A Singular Spectrum Analysis based Trend-Following Trading System

被引：0

作者：

Leles, Michel C. R. ^{[1
]}

Cardoso, Adriano S. V. ^{[1
]}

Moreira, Mariana G. ^{[1
]}

Sbruzzi, Elton F. ^{[2
]}

Nascimento, Cairo L., Jr. ^{[3
]}

Guimaraesf, Homero N. ^{[4
]}

机构：

[1] Univ Fed Sao Joao del Rei, Ctr Studies Elect Engn & Automat, BR-36420000 Ouro Branco, MG, Brazil

[2] Fluminense Fed Univ, Sch Ind & Met Engn, BR-27255125 Volta Redonda, RJ, Brazil

[3] Inst Tecnol Aeronaut, Div Elect Engn, BR-12228900 Sao Jose Dos Campos, SP, Brazil

[4] Univ Fed Minas Gerais, Dept Elect Engn, BR-31270901 Belo Horizonte, MG, Brazil

来源：

12TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON2018) | 2018年

基金：

巴西圣保罗研究基金会;

关键词：

Singular Spectrum Analysis; Stock Markets; Trend-Following Trading Systems; RULES; PROFITABILITY;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：66 / 70

页数：5

共 23 条

[1]

Alexandrov T, 2009, REVSTAT-STAT J, V7, P1

[2]

[Anonymous], 2007, J DATA SCI

[3]

[Anonymous], 2017, IN PRESS

[4] SIMPLE TECHNICAL TRADING RULES AND THE STOCHASTIC PROPERTIES OF STOCK RETURNS [J].

BROCK, W ;

LAKONISHOK, J ;

LEBARON, B .

JOURNAL OF FINANCE, 1992, 47 (05) :1731-1764

[5] Advanced spectral methods for climatic time series [J].

Ghil, M ;

Allen, MR ;

Dettinger, MD ;

Ide, K ;

Kondrashov, D ;

Mann, ME ;

Robertson, AW ;

Saunders, A ;

Tian, Y ;

Varadi, F ;

Yiou, P .

REVIEWS OF GEOPHYSICS, 2002, 40 (01) :3-1

[6]

Golyandina N., 2001, Analysis of time series structure: SSA and related techniques

[7]

Golyandina N., 2013, Singular Spectrum Analysis for Time Series

[8] Variations of singular spectrum analysis for separability improvement: non-orthogonal decompositions of time series [J].

Golyandina, Nina ;

Shlemov, Alex .

STATISTICS AND ITS INTERFACE, 2015, 8 (03) :277-294

[9]

Golyandina N, 2010, STAT INTERFACE, V3, P259

[10] Monte Carlo Singular Spectrum Analysis (SSA) Revisited: Detecting Oscillator Clusters in Multivariate Datasets [J].

Groth, Andreas ;

Ghil, Michael .

JOURNAL OF CLIMATE, 2015, 28 (19) :7873-7893

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