Abstract
Integrated report is a report that combines financial and non-financial information and is issued by mainly listed companies. Although it is not mandatory for listed companies to publish integrated report in Japan, number of listed companies issuing an integrated report are increasing every year. It is because creating high quality integrate report is a merit for listed companies, that is, to attract ESG investment from institutional investors.The objective of this study is to discover the descriptive features of integrated reports that are selected as GPIF's “Most-improved Integrated Reports”. To do so, a machine learning based model is proposed with explanatory variables that generated from text data. Also, two specialized dictionaries for integrated report are developed. Then, three models with different explanatory variables are built and compared with AUC-PR. As a result, the model with explanatory variables generated by co-occurrence pairs using two technical dictionaries shows the highest AUC. This result indicates that these explanatory variables are candidates of descriptive features of the awarded integrated reports. Then, by computing Gini importance for each explanatory variable, it reveals that characteristic co-occurrence pairs for the awarded integrated reports. That is, the awarded integrated reports include more statements regarding social contribution and governance and less statements regarding numerical descriptions, compared to the previous integrated reports.
Published Version
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