Abstract
Singular Spectrum Analysis (SSA) is a non parametric approach that can be used to decompose a time series into trends, oscillations and noise. In online applications, the SSA algorithm must be recalculated for each new sample available. The so called Causal SSA have been used in this context. This paper aims to contribute to the application of Causal SSA as an indicator for trading rules design by means of comparing them to previously proposed ones based on classic indicators. Using historical time series from some of the major Brazilian stocks, different simulations are carried out. Some performance metrics are applied along with several statistical tests, thereby supporting a detailed analysis of the experimental results obtained. The results analysis indicates that, no matter what technical trading rules, it is worth to apply the SSA method rather than classic approaches.
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