Stock Forecast Methods

Stock Market Trading and Investing

The Power of Using Multi-model Forecast in Stock Market

The Power of Using Multi-model Forecast in Stock MarketA multi-model forecast provides a significant improvement over the best individual forecast. It can be explained by existence of many different independent factors contributing to the error in each forecast which distributed around actual value.

Today, airplanes are equipped with a few different altimeters – barometric altimeter, radar altimeter, and GPS. Not to mention, pilots also use a visual estimation of altitude. Why do we need to use so many measurement tools? The first reason is that any of them can fail. There is another reason why multiple measurements are used everywhere – it is accuracy. For example, many different methods are used in weather forecasting to improve the accuracy of forecast.

The phenomenon of multiple forecast improvement can be compared with Expert Method. This method can be illustrated by the following. As example, an experimentalist shows a pen and asks a group of several people to write down their estimate of the length. Then he collects notes and calculates the average number – normally it is almost a precise result. Why it works? Because everyone makes errors in different directions so that averaging self-compensates erroneous deviations.

Concerning the stock market forecast, as experiments show, a multi-model forecast provides a significant (10-25%) improvement over the best individual forecast. Also tests show the advantage of using information even from multiple forecasts of different quality. That is because there are many different independent factors contributing to the error in each forecast and the results from different models are normally distributed around actual value.

Evidently, the methods should be different by their nature. Traditionally, fundamental factors and technical analysis are the major stock market tools. Within technical analysis, there are several different models: technical indicators, chart pattern analysis, Elliott Wave theory, cycle analysis, candlesticks model, trend lines analysis, regression models, etc. Most of these methods are statistically proven and widely used that often create self-fulfilling results.

As a rule, learning and also correctly using many of technical analysis methods may require a lot of time, especially, in a modern dynamic trading environment. Fortunately, the forecast methods combined with computer power have become a good solution to make the works less time-consuming and more effective. Except different linear and non-linear solvers and analytical methods, these days, Neural Network (NN) can help to automate a lot of computational tasks. A properly trained NN may enable predictions to the highest accuracy.

However, implementing NN application can be difficult for non-experts. Besides, one of the big obstacles of implementing NN predicting system is a formalization of inputs. Luckily, there are some software tools that already well-developed and do not require a deep technical understanding. These tools are optimized for each method and users might not notice even all computational power behind the buttons, windows, and charts.

Useful resources:

Advertisements

March 12, 2012 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