A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Weapons equipment portfolios selection based on equipment system contribution rates

Equipment selection is an essential work in the research and development planning of equipment.The scientific and rational development of weapons equipment portfolios is of considerable significance to the optimization of equipment architecture design,the adequate resources allocation,and the joint combat performance.From the system view,this paper proposes a method of weapons equipment portfolios selection(WEPS)based on the contribution rate of weapon systems,providing a new idea for weapon equipment portfolio selection.Firstly,we analyze the WEPS problem and the concept of the contribution rate under the systems background.Secondly,we propose a combat network modeling method for weapon equipment systems based on the function chain.Thirdly,we propose a WEPS method based on the contribution rate,fully considering the correlation relationships between potential weapons and the old weapon systems by the combat network model,under the limitation of capability demands and budget resources,with the objective to maximally increasing the combat ability of weapon systems.Finally,we make a case study with a specific WEPS problem where the whole calculation processes and results are analyzed and exhibited to verify the feasibility and effectiveness of the proposed method model.

System portfolio selection based on GRA method under hesitant fuzzy environment

The hesitant fuzzy set(HFS) is an important tool to deal with uncertain and vague information.In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data;it is usually described by qualitative information and expressed as multiple possible values.We propose a method of equipment system portfolio selection under hesitant fuzzy environment.The hesitant fuzzy element(HFE) is used to describe the index and attribute values of the equipment system.The hesitation degree of HFEs measures the uncertainty of the criterion data of the equipment system.The hesitant fuzzy grey relational analysis(GRA) method is used to evaluate the score of the equipment system, and the improved HFE distance measure is used to fully consider the influence of hesitation degree on the grey correlation degree.Based on the score and hesitation degree of the equipment system,two portfolio selection models of the equipment system and an equipment system portfolio selection case is given to illustrate the application process and effectiveness of the method.

Uncertainty Theory Based Novel Multi-Objective Optimization Technique Using Embedding Theorem with Application to R &D Project Portfolio Selection

This paper introduces a novice solution methodology for multi-objective optimization problems having the coefficients in the form of uncertain variables. The embedding theorem, which establishes that the set of uncertain variables can be embedded into the Banach space C[0, 1] × C[0, 1] isometrically and isomorphically, is developed. Based on this embedding theorem, each objective with uncertain coefficients can be transformed into two objectives with crisp coefficients. The solution of the original m-objectives optimization problem with uncertain coefficients will be obtained by solving the corresponding 2 m-objectives crisp optimization problem. The R & D project portfolio decision deals with future events and opportunities, much of the information required to make portfolio decisions is uncertain. Here parameters like outcome, risk, and cost are considered as uncertain variables and an uncertain bi-objective optimization problem with some useful constraints is developed. The corresponding crisp tetra-objective optimization model is then developed by embedding theorem. The feasibility and effectiveness of the proposed method is verified by a real case study with the consideration that the uncertain variables are triangular in nature.

Service-oriented weapon systems of system portfolio selection method

Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision makers.However,the existing weapon system-of-systems(SoS)is tightly coupled.Because of the diversity and connectivity of mission requirements,it is difficult to describe the direct mapping relationship from the mission to the weapon system.In the latest service-oriented research,the introduction of service modules to build a service-oriented,flexible,and combinable structure is an important trend.This paper proposes a service-oriented weapon system portfolio selection method,by introducing service to serve as an intermediary to connect missions and system selection,and transferring the weapon system selection into the service portfolio selection.Specifically,the relation between the service and the task is described through the service-task mapping matrix;and the relation between the service and the weapon system is constructed through the servicesystem mapping matrix.The service collaboration network to calculate the flexibility and connectivity of each service portfolio is then established.Through multi-objective programming,the optimal service portfolios are generated,which are further decoded into weapon system portfolios.

Portfolio selection: a fuzzy-ANP approach

This study developed specific criteria and a fuzzy analytic network process(FANP)to assess and select portfolios on the Tehran Stock Exchange(TSE).Although the portfolio selection problem has been widely investigated,most studies have focused on income and risk as the main decision-making criteria.However,there are many other important criteria that have been neglected.To fill this gap,first,a literature review was conducted to determine the main criteria for portfolio selection,and a Likert-type questionnaire was then used to finalize a list of criteria.Second,the finalized criteria were applied in an FANP to rank 10 different TSE portfolios.The results indicated that profitability,growth,market,and risk are the most important criteria for portfolio selection.Additionally,portfolios 6,7,2,4,8,1,5,3,9,and 10(A6,A7,A2,A4,A8,A1,A5,A3,A9,and A10)were found to be the best choices.Implications and directions for future research are discussed.

Weapon system portfolio selection based on structural robustness

The system portfolio selection is a fundamental frontier issue in the development planning and demonstration of weapon equipment.The scientific and reasonable development of the weapon system portfolio is of great significance for optimizing the design of equipment architecture,realizing effective resource allocation,and increasing the campaign effectiveness of integrated joint operations.From the perspective of system-ofsystems,this paper proposes a unified framework called structure-oriented weapon system portfolio selection(SWSPS)to solve the weapon system portfolio selection problem based on structural invulnerability.First,the types of equipment and the relationship between the equipment are sorted out based on the operation loop theory,and a heterogeneous combat network model of the weapon equipment system is established by abstracting the equipment and their relationships into different types of nodes and edges respectively.Then,based on the combat network model,the operation loop comprehensive evaluation index(OLCEI)is introduced to quantitatively describe the structural robustness of the combat network.Next,a weapon system combination selection model is established with the goal of maximizing the operation loop comprehensive evaluation index within the constraints of capability requirements and budget limitations.Finally,our proposed SWSPS is demonstrated through a case study of an armored infantry battalion.The results show that our proposed SWSPS can achieve excellent performance in solving the weapon system portfolio selection problem,which yields many meaningful insights and guidance to the future equipment development planning.

Scenario-based approach for project portfolio selection in army engineering and manufacturing development

The decisions concerning portfolio selection for army engineering and manufacturing development projects determine the benefit of those projects to the country concerned.Projects are typically selected based on ex ante estimates of future return values,which are usually difficult to specify or only generated after project launch.A scenario-based approach is presented here to address the problem of selecting a project portfolio under incomplete scenario information and interdependency constraints.In the first stage,the relevant dominance concepts of scenario analysis are studied to handle the incomplete information.Then,a scenario-based programming approach is proposed to handle the interdependencies to obtain the projects,whose return values are multi-criteria with interval data.Finally,an illustrative example of army engineering and manufacturing development shows the feasibility and advantages of the scenario-based multi-objective programming approach.

Weapons system portfolio selection based on the contribution rate evaluation of system of systems

The weapons system portfolio selection problem arises at the equipment demonstration stage and deals with the military application requirements.Further,the contribution rate of the system is one of the important indicators to evaluate the role of a system,which can facilitate the weapons system portfolio selection.Therefore,combining the system contribution rate with system portfolio selection is the focus of this study.It also focuses on calculating the contribution rates of multiple equipment systems with various types of capabilities.The contribution rate is measured by establishing a hierarchical multi-criteria value model from three dimensions.Based on the value model,the feasible portfolios are developed under certain cost constraints and the optimal weapons system portfolios are obtained by using the classification optimization selection strategy.Finally,an illustrative example is presented to verify the feasibility of the proposed model.

Optimal stopping investment in a logarithmic utility-based portfolio selection problem

Background:In this paper,we study the right time for an investor to stop the investment over a given investment horizon so as to obtain as close to the highest possible wealth as possible,according to a Logarithmic utility-maximization objective involving the portfolio in the drift and volatility terms.The problem is formulated as an optimal stopping problem,although it is non-standard in the sense that the maximum wealth involved is not adapted to the information generated over time.Methods:By delicate stochastic analysis,the problem is converted to a standard optimal stopping one involving adapted processes.Results:Numerical examples shed light on the efficiency of the theoretical results.Conclusion:Our investment problem,which includes the portfolio in the drift and volatility terms of the dynamic systems,makes the problem including multi-dimensional financial assets more realistic and meaningful.

Semi entropy of uncertain random variables and its application to portfolio selection

Semi entropy is a measure to characterize the indeterminacy of the uncertain random variable considering the values of the uncertain random variable which are lower than the mean.As important roles of semi entropy in finance,this paper presents the concept of semi entropy for uncertain random variables.In order to compute semi entropy for uncertain random variables,Monte-Carlo approach is provided.As an application of semi entropy,portfolio selection problems are optimized based on mean-semi entropy mode.

Portfolio Selection Model with Derivative Securities

Traditional portfolio theory assumes that the return rate of portfolio follows normality. However, this assumption is not true when derivative assets are incorporated. In this paper a portfolio selection model is developed based on utility function which can capture asymmetries in random variable distributions. Other realistic conditions are also considered, such as liabilities and integer decision variables. Since the resulting model is a complex mixed integer nonlinear programming problem, simulated annealing algorithm is applied for its solution. A numerical example is given and sensitivity analysis is conducted for the model.

Optimal Portfolio Selection of Wind Power Plants Using a Stochastic Risk-Averse Optimization Model, Considering the Wind Complementarity of the Sites and a Budget Constraint

This work focuses on the best financial resources allocation to define a wind power plant portfolio, considering a set of feasible sites. To accomplish the problem formulation and solution, the first step was to establish a long-term wind series reconstruction methodology for generating scenarios of wind energy, applying it to study five different locations of the Brazilian territory. Secondly, a risk-averse stochastic optimization model was implemented and used to define the optimal wind power plant selection that maximizes the portfolio financial results, considering an investment budget constraint. In a sequence, a case study was developed to illustrate a practical situation of applying the methodology to the portfolio selection problem, considering five wind power plants options. The case study was supported by the proposed optimization model, using the scenarios of generation created by the reconstruction methodology. The obtained results show the model performance in terms of defining the best financial resources allocation considering the effect of the complementarity between sites, making it feasible to select the optimal set of wind power plants, characterizing a wind plant optimal portfolio that takes into account the budget constraint. The adopted methodology makes it possible to realize that the diversification of the portfolio depends on the investor risk aversion. Although applied to the Brazilian case, this model can be customized to solve a similar problem worldwide.

A Novel Momentum-Based Measure for Online Portfolio Algorithm

In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-the-winner strategy and to evaluate the significance of this effect, a novel measure of risk asset price momentum trend is introduced for online investment portfolio research. Firstly, a novel approach is introduced to quantify the momentum trend effect, which is determined by the product of the slope of the linear regression model and the absolute value of the linear correlation coefficient. Secondly, a new investment portfolio optimization problem is established based on the prediction of future returns. Thirdly, the Lagrange multiplier method is used to obtain the analytical solution of the optimization model, and the soft projection optimization algorithm is used to map the analytical solution to obtain the investment portfolio of the model. Finally, experiments are conducted on five benchmark datasets and compared with popular investment portfolio algorithms. The empirical findings indicate that the algorithm we are introduced is capable of generating higher investment returns, thereby establishing its efficacy for the management of the online investment portfolios.

A Comprehensive Price Prediction System Based on Inverse Multiquadrics Radial Basis Function for Portfolio Selection

Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction. Here, we propose a comprehensive price prediction (CPP) system based on inverse multiquadrics (IMQ) radial basis function. First, the novel radial basis function (RBF) system based on IMQ function rather than traditional Gaussian (GA) function is proposed and centers on multiple price prediction strategies, aiming at improving the efficiency and robustness of price prediction. Under the novel RBF system, we then create a portfolio update strategy based on kernel and trace operator. To assess the system performance, extensive experiments are performed based on 4 data sets from different real-world financial markets. Interestingly, the experimental results reveal that the novel RBF system effectively realizes the integration of different strategies and CPP system outperforms other systems in investing performance and risk control, even considering a certain degree of transaction costs. Besides, CPP can calculate quickly, making it applicable for large-scale and time-limited financial market.

Dominance-Based Rough Set Approach in Selection of Portfolio of Sustainable Development Projects

In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFDC needs a tool for decision support to select the projects that are proposed by the contractors and partners of its territory. In decision making, a balanced set of 22 indicators is considered. These indicators derive from five perspectives: economic, social, demographic, health and wellness. The DRSA proposal is suitable for the data processing with multiple indicators providing on many examples to infer decision rules related to the preference model. In this paper we show that decision rules developed with the use of rough set theory allow us to simplify the process of selecting a portfolio for sustainable development by reducing a number of redundant indicators and identifying the critical values of selected indicators.

Chemical Selectivity of Laser Ion Source for on-Line Mass Separator

Thulium(Tm)atoms are resonantly ionized in a hot tube by stepwise excitations us-ing three dye lasers pumped by a series of copper vapor pulsed at a 10 kHz rate.The chemicalselectivity of the laser ion source is measured as a function of temperature of the tubes made ofTa,Nb-Zr and TaC.The chemical selectivity rises from 50 to 10000 with decreasing tube temp-erature and strongly depends on the tube material.A chemical selectivity of about 10000 withhigh efficiencies is obtained with the Nb-Zr and TaC tubes.Such a laser ion source can be usedin on-line mass separator to obtain isobarically pure ion beams.

Expanded models of the project portfolio selection problem with learning effect

This research develops two new models for project portfolio selection, in which the candidate projects are composed of multiple repetitive units. To reflect some real situations, the learning effect is considered in the project portfolio selection problem for the first time. The mathematical representations of the relationship between learning experience and investment cost are provided. One numerical example under different scenarios is demonstrated and the impact of considering learning effect is then discussed.

Forward robust portfolio selection: The binomial case

We introduce a new approach for optimal portfolio choice under model ambiguity by incorporating predictable forward preferences in the framework of Angoshtari et al.[2].The investor reassesses and revises the model ambiguity set incrementally in time while,also,updating his risk preferences forward in time.This dynamic alignment of preferences and ambiguity updating results in time-consistent policies and provides a richer,more accurate learning setting.For each investment period,the investor solves a worst-case portfolio optimization over possible market models,which are represented via a Wasserstein neighborhood centered at a binomial distribution.Duality methods from Gao and Kleywegt[10];Blanchet and Murthy[8]are used to solve the optimization problem over a suitable set of measures,yielding an explicit optimal portfolio in the linear case.We analyze the case of linear and quadratic utilities,and provide numerical results.

考虑投资不确定性的项目组合选择IGDT模型

受政策的改变以及对新兴领域项目投资缺少足够认知的影响,管理者的项目投资行为往往呈现一定的不确定性。针对企业管理者在进行项目组合投资决策时存在一定风险偏好的情况,本文基于信息间隙决策理论(Information Gap Decision Theory,IGDT),引入净现值偏差系数对管理者的风险偏好进行描述,构建了考虑投资不确定性的项目组合选择新模型。采用包络约束对投资金额不确定性进行描述,给出风险规避与机会寻求两种策略下的项目组合选择IGDT优化模型,并将其转化成单层规划模型进行求解。通过算例仿真,验证了模型的有效性。结果表明:IGDT优化模型不仅满足了管理者投资的预期净现值要求,而且给出了最优的投资金额与项目选择结果。本文构建模型可以为管理者在项目风险管控和资金管理优化方面提供参考。