This study explores the application of Markowitz's Modern Portfolio Theory (Mean-Variance Theory) under a no short-selling constraint in Vietnam's stock market, focusing on the VN30 index from 2012 to 2024. It compares three investment strategies: (A) naive 1/N allocation, (B) Markowitz optimization targeting the same standard deviation as strategy A, and (C) Markowitz optimization maximizing the Sharpe ratio. The findings reveal that strategy C significantly outperforms cumulative returns and risk-adjusted performance, demonstrating that when properly adjusted, the Markowitz model can still generate alpha in emerging markets like Vietnam. However, the study emphasizes prudent risk management and warns against overreliance on theoretical Optimization without considering market realities.

Nowadays, the selection and management of the optimal portfolio are the most primary fields of financial decision-making. Thereby, selecting a portfolio capable of providing the highest efficiency and, at the same time, the lowest investment risk has been turned into one of the most critical concerns among financial activists. However, in this selection, the two factors above are not the only determining ones. Various factors are affecting financial markets' behavior under different possible scenarios, which should be identified. In this paper, we examine the high sensitivity of the Iranian capital market to the exchange rate fluctuations in the different scenarios due to the lack of a unified view of the value of that rate among experts as one of the mentioned factors and obtain its value using Dempster–Shafer theory (DST). Then, a portfolio selection model that prefers stocks with higher ranks is proposed. Representative results of the real-life case study reveal that the submitted approach is productive and practically applicable.

This article introduces an innovative fuzzy-based approach for developing a comprehensive portfolio optimization model that effectively accounts for inherent uncertainty while incorporating the investor's unique perspective on the dynamic stock market. The multi-objective optimization framework employs Conditional Drawdown at Risk to enhance investor flexibility in determining risk tolerance and optimal investment strategies tailored to specific needs. The research is notable for its pioneering use of intelligent methods to systematically collect valuable data from the Tehran Stock Exchange under fuzzy uncertainty. It incorporates important constraints such as cardinality and ceiling and floor limits for each investment period, allowing for a detailed analysis of various stock market scenarios and potential future outcomes. A case study is conducted with 25 diverse assets from the top five industry groups based on profit per share, from which five shares are thoughtfully selected to effectively demonstrate the model's unique effectiveness. The analysis rigorously assesses the model's performance in real-world conditions, highlighting the importance of accurately understanding the current stock market outlook and trends. To validate the model, the research compares results with a portfolio constructed under similar conditions of certainty and risk. The findings indicate that portfolios created under certainty yield significantly higher values, suggesting that successful portfolio construction is heavily influenced by the prevailing market conditions experienced by investors.

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document- centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document-centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.

This research aims to enhance portfolio selection by integrating machine learning regression algorithms for predicting stock returns with various risk measures. These measures include mean-value-at-risk (VaR) variance (Var), semi-variance mean-absolute-deviation (MAD) and conditional value-at-risk (C-VaR). Addressing gaps in existing literature. Traditional methods lack adaptability to dynamic market conditions. We propose a hybrid approach optimized by genetic algorithms. The study employs multiple machine learning models. These include Random Forest, AdaBoost XGBoost, Support Vector Machine Regression (SVR) K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN). These models are used to forecast stock returns. Utilizing monthly data from the Tehran Stock Exchange, the results indicate that the genetic algorithm prediction model combined with mean-VaR, Var semi-variance and MAD, produces the most efficient portfolios. These portfolios offer superior returns with minimized risk compared to other models. This hybrid strategy provides a robust and efficient method for investors aiming to optimize returns while managing risk effectively. To implement this approach successfully it is crucial to balance investments. This involves both traditional and alternative asset classes, ensuring diversification. It also capitalizes on market opportunities. Regular review and adjustment of fund allocation are essential. Maintain an optimized strategy for maximum returns and minimal risk.

In this study, we attempted to conduct a comprehensive review of the existing and pertinent literature on the topic of factor investment. We performed Scientometric analysis of studies published in reputable finance journals, i.e., The Journal of Portfolio Management, The Financial Analysts Journal, The Journal of Asset Management and others, during the years 2014 to 2023. To obtain the research data for our study, we gathered and examined a collection of 76 bibliographic records sourced from the Web of Science database. This database provided a comprehensive and reliable source of scholarly publications in the field of finance. To analyze the data, we employed Scientometric networks as part of our analytical approach. Scientometric networks allowed us to explore the relationships and connections between different publications, authors, and keywords within the domain of factor investment. To visualize and present the research findings, we utilized the Bibliometrix package for R, a powerful tool specifically designed for bibliometric analysis. This package enabled us to generate insightful visualizations that showcased the key patterns, trends, and interconnections within the literature on factor investment. By employing Scientometric analysis and leveraging the capabilities of the Bibliometrix package, we aimed to provide a comprehensive overview of the existing scholarly research in this field and contribute to the understanding of factor investment.

Portfolio optimization aims to minimize risk and maximize return on investment by determining the best combination of securities and proportions. The variance in portfolio optimization models is typically used for a measure of risk. Over the last few decades, portfolio optimization utilizing a variety of risk measures has grown significantly, and many studies have been conducted. Therefore, this paper provides a systematic review of risk measures for portfolio optimization using bibliometric analysis and maps to analyze the evolution and trends of 682 articles published between 2000 and 2022. Throughout this analysis, communication networks among articles, authors, sources, countries, and keywords are explored. Furthermore, a classification of risks and risk measures were presented to demonstrate a comprehensive overview of the field, and the top 50 papers were analyzed to determine which risk measures were most often used in recent studies.

Since late 2019 and throughout 2020, the global economy has been experiencing difficult times due to the outbreak of the lethal Coronavirus (COVID-19). This study looks at the financial impact of this epidemic on the global economy using Malaysian market index i.e. FTSE Bursa Malaysia KLCI before and during COVID-19. Measuring the financial impact of this epidemic on the Malaysia economy may help policy makers to develop measures to avert similar financial catastrophic impacts on the global economy. The study uses Sharpe optimal and naïve diversification model to solve a scenario that factors in the level of corporate social responsibility(CSR) exhibited before and during the epidemic to measure the financial impact on the stock portfolio. The results show that the emergence of COVID-19exacerbated the already weak Malaysian economy. Our findings may help the policy makers in Malaysia to develop and maintain techniques and policies that may mitigate the negative financial impact and handle similar epidemics in the future. Future studies could cover the financial impact of CSR using variable scoring and apply the portfolio model with practical and prevailing constraints.

The primary concern in all portfolio management systems is to find a good tradeoff between risk and expected return and a good balance between accepted risk and actual return indicates the performance of a particular portfolio. This paper develops “A-Y Model” to measure the performance of a portfolio and analyze it during the bull and the bear market. This paper considers the daily information of one year before and one year after Iran & apos; s 2013 precedential election. The proposed model of this paper provides lost profit and unrealized loss to measure the portfolio performance. The proposed study first ranks the resulted data and then uses some non-parametric methods to see whether there is any change because of the changes in markets on the performance of the portfolio. The results indicate that despite increasing profitable opportunities in bull market, the performance of the portfolio did not match the target risk. As a result, using A-Y Model as a risk and return base model to measure portfolio management & apos; s performance appears to reduce risks and increases return of portfolio.