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.

This article introduces a comprehensive analysis of 20 leading companies, scrutinized through their financial metrics across various sectors. By deploying multi-criteria decision-making (MCDM) techniques, we aim to offer investors a clear and objective perspective on which companies stand out as the best investment options. Among the MCDM techniques, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized, renowned for its efficiency in handling complex decision-making scenarios which is conducted by two clauses. 1) Implementing TOPSIS with assigning equal weights and same share to every chosen metrics as criteria and 2) employ BWM (Best Worst Method) to calculate these weights base on their significance and relevancy to the prosses of ranking. According to the Result gained from the computation, ranks 1 to 5 belong to the similar companies with both assumptions which are Ford Motor Co, BP plc, Tesla Inc, General Motors Co and Exxon Mobil Corp. The consistency in rankings across two different weighting assumptions highlights the robustness of the criteria used, ensuring stable and reliable outcomes. This enhances the credibility of the findings, making them more trustworthy and citable for those who seek reliable and robust methodologies for informed investment decisions.

Portfolio optimization is a widely recognized strategy for investing that involves selecting a combination of assets that offers the optimal balance between potential gains and volatility. Traditional portfolio optimization typically focuses on a single period, considering only the current market conditions. However, multi-period portfolio optimization takes a more comprehensive approach by incorporating the dynamic nature of financial markets over multiple periods. Hence in this study, we focus on multi-period portfolio optimization. We conduct a bibliometric analysis of articles on multi-period portfolio optimization in the Web of Science (WoS) database. Through quantitative methods and the utilization of the Bibliometrix R package, we analyze publication trends, key research sites, and historical output in this field. Our findings provide valuable insights into the current state of research on multi-period portfolio optimization. This bibliometric analysis contributes to the existing literature on multi-period portfolio optimization and serves as a valuable resource for researchers, policymakers, and practitioners in the field of finance.

The concept of fiscal transparency holds significant importance; however, its attainment poses considerable challenges. Current discourse suggests that the concept of transparency lacks sophistication. To tackle this hurdle regarding public expenditure, analytical approaches that pertain to the general concept of transparency can be utilized. The present paper aims to ascertain the determinants of the openness of the provincial budget and its subcategories by human development, technology access, as well as industrialization progress, in addition to other relevant indicators. This study aims to ascertain the fundamental principles that underpin the achievement of efficient transparency in public expenditure. In terms of outcomes, the overall model has limited relations to the hypotheses. For the elemental dependent variables, the impact of various forms of transparency on the attainment of public policy goals varies, but generally have similar results. The study has attempted to conclude the components for an optimal transparent budget system, thus providing a suggestion for effective schemes later on.

The aim of this study is to experimentally examine the relationship between supply chain finance (SCF) and the financial performance of Vietnamese listed non-financial enterprises. A conceptual framework was developed and tested using secondary data collected from 659 companies listed on the Vietnam Stock Exchange in 2022. The path method is used using Stata 18 software. The results highlight that SCF has a positive impact on the financial performance of listed non-financial enterprises in Vietnam. These findings will help businesses better understand how SCF implementations benefit their financial performance in a global environment, especially in emerging markets.

This study aims to evaluate the impact of transparency of budget structure on risk of getting into financial distress in Vietnam. The article uses data regarding equity proportion from the financial reports of business entities on Hanoi Stock Exchange (HNX), divided into four main categories, namely large ownership, institutional ownership, managerial ownership, and state ownership ratio, to find the relation between the allocation of equity and the chance a company having financial failure. From the mentioned information, the research attempts to explain the relations, as well as suggestions for companies to prepare and avoid financial distress from an equity structure perspective. Results from the study’s sample show insignificant correlations between the share of owners and financial distress situation of a company, which plays a part to help the overall estimation of risk in businesses as a whole.

In line with Sharia principles, this study explores the correlation between increased returns and heightened risks, aligned with Al-Ghunm bi Al-Ghurm. Daily data from the Jakarta Islamic Index 70, spanning December 2021 to November 2023, was analysed using quantitative methods. Four key trading strategies were assessed: buy-and-hold and three Ichimoku strategies (Conservative, Moderate, and Aggressive). The results indicate the effectiveness of a moderate Ichimoku strategy because of its well-balanced approach. Despite no significant performance differences between Ichimoku's trading rules and buy-and-hold, the study emphasizes nuanced decision-making for diverse investor preferences. The insights benefited Indonesian Sharia investors, suggesting enhancements in risk mitigation techniques, and considering external factors. Enriching technical analysis and Sharia-compliant investments, this study underscores the importance of aligning financial objectives with ethical considerations, contributing to the evolving landscape of the Indonesian Sharia stock market.

Loan Self Control (LSC) can be defined as the ability of self-control related to behavioral control, cognitive control, and decisional control in managing loans or debts for financial welfare. The study aims to determine the factors of loan self-control in Kredit Usaha Rakyat (KUR) banking customers of Micro Small Medium Enterprise (MSMEs) in Indonesia so that they can obtain three-time financing from banks. This study used 87 samples collected by purposive sampling technique. Primary data was collected using an online survey. Factor analysis formed 3 loan self-control factors, namely Behavioral control, Cognitive control and decisional control from 14 indicators tested.

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.

In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.