Structure of local interactions in complex financial dynamics.
Ontology highlight
ABSTRACT:
SUBMITTER: Jiang XF
PROVIDER: S-EPMC4060508 | biostudies-other | 2014
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks. In this paper, we design a method of network reconstruction for the financial market by using the insights from machine learning tool. To do so, we analyze the time series of financial indices of S&P 500 around some financial crises from 1998 to 2012 by using feature ranking approach where we use the returns of stocks in a certain day to predict the feature ranks of the next day. We use two different feature ranking approaches-Random Forest and Gradient Boosting-to rank the importance of each node for predicting the returns of each other node, which produces the feature ranking matrix. To construct threshold network, we assign a threshold which is equal to mean of the feature ranking matrix. The dynamics of network topology in threshold networks constructed by new approach can identify the financial crises covered by the monitored time series. We observe that the most influential companies during global financial crisis were in the sector of energy and financial services while during European debt crisis, the companies are in the communication services. The Shannon entropy is calculated from the feature ranking which is seen to increase over time before market crash. The rise of entropy implies the influences of stocks to each other are becoming equal, can be used as a precursor of market crash. The technique of feature ranking can be an alternative way to infer more accurate network structure for financial market than existing methods, can be used for the development of the market.
| S-EPMC8413283 | biostudies-literature
Project description:Susceptible-Infected-Recovered (SIR) models have long formed the basis for exploring epidemiological dynamics in a range of contexts, including infectious disease spread in human populations. Classic SIR models take a mean-field assumption, such that a susceptible individual has an equal chance of catching the disease from any infected individual in the population. In reality, spatial and social structure will drive most instances of disease transmission. Here we explore the impacts of including spatial structure in a simple SIR model. We combine an approximate mathematical model (using a pair approximation) and stochastic simulations to consider the impact of increasingly local interactions on the epidemic. Our key development is to allow not just extremes of 'local' (neighbour-to-neighbour) or 'global' (random) transmission, but all points in between. We find that even medium degrees of local interactions produce epidemics highly similar to those with entirely global interactions, and only once interactions are predominantly local do epidemics become substantially lower and later. We also show how intervention strategies to impose local interactions on a population must be introduced early if significant impacts are to be seen.
| S-EPMC8589636 | biostudies-literature
Project description:What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correlation functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discovered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns. Further, we introduce an agent-based model with a novel mechanism, that is, the asymmetric trading preference in volatile and stable markets, to understand the microscopic origin of the volatility-return correlation nonlocal in time.
| S-EPMC4344208 | biostudies-literature
Project description:The hidden nature of causality is a puzzling, yet critical notion for effective decision-making. Financial markets are characterized by fluctuating interdependencies which seldom give rise to emergent phenomena such as bubbles or crashes. In this paper, we propose a method based on symbolic dynamics, which probes beneath the surface of abstract causality and unveils the nature of causal interactions. Our method allows distinction between positive and negative interdependencies as well as a hybrid form that we refer to as "dark causality." We propose an algorithm which is validated by models of a priori defined causal interaction. Then, we test our method on asset pairs and on a network of sovereign credit default swaps (CDS). Our findings suggest that dark causality dominates the sovereign CDS network, indicating interdependencies which require caution from an investor's perspective.
| S-EPMC6561208 | biostudies-literature
Project description:The dynamics of probe molecules is commonly used to investigate the structural dynamics of room-temperature ionic liquids; however, the extent to which this dynamics reflects the dynamics of the ionic liquids or is probe specific has remained debated. Here, we explore to what extent the vibrational and rotational dynamics of the dicyanamide anion, a common ionic liquid anion, correlates with the structural relaxation of ionic liquids. We use polarization-resolved, ultrafast infrared spectroscopy to probe the temperature- and probe-concentration-dependent dynamics of samples with small amounts of 1-ethyl-3-methylimidazolium ([emim]+) dicyanamide ([DCA]-) dissolved in four [emim]+-based ionic liquids with tetrafluoroborate ([BF4]-), bis(trifluoromethylsulfonyl)imide ([NTf2]-), ethylsufate ([EtSO4]-), and triflate ([OTf]-) as anions. The transient spectra after broad-band excitation at 2000-2300 cm-1, resonant with the symmetric and antisymmetric C?N stretching vibrations, initially contain oscillatory signatures due to the vibrational coherence between both modes. Vibrational population relaxation occurs on two distinct time scales, ?6-7 and ?15-20 ps. The vibrational dynamics is rather insensitive to the details of the ionic liquid anion and temperature, except for the slow vibrational relaxation component. The decay of the excitation anisotropy, a measure of the rotational dynamics of [DCA]-, markedly depends on temperature, and the obtained decay time exhibits an activation energy of ?15-21 kJ/mol. Remarkably, neither the rotation time nor the activation energy can be simply explained by the variation of the macroscopic viscosity. Hence, our results suggest that the dynamics of dicyanamide is only in part representative of the ionic liquid structural dynamics. Rather, the dynamics of the probe anion seems to be determined by the specific interaction of [DCA]- with the ionic liquid's ions for the class of [emim]+-based ionic liquids studied here.
| S-EPMC6398149 | biostudies-literature
Project description:Protein folding codes embodying local interactions including surface and secondary structure propensities and residue-residue contacts are optimized for a set of training proteins by using spin-glass theory. A screening method based on these codes correctly matches the structure of a set of test proteins with proteins of similar topology with 100% accuracy, even with limited sequence similarity between the test proteins and the structural homologs and the absence of any structurally similar proteins in the training set.
| S-EPMC50058 | biostudies-other
Project description:A method for the local refinement of protein structures that targets improvements in local stereochemistry while preserving the overall fold is presented. The method uses force field-based minimization and sampling via molecular dynamics simulations with a modified force field to bring bonds, angles, and torsion angles into an acceptable range for high-resolution protein structures. The method is implemented in the locPREFMD web server and was tested on computational models submitted to CASP11. Using MolProbity scores as the main assessment criterion, the locPREFMD method significantly improves the stereochemical quality of given input models close to the quality expected for experimental structures while maintaining the C? coordinates of the initial model.
| S-EPMC4962792 | biostudies-literature
Project description:The elastic network model (ENM) is a widely used method to study native protein dynamics by normal mode analysis (NMA). In ENM we need information about all pairwise distances, and the distance between contacting atoms is restrained to the native value. Therefore ENM requires O(N2) information to realize its dynamics for a protein consisting of N amino acid residues. To see if (or to what extent) such a large amount of specific structural information is required to realize native protein dynamics, here we introduce a novel model based on only O(N) restraints. This model, named the 'contact number diffusion' model (CND), includes specific distance restraints for only local (along the amino acid sequence) atom pairs, and semi-specific non-local restraints imposed on each atom, rather than atom pairs. The semi-specific non-local restraints are defined in terms of the non-local contact numbers of atoms. The CND model exhibits the dynamic characteristics comparable to ENM and more correlated with the explicit-solvent molecular dynamics simulation than ENM. Moreover, unrealistic surface fluctuations often observed in ENM were suppressed in CND. On the other hand, in some ligand-bound structures CND showed larger fluctuations of buried protein atoms interacting with the ligand compared to ENM. In addition, fluctuations from CND and ENM show comparable correlations with the experimental B-factor. Although there are some indications of the importance of some specific non-local interactions, the semi-specific non-local interactions are mostly sufficient for reproducing the native protein dynamics.
| S-EPMC3953337 | biostudies-literature
Project description:In animal groups, individual decisions are best characterized by probabilistic rules. Furthermore, animals of many species live in small groups. Probabilistic interactions among small numbers of individuals lead to a so-called intrinsic noise at the group level. Theory predicts that the strength of intrinsic noise is not a constant but often depends on the collective state of the group; hence, it is also called a state-dependent noise or a multiplicative noise. Surprisingly, such noise may produce collective order. However, only a few empirical studies on collective behaviour have paid attention to such effects owing to the lack of methods that enable us to connect data with theory. Here, we demonstrate a method to characterize the role of stochasticity directly from high-resolution time-series data of collective dynamics. We do this by employing two well-studied individual-based toy models of collective behaviour. We argue that the group-level noise may encode important information about the underlying processes at the individual scale. In summary, we describe a method that enables us to establish connections between empirical data of animal (or cellular) collectives and the phenomenon of noise-induced states, a field that is otherwise largely limited to the theoretical literature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.
| S-EPMC7423372 | biostudies-literature
Project description:Global financial crises have led to the understanding that classical econometric models are limited in comprehending financial markets in extreme conditions, partially since they disregarded complex interactions within the system. Consequently, in recent years research efforts have been directed towards modeling the structure and dynamics of the underlying networks of financial ecosystems. However, difficulties in acquiring fine-grained empirical financial data, due to regulatory limitations, intellectual property and privacy control, still hinder the application of network analysis to financial markets. In this paper we study the trading of cryptocurrency tokens on top of the Ethereum Blockchain, which is the largest publicly available financial data source that has a granularity of individual trades and users, and which provides a rare opportunity to analyze and model financial behavior in an evolving market from its inception. This quickly developing economy is comprised of tens of thousands of different financial assets with an aggregated valuation of more than 500 Billion USD and typical daily volume of 30 Billion USD, and manifests highly volatile dynamics when viewed using classic market measures. However, by applying network theory methods we demonstrate clear structural properties and converging dynamics, indicating that this ecosystem functions as a single coherent financial market. These results suggest that a better understanding of traditional markets could become possible through the analysis of fine-grained, abundant and publicly available data of cryptomarkets.
| S-EPMC7067835 | biostudies-literature