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The purpose of this paper is to identify differences in managing a single project compared with that of a project portfolio, where focus and requirements are expanded, and where clear links to organizational objectives exist. Further, the aim is to propose a methodology for the management of risk within the context of a project portfolio.

The concepts and framework described in this paper have emerged primarily from an in‐depth action research study in a major provider of transport solutions. The work has been conducted within one division, with presence in most of mainland Europe, Scandinavia, and the UK.

The paper finds that the proposed methodology would manage portfolio risk in two ways. First, it provides a means for single projects to gain experiences from other projects within the portfolio. Second, portfolio common risks and trends of issues can be identified. Such risks can become risks for succeeding projects, or require action from outside the single project.

The paper shows that the pilot study consisted of 16 projects within one project portfolio. Other project portfolios, with other prerequisites, might result in different findings, since some factors not included in this research such as cultural aspects or organizational factors could affect the findings.

In this paper the identification and analysis of commonalities and risk trends between projects provide the possibility to manage risks from a portfolio perspective.

The paper sees that existing risk management processes do not support projects in managing risk within a project portfolio. Instead, the proposed methodology provides the project portfolio manager with a consolidated view of the total risk exposure within the portfolio. Additionally, this methodology finds risks and trends not otherwise possible to identify.

Sees the objective of teaching financial management to be to help managers and potential managers to make sensible investment and financing decisions. Acknowledges that financial theory teaches that investment and financing decisions should be based on cash flow and risk. Provides information on payback period; return on capital employed, earnings per share effect, working capital, profit planning, standard costing, financial statement planning and ratio analysis. Seeks to combine the practical rules of thumb of the traditionalists with the ideas of the financial theorists to form a balanced approach to practical financial management for MBA students, financial managers and undergraduates.

It is the purpose of this article to empirically test the risk parameters for larger foreign‐exchange portfolios and to suggest real‐world policies and procedures for the management of market risk with the aid of value at risk (VaR) methodology. The aim of this article is to fill a void in the foreign‐exchange risk management literature and particularly for large portfolios that consist of long and short positions of multi‐currencies of numerous developed and emerging economies.

In this article, a constructive approach for the management of risk exposure of foreign‐exchange securities is demonstrated, which takes into account proper adjustments for the illiquidity of both long and short trading/investment positions. The approach is based on the renowned concept of VaR along with the innovation of a software tool utilizing matrix‐algebra and other optimization techniques. Real‐world examples and reports of foreign‐exchange risk management are presented for a sample of 40 distinctive countries.

A number of realistic case studies are achieved with the objective of setting‐up a practical framework for market risk measurement, management and control reports, in addition to the inception of a practical procedure for the calculation of optimum VaR limits structure. The attainment of the risk management techniques is assessed for both long and short proprietary trading and/or active investment positions.

The main contribution of this article is the introduction of a practical risk approach to managing foreign‐exchange exposure in large proprietary trading and active investment portfolios. Key foreign‐exchange risk management methods, rules and procedures that financial entities, regulators and policymakers should consider in setting‐up their foreign‐exchange risk management objectives are examined and adapted to the specific needs of a model of 40 distinctive economies.

Although a substantial literature has examined the statistical and economic meaning of VaR models, this article provides real‐world techniques and optimum asset allocation strategies for large foreign‐exchange portfolios in emerging and developed financial markets. This is with the objective of setting‐up the basis of a methodology/procedure for the measurement, management and control of foreign‐exchange exposures in the day‐to‐day trading and/or asset management operations.

This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios.

This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart.

To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach.

The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk.

The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.

The purpose of this paper is to present a review of recent risk management literature applied to projects, programs and project portfolios performed inside an organization with the aim of finding areas of opportunity to continue research and the development of current guides and methodologies.

The paper uses a review of recent literature published by international organizations and journals specializing in the field of project, programs, and portfolios.

The review shows that project risk management is a well developed domain in comparison to the program risk management and portfolio risk management fields, for which specifically written methodologies are difficult to find. The review also demonstrates the need to include better tools to perform a continuous control and monitoring process. Integrating a vulnerability approach is also necessary in order to consider the project, program or portfolio characteristics which mediate between consequences and the exposure to hazards and opportunities.

The review does not consider white papers or popular media.

The limitations found in current risk management methodologies show the challenges researchers must undertake to continue improving this domain for projects performed inside an organization. The paper exhibits the areas of opportunity where methodologies and guides can be further improved to evolve towards better management structures.

This study provides an extensive risk assessment framework for nonlisted real estate funds' (NREFs) portfolio management in India across their life cycle; that is, the investment stage, the monitoring stage and the exit stage in an emerging market context. The study of risk across these three stages is a new addition to the literature and assumes importance in the context of real estate portfolio management for NREFs in the emerging markets (e.g. India), which are predominantly an opportunistic investment play.

The risk assessment framework is built on the multiactor/multicriteria risk priorities, using analytical hierarchy process (AHP), obtained from 35 experts in four real estate fund management professional groups; namely, investors/fund managers, valuers, consultants and international developers.

The results demonstrate that the real estate portfolio management risk priorities change across the three life cycle stages of the fund. At the investment stage, specific risks are most critical; at the monitoring stage, it is important to concentrate on all three risks – specific, systematic and management risks; and at the exit stage, systematic risk plays a crucial role. Real estate portfolio management risk evaluation at the subfactor level shows that investee/partner and location selection needs to be critically evaluated at the time of the investment; project execution and quality of development must be monitored during the construction/monitoring period; and repatriation of the funds, currency volatility and exit risk (resale) are critical at the exit stage of the fund.

The understanding of the real estate portfolio management risk transformation across the life cycle stages is crucial for NREF managers for risk minimization, transfer and mitigation strategy formulation in their real estate portfolios. Unlike previous research that evaluates investment risk, this study breaks the NREF's risks into the investment, monitoring and exit stages. The key risk factors for each stage depend on the NREF's real estate activities for that stage. These activities, in turn, give rise to a typical risk profile for that stage. The findings are crucial for the various stakeholders of real estate fund management and policymakers in an emerging market context; particularly India, one of the fastest growing major economies in the world.

This risk assessment framework for simultaneously assessing risk across the three life cycle stages of NREFs is a new addition to the literature.

This paper aims to identify a possible mismatch between the theory found in academic research and the practices of investment managers in Brazil.

The chosen approach is a field survey. This paper considers 78 survey responses from 274 asset management companies. Data obtained are analyzed using independence tests between two variables and multiple regressions.

The results show that most Brazilian investment managers have not adopted current best practices recommended by the financial academic literature and that there is a significant gap between academic recommendations and asset management practices. The modern portfolio theory is still more widely used than the post-modern portfolio theory, and quantitative portfolio optimization is less often used than the simple rule of defining a maximum concentration limit for any single asset. Moreover, the results show that the normal distribution is used more than parametrical distributions with asymmetry and kurtosis to estimate value at risk, among other findings.

This study may be considered a pioneering work in portfolio construction, risk management and performance evaluation in Brazil. Although academia in Brazil and abroad has thoroughly researched portfolio construction, risk management and performance evaluation, little is known about the actual implementation and utilization of this research by Brazilian practitioners.

This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.

The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.

In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.

The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

This study aims to advance both theory and praxis for event portfolio management in cities and destinations. An experiment has been conducted with professional event practitioners in a city to determine their opinions and strategies for balancing value and risk within their event portfolio. The first objective is to rank 14 of the city's recurring events in terms of both value and risk. Second, the events are plotted in a two-dimensional chart of value versus risk with the objective to differentiate between the 14 events. The third objective is to describe the event characteristics that event professionals associate with value and risk.

Results derive from an experiment involving the forced Q-sort procedure and professional event managers from a city renowned as an “event capital”. Empirical evidence is analysed by the constant comparative method of how events are being evaluated by ten professionals working for a DMO.

Economic impact and image effects are characteristics of high-value events as is an opportunity to create relations with event owners for future collaboration. Local community involvement is important for all events. The issue of portfolio fit was a common argument for weak-value events.

Results are based on the opinions of ten DMO employees in one large city. Conclusions help build event portfolio theory.

The results and methods are useful for event strategists and evaluators. In particular, the management of event portfolios and policies covering events in cities and destinations can benefit from the documented method for explicitly balancing risks with perceived value.

A portfolio perspective is also suggested as an approach to analyse the total tourist attractions portfolio of a destination.

Opinions regarding public value and risk by civil servants who work with events have not been studied before. The constant comparative method produces results that can be applied to policies governing events. In terms of theory development, concepts from financial portfolio management, product portfolio management and risk management are used to develop event portfolio design and management, and insights are gained on trade-offs in the process. The plot of the events in a two-dimensional chart of value versus risk clearly differentiated the 14 events and is an original contribution.

New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.

This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.

The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.

The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.