Framework For Portfolio Selection Research Articles

Framework for Integrated Project Portfolio Selection and Adjustment

Project portfolio frameworks usually present the selection and adjustment phases sequentially, lacking the perspective that these phases can be performed iteratively, allowing feedback looping. This paper aims to narrow this gap by investigating the interactions between selection and adjustment phases, and the effects on project portfolio efficiency. Besides, the effect of the control variables—time, effort, and requirements–is investigated along the project design phase. The research approach was based on simulation with five anecdotal projects and real portfolio data gathered in a case-based approach with ten projects. As a result, an integrated portfolio selection and adjustment framework applying data envelopment analysis (DEA) is presented. The dominant portfolios are determined based on mean-Gini selection to construct an efficient frontier comparing return and risk of each one. The proposed framework enables adjustment by evaluating the impact of control variables via two-stage DEA. The results show that the proposed framework indeed increases the number of suitable portfolios. Furthermore, the framework demonstrates how efficiency is impacted by the control variables of a project.

An optimization–diversification approach to portfolio selection

The classical approaches to optimal portfolio selection call for finding a feasible portfolio that optimizes a risk measure, or a gain measure, or a combination thereof by means of a utility function or of a performance measure. However, the optimization approach tends to amplify the estimation errors on the parameters required by the model, such as expected returns and covariances. For this reason, the Risk Parity model, a novel risk diversification approach to portfolio selection, has been recently theoretically developed and used in practice, mainly for the case of the volatility risk measure. Here we first provide new theoretical results for the Risk Parity approach for general risk measures. Then we propose a novel framework for portfolio selection that combines the diversification and the optimization approaches through the global solution of a hard nonlinear mixed integer or pseudo Boolean problem. For the latter problem we propose an efficient and accurate Multi-Greedy heuristic that extends the classical single-threaded greedy approach to a multiple-threaded setting. Finally, we provide empirical results on real-world data showing that the diversified optimal portfolios are only slightly suboptimal in-sample with respect to optimal portfolios, and generally show improved out-of-sample performance with respect to their purely diversified or purely optimized counterparts.

The evolution of project portfolio selection methods: from incremental to radical innovation

PurposeThe purpose of this paper is to identify how project portfolio selection (PPS) methods have evolved and which approaches are more suitable for radical innovation projects. This paper addressed the following research question: how have the selection approaches evolved to better fit within radical innovation conditions? The current literature offers a number of selection approaches with different and, in some cases, conflicting nature. Therefore, there is a lack of understanding regarding when and how to use these approaches in order to select a specific type of innovation projects (from incremental to more radical ones).Design/methodology/approachGiven the nature of the research question, the authors perform a systematic literature review method and analyze 48 portfolio selection approaches. The authors then classified and characterized these articles in order to identify techniques, tools, required data and types of examined projects, among other aspects.FindingsThe authors identify four key features related to the selection of radical innovation projects: dynamism, interdependency management, uncertainty treatment and required input data. Based on the content analysis, the authors identified that approaches based on different sources and nature of data are more appropriated for uncertain conditions, such as behavioral methods, information gap theory, real options and integrated approaches.Originality/valueThe research provides a comprehensive framework about PPS methods and how they have been evolving over time. This portfolio selection framework considers the particular aspects of incremental and radical innovation projects. The authors hope that the framework contributes to reinvigorating the literature on selection approaches for innovation projects.

(1805-4474)A new framework for high-technology project evaluation and project portfolio selection based on Pythagorean fuzzy WASPAS, MOORA and mathematical modeling

High-technology projects are known as tools that help achieving productive forces through scientific and technological knowledge. These knowledge-based projects are associated with high levels of risks and returns. The process of high-technology project and project portfolio selection has technical complexities and uncertainties. This paper presents a novel two-parted method of high-technology project portfolio selection. In the first part, a new decision-making model under Pythagorean fuzzy set (PFS) uncertainty is introduced that is last aggregation and avoids defuzzification until the last step of the process. In the last step, a new PFS ranking method is used to make crisp and comparable values. Outcomes from this part form the objective function of a new integer programming (IP) of the project portfolio selection. To display the model’s application, data from a real case study of high-technology project evaluation and selection is presented, and the steps of the approach are illustrated in addition to presenting the efficacy of the model.

Portfolio selection through Maslow’s need hierarchy theory

ABSTRACTInspired by Maslow’s need hierarchy theory, we construct a new portfolio selection framework using the bi-level optimization technique in which the lower-level need relates to safety (low risk) while the upper-level need is concerned with self-actualization (high payoff). Specially, we consider a bi-level portfolio selection model using variance and conditional value-at-risk associated with the lower-level need and upper-level need, respectively. Accordingly, we propose a procedure to solve this bi-level optimization problem without the normal distribution assumption. Empirical study on the American stock market and U.K. stock market shows that our new model can determine optimal portfolios with moderate diversification. Out of sample performance also confirms that in our framework investors can obtain higher returns in a safe way compared to models in the traditional framework.

A Multi-Greedy Approach to Optimal Diversified Portfolio Selection

The classical approaches to optimal portfolio selection call for finding a feasible portfolio that optimizes a risk measure, or a gain measure, or a combination thereof by means of a utility function or of a performance measure. However, the optimization approach tends to amplify the estimation errors on the parameters required by the model, such as expected returns and covariances. For this reason, the risk parity model, a novel risk diversification approach to portfolio selection, has been recently theoretically developed and used in practice, mainly for the case of the volatility risk measure. Here we first provide new theoretical results for the risk parity approach for general risk measures. Then we propose a novel framework for portfolio selection that combines the diversification and the optimization approaches through the solution of a hard nonlinear mixed integer or pseudo Boolean problem. For the latter problem we propose an efficient and accurate Multi-Greedy heuristic that extends the classical single-threaded greedy approach to a multiple-threaded setting. Finally, we provide empirical results on real-world data showing that the diversified optimal portfolios are only slightly suboptimal in-sample with respect to optimal portfolios, and generally show improved out-of-sample performance with respect to their purely diversified or purely optimized counterparts.

Gaussian Process Priors for Bayesian Portfolio Selection

This article shows how asset characteristics can be incorporated into the Bayesian portfolio selection framework. We use Gaussian process priors to model the belief that assets with similar characteristics are likely to have similar expected returns. The resulting Bayesian shrinkage estimator biases expected return estimates of assets with similar characteristics towards each other. A closed-form solution of the optimal portfolio weights in the Gaussian process prior framework is derived. Our simulation results and our empirical analysis suggest that portfolio selection with Gaussian process priors is a competitive alternative to benchmark portfolio selection approaches from the literature.

Enhancing Risk Parity by Including Views

Within the finance literature, there is an apparent gap between the inherent ignorance of expected returns of a risk parity approach on the one hand, and the assumed certainty of expected returns in a mean–variance approach on the other. The authors propose a portfolio selection framework that allows an investor to position herself between these two extremes. Depending on the confidence in one’s expected return estimates, the optimal portfolio will be tilted more toward the risk parity portfolio or the mean–variance portfolio. The authors illustrate the framework for an investor in an asset allocation context.

A novel project portfolio selection framework: An application of fuzzy DEMATEL and multi-choice goal programming

Project portfolio selection is an important problem for having an ecient and e ective project management. This paper proposes a new framework to identify the optimal project portfolio. First, the in uencing criteria are derived with respect to higher priorities from the fuzzy DEMATEL method under the balanced scorecard framework. Afterwards, a utility-based multi-choice goal programming technique is applied to determine the project portfolio in regard to the chosen criteria and some other operational limitations. The synergy amongst projects and the outsourcing option are also taken into account in order to provide a more realistic selection process. Finally, applicability and validity of the proposed integrated model are tested by a case study conducted in a pharmaceutical company.

Supply Chain Predictability of Returns and Portfolio Selection

We investigate empirically whether returns of stocks from one industry predict those of another. We analyze this question through a VAR process and find downstream predictability in the supply chain along with synergy predictability. We use a portfolio selection framework in order to analyze whether the predictability, in addition to being statistically significant, is also economically relevant. Here, we find significant out-of-sample excess returns, and a noteworthy increase in Sharpe ratio.

Prize-linked savings mechanism in the portfolio selection framework

Prize-linked savings (PLS) instruments implement the lottery-like component into the structure of traditional financial products. Following existing research based on both real and experimental data, such programs appeared highly successful in raising the overall savings rates within the given environments. PLS accounts seem to be treated by decision-makers as substitutes to ordinary lotteries, but this does not hold when comparing PLS with traditional interest-bearing savings products. This paper explains such empirical observations in a framework of portfolio selection problem. For that purpose, two models have been presented and used for deriving optimal portfolios in a presence of PLS, lottery and savings products. As shown in the analysis, the standard mean-variance model does not allow for a PLS instrument to be of optimum choice, whereas in the case of behavioural portfolio model allocating all disposable income.

OR Forum—Design of Risk Weights

Banking regulations set minimum levels of capital for banks. These requirements are generally formulated through a ratio of capital to risk-weighted assets. A risk-weighting scheme assigns a weight to each asset or category of assets and effectively functions as a linear constraint on a bank’s portfolio choice; it also changes the incentives for banks to hold various kinds of assets. In this paper, we investigate the design of risk weights to align regulatory and private objectives in a simple mean-variance framework for portfolio selection. By setting risk weights proportional to profitability rather than risk, the regulator can induce a bank to reduce its overall level of risk without distorting its asset mix. Because the regulator is unlikely to know the true profitability of assets, we introduce an adaptive formulation in which the regulator sets weights by observing a bank’s portfolio. The adaptive scheme converges to the same combination of weights and portfolio choice that would hold if the regulator knew the asset profitability. We also investigate other objectives, including steering banks to a target mix of assets, adding robustness, mitigating procyclicality, and reducing system-wide risk in a setting with multiple heterogeneous banks.

Optimal payoffs under state-dependent preferences

Most decision theories, including expected utility theory, rank-dependent utility theory and cumulative prospect theory, assume that investors are only interested in the distribution of returns and not in the states of the economy in which income is received. Optimal payoffs have their lowest outcomes when the economy is in a downturn, and this feature is often at odds with the needs of many investors. We introduce a framework for portfolio selection within which state-dependent preferences can be accommodated. Specifically, we assume that investors care about the distribution of final wealth and its interaction with some benchmark. In this context, we are able to characterize optimal payoffs in explicit form. Furthermore, we extend the classical expected utility optimization problem of Merton to the state-dependent situation. Some applications in security design are discussed in detail and we also solve some stochastic extensions of the target probability optimization problem.

Portfolio management with robustness in both prediction and decision: A mixture model based learning approach

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying distribution can be always well approximated by utilizing a mixture distribution, if we are able to ensure that the component list of a mixture distribution includes all possible distributions corresponding to the scenario analysis of potential market modes. Adopting a mixture distribution enables us to (1) reduce the problem of distribution prediction to a parameter estimation problem in which the mixture weights of a mixture distribution are estimated under a Bayesian learning scheme and the corresponding credible regions of the mixture weights are obtained as well and (2) harmonize information from different channels, such as historical data, market implied information and investors׳ subjective views. We further formulate a robust mean-CVaR portfolio selection problem to deal with the inherent uncertainty in predicting the future return distributions. By employing the duality theory, we show that the robust portfolio selection problem via learning with a mixture model can be reformulated as a linear program or a second-order cone program, which can be effectively solved in polynomial time. We present the results of simulation analyses and primary empirical tests to illustrate a significance of the proposed approach and demonstrate its pros and cons.

On the distribution and estimation of trading costs

Abstract This paper investigates the uncertainty about the trading costs associated with a given portfolio strategy. I derive accurate approximations of the ex ante probability distributions of proportional trading costs and portfolio turnover under the conventional assumption of normal asset returns. Based on these approximations, I express the expected trading costs as a function of asset and portfolio characteristics. All else equal, the expected trading costs increase with: i) the deviations of the expected asset returns from the expected portfolio return, ii) the assets' volatility and iii) the portfolio volatility. At the same time, they decrease with the covariance between the assets and the portfolio. Furthermore, I propose novel estimators of the expected turnover and trading costs and show that they offer small bias and low variance, even when the sample size is small. Finally, I incorporate my results into a portfolio selection framework to produce portfolios with low levels of risk and trading costs. Several experiments with real and simulated data confirm the practical value of the results.

Copula-opinion pooling with complex opinions

The Black–Litterman model (BL) (Black and Litterman 1992) provides a simple and transparent Bayesian framework for portfolio selection, building on the familiar mean-variance framework first presen...

A Comprehensive Framework for Sustainable Project Portfolio Selection Based on Structural Equation Modeling

Project selection problems are inherently complex problems with multiple and often conflicting objectives. The complexity of project selection problems is due to the high number of projects from which a subset (portfolio) has to be chosen. Various analytical methods, ranging from the simple weighted sum to complex mathematical programming have been proposed to solving these problems. We propose an integrated approach for strategic and sustainable project portfolio selection, which is composed of two distinct but interrelated modules. In the first module, we use the strategic planning and sustainability concepts to select a set of promising projects. In the second module, we use a project portfolio selection procedure to choose among the promising projects identified in the first module. A structural equation model is used to analyze and explain the relationships among different factors in the proposed framework. More specifically, we investigate the effects of: (1) strategic level performance on sustainability, post-implementation, and overall performance; (2) implementation performance on post-implementation and overall performance; (3) portfolio selection performance on implementation and overall performance; and (4) post-implementation performance on overall performance. A case study in investment banking is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

Enhancing Risk Parity by Including Views

Within the finance literature there is an apparent gap between the inherent risk premium ignorance of a risk parity approach on the one hand and the assumed risk premium clairvoyance of a mean variance approach on the other. We propose a portfolio selection framework that allows an investor to position herself between these two extremes. Depending on the confidence in one’s risk premium estimates, the optimal portfolio will be tilted more towards the risk parity portfolio or to the mean variance portfolio. We illustrate the framework for an investor in an asset allocation context.

A combined CFAHP-FTOPSIS approach for portfolio selection

Purpose – Owing to the importance of the portfolio problem in modern financial investment fields, the aim of this paper is to rank and choose suitable stocks in stock markets under vague environment. Design/methodology/approach – Many new principles and methods have been introduced to help the investor to decide which stocks are worth to be chosen. In real word, people prefer to express their attributes by using linguistic terms, so the used data are often imprecise and vague. In this paper, the second method of constrained fuzzy analysis hierarchy process (CFAHP) is used to evaluate the relative weights of the criteria and rank alternatives by fuzzy technique for order preference by similarity to an ideal solution (FTOPSIS). Findings – In this paper, empirical result shows that the proposed method is capable of helping investor to allocate asset in portfolio problems under vague environment. Research limitations/implications – The proposed method gives a new mathematical framework for portfolio selection. Investors can decide the asset allocation by using the combination of CFAHP and FTOPSIS. Practical implications – Besides the benefit of sorting feature, the proposed method can be used to obtain the asset allocation. Social implications – The aim of the portfolio selection theory is to rank and decide the asset allocations. But it is still a difficult task in deciding which assets should be selected under uncertain environment. The proposed method in this paper has been proved to be an efficient method to deal with vague information in decision-making programs. Originality/value – This study analyzes the effectiveness of the combined method of CFAHP and TOPSIS in portfolio problem.

Portfolio Management with Dual Robustness in Prediction and Optimization: A Mixture Model Based Learning Approach

We develop in this paper a novel portfolio selection framework with a feature of dual robustness in both return distribution modeling and portfolio optimization. While predicting the return distributions of the future market always represents the most compelling challenge in investment, any underlying distribution can be always well approximated by using a mixture distribution, if we are able to ensure that the component list of a mixture distribution includes all distributions corresponding to scenario analysis of the potential market modes. Adopting a mixture distribution enables us not only to reduce the prediction problem for distributions to a parameter estimation problem of specifying the mixture weights of the component distributions using a Bayesian learning scheme and estimating the corresponding credible regions of the estimations, but also to harmonize information from different channels, such as historical data, market implied information and the investor subjective views. We establish further a robust mean-CVaR portfolio selection problem formulation to deal with the inherent probability uncertainty. By using duality theory, we show that the robust portfolio selection problem via learning with a mixture model can be reformulated as linear program or second-order cone program, which can be effectively solved in polynomial time. We present simulation analysis and some primary empirical results to illustrate the significance of the proposed approach and demonstrate the pros and cons of the method.