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A new trading system (TS) is proposed in this paper, based on singular spectrum analysis (SSA) and on a decision-making process that relies on performance measures adjusted by profit and risk. The aim is to contribute to the literature of technical analysis (TA) by proposing a general and adaptive tool for supporting trading strategies of investors and market practitioners. Recent advances of trading rules based on SSA are pooled together in a complimentary fashion, with the intention that this group of strategies outperforms its individual constituents, as occur with ensembles. Simulations are presented showing the improvements of the proposed TS over classic tools of TA and the benchmark Buy & Hold strategy for the Brazilian stock market.

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.

The Singular Spectrum Analysis (SSA) is powerful method, capable of working with arbitrary statistical process and it is adaptive to the underlaying data. Many variations of the standard methodology have been prosed in recent years improving the performance, adjusting to specific problems or objectives, or addressing some shortcomings. One of such drawbacks occurs when the spectrum spreads and varies over time, demanding many elementary matrices to reconstruct an approximation of the original series, hampering the method applicability. Also, another difficulty arises when large datasets are analyzed. There are computational issues and also problems with the method ability to maintain satisfactory separability. To circumvent these issues, a new method has been proposed. The original time series is divided into smaller and consecutive segments, with some superposition between them. Then, standard SSA is applied to each segment and the results are concatenated properly. This paper provides an implementation of this algorithm and some experiments are shown to illustrate the improvements achieved. Keywords: Singular spectrum analysis, Non-stationary signals, Segmentation

Singular Spectrum Analysis (SSA) is a powerful non-parametric framework to analysis and enhancement of time-series. SSA may be capable of decomposing a time-series into its meaningful components: trends, oscillations and noise. However, if the signal under analysis is non-stationary, with its spectrum spreading and varying in time, the reliability of the reconstruction is guaranteed only when many elementary matrices are used. As a consequence, the capability to discriminate dominant structures from time-series may be impaired. To circumvent this issue, a new method, called overlap-SSA (ov-SSA), is proposed for segmentation, analysis and reconstruction of long-term and/or non-stationary signals. The raw time series is divided into smaller, consecutive and overlapping segments, and standard SSA procedures are applied to each segment with the resulting series being concatenated. This variation of SSA seeks to: improve reconstruction and component separability for non-stationary time-series; enable the analysis for large datasets, avoiding the issues of concatenation of many segments; and present some benefits of the segmentation in terms of better time–frequency characterization. These advantages are illustrated in several synthetic and experimental datasets.

Singular Spectrum Analysis (SSA) is a nonparametric approach that can be used to decompose a time series as 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. In this manuscript, an alternative version of online SSA, the Recursive SSA, is proposed as a technical indicator. Based on a “forgetting factor” parameter, it is possible to control the amount of previous samples that are used in the SSA algorithm. This functionality may confer adaptive and hybrid features, thereby providing a crucial characteristic to technical indicators. The Recursive SSA technical trading rules (SSA-TTR) are applied to the DJIA time-series and compared against popular technical indicators. The results show the advantages of SSA-TTR over the popular ones.

A comparison between Moving Averages (MA) and two versions of Singular Spectrum Analysis (SSA) methodology — the Caterpillar and the Toeplitz — is presented. Caterpillar had already been studied in this manner but the same is not true for the Toeplitz SSA. Toeplitz SSA assumes the stationarity of the time-series, which means that the process needs to be mean-reverting. However, such assumption is not a necessary condition for the Caterpillar SSA. In this paper both approaches are applied to a trend estimation problem in order to be used as an indicator in trend-following technical rules design. Similarities and differences between these techniques are addressed. The obtained results suggest that, although SSA approaches provides more flexibility to achieve a desired trend resolution compared to the traditional MA, the Toeplitz SSA exhibit some issues that might put it off its use in this particular application.

The connection between Singular Spectrum Analysis (SSA) decomposition and short-term market movements is investigated. Since SSA is a non-parametric approach, suitable to decompose general time-series into meaningful components, such as trends, oscillations and noise, it is proposed as a new oscillator-type Technical Indicator, replacing popular ones. New Technical Trading Rules (TTRs) are designed and applied to some major global stock indexes to illustrate the benefits in terms of revealing market movements. The performance is evaluated according to different risk-adjustment metrics and the empirical results reveal that the SSA-TTRs may outperform some popular technical oscillators and also the Buy & Hold strategy.

Singular Spectrum Analysis (SSA) is a non-parametric approach that can be used to decompose a time-series as trends, oscillations and noise. Trend-following strategies rely on the principle that financial markets move in trends for an extended period of time. Moving Averages (MAs) are the standard indicator to design such strategies. In this study, SSA is used as an alternative method to enhance trend resolution in comparison with the traditional MA. New trading rules using SSA as indicator are proposed. This paper shows that for the Down Jones Industrial Average (DJIA) and Shangai Securities Composite Index (SSCI) time-series the SSA trading rules provided, in general, better results in comparison to MA trading rules.