scholarly journals Synergistic Information Transfer in the Global System of Financial Markets

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1000
Author(s):  
Tomas Scagliarini ◽  
Luca Faes ◽  
Daniele Marinazzo ◽  
Sebastiano Stramaglia ◽  
Rosario N. Mantegna
Keyword(s):  
Stock Market ◽  
Granger Causality ◽  
Information Flow ◽  
Information Transfer ◽  
Partial Information ◽  
Higher Order ◽  
Transfer Entropy ◽  
Order Effects ◽  
Target Market ◽  
Stock Market Index

Uncovering dynamic information flow between stock market indices has been the topic of several studies which exploited the notion of transfer entropy or Granger causality, its linear version. The output of the transfer entropy approach is a directed weighted graph measuring the information about the future state of each target provided by the knowledge of the state of each driving stock market index. In order to go beyond the pairwise description of the information flow, thus looking at higher order informational circuits, here we apply the partial information decomposition to triplets consisting of a pair of driving markets (belonging to America or Europe) and a target market in Asia. Our analysis, on daily data recorded during the years 2000 to 2019, allows the identification of the synergistic information that a pair of drivers carry about the target. By studying the influence of the closing returns of drivers on the subsequent overnight changes of target indexes, we find that (i) Korea, Tokyo, Hong Kong, and Singapore are, in order, the most influenced Asian markets; (ii) US indices SP500 and Russell are the strongest drivers with respect to the bivariate Granger causality; and (iii) concerning higher order effects, pairs of European and American stock market indices play a major role as the most synergetic three-variables circuits. Our results show that the Synergy, a proxy of higher order predictive information flow rooted in information theory, provides details that are complementary to those obtained from bivariate and global Granger causality, and can thus be used to get a better characterization of the global financial system.

scholarly journals Quantifying the Information Flow between Ghana Stock Market Index and Its Constituents Using Transfer Entropy

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Prince Mensah Osei ◽  
Anokye M. Adam
Keyword(s):  
Stock Market ◽  
Information Flow ◽  
Information Exchange ◽  
Oil And Gas ◽  
Transfer Entropy ◽  
Stock Market Index ◽  
Insurance Sector ◽  
Market Index ◽  
The Individual ◽  
Effective Transfer

We quantify the strength and the directionality of information transfer between the Ghana stock market index and its component stocks as well as observe the same among the individual stocks on the market using transfer entropy. The information flow between the market index and its components and among individual stocks is measured by the effective transfer entropy of the daily logarithm returns generated from the daily market index and stock prices of 32 stocks ranging from 2nd January 2009 to 16th February 2018. We find a bidirectional and unidirectional flow of information between the GSE index and its component stocks, and the stocks dominate the information exchange. Among the individual stocks, SCB is the most active stock in the information exchange as it is the stock that receives the highest amount of information, but the most informative source is EGL (an insurance company) that has the highest net information outflow while the most information sink is PBC that has the highest net information inflow. We further categorize the stocks into 9 stock market sectors and find the insurance sector to be the largest source of information which confirms our earlier findings. Surprisingly, the oil and gas sector is the information sink. Our results confirm the fact that other sectors including oil and gas mitigate their risk exposures through insurance companies and are always expectant of information originating from the insurance sector in relation to regulatory compliance issues. It is our firm conviction that this study would allow stakeholders of the market to make informed buy, sell, or hold decisions.

scholarly journals Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 663 ◽  
Author(s):  
Xudong Wang ◽  
Xiaofeng Hui
Keyword(s):  
Stock Market ◽  
Information Flow ◽  
Information Transfer ◽  
Spanning Trees ◽  
Information Source ◽  
Kernel Density ◽  
Transfer Entropy ◽  
Chinese Stock Market ◽  
Technology Sector ◽  
Effective Transfer

This paper applies effective transfer entropy to research the information transfer in the Chinese stock market around its crash in 2015. According to the market states, the entire period is divided into four sub-phases: the tranquil, bull, crash, and post-crash periods. Kernel density estimation is used to calculate the effective transfer entropy. Then, the information transfer network is constructed. Nodes’ centralities and the directed maximum spanning trees of the networks are analyzed. The results show that, in the tranquil period, the information transfer is weak in the market. In the bull period, the strength and scope of the information transfer increases. The utility sector outputs a great deal of information and is the hub node for the information flow. In the crash period, the information transfer grows further. The market efficiency in this period is worse than that in the other three sub-periods. The information technology sector is the biggest information source, while the consumer staples sector receives the most information. The interactions of the sectors become more direct. In the post-crash period, information transfer declines but is still stronger than the tranquil time. The financial sector receives the largest amount of information and is the pivot node.

scholarly journals Calculating Transfer Entropy from Variance-Covariance Matrices Provides Insight into Allosteric Communication in ERK2

2020 ◽  
Author(s):  
Luisa Garcia Michel ◽  
Clara Keirns ◽  
Benjamin Ahlbrecht ◽  
Daniel Barr
Keyword(s):  
Information Flow ◽  
Information Transfer ◽  
Experimental Studies ◽  
Mapk Signaling ◽  
Coupled Systems ◽  
Transfer Entropy ◽  
Entropy Analysis ◽  
Biomolecular Systems ◽  
Approximate Methods ◽  
Advantages And Disadvantages

<p>Transfer entropy methods provide an approach to understanding asymmetric information flow in coupled systems, with particular application to understanding allosteric interactions in biomolecular systems. Transfer entropy analysis holds the potential to reveal pathways or networks of residues that are coupled in their information flow and thus give new insights into folding and binding dynamics. Most current methods for calculating transfer entropy require very long simulations and almost equally long calculations of joint probability histograms to compute the information transfer that make these methods either functionally intractable or statistically unreliable. Available approximate methods based on graph and network theory approaches are rapid but lose sensitivity to the chemical nature of the biomolecules and thus are not applicable in mutation studies. We show that reliable estimates of the transfer entropy can be obtained from the variance-covariance matrix of atomic fluctuations, which converges quickly and retains sensitivity to the full chemical profile of the biomolecular system. We validate our method on ERK2, a well-studied kinase involved in the MAPK signaling cascade for which considerable computational, experimental, and mutation data are available. We present the results of transfer entropy analysis on data obtained from molecular dynamics simulations of wild type active and inactive ERK2, along with mutants Q103A, I84A, L73P, and G83A. We show that our method is consistent with the results of computational and experimental studies on ERK2, and we provide a method for interpreting networks of interconnected residues in the protein from a perspective of allosteric coupling. We introduce new insights about possible allosteric activity of the extreme N-terminal region of the kinase, which to date has been under-explored in the literature and may provide an important new direction for kinase studies. We also describe evidence that suggests activation may occur by different paths or routes in different mutants. Our results highlight systematic advantages and disadvantages of each method for calculating transfer entropy and show the important role of transfer entropy analysis for understanding allosteric behavior in biomolecular systems.</p>

THE DYNAMICS OF PRICE–VOLUME INFORMATION TRANSFER IN THE CRYPTOCURRENCY MARKETS

2020 ◽  
Vol 23 (05) ◽  
pp. 2050014
Author(s):  
JINGLAN ZHENG ◽  
CHUN-XIAO NIE
Keyword(s):  
Information Flow ◽  
Information Transfer ◽  
Transfer Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Dynamic Information ◽  
Market Values ◽  
Daily Data ◽  
Flow Of Information

This study examines the information flow between prices and transaction volumes in the cryptocurrency market, where transfer entropy is used for measurement. We selected four cryptocurrencies (Bitcoin, Ethereum, Litecoin and XRP) with large market values, and Bitcoin and BCH (Bitcoin Cash) for hard fork analysis; a hard fork is when a single cryptocurrency splits in two. By examining the real price data, we show that the long-term time series includes too much noise obscuring the local information flow; thus, a dynamic calculation is needed. The long-term and short-term sliding transfer entropy (TE) values and the corresponding [Formula: see text]-values, based on daily data, indicate that there is a dynamic information flow. The dominant direction of which is [Formula: see text]. In addition, the example based on minute Bitcoin data also shows a dynamic flow of information between price and transaction volume. The price–volume dynamics of multiple time scales helps to analyze the price mechanism in the cryptocurrency market.

scholarly journals Calculating Transfer Entropy from Variance-Covariance Matrices Provides Insight into Allosteric Communication in ERK2

2020 ◽  
Author(s):  
Luisa Garcia Michel ◽  
Clara Keirns ◽  
Benjamin Ahlbrecht ◽  
Daniel Barr
Keyword(s):  
Information Flow ◽  
Information Transfer ◽  
Experimental Studies ◽  
Mapk Signaling ◽  
Coupled Systems ◽  
Transfer Entropy ◽  
Entropy Analysis ◽  
Biomolecular Systems ◽  
Approximate Methods ◽  
Advantages And Disadvantages

<p>Transfer entropy methods provide an approach to understanding asymmetric information flow in coupled systems, with particular application to understanding allosteric interactions in biomolecular systems. Transfer entropy analysis holds the potential to reveal pathways or networks of residues that are coupled in their information flow and thus give new insights into folding and binding dynamics. Most current methods for calculating transfer entropy require very long simulations and almost equally long calculations of joint probability histograms to compute the information transfer that make these methods either functionally intractable or statistically unreliable. Available approximate methods based on graph and network theory approaches are rapid but lose sensitivity to the chemical nature of the biomolecules and thus are not applicable in mutation studies. We show that reliable estimates of the transfer entropy can be obtained from the variance-covariance matrix of atomic fluctuations, which converges quickly and retains sensitivity to the full chemical profile of the biomolecular system. We validate our method on ERK2, a well-studied kinase involved in the MAPK signaling cascade for which considerable computational, experimental, and mutation data are available. We present the results of transfer entropy analysis on data obtained from molecular dynamics simulations of wild type active and inactive ERK2, along with mutants Q103A, I84A, L73P, and G83A. We show that our method is consistent with the results of computational and experimental studies on ERK2, and we provide a method for interpreting networks of interconnected residues in the protein from a perspective of allosteric coupling. We introduce new insights about possible allosteric activity of the extreme N-terminal region of the kinase, which to date has been under-explored in the literature and may provide an important new direction for kinase studies. We also describe evidence that suggests activation may occur by different paths or routes in different mutants. Our results highlight systematic advantages and disadvantages of each method for calculating transfer entropy and show the important role of transfer entropy analysis for understanding allosteric behavior in biomolecular systems.</p>

scholarly journals Granger Causality Approach to Examining Stock Market Integration in West Africa

2019 ◽  
pp. 838-845
Author(s):  
Eseosa David Obadiaru ◽  
Adebayo John Oloyede ◽  
Alex Ehimare Omankhanlen ◽  
Olusegun Barnabas Obasaju
Keyword(s):  
Stock Market ◽  
West Africa ◽  
Granger Causality ◽  
Market Integration ◽  
The United States ◽  
Stock Market Index ◽  
Causality Test ◽  
Stock Market Integration ◽  
The United Kingdom ◽  
Market Index

Stock markets have been found to be increasingly interdependent overtime due to activities related to internationalization, diversification, integration, and globalization. This study assesses the lead/lag interactions between equity markets in the West Africa viz a viz the United States (US) and the United Kingdom (UK) markets. Stock market index data were analyzed from 2008 - 2016 using the Granger causality test. Findings from the study indicates both uni-directional and bi-directional causality between most of the market pairs implying that none of the market exists in autarky.

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