Assessment of Feasibility of the Use of XBRL in Financial Research

The bulk of contemporary financial research is performed today using the data from the financial aggregators such as COMPUSTAT. The goal of this work is to establish feasibility of using XBRL based financial statements for the financial research using detection of earnings management as a test case. The study shows the possible ways of parsing XBRL based financial statements with the purpose of extraction of data for the predefined financial variables. It evaluates arbitrary data received from the statements residing at U.S. SEC EDGAR website and makes a determination about the sufficiency and the suitability of such data for the described research algorithms. The feasibility of data is verified with the use of the mentioned algorithms taken from the earnings management literature and the algorithm of detection defined by the author of this study. The research work proves that despite relatively low yield of suitable data extracted from XBRL, it can be used as a data source in financial research.

Autorentext

Dr. Igor Pustylnick was born in Lgov, USSR. He holds the Radio Physics degree from Voronezh State University (1987), MBA from Athabasca University (2006) and DBA degree from UFM (2010). His research interests include detection of fraud and earnings management in corporate statements and feasibility of XBRL based data.