Automatic assessment of information disclosure quality in Chinese annual reports

Abstract

Information disclosure in annual reports is a mandatory requirement for publicly traded companies in China. The quality of information disclosure will reduce information asymmetry and therefore support market efficiency. Currently, the evaluation of the information disclosure quality in Chinese reports is conducted manually. It remains an untapped field for NLP and text mining community. The goal of this paper is to develop automatic assessment system for information disclosure quality in Chinese annual reports. Our assessment system framework incorporates different technologies including Chinese document modeling, Chinese readability index construction, and multi-class classification. Our explorative and systematic experiment results show that: 1) our automatic assessment system can produce solid predictive accuracy for disclosure quality, especially in "excellent" and "fail" categories; 2) our system for Chinese annual reports assessment achieves better predictive accuracy in certain perspective than the counterparts of the English annual reports prediction; 3) our readability index for Chinese documents, as well as other findings from system performance, may provide enlightenment for a better understanding about the quality features of Chinese company annual reports. © Springer-Verlag Berlin Heidelberg 2013.