Stock Forecast Methods

Stock Market Trading and Investing

Unusual Use of Parabolic SAR

Parabolic SAR can be improved and successfully used for predicting stock market prices, especially, in trending markets

Traditionally, Parabolic SAR* is considered as a trend following indicator. Probably, few traders would think about using it for prediction. But after testing, I started to believe that it can be successfully used for prediction in conjunction with other indicators, especially, in a trending market. The explanation why it works can be the following. When a trend reverses, the probability of its continuation is more than 50% in average. The software with Neural Network (NN) is able to catch it statistically and show the result in extrapolated curves. The function of SAR in this method is to provide NN with reversal point signals.

The initial hypothesis was the following. Since SAR is able to give a strong signal when a price trend is reversing, this signal can be used for predicting if a new trend is prone to last. To compare predictive ability of SAR with other indicators, it has been implemented into the technical analysis module of Fundamental-Technical Analyzer FTA-2. SAR calculations have been used to collect statistics based on the forecast simulations for major indexes and ETFs during August-October 2011 period. As a result, SAR’s position was mostly in “top ten” indicators’ list.

Unusual Use of Parabolic SAR
The research and presented chart are made by Fundamental-Technical Analyzer FTA-2, one of the software modules that enables composing Neural Network forecasts of many indicators with weights accordingly to each indicator’s predictive ability.

Omitting logical rules for acceleration factor and reversal conditions, a recurring core formula for Parabolic SAR is the following:

   SAR (current point) = AF * [EP - SAR (previous point)] + SAR (previous point) 

AF – Acceleration Factor (normally starts from 0.02 and increases by 0.02 if each next point reaches a new extreme, saturates until 0.2);
EP – Extreme Point (lowest low or highest high).

Conclusions. SAR is especially effective in a trending market. To make it more effective in a sideways market, it is a good idea to use it in conjunction with other indicators. Parabolic SAR can be enriched and successfully used for predicting stock market prices. Combing it with Neural Network allows extracting more statistically stable patterns and, therefore, providing a better accuracy in the forecast. As simulations showed, improved results can be achieved if SAR is transformed into more sensitive indicator by subtracting it from close price (it indicates the degree of SAR and price convergence).

*) SAR stands for Stop-And-Reverse. It has been used by many traders for decades. Its major application is in trading systems to define a trailing stop, i.e., to protect profit when a price trend changes. The term “parabolic” appeared to characterize the indicator parabola shape that is due to using an accelerating factor in the formula.


November 26, 2011 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , | Leave a comment

New Trading Decision Support Systems Group on LinkedIn

New Trading Decision Support Systems group on LinkedInThe new group Trading Decision Support Systems is intended to be a resource for individual/institutional traders/investors and software developers in stock market area to share ideas, initiate and participate discussions, benefit from the collective intelligence, and to expand network. It will be primarily focused on such topics as:

  • Trading EOD and intraday different asset classes: trading tips, strategies, why, how, and results.
  • Trading systems: algorithms, methods, technologies, human factor, and statistics.
  • Software tools to support traders decisions: forecast methods, simulations, back-testing, and optimization.
  • Technical Analysis: indicators and chart patterns.
  • Fundamental Analysis: financial ratios and predictive models.
  • News: analysis and formalization by converting to measurable variables to automate systems with contributing news factor.
  • Numerical methods, data processing, artificial intelligence, and modeling in stock market areas.

Many things remain unchangeable in a trading world – supply-demand price balance, greed-fear driven mistakes, as well as, ability to think, make right decisions, and find the best solutions. When once winning approaches, strategies, or methods failed, many traders are prone to analyze the reasons why it happened. Then they create new approaches and develop new successful systems. If systems are automated, it is easy and fast to test them, collect and analyze back-testing and live statistics, and then make necessary improvements. That is why it is important to implement the best ideas in software applications that can be also used by others.

The computational technologies are changing. Systems empowered by Artificial Intelligence have self-learning abilities that enable them to adapt to market changes. One of the purposes of this group is to bring together the developers of decision support software and traders-users for mutual benefits: the developers get more ideas about their products’ improvements and make a better progress in developing software for traders, the users arise issues relating to their needs and wants. Hopefully everyone will find something useful participating in this group.

You are welcome to join this newly created networking group. Be the first to start a relevant discussion, promote your product or service. Please join Trading Decision Support Systems group on LinkedIn!

June 22, 2011 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , , , , , , , , , , , , | 1 Comment

Stocks’ Forecast for the Last Two Weeks of October 2010

The chart below shows S&P-500 index forecast for October 18-29, 2010. It has been calculated using pattern recognition forecasting and indicates some move down and then up.

Stocks' Forecast for the Last Two Weeks of October 2010

This time most technical indicators do not signal any significant move – neither up, nor down. However, a big fluctuation can be expected due to the third quoter reporting season. Statistically, in most cases, it is a “bumpy road” from one to another day. At this an unbalanced stage, any particular overly good or bad news may drive stocks in any direction significantly. And it can be an opportunity for skilled traders.

October 16, 2010 Posted by | Stock Market Forecast | , , , , , , , | Leave a comment

S&P-500 Forecast for the First Two Weeks of August on the Basis of Technical Indicators Signals

There are many technical indicators. And there are many interpretations of each indicator’s signal. Some stock investors and traders use particular favorite indicators and insist on own interpretation. Who is right? What if to allow a computer program to decide using back-testing which indicator should be trusted more and another less for particular market conditions and a specific stock?

One of computer programs that enables to compose the forecast with weights accordingly to predictive ability of each technical indicator is Investment Analyzer InvAn-4. It performs a short-term (10 trading days) forecast using Neural Network. The chart below shows an example of such forecast. It is S&P-500 forecast for the first two weeks of August, 2010.

S&P-500 Forecast for the First Two Weeks of August on the Basis of Technical Indicators Signals

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

August 3, 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

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