Research

22 pages, 2107 KiB

Open AccessArticle

Transaction Entropy: An Alternative Metric of Market Performance

by Hua Zhong, Xiaohao Liang and Yougui Wang

Entropy 2023, 25(8), 1140; https://doi.org/10.3390/e25081140 - 30 Jul 2023

Viewed by 1010

Market uncertainty has a significant impact on market performance. Previous studies have dedicated much effort towards investigations into market uncertainty related to information asymmetry and risk. However, they have neglected the uncertainty inherent in market transactions, which is also an important aspect of [...] Read more.

Market uncertainty has a significant impact on market performance. Previous studies have dedicated much effort towards investigations into market uncertainty related to information asymmetry and risk. However, they have neglected the uncertainty inherent in market transactions, which is also an important aspect of market performance, besides the quantity of transactions and market efficiency. In this paper, we put forward a concept of transaction entropy to measure market uncertainty and see how it changes with price. Transaction entropy is defined as the ratio of the total information entropy of all traders to the quantity of transactions, reflecting the level of uncertainty in making successful transactions. Based on the computational and simulated results, our main finding is that transaction entropy is the lowest at equilibrium, it will decrease in a shortage market, and increase in a surplus market. Additionally, we make a comparison of the total entropy of the centralized market with that of the decentralized market, revealing that the price-filtering mechanism could effectively reduce market uncertainty. Overall, the introduction of transaction entropy enriches our understanding of market uncertainty and facilitates a more comprehensive assessment of market performance. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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17 pages, 4629 KiB

Open AccessArticle

A Goodwin Model Modification and Its Interactions in Complex Networks

by Francisco Yáñez Rodríguez and Alberto P. Muñuzuri

Entropy 2023, 25(6), 894; https://doi.org/10.3390/e25060894 - 02 Jun 2023

Cited by 2 | Viewed by 1242

Abstract

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. [...] Read more.

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. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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Graphical abstract

31 pages, 12091 KiB

Open AccessArticle

Laplacian Spectra of Persistent Structures in Taiwan, Singapore, and US Stock Markets

by Peter Tsung-Wen Yen, Kelin Xia and Siew Ann Cheong

Entropy 2023, 25(6), 846; https://doi.org/10.3390/e25060846 - 25 May 2023

Cited by 3 | Viewed by 1331

Abstract

An important challenge in the study of complex systems is to identify appropriate effective variables at different times. In this paper, we explain why structures that are persistent with respect to changes in length and time scales are proper effective variables, and illustrate [...] Read more.

An important challenge in the study of complex systems is to identify appropriate effective variables at different times. In this paper, we explain why structures that are persistent with respect to changes in length and time scales are proper effective variables, and illustrate how persistent structures can be identified from the spectra and Fiedler vector of the graph Laplacian at different stages of the topological data analysis (TDA) filtration process for twelve toy models. We then investigated four market crashes, three of which were related to the COVID-19 pandemic. In all four crashes, a persistent gap opens up in the Laplacian spectra when we go from a normal phase to a crash phase. In the crash phase, the persistent structure associated with the gap remains distinguishable up to a characteristic length scale

ϵ^{*}

where the first non-zero Laplacian eigenvalue changes most rapidly. Before

ϵ^{*}

, the distribution of components in the Fiedler vector is predominantly bi-modal, and this distribution becomes uni-modal after

ϵ^{*} .

Our findings hint at the possibility of understanding market crashs in terms of both continuous and discontinuous changes. Beyond the graph Laplacian, we can also employ Hodge Laplacians of higher order for future research. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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35 pages, 1600 KiB

Open AccessFeature PaperArticle

Local Phase Transitions in a Model of Multiplex Networks with Heterogeneous Degrees and Inter-Layer Coupling

by Nedim Bayrakdar, Valerio Gemmetto and Diego Garlaschelli

Entropy 2023, 25(5), 828; https://doi.org/10.3390/e25050828 - 22 May 2023

Viewed by 975

Abstract

Multilayer networks represent multiple types of connections between the same set of nodes. Clearly, a multilayer description of a system adds value only if the multiplex does not merely consist of independent layers. In real-world multiplexes, it is expected that the observed inter-layer [...] Read more.

Multilayer networks represent multiple types of connections between the same set of nodes. Clearly, a multilayer description of a system adds value only if the multiplex does not merely consist of independent layers. In real-world multiplexes, it is expected that the observed inter-layer overlap may result partly from spurious correlations arising from the heterogeneity of nodes, and partly from true inter-layer dependencies. It is therefore important to consider rigorous ways to disentangle these two effects. In this paper, we introduce an unbiased maximum entropy model of multiplexes with controllable intra-layer node degrees and controllable inter-layer overlap. The model can be mapped to a generalized Ising model, where the combination of node heterogeneity and inter-layer coupling leads to the possibility of local phase transitions. In particular, we find that node heterogeneity favors the splitting of critical points characterizing different pairs of nodes, leading to link-specific phase transitions that may, in turn, increase the overlap. By quantifying how the overlap can be increased by increasing either the intra-layer node heterogeneity (spurious correlation) or the strength of the inter-layer coupling (true correlation), the model allows us to disentangle the two effects. As an application, we show that the empirical overlap observed in the International Trade Multiplex genuinely requires a nonzero inter-layer coupling in its modeling, as it is not merely a spurious result of the correlation between node degrees across different layers. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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12 pages, 329 KiB

Open AccessArticle

Entropy of Financial Time Series Due to the Shock of War

by Ewa A. Drzazga-Szczȩśniak, Piotr Szczepanik, Adam Z. Kaczmarek and Dominik Szczȩśniak

Entropy 2023, 25(5), 823; https://doi.org/10.3390/e25050823 - 21 May 2023

Cited by 4 | Viewed by 1154

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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19 pages, 5540 KiB

Open AccessArticle

Organizational Labor Flow Networks and Career Forecasting

by Frank Webb, Daniel Stimpson, Miesha Purcell and Eduardo López

Entropy 2023, 25(5), 784; https://doi.org/10.3390/e25050784 - 11 May 2023

Cited by 1 | Viewed by 1290

Abstract

The movement of employees within an organization is a research area of great relevance in a variety of fields such as economics, management science, and operations research, among others. In econophysics, however, only a few initial incursions have been made into this problem. [...] Read more.

The movement of employees within an organization is a research area of great relevance in a variety of fields such as economics, management science, and operations research, among others. In econophysics, however, only a few initial incursions have been made into this problem. In this paper, based on an approach inspired by the concept of labor flow networks which capture the movement of workers among firms of entire national economies, we construct empirically calibrated high-resolution networks of internal labor markets with nodes and links defined on the basis of different descriptions of job positions, such as operating units or occupational codes. The model is constructed and tested for a dataset from a large U.S. government organization. Using two versions of Markov processes, one without and another with limited memory, we show that our network descriptions of internal labor markets have strong predictive power. Among the most relevant findings, we observe that the organizational labor flow networks created by our method based on operational units possess a power law feature consistent with the distribution of firm sizes in an economy. This signals the surprising and important result that this regularity is pervasive across the landscape of economic entities. We expect our work to provide a novel approach to study careers and help connect the different disciplines that currently study them. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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10 pages, 711 KiB

Open AccessArticle

Multi-Criteria Analysis of Startup Investment Alternatives Using the Hierarchy Method

by Tamara Kyrylych and Yuriy Povstenko

Entropy 2023, 25(5), 723; https://doi.org/10.3390/e25050723 - 27 Apr 2023

Cited by 1 | Viewed by 1224

Abstract

In this paper, we discuss the use of multi-criteria analysis for investment alternatives as a rational, transparent, and systematic approach that reveals the decision-making process during a study of influences and relationships in complex organizational systems. It is shown that this approach considers [...] Read more.

In this paper, we discuss the use of multi-criteria analysis for investment alternatives as a rational, transparent, and systematic approach that reveals the decision-making process during a study of influences and relationships in complex organizational systems. It is shown that this approach considers not only quantitative but also qualitative influences, statistical and individual properties of the object, and expert objective evaluation. We define the criteria for evaluating startup investment prerogatives, which are organized in thematic clusters (types of potential). To compare the investment alternatives, Saaty’s hierarchy method is used. As an example, the analysis of three startups is carried out based on the phase mechanism and Saaty’s analytic hierarchy process to identify investment appeal of startups according to their specific features. As a result, it is possible to diversify the risks of an investor through the allocation of resources between several projects, in accordance with the received vector of global priorities. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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18 pages, 2256 KiB

Open AccessArticle

A Network Model Approach to International Aid

by Joe Scattergood and Steven Bishop

Entropy 2023, 25(4), 641; https://doi.org/10.3390/e25040641 - 11 Apr 2023

Viewed by 1258

Abstract

Decisions made by international aid donors regarding the allocation of their aid budgets to recipients can be mathematically modelled using network theory. The many countries and multilateral organisations providing developmental aid, mostly to developing countries, have numerous competing or conflicting interests, biases and [...] Read more.

Decisions made by international aid donors regarding the allocation of their aid budgets to recipients can be mathematically modelled using network theory. The many countries and multilateral organisations providing developmental aid, mostly to developing countries, have numerous competing or conflicting interests, biases and motivations, often obscured by a lack of transparency and confused messaging. Using network theory, combined with other mathematical methods, these inter-connecting and inter-dependent variables are identified, revealing the complicated properties and dynamics of the international aid system. Statistical techniques are applied to the vast amount of available, open data to first understand the complexities and then identify the key variables, focusing principally on bilateral aid flows. These results are used to create a weighted network model which is subsequently adapted for use by a hypothetical aid recipient. By incorporating modern portfolio theory into this weighted network model and taking advantage of a donor’s reasons for allocating their aid budgets to that recipient, a simulation is carried out treating the problem as an optimal investment portfolio of aid determinant ‘assets’ which illustrates how a recipient can maximise their aid receipts. Suggestions are also made for further uses and adaptations of this weighted network model. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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20 pages, 1103 KiB

Open AccessArticle

The Risk Contagion between Chinese and Mature Stock Markets: Evidence from a Markov-Switching Mixed-Clayton Copula Model

by Hongli Niu, Kunliang Xu and Mengyuan Xiong

Entropy 2023, 25(4), 619; https://doi.org/10.3390/e25040619 - 06 Apr 2023

Cited by 2 | Viewed by 1130

Abstract

Exploring the risk spillover between Chinese and mature stock markets is a promising topic. In this study, we propose a Markov-switching mixed-Clayton (Ms-M-Clayton) copula model that combines a state transition mechanism with a weighted mixed-Clayton copula. It is applied to investigate the dynamic [...] Read more.

Exploring the risk spillover between Chinese and mature stock markets is a promising topic. In this study, we propose a Markov-switching mixed-Clayton (Ms-M-Clayton) copula model that combines a state transition mechanism with a weighted mixed-Clayton copula. It is applied to investigate the dynamic risk dependence between Chinese and mature stock markets in the Americas, Europe, and Asia–Oceania regions. Additionally, the conditional value at risk (CoVaR) is applied to analyze the risk spillovers between these markets. The empirical results demonstrate that there is mainly a time-varying but stable positive risk dependence structure between Chinese and mature stock markets, where the upside and downside risk correlations are asymmetric. Moreover, the risk contagion primarily spills over from mature stock markets to the Chinese stock market, and the downside effect is stronger. Finally, the risk contagion from Asia–Oceania to China is weaker than that from Europe and the Americas. The study provides insights into the risk association between emerging markets, represented by China, and mature stock markets in major regions. It is significant for investors and risk managers, enabling them to avoid investment risks and prevent risk contagion. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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11 pages, 1586 KiB

Open AccessArticle

The Hurst Exponent as an Indicator to Anticipate Agricultural Commodity Prices

by Leticia Pérez-Sienes, Mar Grande, Juan Carlos Losada and Javier Borondo

Entropy 2023, 25(4), 579; https://doi.org/10.3390/e25040579 - 28 Mar 2023

Cited by 3 | Viewed by 1392

Abstract

Anticipating and understanding fluctuations in the agri-food market is very important in order to implement policies that can assure fair prices and food availability. In this paper, we contribute to the understanding of this market by exploring its efficiency and whether the local [...] Read more.

Anticipating and understanding fluctuations in the agri-food market is very important in order to implement policies that can assure fair prices and food availability. In this paper, we contribute to the understanding of this market by exploring its efficiency and whether the local Hurst exponent can help to anticipate its trend or not. We have analyzed the time series of the price for different agri-commodities and classified each day into persistent, anti-persistent, or white-noise. Next, we have studied the probability and speed to mean reversion for several rolling windows. We found that in general mean reversion is more probable and occurs faster during anti-persistent periods. In contrast, for most of the rolling windows we could not find a significant effect of persistence in mean reversion. Hence, we conclude that the Hurst exponent can help to anticipate the future trend and range of the expected prices in this market. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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23 pages, 1738 KiB

Open AccessFeature PaperArticle

Extraction of Important Factors in a High-Dimensional Data Space: An Application for High-Growth Firms

by Takuya Wada, Hideki Takayasu and Misako Takayasu

Entropy 2023, 25(3), 488; https://doi.org/10.3390/e25030488 - 10 Mar 2023

Viewed by 1332

Abstract

We introduce a new non-black-box method of extracting multiple areas in a high-dimensional big data space where data points that satisfy specific conditions are highly concentrated. First, we extract one-dimensional areas where the data that satisfy specific conditions are mostly gathered by using [...] Read more.

We introduce a new non-black-box method of extracting multiple areas in a high-dimensional big data space where data points that satisfy specific conditions are highly concentrated. First, we extract one-dimensional areas where the data that satisfy specific conditions are mostly gathered by using the Bayesian method. Second, we construct higher-dimensional areas where the densities of focused data points are higher than the simple combination of the results for one dimension, and then we verify the results through data validation. Third, we apply this method to estimate the set of significant factors shared in successful firms with growth rates in sales at the top 1% level using 156-dimensional data of corporate financial reports for 12 years containing about 320,000 firms. We also categorize high-growth firms into 15 groups of different sets of factors. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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30 pages, 8912 KiB

Open AccessArticle

Investigating Deep Stock Market Forecasting with Sentiment Analysis

by Charalampos M. Liapis, Aikaterini Karanikola and Sotiris Kotsiantis

Entropy 2023, 25(2), 219; https://doi.org/10.3390/e25020219 - 23 Jan 2023

Cited by 5 | Viewed by 3314

Abstract

When forecasting financial time series, incorporating relevant sentiment analysis data into the feature space is a common assumption to increase the capacities of the model. In addition, deep learning architectures and state-of-the-art schemes are increasingly used due to their efficiency. This work compares [...] Read more.

When forecasting financial time series, incorporating relevant sentiment analysis data into the feature space is a common assumption to increase the capacities of the model. In addition, deep learning architectures and state-of-the-art schemes are increasingly used due to their efficiency. This work compares state-of-the-art methods in financial time series forecasting incorporating sentiment analysis. Through an extensive experimental process, 67 different feature setups consisting of stock closing prices and sentiment scores were tested on a variety of different datasets and metrics. In total, 30 state-of-the-art algorithmic schemes were used over two case studies: one comparing methods and one comparing input feature setups. The aggregated results indicate, on the one hand, the prevalence of a proposed method and, on the other, a conditional improvement in model efficiency after the incorporation of sentiment setups in certain forecast time frames. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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18 pages, 733 KiB

Open AccessEditor’s ChoiceArticle

Dependency Structures in Cryptocurrency Market from High to Low Frequency

by Antonio Briola and Tomaso Aste

Entropy 2022, 24(11), 1548; https://doi.org/10.3390/e24111548 - 28 Oct 2022

Cited by 4 | Viewed by 1865

Abstract

We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve [...] Read more.

We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Econophysics)

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