Search Results for Mohammed Alsendi
Abstract
The study aims to achieve several objectives, the most important of which is to analyze the nature of the financing behavior adopted by business companies, by measuring the impact of financing behavior—represented by short-term debt, common stock, and retained earnings—on Economic Value Added (EVA) using the EVA model for a selected sample of business companies listed on the Iraq Stock Exchange during the period from 2005 to 2024. The study relied on the descriptive-analytical approach to present the theoretical and analytical aspects of the research variables, and the quantitative approach to test the research hypotheses in order to address the research problem, which revolves around the question: To what extent does financing behavior affect the Economic Value Added of industrial companies listed on the Iraq Stock Exchange? The study assumes a main hypothesis positing the existence of a statistically significant impact of financing behavior on the economic value of industrial companies. To test the research hypotheses, time series data and multiple linear regression analysis were employed. The results revealed a statistically significant negative impact of common stock financing on Economic Value Added, a statistically significant positive impact of retained earnings financing on Economic Value Added, and no significant impact of short-term financing on Economic Value Added.
The study concludes with a set of recommendations for financial decision-makers in the business companies of the study sample to help enhance their economic value, including: adopting an effective financing policy that ensures the optimal use of shares as a financing tool, achieving a balance between equity financing and debt financing.
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 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.