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Information Theory and Economic Network
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Information Theory and Economic Network

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 March 2021) | Viewed by 75061

Special Issue Editor

Dr. Angeliki Papana

E-Mail Website
Guest Editor
1. Department of Economics, University of Macedonia, Egnatias 156, 546 36 Thessaloniki, Greece
2. Polytechnic School, Aristotle University of Thessaloniki, University Campus, 541 24 Thessaloniki, Greece
Interests: time series analysis; Granger causality; complex networks; Monte Carlo simulations; resampling methods; dynamical systems; chaos
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information theory has provided an ensemble of tools for the identification of the interdependences and the connectivity pattern in complex multivariate systems. Measures from information theory, such as Shannon entropy, have been used in a variety of financial applications, such as to define stock market efficiency and to examine the effects of financial crisis on foreign exchange markets.

The structure of a complex system can be represented as a complex network, where the nodes are the observed variables and the connections are formed utilizing a connectivity measure. Weighted or binary, symmetric or directed networks can then be formed. Network indices quantify different characteristics of the network, such as centrality, integration, segregation and resilience measures and motifs. Methods of complex networks offer a better understanding and characterization of the relationships within large data sets, while offer an effective visualization of the corresponding findings. Information measures, such as transfer entropy, in conjunction with graph theory, have been vastly applied for the empirical study of real-world networks, such as for understanding financial flows and market interdependencies.

The scope of this Special Issue is to provide insights on the analysis of complex networks with applications on economic or financial variables, exploiting tools from information theory. This Special Issue will accept unpublished original papers and comprehensive reviews focused (but not restricted) on methodological innovations on connectivity analysis and construction, analysis, visualization, interpretation of financial networks and their evolution in time.

Dr. Angeliki Papana
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information theory
  • Shannon entropy
  • transfer entropy
  • mutual information
  • connectivity
  • Granger causality
  • complex networks
  • finance
  • economic variables

Published Papers (22 papers)

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Research

33 pages, 12047 KiB  
Article
A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model
by Claudiu Vințe, Marcel Ausloos and Titus Felix Furtună
Entropy 2021, 23(4), 484; https://doi.org/10.3390/e23040484 - 19 Apr 2021
Cited by 6 | Viewed by 5052
Abstract
Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute [...] Read more.
Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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25 pages, 3684 KiB  
Article
Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy
by Muzi Chen, Yuhang Wang, Boyao Wu and Difang Huang
Entropy 2021, 23(4), 434; https://doi.org/10.3390/e23040434 - 07 Apr 2021
Cited by 8 | Viewed by 2065
Abstract
The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and [...] Read more.
The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and bear market stages to investigate interactive patterns in the Chinese financial market. We employ the Least Absolute Shrinkage and Selection Operator (LASSO) method to construct the stock network, compare the heterogeneity of bull and bear markets, and further use the Map Equation method to analyse the evolution of modules in the SSE A-shares market. Empirical results show that (1) the connected effect is more significant in bear markets than bull markets and gives rise to abnormal volatilities in the stock market; (2) a system module can be found in the network during the first four stages, and the industry aggregation effect leads to module differentiation in the last four stages; (3) some stocks have leading effects on others throughout eight periods, and medium- and small-cap stocks with poor financial conditions are more likely to become risk sources, especially in bear markets. Our conclusions are beneficial to improving investment strategies and making regulatory policies. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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16 pages, 461 KiB  
Article
Performance Evaluation of Construction Companies Using Integrated Entropy–Fuzzy VIKOR Model
by Weng Siew Lam, Weng Hoe Lam, Saiful Hafizah Jaaman and Kah Fai Liew
Entropy 2021, 23(3), 320; https://doi.org/10.3390/e23030320 - 08 Mar 2021
Cited by 44 | Viewed by 3525
Abstract
The construction sector plays an important role in a country’s economic development. The financial performance of a company is a good indicator of its financial health and status. In Malaysia, the government encourages the construction industry to develop an advanced infrastructure related to [...] Read more.
The construction sector plays an important role in a country’s economic development. The financial performance of a company is a good indicator of its financial health and status. In Malaysia, the government encourages the construction industry to develop an advanced infrastructure related to health, transport, education and housing. In view of the COVID-19 pandemic, the operations and financial performance of construction sector companies have been affected recently. Additionally, uncertainty plays a vital role in the multi-criteria decision-making (MCDM) process. Based on previous studies, there has been no comprehensive study conducted on the evaluation of the financial performance of construction companies by integrating entropy and fuzzy VIKOR models. Therefore, this paper aims to propose an MCDM model to evaluate and compare the financial performance of construction companies with an integrated entropy–fuzzy VIKOR model. A case study is carried out by evaluating the listed construction companies in Malaysia with the proposed model. The findings of this paper indicate that the company ECONBHD achieves the best financial performance over the study period. The significance of this paper is to determine the priority of the financial ratios and ranking of the construction companies with the proposed entropy–fuzzy VIKOR model. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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16 pages, 6760 KiB  
Article
The Effect of a Hidden Source on the Estimation of Connectivity Networks from Multivariate Time Series
by Christos Koutlis and Dimitris Kugiumtzis
Entropy 2021, 23(2), 208; https://doi.org/10.3390/e23020208 - 08 Feb 2021
Cited by 2 | Viewed by 1575
Abstract
Many methods of Granger causality, or broadly termed connectivity, have been developed to assess the causal relationships between the system variables based only on the information extracted from the time series. The power of these methods to capture the true underlying connectivity structure [...] Read more.
Many methods of Granger causality, or broadly termed connectivity, have been developed to assess the causal relationships between the system variables based only on the information extracted from the time series. The power of these methods to capture the true underlying connectivity structure has been assessed using simulated dynamical systems where the ground truth is known. Here, we consider the presence of an unobserved variable that acts as a hidden source for the observed high-dimensional dynamical system and study the effect of the hidden source on the estimation of the connectivity structure. In particular, the focus is on estimating the direct causality effects in high-dimensional time series (not including the hidden source) of relatively short length. We examine the performance of a linear and a nonlinear connectivity measure using dimension reduction and compare them to a linear measure designed for latent variables. For the simulations, four systems are considered, the coupled Hénon maps system, the coupled Mackey–Glass system, the neural mass model and the vector autoregressive (VAR) process, each comprising 25 subsystems (variables for VAR) at close chain coupling structure and another subsystem (variable for VAR) driving all others acting as the hidden source. The results show that the direct causality measures estimate, in general terms, correctly the existing connectivity in the absence of the source when its driving is zero or weak, yet fail to detect the actual relationships when the driving is strong, with the nonlinear measure of dimension reduction performing best. An example from finance including and excluding the USA index in the global market indices highlights the different performance of the connectivity measures in the presence of hidden source. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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33 pages, 2376 KiB  
Article
To Freeze or Not to Freeze? Epidemic Prevention and Control in the DSGE Model Using an Agent-Based Epidemic Component
by Jagoda Kaszowska-Mojsa and Przemysław Włodarczyk
Entropy 2020, 22(12), 1345; https://doi.org/10.3390/e22121345 - 27 Nov 2020
Cited by 5 | Viewed by 2887
Abstract
The ongoing COVID-19 pandemic has raised numerous questions concerning the shape and range of state interventions the goals of which are to reduce the number of infections and deaths. The lockdowns, which have become the most popular response worldwide, are assessed as being [...] Read more.
The ongoing COVID-19 pandemic has raised numerous questions concerning the shape and range of state interventions the goals of which are to reduce the number of infections and deaths. The lockdowns, which have become the most popular response worldwide, are assessed as being an outdated and economically inefficient way to fight the disease. However, in the absence of efficient cures and vaccines, there is a lack of viable alternatives. In this paper we assess the economic consequences of the epidemic prevention and control schemes that were introduced in order to respond to the COVID-19 pandemic. The analyses report the results of epidemic simulations that were obtained using the agent-based modelling methods under the different response schemes and their use in order to provide conditional forecasts of the standard economic variables. The forecasts were obtained using the dynamic stochastic general equilibrium model (DSGE) with the labour market component. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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19 pages, 540 KiB  
Article
Information Network Modeling for U.S. Banking Systemic Risk
by Giancarlo Nicola, Paola Cerchiello and Tomaso Aste
Entropy 2020, 22(11), 1331; https://doi.org/10.3390/e22111331 - 23 Nov 2020
Cited by 11 | Viewed by 3048
Abstract
In this work we investigate whether information theory measures like mutual information and transfer entropy, extracted from a bank network, Granger cause financial stress indexes like LIBOR-OIS (London Interbank Offered Rate-Overnight Index Swap) spread, STLFSI (St. Louis Fed Financial Stress Index) and USD/CHF [...] Read more.
In this work we investigate whether information theory measures like mutual information and transfer entropy, extracted from a bank network, Granger cause financial stress indexes like LIBOR-OIS (London Interbank Offered Rate-Overnight Index Swap) spread, STLFSI (St. Louis Fed Financial Stress Index) and USD/CHF (USA Dollar/Swiss Franc) exchange rate. The information theory measures are extracted from a Gaussian Graphical Model constructed from daily stock time series of the top 74 listed US banks. The graphical model is calculated with a recently developed algorithm (LoGo) which provides very fast inference model that allows us to update the graphical model each market day. We therefore can generate daily time series of mutual information and transfer entropy for each bank of the network. The Granger causality between the bank related measures and the financial stress indexes is investigated with both standard Granger-causality and Partial Granger-causality conditioned on control measures representative of the general economy conditions. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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19 pages, 352 KiB  
Article
Welfare Cost of Model Uncertainty in a Small Open Economy
by Jocelyn Tapia Stefanoni
Entropy 2020, 22(11), 1221; https://doi.org/10.3390/e22111221 - 27 Oct 2020
Cited by 1 | Viewed by 1788
Abstract
This paper extends the canonical small open-economy real-business-cycle model, when considering model uncertainty. Domestic households have multiplier preferences, which leads them to take robust decisions in response to possible model misspecification for the economy’s aggregate productivity. Using perturbation methods, the paper extends the [...] Read more.
This paper extends the canonical small open-economy real-business-cycle model, when considering model uncertainty. Domestic households have multiplier preferences, which leads them to take robust decisions in response to possible model misspecification for the economy’s aggregate productivity. Using perturbation methods, the paper extends the literature on real business cycle models by deriving a closed-form solution for the combined welfare effect of the two sources of uncertainty, namely risk and model uncertainty. While classical risk has an ambiguous effect on welfare, the addition of model uncertainty is unambiguously welfare-deteriorating. Hence, the overall effect of uncertainty on welfare is ambiguous, depending on consumers preferences and model parameters. The paper provides numerical results for the welfare effects of uncertainty measured by units of consumption equivalence. At moderate (high) levels of risk aversion, the effect of risk on household welfare is positive (negative). The addition of model uncertainty—for all levels of concern about model uncertainty and most risk aversion values—turns the overall effect of uncertainty on household welfare negative. It is important to remark that the analytical decomposition and combination of the effects of the two types of uncertainty considered here and the resulting ambiguous effect on overall welfare have not been derived in the previous literature on small open economies. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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20 pages, 1137 KiB  
Article
Exploitation of Information as a Trading Characteristic: A Causality-Based Analysis of Simulated and Financial Data
by Catherine Kyrtsou, Christina Mikropoulou and Angeliki Papana
Entropy 2020, 22(10), 1139; https://doi.org/10.3390/e22101139 - 08 Oct 2020
Cited by 2 | Viewed by 2256
Abstract
In financial markets, information constitutes a crucial factor contributing to the evolution of the system, while the presence of heterogeneous investors ensures its flow among financial products. When nonlinear trading strategies prevail, the diffusion mechanism reacts accordingly. Under these conditions, information englobes behavioral [...] Read more.
In financial markets, information constitutes a crucial factor contributing to the evolution of the system, while the presence of heterogeneous investors ensures its flow among financial products. When nonlinear trading strategies prevail, the diffusion mechanism reacts accordingly. Under these conditions, information englobes behavioral traces of traders’ decisions and represents their actions. The resulting effect of information endogenization leads to the revision of traders’ positions and affects connectivity among assets. In an effort to investigate the computational dimensions of this effect, we first simulate multivariate systems including several scenarios of noise terms, and then we apply direct causality tests to analyze the information flow among their variables. Finally, empirical evidence is provided in real financial data. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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27 pages, 677 KiB  
Article
A Consistent Nonparametric Test for Granger Non-Causality Based on the Transfer Entropy
by Cees Diks and Hao Fang
Entropy 2020, 22(10), 1123; https://doi.org/10.3390/e22101123 - 03 Oct 2020
Cited by 6 | Viewed by 2306
Abstract
To date, testing for Granger non-causality using kernel density-based nonparametric estimates of the transfer entropy has been hindered by the intractability of the asymptotic distribution of the estimators. We overcome this by shifting from the transfer entropy to its first-order Taylor expansion near [...] Read more.
To date, testing for Granger non-causality using kernel density-based nonparametric estimates of the transfer entropy has been hindered by the intractability of the asymptotic distribution of the estimators. We overcome this by shifting from the transfer entropy to its first-order Taylor expansion near the null hypothesis, which is also non-negative and zero if and only if Granger causality is absent. The estimated Taylor expansion can be expressed in terms of a U-statistic, demonstrating asymptotic normality. After studying its size and power properties numerically, the resulting test is illustrated empirically with applications to stock indices and exchange rates. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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17 pages, 346 KiB  
Article
Statistical Surveillance of Structural Breaks in Credit Rating Dynamics
by Haipeng Xing, Ke Wang, Zhi Li and Ying Chen
Entropy 2020, 22(10), 1072; https://doi.org/10.3390/e22101072 - 24 Sep 2020
Cited by 3 | Viewed by 2044
Abstract
The 2007–2008 financial crisis had severe consequences on the global economy and an intriguing question related to the crisis is whether structural breaks in the credit market can be detected. To address this issue, we chose firms’ credit rating transition dynamics as a [...] Read more.
The 2007–2008 financial crisis had severe consequences on the global economy and an intriguing question related to the crisis is whether structural breaks in the credit market can be detected. To address this issue, we chose firms’ credit rating transition dynamics as a proxy of the credit market and discuss how statistical process control tools can be used to surveil structural breaks in firms’ rating transition dynamics. After reviewing some commonly used Markovian models for firms’ rating transition dynamics, we present several surveillance rules for detecting changes in generators of firms’ rating migration matrices, including the likelihood ratio rule, the generalized likelihood ratio rule, the extended Shiryaev’s detection rule, and a Bayesian detection rule for piecewise homogeneous Markovian models. The effectiveness of these rules was analyzed on the basis of Monte Carlo simulations. We also provide a real example that used the surveillance rules to analyze and detect structural breaks in the monthly credit rating migration of U.S. firms from January 1986 to February 2017. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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17 pages, 3903 KiB  
Article
Competitive Conditions in Global Value Chain Networks: An Assessment Using Entropy and Network Analysis
by Georgios Angelidis, Evangelos Ioannidis, Georgios Makris, Ioannis Antoniou and Nikos Varsakelis
Entropy 2020, 22(10), 1068; https://doi.org/10.3390/e22101068 - 23 Sep 2020
Cited by 12 | Viewed by 3314
Abstract
We investigated competitive conditions in global value chains (GVCs) for a period of fifteen years (2000–2014), focusing on sector structure, countries’ dominance and diversification. For this purpose, we used data from the World Input–Output Database (WIOD) and examined GVCs as weighted directed networks, [...] Read more.
We investigated competitive conditions in global value chains (GVCs) for a period of fifteen years (2000–2014), focusing on sector structure, countries’ dominance and diversification. For this purpose, we used data from the World Input–Output Database (WIOD) and examined GVCs as weighted directed networks, where countries are the nodes and value added flows are the edges. We compared the in-and out-weighted degree centralization of the sectoral GVC networks in order to detect the most centralized, on the import or export side, respectively (oligopsonies and oligopolies). Moreover, we examined the in- and out-weighted degree centrality and the in- and out-weight entropy in order to determine whether dominant countries are also diversified. The empirical results reveal that diversification (entropy) and dominance (degree) are not correlated. Dominant countries (rich) become more dominant (richer). Diversification is not conditioned by competitiveness. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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22 pages, 806 KiB  
Article
The Flow of Information in Trading: An Entropy Approach to Market Regimes
by Anqi Liu, Jing Chen, Steve Y. Yang and Alan G. Hawkes
Entropy 2020, 22(9), 1064; https://doi.org/10.3390/e22091064 - 22 Sep 2020
Cited by 14 | Viewed by 4462
Abstract
In this study, we use entropy-based measures to identify different types of trading behaviors. We detect the return-driven trading using the conditional block entropy that dynamically reflects the “self-causality” of market return flows. Then we use the transfer entropy to identify the news-driven [...] Read more.
In this study, we use entropy-based measures to identify different types of trading behaviors. We detect the return-driven trading using the conditional block entropy that dynamically reflects the “self-causality” of market return flows. Then we use the transfer entropy to identify the news-driven trading activity that is revealed by the information flows from news sentiment to market returns. We argue that when certain trading behavior becomes dominant or jointly dominant, the market will form a specific regime, namely return-, news- or mixed regime. Based on 11 years of news and market data, we find that the evolution of financial market regimes in terms of adaptive trading activities over the 2008 liquidity and euro-zone debt crises can be explicitly explained by the information flows. The proposed method can be expanded to make “causal” inferences on other types of economic phenomena. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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16 pages, 2300 KiB  
Article
Financial Performance Analysis in European Football Clubs
by David Alaminos, Ignacio Esteban and Manuel A. Fernández-Gámez
Entropy 2020, 22(9), 1056; https://doi.org/10.3390/e22091056 - 21 Sep 2020
Cited by 12 | Viewed by 8254
Abstract
The financial performance of football clubs has become an essential element to ensure the solvency and viability of the club over time. For this, both the theory and the practical and regulatory evidence show the need to study financial factors, as well as [...] Read more.
The financial performance of football clubs has become an essential element to ensure the solvency and viability of the club over time. For this, both the theory and the practical and regulatory evidence show the need to study financial factors, as well as sports and corporate factors to analyze the possible flow of income and for good management of the club’s accounts, respectively. Through these factors, the present study analyzes the financial performance of European football clubs using neural networks as a methodology, where the popular multilayer perceptron and the novel quantum neural network are applied. The results show the financial performance of the club is determined by liquidity, leverage, and sporting performance. Additionally, the quantum network as the most accurate variant. These conclusions can be useful for football clubs and interest groups, as well as for regulatory bodies that try to make the best recommendations and conditions for the football industry. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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29 pages, 1763 KiB  
Article
Social Entropy and Normative Network
by Emil Dinga, Cristina-Roxana Tănăsescu and Gabriela-Mariana Ionescu
Entropy 2020, 22(9), 1051; https://doi.org/10.3390/e22091051 - 20 Sep 2020
Cited by 10 | Viewed by 5060
Abstract
The paper introduces a new concept of social entropy and a new concept of social order, both based on the normative framework of society. From these two concepts, typologies (logical and historical) of societies are inferred and examined in their basic features. To [...] Read more.
The paper introduces a new concept of social entropy and a new concept of social order, both based on the normative framework of society. From these two concepts, typologies (logical and historical) of societies are inferred and examined in their basic features. To these ends, some well-known concepts such as entropy, order, system, network, synergy, norm, autopoieticity, fetality, and complexity are revisited and placed into an integrated framework. The core body of this paper addresses the structure and the mechanism of social entropy, understood as an institutionally working counterpart of social order. Finally, this paper concludes that social entropy is an artefact, like society itself, and acts through people’s behavior. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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17 pages, 804 KiB  
Article
Cubic Vague Set and its Application in Decision Making
by Khaleed Alhazaymeh, Yousef Al-Qudah, Nasruddin Hassan and Abdul Muhaimin Nasruddin
Entropy 2020, 22(9), 963; https://doi.org/10.3390/e22090963 - 31 Aug 2020
Cited by 10 | Viewed by 2384
Abstract
From the hybrid nature of cubic sets, we develop a new generalized hybrid structure of cubic sets known as cubic vague sets (CVSs). We also define the concept of internal cubic vague sets (ICVSs) and external cubic vague sets (ECVSs) with examples and [...] Read more.
From the hybrid nature of cubic sets, we develop a new generalized hybrid structure of cubic sets known as cubic vague sets (CVSs). We also define the concept of internal cubic vague sets (ICVSs) and external cubic vague sets (ECVSs) with examples and discuss their interesting properties, including ICVSs and ECVSs under both P and R-Order. Moreover, we prove that the R and R-intersection of ICVSs (or ECVSs) need not be an ICVS (or ECVS). We also derive the different conditions for P-union (P-intersection, R and R-intersection) operations of both ICVSs (ECVSs) to become an ICVS (ECVS). Finally, we introduce a decision-making based on the proposed similarity measure of the CVSs domain and a numerical example is given to elucidate that the proposed similarity measure of CVSs is an important concept for measuring entropy in the information/data. It will be shown that the cubic vague set has the novelty to accurately represent and model two-dimensional information for real-life phenomena that are periodic in nature. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
14 pages, 1854 KiB  
Article
A New Look on Financial Markets Co-Movement through Cooperative Dynamics in Many-Body Physics
by María Nieves López-García, Miguel Angel Sánchez-Granero, Juan Evangelista Trinidad-Segovia, Antonio Manuel Puertas and Francisco Javier De las Nieves
Entropy 2020, 22(9), 954; https://doi.org/10.3390/e22090954 - 29 Aug 2020
Cited by 7 | Viewed by 2456
Abstract
One of the main contributions of the Capital Assets Pricing Model (CAPM) to portfolio theory was to explain the correlation between assets through its relationship with the market index. According to this approach, the market index is expected to explain the co-movement between [...] Read more.
One of the main contributions of the Capital Assets Pricing Model (CAPM) to portfolio theory was to explain the correlation between assets through its relationship with the market index. According to this approach, the market index is expected to explain the co-movement between two different stocks to a great extent. In this paper, we try to verify this hypothesis using a sample of 3.000 stocks of the USA market (attending to liquidity, capitalization, and free float criteria) by using some functions inspired by cooperative dynamics in physical particle systems. We will show that all of the co-movement among the stocks is completely explained by the market, even without considering the market beta of the stocks. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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17 pages, 866 KiB  
Article
Risk-Neutrality of RND and Option Pricing within an Entropy Framework
by Xisheng Yu
Entropy 2020, 22(8), 836; https://doi.org/10.3390/e22080836 - 30 Jul 2020
Cited by 4 | Viewed by 2382
Abstract
This article constructs an entropy pricing framework by incorporating a set of informative risk-neutral moments (RNMs) extracted from the market-available options as constraints. Within the RNM-constrained entropic framework, a unique distribution close enough to the correct one is obtained, and its risk-neutrality is [...] Read more.
This article constructs an entropy pricing framework by incorporating a set of informative risk-neutral moments (RNMs) extracted from the market-available options as constraints. Within the RNM-constrained entropic framework, a unique distribution close enough to the correct one is obtained, and its risk-neutrality is deeply verified based on simulations. Using this resultant risk-neutral distribution (RND), a sample of risk-neutral paths of the underlying price is generated and ultimately the European option’s prices are computed. The pricing performance and analysis in simulations demonstrate that this proposed valuation is comparable to the benchmarks and can produce fairly accurate prices for options. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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13 pages, 2113 KiB  
Article
Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic
by Salim Lahmiri and Stelios Bekiros
Entropy 2020, 22(8), 833; https://doi.org/10.3390/e22080833 - 30 Jul 2020
Cited by 37 | Viewed by 4028
Abstract
The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), [...] Read more.
The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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17 pages, 1093 KiB  
Article
Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data
by Yan Yan, Boyao Wu, Tianhai Tian and Hu Zhang
Entropy 2020, 22(7), 773; https://doi.org/10.3390/e22070773 - 15 Jul 2020
Cited by 19 | Viewed by 3408
Abstract
Complex network is a powerful tool to discover important information from various types of big data. Although substantial studies have been conducted for the development of stock relation networks, correlation coefficient is dominantly used to measure the relationship between stock pairs. Information theory [...] Read more.
Complex network is a powerful tool to discover important information from various types of big data. Although substantial studies have been conducted for the development of stock relation networks, correlation coefficient is dominantly used to measure the relationship between stock pairs. Information theory is much less discussed for this important topic, though mutual information is able to measure nonlinear pairwise relationship. In this work we propose to use part mutual information for developing stock networks. The path-consistency algorithm is used to filter out redundant relationships. Using the Australian stock market data, we develop four stock relation networks using different orders of part mutual information. Compared with the widely used planar maximally filtered graph (PMFG), we can generate networks with cliques of large size. In addition, the large cliques show consistency with the structure of industrial sectors. We also analyze the connectivity and degree distributions of the generated networks. Analysis results suggest that the proposed method is an effective approach to develop stock relation networks using information theory. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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21 pages, 765 KiB  
Article
Portfolio Optimization for Binary Options Based on Relative Entropy
by Peter Joseph Mercurio, Yuehua Wu and Hong Xie
Entropy 2020, 22(7), 752; https://doi.org/10.3390/e22070752 - 09 Jul 2020
Cited by 4 | Viewed by 6398
Abstract
The portfolio optimization problem generally refers to creating an investment portfolio or asset allocation that achieves an optimal balance of expected risk and return. These portfolio returns are traditionally assumed to be continuous random variables. In An Entropy-Based Approach to Portfolio Optimization, [...] Read more.
The portfolio optimization problem generally refers to creating an investment portfolio or asset allocation that achieves an optimal balance of expected risk and return. These portfolio returns are traditionally assumed to be continuous random variables. In An Entropy-Based Approach to Portfolio Optimization, we introduced a novel non-parametric optimization method based on Shannon entropy, called return-entropy portfolio optimization (REPO), which offers a simple and fast optimization algorithm for assets with continuous returns. Here, in this paper, we would like to extend the REPO approach to the optimization problem for assets with discrete distributed returns, such as those from a Bernoulli distribution like binary options. Under a discrete probability distribution, portfolios of binary options can be viewed as repeated short-term investments with an optimal buy/sell strategy or general betting strategy. Upon the outcome of each contract, the portfolio incurs a profit (success) or loss (failure). This is similar to a series of gambling wagers. Portfolio selection under this setting can be formulated as a new optimization problem called discrete entropic portfolio optimization (DEPO). DEPO creates optimal portfolios for discrete return assets based on expected growth rate and relative entropy. We show how a portfolio of binary options provides an ideal general setting for this kind of portfolio selection. As an example we apply DEPO to a portfolio of short-term foreign exchange currency pair binary options from the NADEX exchange platform and show how it outperforms leading Kelly criterion strategies. We also provide an additional example of a gambling application using a portfolio of sports bets over the course of an NFL season and present the advantages of DEPO over competing Kelly criterion strategies. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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19 pages, 1014 KiB  
Article
Non-Uniform Embedding Scheme and Low-Dimensional Approximation Methods for Causality Detection
by Angeliki Papana
Entropy 2020, 22(7), 745; https://doi.org/10.3390/e22070745 - 06 Jul 2020
Cited by 1 | Viewed by 2234
Abstract
Information causality measures have proven to be very effective in uncovering the connectivity patterns of multivariate systems. The non-uniform embedding (NUE) scheme has been developed to address the “curse of dimensionality”, since the estimation relies on high-dimensional conditional mutual information (CMI) terms. Although [...] Read more.
Information causality measures have proven to be very effective in uncovering the connectivity patterns of multivariate systems. The non-uniform embedding (NUE) scheme has been developed to address the “curse of dimensionality”, since the estimation relies on high-dimensional conditional mutual information (CMI) terms. Although the NUE scheme is a dimension reduction technique, the estimation of high-dimensional CMIs is still required. A possible solution is the utilization of low-dimensional approximation (LA) methods for the computation of CMIs. In this study, we aim to provide useful insights regarding the effectiveness of causality measures that rely on NUE and/or on LA methods. In a comparative study, three causality detection methods are evaluated, namely partial transfer entropy (PTE) defined using uniform embedding, PTE using the NUE scheme (PTENUE), and PTE utilizing both NUE and an LA method (LATE). Results from simulations on well known coupled systems suggest the superiority of PTENUE over the other two measures in identifying the true causal effects, having also the least computational cost. The effectiveness of PTENUE is also demonstrated in a real application, where insights are presented regarding the leading forces in financial data. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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27 pages, 2343 KiB  
Article
Exchange-Traded Funds on European Markets: Has Critical Mass been Reached? Implications for Financial Systems
by Adam Marszk and Ewa Lechman
Entropy 2020, 22(6), 686; https://doi.org/10.3390/e22060686 - 19 Jun 2020
Cited by 1 | Viewed by 2382
Abstract
Exchange-traded funds (ETFs) are one of the most rapidly expanding categories of financial products in Europe. One of the key yet still unanswered questions is whether European ETF markets have reached the size at which they could affect the financial systems. In our [...] Read more.
Exchange-traded funds (ETFs) are one of the most rapidly expanding categories of financial products in Europe. One of the key yet still unanswered questions is whether European ETF markets have reached the size at which they could affect the financial systems. In our study, we examine 13 European countries during the period 2004–2017 in order to trace whether the share of ETFs in the total assets of investment funds has reached the ‘critical’ level that makes possible their further growth and can be associated with an influence on the financial system. We use a novel methodological approach that identifies the ‘critical mass’ along diffusion trajectories. Our results show that, in 10 countries, the share of ETFs in assets of investment funds increased. Still, in most countries, the share of ETFs did not exceed 1%. Estimates of the diffusion models indicate that the process of growing shares of ETFs was most dynamic and relatively most stable in Switzerland and United Kingdom. Results of the critical mass analysis imply that its achievement may be forecasted exclusively in these two cases. However, even in such cases there is no substantial evidence for a possible significant influence of ETFs on the local financial systems. Full article
(This article belongs to the Special Issue Information Theory and Economic Network)
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