A generalized spin model of financial markets

We reformulate the Cont-Bouchaud model of financial markets in terms of classical “super-spins” where the spin value is a measure of the number of individual traders represented by a portfolio manager of an investment agency. We then extend this simplified model by switching on interactions among the super-spins to model the tendency of agencies getting influenced by the opinion of other managers. We also introduce a fictitious temperature (to model other random influences), and time-dependent local fields to model a slowly changing optimistic or pessimistic bias of traders. We point out close similarities between the price variations in our model with N super-spins and total displacements in an N-step Levy flight. We demonstrate the phenomena of natural and artificially created bubbles and subsequent crashes as well as the occurrence of “fat tails” in the distributions of stock price variations. Received 13 October 1998     

E-R modeling and visualization of large mutual fund data

Existing visualization techniques used by mutual fund managers focus on the active portfolio management strategies. However, an important aspect of mutual funds is to visualize and understand the fund composition observed at specific points in time. Such a visualization will leverage the fund manager with the power to investigate and to capture forecasting differences and future performance. Our research here is to design a new visualization system that has an Entity-Relationship (E-R) model at the heart of the system and reveals not only the aggregate behavior of the mutual funds, but also the behavior of the mutual fund managers who manage these funds. We find some preliminary evidence that mutual funds in the world market represent a strong geographical pattern, and some potential niche markets are now extracted that stands out in the global scenario such as the returns from funds that belong to a specific country. For the analysis of security distribution, it helps to distinguish the most popular securities from other ordinary ones for an increased realization of profit. Also based on our experience with such huge world fund data to achieve a trade-off between time efficiency and graphics quality, we recommend the approach of splatting visualization, which indicates the influence of a particular security in a fund’s portfolio.     

Physics in organizations: A percolating approach

A percolation-based model to describe organizational structures is proposed, and its main characteristics are delineated. The model is applied in the study of a frequent organizational drawback faced by managers, directors, etc.: the 'leadership hindrance'. It is demonstrated that this and other organizational problems are circumvented by the reshaping of the enterprise 'susceptibility profile', conducted by actions of the leader and his/her staff. The interplay between short- and long-range interactions within the organizational structure as the determinants of extensivity and non-extensivity is discussed. The potentialities of the percolation model for a broader range of organizational applications are addressed.     

含有误差校正的小波神经网络交通流量预测

交通流量的准确预测对于高速路管理者进行决策至关重要。建立了小波神经网络(WNN)交通流量预测模型,并通过预测训练误差和测试误差校正预测结果来提高预测精度。首先构建WNN模型对交通流量进行初步预测,然后利用经验模态分解(EMD)和WNN模型对训练误差和测试误差进行预测。分别用训练误差预测值、测试误差预测值和两种误差预测值的加权对流量初步预测结果进行修正得到最终预测值。采用四川省成灌高速路交通流量数据进行了仿真对比实验,仿真结果表明含有误差校正的小波神经网络模型能有效提高交通流量预测精度,并且利用两种误差加权修正模型的预测精度高于利用测试误差的修正模型和利用训练误差的修正模型。     

Inferring Users’ Social Roles with a Multi-Level Graph Neural Network Model

Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.     

Proximal and remote sensing techniques for mapping of soil contamination with heavy metals

Heavy metal soil contamination is a severe environmental problem globally, and its mapping is vital for environmental managers and policymakers to determine its distributions and hotspots. This paper reviewed multiple proximal and remote sensing spectroscopy for convenient and inexpensive method of obtaining soil reflectance spectroscopy or environmental covariates, which can be used for mapping heavy metal soil contamination. Furthermore, spatial prediction using proximal remote-sensed data and environmental covariates was discussed. We suggested that mapping of the spatial distributions of metal species may be important due to the different bioavailabilities and toxicities of various species. The assimilation of multiple proximal/remote-sensed sensors may promote the horizontal and vertical mapping of soil heavy metals. Moreover, combining the advantages of satellite and unmanned aerial vehicle-based hyperspectral imaging systems will facilitate the development of a space–aeronautic incorporation hyperspectral observation technology that can monitor soil environment rapidly and accurately at a large scale.     

Health risk assessment for nanoparticles: A case for using expert judgment

Uncertainties in conventional quantitative risk assessment typically relate to values of parameters in risk models. For many environmental contaminants, there is a lack of sufficient information about multiple components of the risk assessment framework. In such cases, the use of default assumptions and extrapolations to fill in the data gaps is a common practice. Nanoparticle risks, however, pose a new form of risk assessment challenge. Besides a lack of data, there is deep scientific uncertainty regarding every aspect of the risk assessment framework: (a) particle characteristics that may affect toxicity; (b) their fate and transport through the environment; (c) the routes of exposure and the metrics by which exposure ought to be measured; (d) the mechanisms of translocation to different parts of the body; and (e) the mechanisms of toxicity and disease. In each of these areas, there are multiple and competing models and hypotheses. These are not merely parametric uncertainties but uncertainties about the choice of the causal mechanisms themselves and the proper model variables to be used, i.e., structural uncertainties. While these uncertainties exist for PM2.5 as well, risk assessment for PM2.5 has avoided dealing with these issues because of a plethora of epidemiological studies. However, such studies don’t exist for the case of nanoparticles. Even if such studies are done in the future, they will be very specific to a particular type of engineered nanoparticle and not generalizable to other nanoparticles. Therefore, risk assessment for nanoparticles will have to deal with the various uncertainties that were avoided in the case of PM2.5. Consequently, uncertainties in estimating risks due to nanoparticle exposures may be characterized as ‘extreme’. This paper proposes a methodology by which risk analysts can cope with such extreme uncertainty. One way to make these problems analytically tractable is to use expert judgment approaches to study the degree of consensus and/or disagreement between experts on different parts of the exposure–response paradigm. This can be done by eliciting judgments from a wide range of experts on different parts of the risk causal chain. We also use examples to illustrate how studying expert consensus/disagreement helps in research prioritization and budget allocation exercises. The expert elicitation can be repeated over the course of several years, over which time, the state of scientific knowledge will also improve and uncertainties may possibly reduce. Results from expert the elicitation exercise can be used by risk managers or managers of funding agencies as a tool for research prioritization.     

Random matrix theory and fund of funds portfolio optimisation

The proprietary nature of Hedge Fund investing means that it is common practise for managers to release minimal information about their returns. The construction of a fund of hedge funds portfolio requires a correlation matrix which often has to be estimated using a relatively small sample of monthly returns data which induces noise. In this paper, random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using hedge fund returns data. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to distinct groups of strategies that are applied by hedge fund managers. The inverse participation ratio is used to quantify the number of components that participate in each eigenvector. Finally, the correlation matrix is cleaned by separating the noisy part from the non-noisy part of C. This technique is found to greatly reduce the difference between the predicted and realised risk of a portfolio, leading to an improved risk profile for a fund of hedge funds.     

部分道路关闭引起的交通激波特性研究

本文根据实际交通中经常遇到的交通事故或部分道路施工等情况, 建立了部分道路关闭的交通流模型. 采用平均场理论分析和确定性NS元胞自动机规则分别对模型进行解析和数值模拟, 结果表明, 系统存在三种稳定的物理状态:低密度相、激波相和高密度相, 并找到了系统发生相变的临界密度. 理论分析和数值模拟能很好地符合.     

Information management and information technologies: keys to professional and business success.

Personal computers, spreadsheets, decision support software, electronic mail and video disks are just a few of the innovations of information technology which attract the attention of information professionals and managers alike: they are all concerned with the rapidly changing face of information technology and how to cope with a changing competitive environment, personally, and for the benefit of their companies. This paper is the first in a monthly series which tries to illuminate some of the factors and changes which shape our future as professionals and managers. In so doing, it guides and motivates the reader to become "information literate," a prerequisite for personal advancement in an information-based economy. This first paper outlines the relationship between technological innovations, use of information tools and information management and what to consider in order to benefit from the information revolution. It explains the risks of becoming professionally obsolete and alerts the reader to get personally involved to remain or become "information and computer literate."     

Modelling carrier dynamics and small-signal modulation response in quantum-well lasers

This paper discusses models of carrier dynamics and modulation response in quantum-well lasers. We present a detailed carrier dynamics model that takes into account the different roles played by holes and electrons in such quantum-well lasers, the internal Coulomb field resulting from the mutual interaction of electrons and holes, and the effect of that field on the energy bands. We further address the ability to construct a simpler model that in the limited context of small-signal excitations yields results that are reasonably consistent with those of the detailed model. We compare that simple model to other common simple models and demonstrate the significance of identifying the important parameters in a simple model via self-consistent extraction from detailed calculations. We identify the equilibrium ratio of confined carriers to confinement region carriers, the local capture time and the transport time as the crucial parameters of any simple model.     

Spontaneous Magnetization in the Plane

The Arak process is a solvable stochastic process which generates coloured patterns in the plane. Patterns are made up of a variable number of random non-intersecting polygons. We show that the distribution of Arak process states is the Gibbs distribution of its states in thermodynamic equilibrium in the grand canonical ensemble. The sequence of Gibbs distributions forms a new model parameterised by temperature. We prove that there is a phase transition in this model, for some non-zero temperature. We illustrate this conclusion with simulation results. We measure the critical exponents of this off-lattice model and find they are consistent with those of the Ising model in two dimensions.     

Performance of default risk model with barrier option framework and maximum likelihood estimation: Evidence from Taiwan

We investigate the performance of a default risk model based on the barrier option framework with maximum likelihood estimation. We provide empirical validation of the model by showing that implied default barriers are statistically significant for a sample of construction firms in Taiwan over the period 1994-2004. We find that our model dominates the commonly adopted models, Merton model, Z-score model and ZETA model. Moreover, we test the n-year-ahead prediction performance of the model and find evidence that the prediction accuracy of the model improves as the forecast horizon decreases. Finally, we assess the effect of estimated default risk on equity returns and find that default risk is able to explain equity returns and that default risk is a variable worth considering in asset-pricing tests, above and beyond size and book-to-market.     

Diversity-induced resonance in a model for opinion formation

We perform a computational study of a variant of the “train” model for earthquakes [Phys. Rev. A 46, 6288 (1992)], where we assume a static friction that is a stochastic function of position rather than being velocity dependent. The model consists of an array of blocks coupled by springs, with the forces between neighbouring blocks balanced by static friction. We calculate the probability, P(s), of the occurrence of avalanches with a size s or greater, finding that our results are consistent with the phenomenology and also with previous models which exhibit a power law over a wide range. We show that the train model may be mapped onto a stochastic sandpile model and study a variant of the latter for non-spherical grains. We show that, in this case, the model has critical behaviour only for grains with large aspect ratio, as was already shown in experiments with real ricepiles. We also demonstrate a way to introduce randomness in a physically motivated manner into the model.     

A Markov model of financial returns

We address the general problem of how to quantify the kinematics of time series with stationary first moments but having non stationary multifractal long-range correlated second moments. We show that a Markov process is sufficient to model important aspects of the multifractality observed in financial time series and propose a kinematic model of price fluctuations. We test the proposed model by analyzing index closing prices of the New York Stock Exchange and the DEM/USD tick-by-tick exchange rates obtained from Reuters EFX. We show that the model captures the characteristic features observed in actual financial time series, including volatility clustering, time scaling and fat tails in the probability density functions, power-law behavior of volatility correlations and, most importantly, the observed nonuniversal multifractal singularity spectrum. Motivated by our finding of strong agreement between the model and the data, we argue that at least two independent stochastic Gaussian variables are required to adequately model price fluctuations.     

Role of Modified Chaplygin Gas in Bianchi Type-I Universe

We have studied the evolution of a homogeneous, anisotropic universe given by a Bianchi type-I cosmological model with modified Chaplygin gas. We have assumed that the equation of state of this modified model is valid from the radiation era to the ΛCMD model. We have used state-finder parameters in characterizing different phase of the model.     

Sensitivity to Initial Conditions in Self-Organized Critical Systems

We discuss sensitivity to initial conditions in a model for avalanches in granular media displaying self-organized criticality. We show that damage, due to a small perturbation in initial conditions, does not spread. The damage persists in a statistically time-invariant and scale-free form. We argue that the origin of this behavior is the Abelian nature of the model, which generalizes our results to all models with Abelian properties, including the BTW model and the Manna model. An ensemble average of the damage leads to seemingly time dependent damage spreading. Scaling arguments show that this numerical result is due to the time lag before avalanches reach the initial perturbation.     

Crossing the phantom divide in extended Dvali–Gabadadze–Porrati gravity

We propose a phantom crossing Dvali–Gabadadze–Porrati (DGP) model. In our model, the effective equation of state of the DGP gravity crosses the phantom divide line. We demonstrate crossing of the phantom divide does not occur within the framework of the original DGP model or the DGP model developed by Dvali and Turner. By extending their model, we construct a model that realizes crossing of the phantom divide. We find that the smaller the value of the new introduced parameter β is, the older epoch crossing of the phantom divide occurs in. Our model can account for late-time acceleration of the universe without dark energy. We investigate and show the property of Phantom crossing DGP model.     

Reaction-diffusion fronts in periodically layered media

We compute the effective wavefront speeds of reaction-diffusion equations in periodically layered media with coefficients that have small-amplitude oscillations around a uniform mean state. We compare them with the corresponding wavefront speeds in the uniform state. We analyze a one-dimensional model where wave propagation is along the layering direction of the medium and a two-dimensional shear flow model where wave propagation is othogonal to the layering direction. We find that the effective wave speed is smaller in the one-dimensional model and is larger in the two-dimensional model for both bistable cubic and quadratic nonlinearities of the Kolmogorov-Petrovskii-Piskunov form. We derive approximate expressions for the wave speeds in the bistable case.Dedicated to Jerry Percus on the occasion of his 65th birthday.