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Mutual fund (MF) is one of the applicable and popular tools in investment market. The aim of this paper is to propose an approach for performance evaluation of mutual fund by considering internal structure and financial data uncertainty. To reach this goal, the robust network data envelopment analysis (RNDEA) is presented for extended two-stage structure. In the RNDEA method, leader–follower (non-cooperative game) and robust optimization approaches are applied in order to modeling network data envelopment analysis (NDEA) and dealing with uncertainty, respectively. The proposed RNDEA approach is implemented for performance assessment of 15 mutual funds. Illustrative results show that presented method is applicable and effective for performance evaluation and ranking of MFs in the presence of uncertain data. Also, the results reveal that the discriminatory power of robust NDEA approach is more than the discriminatory power of deterministic NDEA models. [ABSTRACT FROM AUTHOR]

The 1/N investment strategy, characterized by equally allocating wealth among available investment options, has garnered significant scholarly attention. Simultaneously, risk assessment and management play a critical role in financial decision-making, leading to the development of a diverse array of risk measure models. This paper aims to synthesize these two strategies and propose a novel approach for constructing a naive diversification strategy by incorporating commonly employed risk measures in financial analysis. The research involves an in-depth exploration of various risk measures utilized by financial professionals to enhance effective risk management. These measures include Mean-Variance (MV), Mean Absolute Deviation (MAD), Semi Standard Deviation (MSV), Value at Risk (VaR), Conditional Value at Risk (CVaR), Entropic Value at Risk (EVaR), Drawdown at Risk (DaR) of uncompounded cumulative returns, Conditional Drawdown at Risk (CDaR) of uncompounded cumulative returns, and Entropic Drawdown at Risk (EDaR) of uncompounded cumulative returns. To validate the efficacy of the proposed model, a real-world empirical case study utilizing the annual financial statements of the NASDAQ100 (NDX) database is conducted. The empirical findings from this study carry practical implications for investors and risk managers engaged in portfolio management. [ABSTRACT FROM AUTHOR]

The main aim of this research is to present an innovative method known as fuzzy network data envelopment analysis (FNDEA) in order to assess the performance of network decision-making units (DMUs) that possess a two-stage structure while taking into account the uncertainty of data. To attain this goal, we utilize various methodologies including the non-cooperative game (leader-follower) NDEA method, the concept of Z-number, credibility theory, and chance-constrained programming (CCP) to develop a model for the fuzzy NDEA approach. The FNDEA approach offers several advantages, such as the linearity of the presented FNDEA models, the ability to rank two-stage DMUs in situations of ambiguity, the provision of a unique efficiency decomposition method in an uncertain environment, and the capability to handle Z-information. To demonstrate the applicability and effectiveness of the proposed approach, we implement the Z-number network data envelopment analysis (ZNDEA) approach in assessing the performance of Iranian private insurance companies. The results of this implementation reveal that the proposed ZNDEA method is suitable and effective for measuring and ranking insurance companies in situations where data ambiguity is present. [ABSTRACT FROM AUTHOR]

In the wake of recent turbulent events in the global economy, the need for robust methods to navigate uncertainties in financial markets has become increasingly apparent. Robust portfolio optimization (RPO) offers a solution by devising investment strategies that perform well even under adverse scenarios of uncertain inputs such as returns and covariances. This paper conducts a systematic review of recent developments and extensions in the field of RPO. Leveraging bibliometric analysis and visual mapping techniques, we scrutinize 1085 articles published between 2000 and 2023. Our analysis traces the evolution and trends within RPO, examining the interconnectedness among articles, authors, sources, countries, and keywords. The insights gleaned from our study can guide future research endeavors in this domain and aid practitioners in making more informed investment decisions. [ABSTRACT FROM AUTHOR]

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors. [ABSTRACT FROM AUTHOR]

After modern portfolio theory and the Markowitz model, which considered risk and risk aversion in order to optimize investment portfolios and caused a great revolution in the field of economics, now behavioral finance topics such as ambiguity and ambiguity aversion have also been considered. Given the positive results achieved by machine learning algorithms, deep learning, and time series in predicting stock returns, this article used three methods that performed better than in most studies in the field of stock return prediction. Long short-term memory models are used in deep learning, random forests in machine learning, and autoregressive integrated moving average (ARIMA) models in time series. This article compares forecasting models in order to determine which model is more accurate and then predicts the returns based on that model. This article combines their predictive results with risk and ambiguity metrics in order to develop optimization models. An evaluation was conducted based on historical data for the six-year period from 2017 to 2022 for the top five industries in the Iranian stock market. According to the experiments, the deep learning model performed better than other models when it came to long-term predictions. Hence, this article presents a model for optimizing stock portfolios by integrating investor unknowns with forecasting models for the first time. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps. Findings: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

This paper applied the Measure of Attractiveness of Investment (MAI) as a representative indicator to enable investors to choose stocks according to attractiveness measured by some financial ratios of companies. It is integrated with β as the inherent risk of the market to construct a portfolio accounting for the companies' fundamental strength and the investment's long-term character. One may not know what the future investment environment will look like, but one can be better prepared for whatever comes through taking into account the uncertainty. The underlying key contribution of this study is related to the integration of MAI and β to construct a portfolio based on the companies' fundamental ratios and attractiveness of investments' long-term measures under uncertain conditions. To overcome the uncertainty, the Bertism and Sim algorithm is utilized which has some advantages such as linearity and the possibility of the adjusting protection level regarding the uncertainty degree. The empirical example shows that the proposed approach can be well implemented to deal with portfolio selection in uncertain conditions. [ABSTRACT FROM AUTHOR]

This study considers a time-consistent multi-period rolling portfolio optimization issue in the context of a fuzzy situation. Rolling optimization with a risk aversion component attempts to separate the time periods and psychological effects of one's investment in a mathematical model. Furthermore, a resilient portfolio selection may be attained by taking into account fuzzy scenarios. Credibilistic entropy of fuzzy returns is used to measure portfolio risk because entropy, as a measure of risk, is not dependent on any certain sort of symmetric membership function of stock returns and may be estimated using nonmetric data. Mathematical modeling is performed to compare the Rolling Model (RM) and the Unified Model (UM). Two empirical studies from the Tehran stock market (10 stocks from April 2017 to April 2019) and the global stock market (20 stocks from April 2021 to April 2023) are utilized to illustrate the applicability of the suggested strategy. The findings reveal that RM can limit the risk of the portfolio at each time, but the portfolio's return is smaller than that of UM. Furthermore, the suggested models outperform the standard deterministic model. [ABSTRACT FROM AUTHOR]

This paper proposes a robust window data envelopment analysis (RWDEA) approach for assessing the dynamic performance of decision making units (DMU) in the presence of panel and uncertain data. To present the RWDEA method, generalized data envelopment analysis (GDEA) model, window analysis (WA) method, and robust optimization (RO) approach are taken into account. The proposed RWDEA approach can be used under different returns to scale (RTS) assumptions, including constant returns to scale (CRS), non-increasing returns to scale (NIRS), non-decreasing returns to scale (NDRS), and variable returns to scale (VRS). Notably, the RWDEA model is linear and can fully rank DMUs under deep uncertainty. To solve and show the validity of the proposed approach, the RWDEA model is implemented for evaluating the efficiency of the intellectual capital of 10 automotive and parts manufacturing companies. The results indicate that the RWDEA approach is applicable and useful for the dynamic efficiency assessment of DMUs in the presence of uncertain panel data. The RWDEA approach, by considering the uncertainties in the data and using panel data, provides more reliable results in comparison with the classical DEA models. [ABSTRACT FROM AUTHOR]

Fuzzy data envelopment analysis (FDEA) is one of the most applicable approaches for performance assessment of peer decision making units under ambiguity which is evolving rapidly and gaining popularity under uncertain data envelopment analysis field. The goal of this paper is to review some FDEA models based on applied possibility, necessity, credibility, general fuzzy measures and chance-constrained programming to deal with data ambiguity. The study presents a comprehensive and structured literature review of fuzzy chance-constrained data envelopment analysis (FCCDEA) studies including 87 studies from 2000 to 2020. The main contributions of this research include the following details: (1) Review of fuzzy chance-constrained programming, (2) Survey of FCCDEA models based on different fuzzy measures, (3) Analysis of FCCDEA applications and features, (4) Classification of FCCDEA studies from modeling and uncertainty type viewpoints, (5) Bibliometric analysis of FCCDEA literature, and (6) Extraction of main research gaps and guidelines for future research directions. [ABSTRACT FROM AUTHOR]

Decision-makers (DMs) are not sufficiently exposed to concepts such as efficiency and risk in innovative activities from the perspective of organizational strategy. The challenges become even greater when these DMs lack expertise in technology and deal with uncertain circumstances. In this sense, exchanging expert knowledge between DMs and technical teams will strengthen the link between technology planning and strategic management. The purpose of this study is to bridge the knowledge gap between these two groups. It introduces a framework to translate the organization's strategy into technological decisions at an acceptable innovation risk level. This framework considers aspects such as knowledge, type of innovation, and innovation process. This study focuses on determining whether activities should be accepted or rejected by examining the uncertainty and efficiency of innovation. It also introduces a novel perspective on the hybrid "success-failure" uncertainty of innovation, and a new measure called "efficiency probability," which DMs and technology developers can use to intuitively engage in the innovation process. This paper seeks to propose a practical strategy map for new product development under uncertain conditions. To achieve this goal, the Fuzzy Front-End (FFE) concept, fuzzy data envelopment analysis (FDEA) model, and adjustable possibilistic programming (APP) approach are applied. The results of this study indicate that innovative activities typically have low efficiency and high uncertainty. Therefore, the decision to implement or abandon them requires reviewing and balancing the goals and strategic approach of the organization with technological and business features. [ABSTRACT FROM AUTHOR]

Portfolio construction is one of the most critical problems in financial markets. In this paper, a new two-phase robust portfolio selection and optimization approach is proposed to deal with the uncertainty of the data, increasing the robustness of investment process against uncertainty, decreasing computational complexity, and comprehensive assessments of stocks from different financial aspects and criteria are provided. In the first phase of this approach, all candidate stocks' efficiency is measured using a robust data envelopment analysis (RDEA) method. Then in the second phase, by applying robust mean-semi variance-liquidity (RMSVL) and robust mean-absolute deviation-liquidity (RMADL) models, the amount of investment in each qualified stock is determined. Finally, the proposed approach is implemented in a real case study of the Tehran stock exchange (TSE). Additionally, a sensitivity analysis of all robust models of this study is examined. Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain data. [ABSTRACT FROM AUTHOR]

This paper reviews the milestone approaches for handling uncertainty in data envelopment analysis (DEA). This paper presents the detailed classifications of robust data envelopment analysis (RDEA). RDEA is appropriate for measuring the efficiencies of decision‐making units in the presence of the data and distributional uncertainties. This paper reviews scenario‐based and uncertainty set of DEA models. It covers 73 studies from 2008 to 2019. The paper concludes with suggestions about the guidelines for future researches in the field of RDEA. [ABSTRACT FROM AUTHOR]

Possibilistic programming approach is one of the most popular methods used to cope with epistemic uncertainty in optimization models. In this paper, several robust fuzzy data envelopment analysis (RFDEA) models are proposed by the use of different fuzzy measures including possibility, necessity and credibility measures. Despite the regular fuzzy DEA methods, the proposed models are able to endogenously adjust the confidence level of each constraints and produce both conservative and non-conservative methods based on various fuzzy measures. The developed RFDEA models are then linearized and numerically compared to regular fuzzy DEA models. Illustrative results in all of the FDEA and RFDEA models show that, maximum efficiency is obtained for possibility, credibility and necessity-based models, respectively. [ABSTRACT FROM AUTHOR]

Preemption is one of the important features in scheduling problems. When preemption is allowed, a customer can immediately receive the service on the arrival time whenever the server is idle or the preemption is justified. While a customer receives a service, one or more preemptions may occur. The primary objective of this paper is to minimize the average waiting time that all customers spend on the system. We consider some thresholds for the maximum allowed number of preemptions and their times to prevent from having a very long waited customer. A heuristic algorithm is presented for the problem, and the implementation of the proposed approach is demonstrated using some numerical examples. In addition, the performance of the proposed algorithm is compared with another existing algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the on-line production order scheduling problem where each preemption causes a penalty, and the net profit is the total revenues of completed jobs minus the total penalties caused by preemptions. We discuss a preemptive version of the single-machine scheduling problem with the total flow time criterion. Two models with different kinds of preemption cost penalties are considered in this paper. In the first model, cost penalties are constant. In the second model, the preemption penalties are proportional to the lost part of the preempted job. Uncertain release times and processing times are modeled by an M/M/1 queue system. We calculate a lower bound probability for preemption of each job. Also, some financial analyses of the models are presented. [ABSTRACT FROM AUTHOR]

There has been extensive research over the decades on the preemptive scheduling. However, little attention has been devoted to problems where a certain time penalty such as setup times is incurred when preemption is allowed. In this paper, we consider the single-machine scheduling problem of minimizing the total flow time subject to job release dates and preemption penalties. When a job is started, whether initially or after being preempted, a job-dependent setup time must take place. The problem is strongly NP-hard even if the setup time is one unit. Therefore, we present a heuristic approach and validate it using some numerical examples. [ABSTRACT FROM AUTHOR]