Stock Forecast Methods

Stock Market Trading and Investing

New Stock Market Prediction Software SMFT-2 Released

Addaptron Software announced the release of Stock Market Forecast Tools SMFT-2. It is a new integrated system, the next generation of the software that intended to replace older SMFT-1 version. As a result of a few recent development projects, the new version is based on new advanced methods, provides more accurate prediction, and is easier to use.

Prediction modules is built with back-test calculation to estimate the accuracy of forecast within the recent performance periods. Additionally, the back-testing computations is important if more than one method is used. It allows estimating the weight of each method in a composed result; the weights that are proportional to the ability of the methods to predict the price.

New Stock Market Prediction Software SMFT-2

SMFT-2 currently includes five major modules:

  • TA Predictor – prediction for day or week period based on technical analysis, pattern recognition and Neural Networks (generates composite result). Back-analysis models optimization and batch calculation for comparative analysis included.
  • Waves – Elliott Wave model: back-test optimization, up to 10 waves forecast.
  • Cycles – prediction based on cycle analysis.
  • Week day – search for maximum performance using price behavior depending on week day. It allows discovering the best entry/exit days of week; batch calculation included.
  • Month day – search for maximum performance using price behavior depending on month day. It allows discovering the best entry/exit days of month; batch calculation included.

The implemented methods are statistically proven and widely used. All modules share the same EOD (end-of-day) input data. The software is provided with a free Downloader that allows downloading EOD historical quotes files from the Internet for free. A fully-functional software SMFT-2 is free during initial 30-day period. The software and associated documentation are delivered via download links over the Internet. For technical requirements, installation instruction, and download link, visit SMFT-2 download page.

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March 4, 2015 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , , , , , , | Leave a comment

Stock Market Forecast using Technical Analysis

Many discuss if Stock Market Technical Analysis (SMTA) can be used to predict prices. Normally, there are two camps – the side who believes in SMTA predictability and other who does not. In many particular cases, both sides have a solid evidence to support their point of view.

Stock Market Forecast using Technical Analysis

From a statistical point of view it looks reasonable to search for a good predictability in the areas where random or multi-news driven fluctuations are self-compensating each other. One of such areas is stock market indexes, for example, S&P-500. This index consists of 500 biggest companies which make difficult to easily disrupt an equilibrium that is formed during relatively long time frames.

Indeed, as experiments show, one of the most consistent SMTA prediction ratios can be observed for S&P-500 index. Normally, the ratio of its successful/unsuccessful predictions is within the range of 60-68%. Average root-mean-square deviation of predicted-actual values during one week can fluctuate within 2-9 absolute values of index prices (Open, High, Low, Close prices).

Recently, Addaptron Software introduced a new service – Next Day S&P 500 Index Forecast The forecast is calculated using a multi-model SMTA forecast system. User can order the index forecast for the next day, i.e., candlestick values – Open, High, Low, Close prices. The forecast will be sent to your email address by automated tool.

December 12, 2012 Posted by | Stock Market Forecast | , , , , , | Leave a comment

Elliott Wave: Extracting Extremes and Predicting next One by Neural Network

Article first published as Elliott Wave: Extracting Extremes and Predicting next One by Neural Network on Technorati.

Improved Elliott Wave model can be successfully used as an additional input for making investing decisions in modern market conditions

The Elliott Wave idea is to use in stock market forecast. It is based on a crowd psychology that changes between optimistic and pessimistic trends creating patterns that can be fitted to reoccurred sequences. To use waves for prediction the assumption is made that waves are developing in the sequence of Fibonacci, harmonic, or fractal ratios. So that each wave has a programmed position and characterized by a particular direction and duration with extreme as a reversal point.

In fact, the model has been used by stock market analysts for almost a century. Although it looks very attractive due to its strict formalism and deterministic outcome, its predictive power is weak because of a few reasons. Firstly, the predictive values are dependent on waves that were counted – determining where first and next wave start can be subjective. Secondly, according to Efficient Market Hypothesis, using an exact Elliott Wave model by many traders could lead to the disappearance of the patterns they anticipate. And finally, nowadays one’s trading success based on predictions is rather a chance game in a modern market with its irrational behavior.

The purpose of this research was to explore if Elliott Wave principle can be used these days in stock market forecast. To eliminate the subjectivity in counting waves, Neural Network (NN) was used to analyze and predict waves. Also instead of assuming that waves obey only the sequence of Fibonacci, harmonic, or fractal ratios, a more general approach used – the software processes all extracted waves. Besides, employing NN enables identifying both the price and date of extremes. The first experiment has been done using an artificial data set. The data consist of two sinusoidal functions with different periods. The second group of experiments has been done on real market data.

The main conclusion is that the Elliott Wave idea can be used in predicting stock market. Although it does not generate always accurate and consistent forecasts, its result can be successfully used as an additional input for making a trading or investing decision in modern market conditions.

Elliott Wave: Extracting Extremes and Predicting next One by Neural Network

Charts and calculation results by SMFT-1 (TA-1 sub-system Waves module interface)

October 12, 2011 Posted by | Stock Market Forecast | , , , , , , | Leave a comment

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

Pattern Prediction Worked This Time – Is Stock Market Predictive?

The last two-week prediction using pattern similarity was pretty good:

Pattern Prediction Worked This Time - Is Stock Market Predictive

The question is if we can trust and use the only one such prediction for investing. Pattern recognition system is based on the statistical classification of patterns assuming that the patterns are generated by a probabilistic system. “Probabilistic” means that sometimes an expected thing happens, sometimes does not. Yes, some patterns can be repeatable in the future, therefore, the selected present patterns can be used to predict the future pattern. However, statistics evidence that the prediction can be wrong.

Basically, average stock prices are almost continuous function of fundamentals. The stock market performance depends on the strength of the companies and the economy as a whole. Such things are quite transparent and predictive to a certain extend. Least predictive factors (noise) are natural disaster,
political event, published news, investors’ psychological reaction to news or event, etc.

If we try to calculate the value of “patterns” predictive factor, it might be a relatively small amount – around 20-40%. So that it is wise to analyze information from multiple sources to make a more comprehensive conclusion. There is always a risk involved in stock investing. In fact, historically a stock market crash is a result of combination of all factors – short, medium, and long terms, predictive and non-predictive. “Non-predictive” means – there is no way to know for sure about this in advance.

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

August 28, 2010 Posted by | Stock Market Forecast | , , , , , , | Leave a comment

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

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