Financial Footnote Analysis: Developing a Text Mining Approach

Financial footnotes analysis provides an opportunity to communicate with stakeholders beyond the numbers in the main body of financial statements. The combination of values in financial reports and their disclosure in footnote parts supports financial decisions in a wisely manner. Nevertheless, the unstructured nature of footnotes poses a barrier for an accurate, automatic, and real-time financial analysis. To address this issue, this paper implements a text classification procedure to evaluate the benefits of text mining deployment to react to the manual financial footnote analysis. This supports the classification of textual parts of financial footnotes automatically into related financial categories, which are relevant for financial analysts, in order to avoid reading entire textual parts manually. This research provides useful insights about the impact of using text mining for an automatic financial footnote analysis in terms of time saving and increasing accuracy.