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 Searching for histogram patterns in USD/ZAR foreign exchange financial time series

2009 ◽  
Vol 6 (4) ◽  
pp. 575-584
Author(s):  
JH Van Rooyen
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
High Frequency ◽  
Financial Risk ◽  
Pattern Analysis ◽  
Financial Time Series ◽  
Rate Data ◽  
Data Set ◽  
Frequency Distributions ◽  
Financial Time

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 data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from tick high frequency currency exchange rates over a period of one week. Shnoll et al. use the notion of macroscopic fluctuations (MF) to explain the behaviour 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, which is a follow up of research by Van ZylBulitta, VH, Otte, R and Van Rooyen, JH, 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 and the repetition of histogram patterns

scholarly journals Financial Time Series Forecasting Using Directed-Weighted Chunking SVMs

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Yongming Cai ◽  
Lei Song ◽  
Tingwei Wang ◽  
Qing Chang
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Data Set ◽  
Promising Alternative ◽  
Financial Time ◽  
Operation Speed ◽  
Support Vectors

Support vector machines (SVMs) are a promising alternative to traditional regression estimation approaches. But, when dealing with massive-scale data set, there exist many problems, such as the long training time and excessive demand of memory space. So, the SVMs algorithm is not suitable to deal with financial time series data. In order to solve these problems, directed-weighted chunking SVMs algorithm is proposed. In this algorithm, the whole training data set is split into several chunks, and then the support vectors are obtained on each subset. Furthermore, the weighted support vector regressions are calculated to obtain the forecast model on the new working data set. Our directed-weighted chunking algorithm provides a new method of support vectors decomposing and combining according to the importance of chunks, which can improve the operation speed without reducing prediction accuracy. Finally, IBM stock daily close prices data are used to verify the validity of the proposed algorithm.

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.

scholarly journals CONSTRUCTION OF MODELS FOR BOUNDED PRICE PROCESSES: THE CASE OF THE HKD EXCHANGE RATE

2015 ◽  
Vol 10 (02) ◽  
pp. 1550011
Author(s):  
HONG BEN YEE ◽  
NIKOLAI DOKUCHAEV
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Financial Time Series ◽  
Two Dimensional ◽  
One Dimensional ◽  
Financial Time ◽  
Ergodic Properties ◽  
1D Model ◽  
Weak Side ◽  
2D Model

This paper discusses construction of evolution models for financial time series evolving within a given interval. We calibrated a model for the case of the USD/HKD exchange rate after the separation of strong and weak side convertibility undertakings, in which the rate is confined to a specified corridor. This process represents an interesting example of a tradable bounded process. A one-dimensional (1D) model was able to replicate the bounded distribution of the process, but a two-dimensional (2D) model better captured dynamics as measured by the volatility without losing features of the 1D model. We briefly consider the ergodic properties of these models.

Application of Pi-Sigma Neural Networks and Ridge Polynomial Neural Networks to Financial Time Series Prediction

2009 ◽  
pp. 271-293 ◽  
Author(s):  
Rozaida Ghazali ◽  
Dhiya Al-Jumeily
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Exchange Rate ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
The United States ◽  
Financial Time ◽  
Higher Order Neural Networks ◽  
The Exchange Rate ◽  
Japanese Yen

This chapter discusses the use of two artificial Higher Order Neural Networks (HONNs) models; the Pi- Sigma Neural Networks and the Ridge Polynomial Neural Networks, in financial time series forecasting. The networks were used to forecast the upcoming trends of three noisy financial signals; the exchange rate between the US Dollar and the Euro, the exchange rate between the Japanese Yen and the Euro, and the United States 10-year government bond. In particular, we systematically investigate a method of pre-processing the signals in order to reduce the trends in them. The performance of the networks is benchmarked against the performance of Multilayer Perceptrons. From the simulation results, the predictions clearly demonstrated that HONNs models, particularly Ridge Polynomial Neural Networks generate higher profit returns with fast convergence, therefore show considerable promise as a decision making tool. It is hoped that individual investor could benefit from the use of this forecasting tool.

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