Physica A: Statistical Mechanics and its Applications最新文献

英文中文

Studying the impact of individual emotional states on the co-evolution of information, behavior and disease in multiplex networks

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-05 DOI: 10.1016/j.physa.2025.130480

Bingjie Wu , Liang’an Huo

As the information technology era advances, information is rapidly disseminated, and it directly influence individuals’ behavioral choices, which will also have a significant impact on disease transmission, and the individual emotional states play a crucial role in this process. This paper proposes a new coupled model, aiming to investigate the co-evolutionary interactions of individual emotional states with information, behavior and disease transmission. Meanwhile, it innovatively introduces a threshold model, to quantify the process of individual emotional state change. The model considers that individuals’ emotional states are influenced by two main factors: the global information dissemination and the local disease severity. It also analyzes in depth how the individual emotional state affects the individual’s willingness to receive information, willingness to vaccinate, and susceptibility, which are important factors in the disease transmission process. The model is analyzed utilizing the MMCA (Microscopic Markov Chain Approach), aiming to obtain state transformation equation and derive the disease outbreak thresholds. Simulation experiments show that individual emotions tend to have a complex impact on the model transmission process. Overall, during disease transmission, individuals should appropriately regulate their emotional changes, make more positive and rational decisions, while effectively controlling the negative information dissemination, which is of vital importance for the maintenance of public health and social stability.

{"title":"Studying the impact of individual emotional states on the co-evolution of information, behavior and disease in multiplex networks","authors":"Bingjie Wu , Liang’an Huo","doi":"10.1016/j.physa.2025.130480","DOIUrl":"10.1016/j.physa.2025.130480","url":null,"abstract":"<div><div>As the information technology era advances, information is rapidly disseminated, and it directly influence individuals’ behavioral choices, which will also have a significant impact on disease transmission, and the individual emotional states play a crucial role in this process. This paper proposes a new coupled model, aiming to investigate the co-evolutionary interactions of individual emotional states with information, behavior and disease transmission. Meanwhile, it innovatively introduces a threshold model, to quantify the process of individual emotional state change. The model considers that individuals’ emotional states are influenced by two main factors: the global information dissemination and the local disease severity. It also analyzes in depth how the individual emotional state affects the individual’s willingness to receive information, willingness to vaccinate, and susceptibility, which are important factors in the disease transmission process. The model is analyzed utilizing the MMCA (Microscopic Markov Chain Approach), aiming to obtain state transformation equation and derive the disease outbreak thresholds. Simulation experiments show that individual emotions tend to have a complex impact on the model transmission process. Overall, during disease transmission, individuals should appropriately regulate their emotional changes, make more positive and rational decisions, while effectively controlling the negative information dissemination, which is of vital importance for the maintenance of public health and social stability.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130480"},"PeriodicalIF":2.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An approach for interdisciplinary knowledge discovery: Link prediction between topics

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-05 DOI: 10.1016/j.physa.2025.130517

Huo Chaoguang , Han Yueji , Huo Fanfan , Zhang Chenwei

Predicting interdisciplinary links between topics can unveil potential interdisciplinary knowledge relationships and foster innovation. Considering keywords extracted from interdisciplinary research as topics, we propose a topic link prediction method based on graph neural networks. We emphasize the integration of topic semantic content features, author direct-collaboration features, and indirect-collaboration features to improve prediction performance. The interdisciplinary topic link prediction models are constructed using Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Sample and Aggregate (GraphSAGE), BERT, and Node2Vec. These models are validated by using digital humanities data as a case study. We find that the integration of semantic content, direct-collaboration, and indirect-collaboration features significantly improved the Area Under the Curve (AUC) by 20.68 % and the Average Precision (AP) by 16.52 %, compared to relying solely on the co-occurrence network. For topic reorganization, we find that the features we designed make more sense than GNN algorithms alone, and that weak relationships contribute more to topic link prediction than strong relationships. Our approach provides valuable research insights and references for scholars engaged in interdisciplinary knowledge. Notably, this is an innovative approach to interdisciplinary knowledge discovery through knowledge reorganization.

{"title":"An approach for interdisciplinary knowledge discovery: Link prediction between topics","authors":"Huo Chaoguang , Han Yueji , Huo Fanfan , Zhang Chenwei","doi":"10.1016/j.physa.2025.130517","DOIUrl":"10.1016/j.physa.2025.130517","url":null,"abstract":"<div><div>Predicting interdisciplinary links between topics can unveil potential interdisciplinary knowledge relationships and foster innovation. Considering keywords extracted from interdisciplinary research as topics, we propose a topic link prediction method based on graph neural networks. We emphasize the integration of topic semantic content features, author direct-collaboration features, and indirect-collaboration features to improve prediction performance. The interdisciplinary topic link prediction models are constructed using Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Sample and Aggregate (GraphSAGE), BERT, and Node2Vec. These models are validated by using digital humanities data as a case study. We find that the integration of semantic content, direct-collaboration, and indirect-collaboration features significantly improved the Area Under the Curve (AUC) by 20.68 % and the Average Precision (AP) by 16.52 %, compared to relying solely on the co-occurrence network. For topic reorganization, we find that the features we designed make more sense than GNN algorithms alone, and that weak relationships contribute more to topic link prediction than strong relationships. Our approach provides valuable research insights and references for scholars engaged in interdisciplinary knowledge. Notably, this is an innovative approach to interdisciplinary knowledge discovery through knowledge reorganization.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130517"},"PeriodicalIF":2.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamical phase transitions in two-dimensional Brownian matter

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-04 DOI: 10.1016/j.physa.2025.130482

Nathan O. Silvano , Daniel G. Barci

We investigate collective behavior of a system of two-dimensional interacting Brownian particles in the hydrodynamic regime. By means of the Martin–Siggia–Rose–Jenssen–de Dominicis formalism, we built up a generating functional for correlations functions. In the continuum limit, we uncover an exact symmetry under area-preserving diffeomorphism transformations that characterizes a liquid state. This symmetry leads to the conservation of local vorticity. By computing the generating functional within the saddle-point plus Gaussian fluctuations approximation, we reveal the emergence of a

U (1)

gauge symmetry that allows us to describe the dynamics of density fluctuations as a gauge theory. We solve the corresponding equations of motion for short as well as long ranged interactions showing up the presence of multiple dynamical regimes and associated dynamical phase transitions, even for pure repulsive interactions.

引用次数: 0

Analysis of the impact of large vehicles in merging areas based on driver characteristics under vehicle-road coordination

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-04 DOI: 10.1016/j.physa.2025.130497

Shuhua Li, Yongsheng Qian, Junwei Zeng, Xu Wei

The lane-changing behavior of vehicles in highway merging areas is a crucial factor affecting road flow and safety. Due to the size and power performance limitations of large vehicles, they cause more significant interference to the surrounding vehicles, thereby reducing the traffic capacity of the main road and ramp in the merging area. To study the impact of large vehicles on the driving behavior of different types of drivers in highway ramp merging sections, this paper analyzes the driving behavior differences of different driver types based on the vehicle driving state data from the Mirror-Traffic database. It constructs a vehicle gap acceptance model for highway ramp merging sections under the influence of large vehicles and verifies the rationality of the model. On this basis, taking the unidirectional three-lane highway ramp merging area under the condition of vehicle-road coordination as the research object, the game benefit function of merging vehicles in the merging area is redefined. To achieve optimal traffic efficiency in the merging area and minimize vehicle travel time and delay, an induction optimization method and a cooperative control strategy are proposed, and a group vehicle induction distribution optimization model is established. The multi-vehicle cooperative induction distribution method can adjust the merging speed and position of vehicles, optimizing vehicle merging trajectories. Simulation results show that under the condition of vehicle-road coordination, the average speed of the group of vehicles increased by 5–15 %, the average flow increased by 8–16 %, vehicle collision safety was improved, vehicle delay time was significantly reduced, and with the increase in traffic flow in the merging area, the optimization effect became more significant.

{"title":"Analysis of the impact of large vehicles in merging areas based on driver characteristics under vehicle-road coordination","authors":"Shuhua Li, Yongsheng Qian, Junwei Zeng, Xu Wei","doi":"10.1016/j.physa.2025.130497","DOIUrl":"10.1016/j.physa.2025.130497","url":null,"abstract":"<div><div>The lane-changing behavior of vehicles in highway merging areas is a crucial factor affecting road flow and safety. Due to the size and power performance limitations of large vehicles, they cause more significant interference to the surrounding vehicles, thereby reducing the traffic capacity of the main road and ramp in the merging area. To study the impact of large vehicles on the driving behavior of different types of drivers in highway ramp merging sections, this paper analyzes the driving behavior differences of different driver types based on the vehicle driving state data from the Mirror-Traffic database. It constructs a vehicle gap acceptance model for highway ramp merging sections under the influence of large vehicles and verifies the rationality of the model. On this basis, taking the unidirectional three-lane highway ramp merging area under the condition of vehicle-road coordination as the research object, the game benefit function of merging vehicles in the merging area is redefined. To achieve optimal traffic efficiency in the merging area and minimize vehicle travel time and delay, an induction optimization method and a cooperative control strategy are proposed, and a group vehicle induction distribution optimization model is established. The multi-vehicle cooperative induction distribution method can adjust the merging speed and position of vehicles, optimizing vehicle merging trajectories. Simulation results show that under the condition of vehicle-road coordination, the average speed of the group of vehicles increased by 5–15 %, the average flow increased by 8–16 %, vehicle collision safety was improved, vehicle delay time was significantly reduced, and with the increase in traffic flow in the merging area, the optimization effect became more significant.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130497"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vehicle navigation path optimization based on complex networks

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-04 DOI: 10.1016/j.physa.2025.130509

Changxi Ma, Mingxi Zhao, Yang Liu

Vehicle navigation path optimization, an essential means to prevent and alleviate traffic congestion, assists users in finding optimal routes from origin to destination based on acquired traffic information. This paper proposes a vehicle navigation path optimization approach that incorporates complex networks. Initially, a complex network-based multi-objective optimization model is developed to address total travel time and cost objectives. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is enhanced by integrating a machine learning approach and designing a competitive selection operator, along with crossover and mutation operators based on hierarchical clustering, to create a multi-objective vehicle navigation path optimization algorithm. Finally, case studies validate the model and algorithm’s effectiveness. Experimental results demonstrate the superiority of the proposed machine learning and NSGA-II hybrid algorithm over traditional NSGA-II and NSGA-III. This research achieves rational and balanced distribution of traffic flow across road segments by appropriately guiding vehicles, thereby improving traffic network efficiency.

车辆导航路径优化是预防和缓解交通拥堵的重要手段，它可以帮助用户根据获取的交通信息找到从出发地到目的地的最佳路线。本文提出了一种结合复杂网络的车辆导航路径优化方法。首先，针对总行程时间和成本目标，建立了基于复杂网络的多目标优化模型。随后，通过整合机器学习方法和设计竞争选择算子，以及基于分层聚类的交叉和突变算子，增强了非支配排序遗传算法 II（NSGA-II），从而创建了一种多目标车辆导航路径优化算法。最后，案例研究验证了模型和算法的有效性。实验结果表明，所提出的机器学习和 NSGA-II 混合算法优于传统的 NSGA-II 和 NSGA-III。这项研究通过适当引导车辆，实现了交通流在各路段的合理均衡分配，从而提高了交通网络的效率。

{"title":"Vehicle navigation path optimization based on complex networks","authors":"Changxi Ma, Mingxi Zhao, Yang Liu","doi":"10.1016/j.physa.2025.130509","DOIUrl":"10.1016/j.physa.2025.130509","url":null,"abstract":"<div><div>Vehicle navigation path optimization, an essential means to prevent and alleviate traffic congestion, assists users in finding optimal routes from origin to destination based on acquired traffic information. This paper proposes a vehicle navigation path optimization approach that incorporates complex networks. Initially, a complex network-based multi-objective optimization model is developed to address total travel time and cost objectives. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is enhanced by integrating a machine learning approach and designing a competitive selection operator, along with crossover and mutation operators based on hierarchical clustering, to create a multi-objective vehicle navigation path optimization algorithm. Finally, case studies validate the model and algorithm’s effectiveness. Experimental results demonstrate the superiority of the proposed machine learning and NSGA-II hybrid algorithm over traditional NSGA-II and NSGA-III. This research achieves rational and balanced distribution of traffic flow across road segments by appropriately guiding vehicles, thereby improving traffic network efficiency.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130509"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The collision-avoiding flocking of a Cucker–Smale model with pinning control and external perturbation

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-03 DOI: 10.1016/j.physa.2025.130466

Jianlong Ren, Qiming Liu, Ping Li

The Cucker–Smale model is an essential and effective tool for analyzing flocking behavior. A modified Cucker–Smale model with pinning control and external perturbation is proposed in this paper. Primarily, by imposing appropriate restrictions on external perturbation, the system can achieve asymptotic flocking; simultaneously, pinning control improves the speed of flocking. Furthermore, collision-avoiding between any two agents in the flocking process can be derived. The results show that the convergence time depends on control parameters and the convergence speed of the perturbation. Lastly, some numerical simulations are given to illustrate the effectiveness of the theoretical results.

引用次数: 0

Traffic flow phenomena in large sporting events – empirical analysis and macroscopic simulation of the Vasaloppet

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-01 DOI: 10.1016/j.physa.2025.130481

Klaus Bogenberger , Martin Treiber , Patrick Malcolm

In large sporting events, traffic flow phenomena similar to car traffic can be observed. The Vasaloppet, a long-distance cross-country skiing race in Sweden, exemplifies this. Athletes use classical techniques and race in tracks akin to small lanes, where athlete traffic jams can impede performance. This study empirically analyzes these phenomena using eight years of split-time data and GPS tracks. We identified critical bottlenecks, jams, and stress points where athletes must wait while the race clock continues to tick. The nature of movement under free-flow conditions is largely dispersive due to varying abilities and grouping by skill level. Using comprehensive data and observations from the Vasaloppet, we developed a two-phase macroscopic simulation model to predict and understand athlete flow during races. One phase models free-flow conditions, and the other congested conditions. A multi-class approach was implemented, with each class having different speed distributions based on official split time data. Using this model, we simulated the race under status-quo conditions, as well as with two potential measures to reduce waiting time for participants: a staggered start and a widening of the race course. Both measures result in massively reduced congestion and waiting times compared to the status quo. While these measures are unlikely to be implemented in future Vasaloppet races due to tradition and nature conservation respectively, our model could be applied to various mass sporting events, such as other cross-country skiing, running, cycling, and inline skating events. It can aid organizers who can test different measures to resolve athlete traffic jams.

{"title":"Traffic flow phenomena in large sporting events – empirical analysis and macroscopic simulation of the Vasaloppet","authors":"Klaus Bogenberger , Martin Treiber , Patrick Malcolm","doi":"10.1016/j.physa.2025.130481","DOIUrl":"10.1016/j.physa.2025.130481","url":null,"abstract":"<div><div>In large sporting events, traffic flow phenomena similar to car traffic can be observed. The Vasaloppet, a long-distance cross-country skiing race in Sweden, exemplifies this. Athletes use classical techniques and race in tracks akin to small lanes, where athlete traffic jams can impede performance. This study empirically analyzes these phenomena using eight years of split-time data and GPS tracks. We identified critical bottlenecks, jams, and stress points where athletes must wait while the race clock continues to tick. The nature of movement under free-flow conditions is largely dispersive due to varying abilities and grouping by skill level. Using comprehensive data and observations from the Vasaloppet, we developed a two-phase macroscopic simulation model to predict and understand athlete flow during races. One phase models free-flow conditions, and the other congested conditions. A multi-class approach was implemented, with each class having different speed distributions based on official split time data. Using this model, we simulated the race under status-quo conditions, as well as with two potential measures to reduce waiting time for participants: a staggered start and a widening of the race course. Both measures result in massively reduced congestion and waiting times compared to the status quo. While these measures are unlikely to be implemented in future Vasaloppet races due to tradition and nature conservation respectively, our model could be applied to various mass sporting events, such as other cross-country skiing, running, cycling, and inline skating events. It can aid organizers who can test different measures to resolve athlete traffic jams.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130481"},"PeriodicalIF":2.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid projective synchronization of complex-valued memristive neural networks via concise prescribed-time control strategies

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-01 DOI: 10.1016/j.physa.2025.130465

Hao Pu , Fengjun Li , Qingyun Wang , Jie Ran

This article aims to consider the prescribed-time hybrid projective synchronization of fully complex-valued memristive delayed neural networks with discontinuous activation. Above all, a new prescribed-time stability lemma is established, of which the settling time is directly a parameter of the auxiliary function and the conservatism of the conditions is reduced. Unlike common research methods, to simplify the operation, the complex-valued memristive neural network model is converted into one with uncertain parameters in view of the convex analysis approach. Subsequently, applying Filippov’s solution theory, prescribed-time stability theory, inequality techniques, and non-separation method, several novel and concise sufficient criteria are established to ensure the considered systems achieve prescribed-time synchronization by designing some controllers. Additionally, unlike common power-law type prescribed-time controllers, the ones designed in this paper are simpler because they do not involve sign function and time-delay term and have relatively fewer terms. Especially, one of their control gains is a time variable rather than a constant. And the prescribed synchronization time is independent of any initial values and parameters of the system, and can be preset arbitrarily according to actual needs. Compared with existing works, the hybrid projective coefficients of this paper are complex-valued that can be adjusted rather than real-valued, and projective synchronization, complete synchronization and anti-synchronization are its special cases. Eventually, numerical simulation results are furnished to manifest the effectiveness of the acquired theoretical outcomes.

{"title":"Hybrid projective synchronization of complex-valued memristive neural networks via concise prescribed-time control strategies","authors":"Hao Pu , Fengjun Li , Qingyun Wang , Jie Ran","doi":"10.1016/j.physa.2025.130465","DOIUrl":"10.1016/j.physa.2025.130465","url":null,"abstract":"<div><div>This article aims to consider the prescribed-time hybrid projective synchronization of fully complex-valued memristive delayed neural networks with discontinuous activation. Above all, a new prescribed-time stability lemma is established, of which the settling time is directly a parameter of the auxiliary function and the conservatism of the conditions is reduced. Unlike common research methods, to simplify the operation, the complex-valued memristive neural network model is converted into one with uncertain parameters in view of the convex analysis approach. Subsequently, applying Filippov’s solution theory, prescribed-time stability theory, inequality techniques, and non-separation method, several novel and concise sufficient criteria are established to ensure the considered systems achieve prescribed-time synchronization by designing some controllers. Additionally, unlike common power-law type prescribed-time controllers, the ones designed in this paper are simpler because they do not involve sign function and time-delay term and have relatively fewer terms. Especially, one of their control gains is a time variable rather than a constant. And the prescribed synchronization time is independent of any initial values and parameters of the system, and can be preset arbitrarily according to actual needs. Compared with existing works, the hybrid projective coefficients of this paper are complex-valued that can be adjusted rather than real-valued, and projective synchronization, complete synchronization and anti-synchronization are its special cases. Eventually, numerical simulation results are furnished to manifest the effectiveness of the acquired theoretical outcomes.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130465"},"PeriodicalIF":2.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing clustered factors in the cryptocurrency market with Random Matrix Theory

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-02-28 DOI: 10.1016/j.physa.2025.130473

Laura Molero González , Roy Cerqueti , Raffaele Mattera , Miguel Ángel Sánchez Granero , Juan Evangelista Trinidad Segovia

The cryptocurrency market is a dynamic and complex system. Factor models can identify latent factors that systematically influence asset returns and are useful for unraveling such complexities. The latent factors can represent the underlying economic, financial, or investor behavioral phenomena driving the price movements of cryptocurrencies. In this paper, we approach the problem from the perspective of Random Matrix Theory (RMT) and assume that while some factors affect all cryptocurrencies, some others are cluster-specific. In particular, we distinguish between stablecoins and non-stablecoins. We find that there are up to two global factors for cryptocurrencies. The results at the cluster level highlight that stablecoins are affected by a larger number of factors than standard cryptocurrencies.

引用次数: 0

Forecasting the unforecastable: An independent component analysis for majority game-like global cryptocurrencies

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-02-28 DOI: 10.1016/j.physa.2025.130472

Oliver Kirsten , Bernd Süssmuth

Cryptocurrencies do not have proper economic fundamentals. Consequently, economic variables cannot predict crypto prices. According to economic theory, cryptocurrencies are unbacked assets that are inherently unforecastable. However, a growing strand of literature suggests global crypto markets to be informationally inefficient. It implies the possibility of return predictability based on past information. Forecasting the allegedly unforecastable becomes feasible. Keeping it sophisticatedly simple, past infomation can be captured by autoregressive integrated moving average (ARIMA) processes of principal components. However, Principal Component Analysis (PCA) for crypto price series is due to their non-Gaussian property not applicable and requires the assumption of a stochastic trend model. Making use of the Central Limit Theorem, Independent Component Analysis (ICA) overcomes this deficiency. We show that ICA combined with ARIMA modeling more than triples the predictability of global crypto price dynamics.

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Physica A: Statistical Mechanics and its Applications

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀