Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets

Bekiros, Stelios; Nguyen, Duc Khuong; Sandoval, Leonidas; Uddin, Gazi Salah

doi:10.1016/j.ejor.2016.06.052

Cited by 127 publications

(69 citation statements)

References 53 publications

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“…They used information-theoretic quantities to measure the centrality of economic networks. An important result of [17] is that the authors proved evidence of the disparity of correlation and entropy-based centrality measurements for all markets between pre-and post-crisis periods.…”

Section: Network Complexity Based On Information Theorymentioning

confidence: 96%

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Computational analysis of structural properties of economic and financial networks

Emmert‐Streib¹,

Musa²,

Baltakys³

et al. 2018

JNTF

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In recent years, methods from network science are gaining rapidly interest in economics and finance. A reason for this is that in a globalized world the interconnectedness among economic and financial entities are crucial to understand and networks provide a natural framework for representing and studying such systems. In this paper, we are surveying the use of networks and network-based methods for studying economy related questions. We start with a brief overview of graph theory and basic definitions. Then we discuss descriptive network measures and network 1 arXiv:1710.04455v1 [q-fin.CP] 12 Oct 2017 complexity measures for quantifying structural properties of economic networks. Finally, we discuss different network and tree structures as relevant for applications.

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Section: Network Complexity Based On Information Theorymentioning

confidence: 96%

“…Further, they also determined a positive correlation between the hierarchical complexity of a business group and their productivity. The last contribution we mention in this section is due to Bekiros et al [17]. They used information-theoretic quantities to measure the centrality of economic networks.…”

Section: Network Complexity Based On Information Theorymentioning

confidence: 99%

Computational analysis of structural properties of economic and financial networks

Emmert‐Streib¹,

Musa²,

Baltakys³

et al. 2018

JNTF

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“…Thus, our analysis is mostly grounded on Mutual Information [21] and Symbolic Transfer of Entropy [22] (STE), which allows to quantitatively study individual behavioral aspects, like synchronization and information flows, key elements to identify higher properties like structural hubs, coordinated communities, critical transitions or sudden collapses [23]. STE analysis is in fact a rather new tool in the context of financial markets, which has mostly being used to analyze cross-market effects [24,25] and to identify dynamic causal linkages as a way to complement other techniques such as network analysis [26,27], which might have important consequences in optimizing portfolio composition. In this sense, Mutual Information and mostly STE respectively represent an alternative approach to statistically validated synchronous networks [28] and its much more recent evolution under the form of statistically validated lead-lag networks [29,30].…”

Section: Introductionmentioning

confidence: 99%

Mapping individual behavior in financial markets: synchronization and anticipation

et al. 2019

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In this paper we develop a methodology, based on Mutual Information and Transfer of Entropy, that allows to identify, quantify and map on a network the synchronization and anticipation relationships between financial traders. We apply this methodology to a dataset containing 410,612 real buy and sell operations, made by 566 non-professional investors from a private investment firm on 8 different assets from the Spanish IBEX market during a period of time from 2000 to 2008. These networks present a peculiar topology significantly different from the random networks. We seek alternative features based on human behavior that might explain part of those 12,158 synchronization links and 1031 anticipation links. Thus, we detect that daily synchronization with price (present in 64.90% of investors) and the one-day delay with respect to price (present in 4.38% of investors) play a significant role in the network structure. We find that individuals reaction to daily price changes explains around 20% of the links in the Synchronization Network, and has significant effects on the Anticipation Network. Finally, we show how using these networks we substantially improve the prediction accuracy when Random Forest models are used to nowcast and predict the activity of individual investors.

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“…Volatility series exhibit remarkable features suggesting the existence of some amount of 'order' out of the seemingly random structure. The degree of 'order' is intrinsically linked to the information, embedded in the volatility patterns, whose extraction and quantification might shed light on microscopic phenomena in finance [8][9][10][11][12][13][14][15]. The Volatility Index (VIX), defined as the near-term volatility conveyed by S&P 500 stock index option prices, has been suggested to monitor investor sentiment.…”

Section: Introductionmentioning

confidence: 99%

Information measure for financial time series: Quantifying short-term market heterogeneity

Ponta

Carbone

2018

Physica A: Statistical Mechanics and its Applications

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A well-interpretable measure of information has been recently proposed based on a partition obtained by intersecting a random sequence with its moving average. The partition yields disjoint sets of the sequence, which are then ranked according to their size to form a probability distribution function and finally fed in the expression of the Shannon entropy. In this work, such entropy measure is implemented on the time series of prices and volatilities of six financial markets. The analysis has been performed, on tick-by-tick data sampled every minute for six years of data from 1999 to 2004, for a broad range of moving average windows and volatility horizons. The study shows that the entropy of the volatility series depends on the individual market, while the entropy of the price series is practically a market-invariant for the six markets. Finally, a cumulative information measure -the 'Market Heterogeneity Index'-is derived from the integral of the proposed entropy measure. The values of the Market Heterogeneity Index are discussed as possible tools for optimal portfolio construction and compared with those obtained by using the Sharpe ratio a traditional risk diversity measure. arXiv:1710.07331v2 [q-fin.ST]

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Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets

Cited by 127 publications

References 53 publications

Computational analysis of structural properties of economic and financial networks

Computational analysis of structural properties of economic and financial networks

Mapping individual behavior in financial markets: synchronization and anticipation

Information measure for financial time series: Quantifying short-term market heterogeneity

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