- Published on 22 November 2017
New study of the trading interactions that determine the stock price using AI algorithms reveals unexpected microstructure for stock evolution, useful for financial crash modeling
Every day, thousands of orders for selling or buying stocks are registered and processed within milliseconds. Electronic stock exchanges, such as NASDAQ, use what is referred to as microscopic modelling of the order flow - reflecting the dynamics of order bookings - to facilitate trading. The study of such market microstructures is a relatively new research field focusing on the trading interactions that determine the stock price. Now, a German team from the University of Duisburg-Essen has analysed the statistical regularities and irregularities in the recent order flow of 96 different NASDAQ stocks. Since prices are strongly correlated during financial crises, they evolve in a way that is similar to what happens to nerve signals during epileptic seizures. The findings of the Duisburg-Essen group, published in EPJ B, contribute to modelling price evolution, and could ultimately be used to evaluate the impact of financial crises.
- Published on 20 November 2017
Complex systems models reveal that socio-political instabilities are so predictable that the need to reduce the time lag in political decision-making is blatantly obvious
The Brexit is the perfect example of a time-delayed event. It will happen, if at all, only several years after the referendum vote. Dynamical systems with time delays, like societies making political decisions, have attracted considerable attention from physicists specialised in complex systems. In this new study published in EPJ B, Claudius Gros from Goethe University Frankfurt, Germany has shown that over time, the stability of our democracies can only be preserved by finding ways to reduce the time span governments and other political actors typically need to respond to the wishes of citizens, particularly when confronted with external shocks. That’s because citizens’ opinions are now forming much more quickly than ever before, relative to the time lags that policy decision making involves. This means that drastic changes in modes of governance may be required in order to keep democratic societies stable.
- Published on 28 August 2017
Lessons from self-trapped electrons in crystal lattice offer better predictive power for transport model
Ever heard of polarons? They are a kind of quasi-particle resulting from electrons self-trapping in a vibrating crystal lattice. Polarons can be harnessed to transport energy under certain conditions related to the relative vibrations of the electrons and the lattice itself. The theory explaining how polarons carry energy in crystals can be applied to long molecules called polypeptides—which can fold into proteins. In a new study published in EPJ B, Jingxi Luo and Bernard Piette from Durham University, UK, present a new mathematical model describing how polarons can be displaced in a directed way with minimum energy loss in linear peptide chains—which were used as a proxy for the study of proteins. The model therefore accounts for the energy transport mechanism explaining how energy generated inside a biological cell moves along transmembrane proteins towards the cell's exterior.
- Published on 28 August 2017
Chinese scientists show how the network structure affects the accuracy of methods predicting the future evolution of a network, like those used to predict protein interactions
Nature and society are full of so-called real-world complex systems, such as protein interactions. Theoretical models, called complex networks, describe them and consist of nodes representing any basic element of that network, and links describing interactions or reactions between two nodes. In the case of protein-interaction studies, reconstruction of complex networks is key as the data available is often inaccurate and our knowledge of the exact nature of these interactions is limited. For reconstruction of networks, link predict -- the likelihood of the existence of a link between two nodes -- matters. Now, Chinese scientists have looked at the influence of the network structure to shed some light on the robustness of the latest methods used to predict the behaviour of such complex networks. Jin-Xuan Yang and Xiao-Dong Zhang from Shanghai Jiao Tong University in China have just published their work in EPJ B, providing a good reference for the choice of a suitable algorithm for link prediction depending on the chosen network structure. In this paper, the authors use two parameters of networks—the common neighbours index and the so-called Gini coefficient index—to reveal the relation between the structure of a network and the accuracy of methods used to predict future links.
- Published on 28 August 2017
In the study of phase transitions and critical phenomena, it is important to understand finite size corrections to thermodynamic quantities. Finite-size scaling concerns the critical behavior of systems in which one or more directions are finite. It is valuable in the analysis of experimental and numerical data in many situations, for example for films of finite thickness.
As soon as one has a finite system one must consider the question of boundary conditions on the outer surfaces or “walls” of the system because the critical behavior near boundaries normally differs from the bulk behavior.
The author of this EPJ B Colloquium investigates the effects of boundary conditions on finite-size corrections through the study of model systems, especially those which have exact results and can be analysed without numerical errors, such as the Ising model, the dimer model, the resistor network and the spanning tree model.
- Published on 11 July 2017
Lifetime simulation of biological populations reveals dramatic population fluctuations before extinction
Populations of endangered species reach a critical point in their life where they either survive or evolve towards extinction. Therefore, efforts to predict and even prevent the extinction of biological species require a thorough understanding of the underlying mechanisms. In a new study published in EPJ B, Hatem Barghathi and colleagues from Missouri University of Science and Technology, USA, have investigated how environmental disturbance at random times could cause strong fluctuations in the number of individuals in biological populations. This, in turn, makes extinction easier, even for large populations. They found that environmental disorder can lead to a period of slow population increase interrupted by sudden population collapses. These findings also have implications for solving the opposite problem when attempting to predict, control and eradicate population of viruses in epidemics.
- Published on 17 May 2017
Physicists are providing a greater level of autonomy for self-taught systems by combining how they respond to their learning as they evolve
Cars that can drive autonomously have recently made headlines. In the near future, machines that can learn autonomously will become increasingly present in our lives. The secret to efficient learning for these machines is to define an iterative process to map out the evolution of how key aspects of these systems change over time. In a study published in EPJ B, Agustín Bilen and Pablo Kaluza from Universidad Nacional de Cuyo, Mendoza, Argentina show that these smart systems can evolve autonomously to perform a specific and well-defined task over time. Applications range from nanotechnology to biological systems, such as biological signal transduction networks, genetic regulatory networks with adaptive responses, or genetic networks in which the expression level of certain genes in a network oscillates from one state to another.
- Published on 12 April 2017
Physicists prove important constraints for fermion gases with spin population imbalance
Fermions are ubiquitous elementary particles. They span from electrons in metals, to protons and neutrons in nuclei and to quarks at the sub-nuclear level. Further, they possess an intrinsic degree of freedom called spin with only two possible configurations, either up or down. In a new study published in EPJ B, theoretical physicists explore the possibility of separately controlling the up and down spin populations of a group of interacting fermions. Their detailed theory describing the spin population imbalance could be relevant, for instance, to the field of spintronics, which exploits polarised spin populations.
- Published on 17 March 2017
This Colloquium paper published in EPJ B by R. Kutner and J. Masoliver revisits the most significant achievements and future possibilities for continuous-time random walk (CTRW), a versatile and widely applied formalism.
- Published on 07 March 2017
Physicists define a smart way of inducing large-amplitude vibrations in graphene models, which could open the door for novel electronic applications
Graphene, the one-atom-thick material made of carbon atoms, still holds some unexplained qualities, which are important in connection with electronic applications where high-conductivity matters, ranging from smart materials that collectively respond to external stimuli in a coherent, tunable fashion, to light-induced, all-optical networks. Materials like graphene can exhibit a particular type of large-amplitude, stable vibrational modes that are localised, referred to as Discrete Breathers (DBs). The secret to enhancing conductivity by creating DBs lies in creating the external constraints to make atoms within the material oscillate perpendicular to the direction of the graphene sheet. Simulations-based models describing what happens at the atomic level are not straightforward, making it necessary to determine the initial conditions leading to the emergence of DBs. In a new paper published in EPJ B, Elham Barani from the Ferdowsi University of Mashhad, Iran, and colleagues from Russia, Iran and Singapore use a systematic approach to identify the initial conditions that lend themselves to exciting DBs in graphene, ultimately opening the door to understanding the keys to greater conductivity.