Market cycles can help maximize return on investment.
One of the characters of the market is that it has strong and fairly consistent cycles. Its performance curve can be seen as a sum of the cyclic functions with different periods and amplitudes. Some cycles that have been known to investors for a long time, for example, a four-year presidential cycle or annual and quarterly budget reporting cycles. By identifying cycles, it is possible to anticipate highs and lows, as well as to determine trends. So that cycles can be a good opportunity to maximize the return on investment.
It is difficult to identify cycles using simple graphical analysis.
It is not easy to analyze the repetition of typical patterns in a performance curve because often the cycles obscure each other; sometimes they overlap to form an abnormal extremum or shift to form a flat period. The presence of several cycles of different periods and magnitudes in conjunction with linear and non-linear trends can form a complex pattern of the curve. Obviously, a simple graphical analysis has some limitation in identifying cycle parameters and using them for forecasting. Therefore, a mathematical statistical model implemented in a computer program could be a solution.
Please note: no predictive model guarantees 100% accuracy.
Unfortunately, any predictive model has its own limitation. The main obstacle to using cycle analysis for market forecasting is cycle instability. Due to the probabilistic nature of the market, cycles sometimes repeat themselves, sometimes not. In order to avoid overconfidence and hence losses, it is important to remember the semi-cyclical nature of the market. In other words, prediction based on cycle analysis as well as any other technique cannot guarantee 100% prediction accuracy.
Back-testing helps improve the accuracy of predictions.
One of the techniques to improve the accuracy of a prediction is back-testing. It is the process of prediction testing on past periods. In the beginning, instead of calculating the prediction for the upcoming time period, we could simulate the forecast on relevant past data to estimate the accuracy of the prediction with certain parameters. Then, optimizing these parameters could help achieve better forecast accuracy.
The software makes it possible to use cycle analysis for forecasting stock price.
To discover different patterns in the movement of prices, including cycles, investors use different software tools. They are able to extract the basic cycles of the stock market (indices, sectors or well-traded stocks). To build an extrapolation (i.e. forecast) they normally use the following two-step approach: (1) apply spectral analysis (time series) to decompose the curve into basic functions , (2) compose these functions beyond historical data. The best software tools should also include a back-testing feature.
The stock market is a living system – around may be joy or fear, but its buy-sell impulse still exists. To experience different patterns in market movement, including cycles, investors use different software tools. Sometimes these computer tools are called "stock market software". Stock market software tools help investors and traders to research, analyze and predict the stock market.