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How a micro-founded discipline such as economics could deal with the increasing global economic reality? This question has been asked frequently since the last economic crisis that appeared in 2008. In this challenging context, some commentators have turned their attention to a new area of knowledge coming from physics: econophysics which mainly focuses on a macro-analysis of economic systems. By showing that concepts used by econophysicists are consistent with an existing economic knowledge (developed by J.S. Mill), the purpose of this paper is to claim that an interdisciplinary perspective is possible between these two communities.

The authors propose a historical and conceptual analysis of the key concept of emergence to emphasize the potential bridge between econophysics and economics.

Six methodological arguments will be developed in order to show the existence of conceptual bridges as a necessary condition for the elaboration of a common language between economists and econophysics which would not be superfluous, in this challenging context, to clarify the growing complexity of economic phenomena.

Although the economics and econophysics study same the complex economic phenomena, very few collaborations exist between them. This paper paves a conceptual/methodological path for more collaboration between the two fields.

This paper aims to present a framework enriching currency risk analyses based on information theory.

Information-theoretic measures of predictability (entropy rate) and co-dependence (mutual information) are used to enhance existing methods of analysing and measuring currency risk.

The currency exchange rates have varying degrees of predictability, which should be accounted for in currency risk analyses. In case of baskets of currencies, a network approach rooted in portfolio theory may be useful.

The currency exchange rate time series must be discretised for the information-theoretic analysis (although the results are robust). An agent-based simulation may be a necessary further study to show what the impact of accounting for predictability in managing currency risk is.

Practical analyses measuring currency risk should take predictability of currency rate changes into account wherever the currency exposure is actively managed.

The paper introduces predictability into measuring currency risk, which has previously been ignored, despite the nature of the risk being inherently tied to uncertainty of the currency rate changes. The paper also introduces a portfolio theory-based approach to quantifying currency risk, which accounts for non-linear co-dependence in the currency markets.

Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the academic arena. This study aims to pioneer an econophysics approach coupled with an autoregressive data analysis technique for bulk shipbuilding order forecasting.

By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The model has been evaluated through autoregressive integrated moving average analysis, and the outcome indicates a relatively stable good fit.

The outcomes of the econophysics model indicate a relatively stable good fit. Although relevant maritime data and its quality need to be improved, the flexibility in refining the predictive variables ensure the robustness of this econophysics-based forecasting model.

By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The research result helps shipping investors make decision in a capital-intensive and uncertainty-prone environment.

Mobile phones are radically transforming micro-finance in Sub-Saharan Africa, and Kenya, in particular. The introduction of the micro-financial transaction mobile phone application, “MPesa,” created a means to facilitate micro-transactions without the need for an intermediary, such as a banking system. The purpose of this paper is to posit an econophysics model to predict the value of Mpesa for Kenyan and South African consumers. The econophysics framework posits several fitness matrices and a distance measure that can account for the concepts of mass, distance, momentum, velocity, action, and force. The authors begin with a table of the match between the physics concepts and the economic concepts followed by the vector model that utilizes these concepts for the MPesa application case. In this paper, the authors will argue that MPesa succeeded in Sub-Saharan African countries, such as Kenya, because the fit between what this group of customers needed and the solutions Safaricom’s MPesa offered was a better fit with a smaller distance to adoption than in the South African case.

The research develops an econophysics approach to the assessment of micro-finance development in Sub-Saharan countries.

The research shows clearly the reasons of the success of MPesa in Kenya in comparison of its relative failure in South Africa: the distance between customers’ expectations and the system supply.

The research is limited to two case studies and needs to be extended to other contexts, in order to demonstrate its robustness, especially with regard to the intangible dimension, e.g., the distance between a system potential and what it really offers.

The research shows the importance of system’s characteristics in its success.

The social implications are very high, especially in this case, where micro-finance is a high stake for developing societies.

This is one of the first works to develop an econophysics approach for the evaluation of the key characteristics of a system.

Popular understandings of the financial crisis tend to focus on the rents extracted by elite personnel in the financial sector. Professional discussions, however, have addressed the faulty assumptions underlying theory and practice – in particular, the assumption that returns to financial assets follow the Gaussian distribution, in the face of much empirical evidence that these have power law distributions with far higher kurtosis. It turns out that the power law tails of returns to financial assets are also a feature of the distribution of company rates of profit, a discovery that stems from proposals to ‘dissolve’ the traditional transformation problem by abandoning the condition of a uniform rate of profit and instead considering its distribution.Marx himself was aware of the importance of considering the distributional properties of economic variables, based on his reading of Quetelet. In fact, heavy-tailed distributions characterise a wide range of variables in capitalist economies, the best-known probably being the Paretian tail component in distributions of income and wealth. Nor is this simply an empirical fact – such distributions emerge readily from a range of agent-based simulations.Capitalist economies are, in a particular technical sense, complex self-organising systems perpetually on the brink of crisis. This modern understanding is prefigured in Marx’s discussion of how the compulsive character of social relations emerges from the atomistic exercise of human free will in commercial society. The developing literature of probabilistic Marxism successfully applies these insights to the wider fields of econophysics and complexity, demonstrating the continuing relevance of Marx’s thought.

When investors experience extreme uncertainty, they seek “safe havens” to reduce their risk, to limit their losses and to protect the value of their portfolios. The purpose of this paper is to examine the safe-haven properties of Bitcoin compared to the stock market.

Based on intraday data, this study compares the price efficiencies of Bitcoin and Morgan Stanley Capital Index (MSCI) using Multifractal Detrended Fluctuation Analysis for the second half of 2020. This study then evaluates Bitcoin’s safe-haven property using Detrended Partial-Cross-Correlation Analysis (DPCCA).

This study finds that the price efficiency of Bitcoin is lower than that of MSCI. Further, Bitcoin was not a safe haven at any time for the MSCI index. The net cross-correlations between Bitcoin and MSCI are weak and they vary at different time scales.

The behavior of market prices varies over time. Therefore, it is important to replicate this study for other time periods.

The paper sheds light on the price behavior of Bitcoin during a period of instability. The results suggest that the construction of portfolios should differ based on the time horizons of the investors.

The authors compare Bitcoin against a global equity index instead of a specific country index or commodity. They also demonstrate the applicability of DPCCA in finance research.

The purpose of this paper is to provide a decision support model for the early diagnosis of housing bubbles in the UK during the maturity process of the phenomenon.

The development process of the model is divided into four stages. These stages are driven by the normal distribution theorem coupled with the case study approach. The application of normal distribution theory is allowed through the usage of several parametric tools. The case studies tested in this research include the last two UK housing bubbles, 1986 to 1989 and 2001/2002 to 2007. The central hypothesis of the model is that during housing bubbles, all speculative activities of market participants follow an approximate synchronisation, and therefore, an irrational, synchronous and periodic increase on a wide range of relevant variables must occur to anticipate the bubble component. An empirical application of the model is conducted on UK housing market data over the period of 1983-2011.

The new approach successfully identifies the well-known UK historical bubble episodes over the period of 1983-2011. The study further determines that for uncovering housing bubbles in the UK, house price changes have the same weight with the debt–burden ratio when their velocity is positive. Finally, the application of this model has led us to conclude that the model’s outputs fluctuate approximately in line with phases of the UK real estate cycle.

This paper proposes a new measure for studying the presence of housing bubbles. This measure is not simply an ex post detection technique but dating algorithms that use data only up to the point of analysis for an on-going bubble assessment, giving an early warning diagnostic that can assist market participants and regulators in market monitoring.

This paper examines the impact of Russian invasion of Ukraine on the intraday efficiency of four major energy markets, namely, diesel oil, Brent oil, light oil and natural gas.

This study applies the multifractal detrended fluctuation analysis (MFDFA) to high-frequency returns (30-min intervals) for the period from October 21, 2021, to May 20, 2022. The data sample of 5,141 observations is divided into two sub-samples, before and after the invasion of 24th February 2022. Additionally, the magnitude of long memory index is employed to investigate the presence of herding behavior around the invasion period.

Results confirm the presence of multifractality in energy markets and reveal significant changes of multifractal strength due to the invasion, indicating a decline of intraday efficiency for oil markets. Surprisingly, the natural gas market, being the least efficient before the invasion, turns out to be more efficient after the invasion. The findings also suggest that investors in these energy markets are likely to show herding, more prominently after the invasion.

The multifractal patterns, in particular the long memory property of energy markets, can help investors develop profitable investment strategies. Furthermore, the improved efficiency observed in the natural gas market, after the invasion, highlights its unique traits and underlying complexity.

This study is the first attempt to assess the impact of the Russia–Ukraine war on the efficiency of global commodity markets. This is quite important because the adverse effects of the war on financial markets may potentially cause destabilizing outcomes and negative effects on social welfare on a global scale.

The term “agent-based modelling” (ABM) is a buzzword which is widely used in the scientific literature even though it refers to a variety of methodologies implemented in different disciplinary contexts. The numerous works dealing with ABM require a clarification to better understand the lines of thinking paved by this approach in economics. All modelling tasks are a means and a source of knowledge, and this epistemic function can vary depending on the methodology. this paper is to present four major ways (deductive, abductive, metaphorical and phenomenological) of implementing an agent-based framework to describe economic systems. ABM generates numerous debates in economics and opens the room for epistemological questions about the micro-foundations of macroeconomics; before dealing with this issue, the purpose of this paper is to identify the kind of ABM the author can find in economics.

The profusion of works dealing with ABM requires a clarification to understand better the lines of thinking paved by this approach in economics. This paper offers a conceptual classification outlining the major trends of ABM in economics.

There are four categories of ABM in economics.

This paper suggests a methodological categorization of ABM works in economics.