Readability of MD&A Extracted from iXBRL: Computational Linguistic Approach

Abstract

This paper clarifies determinants of the readability of Management and Discussion & Analysis (MD&A) section from annual reports of Japanese companies extracted from Inline Extensible Business Reporting Language (iXBRL). Previous studies have focused on English-language information, with no studies discussing the characteristics of Japanese MD&A using large-sample data. Thus, we extracted the character information Japanese companies from iXBRL and analysed the readability using text mining. We found that 1) companies with large market value at the end of the term and companies with a high age have low readability, 2) companies with a large market value at the end of the term and companies with many foreign segments have many characters, and 3) companies with high age have fewer characters. Further, MD&A in Japan had greater readability than comparable United States documents. Our results suggest that firms with asymmetric information use simpler words for shareholders and, further, are conscious of shareholders who have poor Japanese. The academic contribution of this paper is to show the usefulness of iXBRL as well as the readability of Japanese MD&A using large-sample data through a computational linguistic approach. In addition, this research compares the results of Li (2008), which targeted the United States, with the results for Japan.