Stock market forecasting using cycle analysis

Stock market cycles can help increase return on investment.

One of the market characteristics is that there are powerful and quite consistent cycles. Its performance curve can be considered as a sum of cyclic functions with different periods and amplitudes. Some cycles known to investors for a long time, such as a four-year presidential cycle or annual and quarterly fiscal reporting cycles. By identifying cycles, it is possible to predict peaks and troughs, as well as to identify trends. So cycles can be a good opportunity to maximize return on investment.

It is difficult to identify cycles using a simple chart analysis.

It is not easy to analyze the repetition of typical patterns in the performance curve because cycles are often masked; sometimes they overlap to form an unusual extremum or shift to form a flat period. The presence of multiple cycles with different periods and quantities in relation to linear and nonlinear tendencies can form a complex curve model. Obviously, the simple analysis of the diagram has a certain limit in identifying the parameters of the cycles and using them for forecasting. Therefore, a mathematical statistical model implemented in a computer program can be a solution.

Keep in mind: no predictive model guarantees 100% accuracy.

Unfortunately, each forecast model has its own limit. The main obstacle to using cycle analysis to forecast the stock market is cycle instability. Due to the probabilistic nature of the market, cycles are sometimes repeated, sometimes not. To avoid overconfidence and therefore losses, it is important to remember the semi-cyclical nature of the market. In other words, the forecast based on cycle analysis, like any other technique, cannot guarantee 100% accuracy of the forecast.

Reverse testing helps to improve forecasting accuracy.

One of the techniques to improve forecasting accuracy is back testing. This is a process of testing the forecast for previous periods of time. In the beginning, instead of calculating the forecast for the period ahead, we could simulate the forecast on the relevant past data to assess the accuracy of the forecast with certain parameters. Then the optimization of these parameters could help to achieve better accuracy in the forecast.

The software makes it possible to use cycle analysis to forecast stock prices.

To find different patterns in price movements, including cycles, investors use different software tools. They are able to derive major cycles of the stock market (indices, sectors or well-traded stocks). To construct an extrapolation (ie forecast), they typically use the following two-step approach: (1) applying spectral (time series) analysis to decompose the curve into basic functions, (2) compiling these functions outside the historical data . Also, the best software tools should include a back-testing feature.

Conclusion

The stock market is a living system – there may be joy or fear around, but its impulse to buy and sell always exists. To find different patterns in the market movement, including cycles, investors use different software tools. These computer tools are sometimes called “stock market software”. Stock market software tools help investors and traders research, analyze and forecast the stock market.