scholarly journals Universality of delay-time averages for financial time series: analytical results, computer simulations, and analysis of historical stock-market prices

2021 ◽  
Author(s):  
Stefan Ritschel ◽  
Andrey G Cherstvy ◽  
Ralf Metzler
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Computer Simulations ◽  
Delay Time ◽  
Financial Time Series ◽  
Market Prices ◽  
Financial Time ◽  
Analytical Results ◽  
Time Averages

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.

Multiscale weighted Rényi entropy causality plane for financial time series

2019 ◽  
Vol 30 (05) ◽  
pp. 1950037 ◽  
Author(s):  
Jing Gao ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Time Series ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Market Development ◽  
Permutation Entropy ◽  
Statistical Measure ◽  
Stock Market Development ◽  
Financial Time

The permutation entropy (PE) is a statistical measure which can describe complexity of time series. In recent years, the research on PE is increasing gradually. As part of its application, the complexity–entropy causality plane (CECP) and weighted CECP (WCECP) have been recently used to distinguish the stage of stock market development. In this paper, we focus on weighted Rényi entropy causality plane (WRECP), and then extend WCECP and WRECP into multiscale WCECP (MWCECP) and multiscale WRECP (MWRECP) by introducing a new parameter scale. By data simulating and analyzing, we show the power of WRECP. Besides, we discuss the MWCECP and the MWRECP of adjacent scales. It reveals a gradual relationship between adjacent weighted scale entropies.

The multiscale analysis between stock market time series

2015 ◽  
Vol 26 (06) ◽  
pp. 1550071 ◽  
Author(s):  
Wenbin Shi ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Correlation Coefficient ◽  
Multiscale Analysis ◽  
Cross Correlation ◽  
Multiple Scales ◽  
Financial Time Series ◽  
Sample Entropy ◽  
Financial Time ◽  
Cross Correlation Analysis

This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.

Improved RBF neural network algorithm in financial time series prediction

2021 ◽  
pp. 1-9
Author(s):  
Jian Zhu ◽  
Haiming Long ◽  
Saihong Liu ◽  
Wenzhi Wu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Stock Market ◽  
Financial Time Series ◽  
Rbf Neural Network ◽  
Time Series Prediction ◽  
Market Price ◽  
Prediction Errors ◽  
Financial Time ◽  
Sample Data

The financial market is often unpredictable and extremely susceptible to political, economic and other factors. How to achieve accurate predictions of financial time series is very important for scientific research and financial enterprise management. Based on this, this article takes the application of the improved RBF neural network(NN) algorithm in financial time series forecasting as the research object, and explores how to use the improved RBF NN algorithm to predict the stock market price, with a view to reducing investment risks and increasing returns for the majority of stock investors to provide help. This article uses the stock market prices of three listed companies in May 2019 as the data samples for this survey, including 72 training sample data and 21 test sample data. These three stocks were predicted by using the improved RBF NN algorithm Experiments, the experimental results show that the prediction errors of the improved RBF NN algorithm for the three stocks are 2.14%, 0.69% and 1.47%, while the traditional RBF NN algorithm’s prediction errors for the stocks are 5.74%, 2.38% and 11.37%. This shows that the improved algorithm is significantly more accurate and more effective than traditional algorithms. Therefore, the application of the improved RBF NN algorithm in financial time series prediction will be more extensive in the future.

scholarly journals Wavelet Variance, Covariance and Correlation Analysis of BSE and NSE Indexes Financial Time Series

2016 ◽  
Vol 1 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Anuj Kumar ◽  
Sangeeta Pant ◽  
Lokesh Kumar Joshi
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Market ◽  
Financial Time Series ◽  
Market Behavior ◽  
Financial Time ◽  
Index Data ◽  
Wavelet Variance ◽  
Cross Country

The mostly used measure to analyze the stock market behavior is wavelet correlation analysis. Cross-country correlations have been largely used to obtain a static estimate of the comovements of actual returns across country. In this paper wavelet based variance, covariance and correlation analysis of BSE and NSE indexes financial time series have been done using index data from April 1990 to March 2006.

scholarly journals EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices

2018 ◽  
Vol 13 (2) ◽  
pp. 268-279
Author(s):  
Li Tang ◽  
Ping He Pan ◽  
Yong Yi Yao
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Prediction Model ◽  
Computational Intelligence ◽  
Financial Time Series ◽  
Extraction Procedure ◽  
Stock Index ◽  
Stock Market Index ◽  
Financial Time ◽  
Market Index

This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clustering for prediction via regression. The EPAK model is then used as a kernel for predicting each of all the sector indices of the stock market. The sector indices predictions are then synthesized via weighted average to generate the prediction of the stock market index, yielding a complex prediction model for the stock market index. The EPAK model and the complex prediction model for stock index are tested on real historical financial time series in Chinese stock index including CSI 300 and ten sector indices, with results confirming the effectiveness of the proposed models.

scholarly journals SEPARACJA FINANSOWYCH SZEREGÓW CZASOWYCH Z WYKORZYSTANIEM DEKORELACJI Z OPÓŹNIENIAMI

2020 ◽  
Vol 20 (4) ◽  
pp. 305-315
Author(s):  
Ryszard Szupiluk ◽  
Paweł Rubach
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Time Series ◽  
Methodological Issues ◽  
Market Data ◽  
Financial Time ◽  
Stock Indices

In this paper, we present the separation of financial time series using algorithms based on the decoralation procedure. The SOBI and AMUSE algorithms are used, tested and compared on real stock market data. We also present a discussion of theoretical and methodological issues related to the application of separation algorithms. The study is carried out using the WIG20 and SP500 stock indices.

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