当前位置: X-MOL 学术J. Intell. Fuzzy Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Identifying financial ratios associated with companies’ performance using fuzzy logic tools
Journal of Intelligent & Fuzzy Systems ( IF 2 ) Pub Date : 2020-11-18 , DOI: 10.3233/jifs-190109
Amos Baranes 1 , Rimona Palas 2 , Eli Shnaider 3 , Arthur Yosef 3
Affiliation  

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.

中文翻译:

使用模糊逻辑工具识别与公司绩效相关的财务比率

本研究介绍了用于评估在主要世界股票市场上交易的公司的公司绩效的计算机模型。这项研究的主要贡献是利用“软回归”建模工具,这是一种在财务报表分析中基于模糊逻辑的软计算工具。具体来说,该工具用于识别最重要的财务比率,以解释属于2000–3999年制造业的公开交易公司的业绩(反映为营业收入利润率)。我们使用了从XBRL数据库中提取的2012年至2016年的数据。研究的主要结果和结论如下:1.研究确定了与制造业相关的财务比率。还揭示了各种财务比率的相对重要性。2。对2012年和2016年结果的详细比较显示了长期的高度一致性和稳定性3.并非所有财务比率都对所有行业都具有同等相关性4.我们模型中属于同一财务比率类别的代理变量可以互换。没关系,使用哪个比率属于同一类别,2012年和2016.5的结果都非常相似,所有得出的指标都表明该模型高度可靠且健壮。这项研究的主要贡献是提出一种基于模糊逻辑的软计算建模工具,该工具直观,稳定且不基于限制性假设。属于同一类财务比率的代理变量在我们的模型中可以互换。没关系,使用哪个比率属于同一类别,2012年和2016.5的结果都非常相似,所有得出的指标都表明该模型高度可靠且健壮。这项研究的主要贡献是提出一种基于模糊逻辑的软计算建模工具,该工具直观,稳定且不基于限制性假设。属于同一类财务比率的代理变量在我们的模型中可以互换。没关系,使用哪个比率属于同一类别,2012年和2016.5的结果都非常相似,所有得出的指标都表明该模型高度可靠且健壮。这项研究的主要贡献是提出了一种基于模糊逻辑的软计算建模工具,该工具直观,稳定且不基于限制性假设。
更新日期:2020-11-21
down
wechat
bug