An Investment Portfolio Management Using Software Agents that Rely on Fuzzy Logic and with Different Decision Arguments Combinations

During the development of the intellectual multi-agent investment management information system （designed for the formation of investor＇s investment decisions）, it was established that there is a lack of both necessary comprehensive researches and analysis to invoke more than one decision-making aspect （argument） for the making of investment decision, and recommendations to combine these diverse parameters. It is possible to find a lot of articles and researches in which investment decisions or investment tactics are decided on the ground of either technical or fundamental analysis, or modeling and on the ground of intellectual calculating technique （for example, fuzzy logic, neural net, genetic programming）, whereas the issues of the coordination of different techniques are not decided at all. To fill this niche, the article offers the decision applied in multi-agent investment management information system which allows to provide rationale for investment decision taking into account four aspects （arguments）, i.e., to form the recommendation to purchase/hold/sell a security paper having evaluated the following four aspects （arguments）： （a） fundamental analysis, （b） technical analysis, （c） experts and analysts＇ recommendations and （d） risk assessment. These aspects （arguments） are chosen taking into account the real most commonly occurring process of investor＇s investment decision-making. The article gives the implementation of aspects （arguments） assessment by four corresponding software agents whose decisions are implemented with a help of fuzzy logic. Besides, the article offers the technique of the unification of these aspects （arguments）. The offered intellectual multi-agent investment management information system can be tested on the internet： www.sprendimutechnologijos.lt/webapp （MADSYS project）.

Competency Evaluation and Promotion for Portfolio Management Based on Organizational Project Management Maturity Model

The objective of this paper is to present an and promote the capabilities of organizational portfolio ment Maturity Model （ OPM3 ）, the process areas of approach to comprehensively and quantitatively evaluate management. Based on the Organizational Project Manage- organizational project portfolio management are identified through the questionnaire survey and further analysis, and five capability levels are put forward and described. Then the methods of Delphi, AHP and multi-layer fuzzy comprehensive evaluation are applied to construct a mod- el of assessment and promotion. Finally, an illustrative example is presented to verify the proposed approach The result objectively and accurately describes the project portfolio management capabilities of the organization, and shows that it is able to provide a theoretical basis for an organization to improve and enhance the project port- folio management

The Impact of Project Portfolio Management on Enterprise Strategic Objectives

In today’s era,with the increase in the number of enterprise innovations,enterprises must adopt project portfolio management for various innovations,select alternative projects from the perspective of enterprise strategy.This paper primarily explores the use of project portfolio management in enterprise project management,hoping to improve the quality of enterprise project management and the utilization efficiency of project portfolio management in enterprise project management.

A New Approach to University IT Project Portfolio Management Based on Multi-Criteria Methods and the COBIT 5 Governance Framework

Project portfolio management is a major challenge for some organizations.In most organizations,there are a large number of projects active at the same time,some not necessarily delivering value or not aligned with their strategic goals.Also universities face a lot of uncertainties when selecting and prioritizing the projects that make up their portfolio.In addition,the achievement of those who are aligned with the strategy of the university becomes a great challenge.So to ensure good project portfolio management,the implementation of selection and prioritization methods and processes becomes important.For the project portfolio management to be effective,it is necessary to establish a structured method adapted to the needs and strategy of the university.In this context,this paper proposes a method for selecting and prioritizing projects within the framework of the portfolio management dedicated to universities,which can promote harmony between the university’s strategy,the needs and the priority objectives for enable better decision-making.This method is based on the processes of the COBIT 5 good practice framework,and on the multi-criteria decision-making methods AHP,TOPSIS and the WSM technique,thus,it proposes seven project selection criteria based on the five axes IT governance strategies and two catalysts derived from COBIT 5 enablers.The evaluation and validation of this method was applied in the portfolio management of the Abdelmalek Essaadi Moroccan University(AUE).The result shows that this proposed method has made it possible to make a better selection and prioritization of the portfolio of projects of Abdelmleek Essaadi University having the most value.

Space Enterprise Strategy Guiding-Based Study on Project Portfolio Management and Flow Optimization

Project portfolio management(PPM) is the centralized management method, process and technology in multiple projects. When multiple projects in the space industry are implemented, it provides an effective methodology to resolve the problems at the same time such as conflicts among models, decrease in design efficiency,and increase in target deviation. Hence, a PPM dedicated to multiple projects management in space enterprise is presented in this paper. Firstly, an analysis of features and contents in space enterprise portfolio management mode is performed by using PPM based on its specific strategic characteristics. Then, the principle and selection methods of PPM are provided as a reference for the future development of an enterprise. Finally, a multiple-level organization architecture including decision making layers, function management layers and project execution layers is proposed so as to adapt to possible changes in the multiple projects and correspond to the strategic development. As a consequence, a perfect matching mechanism to fit the changes in PPM modes is reached. In addition, the flow chart of PPM is designed and optimized by analyzing the implementation procedure of strategic target and project portfolio life-cycle, which is expected to realize the purpose of improving space enterprise management efficiency, project management capacity, innovation development and economic benefits.

IT APPLICATIONS PORTFOLIO MANAGEMENT UNDER BUSINESS AND IMPLEMENTATION UNCERTAINTY

Corporations need to improve business processes in order to enhance velocity and service levels while reducing their processing costs and differentiating themselves in the face of competition. The levitation of importance beyond support roles has raised IT investment decisions to high priority in chief executive officers＇ agendas. Corporate planning groups as well as lines of business are increasingly applying techniques of IT applications portfolio management in a more systematic fashion to improve decision-making and resource-allocation processes. Recent advances in software engineering and IT service delivery methodologies have achieved the logical separation of business functions from implementation. This separation has made a new breed of innovative IT project possible with a new project risk structure; the adjustment of portfolio management techniques is appropriate. We present an integrated portfolio management model so that the corporation can focus on organic growth through sources at both the department and top management levels. The research gives clear advice as to how top management can seek economic growth by selecting an entrepreneurial strategic posture, implying a strong risk-taking propensity. By integrating a risk-return model and risk-tolerance paradigm to cope with today＇s risk structure, overall capabilities can improve the decision process and the corporation＇s performance as well. The application of the integrated technique to a Japanese manufacturing firm is described.

Multistage Stochastic Programming Model for the Portfolio Problem of a Property-Liability Insurance Company

The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod model for its portfolio problem. The model is a multistage stochastic programming which considers transaction costs, cash flow between time periods, and the matching of asset and liability; it does not depend on the assumption for normality of return distribution. Additionally, an investment constraint is added. The numerical example manifests that the multiperiod model can more effectively assist the property-liability insurer to determine the optimal composition of insurance and investment portfolio and outperforms the single period one.

A Robust Method for Ordering Performances of Multi-assets, Based Purely on Their Return Series

This study propose a new robust method to rank the performances of multi-assets （portfolios）, based purely on their return time series. This method makes no assumption on the distributions. Topsoe distance is symmetrized Kullback-Leibler divergence by average of the probabilities. The square root of Topsoe distance is a metric. We extend this metric from probability density functions to real number series on （0, 1 ]. We call it ST-metric. We show the consistency of ST-metric with mean-variance theory and stochastic dominance method of order one and two. We demonstrate the advantages of ST-metric over mean-variance rule and stochastic dominance method of order one and two.

The State Equations Methods for Stochastic Control Problems

The state equations of stochastic control problems,which are controlled stochastic differential equations,are proposed to be discretized by the weak midpoint rule and predictor-corrector methods for the Markov chain approximation approach. Local consistency of the methods are proved.Numerical tests on a simplified Merton's portfolio model show better simulation to feedback control rules by these two methods, as compared with the weak Euler-Maruyama discretisation used by Krawczyk.This suggests a new approach of improving accuracy of approximating Markov chains for stochastic control problems.

ST-metric Estimation of Factor Exposures

Non-parametric methods are treasured in data analysis,particularly in finance.ST-metric is a new concept,introduced by Tulunay(2017).It offers non-parametric methods and a new geometric view to data analysis.In that paper,ST-metric concept has been applied to performance measures of portfolios.In this current paper,we purpose another ST-metric method for finding factor exposures in the five-style-factors model.Here the style factors are value,size,minimum volatility,quality and momentum.The main idea is to find the factor exposures(weights)of the five-factors-model by minimizing the ST-metric between benchmark returns and the constructed factor model returns.We compare ST-metric method with Tracking Error method(TE-method)which is used for factor analysis of major indexes,decomposed into the style factors(tradable via Exchange Traded Funds(ETFs))by Ang et al.(2018).We show that ST-metric method gives better estimation of the factor exposures(weights)than tracking error method,in general,and further how ST-metric values vary with respect to fluctuations.This explains the reason behind the efficiency of the ST-metric method.We support this idea with empirical evidences.

A Quantitative Description of Complex Adaptive System:The Self-Adaptive Mechanism of the Material Purchasing Management System Towards the Changing Environment

This paper demonstrates a new interpretation of the material purchasing management system(MPMS) from the perspective of complex adaptive systems(CAS).Within the framework of CAS,the authors design the self-adaptive mechanism of the MPMS responding to the changing environment,such as the change of the price,by using risk measurement theory,modern portfolio theory(MPT) and the information of the material's modifying priority.As a bottom-up systems view,CAS focuses on the individual level and studies system's overall complexity by analyzing the mutual competition and adaptation among the individuals.This paper demonstrates a quantitative description of CAS by discussing the MPMS which can be viewed as a kind of CAS,and makes numerical simulations of Daqing oilfield MPMS.Compared to the benchmarks,the authors set the simulations show that the self-adaptive mechanism adapts well to the change of the material's market price.Hence,this paper accomplishes a numerical simulation of CAS's quantitative self-adaptive mechanism responding to the environment's change.

A Worst-Case Risk Measure by G-VaR

G-VaR,which is a type of worst-case value-at-risk(VaR),is defined as measuring risk incorporating model uncertainty.Compared with most extant notions of worst-case VaR,G-VaR can be computed using an explicit formula,and can be applied to large portfolios of several hundred dimensions with low computational cost.We also apply G-VaR to robust portfolio optimization,thereby providing a tractable means to facilitate optimal allocations under the condition of market ambiguity.

A Composite Risk Measure Framework for Decision Making Under Uncertainty

In this paper,we present a unified framework for decision making under uncertainty.Our framework is based on the composite of two risk measures,where the inner risk measure accounts for the risk of decision if the exact distribution of uncertain model parameters were given,and the outer risk measure quantifies the risk that occurs when estimating the parameters of distribution.We show that the model is tractable under mild conditions.The framework is a generalization of several existing models,including stochastic programming,robust optimization,distributionally robust optimization.Using this framework,we study a few new models which imply probabilistic guarantees for solutions and yield less conservative results compared to traditional models.Numerical experiments are performed on portfolio selection problems to demonstrate the strength of our models.

The Convergence of Set-Valued Scenario Approach for Downside Risk Minimization

Scenario approach is a widely used tool in portfolio risk management,however,it often runs into dilemma when determining the distribution of asset returns with insufficient information,which will be used to simulate the scenarios.Also the quality of generated scenarios are not guaranteed even when the distribution of asset returns is known exactly.A set-valued scenario approach was proposed by Zhu,et al.(2015)as a possible remedy.As a necessary supplement of the results proposed by Zhu,et al.(2015),this paper theoretically investigates the convergent property of the numerical solution based on the set-valued scenario approach under the condition that the underlying distribution is known.