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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Study on Singular Spectrum Analysis as a New Technical Oscillator for Trading Rules Design

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Author(s):
Rodrigues Leles, Michel Carlo [1] ; Mozelli, Leonardo Amaral [2] ; Nascimento Junior, Cairo Lucio [3] ; Sbruzzi, Elton Felipe [4] ; Guimaraes, Homero Nogueira [2]
Total Authors: 5
Affiliation:
[1] Univ Fed Sao Joao del Rei, CELTA Ctr Studies Elect Engn & Automat, BR-36420000 Ouro Branco, MG - Brazil
[2] Univ Fed Minas Gerais, Dept Elect Engn, BR-31270901 Belo Horizonte, MG - Brazil
[3] Inst Tecnol Aeronaut, Div Elect Engn, BR-12228900 Sao Jose Dos Campos, SP - Brazil
[4] Fluminense Fed Univ, Sch Ind & Met Engn, BR-27255125 Volta Redonda, RJ - Brazil
Total Affiliations: 4
Document type: Journal article
Source: FLUCTUATION AND NOISE LETTERS; v. 17, n. 4 DEC 2018.
Web of Science Citations: 0
Abstract

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. (AU)

FAPESP's process: 16/04992-6 - Employing computational intelligence techniques and Big Data analytics in a multi-agent system experiment of finance
Grantee:Cairo Lúcio Nascimento Júnior
Support Opportunities: Research Grants - eScience and Data Science Program - Regular Program Grants
FAPESP's process: 17/20248-8 - Employing computational intelligence techniques and Big Data analytics in a multi-agent system experiment of finance
Grantee:Michel Carlo Rodrigues Leles
Support Opportunities: Scholarships in Brazil - Post-Doctoral