Stock index tracking by Pareto efficient genetic algorithm

This paper proposes a heuristic searching approach on construction of a tracking portfolio, which is able to get the average market return and can even outperform some hedge funds that are managed actively. The tracking portfolio is expected to replicate the performance of a benchmark index return with a part of its component stocks while reducing the cost of transaction by limiting the number of rebalancing and unnecessary investment on less influential component stocks. The mathematical model being proposed is based on a hybrid genetic algorithm with a self-adaptive evolving mechanism. In order to enhance the model efficiency, we optimize the original genetic algorithm by applying Pareto efficiency as utility measure and goal programming for the inevitable conflicts of multiple objectives/interests. The proposed approach provides a comprehensive solution to index tracking problem by considering as many practical issues as possible. The constructed portfolio has a satisfactory performance on experiments based on CSI300, FTSE100 and HSI data. The proposed formulation of index tracking is therefore believed to be a good alternative to many current techniques.     

Using investment satisfaction capability index based particle swarm optimization to construct a stock portfolio

The goal of this study is to construct an enhanced process based on the investment satisfied capability index (ISCI). The process is divided into two stages. The first stage is to apply the Process Capability Indices (PCI) for quality management so as to develop a new performance appreciation method. Investors can utilize the ISCI index to rapidly evaluate individual stock performance and then select those stocks which can lead to achieve investment satisfaction. In the second stage, a particle swarm optimization (PSO) algorithm with moving interval windows is applied to find the optimal investment allocation of the stocks in this portfolio. Based on those algorithms we can ensure investment risk control and obtain a more profitable stock investment portfolio.     

Mirror‐based reflection in AmbientTalk

This paper introduces a novel mechanism to perform intercession (a form of reflection) in an object‐oriented programming language with the goal of making the language extensible from within itself. The proposed mechanism builds upon a mirror‐based architecture, leading to a reusable reflective application programming interface that cleanly separates interface from implementation details. However, support for intercession has been limited in contemporary mirror‐based architectures. This is due to the fact that mirror‐based architectures only support reflection explicitly triggered by metaprograms, while intercession requires reflection implicitly triggered by the language interpreter. This work reconciles mirrors with intercession in the context of an actor‐based, object‐oriented programming language named AmbientTalk. We describe this language's full reflective architecture, highlighting its novel mirror‐based approach to reflect upon both objects and concurrently executing actors. Subsequently, we apply AmbientTalk's mirror‐based reflection to implement two language features, which crucially depend on intercession, to wit future‐type message passing and leased object references. Copyright © 2008 John Wiley & Sons, Ltd.     

Detail‐Preserving Explicit Mesh Projection and Topology Matching for Particle‐Based Fluids

We propose a new explicit surface tracking approach for particle‐based fluid simulations. Our goal is to advect and update a highly detailed surface, while only computing a coarse simulation. Current explicit surface methods lose surface details when projecting on the isosurface of an implicit function built from particles. Our approach uses a detail‐preserving projection, based on a signed distance field, to prevent the divergence of the explicit surface without losing its initial details. Furthermore, we introduce a novel topology matching stage that corrects the topology of the explicit surface based on the topology of an implicit function. To that end, we introduce an optimization approach to update our explicit mesh signed distance field before remeshing. Our approach is successfully used to preserve the surface details of melting and highly viscous objects, and shown to be stable by handling complex cases involving multiple topological changes. Compared to the computation of a high‐resolution simulation, using our approach with a coarse fluid simulation significantly reduces the computation time and improves the quality of the resulting surface.     

Modelling stock selection using ordered weighted averaging operator

The main objective of stock selection is to select a set of assets in the stock market with high‐expected returns. There are many financial variables that affect the performance of stock firms. This paper proposes a novel linear programming model based on the ordered weighted averaging (OWA) operator for identifying superior stocks without requiring the re‐ordering process. The paper first converts a stock selection problem into a preference voting system by considering two different perspectives: an investor perspective in which the goal is to select stocks with the highest return, and a creditor perspective in which the goal is to maximize the repayment ability. The OWA operator is then used to formulate a linear programming model for identifying superior stocks. The usefulness of the proposed method in this paper is shown through an application in the Tehran stock market.     

Additive‐state‐decomposition‐based tracking control framework for a class of nonminimum phase systems with measurable nonlinearities and unknown disturbances

This paper aims to propose an additive‐state‐decomposition‐based tracking control framework, based on which the output feedback tracking problem is solved for a class of nonminimum phase systems with measurable nonlinearities and unknown disturbances. This framework is to ‘additively’ decompose the output feedback tracking problem into two more tractable problems, namely an output feedback tracking problem for a linear time invariant system and a state feedback stabilization problem for a nonlinear system. Then, one can design a controller for each problem respectively using existing methods, and these two designed controllers are combined together to achieve the original control goal. The main contribution of the paper lies on the introduction of an additive state decomposition scheme and its implementation to mitigate the design difficulty of the output feedback tracking control problem for nonminimum phase nonlinear systems. To demonstrate the effectiveness, an illustrative example is given. Copyright © 2013 John Wiley & Sons, Ltd.     

Sequences with Low‐Discrepancy Blue‐Noise 2‐D Projections

Distributions of samples play a very important role in rendering, affecting variance, bias and aliasing in Monte‐Carlo and Quasi‐Monte Carlo evaluation of the rendering equation. In this paper, we propose an original sampler which inherits many important features of classical low‐discrepancy sequences (LDS): a high degree of uniformity of the achieved distribution of samples, computational efficiency and progressive sampling capability. At the same time, we purposely tailor our sampler in order to improve its spectral characteristics, which in turn play a crucial role in variance reduction, anti‐aliasing and improving visual appearance of rendering. Our sampler can efficiently generate sequences of multidimensional points, whose power spectra approach so‐called Blue‐Noise (BN) spectral property while preserving low discrepancy (LD) in certain 2‐D projections. In our tile‐based approach, we perform permutations on subsets of the original Sobol LDS. In a large space of all possible permutations, we select those which better approach the target BN property, using pair‐correlation statistics. We pre‐calculate such “good” permutations for each possible Sobol pattern, and store them in a lookup table efficiently accessible in runtime. We provide a complete and rigorous proof that such permutations preserve dyadic partitioning and thus the LDS properties of the point set in 2‐D projections. Our construction is computationally efficient, has a relatively low memory footprint and supports adaptive sampling. We validate our method by performing spectral/discrepancy/aliasing analysis of the achieved distributions, and provide variance analysis for several target integrands of theoretical and practical interest.     

HairControl: A Tracking Solution for Directable Hair Simulation

We present a method for adding artistic control to physics‐based hair simulation. Taking as input an animation of a coarse set of guide hairs, we constrain a subsequent higher‐resolution simulation of detail hairs to follow the input motion in a spatially‐averaged sense. The resulting high‐resolution motion adheres to the artistic intent, but is enhanced with detailed deformations and dynamics generated by physics‐based simulation. The technical core of our approach is formed by a set of tracking constraints, requiring the center of mass of a given subset of detail hair to maintain its position relative to a reference point on the corresponding guide hair. As a crucial element of our formulation, we introduce the concept of dynamically‐changing constraint targets that allow reference points to slide along the guide hairs to provide sufficient flexibility for natural deformations. We furthermore propose to regularize the null space of the tracking constraints based on variance minimization, effectively controlling the amount of spread in the hair. We demonstrate the ability of our tracking solver to generate directable yet natural hair motion on a set of targeted experiments and show its application to production‐level animations.     

Compensation for state‐dependent nonlinearity in a modified repetitive control system

This paper presents an estimation and compensation of state‐dependent nonlinearity for a modified repetitive control system. It is based on the equivalent‐input‐disturbance (EID) approach. The nonlinearity is estimated by an EID estimator and compensated by incorporation of the estimate into the repetitive control input. A two‐dimensional model of the EID‐based modified repetitive control system is established that enables the preferential adjustment of control and learning actions by means of 2 tuning parameters. The singular‐value‐decomposition technique and Lyapunov stability theory are used to derive a linear‐matrix‐inequality–based asymptotic stability condition. Exploiting the stability condition and an overall performance evaluation index, a design algorithm is developed. Simulation results for the tracking control of a chuck‐workpiece system show that the method not only compensates state‐dependent nonlinearity but also improves the tracking performance for the periodic reference input, thereby demonstrating the validity of the method.     

OPTIMAL TRACKING CONTROL FOR DISCRETE TIME‐DELAY SYSTEMS WITH PERSISTENT DISTURBANCES

Optimal tracking control (OTC) for discrete time‐delay systems affected by persistent disturbances with quadratic performance indexes is considered. Optimal tracking controller is designed based on a sensitivity approximation approach. By introducing a sensitivity parameter, we transform the original OTC problem into a series of difference equations without time‐advance on time‐delay terms. The obtained OTC law consists of analytic feedback and feedforward terms, and a compensation term, which is the sum of the infinite series of adjoint vectors. The compensation term can be obtained with an iterated formula for the adjoint vectors. A simulation example shows that the approximation approach is effective in tracking the reference input and robust with respect to exogenous persistent disturbances.     

A large-scale goal programming model-based decision support for formulating global financing strategy

A large scale goal programming (GP) model-based decision support system (DSS) is developed to support the formulation of a global financing strategy for a multinational corporation (MNC). The GP model-based DSS provides a major technological breakthrough by presenting a solution to the critical issues in global financial planning that is an improvement over existing approaches. The DSS allows the financial managers of an MNC to choose an appropriate global financing strategy to satisfice multiple, and often conflicting, management goals and provides a tool for analyzing the trade-offs among financing costs, foreign exchange risks, political risks, capital structure goal, and agency costs.     

Development of a decision support system for supplier evaluation and order allocation

This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.     

Tax impact on multi-stage mean-variance portfolio allocation

We investigate the sensitivity to tax change of multi‐stage portfolio allocation over a discrete time investment horizon. Special taxation rules within wrappers grouped a number of risky assets are integrated with multi‐stage linear or quadratic stochastic programming in the mean‐variance framework. The uncertainty on the returns of assets is specified as a scenario tree generated by a simulation‐based approach. We adjust different values on capital gains tax under different asset bounds and risk levels. The tax impact in the yearly reallocation of the investments for a typical case with an annual fixed withdrawal that utilizes completely the option of taper relief is also explored. Our computational results show that taxes, combined with other effects such as risk and investment upper bounds, have a significant performance impact on portfolio allocation as well as diversification over wrappers. Yet, investment strategies can be made robust to changes in taxation.     

A decision support system for fisheries management using operations research and systems science approach

In this paper, we present a general framework, using a systems science approach, for developing a decision support system (DSS) for fisheries management. Decision support systems are quantitative tools for managers to evaluate outcomes of their policies prior to implementation. Our fishery model considers multiple stocks and fisheries simultaneously in balancing catch among targeted and protected fish abundances. Since in the Northeastern US multispecies fishery the distribution of abundance, catch-per-unit-effort and bycatch vary geographically, we focus on a spatial management approach to address their spatial variability. The core component of this DSS applies operations research techniques of simulation and optimization to determine the optimal inter-annual and intra-annual fishing plans in terms of fishing efforts in each sub-area and the time period. The result is the recommended amount to catch from each fish species at each sub-area at any time period so that while management objectives for sustainability of fish stocks are satisfied, the value of landings is maximized. The graphical user interface of the proposed DSS helps users to define inputs, to set constraints and sub-area boundaries, and to visualize the outcomes.     

Fuzzy Bayesian system reliability assessment based on prior two‐parameter exponential distribution under different loss functions

The fuzzy Bayesian system reliability assessment based on prior two‐parameter exponential distribution under squared error symmetric loss function and precautionary asymmetric loss function is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. Because the goal of the paper is to obtain fuzzy Bayes point estimators of system reliability assessment, prior distributions of location‐scale family has been changed to scale family with change variable. On the other hand, also the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability have been provided. In order to achieve this purpose, we transform the original problem into a non‐linear programming problem. This non‐linear programming problem is then divided into four sub‐problems for the purpose of simplifying computation. Finally, the sub‐problems can be solved by using any commercial optimizers, e.g. GAMS or LINGO. Copyright © 2010 John Wiley & Sons, Ltd.     

Observer‐based tracking control for switched stochastic systems based on a hybrid 2‐D model

This paper is concerned with the observer‐based output tracking problem for a class of linear switched stochastic systems with time delay and disturbance by using repetitive control approach. More precisely, a two‐dimensional hybrid model is incorporated to obtain and optimize the repetitive controller. In particular, the repetitive controller is used to improve the tracking performance through its continuous learning actions. In addition, an equivalent‐input‐disturbance estimator is incorporated into the repetitive control design approach to reduce the effect of the external disturbances. The main aim of the control design is to track the periodic reference signal with the measured output of the system under consideration even in the presence of an unknown bounded disturbance. By constructing a suitable Lyapunov‐Krasovskii functional and using average dwell time approach and Jensen inequality, sufficient conditions are obtained in terms of linear matrix inequalities to guarantee the mean‐square exponential stability of the considered system. Eventually, a numerical example is provided to demonstrate the effectiveness of the developed method.     

Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles

This paper studies the problem of stabilizing reference trajectories (also called as the trajectory tracking problem) for underactuated marine vehicles under predefined tracking error constraints. The boundary functions of the predefined constraints are asymmetric and time‐varying. The time‐varying boundary functions allow us to quantify prescribed performance of tracking errors on both transient and steady‐state stages. To overcome difficulties raised by underactuation and nonzero off‐diagonal terms in the system matrices, we develop a novel transverse function control approach to introduce an additional control input in backstepping procedure. This approach provides practical stabilization of any smooth reference trajectory, whether this trajectory is feasible or not. By practical stabilization, we mean that the tracking errors of vehicle position and orientation converge to a small neighborhood of zero. With the introduction of an error transformation function, we construct an inverse‐hyperbolic‐tangent‐like barrier Lyapunov function to show practical stability of the closed‐loop systems with prescribed transient and steady‐state performances. To deal with unmodeled dynamic uncertainties and external disturbances, we employ neural network (NN) approximators to estimate uncertain dynamics and present disturbance observers to estimate unknown disturbances. Subsequently, we develop adaptive control, based on NN approximators and disturbance estimates, that guarantees the prescribed performance of tracking errors during the transient stage of on‐line NN weight adaptations and disturbance estimates. Simulation results show the performance of the proposed tracking control.     

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e‐service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back‐propagation neural network (BPNN)‐based forex forecasting subsystem and Web‐based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e‐service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.     

A two-stage linear goal programming approach to eliciting interval weights from additive interval fuzzy preference relations

This article presents a linear goal programming framework to obtain normalized interval weights from interval fuzzy preference relations (IFPRs). A parameterized transformation equation is put forward to convert a normalized interval weight vector into IFPRs with additive consistency. Based on a linearization approximate relation of the transformation equation, a two-stage linear goal programming approach is developed to elicit interval weights and determine an appropriate parameter value from an additive IFPR. The first stage devises a linear goal programming model to generate optimal interval weight vectors by minimizing the absolute deviation between sides of the parameterized linearization approximate relation. The second stage aims to find a benchmark among the optimal solutions derived from the previous stage by minimizing the absolute deviation between the parameter and 1. The obtained benchmark is the closest to the original IFPR and can sufficiently reflect uncertainty of original judgments. A procedure is further proposed for solving group decision making problems with IFPRs. Two numerical examples including a comparative study with existing approaches are provided to illustrate validity and practicality of the proposed model.     

Adaptive tracking control for a class of uncertain pure‐feedback systems

This paper concerns the global output tracking for uncertain pure‐feedback systems. By adding an integrator, the original systems with nonaffine‐in‐control are transformed into the augmented affine‐in‐control systems. Through a novel change of coordinates, an adaptive control scheme is proposed via backstepping approach, which eliminates the need of overparametrization. It is proven that the proposed control scheme is sufficient to ensure the global asymptotic tracking, as well as the boundedness of all the closed‐loop signals. An illustrative example is provided to demonstrate the feasibility of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.