Article
Physics, Multidisciplinary
Hung T. Diep, Gabriel Desgranges
Summary: By studying the time evolution of stock markets using a statistical physics approach, we have found the impact of market atmosphere and specific measures on price fluctuations. Our model replicates important features in finance, such as the stability of price variation and the dynamic nature of volatility clustering.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Mathematics
Maria Nieves Lopez-Garcia, Miguel Angel Sanchez-Granero, Juan Evangelista Trinidad-Segovia, Antonio Manuel Puertas, Francisco Javier De las Nieves
Summary: By studying the co-movement functions among stocks in a given market, it was found that stocks with similar volatility tend to have greater co-movement, while stocks with dissimilar volatility have smaller co-movement. Additionally, during crisis periods, the volatility and log-price co-movement are much higher compared to calmer periods.
Article
Multidisciplinary Sciences
Irfan Javid, Rozaida Ghazali, Irteza Syed, Muhammad Zulqarnain, Noor Aida Husaini
Summary: A stock market collapse refers to a situation where stock prices drop by more than 10% across major indexes. Predicting such crises is challenging, but a model combining Hybridized Feature Selection and deep learning algorithms can improve accuracy.
Article
Business, Finance
Hui Hong, Lijun Jiang, Cheng Zhang, Zhonggang Yue
Summary: This research specifies the difference in herding across China's conventional and new energy stock markets and finds that herding is stronger for new energy stocks. The study also reveals that during the COVID-19 pandemic, new energy stock investors tend to follow market consensus, while conventional energy stock investors are not influenced. Furthermore, information arrivals from the new energy market help weaken herding in the conventional energy market, highlighting the importance of information dissemination.
RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE
(2024)
Article
Business, Finance
Wan-Ru Yang, Ming-Che Chuang
Summary: This article examines whether investors engage in herd behavior during periods of high market volatility. A modified herding model incorporating the Kalman filter and GARCH methodology is used to estimate time-varying herding corresponding to influential events. Our proposed model provides comprehensive findings on the relationship between investor herding and market conditions, which has been a subject of debate in previous literature. We find that investors do exhibit herd behavior in volatile markets, including the dot-com bubble in 2001 and the global financial crisis in 2009. However, in recent years, anti-herding has become more prevalent, and herding is minimal even in turbulent markets like the Covid-19 pandemic in 2020.
FINANCE RESEARCH LETTERS
(2023)
Article
Business, Finance
Shiwei Su, Songbo Jia, Guangping Shi
Summary: This paper examines the correlation between leverage adjustment behavior and the risk of stock price crash, using the data of China's A-share listed companies from 2011 to 2021. The findings suggest that the leverage adjustment behavior increases firms' risk of stock price crash, especially for state-owned listed companies. Furthermore, the analysis reveals that the risk of stock price crash further amplifies when there is high information asymmetry.
FINANCE RESEARCH LETTERS
(2023)
Review
Computer Science, Artificial Intelligence
Wenjie Lu, Jiazheng Li, Jingyang Wang, Lele Qin
Summary: This study proposes a new method combining convolutional neural networks, bi-directional long short-term memory networks, and attention mechanism for stock closing price prediction. The method performs the best among others, with the lowest mean absolute error and root mean square error, and the highest coefficient of determination.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Business, Finance
Yongbin Shi, Miao Yu, Liujun Chen, Plamen Ch Ivanov, Yougui Wang
Summary: The research reveals that over a 16-year period, the rank mobility of closing prices of stocks at the Shanghai A-share market increases as a power law with time scale, eventually converging to a constant level, indicating a fundamental dynamics of Chinese stock price movements.
FINANCE RESEARCH LETTERS
(2021)
Article
Mathematics
Florin Turcas, Florin Cornel Dumiter, Marius Boita
Summary: Exact sciences have yielded results that can potentially be applied to economics, particularly in the field of investment strategies. Statistics, geometry, and financial mathematics are tools that can be utilized in the capital market. In addition to exact science, factors like human psychology and behavioral unpredictability also play crucial roles in financial markets.
Article
Computer Science, Artificial Intelligence
Tengteng Liu, Xiang Ma, Shuo Li, Xuemei Li, Caiming Zhang
Summary: Stock price prediction is a crucial research topic with vast application potential. This study introduces a new model called VML, which decomposes stock price series using VMD and predicts subseries using MAML algorithm and LSTM network. The proposed method aims to improve prediction accuracy and reduce investment risk.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Business, Finance
Shicheng Li, Xiaoyong Huang, Zhonghou Cheng, Wei Zou, Yugen Yi
Summary: This paper proposes a method called AE-ACG for stock price movement prediction, which combines convolutional neural networks and gated recurrent units to efficiently extract features from financial time series data, and utilizes skip connections and attention mechanism to leverage hierarchical features and distinguish the importance of historical data across different periods.
FINANCE RESEARCH LETTERS
(2023)
Article
Business, Finance
Qian Wang, Chunyan Zhou, Lei Wang, Yu Wei
Summary: Behavioral finance research has found that the end-word tones of stock names can have a significant impact on the cognitive biases of investors and their consequences in the Chinese IPO market. This study reveals that stocks with easy pronunciation of the end-word tones tend to have higher price-to-earnings ratios and abnormal returns compared to stocks with difficult pronunciation. However, after 12 months, the cumulative abnormal returns of easy-pronunciation stocks decrease more than difficult-pronunciation stocks. Furthermore, the effects of end-word tone on P/E ratios and abnormal returns of IPO stocks in China are generally significant throughout the first day of IPO to 12 months after, but change from positive to negative around six months after the IPO day.
FINANCE RESEARCH LETTERS
(2023)
Article
Business, Finance
Min Bai, Yafeng Qin, Huiping Zhang
Summary: This paper examines large price declines of individual stocks in 22 emerging markets, finding that crashes in these markets are often not accompanied by information events and are typically followed by price reversals. Further analysis indicates that crashes in countries with better information transparency or lower openness are less likely to reverse in the short run, suggesting that factors such as information environment and market integration may influence the large swings in emerging market stock prices.
INTERNATIONAL REVIEW OF ECONOMICS & FINANCE
(2021)
Article
Computer Science, Artificial Intelligence
Yong Shi, Yuanchun Zheng, Kun Guo, Xinyue Ren
Summary: This paper analyzes the impact of herding on stock market fluctuations and proposes a method for modeling stock market sentiment based on sentiment propagation networks. The experimental results confirm the significant effect of investor behavior on the stock market.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Computer Science, Information Systems
Zhichao Chang, Zuping Zhang
Summary: Machine learning has been proven effective in boosting stock price predictions. However, most researchers focus on historical data and design prediction models. To address this issue, the authors propose a novel model called CNN-SC that incorporates sentiment analysis in expert comments for stock price prediction. Comparisons with other methods show the superiority of CNN-SC, accurately predicting short-term stock price fluctuations.
Article
Multidisciplinary Sciences
Liujun Xu, Jinrong Liu, Peng Jin, Guoqiang Xu, Jiaxin Li, Xiaoping Ouyang, Ying Li, Cheng-Wei Qiu, Jiping Huang
Summary: The curved space-time produced by black holes leads to the intriguing trapping effect. So far, metadevices have enabled analogous black holes to trap light or sound in laboratory spacetime. However, trapping heat in a conductive environment is still challenging because diffusive behaviors are directionless.
NATIONAL SCIENCE REVIEW
(2023)
Article
Physics, Multidisciplinary
X. C. Zhou, W. Y. Lin, F. B. Yang, X. D. Zhou, J. Shen, J. P. Huang
Summary: Recent research discovered a hysteresis phenomenon in the electric conductance during the metal-insulator transition in the vanadium trioxide system. An effective medium theory was developed to predict the relationships between the conductance and the phase ratio. The theory explained the hysteresis as a result of the hybrid impacts of phase symmetry and spatial distribution asymmetry. The predicted relationships were consistent with experimental results, demonstrating the asymmetrical dynamic behaviors during the warming and cooling processes.
Article
Multidisciplinary Sciences
Peng Jin, Jinrong Liu, Liujun Xu, Jun Wang, Xiaoping Ouyang, Jian-Hua Jiang, Jiping Huang
Summary: Thermal metamaterials provide rich control of heat transport by breaking the Onsager reciprocity and introducing thermal convection, leading to a regime beyond effective heat conduction. A continuous switch from thermal cloaking to thermal concentration is demonstrated in a liquid-solid hybrid thermal metamaterial with external tuning. This switch is achieved by tuning the liquid flow, resulting in a topology transition in the virtual space of the thermotic transformation. These findings illustrate the extraordinary heat transport in complex multicomponent thermal metamaterials and pave the way toward an unprecedented regime of heat manipulation.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Physics, Applied
Zeren Zhang, Fubao Yang, Jiping Huang
Summary: By utilizing transformation-invariant metamaterials, intelligent chameleon-like metashells for mass diffusion are proposed, which automatically change their efficient parameters to adapt to environmental change and cost no energy. Moreover, an irregular-shaped chameleon-like concentrator and a circular chameleon-like rotator are designed. Experimental suggestions combined with layered structure devices are provided to validate the proposal. This study may inspire the intelligentization of mass-diffusion metamaterials in electronics and plasma physics.
PHYSICAL REVIEW APPLIED
(2023)
Article
Physics, Multidisciplinary
Chuan-Xin Zhang, Tian-Jiao Li, Liu-Jun Xu, Ji-Ping Huang
Summary: Accurate and fast prediction of thermal radiation properties is crucial for material applications. However, existing models do not account for deviations caused by volcanic eruptions, pollution, and human activities that exacerbate dust production in water droplets. This study investigates the influence of dust particles on light transmission and energy distribution in water droplets, highlighting the significant role of dust particles in thermal radiation and providing insights into electromagnetic properties. This research emphasizes the importance of accounting for dust particles in atmospheric models and their potential impact on radiative balance.
CHINESE PHYSICS LETTERS
(2023)
Article
Thermodynamics
Min Lei, Chaoran Jiang, Fubao Yang, Jun Wang, Jiping Huang
Summary: In this study, a novel programmable all-thermal encoding strategy is proposed, which utilizes macroscopic conductive heat for digital encoding under purely thermal fields. Switchable cloak-concentrator metadevices are used to distinguish and modulate binary signals, and temperature-responsive phase change materials are employed to make the encoding operation programmable. This scheme presents a practical paradigm for all-thermal logical metadevices and opens up new avenues for implementing modern information technologies in ubiquitous diffusion systems.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2023)
Article
Physics, Multidisciplinary
Yanshuang Chen, Zefang Ye, Kexin Wang, Jiping Huang, Hua Tong, Yuliang Jin, Ke Chen, Hajime Tanaka, Peng Tan
Summary: Glasses can relax internally even when their structure is frozen. In a two-dimensional glass former, non-constrained bonds survive the freezing of structural relaxation, leading to persistent internal relaxation. This study directly visualizes the internal relaxations in a glass through observations of a mechanically driven granular system, revealing the emergence of slow beta relaxation as the structure relaxation freezes.
Article
Physics, Applied
Fubao Yang, Peng Jin, Min Lei, Gaole Dai, Jun Wang, Jiping Huang
Summary: The proposed space-time-coding electromagnetic metasurface introduces the temporal dimension into artificial structure design, expanding its digital application in information processing. However, the absence of temporal dimension in thermal digital metamaterial limits the synergetic modulation of thermal signal in time and space. This study introduces temporal modulation into existing spatially variable thermal coding structures and proposes a space-time thermal binary coding scheme, demonstrating a practical strategy for thermal binary coding and providing a prototype for spatiotemporal regulation of thermal signal.
PHYSICAL REVIEW APPLIED
(2023)
Article
Materials Science, Multidisciplinary
Min Lei, Liujun Xu, Jiping Huang
Summary: Emerging multiphysics metamaterials face limitations in functionality and tunability due to fixed multiphysics functionality and challenging continuous tunability. To overcome these, spatiotemporal multiphysics metamaterials are proposed, enabling multiple functions for each physical field and continuous switching. Rotatable checkerboard structures with different rotation times, material composition, and geometric shapes have been developed to allow for flexible function switching. The results offer a promising platform for adaptive and intelligent multiphysics field manipulation.
MATERIALS TODAY PHYSICS
(2023)
Review
Physics, Applied
Zeren Zhang, Liujun Xu, Teng Qu, Min Lei, Zhi-Kang Lin, Xiaoping Ouyang, Jian-Hua Jiang, Jiping Huang
Summary: This review introduces the principles, materials advances, and applications of metamaterials that modulate the diffusion of heat, particles, and plasmas. It discusses the use of the transformation principle and metamaterials to control diffusion, going beyond the conventional scope of metamaterials. Future directions include research into topological diffusion and machine-learning-assisted materials design.
NATURE REVIEWS PHYSICS
(2023)
Article
Chemistry, Multidisciplinary
Peng Jin, Liujun Xu, Guoqiang Xu, Jiaxin Li, Cheng-Wei Qiu, Jiping Huang
Summary: Heat-enhanced thermal diffusion metamaterials powered by deep learning enable automatic temperature sensing and adjustment with high tunability and stable thermal performance.
ADVANCED MATERIALS
(2023)
Article
Multidisciplinary Sciences
Liujun Xu, Jinrong Liu, Guoqiang Xu, Jiping Huang, Cheng- Wei Qiu
Summary: In an active thermal lattice composed of a stationary solid matrix and rotating solid particles, giant thermal chirality is generated by breaking the Onsager reciprocity relation through rotation, which is about two orders of magnitude larger than ever reported. Anisotropic thermal chirality is achieved by breaking the rotation invariance of the active lattice, bringing effective thermal conductivity to a region unreachable by the thermal Hall effect.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Multidisciplinary Sciences
Xinchen Zhou, Xiang Xu, Jiping Huang
Summary: The study presents an adaptive multi-temperature control system using liquid-solid phase transitions for effective thermal management. By leveraging the properties of specific materials, a multi-temperature maintenance container was created, and temperature variations were successfully controlled within a range of only 0.14-2.05%, offering a practical solution for reliable transportation of goods.
NATURE COMMUNICATIONS
(2023)
Article
Engineering, Mechanical
Pengfei Zhuang, Jiping Huang
Summary: Thermal metamaterials based on transformation theory can precisely control heat flow and electric current by manipulating the spatial distributions of material parameters. This study presents a dual-function metamaterial that can simultaneously concentrate (or cloak) and rotate the thermoelectric (TE) field. The proposed control methods, including a temperature-switching TE rotating concentrator cloak and an electrically controlled TE rotating concentrator, enable precise manipulation of the TE field. The theoretical predictions and finite-element simulations show good agreement, providing a unified framework for manipulating the direction and density of the TE field and contributing to the study of thermal management.
INTERNATIONAL JOURNAL OF MECHANICAL SYSTEM DYNAMICS
(2023)
Article
Physics, Fluids & Plasmas
Haohan Tan, Yuguang Qiu, Liujun Xu, Jiping Huang
Summary: Thermal conduction force plays an important role in various fields, but regulating its effect is challenging due to two restrictions. This study demonstrates that thermal conduction force can exist unexpectedly at a zero average temperature gradient in dielectric crystals. The force direction can be highly tunable, providing valuable insights into thermal conduction force and potential applications in manipulating local thermal conductivity.
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
