes. Some cycles known by investors for long, for example, four-year presidential cycle or annual and quarterly fiscal reporting cycles. By identifying the cycles it is possible to anticipate tops and bottoms, as well as, to determine trends. So that the stock market cycles can be a good opportunity to maximize return on investments.

It is hard to identify cycles using a simple chart analysis.
It is not easy to analyze the repetition of typical patterns in stock market performance because often cycles mask themselves; sometimes they overlap to form an abnormal extremum or offset to form a flat period. The presence of multiple cycles of different periods and magnitudes in conjunction with linear and non-linear trends can form a complex pattern of the curve. Evidently, a simple chart analysis has a certain limit in identifying cycles parameters and using them for predicting. Therefore, a mathematical statistical model implemented in a computer program could be a solution.

Be aware: no predictive model guarantees 100% precision.
Unfortunately, any predictive model has own limit. The major obstacle in using cycle analysis for the stock market prediction is a cycle instability. Due to a probabilistic nature of the stock market cycles, the cycles sometimes repeat, sometimes not. In order to avoid excessive confidence and, therefore, losses it is important to remember about a semi-cyclical nature of the stock market. In other words, the prediction based on cycle analysis, as well as, any other technique cannot guarantee 100% accuracy of prediction.

Back-testing helps to improve prediction accuracy.
One of the techniques to improve a prediction accuracy is back-testing. It is the process of testing prediction on prior time periods. At the beginning, instead of calculating the prediction for the time period forward, we could simulate the forecast on relevant past data in order to estimate the accuracy of prediction with certain parameters. Then the optimization of these parameters could help to reach a better precision in forecast.

Stock Market Predictor SMAP-3 is a computer program that is based on cycle analysis.
To discover different patterns in the market movement, including cycles, investors use different software tools. One of the them is Stock Market Predictor SMAP-3. It is able to extract basic cycles of the stock market (indexes, sectors, or well-traded shares). To build an extrapolation, SMAP-3 uses the following two-step approach: (1) applying spectral (time series) analysis to decompose the curve into basic functions, (2) composing these functions beyond the historical data.

Predicting Stock Market Using Cycle Analysis

Conclusion
The stock market is an alive system – around can be joy or fear but its buy-sell pulse always exists. To discover different patterns in the market movement, including cycles, investors use different software tools. Sometimes, these computer tools are called “stock market software.” The stock market software tools help investors and traders to research, analyze, and predict the stock market.

© Alex Shmatov. Published with permission of the copyright owner. Further reproduction strictly prohibited without permission.

May 22, 2010 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , , , , , , , , , , , | Leave a comment