Analyzing Going Concern Disclosures in Statutory Financial Statements through Text Mining: The Case of General Electric
Analyzing Going Concern Disclosures in Statutory Financial Statements through Text Mining: The Case of General Electric

Author(s): Georgi Hristov
Subject(s): Economy, Business Economy / Management, Micro-Economics, Accounting - Business Administration, Socio-Economic Research
Published by: Университет за национално и световно стопанство (УНСС)
Keywords: going concern; text mining; disclosures; US GAAP; IFRS
Summary/Abstract: This study explores the use of text mining to analyze going concern disclosures in General Electric’s consolidated financial statements from 2018 to 2023. By examining the frequency and co-occurrence of terms like „bankruptcy risk“ and „liquidity risk“ under US GAAP and IFRS, we assess whether text mining can identify financial distress signals and align with formal going concern opinions. Both frameworks provide limited direct references to „going concern“ and related risks, with marginal occurrences in IFRS disclosures and virtually none in US GAAP. While IFRS includes slightly more explicit mentions of „going concern“, the differences are not substantial. The results show an increasing frequency of financial distress terms, yet minimal changes in the formal going concern opinion. This highlights the limitations of text mining alone and calls for refined methods in financial statement analysis.

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