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The exploitation of limited energy resources can generate socio-economic inequalities calling for a combination of justice with the socio-technical study of modern energy systems to advance its understanding and remediation. Within this context, this paper uses energy justice and econophysics as theoretical and methodological frameworks to discuss issues of energy poverty in Mexico. The results emerging from this research illustrate that, according to data from 2014, around 61% of Mexican households suffered from energy poverty due to issues of accessibility or affordability of modern energy services and fuels (with 11.54% of households facing both types of energy poverty). This paper provides a novel approach that combines advanced quantitative methods based on econophysics, with conceptual frameworks from social sciences like energy justice to discuss issues of energy poverty in Mexico. Furthermore, this research performs an approximation to energy consumption in Mexican households based on monetary expenditure, the heat value of fuels and unit prices during 2014. These methods contribute to the understanding and characterisation of household energy needs and energy consumption in Mexico, reducing the existing gap in the academic literature on the analysis and critical thinking of energy poverty in Global South contexts (specifically in Latin America).

The objective of this article is to study the behavior of the stock markets in emerging countries (BRICS) and developed countries (USA, England, Germany, and Japan), aiming to identify the evolution of their degree of efficiency between June 2007 and July 2021 based on the hypothesis of fractal markets. Using the Hurst exponent, the fractal dimension, and entropy approximation, it was built a market efficiency index. Among the main results, inconstancy of the efficiency indices over time was identified, which is consistent with previous studies within the field of econophysics. In addition, most of the inefficiency is due to the presence of deterministic elements in asset price variations, with a similarity in the efficiency level between the groups of emerging and developed countries, except for the case of China, which presented a singular behavior, which motivates new studies in this market. Finally, the results indicate the relevance of the cointegration effects of the analyzed markets, which is reflected in the inefficiencies of these markets over time.

Econophysics is a branch of economics that applies concepts and methods from physics to the financial markets. This article focuses on the approaches to quantum finance developed by Kirill Ilinski and Belal E. Baaquie to deal with the uncertainty characterizing financial time series. Allegedly, their models rest on a formal analogy between quantum mechanics and finance. In order to evaluate them, we raise the question what is really quantum in quantum econophysics. We then argue that the supposed analogy breaks in an important manner, which is relevant to explain the empirical success of the proposed models.

In this paper we will show that, in the financial market, the concept of chaos is removed and replaced with that of complexity. Indeed, it has always shown the characteristics of a dynamical system...

Econophysics is a relatively young discipline, being an interdisciplinary approach that applies methods and tools from physics in the economics, for studying the financial markets, or other economical phenomena. The objective is to create a mapping of the most used keywords in this topic, as well as the connections between countries and authors, based on citations. This article studies the literature on the topic of econophysics, using the Web of Science database. Bibliometric analysis was made with the free software VOSviewer, after extracting the data from 1364 articles, since the conception of the topic in 1996 until the year 2020. The software offered a useful network representation between the main keywords used in the field, or between countries and citations, and finally between authors, based on citations. The results evidenced a constant interest on the topic, with main keywords: †econophysics†, †financial markets†, †dynamics†, †model†and †stock market†, and the relationships between countries and authors were shown, with the United States, China and Italy being the leading ones. We also found that the most cited authors are H.E. Stanley, the inventor of the word econophysics, Wei-Xing Zhou and Didier Sornette. This article shows the main concepts used in econophysics, which can serve as an indicator of its directions of research, as well as the top contributors in the field, since its inception. Future directions include expanding the analysis to other databases, or to concepts relevant to econophysics, to identify trends in research and the growth of the field.

The influence of physics on economics has been largely analysed; the opposite influence also exists even if it has been less studied. In the last decades the relation between these two disciplines ...

We investigate a heterogeneous Ising model in the context of tax evasion dynamics, where different types of agents are parameterised via local temperatures and magnetic fields. Our work focuses on the dynamic behavioural change of agents after an audit, which either corresponds to a temporal reduction or enhancement of compliance, also known under the terms of ‘bomb crater effect’ and ‘target effect’, respectively. We analyse this effect for different types of agents: endogenously non-compliant types; agents that have a tendency to copy (non-)compliant behaviour from their social environment; ethical agents with strong endogenous moral attitudes; and random types that show large fluctuations between compliant and non-compliant behaviour. Each type influences overall tax evasion differently and our model predicts that, interestingly, increasing the audit probability can have the counter-intuitive effect of increasing tax evasion under certain circumstances. We analyse audit strategies that can suppress this effect, and thus contribute to the burgeoning literature on the actual impact of tax audits.

We study the record statistics in the Korean housing market. To characterize the statistical properties of records, we analyze the record rate and the expected number of records for the transaction price of apartments and the volatility. From the numerical analysis, we find that the record rate of price in the overheated region is well described by the record rate obtained from the symmetric independent and identically distributed (IID) random sequence when the price change is relatively small. On the other hand, during the period for the subprime mortgage crisis, the record rate of price in the overheated regions is well approximated by that for the correlated sequence. More interestingly, when the price continuously increases the record rate of price shows a strong seasonal effect in the overheated region, while that in the normal region is simply described by the symmetric IID random sequence with constant drift on the average.

Digitization of publications, advancement in communication technology, and the availability of bibliographic data have made it easier for the researchers to study the growth and dynamics of any discipline. We present a study on "Econophysics" metadata extracted from the Web of Science managed by the Clarivate Analytics from 2000-2019. The study highlights the growth and dynamics of the discipline by measures of a number of publications, citations on publications, other disciplines contribution, institutions participation, country-wise spread, etc. We investigate the impact of self-citations on citations with every five-year interval. Also, we find the contribution of other disciplines by analyzing the cited references. Results emerged from micro, meso and macro-level analysis of collaborations show that the distributions among authors collaboration and affiliations of authors follow a power law. Thus, very few authors keep producing most of the papers and are from a few institutions. We find that China is leading in the production of a number of authors and a number of papers; however, shares more of national collaboration rather than international, whereas the USA shares more international collaboration. Finally, we demonstrate the evolution of the author's collaborations and affiliations networks from 2000-2019. Overall the analysis reveals the "small-world" property of the network with average path length 5. As a consequence of our analysis, this study can serve as in-depth knowledge to understand the growth and dynamics of the Econophysics network both qualitatively and quantitatively., 14 pages, 16 figures

For the last three decades, physicists have been moving beyond the boundaries of their discipline, using their methods to study various problems usually instigated by economists. This trend labeled ‘econophysics’ can be seen as a hybrid area of knowledge that exists between economics and physics. Econophysics did not spring from nowhere—the existing literature agrees that econophysics emerged in the 1990s and historical studies on the field mainly deal with what happened during that decade. This article aims at investigating what happened before the 1990s by clarifying the epistemic background that might have paved the way to the emergence of econophysics. This historical exploration led me to highlight the active role played by the Santa Fe Institute by promoting interdisciplinary research on complexity in 1980s. Precisely, by defining three research themes on economic complexity, the SFI defined a research agenda and a way of extending physics\biology to economics. This article offers a possible archeology of econophysics to clarify what could have contributed to the development of a particular episteme in the 1980s easing the advent of econophysics in the 1990s.

In this work, we modify the affine wealth model of wealth distributions to examine the effects of nonconstant redistribution on the very wealthy. Previous studies of this model, restricted to flat redistribution schemes, have demonstrated the presence of a phase transition to a partially wealth-condensed state, or "partial oligarchy," at the critical value of an order parameter. These studies have also indicated the presence of an exponential tail in wealth distribution precisely at criticality. Away from criticality, the tail was observed to be Gaussian. In this work, we generalize the flat redistribution within the affine wealth model to allow for an essentially arbitrary redistribution policy. We show that the exponential tail observed near criticality in prior work is, in fact, a special case of a much broader class of critical, slower-than-Gaussian decays that depend sensitively on the corresponding asymptotic behavior of the progressive redistribution model used. We thereby demonstrate that the functional form of the tail of the wealth distribution in a near-critical society is not universal in nature but rather entirely determined by the specifics of public policy decisions. This is significant because most major economies today are observed to be near-critical., Comment: 20 pages, 2 figures

This paper proposes a novel dynamic function to capture the phenomenon of momentum in financial markets. It occurs when high returns subsequently follow a security’s past high returns, and subsequent low returns follow its past low returns. By exploring an analogy to the momentum definition from physics, we model financial momentum as the product of a stock’s return and its number of shares outstanding with considerations of the difference between its market price and intrinsic price. In contrast to traditional proxies, where the momentum is described as the rate of acceleration of a security’s price or volume and is calculated based on past 12-month returns or the associated accumulation in returns, the dynamics of our model exhibit advantageous trading characteristics for common momentum-based strategies.

This article investigates the dynamical complexity and fractal characteristics changes of the Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns in the period before and after the outbreak of the COVID-19 pandemic. More specifically, we applied the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method to investigate the temporal evolution of the asymmetric multifractal spectrum parameters. In addition, we examined the temporal evolution of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. Our research was motivated to contribute to the comprehension of the pandemic’s impact and the possible changes it caused in two currencies that play a key role in the modern financial system. Our results revealed that for the overall trend both before and after the outbreak of the pandemic, the BTC/USD returns exhibited persistent behavior while the EUR/USD returns exhibited anti-persistent behavior. Additionally, after the outbreak of COVID-19, there was an increase in the degree of multifractality, a dominance of large fluctuations, as well as a sharp decrease of the complexity (i.e., increase of the order and information content and decrease of randomness) of both BTC/USD and EUR/USD returns. The World Health Organization (WHO) announcement, in which COVID-19 was declared a global pandemic, appears to have had a significant impact on the sudden change in complexity. Our findings can help both investors and risk managers, as well as policymakers, to formulate a comprehensive response to the occurrence of such external events.

The methods of complex networks have been extensively used to characterize information flow in complex systems, such as risk propagation in complex financial networks. However, network dynamics are ignored in most cases despite systems with similar topological structures exhibiting profoundly different dynamic behaviors. To observe the spatiotemporal patterns of risk propagation in complex financial networks, we combined a dynamic model with empirical networks. Our analysis revealed that hub nodes play a dominant role in risk propagation across the network and respond rapidly, thus exhibiting a degree-driven effect. The influence of key dynamic parameters, i.e., infection rate and recovery rate, was also investigated. Furthermore, the impacts of two typical characteristics of complex financial systems—the existence of community structures and frequent large fluctuations—on the spatiotemporal patterns of risk propagation were explored. About 30% of the total risk propagation flow of each community can be explained by the top 10% nodes. Thus, we can control the risk propagation flow of each community by controlling a few influential nodes in the community and, in turn, control the whole network. In extreme market states, hub nodes become more dominant, indicating better risk control.

A adaptação de metodologias das ciências físicas ao estudo de fenômenos sociais e econômicos deuorigem à ideia de sociofísica e econofísica como campos de estudo. A ideia do estudo do sociedadecomo uma “física social” já estava presente no ideário de precursores do sociologia como Comte eQuéletet, mas essa abordagem é considerada superada ou mesmo perigosa por grande parte doscientistas sociais, o que influencia sua opinião sobre a sociofísica, embora esta também possa serconsiderada como um fenômeno distinto das ideias originais de física social. Outras objeçõescomuns à sociofísica relacionam-se à maior complexidade dos fenômenos sociais em relação aosfenômenos físicos e ao fato de que o próprio observador seja parte do objeto estudo nos estudossociais. É uma ideia recorrente que a complexidade do ser humano impossibilita a abordagempor meio de equações matemáticas. Essa ideia não nos parece totalmente justificada porque, nosestudos de sociofísica e econofísica que analisamos, os modelos matemáticos não aparecem comoas únicas ferramentas utilizadas, mas como recursos adicionais de análise, havendo referências aobservações e dados vindos de estudos sociológicos, psicológicos e econômicos. Não é razoável que oestudo de sistemas mais complexos seja prejudicado pela inclusão de mais recursos e possibilidadesmetodológicas. Objeções semelhantes também são feitas à econofísica, que também pode serentendida como uma ciência social, mas o maior conflito entre econofísicos e economistas refere-seao fato de que esses profissionais frequentemente usam os mesmos conceitos e metodologias,de modo que seja difícil definir fronteiras entre seus campos de atuação. De forma geral, aabordagem da econofísica não é estranha aos economistas, que estão mais habituados a análisesestatísticas e modelos matemáticos. A maioria dos estudos de econofísica e sociofísica poderia sertambém classificada em outros campos de estudo: economia financeira, sociologia matemática,sistemas dinâmicos, dinâmica de sistemas, teoria dos jogos, teoria da complexidade etc. A teoriada complexidade, sob a denominação “sistemas complexos”, tem sido citada como uma áreadestacadamente multidisciplinar, oferecendo uma oportunidade de maior interação de econofísicose sociofísicos com profissionais de outras áreas.

Econophysics, or the theory of construction of models using physical assumptions in economics, already has a long history. For this purpose, the gravitational law of attraction was probably first used in the modeling of trade models between two subjects. The laws of gravitational and electrostatic attraction were then used to study migration between countries. The article adopts two new models (containing dummy variables) of migration from Georgia to leading European countries and USA using multivariate linear regression. They are based on the Lewer and van der Berg generalized gravitational model of migration between countries of The Organisation for Economic Co-operation and Development (OECD). Their model contains dummy parameters of border, the proximity of the state languages and colonial links in past. In this article we introduce a new dummy parameter «religion» of the model of migration from Georgia; It is hypothesized that migration between neighbor countries may not be analogous to the Newton›s law of universal gravitation. In our view, it would be more appropriate to use other physical assumptions to construct such a migration model, for example a mathematical model of the law of equalization of temperatures in an area with different temperatures at different points.

The growth and reduction of cycle times is a characteristic feature of modern economy. Increasingly, cyclical factor is cause of imbalance and violation of global and national economies stability. However, the question of regional industry cycles asynchrony has not been studied. It was previously believed that the regional industry cycle, its stages of growth and decline, is consistent with the national one. The work shows that it is precisely the asynchrony of regional industrial cycles that underlies imbalance and reduces stability of country's economic development. The study presented by the authors expands boundaries of understanding the laws of recessions and upswings in industry cycles, since it reveals the causes and consequences of asynchronism of industry cycles in Russian regions. Terminologically, the regional sectoral cycle should be interpreted as a dynamic process of fluctuations in economic activity within life cycle of industry, characterized by repeatability of successive stages of decline and rise in the region's industry. The authors developed a classification of main parameters of regional industry cycle according to their cyclic properties, including leading, lagging and coinciding indicators of the cycle dynamics. A generalization of scientific and methodological approaches to industry cycles as a risk factor for sustainable and balanced development of regions is presented. The causes and consequences of regional sectoral cycles for the sustainability of regional economy are determined. The measures proposed for regional authorities to prevent the negative consequences of phase shift of sectoral regional cycle will be of practical importance.

Unification of natural science and social science is a centuries-old, unmitigated debate. Natural science has a chronological advantage over social science because the latter took time to include many social phenomena in its fold. History of science witnessed quite a number of efforts by social scientists to fit this discipline in a rational if not mathematical framework. On the other hand a tendency among some physicists has been observed especially since the last century to recast a number of social phenomena in the mould of events taking place in physical world and governed by well-known systems and equations of physics. It necessitated the introduction of social physics as a new inter-disciplinary subject. Obviously this attempt is aimed at explaining hitherto unsolved or highly debated issues of social science. Physicists are showing special interest on problems on economics, ranging from some topics of normative economics to the movement of prices of derivatives. Statistics has been widely used in these attempts and at least two sub-disciplines of the subject, namely, stochastic process and time series analysis deserve special mention. All these research activities gave birth to another inter-disciplinary subject named as econophysics. Interestingly, global financial crisis of 2007–08 has revived the need of determination of prices of derivatives in a more accurate manner. This article adumbrates a sketch of the theoretical synthesis between physics and economics and the role played by statistics in this process.

A financial time series, such as a stock market index, foreign exchange rate, or a commodity price, fluctuates heavily and shows scaling behaviors. Scaling and multi-scaling behaviors are measured for a nonstationary time series, such as stock market indices, high-frequency stock prices of individual stocks, or the volatility time series of a stock index. We review the fractality, multi-scaling, and multifractality of the financial time series of a stock market. We introduce a detrended fluctuation analysis of the financial time series to extract fluctuation patterns. Multifractality is measured using various methods, such as generalized Hurst exponents, the generalized partition function method, a detrended fluctuation analysis, the detrended moving average method, and a wavelet transformation.

Subject. The article analyzes the mesodynamics of economic sectors in Russian regions. Objectives. The aim of the study is to undertake quantitative and qualitative monitoring of the components' evolution in twelve basic economic sectors from 2005 to 2017, to describe the regions' homogeneity, stability and balance, and their prospects for investment. Methods. We employ methods of identification, lowess smoothing, and the bootstrap approach to identify models of mesodynamics on 30–50-value samples, without a priori knowledge about the stochastic component distribution law. Results. The findings confirmed the possibility and adequacy of the econophysics paradigm for mesodynamic analysis. We justified and tested methods for general mesodynamic models' identification, using the R programming language. Conclusions. The offered methodology and tools will enable to develop a knowledge base for fundamental laws of mesodynamics. They will also help reveal spatial and temporal features of cyclical development of Russian regions; assess the level of balance and sustainability of regional development in the medium term period; group the regions by their cycle length and define the most promising ones for investment.

This paper empirically investigates whether cryptocurrencies might have a useful role in financial modelling and risk management in the energy markets. To do so, the causal relationship between movements on the energy markets (specifically the price of crude oil) and the value of cryptocurrencies is analysed by drawing on daily data from April 2013 to April 2019. We find that shocks to the US and European crude oil indices are strongly connected to the movements of most cryptocurrencies. Applying a non-parametric statistic, Transferring Entropy (an econophysics technique measuring information flow), we find that some cryptocurrencies (XEM, DOGE, VTC, XLM, USDT, XRP) can be used for hedging and portfolio diversification. Furthermore, the results reveal that the European crude oil index is a source of shocks on the cryptocurrency market while the US oil index appears to be a receiver of shocks.

Modern authors have identified a variety of striking economic patterns, most importantly those involving the distribution of incomes and profit rates. In recent times, the econophysics literature has demonstrated that bottom incomes follow an exponential distribution, top incomes follow a Pareto, and profit rates display a tent-shaped distribution. This paper is concerned with the theory underlying various explanations of these phenomena Traditional econophysics relies on energy-conserving “particle collision” models in which simulation is often used to derive a stationary distribution Those in the Jaynesian tradition rely on entropy maximization, subject to certain constraints, to infer the final distribution. This paper argues that economic phenomena should be derived as results of explicit economic processes For instance, the entry and exit process motivated by supply decisions of firms underlies the drift-diffusion form of wage, interest and profit rates arbitrage. These processes give rise to stationary distributions that turn out to be also entropy maximizing. In the arbitrage approach, entropy maximization is a result. In the Jaynesian approach, entropy maximization is the means.

A review of theoretical and applied results obtained in the framework of the scientific direction in econophysics at the Department of information systems and mathematical methods in economics is given. The first part gives the concept of a financial bubble and methods for finding them. At the beginning of the article, the development of econophysics is given. Therefore, using the research of physicists as a model, econophysics should begin its research not from the upper floors of an economic building (in the form of financial markets, distribution of returns on financial assets, etc.), but from its fundamental foundations or, in the words of physicists, from elementary economic objects and forms of their movement (labor, its productivity, etc.). Only in this way can econophysics find its subject of study and become a "new form of economic theory". Further, the main prerequisites of financial bubble models in the market are considered: the principle of the absence of arbitrage opportunities, the existence of rational agents, a risk-driven model, and a price-driven model. A well-known nonlinear LPPL model (log periodic power law model) was proposed. In the works of V.O. Arbuzov, it was proposed to use procedures for selecting models. Namely, basic selection, "stationarity" filtering, and spectral analysis were introduced. The results of the model were presented in the works of D. Sornette and his students. The second part gives the concept of percolation and its application in Economics. We will consider a mathematical model proposed by J.P. Bouchaud, D. Stauffer, and D. Sornette that recreates the behavior of an agent in the market and their interaction, geometrically describing a phase transition of the second kind. In this model, the price of an asset in a single time interval changes in proportion to the difference between supply and demand in this market. The results are published in the works of A.A. Byachkova, B.I. Myznikova and A.A. Simonov. There are two types of phase transition: the first and second kind. During the phase transition of the first kind, the most important, primary extensive parameters change abruptly: the specific volume, the amount of stored internal energy, the concentration of components, and other indicators. It should be noted that this refers to an abrupt change in these values with changes in temperature, pressure, and not a sudden change in time. The most common examples of phase transitions of the first kind are: melting and crystallization, evaporation and condensation. During the second kind of phase transition, the density and internal energy do not change. The jump is experienced by their temperature and pressure derivatives: heat capacity, coefficient of thermal expansion, or various susceptibilities. Phase transitions of the second kind occur when the symmetry of the structure of a substance changes: it can completely disappear or decrease. For quantitative characterization of symmetry in a second-order phase transition, an order parameter is introduced that runs through non-zero values in a phase with greater symmetry, and is identically equal to zero in an unordered phase. Thus, we can consider percolation as a phase transition of the second kind, by analogy with the transition of paramagnets to the state of ferromagnets. The percolation threshold or critical concentration separates two phases of the percolation grid: in one phase there are finite clusters, in the other phase there is one infinite cluster. The key situation to study is the moment of formation of an infinite cluster on the percolation grid, since this means the collapse of the market, when the overwhelming part of agents for this market has a similar opinion about their actions to buy or sell an asset. The main characteristics of the process are the threshold probability of market collapse, as well as the empirical distribution function of price changes in this market. Keywords: econophysics, behavior of agents in the market, market crash, second-order phase transition, percolation theory, model calibration, agent model calibration, percolation gratings, gradient percolation model, percolation threshold, clusters, fractal dimensions, phase transitions of the first and second kind.

Research background: The application of non-linear analysis and chaos theory modelling on financial time series in the discipline of Econophysics. Purpose of the article: The main aim of the article is to identify the deterministic chaotic behavior of stock prices with reference to Amazon using daily data from Nasdaq-100. Methods: The paper uses nonlinear methods, in particular chaos theory modelling, in a case study exploring and forecasting the daily Amazon stock price. Findings & Value added: The results suggest that the Amazon stock price time series is a deterministic chaotic series with a lot of noise. We calculated the invariant parameters such as the maxi-mum Lyapunov exponent as well as the correlation dimension, managed a two-days-ahead forecast through phase space reconstruction and a grouped data handling method.

Cellular automata are widely used in undergraduate physics courses to educate students in elementary programming and for project work. Cellular automata are coded with simple rules yet provide a rich if well-trodden landscape for exploring aspects of physics such as diffusion and magnetism. Mathematical games, such as the minority game or the prisoner's dilemma, are also amenable to project work with the added dimension of applications in finance, econophysics, and social physics. Conway's classical game of life is both a mathematical game and a cellular automaton. We exploit adaptations of Conway's game of life as an opportunity for undergraduate students to explore new territory within the safe haven of an easy-to-implement cellular automaton. Students may discover new “lifeforms” comprising collections of live, dead, and part-live cells, and explore the escalation of floating-point errors leading to chaos-like behavior, amongst many phenomena not observed in Conway's classical counterpart.

A review of theoretical and applied results obtained in the framework of the scientific direction in econophysics at the Department of Information Systems and Mathematical Methods in Economics is given. The first part gives the concept of a financial bubble and methods for their search. The review covers the period 2010-2019. A review of theoretical and applied results obtained in the framework of the scientific direction in econophysics at the Department of information systems and mathematical methods in Economics is given. The first part gives the concept of a financial bubble and methods for finding them. At the beginning of the article, the development of econophysics is given. Therefore, using the research of physicists as a model, econophysics should begin its research not from the upper floors of an economic building (in the form of financial markets, distribution of returns on financial assets, etc.), but from its fundamental foundations or, in the words of physicists, from elementary economic objects and forms of their movement (labor, its productivity, etc.). Only in this way can econophysics find its subject of study and become a "new form of economic theory". Further, the main prerequisites of financial bubble models in the market are considered: the principle of the absence of arbitrage opportunities, the existence of rational agents, a risk-driven model, and a price-driven model. A well-known nonlinear LPPL model (log periodic power law model) was proposed. In the works of V. O. Arbuzov, it was proposed to use procedures for selecting models. Namely, basic selection, "stationarity" filtering, and spectral analysis were introduced. The results of the model were presented in the works of D. Sornette and his students. The second part gives the concept of percolation and its application in Economics. We will consider a mathematical model proposed by J. P. Bouchaud, D. Stauffer, and D. Sornette that recreates the behavior of an agent in the market and their interaction, geometrically describing a phase transition of the second kind. In this model, the price of an asset in a single time interval changes in proportion to the difference between supply and demand in this market. The key situation to study is the moment of formation of an infinite cluster on the percolation grid, since this means the collapse of the market, when the overwhelming part of agents for this market has a similar opinion about their actions to buy or sell an asset. The main characteristics of the process are the threshold probability of market collapse, as well as the empirical distribution function of price changes in this market.

The topic of wealth and money distribution attracts great attention of economists, as well as researchers from other scientific fields, such as statistical physics and econophysics. An increasing number of models and simulations are being created in order to understand the process of wealth distribution and reaching the steady state of the distribution system. Also, the number of papers dealing with analysis and determining the distribution proportion is constantly growing, and, unlike the previous years, when the Pareto principle was “80-20”, today that principle could be “90-10”and even “90-20”. In this paper we present an agent-based simulation model derived from econophysics that describes the dynamics of wealth distribution. Two models of exponential function are tested: a one-phase model that uses the Newton’s law of cooling and a two-phase exponential function model. We found that exponential decreasing function adequately described the dynamics of wealth distribution, especially in the models without the possibility of borrowing money, and the validity of the Pareto principle “80-20” in these models could be confirmed.

Yakovenko and his co-authors have established that the bottom 97– 99% of individual incomes (labor incomes) follow a near-exponential distribution while the top incomes (property incomes) follow a power law. Initial explanations of these patterns relied on various monetary analogues to the physics principle of energy conservation. Subsequent approaches turned to the stochastic dynamics of economic processes, including those of labor and property income modeled as a drift-diffusion processes. Our paper is in the latter tradition, but our specifications of drift-diffusions are derived from the fundamental economic principle of turbulent arbitrage modeled as a mean-reverting process. This approach is well developed in the domain of interest rate arbitrage as in the case of CIR models. Our contribution is to demonstrate that arbitrage can also explain the observed distributions of wages, rates of return on assets, and property income. In the energy conservation approach, stationary distributions are derived from the assumption of entropy maximization. In both stochastic dynamics approaches, the dynamic paths give rise to stationary distributions that turn out to be entropy maximizing.

Macroeconomic theory and central Banks' policy recommendations have analyzed for decades the link between the expected value of future inflation and its subsequent realization. Agents' inflation expectations have thus become a fundamental input of the economic policy: they allow to know if economic agents are synchronized with the policies and allow the Central Banks to anticipate the market trends. In this paper, we found evidence for the case of Uruguay of a discrepancy between the distribution of agents' inflation expectations and the distribution expected by traditional models. A first consequence is an increase in uncertainty in the estimates; problems related to its asymptotic distribution and the assumptions that arise from this aggregate distribution are analyzed. Another consequence is related to the existence of a structure in the data and the notion of equilibrium in the model. It is concluded that a discussion regarding the nature of the economic phenomenon is essential for the correct specification of the model studied.

The history of economic thought witnessed several prominent economists who took seriously models and concepts in physics for the elucidation and prediction of economic phenomena. Econophysics is an emerging discipline at the intersection of heterodox economics and the physics of complex systems, with practitioners typically engaged in two overlapping but distinct methodological programs. The first is to export mathematical methods used in physics for the purposes of studying economic phenomena. The second is to export mechanisms in physics into economics. A conclusion is drawn that physics transfer is often justified at the level of mathematical transfer but unjustified at the level of mechanistic transfer.

We analyze historical data of stock-market prices for multiple financial indices using the concept of delay-time averaging for the financial time series (FTS). The region of validity of our recent theoretical predictions [Cherstvy A Get al2017New J. Phys.19063045] for the standard and delayed time-averaged mean-squared ‘displacements’ (TAMSDs) of the historical FTS is extended to all lag times. As the first novel element, we perform extensive computer simulations of the stochastic differential equation describing geometric Brownian motion (GBM) which demonstrate a quantitative agreement with the analytical long-term price-evolution predictions in terms of the delayed TAMSD (for all stock-market indices in crisis-free times). Secondly, we present a robust procedure of determination of the model parameters of GBM via fitting the features of the price-evolution dynamics in the FTS for stocks and cryptocurrencies. The employed concept of single-trajectory-based time averaging can serve as a predictive tool (proxy) for a mathematically based assessment and rationalization of probabilistic trends in the evolution of stock-market prices.

In this paper, relying on the clustering of complex networks that can determine large scale features of ‎the network, we study 48 financial markets across the world. To this end, we develop a modularity ‎maximization method for directed and weighted networks. According to the linear correlation measure, ‎we construct the adjacency matrix, and by using the theory of random matrices, we divide the space of ‎eigenvalues of our matrix into two irrelevant and relevant fragments. By considering the temporal ‎window and its evolution over time series, our results demonstrate that in the vicinity of so-called ‎financial crisis clusters, which are often affected by geographical characteristics, are formed and from the ‎perspective of complex networks, they show more random behavior‎.‎‎

This paper examines relations between econophysics and the law of entropy as foundations of economic phenomena. Ontological entropy, where actual thermodynamic processes are involved in the flow of energy from the Sun through the biosphere and economy, is distinguished from metaphorical entropy, where similar mathematics used for modeling entropy is employed to model economic phenomena. Areas considered include general equilibrium theory, growth theory, business cycles, ecological economics, urban–regional economics, income and wealth distribution, and financial market dynamics. The power-law distributions studied by econophysicists can reflect anti-entropic forces is emphasized to show how entropic and anti-entropic forces can interact to drive economic dynamics, such as in the interaction between business cycles, financial markets, and income distributions.

The analysis of ordinal pattern distributions provides a relatively new and interesting approach to nonlinear time series analysis leading, for example, to the concept of permutation entropy. Data analysis methods based on ordinal patterns have been applied in different fields of research such as biomedicine, econophysics and engineering. Main reasons for the increasing success of these analysis methods are that ordinal patterns contain intrinsic information on the dynamical structure of a system and that ordinal pattern-based methods are robust and simple from a computational viewpoint. Whereas some nice asymptotic results have been found for pattern length going to infinity, in practical data analysis short patterns describing certain features of data and models behind them are of some special interest. We demonstrate this point by discussing some statistics based on counting monotone changes and on considering asymmetries in ordinal pattern distributions. The data analysis methods obtained on this base are illustrated by considering some real world data.

This study delves into the herding and bubble detection in the volatility domain of a capital market underlying. Furthermore, it focuses on creating heuristics, so that common investors find it relatively easy to understand the state of the market volatility. Hence, it can be termed that this study is focused on the specific financial innovation regarding bubble and herding detection coupled with investor awareness. The traces of possible volatility bubble emerge when it is positioned against its own lags (both lag1 and lag2). The volatility trigger indicated clear traces of herding and an embedded parabola function. Continuous and repetitive parabola function hinted at a subtle presence of “fractals”. Firstly, the detrended fluctuation analysis has been used with its multifractal variant. Secondly, the regularized form of Hurst calculation and analysis have been used. Both tests reveal the traces of nascent bubble formation owing to prominent herding in CNX Nifty HFT environment. They also indicate a clear link with Hausdorff topological patterns. These patterns would help to create heuristics, enabling investors to be aware of possible bubble and herd situations.

The fundamental purpose of the present research article is to introduce the basic principles of dimensional analysis in the context of the neoclassical economic theory, in order to apply such principles to the fundamental relations that underlay most models of economic growth. In particular, basic instruments from dimensional analysis are used to evaluate the analytical consistency of the neoclassical economic growth model. The analysis shows that an adjustment to the model is required in such a way that the principle of dimensional homogeneity is satisfied.JEL: A12, C02, C65, O40

Given its regionally lopsided development, the sports industry in China now largely features spatial and geographical agglomeration. Profitability is an important parameter for the sustainable development of an industry. It is interesting to make clear whether the profitability of Chinese sports industry features regional agglomeration. We firstly make a statistical analysis and probability density analysis by Random Matrix Theory (RMT) based on monthly provincial profit margin data of the sporting goods manufacturing industry listed from 2011 to 2015. We find that the profit margin of this industry is not randomly distributed over the regions. We also focus on the collective market effects of the profitability of this industry. Through the method of regression analysis on the largest 3 eigenvalues of the profit margin, we find that the eigenvalues reflect the collective market effects in Profit to Cost Ratio (PCR) and Return on Sales (ROS). At last, we search each correlation matrix of the profit margin for the clusters of provinces by using the clustering methods. We find the majority of the provinces can be categorized into clusters and most of the correlated provinces are adjacency or proximity. Our analysis provides a new understanding of the regional characteristics of sports industry profitability and offers potential practical values for the government and entrepreneurs.

Risk contagion is becoming a research hotspot in the field of econophysics with the rise of interdisciplinary studies and gains more and more attention from theoretical circles and practical departments. This paper proposes a new research frame to discuss the microscopic mechanism of risk contagion based on agent modeling technology and complex network theory and reveals nonlinear dynamics characteristic of risk contagion from the perspective of market participants in financial market. Based on the proposed SICM model, financial risk can transmit from susceptible agents to infected agents, to contagious agents, or to immune agents. With the increases of contagious probabilities, the simulation experiments show that (1) the amount of susceptible agents continuously decreases; (2) the amount of infected agents increases first and then decreases; (3) the amount of contagious agents increases first and then decreases with a lower speed, relative to the amount of infected agents; and (4) the amount of immune agents continuously increases. The major contribution of this paper is a new method for studying nonlinear dynamics characteristic of risk contagion, which can be used as a theoretic basis for further researches on the behavioral features of microcosmic subject and the inner mechanisms of risk contagion.

We construct the empirical information network (EIN) of traders using the order flow data of the constituent stocks of SZSE 100 Index in 2013. A statistical validation method is applied to the edges of the network to filter out noises and uncover the intrinsic interaction behaviors of traders. We investigate the correlation between topological structures and statistical properties for their largest connected components. We find that the statistical validated network shows an assortative mixing pattern while the original network exhibits a disassortative mixing pattern. We consider two definitions of edge weight for comparison but there is no significant difference in a same network. We also analyze the mutual relationships among node degree, edge weight and node strength.

Mergers and divestitures are two major economic activities. In this article, an evolutionary model combined with mergers and divestitures is proposed from the perspective of complex network science. More specifically, the Axelrod model is introduced to present the identity of entities on the economic network, the Cobb–Douglas production function is used to calculate the power of each entity, and the probabilities of mergers and divestitures are determined by the Fermi function considering key economic indicators during the evolution. We find that mergers and divestitures have neither good nor bad characteristics by themselves, and that the key to the success of these activities is the ability and the development status of entities in the economic network. Identity dimension, learning ability, initial network size, and density usually play important roles in the progress under the influence of mergers and divestitures. Power, degree, age, identity distance, maximal ingredient, network size, and the frequency of mergers and divestitures likewise can give rise to very different processes in different economic environments. The presented results may have a high reference value when economic sectors develop specific merger and divestiture policies, and we also outline directions for future research that seem important and promising along the same lines.

Considering macroeconomies as systems subject to stochastic forms of entropic equilibria, we shall consider how deviations driven by positive feedbacks as in a speculative bubble can drive such an economy into an anti-entropic state that can suddenly collapse back into an entropic state, with such a collapse taking the form of a Minsky moment. This can manifest itself as shifts in the boundary between the portion of the income distribution that is best modeled as Boltzmann–Gibbs and that best modeled as a Paretian power law.

The study analyses the impact of central bank activities carried out as a result of the crisis in 2008 on cross-correlations observed on stocks exchanges. Using the power law classification scheme (PLCS), histograms of the correlation strength for five groups of entities from different regions of the world were examined. Each of the groups contained 16 quotes selected among the companies listed on the following indexes: All Ordinaries, CAC 40, DAX, DJI, FTSE 100, Hang Seng, Nikkei 225. The analysis was carried out by comparing the correlation strengths histograms for the selected periods before and after 2008 year. Significant differences between the group of markets on which central banks’ intervention took place from those where such intervention was not carried out were pointed. In particular, the cross-correlations among companies in the USA and European markets were compared with the situation on the Australian market. The Japanese and Singapore markets can be considered as the special cases where no direct intervention took place but due to the strong relationship with European and USA economy the cross-correlations have been disturbed.

International audience; In this article the problem of reconstructing the pattern of connection between agents from partial empirical data in a macro-economic model is addressed, given a set of behavioral equations. This systemic point of view puts the focus on distributional and network effects, rather than time-dependence. Using the theory of complex networks we compare several models to reconstruct both the topology and the flows of money of the different types of monetary transactions, while imposing a series of constraints related to national accounts, and to empirical network sparsity. Some properties of reconstructed networks are compared with their empirical counterpart.

Behavioural Finance Theory is a modern approach to finance theory – which, in turn has three wings in its standard versions: the theory of finance based on subjective expected utility theory, in conjunction with the efficient market hypothesis theory (with Bayes’s rule as an auxiliary assumption for updates); the Shafer-Vovk approach via the use of Ville’s arithmetic game version; and that which is based on the work of Bachelier, Osborne and Mandelbrot which is called the Econophysics vision. Dissatisfaction with the theoretical, empirical and experimental fundamentals of these three approaches has led, in the last quarter of a century, to the development of the field of modern behavioural finance theory. This is based on the early work of Thaler, Tversky and Kahneman. In this paper, this view is contrasted with the prior work of Herbert Simon, and is called Classical Behavioural Finance Theory.

While most financial engineering and econophysics studies have focused in daytime trading, much less investigation has been devoted to the non-trading or night periods. In this work, the correlation between overnight and daytime return (correlation ND) and the correlation between daytime return and following over night return (correlation DF) were investigated, which led to several findings. First, a weak negative correlation between overnight and daytime return (correlation ND) was observed in Japanese Stocks Market. Secondly, the application of Volatility Constrained correlation (VC correlation) method led to a significant amplification of this signal which benefits for increasing predictability of day time return compared to standard correlation. Furthermore, the analysis of the amplified signal derived from VC correlation for each stock revealed a linear scale relationship between the standard correlation and VC correlation. Therefore, this result indicates that by using the VC correlation, stronger correlation effect can be observed. Taking together, these findings suggest that the combination of VC approach with financial trading data over night paves the way to improve market predictability.

We use a heterogeneous agent-based two-state sociophysics model to simulate financial markets. Focusing on stock market trader dynamics, we propose a model with two kinds of individual – the contrarian agent and the noise trader – in which the dynamics of buying and selling investors are governed by local and global interactions. We define an antiferromagnetic coupling that relates the option of contrarian agents to global magnetization and a ferromagnetic interaction that connects noise traders to their local neighborhood. Our model presents such stylized facts of real financial markets as clustered volatility, power-law distributed returns, and the long-time correlation of the absolute returns with exponential decay. We also observe that the distribution of logarithmic returns can be fitted by the Student’s t distribution in which its degree of freedom changes with the percentage of contrarian agents in the market.

The detection of informational leadership is a core issue in financial market microstructure. We use effective group transfer entropy (EGTE) as a measure for the predictability of a stochastic process using lagged observations on multiple related processes within the same system. We propose an appropriate bootstrap to derive confidence bounds and show by means of a simulation study that standard linear approaches in economics and finance, such as vector autoregressions and Granger-causality tests, are not well suited to detect information transfer. We empirically examine the markets for cryptocurrencies using intraday data and reveal that the dependencies are mostly of nonlinear nature, highlighting the applicability of EGTE in the context of this new financial product.

This paper focuses on measuring the globally and nationally economic system’s connectedness and industrial sector’s function on the Global Value Chain (GVC), as reinforcements to the present studies on international trade. Firstly, we reconsidered the length-related and position-related measures in literatures about vertical specialization from the perspective of econophysics. Secondly, we redefined the inter-country and inter-sector propagating process of intermediate goods and proposed the concept of Strongest Relevance Path Length (SRPL) based on Revised Floyd–Warshall Algorithm (RFWA), which is the basis of new measurement. Thirdly, we introduced Average and Maximum Strongest Relevance Degree (ASRD and MSRD) to measure the connectedness and compactness of network respectively. Fourthly, enlightened by betweenness centrality, we introduced SRPL-based index to measure industrial sectors’ Pivotability in transferring intermediate goods Fifthly, these indices were applied to the empirical analysis of the economic system by physical statistics. Finally, through cascading failure analysis, we found that both ASRD and MSRD reflecting the overall performance of propagating process of intermediate goods are vulnerable to those sectors with high pivotability.

To model wealth distributions there exist models based on the Boltzmann-Gibbs distribution (BGD), which is obtained by simulating binary economic interactions or exchanges that are similar to particle collisions in physics with conservedenergy (or money in econophysics). Also, BGD can be reproduced by numerical simulations of diffusion for many particles which experience energy fluctuations. This latter case is analogous to non-interacting pollen particles performing Brownian motion. In order to decrease inequality, we also modify the energy-conserved diffusion by taxing the richest agent. In all cases, we calculate the corresponding Gini inequality index and the time evolution of the entropy to show the stability of the statistical distributions.