Search Results for Ashti A. AL-mizori
Abstract
This study aimed to evaluate the financial sustainability of Iraqi industrial companies using machine learning models and to compare their performance with traditional statistical models. Five key financial variables were analyzed: Working Capital Ratio (WCR), Current Ratio (CR), Return on Equity (ROE), Return on Assets (ROA), and Asset Turnover (AT). A Financial Sustainability Index (FSI) was constructed to classify companies. The results showed that WCR and CR were the most influential variables in explaining financial sustainability, while AT had the least impact. All variables demonstrated statistically significant relationships with the FSI. Moreover, machine learning models—particularly XGBoost and Random Forest—outperformed the traditional logistic regression model in predictive accuracy, with variations in variable importance rankings across models. The findings highlight the importance of focusing on working capital management and liquidity improvement to enhance financial sustainability, as well as the use of machine learning tools in financial forecasting to support more accurate strategic decision-making. This study contributes by providing a modern analytical framework to support the financial sustainability of industrial companies, with the potential for future application in other sectors. Specifically, machine learning models improved predictive accuracy by approximately 9.4% over the traditional logistic regression model, while SHAP-based analysis confirmed that WCR and CR carry the greatest weight in predicting financial sustainability. Accordingly, the study recommends that industrial companies prioritize working capital and liquidity management, that financial analysts and investors adopt machine learning models such as XGBoost and Random Forest in financial sustainability forecasting, that more comprehensive sustainability indices combining financial and operational performance be developed, and that decision-makers invest in training on machine learning tools and SHAP interpretation to support more reliable, evidence-based financial decisions.
Abstract
This study aims to determine the extent of the impact of financing on the market value of firms, by using a sample consisting of six financial firms listed in the Iraq Stock Exchange distributed between the two sectors (insurance and investment), and these companies constitute 60% of the research community for the period (2012-2021), based on the quarterly data to form (240) observations, and for the purpose of testing the hypothesis of the study, the Panel Data technology and the outputs of the Eviews-10 statistical program were relied upon, and multiple regression analysis was relied upon to identify the relationship between the explanatory variable (financial leverage) and the controlling variables (company size , fixed assets, growth opportunities, and trading ratio), with the dependent variable (market value), and the estimation results revealed that there is a statistically significant negative effect between the financial leverage and the market value of the firm, and that the firm's trading ratio and its fixed assets positively and significantly affected the value of the company It also showed that there is a significant negative impact of the firm's size and its growth rate in the market value, This result was consistent with financial theory and related studies in its various environments. Accordingly, about 43% of the change was reached, The outcome of the firm's market value is explained through the variables of the current study, which prompts financial managers to increase interest in how to form suitable sources of financing in a way that reflects positively on the firm's value in the market.
Abstract
This study aimed to evaluate the financial sustainability of Iraqi industrial companies using machine learning models and to compare their performance with traditional statistical models. Five key financial variables were analyzed: Working Capital Ratio (WCR), Current Ratio (CR), Return on Equity (ROE), Return on Assets (ROA), and Asset Turnover (AT). A Financial Sustainability Index (FSI) was constructed to classify companies. The results showed that WCR and CR were the most influential variables in explaining financial sustainability, while AT had the least impact. All variables demonstrated statistically significant relationships with the FSI. Moreover, machine learning models—particularly XGBoost and Random Forest—outperformed the traditional logistic regression model in predictive accuracy, with variations in variable importance rankings across models. The findings highlight the importance of focusing on working capital management and liquidity improvement to enhance financial sustainability, as well as the use of machine learning tools in financial forecasting to support more accurate strategic decision-making. This study contributes by providing a modern analytical framework to support the financial sustainability of industrial companies, with the potential for future application in other sectors. Specifically, machine learning models improved predictive accuracy by approximately 9.4% over the traditional logistic regression model, while SHAP-based analysis confirmed that WCR and CR carry the greatest weight in predicting financial sustainability. Accordingly, the study recommends that industrial companies prioritize working capital and liquidity management, that financial analysts and investors adopt machine learning models such as XGBoost and Random Forest in financial sustainability forecasting, that more comprehensive sustainability indices combining financial and operational performance be developed, and that decision-makers invest in training on machine learning tools and SHAP interpretation to support more reliable, evidence-based financial decisions.