Text analysis in financial disclosures

TL;DR

This paper reviews methods for analyzing unstructured textual data in financial disclosures, emphasizing the limitations of current sentiment-focused approaches and proposing broader future research directions.

Contribution

It provides a comprehensive review of NLP techniques in financial text analysis and highlights the need to move beyond sentiment metrics for better disclosure analysis.

Findings

Current methods rely heavily on sentiment analysis.

Unstructured text contains valuable but underutilized information.

Future research should explore broader linguistic and mathematical models.

Abstract

Financial disclosure analysis and Knowledge extraction is an important financial analysis problem. Prevailing methods depend predominantly on quantitative ratios and techniques, which suffer from limitations like window dressing and past focus. Most of the information in a firm's financial disclosures is in unstructured text and contains valuable information about its health. Humans and machines fail to analyze it satisfactorily due to the enormous volume and unstructured nature, respectively. Researchers have started analyzing text content in disclosures recently. This paper covers the previous work in unstructured data analysis in Finance and Accounting. It also explores the state of art methods in computational linguistics and reviews the current methodologies in Natural Language Processing (NLP). Specifically, it focuses on research related to text source, linguistic attributes, firm attributes, and mathematical models employed in the text analysis approach. This work contributes to disclosure analysis methods by highlighting the limitations of the current focus on sentiment metrics and highlighting broader future research areas