This study investigates risk distribution models in the context of auto insurance in emerging markets, with a focus on the National Insurance Company (SAA), regional directorate of Setif, Algeria. The research applies generalized linear models (GLM) and factor analysis to model the frequency of vehicle accidents and their associated risks. A comprehensive approach is employed, beginning with a discussion of the techniques used for data collection and preliminary descriptive analysis. Following this, a theoretical framework is established for understanding the risk distribution models, highlighting the role of GLM in the modelling of accident frequencies within the insurance industry. Different types of factor analysis, including basic coefficient analysis, cross-factor analysis, generalized cross-factor analysis, and mixed factor analysis, are examined in relation to their applicability to insurance risk modelling. Subsequently, generalized linear models are implemented to derive a robust model for accident frequency, utilizing R software for analysis. The results reveal that the pricing system of the National Insurance Company is influenced by multiple, non-deterministic factors, which complicate the prediction of accident rates and insurance costs. These findings underscore the importance of incorporating various risk factors into pricing strategies, rather than relying on deterministic models. The study highlights the necessity of considering a broader range of factors in the development of pricing systems, particularly in emerging markets where data may be incomplete or subject to considerable variability. Furthermore, the use of Mixed Poisson models is suggested as an effective approach for capturing the non-linear relationship between various risk factors and accident occurrence. This research contributes to the existing body of knowledge by providing a nuanced understanding of the application of GLM and factor analysis in the auto insurance sector, particularly in emerging markets.

The global iron and steel sector is currently navigating a period marked by significant volatility, driven by rising overcapacity and stagnating demand. In this challenging environment, businesses are increasingly compelled to compete not only within their local markets but also on the international stage, as the global economy becomes ever more interconnected. This necessitates a thorough evaluation of the financial performance of major firms in the iron and steel industry, particularly those listed on the Borsa İstanbul (BIST). Such assessments are critical for informing strategic decision-making within the sector. This study aims to assess the financial performance of prominent iron and steel companies traded on BIST between 2019 and 2023, employing an advanced multi-criteria decision-making (MCDM) approach. Specifically, an Improved ENTROPY method is combined with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the fiscal performance of these enterprises. The findings indicate that EREGL stands out as the highest-performing company in terms of financial metrics over the specified period. The study offers valuable insights into the financial health and operational efficiency of iron and steel firms, providing key information for investors and policymakers in the sector. Additionally, the proposed methodology presents a robust framework for the evaluation of corporate performance in other industries facing similar global challenges.

Energy dependency plays a pivotal role in shaping the performance of stock markets, particularly in energy-sensitive indices such as the BIST Industrial Index in Turkey. This study presents a comparative evaluation of traditional statistical models and machine learning (ML) techniques in capturing the complex relationship between energy variables and the BIST Industrial Index. A dataset encompassing energy imports, production levels, and energy prices is utilised to assess the effectiveness of Ordinary Least Squares (OLS) regression, Random Forest (RF), and Gradient Boosting (GB) models. The results reveal that ML models substantially outperform traditional statistical methods in their ability to capture nonlinear, intricate relationships between energy metrics and market behaviour. Among the ML models, RF demonstrates the highest predictive accuracy. Feature importance analysis identifies crude oil production as the most significant variable, underscoring the dominant influence of domestic energy dynamics in shaping the BIST Industrial Index. While ML models offer superior forecasting capabilities, they introduce challenges in terms of model interpretability. In contexts where transparency is crucial, statistical models such as OLS remain more favoured for their simplicity and explainability. The findings highlight the need for a balanced approach in model selection, with hybrid models potentially offering the best of both worlds by combining the strengths of traditional and modern methodologies. The insights derived from this study can inform policymakers and investors, particularly within emerging markets, providing a nuanced understanding of the trade-offs between predictive power and model transparency in forecasting energy-sensitive financial indices.

The rapid emergence of blockchain technology has facilitated the rise of Initial Coin Offerings (ICOs), offering an innovative approach to raising capital for startups and entrepreneurial ventures. Unlike conventional financing, where projects rely on internal resources or traditional external investments, ICOs enable firms to secure funding directly from the public through token sales. As this new form of crowdfunding gains momentum, the structure of national economic and financial systems has been identified as a critical factor influencing the success and performance of ICOs. Recent research has increasingly focused on comparing ICO markets with traditional corporate financing to better understand the dynamics at play. In this study, an econometric model was constructed to investigate how variations in a country’s financial and economic structures shape the fundraising outcomes of ICOs. A sample of 100 startups from diverse countries, including the United Kingdom, the United States, Austria, and South Africa, was analysed. The ordinary least squares (OLS) method was employed to estimate the model, defined as: Log(funds) = α + β1(fin) + β2(b) + β3(n) + β4(in) + $\in$. The variables represent key economic and financial indicators hypothesised to affect ICO performance, with rigorous statistical tests conducted using R Studio and Excel. Findings are expected to contribute to the growing body of literature by clarifying the extent to which national financial systems either facilitate or hinder the success of ICO fundraising campaigns. This research also provides valuable insights into the evolving role of financial innovation and regulation in the cryptocurrency ecosystem.

This study examines the impact of intellectual capital and leverage on profitability, along with their implications for firm value, focusing on healthcare provider companies listed on the Indonesia Stock Exchange (IDX) from 2018 to 2022. A purposive sampling method was employed, resulting in a sample of seven healthcare provider companies. Secondary data were extracted from the companies' annual reports, with intellectual capital measured using Pulic’s Value-Added Intellectual Coefficient (VAIC) model, leverage assessed through the debt ratio, profitability evaluated via return on equity (ROE), and firm value gauged by the price-to-book ratio (PBR). Multiple and simple linear regression analyses were conducted to investigate the relationships between these variables. The findings reveal that both intellectual capital and leverage significantly influence profitability, accounting for 69.18% of its variation. Furthermore, profitability is found to significantly impact firm value, explaining 70.79% of its variation. These results indicate that intellectual capital plays a critical role in enhancing profitability, while leverage also contributes to profitability, which in turn, significantly affects firm value. The implications of these findings suggest that healthcare providers in Indonesia could optimize their financial strategies by focusing on intellectual capital and leverage to enhance profitability and, consequently, firm value. This study contributes to the existing literature by providing empirical evidence from the Indonesian healthcare sector and offers insights for both practitioners and policymakers aiming to enhance firm value through strategic financial management.