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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

Multi-Period Telser's Safety-First Portfolio Selection Problem in a Defined Contribution Pension Plan

This paper investigates a multi-period portfolio optimization problem for a defined contribution pension plan with Telser's safety-first criterion.The plan members aim to maximize the expected terminal wealth subject to a constraint that the probability of the terminal wealth falling below a disaster level is less than a pre-determined number called risk control level.By Tchebycheff inequality,Lagrange multiplier technique,the embedding method and Bellman's principle of optimality,the authors obtain the conditions under which the optimal strategy exists and derive the closed-form optimal strategy and value function.Special cases show that the obtained results in this paper can be reduced to those in the classical mean-variance model.Finally,numerical analysis is provided to analyze the effects of the risk control level,the disaster level and the contribution proportion on the disaster probability and the value function.The numerical analysis indicates that the disaster probability in this paper is less than that in the classical mean-variance model on the premise that the value functions are the same in two models.

Continuous-Time Mean-Variance Portfolio Selection Under Non-Markovian Regime-Switching Model with Random Horizon

This paper considers a continuous-time mean-variance portfolio selection with regime-switching and random horizon.Unlike previous works,the dynamic of assets are described by non-Markovian regime-switching models in the sense that all the market parameters are predictable with respect to the filtration generated jointly by Markov chain and Brownian motion.The Markov chain is assumed to be independent of Brownian motion,thus the market is incomplete.The authors formulate this problem as a constrained stochastic linear-quadratic optimal control problem.The authors derive closed-form expressions for both the optimal portfolios and the efficient frontier.All the results are different from those in the problem with fixed time horizon.

AN EXTENDED MODEL FOR PROJECT PORTFOLIO SELECTION WITH PROJECT DIVISIBILITY AND INTERDEPENDENCY

In this paper, we develop an extended model for the project portfolio selection problem over a planning horizon with multiple time periods. The model incorporates the factors of project divisibility and interdependency at the same time for real-life applications. The project divisibility is considered as a strategy, not an unfortunate event as in the literature, in choosing the best execution schedule for the projects, and the classical concept of＂project interdependencies＂ among fully executed projects is then extended to the portions of executed projects. Additional constraints of reinvestment consideration, setup cost, cardinality restriction, precedence relationship and scheduling are also included in the model. For efficient computations, an equivalent mixed integer linear programming representation of the proposed model is derived. Numerical examples under four scenarios are presented to highlight the characteristics of the proposed model. In particular, the positive effects of project divisibility are shown for the first time.

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.

AN UTILITIES BASED APPROACH FOR MULTI-PERIOD DYNAMIC PORTFOLIO SELECTION

This paper proposed a multi-period dynamic optimal portfolio selection model. Assumptions were made to assure the strictness of reasoning. This Approach depicted the developments and changing of the real stock market and is an attempt to remedy some of the deficiencies of recent researches. The model is a standard form of quadratic programming. Furthermore, this paper presented a numerical example in real stock market.

Combinatorial auction algorithm for project portfolio selection and scheduling to maximize the net present value

Scheduling projects at the activity level increases the complexity of decision making of project portfolio selection but also expands the search space to include better project portfolios. An integer programming model is formulated for the project portfolio selection and scheduling problem. An iterative multi-unit combinatorial auction algorithm is proposed to select and schedule project portfolios through a distributed bidding mechanism. Two price update schemes are designed to adopt either a standard or an adaptive Walrasian tatonnement process. Computational tests show that the proposed auction algorithm with the adaptive price update scheme selects and schedules project portfolios effectively and maximizes the total net present value. The price profile generated by the algorithm also provides managerial insights for project managers and helps to manage the scarce resources efficiently.

Time Consistent Multi-period Worst-Case Risk Measure in Robust Portfolio Selection

In this paper,we first construct a time consistent multi-period worst-case risk measure,which measures the dynamic investment risk period-wise from a distributionally robust perspective.Under the usually adopted uncertainty set,we derive the explicit optimal investment strategy for the multi-period robust portfolio selection problem under the multi-period worst-case risk measure.Empirical results demonstrate that the portfolio selection model under the proposed risk measure is a good complement to existing multi-period robust portfolio selection models using the adjustable robust approach.

Optimal Portfolio Selection Strategies under Some Constraints

A portfolio selection problem for any utility function is introduced, where all the market coefficients are random and the wealth process under any admissible trading strategy is not allowed to be below a benchmark wealth process. The problem is completely solved using a decomposition approach. First, the portfolio selection problem is formulated, and its feasibility is characterized. Then, the problem is decomposed to two steps to solve. After a system of equations for a Lagrange multiplier is solved, the portfolio selection problem is derived as the replicating portfolios of contingent claims. Finally, some simulations are demonstrated.

A BRANCH-AND-CUT APPROACH TO PORTFOLIO SELECTION WITH MARGINAL RISK CONTROL IN A LINEAR CONIC PROGRAMMING FRAMEWORK

Marginal risk represents the risk contribution of an individual asset to the risk of the entire portfolio In this paper, we investigate the portfolio selection problem with direct marginal risk control in a linear conic programming framework. ＇The optimization model involved is a nonconvex quadratically constrained quadratic programming （QCQP） problem. We first transform the QCQP problem into a linear conic programming problem, and then approximate the problem by semidefinite programming （SDP） relaxation problems over some subrectangles. In order to improve the lower bounds obtained from the SDP relaxation problems, linear and quadratic polar cuts are introduced for designing a branch-and-cut algorithm, that may yield an e -optimal global solution （with respect to feasibility and optimality） in a finite number of iterations. By exploring the special structure of the SDP relaxation problems, an adaptive branch-and-cut rule is employed to speed up the computation. The proposed algorithm is tested and compared with a known method in the literature for portfolio selection problems with hundreds of assets and tens of marginal risk control constraints.