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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]

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]