Framework For Portfolio Selection Research Articles

Behavioral optimization models for multicriteria portfolio selection

In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

Return distribution predictability and its implications for portfolio selection

The inquiries to return predictability are traditionally limited to conditional mean, while literature on portfolio selection is replete with moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regressions and copulas, using quantile regressions to extract information in marginal distributions and copulas to capture dependence structure. A preference function which captures higher moments is proposed for portfolio selection. An empirical application highlights the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.

An integrated multi-objective framework for solving multi-period project selection problems

Investment managers are multi-objective decision-makers (DMs) who make portfolio decisions by maximizing profits and minimizing risks over a multi-period planning horizon. Portfolio decisions are complex multi-objective problems which include both tangible and intangible factors. We propose an integrated multi-objective framework for project portfolio selection with respect to both the profits and risks objectives. The proposed method is based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and an efficient version of the epsilon-constraint method. TOPSIS is used to reduce the Multi-Objective Decision Making (MODM) problem into a bi-objective problem. The efficient epsilon-constraint method is used to generate non-dominated solutions with a pre-defined and arbitrary resolution on the Pareto front of the aforementioned bi-objective problem. The results from the integrated framework proposed in this study are compared with the results from the conventional epsilon-constraint method based on a series of simulated benchmark cases. A sensitivity analysis is performed to study the sensitivity of the relative importance weights of the objective functions in re-generating the Pareto front. The practical application of the proposed framework illustrates the efficacy of the procedures and algorithms.

Hybrid optimization models of portfolio selection involving financial and ethical considerations

In this paper, we propose a comprehensive three-stage multiple criteria decision making framework for portfolio selection based upon financial and ethical criteria simultaneously. It may be noted that the ethical investment movement that began from the USA in 1960s has gained tremendous momentum the world over recently. The growing instances of corporate scams and scandals have made it incumbent upon the investors to consider the quality of governance of corporations and ethicality of their conduct. Indeed, there has been a spate of reforms relating to corporate laws and capital markets all over the world. Also, the investors are becoming conscious of the desirability of ethical evaluation of the assets. The growing influence of institutional investors has reinforced this consciousness. Hence, the research in the area must take cognizance of these developments to construct models that accord due consideration to ethical criteria besides the financial criteria. We use multiple methodologies toward the purpose. Analytical hierarchy process technique is used to obtain the ethical performance score of each asset based upon investor-preferences. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor-ratings on the financial criteria. Three hybrid portfolio optimization models are developed to obtain well diversified financially and ethically viable portfolios. These models have maximization of financial goal as main objective and differ in the way the ethical goal is pursued by the investor. Numerical illustrations are included to demonstrate the suitability of the models for obtaining diversified portfolios that meet the investor’s financial and ethical goals.

A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection

Project selection is a complex decision making process that is influenced by multiple and often conflicting objectives. The complexity of the project selection problem is due to the high number of projects from which a subset (portfolio) has to be chosen. We present a hybrid fuzzy rule-based multi-objective framework for sustainable project portfolio selection. The multiple and conflicting objectives are considered as the input variables in a Fuzzy Rule-Based (FRB) system developed to estimate the overall fitness (suitability) of the potential project portfolios. A hybrid multi-objective framework integrates and synthesizes the results from a data mining model with the results from a Data Envelope Analysis (DEA) model and an Evolutionary Algorithm (EA) to design the structure of the proposed FRB system. The proposed framework simultaneously considers the accuracy maximization and the complexity minimization objectives. A Genetic Based Machine Learning (GBML) method is utilized to design an alternative FRB system for comparison purposes. The proposed framework and the GBML method are used to assess the alternative project portfolios in a real-world financial services institution. The statistical analysis shows the performance dominance of the proposed hybrid framework over the GBML method based on selected accuracy and interoperability measures.

Return Prediction and Portfolio Selection: A Distributional Approach

The inquiries to return predictability are traditionally limited to the first two moments, mean and volatility. Analogously, literature on portfolio selection also stems from a moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regression and copulas, using the quantile approach to extract information in marginal distributions and copulas to capture dependence structure. A nonlinear utility function is proposed for portfolio selection which utilizes the full underlying return distribution. An empirical application to US data highlights not only the predictability of the stock and bond return distributions, but also the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.

A framework for project portfolio selection with risk reduction approach

The ever-increasing penetration of projects necessitates effective management of multiple projects. Project portfolio management (PPM) is one of the modern project management tools that is utilized for strategy implementation in project-based organizations. This technique includes the following processes: Identification, categorization, evaluation, selection, prioritization, portfolio balancing, and authorization. In this paper, strategy implementation is considered the main goal of the organization. In other words, the more the strategy implementation, the more organizational success. Also, we know that one of the key risk objectives of PPM is detecting inappropriate projects. This ensures that the mix of ongoing projects will be the best available project opportunities. In other words, the importance of selection is enormous. In this paper, first, the main efficiency factors are analyzed and presented new definitions and methods to assess each of them. Based on the literature, four main evaluation factors of a portfolio are: 1) strategy fit, 2) single project success, 3) portfolio balance, and 4) interdependence between projects. Then, a framework is presented based on project strategy and Diamond approach concepts. The framework helps the organization to project evaluation and selection accurately. It also can be used for project prioritization. Some instances such as project definition, project objectives and project success criteria are defined in project strategy. Diamond approach is also used to analyze a project in four dimensions: 1) Novelty, 2) Technology, 3) Complexity, and 4) Pace (NTCP). Key words: Project portfolio management, project selection, strategy, project strategy, risk.

Long-Term U.S. Infrastructure Returns and Portfolio Selection

Industry professionals wish to understand the long-term return behaviour and portfolio characteristics of infrastructure investments, however, there is a relatively short history of empirical data. There is also a paucity of research in regards to the application of finance theory and how it relates to infrastructure investments. This research employs a procedure that constructs monthly U.S. infrastructure returns over the long-term from 1927 to 2010. We employ the Carhart (1997) framework and reveal that low market beta and the value risk premia explains U.S. infrastructure returns. The research also finds that some infrastructure indices (ie. the MSCI U.S. Infrastructure index) are undesirable in a portfolio selection framework while others (ie. the MSCI U.S. Broad and Small Utilities indices) exhibit promising return, risk and portfolio diversification benefits. The findings also reveal that U.S. infrastructure index returns suffer from larger extreme left tail-risk over the long-term in comparison to what has been experienced by investors from recent empirical returns. Overall, the findings reveal that the investment merits of U.S. infrastructure are mixed. This research provides a first glimpse of U.S. infrastructure over the long-term and lays the foundation for the assessment of infrastructure in other countries.

Scenario-based portfolio selection of investment projects with incomplete probability and utility information

In the selection of investment projects, it is important to account for exogenous uncertainties (such as macroeconomic developments) which may impact the performance of projects. These uncertainties can be addressed by examining how the projects perform across several scenarios; but it may be difficult to assign well-founded probabilities to such scenarios, or to characterize the decision makers’ risk preferences through a uniquely defined utility function. Motivated by these considerations, we develop a portfolio selection framework which (i) uses set inclusion to capture incomplete information about scenario probabilities and utility functions, (ii) identifies all the non-dominated project portfolios in view of this information, and (iii) offers decision support for rejection and selection of projects. The proposed framework enables interactive decision support processes where the implications of additional probability and utility information or further risk constraints are shown in terms of corresponding decision recommendations.

Portfolio selection based on the mean–VaR efficient frontier

Value-at-Risk (VaR) has become one of the standard measures for assessing risk not only in the financial industry but also for asset allocations of individual investors. The traditional mean–variance framework for portfolio selection should, however, be revised when the investor's concern is the VaR instead of the standard deviation. This is especially true when asset returns are not normal. In this paper, we incorporate VaR in portfolio selection, and we propose a mean–VaR efficient frontier. Due to the two-objective optimization problem that is associated with the mean–VaR framework, an evolutionary multi-objective approach is required to construct the mean–VaR efficient frontier. Specifically, we consider the elitist non-dominated sorting Genetic Algorithm (NSGA-II). From our empirical analysis, we conclude that the risk-averse investor might inefficiently allocate his/her wealth if his/her decision is based on the mean–variance framework.

Commitment Driven Investing and Time Diversification

Contrary to conventional practices, classic works of P. Samuelson and R. Merton demonstrated the rationality of the myopic view of life-cycle portfolio selection - a rational investor should maintain the same portfolio regardless of the investor’s age. Since then, academic and practitioners have endeavored to find a sensible portfolio selection framework that would justify the rationality of evolving portfolios. This paper provides such a framework as well as theoretical justification for the conventional practices - an investor’s portfolio should be rationally expected to evolve as the investor gets older. This framework differs from the classic approach in its primary objective (to fund a specific financial commitment vs. to maximize expected utility) as well as in the recognition that multiple asset allocation decisions should form a Nash equilibrium solution to the problem of funding the commitment.

Multi-Attribute Portfolio Selection with Genetic Optimization Algorithms

The traditional portfolio theory first proposed by Markowitz only provides a solution to capital allocation to a pre-determined set of assets, regardless of asset quality. To remedy this gap, a multi-attribute asset quality analysis, before asset allocation, is proposed. Thus a two-stage multi-attribute portfolio selection framework that considers asset quality, as well as asset allocation, is formulated. For solving the proposed portfolio selection problem, this study applies genetic algorithms for multi-attribute portfolio selection and analysis. In the first stage, i.e. asset quality evaluation, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the asset allocation stage, allocation of capital to individual high-quality assets is optimized using another genetic algorithm based on Markowitz's mean-variance theory. Through the two-stage asset evaluation and allocation process, an optimal portfolio can be determined in the context of considering both multiple asset return attributes and risk exposures. Experimental results reveal that the proposed multi-attribute portfolio selection framework provides a very feasible and useful tool to assist investors in planning their investment strategy and constructing their portfolios.

Towards a Multiple Criteria Decision Making framework for common stock portfolio selection

An integrated multiple criteria methodological framework is proposed to support decisions that concern the construction and selection of common stock portfolios. The proposed framework combines discrete multiple criteria evaluation methods with a non-linear continuous optimisation model. The preferences and experiences of professionals and experts in the field of portfolio management are strongly taken into consideration, through all the stages of the process. The validity of the above methodology is tested through a large scale illustrative application on the stocks that constitute the FTSE-140 index of the Athens Stock Exchange.

A framework of Web-based Decision Support Systems for portfolio selection with OLAP and PVM

In this paper, we provide an integrated framework for portfolio selection, which is adaptable to the needs of financial organizations and individual investors, and as an organized approach of selecting efficient portfolios for investments. We focus our discussion on the implementation of this framework for a Web-based Decision Support System (DSS) based on our prototype named WPSS—A Web-based Portfolio Selection System for Chinese financial markets. In this system, we adopt technologies such as online analytical processing as an add-on tool for analytical purpose, as well as using Parallel Virtual Machine (PVM) to improve overall performance.

Project portfolio selection through decision support

Project portfolio selection is a crucial decision in many organizations, which must make informed decisions on investment, where the appropriate distribution of investment is complex, due to varying levels of risk, resource requirements, and interaction among the proposed projects. In this paper, we discuss the implementation of an organized framework for project portfolio selection through a decision support system (DSS), which we call Project Analysis and Selection System (PASS). We describe the results of laboratory tests undertaken to measure its usability and quality, compared to manual selection processes, in typical portfolio selection problems. We also discuss the potential of PASS in supporting corporate decision making, through exposure this system has received through demonstrations for several companies.

An integrated framework for project portfolio selection

The task of selecting project portfolios is an important and recurring activity in many organizations. There are many techniques available to assist in this process, but no integrated framework for carrying it out. This paper simplifies the project portfolio selection process by developing a framework which separates the work into distinct stages. Each stage accomplishes a particular objective and creates inputs to the next stage. At the same time, users are free to choose the techniques they find the most suitable for each stage, or in some cases to omit or modify a stage if this will simplify and expedite the process. The framework may be implemented in the form of a decision support system, and a prototype system is described which supports many of the related decision making activities.

A Stochastic Convergence Model for Portfolio Selection

Portfolio selection techniques must provide decision makers with a dynamic model framework that incorporates realistic assumptions regarding financial markets, risk preferences, and required portfolio characteristics. Unfortunately, multistage stochastic programming (SP) models for portfolio selection very quickly become intractable as assumptions are relaxed and uncertainty is introduced. In this paper, I present an alternative model framework for portfolio selection, stochastic convergence (SC), that systematically incorporates uncertainty under a realistic assumption set. The optimal portfolio is derived through an iterative procedure, where portfolio plans are evaluated under many possible future scenarios then revised until the model converges to the optimal plan. This approach allows for scenario analysis over all stochastic components, requires no limitation on the structural form of the objective or constraints, and permits evaluation over any length planning horizon while maintaining model tractability by aggregating the scenario tree at each stage in the solution process. Through focused aggregation schemes, the SC approach allows for the implementation of a lower partial variance riskmetric, which is preferred in investment selection. In simulated tests, the SC model, with scenario aggregation, generated portfolios exhibiting performance similar to those generated using the SP model form with no aggregation. Empirical tests using historical fund returns show that a multiperiod SC decision strategy outperforms various benchmarkstrate gies over a long-term test horizon under both asset-liability matching and return maximization frameworks.

A Stochastic Convergence Model for Portfolio Selection

Portfolio selection techniques must provide decision-makers with a dynamic model framework that incorporates realistic assumptions regarding financial markets, risk preferences and required portfolio characteristics. Unfortunately, multi-stage stochastic programming (SP) models for portfolio selection very quickly become intractable as assumptions are relaxed and uncertainty is introduced. In this paper I present an alternative model framework for portfolio selection, stochastic convergence (SC), that systematically incorporates uncertainty under a realistic assumption set. The optimal portfolio is derived through an iterative procedure where portfolio plans are evaluated under many possible future scenarios then revised until the model converges to the optimal plan. This approach allows for scenario analysis over all stochastic components, requires no limitation on the structural form of the objective or constraints, and permits evaluation over any length planning horizon while maintaining model tractability by aggregating the scenario tree at each stage in the solution process. In simulated tests, the SC model, with scenario aggregation, generated portfolios exhibiting performance similar to those generated using the SP model form with no aggregation. Empirical tests using historical fund returns show that a multi-period SC decision strategy outperforms various benchmark strategies over a long-term test horizon.

Reinsurance retention levels for property/liability firms: A managerial portfolio selection framework

This paper presents a managerial portfolio selection model which analyzes the reinsurance decision of the ceding insurer. Alternative goal functions for management are assumed and then comparative statics, as well as illustrative numerical examples are used to develop testable implications concerning the optimal proportional reinsurance retention level for property/liability insurance firms.

A portfolio selection framework for the houseowner

In this paper the portfolio selection decision of the houseowner is considered in the usual two-parameter framework. It is assumed that houseownership may not be a consequence of wealth maximization alone and that houseowners may therefore subject themselves to certain financial constraints in order to own property. It is shown that portfolio selection decisions for houseowners are generally influenced by the relative size of the outstanding bond on the property as well as the relative value of the house. An empirical study over the 1978–1987 period demonstrates how the framework can be used to investigate portfolio selection decisions.