scholarly journals Financial Time Series: Market Analysis Techniques Based on Matrix Profiles †

2021 ◽  
Vol 5 (1) ◽  
pp. 45
Author(s):  
Eoin Cartwright ◽  
Martin Crane ◽  
Heather J. Ruskin
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Market Analysis ◽  
Analysis Techniques ◽  
Financial Time ◽  
Motif Identification ◽  
Initial Identification ◽  
The Matrix ◽  
Individual Market ◽  
Time Periods

The Matrix Profile (MP) algorithm has the potential to revolutionise many areas of data analysis. In this article, several applications to financial time series are examined. Several approaches for the identification of similar behaviour patterns (or motifs) are proposed, illustrated, and the results discussed. While the MP is primarily designed for single series analysis, it can also be applied to multi-variate financial series. It still permits the initial identification of time periods with indicatively similar behaviour across individual market sectors and indexes, together with the assessment of wider applications, such as general market behaviour in times of financial crisis. In short, the MP algorithm offers considerable potential for detailed analysis, not only in terms of motif identification in financial time series, but also in terms of exploring the nature of underlying events.

Robert Engle and Clive Granger: New Spheres of Economic Research (Nobel Prize in Economics 2003)

2004 ◽  
pp. 37-48
Author(s):  
G. Kantorovich ◽  
M. Touruntseva
Keyword(s):  
Time Series ◽  
Financial Market ◽  
Nobel Prize ◽  
Financial Time Series ◽  
Market Analysis ◽  
Time Series Model ◽  
Economic Research ◽  
Estimation Methods ◽  
Financial Time ◽  
Financial Market Analysis

This paper is dedicated to the achievements of Robert Engle and Clive Granger which allowed to overcome a serious crisis in macroeconomics and financial market analysis. The main concepts of cointegration theory and different estimation methods of cointegration equations are considered in the first part of the paper. The areas of application of cointegration theory and possible extensions are briefly described as well. The financial time series model with conditional heteroskedastisity is analyzed in the second part of the paper. The main prerequisites of the method suggested by R. Engle are formulated and its extensions and areas of application are defined.

scholarly journals Layer histogram patterns in financial time series

2009 ◽  
Vol 6 (3) ◽  
pp. 137-146
Author(s):  
Verena Helen Van Zyl-Bulitta ◽  
R. Otte ◽  
JH Van Rooyen
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Financial Risk ◽  
Pattern Analysis ◽  
Financial Time Series ◽  
Data Set ◽  
Frequency Distributions ◽  
Analysis Techniques ◽  
Financial Time ◽  
Macroscopic Fluctuations

This study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate and index data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from statistically insufficient samples of changes and their patterns in time. Shnoll et al. use the notion of macroscopic fluctuations (MF) to explain the behavior of sequences of histograms. Histogram patterns in time adhere to several laws that could not be detected when using time series analysis methods. In this study special emphasis is placed on the histogram pattern analysis of high frequency exchange rate data set. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings presented here widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics, more specifically the formation of discrete states.

scholarly journals A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 115 ◽  
Author(s):  
Xin Liu ◽  
Jiang Wu ◽  
Chen Yang ◽  
Wenjun Jiang
Keyword(s):  
Time Series ◽  
Portfolio Selection ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Simulation Studies ◽  
Lower Tail ◽  
Financial Time ◽  
The Matrix ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this paper, we propose a clustering procedure of financial time series according to the coefficient of weak lower-tail maximal dependence (WLTMD). Due to the potential asymmetry of the matrix of WLTMD coefficients, the clustering procedure is based on a generalized weighted cuts method instead of the dissimilarity-based methods. The performance of the new clustering procedure is evaluated by simulation studies. Finally, we illustrate that the optimal mean-variance portfolio constructed based on the resulting clusters manages to reduce the risk of simultaneous large losses effectively.

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

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