Identifying financial ratios associated with companies’ performance using fuzzy logic tools

Abstract

This study introduces computerized model for evaluation of corporate performance for companies traded in the main world stock markets. The main contribution of this study is to utilize a “Soft Regression” modeling tool, which is a soft computing tool based on fuzzy logic in financial statement analysis. Specifically, the tool is used to identify the most important financial ratios explaining the performance (as reflected by Operating Income Margin) of publicly traded companies, belonging to the manufacturing industries 2000–3999. We used data extracted from the XBRL database for years 2012 to 2016. The main results and conclusions of the study are: 1. The study identified relevant financial ratios for the manufacturing industry. It also revealed the relative importance of the various categories of financial ratios. 2. Detailed comparison of the results for 2012 and for 2016 indicated high degree of consistency and stability over time. 3. Not all financial ratios are equally relevant for all industries. 4. Proxy variables belonging to the same category of financial ratios are interchangeable in our model. It does not matter, which of the ratios belonging to the same category are used, the results are very similar for both, 2012 and for 2016. 5. All the resulting indicators imply that the model is highly reliable and robust. The main contribution of this study is to present a soft computing modeling tool based on fuzzy logic which is intuitive, stable and not based on restrictive assumptions.