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 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.

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.