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The purpose of this paper is to outline and demonstrate a method for screening and selection of potential portfolio companies (PCs) during the screening phase in corporate venture capital.

The use of the data envelopment analysis (DEA) enables the consideration of individual, heterogeneous and multidimensional decision criteria in portfolio selection and the preceding screening process by the investor.

The result of this method is a relative ranking of the PCs, with all the PCs considered serving as peer group. A weighting of individual criteria is not necessary because it is part of the functionality of DEA. The authors validate the proposed approach in a case study and show that it can be well combined with other models and theoretical frameworks.

The method is particularly useful in two cases. First, if a highly specialized investor wishes to use a variety of individual selection criteria for portfolio selection. Second, if an investor only has insufficient (financial) data on potential PCs, but still wants to make a (pre-) selection based on observable (qualitative) characteristics. This model helps to make consistent, intersubjectively comprehensible decisions based on valid decision criteria and helps to optimize the decision-making process in the context of portfolio selection in CVC.

This method allows the systematic selection of an attractive group from a large number of potential PCs, based on observable characteristics and taking into account individual strategic investment objectives, without having to make assumptions about underlying distributions or weights of decision criteria.

The consideration of the substitution phenomenon in the project portfolio selection problem can improve the robustness of project portfolio selection and help enterprises better achieve their strategic objectives. However, the existence of inter-project risk propagation will have a negative impact on project substitution. This paper proposes a new framework for project portfolio selection and constructs a risk propagation model based on strategic objectives to study the impact of risk propagation on substitution in the project portfolio.

The authors first construct a risk propagation model based on strategic objectives to describe the risk propagation between projects. Then the project substitution phenomenon based on risk propagation is put forward, and the calculation method of substitution loss is given. Finally, a robust project portfolio selection framework based on strategic objectives considering risk propagation is constructed.

The analysis of a case study demonstrates that (1) With the increase of risk intensity, the strategic loss of the same project portfolio increases linearly, and under the same risk intensity, the more projects in the portfolio, the stronger the robustness. (2) Considering risk propagation, the effect of project substitution is significantly weakened, and the strategic loss rate of the project portfolio is significantly increased compared with that of a direct attack.

This study is the first to take the project substitution into account in the project portfolio selection process. Moreover, the authors describe inter-project risk propagation and analyze the impact of risk propagation on the project substitution phenomenon. Finally, the authors extend the evaluation index of robustness. This paper puts forward a new way to solve the problem of project portfolio selection.

The purpose of this paper is to argue that simplifications of portfolio selection may no longer be necessary, based on computational advancements of portfolio theory and powerful computers.

First, the paper reviews the two branches of portfolio optimization and second, presents the results of large‐scale portfolio selection, based on exhaustive sampling in China. Some speedy results support removing the simplification.

The paper finds that for some simplification techniques, the results of simplified models and original models are quite alike, while for other techniques, the results are strikingly distinctive. Moreover, the performance of portfolio optimizers varies from being instantly fast to being unbearably slow, so it pays to be picky.

This paper reports for large‐scale portfolio selection the results of kinds of software and this alone makes the paper unique. Based on the leading software and exhaustive sampling in China, for the first time the difference between the original and simplified models is studied.

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.

The field of socially responsible investment (SRI) has become a central theme in the mutual funds industry. The risk implications associated with this investment approach are less explored. This study further investigates the real contribution to the investor offered by the SRI alternative.The aim of this paper is to throw more light on this debate.

Analyzing a large sample of US companies, this study investigates the tendency to generate risk when the portfolio is built, taking into account SRI. The research is based on the backtest of the real performance obtainable by adopting different investment strategies in which the red line is the selection method based on the principles of corporate social responsibility.

The investor must pay a cost that depends on the degree of rigor in the selection criteria. The risk associated with SRI is influenced by the measure adopted. SRI has a better asymmetric risk behavior than other securities. The results suggest using different selection models according to the investor’s objectives. When the objective is to maximize the average return and the remuneration risk, the SRI selection model should be negative or at least as inclusive as possible. In the event that the investor’s objective is to contain risk indices, a restrictive approach to the selection of investments is advisable.

Academic research has long been investigating the ability to generate profits but often neglects the levels of risk implicit in such investment approaches. The originality of this research consists in the adoption of a model based on the continuous optimization of the portfolio. This approach allows the results to be assessed by the returns actually obtained.

The purpose of this paper is to present a multi-objective model to the optimum portfolio selection using genetic algorithm and analytic hierarchy process (AHP). Portfolio selection is a multi-objective decision-making problem in financial management.

The proposed approach solves the problem in two stages. In the first stage, the portfolio selection problem is formulated as a zero-one mathematical programming model to optimize two objectives, namely, return and risk. A genetic algorithm (GA) with multiple fitness functions called as Multiple Fitness Functions Genetic Algorithm is applied to solve the formulated model. The proposed GA results in several non-dominated portfolios being in the Pareto (efficient) frontier. A decision-making approach based on AHP is then used in the second stage to select the portfolio from among the solutions obtained by GA which satisfies a decision-maker’s interests at most.

The proposed decision-making system enables an investor to find a portfolio which suits for his/her expectations at most. The main advantage of the proposed method is to provide prima-facie information about the optimal portfolios lying on the efficient frontier and thus helps investors to decide the appropriate investment alternatives.

The value of the paper is due to its comprehensiveness in which seven criteria are taken into account in the selection of a portfolio including return, risk, beta ratio, liquidity ratio, reward to variability ratio, Treynor’s ratio and Jensen’s alpha.

This paper aims to propose two portfolio selection models with hesitant value-at-risk (HVaR) – HVaR fuzzy portfolio selection model (HVaR-FPSM) and HVaR-score fuzzy portfolio selection model (HVaR-S-FPSM) – to help investors solve the problem that how bad a portfolio can be under probabilistic hesitant fuzzy environment.

It is strictly proved that the higher the probability threshold, the higher the HVaR in HVaR-S-FPSM. Numerical examples and a case study are used to illustrate the steps of building the proposed models and the importance of the HVaR and score constraint. In case study, the authors conduct a sensitivity analysis and compare the proposed models with decision-making models and hesitant fuzzy portfolio models.

The score constraint can make sure that the portfolio selected is profitable, but will not cause the HVaR to decrease dramatically. The investment proportions of stocks are mainly affected by their HVaRs, which is consistent with the fact that the stock having good performance is usually desirable in portfolio selection. The HVaR-S-FPSM can find portfolios with higher HVaR than each single stock and has little sacrifice of extreme returns.

This paper fulfills a need to construct portfolio selection models with HVaR under probabilistic hesitant fuzzy environment. As a downside risk, the HVaR is more consistent with investors’ intuitions about risks. Moreover, the score constraint makes sure that undesirable portfolios will not be selected.

Firms engaged in new product development (NPD) have to achieve a balanced portfolio of NPD projects. Despite the large number of models purporting to support portfolio optimization, most of them do not take into account political bias in project selection decisions. This paper aims to analyze approaches of organizational politics to NPD project selection and their implications for NPD portfolio management and future research.

A review is made of the current literature at the intersection between organizational politics and NPD project selection. With regard to the underlying assumptions of organizational politics, similarities, differences, practical implications, and research perspectives are identified.

From the paper, insights could be gained into explaining the effects of organizational politics on NPD project selection. However, the differences in assumptions that can be generally observed in organizational politics are also reflected in the studies analyzed. Future research could benefit from integrating different political and methodological perspectives.

In order to reach a balanced NPD portfolio, the potentially dysfunctional biases which characterize political processes from idea generation to project selection should be addressed. A concept of NPD portfolio management is proposed which considers the management of power and politics.

This paper contributes to a more comprehensive overview of political approaches of NPD project selection and serves as a sound basis for future research. The relevance and implications of politics for NPD portfolio management are demonstrated.

The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time.

This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization.

From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods.

This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

This paper proposes selecting a construction project portfolio in the context of a public institution, which makes it possible to assess quantitative and qualitative criteria, thereby meeting the needs of the institution and the existing constraints.

The research design follows a framework using technique for order preference by similarity to ideal solution (TOPSIS) associated with integer linear programming.

The method involves a flow of assessments allowing criteria and weights to be elicited where outcomes are based on the experts' intra-criteria assessment of alternatives and decision-makers' inter-criteria assessment. This is of utmost interest to public organizations, where selections must result in benefits and lower costs, integrating the experts' technical and management perspectives.

Public institutions are characterized by having limited financial and personnel resources for project development despite having a high demand for requests not associated with profits, making it essential to have a framework that enables using multiple criteria to better evaluate the benefits related to these decisions.

The main contributions of this article are: (1) the proposition of a framework for selecting construction project portfolios considering the organization's strategic needs; (2) identifying quantitative and qualitative assessment criteria for project selection; (3) integrating TOPSIS with an optimization process for selecting the construction project portfolios and (4) providing a structured decision process for selecting the portfolio that best represents the interests of the institution within its limited resources and personnel.