Cash conversion cycle

Optimal working capital management can positively effect on the Firm performance, but this relationship can be affected by major characteristics of the firm, making an important subject for research. This research investigates the moderating role of firm characteristics on the relation between working capital management and financial performance of the firms listed in TSE during 2008 – 2017 period. Based on existing researches, three characters are considered as moderating variables in this research include firm size, debt ratio, and Governmental ownership. Financial performance and working capital management are measured using return on assets (ROA) and cash conversion cycle (CCC), respectively. We use from multivariate regression model with panel data for test of research hypotheses. The Results of this study show that, firm size affects the Relation between CCC (as a measure of working capital management) and ROA (as a measure of firm performance). However, debt ratio and Governmental ownership don’t any significant effect on the relationship between working capital management and financial performance of firms

This study aims to develop a working capital management model for companies listed on the Tehran Stock Exchange. The proposed model determines the expected level of working capital for a company, enabling it to create the highest possible value. Additionally, this model can be used to assess the efficiency of working capital management. The discrepancy between the actual level of working capital and the expected level serves as an indicator of inefficiency in working capital management. Initially, based on theoretical foundations and expert opinions, 28 variables affecting working capital were selected. Then, using the operational working capital index, the research models were estimated using multiple regression and genetic algorithm techniques for data from 156 companies over the period from 2011 to 2022. Influential variables were identified and filtered. Finally, suitable working capital management models were identified based on two criteria: (1) the strong correlation between the errors of the fitted models and the working capital efficiency of the company, and (2) the model’s accuracy in identifying companies prone to excess or shortage of working capital. In total, after estimating 119 different models using regression and genetic algorithm methods, four suitable working capital management models were determined. The regression method resulted in models with an average accuracy of 77.27% and 79.54% for the dependent variable of working capital and the cash conversion cycle, respectively. The genetic algorithm method resulted in models with an average accuracy of 89.03% and 82.08%. The final model, with the cash conversion cycle as the dependent variable, was identified as the best model. It includes the variables of the previous year's cash conversion cycle, company-specific risk, gross profit margin, trade credit, growth opportunities, operating cycle, economic policy uncertainty, and exchange rate changes.