Deciphering the Corporate Mind: Capturing Early Warning Signals in Non-Numeric Communication Channels Using Computational Intelligence

Rahul Kumar, Soumya Guha Deb, Shubhadeep Mukherjee

Research output: Chapter in Book/Published conference outputChapter


Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

Original languageEnglish
Title of host publicationAdvances in Accounting Behavioral Research
EditorsKhondkar E. Karim
PublisherEmerald Publishing
Number of pages35
ISBN (Print) 9781804557990
Publication statusPublished - 13 Mar 2023

Publication series

NameAdvances in Accounting Behavioral Research
ISSN (Print)1475-1488


  • Corporate communications
  • corporate failure
  • early warning signals
  • loser firms
  • machine learning
  • text mining


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