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 Searching for long memory effects in time series of central Europe stock market indices

2008 ◽  
Vol 56 (3) ◽  
pp. 187-200
Author(s):  
Luboš Střelec
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Central Europe ◽  
Long Memory ◽  
Hurst Exponent ◽  
Financial Time Series ◽  
Memory Effects ◽  
Stock Index ◽  
Source Data ◽  
Independent Process

This article deals with one of the important parts of applying chaos theory to financial and capital markets – namely searching for long memory effects in time series of financial instruments. Source data are daily closing prices of Central Europe stock market indices – Bratislava stock index (SAX), Budapest stock index (BUX), Prague stock index (PX) and Vienna stock index (ATX) – in the period from January 1998 to September 2007. For analysed data R/S analysis is used to calculate the Hurst exponent. On the basis of the Hurst exponent is characterized formation and behaviour of analysed financial time series. Computed Hurst exponent is also statistical compared with his expected value signalling independent process. It is also operated with 5-day returns (i.e. weekly returns) for the purposes of comparison and identification nonperiodic cycles.

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.

Permutation entropy and statistical analysis of the historical evolution of the Mexican stock market index

2022 ◽  
Author(s):  
M. Rodríguez-Achach ◽  
A. Suárez-Solís ◽  
A. R. Hernández Montoya ◽  
J. E. Escalante-Martínez ◽  
C. Calderón-Ramón
Keyword(s):  
Stock Market ◽  
Time Window ◽  
Stock Index ◽  
Permutation Entropy ◽  
Stock Market Index ◽  
Statistical Tools ◽  
Efficient Market ◽  
Market Index ◽  
Before And After ◽  
Year 2000

The objective of this work is to analyze the Indice de Precios y Cotizaciones (IPC), which is the Mexican stock market index, by using several statistical tools in order to study the tendencies that can shed light on the evolution of the IPC towards a more efficient market. The methodology used is to apply the statistical tools to the Mexican index and compare the results with a mature and well-known market index such as the Dow Jones Industrial Average (DJIA). We employ an autocorrelation analysis, and the volatility of the indexes, applied to the daily returns of the closing price on a moving time window during the studied period (1980–2018). Additionally, we perform an order three permutation entropy analysis, which can quantify the disorder present in the time series. Our results show that there is evidence that the IPC has become more mature since its creation and that it can be considered an efficient market since around year 2000. The behavior of the several techniques used shows a similar behavior to the DJIA which is not observed before that year. There are some limitations mainly because there is no high frequency data that would permit a more detailed analysis, specifically in the periods before and after a crisis is located. Our conclusion is that since around the year 2000, the Mexican stock index displays the typical behavior of other mature markets and can be considered as one.

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.

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