Stock Forecast Methods

Stock Market Trading and Investing

S&P-500 Index Cycle Analysis Forecast for the Next Several Years

There is a simple forecast that is built on assumption that the stock market has a cyclical nature. The cycles may not be stable all the time but the probability of repeating cycles can be high enough to use the cycles by stock traders and investors for their benefits. The cyclical nature of the market can be masked by more powerful factors (fundamental data, bad/good news, global events, etc.) that over-drive the market time-from-time. Many technical analysts use cycle predictions in their comprehensive analysis.

One of the result-sensitive parameters in the cycle analysis prediction is a historical period that used to extract the cycles from a curve. The prediction can be very different depending on the historical period that is chosen. One of the stable cycles that is observed for the recent decade is a seven-year period. The chart below shows S&P-500 index forecast for the next several years. The calculation has been performed using Stock Market Predictor SMAP-3. According to this forecast the stock market might continue its uptrend with natural short-term small-amplitude sub-cycles until 2012-2013.

S&P-500 Index Cycle Analysis Forecast for the Next Several Years

December 29, 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

Latest Cycle Analysis Forecast: Stock Market Uptrend until April 2010

According to a revised cycle analysis forecast, SP-500 index performance until April 7, 2010 expected to be positive. Evidently, previously predicted negative move was reversed by good fundamental news. The chart has been plotted using Stock Market Analyzer-Predictor SMAP-3.

Latest Cycle Analysis Forecast: Stock Market Uptrend until April 2010


Nothing in this piece or in this blog should be construed as investment advice in any way. Always do your 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.

March 19, 2010 Posted by | Stock Market Forecast | , , , , , | 2 Comments

   

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