Predicting Stock Market Using Cycle Analysis

Many 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.

One of the 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. The stock market cycles can be a good opportunity to maximize return on investments.

However, 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.

Any predictive method has own limit. The major obstacle in using cycle analysis 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 cannot guarantee 100% accuracy of prediction.

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 (and then adjust these parameters).

To discover different patterns in the market movement, including cycles, investors use different software tools. One of the tool 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.

As example, the chart below shows S&P-500 forecast for May 17 - June 2, 2010. The calculation has been performed using SMAP-3. The forecast is the following: downtrend may continue until May 21, then a reversal to uptrend until June 02.



Computational details: regular mode (auto); number of line for spectrum analysis - 48; used historical data period - 4 months (from December 17, 2009 to May 14, 2010); back-test deviation - 1.11%; spectrum lines and fitting charts -



In 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." Also, stock market software tools help investors and traders to research, analyze, and predict the stock market.

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Nothing in this piece or 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

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

Predicting Stock Market Using Expert Method

The more methods and information are taken into consideration, the more precise an investment-related solution and, consequently, the more profitable is investing. One of the forecasting methods that uses a collective wisdom is an expert method. This method can be explained by following. As example, an experimentalist shows a pen and asks about 40 people to write down their estimate of the length. Then he collects notes and calculates the average number - normally it is almost 100% accurate. Why it works? Everyone makes errors in different directions so that averaging gives a precise result.

An example of simplified expert method forecast in stock forecasting can be analysts' opinions that collected and averaged. Such information can be found, for instance, on Yahoo Finance webpage "Analyst Opinion" for each stock, it is called "Recommendation Summary". If mean recommendation is equal or close to 1, experts predict strong performance because "1" means "strong buy". If mean recommendation is equal or close to 5, experts predict stock decline because "5" means "sell". It is natural to assume that the more experts express their opinions, the better should be the result of prediction.

Another example of expert forecast could be using your own research of different factors that can contribute certain "opinions" in composed forecast. You can assign different weight for each factor and build an estimation based on weighted averaging. For instance, fundamental analysis may be one the most influential factors, then news factor, technical analysis prediction factor, seasonal price fluctuation factor, etc. All these factors should be added with different weight coefficients. Then the result should be divided by total amount of all weights.

One more idea is to read different current news, analytical articles, blogs, investor forums and draw a summarized conclusion from all opinions, positive and negative predictions. To make this process more automatic, it can be possible to participate on-line polls. There are some websites where you can participate in building a collective forecast for S&P-500 index. You can share your opinion by voting and see the result of composite forecast. If you use more than one method, approach, or tool for prediction, it could be reasonable to give a vote for each one. All participants may benefit from building a simple average forecast. However, do not put too much trust in any method alone - make your own conclusion.

Link to: useful resources

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

Spring 2010: A Short-term Outlook for Stock Market Is Positive

Retailers and consumer discretionary stocks demonstrate a strong performance. Retail sales posted a surprising increase in February (sales rose 0.3%). Although some analysts doubt that the spending gains can be sustained since the unemployment rate still remains high (9.7% in February), fundamentals of many companies gradually started showing improvements.

If the overall US national debt over the next few years rises to 100% of the gross domestic product, it will be alarming signal for the International Monetary Fund and international markets. However, many economists believe that US will be able at least keep the national debt stable relative to the size of the economy.

More positive news:
Recovery hope
It is still manageable at the current level of US federal budget deficit

...and negative ones:
Congressional estimates
Dead Cat
Death of American Capitalism
Wall Street Loses
US debt will keep growing even with recovery

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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.

Stock Market Forecast Using Expert Method

The more methods and information are taken into consideration, the more precise an investment-related solution and, consequently, the more profitable is investing. There is Expert Method. This method can be explained by following. As example, an experimentalist shows a pen and asks about 40 people to write down their estimate of the length. Then he collects notes and calculates the average number - normally it is almost 100% accurate. Why it works? Everyone makes errors in different directions so that averaging gives a precise result.

There is a webpage where you are invited to build a collective forecast for S&P-500 index. Please share your opinion by voting and see the result of composite forecast. If you use more than one method, approach, or tool for prediction, it could be reasonable to give a vote for each one. All participants may benefit from building a simple average forecast. However, do not put too much trust in any method alone - make your own conclusion.

Link to S&P-500 index weekly forecast

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