Portfolio Decision Model Research Articles

Multi-period portfolio choice under loss aversion with dynamic reference point in serially correlated market

This paper explores a novel multi-period portfolio decision model for loss-averse investors with dynamically adapted reference points in a market with serially correlated returns. We demonstrate that the optimal policy is a piecewise linear function of the deviation between current wealth and reference level, and its slopes are a path-dependent function of the historical returns, in sharp contrast to the constant slopes generated by the simplified model that ignores the diminishing sensitivity and assumes independent returns. This distinctive characteristic significantly departs from the conventional V-shaped pattern of the risky position, leading to a more intricate nonlinear functional mapping. Our study underscores the potential pitfalls of relying on the simplified model and offers valuable insights for investors and practitioners seeking to formulate effective portfolio strategies under realistic market conditions. Furthermore, our simulation analysis indicates that the predictability of returns, coupled with a slight degree of diminishing sensitivity, may amplify the disposition effect. Lastly, we establish that the new policy also effectively addresses the multi-period mean-conditional-value-at-risk (CVaR) portfolio optimization problem in the context of correlated returns, thereby expanding the practical applications of our findings.

An Interval-Valued Fuzzy Portfolio Decision Model with Entropy and VaR Constraints

An Interval-Valued Fuzzy Portfolio Decision Model with Entropy and VaR Constraints

Evaluation of smart long-term care information strategy portfolio decision model: the national healthcare environment in Taiwan.

A globally aging population results in the long-term care of people with chronic illnesses, affecting the living quality of the elderly. Integrating smart technology and long-term care services will enhance and maximize healthcare quality, while planning a smart long-term care information strategy could satisfy the variety of care demands regarding hospitals, home-care institutions, and communities. The evaluation of a smart long-term care information strategy is necessary to develop smart long-term care technology. This study applies a hybrid Multi-Criteria Decision-Making (MCDM) method, which uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with the Analytic Network Process (ANP) for ranking and priority of a smart long-term care information strategy. In addition, this study considers the various resource constraints (budget, network platform cost, training time, labor cost-saving ratio, and information transmission efficiency) into the Zero-one Goal Programming (ZOGP) model to capture the optimal smart long-term care information strategy portfolios. The results of this study indicate that a hybrid MCDM decision model can provide decision-makers with the optimal service platform selection for a smart long-term care information strategy that can maximize information service benefits and allocate constrained resources most efficiently.

National IoMT platform strategy portfolio decision model under the COVID-19 environment: based on the financial and non-financial value view.

The Internet of Medical Things (IoMT) is an emerging technology in the healthcare revolution which provides real-time healthcare information communication and reasonable medical resource allocation. The COVID-19 pandemic has had a significant effect on people’s lives and has affected healthcare capacities. It is important for integrated IoMT platform development to overcome the global pandemic challenges. This study proposed the national IoMT platform strategy portfolio decision-making model from the non-financial (technology, organization, environment) and financial perspectives. As a solution to the decision problem, initially, the decision-making trial and evaluation laboratory (DEMATEL) technology were employed to capture the cause-effect relationship based on the perspectives and criteria obtained from the insight of an expert team. The analytic network process (ANP) and pairwise comparisons were then used to determine the weights for the strategy. Simultaneously, this study incorporated IoMT platform resource limitations into the zero–one goal programming (ZOGP) method to obtain an optimal portfolio selection for IoMT platform strategy planning. The results showed that the integrated MCDM method produced reasonable results for selecting the most appropriate IoMT platform strategy portfolio when considering resource constraints such as system installation costs, consultant fees, infrastructure costs, reduction of medical staff demand, and improvement rates for diagnosis efficiency. The decision-making model of the IoMT platform in this study was conclusive and significantly compelling to aid government decision makers in concentrating their efforts on planning IoMT strategies in response to various pandemic and medical resource allocations.

A fuzzy behavioral portfolio decision model with trapezoidal fuzzy return and aspiration

This paper deals with the stock portfolio selection problem involved with trapezoidal fuzzy number returns and multiple mental accounts. A fuzzy behavioral portfolio decision model is proposed to maximize the possibilistic mean value of portfolio return and ensure the portfolio return of each mental account exceeding the given minimum fuzzy aspiration level with a given probability. Then, some programming models are designed to solve the optimal portfolio strategy. Finally, one numerical example is given to illustrate the effectiveness of the proposed fuzzy behavioral portfolio decision approach.

Comparison and Research on Diversified Portfolios with Several Entropy Measures Based on Different Psychological States

In previous studies, there were few portfolio models involving investors’ psychological states, market ambiguity and entropy. Some entropy can make the model have the effect of diversifying investment, which is very important. This paper mainly studies four kinds of entropy. First, we obtained four definitions of entropy from the literature, and gave the function of fuzzy entropy in different psychological states through strict mathematical proof. Then, we construct a fuzzy portfolio entropy decision model based on the investor’s psychological states, and compared it with the possibilistic mean–variance model. Then we presented a numerical example and compared the five different models established. By comparing the results, we find that: (a) The possibilistic mean–Shannon entropy model solves the problem of the possibility of excessive concentration in the possibilistic mean–variance model, but the dispersion is not enough. Conversely, the possibilistic mean–Yager entropy is over–emphasized due to the definition of its own function, such that it gave an investment pattern of equal weight distribution or approximate average distribution. (b) The results of possibilistic mean–proportional entropy can be said to be the middle status of the portfolios of possibilistic mean–Shannon entropy and possibilistic mean–Yager entropy. This portfolio not only achieves a certain rate of return, but also disperses the risk to some extent. (c) The lines of satisfaction for portfolios derived from different models are approximately U–shaped with the increase in return preference. (d) The possibilistic mean–Shannon entropy model tends to have the highest portfolio satisfaction with the same psychological state of the investor.

A Behavioral Portfolio Decision Model with Triangular Fuzzy Number Return and Investor’s Sentiment

This paper deals with a behavioral portfolio decision problem with triangular fuzzy number return. A fuzzy sentimental mean model for behavioral portfolio decision is proposed by taking into account investor’s sentiment and multiple mental accounts. The presented behavioral portfolio decision model maximizes the fuzzy sentimental mean value of portfolio return and ensures the portfolio return of each mental account exceeding the given minimum triangular fuzzy number level with a given possibility degree. Then, multiple programming models are designed to solve the optimal behavioral portfolio strategy. Finally, a numerical example is given to illustrate the validity of the proposed approach.

A Behavioral Portfolio Model with Interval Return and Investor’s Sentiment

This paper proposes a behavioral portfolio decision model with interval returns and investor’s sentiment. A sentiment-adjusted mean model for behavioral portfolio selection is presented by taking into account investor’s sentiment return and multiple mental accounts. The proposed behavioral model maximizes the sentimental mean value of portfolio interval return and ensures the portfolio interval return of each mental account exceeding the given minimum return level with a given possibility degree. Then, multiple programming models are designed to solve the optimal behavioral portfolio strategy. Finally, a numerical example is given to illustrate the validity of the proposed approach.

Information-theoretic portfolio decision model for optimal flood management

The increasing impact of flooding urges more effective flood management strategies to guarantee sustainable ecosystem development. Recent catastrophes underline the importance of avoiding local flood management, but characterizing large scale basin wide approaches for systemic flood risk management. Here we introduce an information-theoretic Portfolio Decision Model (iPDM) for the optimization of a systemic ecosystem value at the basin scale by evaluating all potential flood risk mitigation plans. iPDM calculates the ecosystem value predicted by all feasible combinations of flood control structures (FCS) considering environmental, social and economical asset criteria. A multi-criteria decision analytical model evaluates the benefits of all FCS portfolios at the basin scale weighted by stakeholder preferences for assets’ criteria as ecosystem services. The risk model is based on a maximum entropy model (MaxEnt) that predicts the flood susceptibility, the risk of floods based on the exceedance probability distribution, and its most important drivers. Information theoretic global sensitivity and uncertainty analysis is used to select the simplest and most accurate model based on a flood return period. A stochastic optimization algorithm optimizes the ecosystem value constrained to the budget available and provides Pareto frontiers of optimal FCS plans for any budget level. Pareto optimal solutions maximize FCS diversity and minimize the criticality of floods manifested by the scaling exponent of the Pareto distribution of flood size that links management and hydrogeomorphological patterns. The proposed model is tested on the 17,000 km2 Tiber river basin in Italy. iPDM allows stakeholders to identify optimal FCS plans in river basins for a comprehensive evaluation of flood effects under future ecosystem trajectories.

Time-consistent and self-coordination strategies for multi-period mean-Conditional Value-at-Risk portfolio selection

The multi-period mean-Conditional Value-at-Risk (mean-CVaR) portfolio decision model is prone to time inconsistency problems that drive CVaR investors away from the pre-committed portfolio strategy, although this strategy is regarded as the global optimal strategy at the initial time point. In the existing literature, authors have proposed time-consistent and self-coordination strategies to solve the time inconsistency issue arising from other sequential decision problems. However, these strategies are rarely studied under the multi-period mean-CVaR portfolio decision framework. This work fills in these gaps by providing both computationally tractable methods and analytical solutions for these strategies. The revealed time-consistent strategy is a piecewise linear function of the wealth level, wherein the other parameters can be computed by solving a series of mixed-integer programming problems off line. The self-coordination strategy can be formulated as a convex program with a quadratic constraint. We also prove that the pre-committed strategy and the time-consistent strategy are the extreme cases of the self-coordination strategy. Furthermore, we extend our main findings to a regime-switching market setting.

An optimization portfolio decision model of life cycle activity-based costing with carbon footprint constraints for hybrid green power strategies

A stable electricity supply is indispensable for both economic development and residential life. Social economic growth has caused a significant deficiency of conventional energy; hence, reducing the use of energy and developing new electricity sources are topics that have attracted wide attention from both academia and industries. In a green electric power system, reasonable costs and benefit controls, as well as carbon footprint computations, are considered to be the key barriers encountered. This study focuses on the total cost, feed-in tariff price, and carbon footprint of a power system, and proposes a 0–1 mixed integer linear programming (MILP) decision model for achieving an optimization portfolio of green electric power systems, using activity-based cost (ABC) and lifecycle assessment (LCA) approaches. The major contributions of this study are, as follows: (1) the integrated model can help green power suppliers to more accurately understand how to allocate resources and funding for energy-saving activities to each green electric power system through appropriate cost drivers; (2) the proposed model provides cost and benefit analysis information, which will assist management in planning clean energy production systems; and (3) the obtained portfolio shows that the maximum profits for green power planning contribute to the development of a national energy policy in Taiwan.

A practical expected-value-approach model to assess the relevant procurement costs

As an improvement to a series of expected value approach models, this study presents a supplier portfolio decision model, rather than a number of suppliers decision model. The model assesses risks as costs, considers types of heterogeneity in a procurement process and links the assessed terms in the programming model with accounting concepts. The model is then confirmed by using a procurement decision case with real data. The analysis has several implications for modelling, application and practice. In the light of the counterexamples shown, some thoughts pertaining to procurement decisions in supply management are reconsidered.

Application of Artificial Bee Colony Algorithm on CVaR Portfolio Optimization

Portfolio decision model based on conditional value at risk (CVaR) is a NP hard optimization. It is di‐cult to optimize the portfolio model using traditional optimization algorithm. Artiflcial bee colony algorithm (ABC) is a new heuristic intelligent algorithm. It does not need more information of the problem, such as continuity and convexity. It only needs to flnd the feasible solution to the optimization problem. So we use the artiflcial bee colony algorithm to solve the CVaR portfolio optimization model, and through data analysis, we can prove that the method is efiective and practical.

Portfolio Decision Model based on the Strategic Adjustment Capacity: A Bionic Perspective on Bird Predation and Firm Competition

Portfolio Decision Model based on the Strategic Adjustment Capacity: A Bionic Perspective on Bird Predation and Firm Competition

Study on Application of Markowitz’s Portfolio Selection Theory in Overseas Petroleum Venture Investment Decision

Markowitz’s portfolio selection theory was applied in overseas petroleum venture capital investment, the mean of each project’s AT cash was used as the portfolio’s return, the fluctuate rate return of project was used to reflect portfolio’s risk, the combination of minimum risk portfolio optimization decision model was established. A variety of risk definition method were explored, efficient frontier under variety of risk-defined were developed, according to different risk preferences, the investment decision-makers can choose optimal portfolio to maximize investment, so as to reduce and avoid undue loss.

A strategic decision model for evaluating college and university sustainability investments

Purpose– The paper aims to examine a strategic decision model and its potential value for assisting academic institutional leaders in addressing climate change by making better informed sustainability investment decisions.Design/methodology/approach– Although the vast majority of engagements have focused on IT project selection and prioritization, it will be demonstrated that there is direct applicability to capital investment decisions related to sustainability. Examples of the results are provided with instructions on how to implement a similar approach for organizations challenged with selection and prioritization of sustainability projects. Finally, the model is assessed critically in terms of challenges and lessons learned from client projects.Findings– The strategic decision model has been found to provide a broader framework for decisions, i.e. both qualitative and quantitative. At the same time, the methodology used with the model engages more stakeholders in the process to help build consensus regarding investment and project decisions. As a portfolio-based model, relative value and risk can be viewed easily to enable focus on what the stakeholders believe to be key projects. This perspective and the functionality of the application enable action to be taken by the decision maker to possibly reduce project risk and/or increase potential value of specific projects.Research limitations/implications– It was determined that additional research, data collection and application development would be necessary if the model were to be used for benchmarking for sustainability planning investment decisions.Practical implications– The paper proposes that colleges and universities consider the use of the portfolio decision model and, where appropriate, implement the model as part of their sustainability program. The anticipated benefits of this action would include: greater involvement of key stakeholders in support of required organizational change, improvement in the quality of investment decisions, increased success of sustainability projects and data which will enable future benchmarking.Originality/value– Given this emergent leading role of universities in championing sustainability efforts, the purpose of this paper is to provide college and university leaders a tool to address the challenges related to investment decisions targeted at sustainability projects, such as environmental footprint reduction in support of achieving sustainability for their respective institutions.

Dynamic Portfolio Decisions with Time-VaryingAsset Returns and Labor Income

This article establishes a basic framework on a portfolio model for a financial fund that manages savings for retirement income and for which the inflows to the fund are asset returns as well as labor income. It suggests a practical way to rebalance portfolios as new information on returns and labor income become available. The rebalancing of the portfolio is guided by estimated low-frequency components of asset returns and labor income. For the actual saving and asset allocation decisions, the author uses a new numerical procedure as guidance. First, low-frequency components for asset returns and labor income are estimated through spectral analysis. Second, after filtering out the low-frequency components of actual U.S. time-series data on asset returns and labor income data, the procedure is used to solve for dynamic saving and asset allocation decisions. Third, along the line of Campbell and Viceira [CV, 2002], he explores the effects of varying risk aversion and varying time horizon, across investors. Whereas CV, in their solution technique, use a constant consumption–wealth ratio and a constant equity premium, the author allows both to be time varying. The optimal saving decisions, asset allocation, welfare of investors, as well as fate of the wealth of the investors are explored for the actual time-series data. The dynamic portfolio decision model allows for online decisions, as the low-frequency data on asset returns and labor income are available in real time. The method is set up in a way so that it also helps to make retirement fund decisions for various types of investors.

Allocation of public funds to R&D: a portfolio choice-styled decision model and a biotechnology case study

A decision problem—allocating public research and development (R&D) funding—is faced by a planner who has ambiguous knowledge of welfare effects of the various research areas. We model this as a reverse portfolio choice problem faced by a Bayesian decision-maker. Two elements of the planner’s inferential system are developed: a conditional distribution of welfare ‘returns’ on an allocation, given stated preferences of citizens for the different areas, and a minimum risk criterion for re-allocating these funds, given the performance of a status quo level of funding. A case study of Canadian public research funds expended on various applications of agricultural biotechnology is provided. The decision-making methodology can accommodate a variety of collective expenditure and resource allocation problems.

Collateral in Monetary Policy Operations

Abstract We present a portfolio decision model for banks that permits us to estimate the costs associated with the need to collateralise loans from the central bank. This allows us to calibrate the difference between a restrictive collateral eligibility framework for open market operations, such as that applied by the FED, with a more flexible approach such as that of Eurosystem. We also document that there could potentially appear relevant cost differences between the various collateral mobilisation procedures (pooling and earmarking) that currently coexist in the eurozone.

Study on Active Risk Budgeting and Manager's Portfolio Choice

Abstract This article builds portfolio decision model under risk budgeting framework by decomposing active risk budgeting into gross budgets and structural budgets, and respectively, solves the model when the benchmark is efficient and nonefficient, then analyzes in detail the properties of optimal investment decision under the two different conditions. The results show that the efficiency of portfolio decision lies on the efficiency of benchmark completely; when benchmark is nonefficient, structural budgets determine the structure of portfolio, whereas gross budgets determine the degree which is the optimal portfolio deviating from the benchmark; smaller beta in structural budgets actually hedge the benchmark risk effectively, so it conduces to enhance the total return under the same total risk.