The local Hurst exponent of the financial time series in the vicinity of crashes on the Polish stock exchange market

2008 ◽  
Vol 387 (16-17) ◽  
pp. 4299-4308 ◽  
Author(s):  
Dariusz Grech ◽  
Grzegorz Pamuła
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Exchange Market ◽  
Financial Time

scholarly journals Learning Financial Time Series for Prediction of the Stock Exchange Market

10.29007/mh4m ◽  
2019 ◽  
Author(s):  
Roberto Rosas-Romero ◽  
Juan-Pablo Medina-Ochoa
Keyword(s):  
Time Series ◽  
Financial Services ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Formal System ◽  
Activation Function ◽  
Exchange Market ◽  
Financial Time ◽  
Potential Benefits ◽  
And Performance

This paper presents the extension and application of three predictive models to time series within the financial sector, specifically data from 75 companies on the Mexican stock exchange market. A tool, which generates awareness of the potential benefits obtained from using formal financial services, would encourage more participation in a formal system. The three statistical models used for prediction of financial time series are a regression model, multi-layer perceptron with linear activation function at the output, and a Hidden Markov Model. Experiments were conducted by finding the optimal set of parameters for each predicting model while applying a model to 75 companies. Theory, issues, challenges and results related to the application of artificial predicting systems to financial time series, and performance of the methods are presented.

ESTIMATION OF HURST EXPONENT FOR THE FINANCIAL TIME SERIES

2009 ◽  
Author(s):  
J. Kumar ◽  
P. Manchanda ◽  
A. H. Siddiqi ◽  
M. Brokate ◽  
A. K. Gupta
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Financial Time Series ◽  
Financial Time

Hurst exponent analysis of financial time series

2001 ◽  
Vol 5 (4) ◽  
pp. 269-272 ◽  
Author(s):  
Hong-wei SANG ◽  
Tian Ma ◽  
Shuo-zhong Wang
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Financial Time Series ◽  
Financial Time

scholarly journals Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wuyang Cheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Statistical Physics ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Contact Process ◽  
Composite Index ◽  
Return Time ◽  
Time Series Model ◽  
Financial Time

We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI) and Hang Seng Index (HSI) are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

scholarly journals Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Ali ◽  
Dost Muhammad Khan ◽  
Muhammad Aamir ◽  
Amjad Ali ◽  
Zubair Ahmad
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Composite Index ◽  
Support Vector ◽  
Financial Time ◽  
Svm Model

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

CLUSTERING FINANCIAL TIME SERIES BY NETWORK COMMUNITY ANALYSIS

2011 ◽  
Vol 22 (01) ◽  
pp. 35-50 ◽  
Author(s):  
CARLO PICCARDI ◽  
LISA CALATRONI ◽  
FABIO BERTONI
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Community Analysis ◽  
Network Graph ◽  
Financial Time ◽  
Network Communities ◽  
The Us ◽  
The Stability ◽  
Symbolic Time Series Analysis

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

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