{"title":"XBRL utilization as an automated industry analysis","authors":"A. Suta, Á. Tóth","doi":"10.33927/hjic-2020-19","DOIUrl":null,"url":null,"abstract":"In the last two decades, electronic financial reporting went through a significant evolution, where to date, eXtensible Business Reporting Language (XBRL) has become the leading platform that is already obligatory for listed entities in the United States and was also legislated in the European Union from January 1, 2020. The primary objective of this research was to review the US-listed companies’ 2018 quarterly reports. The study generated an automated industry analysis for the automotive industry from the aspect of four main financial item categories as an alternative to statistics-based, man-ually prepared industry analyses. Statistical tests were carried out between two industrial classification methodologies, the securities’ industry identification marks and the reported Standard Industrial Classification (SIC) codes. The results showed a significant difference between the industry classification methodologies. Automated reporting was more pre-cise with regard to the identification of the listed and reporting entities, however, the data fields of SIC codes within the XBRL data set provided an inaccurate classification, which is a potential area of improvement along with additional recommendations outlined in the Conclusion.","PeriodicalId":43118,"journal":{"name":"Hungarian Journal of Industry and Chemistry","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2020-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hungarian Journal of Industry and Chemistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33927/hjic-2020-19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 1
Abstract
In the last two decades, electronic financial reporting went through a significant evolution, where to date, eXtensible Business Reporting Language (XBRL) has become the leading platform that is already obligatory for listed entities in the United States and was also legislated in the European Union from January 1, 2020. The primary objective of this research was to review the US-listed companies’ 2018 quarterly reports. The study generated an automated industry analysis for the automotive industry from the aspect of four main financial item categories as an alternative to statistics-based, man-ually prepared industry analyses. Statistical tests were carried out between two industrial classification methodologies, the securities’ industry identification marks and the reported Standard Industrial Classification (SIC) codes. The results showed a significant difference between the industry classification methodologies. Automated reporting was more pre-cise with regard to the identification of the listed and reporting entities, however, the data fields of SIC codes within the XBRL data set provided an inaccurate classification, which is a potential area of improvement along with additional recommendations outlined in the Conclusion.