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Dynamic pricing depends on the understanding of uncertain demand. We ask the question whether a stochastic system is sufficient to model this uncertainty. We propose a novel paradigm based on statistical analysis of recurrence quantification measures. The paradigm fits nonlinear dynamics by simultaneously optimizing both the determinism and the trapping time in recurrence plots and identifies an optimal time delay embedding. We firstly apply the paradigm on well-known deterministic and stochastic systems including Duffing systems and multi-fractional Gaussian noise. We then apply the paradigm to optimize the sampling of empirical point process data from RideAustin, a company providing ride share service in the city of Austin, Texas, the USA, thus reconstructing a period-7 attractor. Results show that in deterministic systems, an optimal embedding exists under which recurrence plots exhibit robust diagonal or vertical lines. However, in stochastic systems, an optimal embedding often does not exist, evidenced by the inability to shrink the standard deviation of either the determinism or the trapping time. By means of surrogate testing, we also show that a Poisson process or a stochastic system with periodic trend is insufficient to model uncertainty contained in empirical data. By contrast, the period-7 attractor dominates and well models nonlinear dynamics of empirical data via irregularly switching of the slow and the fast dynamics. Findings highlight the importance of fitting and recreating nonlinear dynamics of data in modeling practical problems. [ABSTRACT FROM AUTHOR]

The lattice gas automaton (LGA) is proposed for a closed economic market of agents with heterogeneous saving interests. There are two procedures in the standard LGA, i.e. "propagation" + "transaction". If the propagation step is removed and the transaction is conducted among all agents, the LGA reduces to a more simplified kinetic model. In addition, two dealing rules are imposed on the transaction phase. Under Rule I, the trading volume depends on the average saving propensities of an arbitrary pair of agents in trade. Under Rule II, the exchange is governed by a stochastic parameter between the saving propensities of two traders. Besides, two sampling methods are introduced for the random selection of two agents in the iterative process. Specifically, Sampling I is the sampling with replacement and is easier to program. Sampling II is the sampling without replacement and owns a higher computing efficiency. There are slight differences between the stationary wealth distributions simulated by using the two transaction rules and sampling approaches. In addition, the accuracy, robustness and efficiency of the econophysics models are validated by typical numerical tests. The reduced LGA without the propagation step owns a higher computational efficiency than the standard LGA. Moreover, the impact of saving propensities of agents in two groups on the wealth distributions is studied, and the influence of proportions of agents is investigated as well. To quantitatively measure the wealth inequality, the Gini coefficients, Kolkata indices, and deviation degrees of all agents and two groups are simulated and analyzed in detail. This work is helpful to further analyze and predict the dynamic process of wealth distribution in the realistic economic market. [ABSTRACT FROM AUTHOR]

Grains account for more than 50% of the calories consumed by people worldwide, and military conflicts, pandemics, climate change, and soaring grain prices all have vital impacts on food security. However, the complex price behavior of the global grain spot markets has not been well understood. A recent study performed multifractal moving average analysis (MF-DMA) of the Grains & Oilseeds Index (GOI) and its sub-indices of wheat, maize, soybeans, rice, and barley and it was found that only the maize and barley sub-indices exhibit an intrinsic multifractal nature with convincing evidence. Here, we utilize multifractal fluctuation analysis (MF-DFA) to investigate the same problem. Extensive statistical tests confirm the presence of intrinsic multifractality in the maize and barley sub-indices and the absence of intrinsic multifractality in the wheat and rice sub-indices. Different from the MF-DMA results, the MF-DFA results suggest that there is also intrinsic multifractality in the GOI and soybeans sub-indices. Our comparative analysis does not provide conclusive information about the GOI and soybeans and highlights the high complexity of the global grain spot markets. [ABSTRACT FROM AUTHOR]

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies. [ABSTRACT FROM AUTHOR]

Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion. [ABSTRACT FROM AUTHOR]

Increasing wealth inequality is a significant global issue that demands attention. While the distribution of wealth varies across countries based on their economic stages, there is a universal trend observed in the distribution function. Typically, regions with lower wealth values exhibit an exponential distribution, while regions with higher wealth values demonstrate a power-law distribution. In this review, we introduce measures that effectively capture wealth inequality and examine wealth distribution functions within the wealth exchange model. Drawing inspiration from the field of econophysics, wealth exchange resulting from economic activities is likened to a kinetic model, where molecules collide and exchange energy. Within this framework, two agents exchange a specific amount of wealth. As we delve into the analysis, we investigate the impact of various factors such as tax collection, debt allowance, and savings on the wealth distribution function when wealth is exchanged. These factors play a crucial role in shaping the dynamics of wealth distribution. [ABSTRACT FROM AUTHOR]

This study examines the response of the BRICS and MSCI emerging stock market indices to the COVID-19 outbreak. For this purpose, this study uses a multifractal detrended fluctuation analysis (MF-DFA) to investigate the market efficiency dynamics of these indices and then ranks them based on their market efficiency. Overall, our results indicate that the returns from all the stock indices exhibit long-range correlations, implying that these markets are not weak-form efficient. Specifically, China showed the highest level of multifractality (i.e., inefficiency), which can be attributed to its highly volatile market structure. Using a subsample analysis, we further explore the impact of COVID-19 on these markets' efficiency by dividing the dataset into pre- and post-COVID periods. The findings indicate that COVID-19 adversely affected the efficiency of all the indices. Surprisingly, improvement in the Chinese market's inefficiency was witnessed, which can be attributed to the prompt and effective measures (i.e., timely imposition of health-related measures such as lockdowns and resident quarantines to contain COVID-19 and financial packages designed to curtail the economic meltdown) introduced by the Chinese government. The findings of this study may help investors, policymakers and regulators in refining their financial and policy decisions according to the new efficiency levels of these markets. [ABSTRACT FROM AUTHOR]

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Institutional investors like hedge funds are particularly resilient in times of volatility, primarily due to their access to a slew of complex trading strategies and extensive research capabilities; both of which are generally outside the reach of common investors. Fortunately, the U.S. Securities and Exchange Commission has mandated that large institutional investors publicly disclose the positions held by them at the end of a quarter, within the following 45 days. We then ask the question "Given our access to snapshots of positions held by large investors, can we extract alpha by approximating their aggregate behaviour?" In this paper, we introduce a stock recommendation model driven by the aggregate behaviour of institutional investors. We interpret stocks as the states of a Markov chain. The corresponding state transition matrix is defined based on the aggregate behaviour of investors. By designing the transition matrix to hold for aperiodicity and irreducibility, the steady state distribution of this chain is treated as a ranked list of stocks. We build a long only, equally weighted portfolio of stocks to trade using these recommendations. We observe that the returns on this portfolio beat existing models in the literature, and standard hedge fund indices both in terms of annualised returns and Sharpe ratio. [ABSTRACT FROM AUTHOR]

The global economy cannot be understood without the interaction of smaller-scale economies. We addressed this issue by considering a simplified economic model that still preserves the basic features, and analyzed the interaction of a set of such economies and the collective emerging dynamic. The topological structure of the economies' network appears to correlate with the collective properties observed. In particular, the strength of the coupling between the different networks as well as the specific connectivity of each node happen to play a crucial role in the determination of the final state. [ABSTRACT FROM AUTHOR]

The current study investigates the impact of the Coronavirus 2019 (COVID-19) pandemic on the volatility of Moroccan stock market sectoral indices. Shannon entropy with multiple estimators and Rényi entropy for different scales were calculated from February 1, 2019 to May 1, 2022, to measure volatility in the Banking, Oil and Gas, Construction and Building Materials, Beverage, Food Producers and Processors, Distributors, and Mining sector’s indices. In this regard, this study uses three periods to quantify the uncertainty in Moroccan sectoral indices before, during, and after the first year of the COVID-19 pandemic in Morocco. The empirical results from Shannon and Rényi entropies indicated higher volatility during the COVID-19 pandemic for all sectoral indices except Oil and Gas. However, the consumer staples sectors have shown a form of resilience compared to other sectors. Indeed, the impact of COVID-19 on the consumer staples sectoral indices’ volatilities was negligible compared to other sectors. In addition, investing in a portfolio composed of Mining or Construction and Building Materials stocks was risky due to the increased volatility before and during the epidemic. However, after the COVID-19 pandemic, the entropy level corresponding to all sectors has rearranged except the Beverage sector, which kept the lowest entropy during the three periods. Thus, it seems that the Beverage sector was a safe investment for the three periods. The findings are crucial for governments, businesses, private and public authorities, and investors to create recovery action plans for sensitive sectors and give investors trust to make smarter investment decisions.

The main purpose of this paper is to calculate the entropy of money in the space of Gross domestic product with the approach of econophysics and investigating the effect of stock market development on it. In this regard, by using annual data in the period of 1370-1398 in the framework of Smooth Transition Autoregressive Model (STAR), the asymmetric behavior of monetary irregularities around a threshold at different levels of stock market value as a variable of analysis is investigated. The results show that at low levels of current value of the stock market (the first regime), net capital inventory and budget deficit of governments have positive effects and the number of companies admitted to the stock exchange organization have a negative effect on monetary entropy. At high levels of current value of the stock market (Second Regime), net capital inventory has negative effect and government budget deficit continued to have a positive effect on monetary entropy. Based on the results of this study, it is clear that the dynamics of the stock market will reduce monetary entropy, which is itself an indicator of wasting and lacking of access to the resources.

Herding has a history of igniting large, irrational market ups and downs, usually based on a lack of fundamental support. Intuitively, most herds start with an external shock. This empirical study seeks to detect shock-induced herding and the creation of nascent bubbles in the Indian stock market. Initially, the multifractal form of the detrended fluctuation analysis was applied. Then the Reformulated Hurst exponent for the Bombay stock exchange (BSE) was determined using Kantelhardt’s calibration. The investigation found evidence of high-level herding and a bubble in 2012, with a high value of Hurst Exponent (0.7349). The other years of the research period (2011, 2013, 2016, 2018, 2020–2021) observed mild to significant herding with comparatively lower Hurst values. The results confirm that herding behavior occurs during a crisis and harsh situations emitting shocks. The study concludes that shock-based herding is prevalent in all six shocks: the economic meltdown, commodities and currency devaluation, geo-political problems, the Central Bank’s decision on liquidity management, and the Pandemic. Additionally, the years following the Financial Crisis and the years of the Pandemic are when herding and bubble are prominent. AcknowledgmentsWe thank Dr. Bikramaditya Ghosh (Associate Professor, Symbiosis International University, Bangalore, India) for motivating us in this research. We also thank Dr. Natchimuthu N (Assistant Professor, Commerce, CHRIST (Deemed to be University), Bangalore, India) and Dr. Mahesh E. (Assistant Professor, Economics, CHRIST (Deemed to be University), Bangalore, India) for their support throughout this study.

International seed trade networks (iSTNs) are complex networks formed by the trade of seeds between economies. We investigate the statistical properties of the international trade networks for maize seeds and rice seeds from the viewpoint of temporal directed and weighted networks. We find that most properties, including node and edge numbers, average in- and out-degrees, total and average trade values, network density and clustering coefficient, showed an increasing trend, which indicates that the increasing globalization of seed trade has led to more and more complete and dense trade networks. We also find that links with larger trade values have greater stability. In addition, we observe positive correlations between in-degree and out-degree, link reciprocity and in- and out-degrees, link reciprocity and in- and out-strengths and clustering coefficient and in- and out-degrees. In contrast, there are negative correlations between clustering coefficient and in- and out-strengths. Furthermore, we find that both networks are assortatively mixed in most years, which indicates that large exporters and importers are more likely to trade with other large exporters and importers. The structural properties of the two iSTNs of maize and rice share many qualitative similarities, but exhibit quantitative differences. [ABSTRACT FROM AUTHOR]

The concept of entropy is not uniquely relevant to the statistical mechanics but, among others, it can play pivotal role in the analysis of a time series, particularly the stock market data. In this area, sudden events are especially interesting as they describe abrupt data changes with potentially long-lasting effects. Here, we investigate the impact of such events on the entropy of financial time series. As a case study, we assume data of the Polish stock market, in the context of its main cumulative index, and discuss it for the finite time periods before and after outbreak of the 2022 Russian invasion of Ukraine. This analysis allows us to validate the entropy-based methodology in assessing changes in the market volatility, as driven by the extreme external factors. We show that some qualitative features of such market variations can be well captured in terms of the entropy. In particular, the discussed measure appears to highlight differences between data of the two considered timeframes in agreement with the character of their empirical distributions, which is not always the case in terms of the conventional standard deviation. Moreover, the entropy of cumulative index averages, qualitatively, the entropies of composing assets, suggesting capability for describing interdependencies between them. The entropy is also found to exhibit signatures of the upcoming extreme events. To this end, the role of recent war in shaping the current economic situation is briefly discussed. [ABSTRACT FROM AUTHOR]

A precise understanding of the market structure of an industry forms the basis for any entrepreneurial decisions. The combination of Bishop's cross-price elasticity of demand and price elasticity of demand forms the mathematical basis for determining the market structure. However, these formulas are time-independent, which does not adequately capture the complex dynamics of today's economy. This paper attempts a systemic dynamic extension of the cross-price elasticity of demand and the price elasticity of demand by adding a temporal dimension to these formulas. This ensures that entrepreneurial and industry-specific decisions can be made more accurately by following a system-dynamic approach. This paper provides a methodological extension based on Estola's econophysical theory and combining it with Bishop's approach for determining the market structure of an industry. Econophysics ensures that the temporal dimension is taken into account in economic formulas and therefore providing a more accurate view on current economic issues. [ABSTRACT FROM AUTHOR]

Repeated crises have proven credit rating agency (CRA) models/methods erroneous and "business-as-usual" unsustainable. Nevertheless, considerable dubious "default risk" management and technoscientific capitalist expertise remain unchanged. Unpacking sovereign ratings, we appreciate how "debt sustainability analysis" (DSA) distortions underpin expertocratic CRA (default) anomaly. Their neoliberal "politics of limits" performance helps market (shareholder) imperatives trump those of democratic (stakeholder) politics. Given surging inflation and debt (distress) to remedy Covid-19-induced shocks, ratings aid constitute and (re)validate the subjectivities/affinities and organizational conditions advancing a "self-equilibrating," "self-generative" agencement political economy of creditworthiness (PEC). Antagonizing sustainable budgetary government's programmatic/expertocratic and operational/democratic asymmetry, econophysics ratings diminish fiscal sovereignty. Universal PEC management through hybrid credit risk/uncertainty qualculation mitigates negative externality contestation shielding CRAs from serious reform. Ratings procyclicality and contagion reinforce this precarious sociotechnical agencement PEC as the status quo. [ABSTRACT FROM AUTHOR]

The formalism of geometrothermodynamics has been developed to describe the properties of thermodynamic systems in terms of concepts of differential geometry. On the other hand, in econophysics, it is argued that under certain conditions the behavior of economic systems can be described by using the laws of classical thermodynamics. These two results are used in this work to propose a geometric description of economic systems. We obtain as a result that most systems can be described by two different geometries corresponding to the Boltzmann–Gibbs and Pareto distributions, which represent two different population groups that are usually present in most economic systems. The geometrothermodynamic analysis shows that no phase transitions are present in the Boltzmann–Gibbs sector, whereas the Pareto sector is characterized by a strong thermodynamic interaction that leads to the appearance of a rich phase transition structure. We argue that those phase transitions could be interpreted as financial crises. [ABSTRACT FROM AUTHOR]

We investigate the repeated averaging model for money exchanges: two agents picked uniformly at random share half of their wealth to each other. It is intuitively convincing that a Dirac distribution of wealth (centered at the initial average wealth) will be the long time equilibrium for this dynamics. In other words, the Gini index should converge to zero. To better understand this dynamics, we investigate its limit as the number of agents goes to infinity by proving the so‐called propagation of chaos, which links the stochastic agent‐based dynamics to a (limiting) nonlinear partial differential equation (PDE). This deterministic description has a flavor of the classical Boltzmann equation arising from statistical mechanics of dilute gases. We prove its convergence towards its Dirac equilibrium distribution by showing that the associated Gini index of the wealth distribution converges to zero with an explicit rate. [ABSTRACT FROM AUTHOR]

With the development of computational research methods and the availability of large electronic databases, interdisciplinary research in physics has boomed in recent decades. By modelling complex systems based on networks, we can study biological, social or even economic systems. Social physics (sociophysics) studies the behaviour of crowds of people, while economic physics (econophysics) tries to model economic phenomena using the methods of statistical physics. At the Hungarian Institute of Physics of Babeº-Bolyai University, under the direction of Zoltán Néda, some interesting research results on collective phenomena and complex systems have been produced in recent years. Zoltán Néda’s research area is interdisciplinarily applied statistical physics, including the statistical physics approach to social phenomena. The researcher has spoken about the nature of complex systems and their modelling methods. [ABSTRACT FROM AUTHOR]

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16 June 2016 to 9 July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

This study bridges finance and physics by applying thermodynamic concepts to model the limit order book (LOB) with high-frequency trading data on the Bitcoin spot. We derive the measures of Market Temperature and Market Entropy from the kinetic and potential energies in the LOB to provide a deeper understanding of order activities and market participant behavior. Market Temperature emerges as a robust indicator of market liquidity, correlating with liquidity measures such as Active Quote Volume, bid–ask spread and match volume. Market Entropy, on the other hand, quantifies the degree of disorder or randomness in the LOB, providing insights into the instantaneous volatility of price in the high-frequency trading market. Our empirical findings not only broaden the theoretical framework of econophysics but also enhance comprehensive understanding of the market microstructure and order book dynamics.

This paper investigates the topological evolution of the Casablanca Stock Exchange (СSE) from the perspective of the Coronavirus 2019 (COVID-19) pandemic. Cross-correlations between the daily closing prices of the Moroccan most active shares (MADEX) index stocks from March 1, 2016 to February 18, 2022 were used to compute the minimum spanning tree (MST) maps. In addition to the whole sample, the analysis also uses three sub-periods to investigate the topological evolution before, during, and after the first year of the COVID-19 pandemic in Morocco. The findings show that, compared to other periods, the mean correlation coefficient increased remarkably through the crisis period; inversely, the mean distance decreased in the same period. The MST and its related tree length support the evidence of the star-like structure, the shrinkage of the MST in times of market turbulence, and an expansion in the recovery period. Besides, the CSE network was less clustered and homogeneous before and after the crisis than in the crisis period, where the banking sector held a key role. The degree and betweenness centrality analysis showed that Itissalat Al-Maghrib and Auto Hall were the most prominent stocks before the crisis. On the other hand, Attijariwafa Bank, Banque Populaire, and Cosumar were the leading stocks during and after the crisis. Indeed, the results of this study can be used to assist policymakers and investors in incorporating subjective judgment into the portfolio optimization problem during extreme events.

This paper investigates the dynamic high-frequency dependence structure of Chinese four major agricultural commodity futures by utilizing a semi-parametric copula-based multivariate model with 5-minute high-frequency trading data. The empirical results show that the daily dependence between the agricultural commodity futures is time-varying and slightly asymmetric, and that this dependence and its asymmetry are more pronounced during the world food crisis (2007–2008) and the global financial crisis (2008–2011). Furthermore, the intraday dependence structure exhibits a lopsided inverted U-shaped pattern with relatively lower dependence level around the opening and closing time, and a peak around the mid-trading day.

We start by summarising very briefly the various prior attempts (during the last one and half a decade), some of which were made as independent research centres and others as visiting centres with extensive visiting programs for luminaries from various basic sciences (Mathematics, Physics, Biology, Economics, and Sociology) and students from various institutions around the world for such interdisciplinary fusion of ideas and researches. Additionally, we briefly discuss the efforts that our institute has made (without any visible success so far, as in the other attempts elsewhere). We then emphasise the critical need for such an international centre to attract stalwarts in the basic disciplinary fields as well as interested students from around the world in order to comprehend the world’s global socio-economic dynamics.

We use the logistic equation to model the dynamics of the GDP and the trade of the six countries with the highest GDP in the world, namely, USA, China, Japan, Germany, UK and India. From the modeling of the economic data, which are made available by the World Bank, we predict the maximum values of the growth of GDP and trade, as well as the duration over which exponential growth can be sustained. We set up the correlated growth of GDP and trade as the phase solutions of an autonomous second-order dynamical system. GDP and trade are related to each other by a power law, whose exponent seems to differentiate the six national economies into two types. Under conducive conditions for economic growth, our conclusions have general validity. [ABSTRACT FROM AUTHOR]

From the years 2001 to 2017, per capita nominal and real (adjusted to inflation) GDP at purchasing power parity (PCGDP-PPP) distributions for cities and regions are fitted to various functions. For most years and regions, real PCGDP-PPP data are very well adjusted to the one-parameter Boltzmann–Gibbs distribution (BGD), in accordance with the exponential behavior predicted by the simple econophysics analogy between conserved money in economic trade and energy in elastic collisions in gases. Overall, fittings are better for large regions in recent years, which may reflect an increasing economic globalization in time. Cities, small regions and large regions values are well fitted by stretched exponential distributions. [ABSTRACT FROM AUTHOR]

Given wealth inequality worldwide, there is an urgent need to identify the mode of wealth exchange through which it arises. To address the research gap regarding models that combine equivalent exchange and redistribution, this study compares an equivalent market exchange with redistribution based on power centers and a non-equivalent exchange with mutual aid using the Polanyi, Graeber, and Karatani modes of exchange. Two new exchange models based on multi-agent interactions are reconstructed following an econophysics-based approach for evaluating the Gini index (inequality) and total exchange (economic flow). Exchange simulations indicate that the evaluation parameter of the total exchange divided by the Gini index can be expressed by the same saturated curvilinear approximate equation using the wealth transfer rate and time period of redistribution, the surplus contribution rate of the wealthy, and the saving rate. However, considering the coercion of taxes and its associated costs and independence based on the morality of mutual aid, a non-equivalent exchange without return obligation is preferred. This is oriented toward Graeber's baseline communism and Karatani's mode of exchange D, with implications for alternatives to the capitalist economy. [ABSTRACT FROM AUTHOR]

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 ( W H O ) 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. [ABSTRACT FROM AUTHOR]

The econophysics "two‐class" approach yields a novel theoretical and empirically robust relation: The per capita income y‾(x) $\overline{y}(x)$ of any bottom fraction (x) of the population equals a(x)∙(1−G) y‾ $\overline{y}$, where a(x) is a coupling coefficient, G the Gini, and y‾ $\overline{y}$ is national per capita income. For the bottom 70%, a(70) = 1, which yields the Sen inequality adjustment to the 1993 UNDP Human Development Index, without any reliance on social welfare functions. Alternately, a(80) = 1.1 yields the bottom 80% per capita income (Vast Majority Income). We propose the latter as a new inequality‐adjusted measure of wellbeing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Financial markets are complex systems, which can be studied under the lens of econophysics, including the study of phenomena such as power laws. We test the hypothesis of a power law in Ibovespa, the main Brazilian stock market index, and cannot dismiss the hypothesis that its stocks are distributed following a power law. This is most interesting, as these stocks are ordered by individual trading volumes, thus a similar pattern always emerges organically even though companies are always exchanging positions and being excluded or added to the index. [ABSTRACT FROM AUTHOR]

The two-body stochastic dealer model is revisited to provide an exact solution to the average order-book profile using the kinetic approach. The dealer model is a microscopic financial model where individual traders make decisions on limit-order prices stochastically and then reach agreements on transactions. In the literature, this model was solved for several cases: an exact solution for two-body traders N = 2 and a mean-field solution for many traders N ≫ 1 . Remarkably, while kinetic theory plays a significant role in the mean-field analysis for N ≫ 1 , its role is still elusive for the case of N = 2 . In this paper, we revisit the two-body dealer model N = 2 to clarify the utility of the kinetic theory. We first derive the exact master-Liouville equations for the two-body dealer model. We next illustrate the physical picture of the master-Liouville equation from the viewpoint of the probability currents. The master-Liouville equations are then solved exactly to derive the order-book profile and the average transaction interval. Furthermore, we introduce a generalised two-body dealer model by incorporating interaction between traders via the market midprice and exactly solve the model within the kinetic framework. We finally confirm our exact solution by numerical simulations. This work provides a systematic mathematical basis for the econophysics model by developing better mathematical intuition. [ABSTRACT FROM AUTHOR]

In this study, the validity of the fractal market hypothesis, which explains market stability and investor horizon, and states financial time series are nonnormally distributed and have self-similarity in financial time series, in two emerging markets, in two developed markets, and in two crypto assets was aimed to investigate via Hurst Exponent-Rescaled Range (R/S) analysis. According to MSCI classification, SP500 and FTSE 100 as developed markets, Borsa Istanbul 100 and Shanghai Index as emerging markets were included in the analysis. In cryptoassets, Bitcoin and Ethereum variables with the highest transaction volume were included in the analysis. According to the findings of the study, the existence of the fractal market hypothesis was accepted in all indices examined, while the role of long memory changes. In all variables, The Hurst exponent is greater than 0.5. According to the Hurst exponential results, the hypothesis that the time series exhibits a permanent behavior in all variables was accepted. The variable with the lowest persistence of long memory is FTSE. While long memory and permanence are higher in emerging stock markets than in developed stock markets, the variable with the strongest long memory and the highest permanence among all variables is Bitcoin. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are relatively new and innovative financial assets. They are a topic of interest to investors and academics due to their distinctive features. Whether financial or not, extraordinary events are one of the biggest challenges facing financial markets. The onset of the COVID-19 pandemic crisis, considered by some authors a "black swan", is one of these events. In this study, we assess integration and contagion in the cryptocurrency market in the COVID-19 pandemic context, using two entropy-based measures: mutual information and transfer entropy. Both methodologies reveal that cryptocurrencies exhibit mixed levels of integration before and after the onset of the pandemic. Cryptocurrencies displaying higher integration before the event experienced a decline in such link after the world became aware of the first cases of pneumonia in Wuhan city. In what concerns contagion, mutual information provided evidence of its presence solely for the Huobi Token, and the transfer entropy analysis pointed out Tether and Huobi Token as its main source. As both analyses indicate no contagion from the pandemic turmoil to these financial assets, cryptocurrencies may be good investment options in case of real global shocks, such as the one provoked by the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

In econophysics, the analysis of the return distribution of a financial asset using statistical physics methods is a long-standing and important issue. This paper systematically conducts an analysis of composite index 1 min datasets over a 17-year period (2005–2021) for both the Shanghai and Shenzhen stock exchanges. To reveal the differences between Chinese and mature stock markets, we precisely measure the property of the return distribution of the composite index over the time scale Δ t , which ranges from 1 min to almost 4000 min. The main findings are as follows: (1) The return distribution presents a leptokurtic, fat-tailed, and almost symmetrical shape that is similar to that of mature markets. (2) The central part of the return distribution is described by the symmetrical Lévy α -stable process, with a stability parameter comparable with a value of about 1.4, which was extracted for the U.S. stock market. (3) The return distribution can be described well by Student's t-distribution within a wider return range than the Lévy α -stable distribution. (4) Distinctively, the stability parameter shows a potential change when Δ t increases, and thus a crossover region at 15 < Δ t < 60 min is observed. This is different from the finding in the U.S. stock market that a single value of about 1.4 holds over 1 ≤ Δ t ≤ 1000 min. (5) The tail distribution of returns at small Δ t decays as an asymptotic power law with an exponent of about 3, which is a widely observed value in mature markets. However, it decays exponentially when Δ t ≥ 240 min, which is not observed in mature markets. (6) Return distributions gradually converge to a normal distribution as Δ t increases. This observation is different from the finding of a critical Δ t = 4 days in the U.S. stock market. [ABSTRACT FROM AUTHOR]

We investigate financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize individuals in a financial market according to their trading strategy, namely, whether they are noise traders or fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the decisions of fundamentalist agents. We introduce a noise parameter, q, to represent the level of anxiety and perceived uncertainty regarding market behavior, enabling the possibility of adrift financial action. We place individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interactions. At any given time, individuals assume one of two possible opinion states±1 regarding buying or selling an asset. The model exhibits fundamental qualitative and quantitative real-world market features such as the distribution of logarithmic returns with fat tails, clustered volatility, and the long-term correlation of returns. We use Student's t distributions to fit the histograms of logarithmic returns, showing a gradual shift from a leptokurtic to a mesokurtic regime depending on the fraction of fundamentalist agents. Furthermore, we compare our results with those concerning the distribution of the logarithmic returns of several real-world financial indices. [ABSTRACT FROM AUTHOR]

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). • Energy poverty in Mexico is analysed based on metrics of affordability and accessibility. • 38.84% of Mexican households suffered energy poverty by accessibility, 34% to affordability, and 11.54% suffered both. • Econophysics and energy justice are demonstrated as valuable methodological and conceptual tools to examine energy poverty. • This study demonstrates the need to consider transport as a core energy service for future energy poverty studies. • Although quantitative methods are crucial to understanding energy poverty, complementary qualitative studies are needed. [ABSTRACT FROM AUTHOR]

We present an agent-based model that examines the microscopic exchange of wealth in a dynamic network to investigate the topological characteristics associated with economic inequality. The model consists of two processes: conservative wealth exchange between connected agents and the rewiring of connections, which depends on the wealth of the agents. The dynamics of wealth and connections are interrelated, as the network structure influences which agents interact with each other. We analyze the time evolution and asymptotic characteristics of the model for different values of a social protection factor (f), which favors the poorest agent in each wealth transaction. Our results show that for f = 0 , wealth and connections condense in a single agent, in accordance with mean-field models of wealth exchange. When f is low, agents from the middle and upper classes become favored, leading to the formation of network hubs. However, as f increases, the restriction of the network on exchanges results in an egalitarian society departing from the outcomes observed in the mean-field exchange models. [ABSTRACT FROM AUTHOR]

The Grains and Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley are daily price indexes reflect the price changes of the global spot markets of staple agro-food crops. In this paper, we carry out a visibility graph (VG) analysis of the GOI and its five sub-indices. Our findings reveal that the degree distributions of the VGs, except for rice, exhibit exponentially truncated power-law tails, while the rice VG conforms to a power-law tail. The average clustering coefficients of the six VGs are quite large (> 0. 5) and exhibit a nice power-law relation with respect to the average degrees of the VGs. For each VG, the clustering coefficients of nodes are inversely proportional to their degrees for large degrees and are correlated to their degrees as a power law for small degrees. All six VGs exhibit small-world characteristics. The degree-degree correlation coefficients show that the VGs for maize and soyabeans indices exhibit weak assortative mixing patterns, while the other four VGs are weakly disassortative. The average nearest neighbor degree functions have similar patterns, and each function shows a more complex mixing pattern that decreases for small degrees, increases for mediate degrees, and decreases again for large degrees. • We perform visibility graph analysis on staple agro-food crop price indices. • Most visibility graphs are small-world networks. • All the visibility graphs exhibit similar mixing patterns. [ABSTRACT FROM AUTHOR]

Extreme inequality represents a grave challenge for impoverished individuals and poses a threat to economic growth and stability. Despite the fulfillment of affirmative action measures aimed at promoting equal opportunities, they often prove inadequate in effectively reducing inequality. Mathematical models and simulations have demonstrated that even when equal opportunities are present, wealth tends to concentrate in the hands of a privileged few, leaving the majority of the population in dire poverty. This phenomenon, known as condensation, has been shown to be an inevitable outcome in economic models that rely on fair exchange. In light of the escalating levels of inequality in the 21st century and the significant state intervention necessitated by the recent COVID-19 pandemic, an increasing number of scholars are abandoning neo-liberal ideologies. Instead, they propose a more robust role for the state in the economy, utilizing mechanisms such as taxation, regulation, and universal allocations. This paper begins with the assumption that state intervention is essential to effectively reduce inequality and to revitalize the economy. Subsequently, it conducts a comparative analysis of various taxation and redistribution mechanisms, with a particular emphasis on their impact on inequality indices, including the Gini coefficient. Specifically, it compares the effects of fortune and consumption-based taxation, as well as universal redistribution mechanisms or targeted redistribution mechanisms aimed at assisting the most economically disadvantaged individuals. The results suggest that fortune taxation are more effective than consumption-based taxation to reduce inequality.

Modern technology has brought novel types of wealth. In contrast to hard cash, digital currency does not have a physical form. It exists in electronic forms only. To date, it has not been clear what impacts its ongoing growth will have, if any, on wealth distribution. Here, we propose to identify all forms of contemporary wealth into two classes: ‘distinguishable’ or ‘identical’. Traditional tangible moneys are all distinguishable. Financial assets and cryptocurrencies, such as bank deposits and Bitcoin, are boson-like, while non-fungible tokens are fermion-like. We derived their ownership-based distributions in a unified manner. Each class follows essentially the Poisson or the geometric distribution. We contrast their distinct features such as Gini coefficients. Furthermore, aggregating different kinds of wealth corresponds to a weighted convolution where the number of banks matters and Bitcoin follows Bose–Einstein distribution. Our proposal opens a new avenue to understand the deepened inequality in modern economy, which is based on the statistical physics property of wealth rather than the individual ability of owners. We call for verifications with real data.

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.

The production, or value added, approach to GDP involves calculating an industry or sector’s output and subtracting its intermediate consumption (the goods and services used to produce the output) to derive its value added. The value added at the macro level depends on business efficiency. It reflects an increase in value that a business creates by undertaking the production process. We assumed that the market creates thousands of vibrating energies, coming from other enterprises, with different frequencies. The purpose of this article is to verify whether the econophysics approach could be successfully used to assess a business from the perspective of the interaction between economic forces. Thus, we propose that the term ‘value added’ be understood as a certain amount of accumulated energy of enterprises that comes from the interaction of basic economic forces and economic vibrating forces of accounting. Using regression models, we show the influence of basic forces, like debt and the stock market, and vibrating ones (i.e., accounts payable, accounts receivable, inventory) on the economic value added by testing US, European, and emerging markets. We confirmed the relevance and appropriateness of the econophysics approach to estimating the economic value added.

A stock market is a complex system consisting of many interacting agents. We consider recent progress with complex networks constructed from cross-correlation of financial time series in the stock market. We review some methods and discuss the challenges in generating such complex networks that have a reasonable threshold.

Krugman has proposed a continuous in time and space model for the emergence of polycentric urban areas in the regional space. By using a discrete version of this model on spatial networks, we predicted the distribution of jobs among the different localities inside a couple of economic regions in Ohio and Texas. The time evolution of the distribution of jobs is governed by a market potential. Employment gradually moves towards locations considered relatively attractive if their market potential is above the spatial average. Similarly, jobs move away from locations with below-average market potential. First, I will show that the market potential satisfies the Fisher equation of natural selection. Second, I will determine the stationary distribution. [ABSTRACT FROM AUTHOR]

Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide on-line availability, increasing capitalization, and potential profits. In the econophysics tradition, we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions, instead following heavy-tailed distributions. Entropy measures are applied, showing that portfolio diversification is a reasonable practice for decreasing return uncertainty. [ABSTRACT FROM AUTHOR]