Examination of the Liquidity, Profitability and Indebtness Relations for Polish Companies with Neural Networks

Abstract

This study tries to investigate the relations of liquidity, indebtness and profitability for non financial listed companies in the Warsaw Stock Exchange, by applying the non parametric method, neural network analysis. After we present the legal environment of the Polish stock market, we investigate the relationship of liquidity, as measured by the selected indicators, with the company’s profitability and with the firm’s indebtness. It is important for the financial managers to know that by improving their company’s profitability, the liquidity is also affected, or that by managing their company’s liquidity efficiently, they can improve the firm’s profitability. Similarly, a relationship between liquidity and indebtness will also be investigated, since high liquidity reduces the firm’s default and bankruptcy risk. Our results showed that the cash conversion cycle was positively related to the return on assets ratio but had a changeable relation with the return on equity, the net profit margin and the gross profit ratio. The current and the quick ratios were positively related to the return on assets and the return on equity ratios, supporting our hypothesis. We found that the cash conversion cycle was positively and negatively related to the debt ratio, in other words had a changeable relation, while the current and the quick ratios were negatively related to the debt ratio, supporting our hypothesis. Our results compared to the relative literature indicated that the selected methodology (parametric vs. non-parametric) did not give any different results regarding the examined relations but complemented each other.