TY - CHAP
T1 - Deciphering the Corporate Mind
T2 - Capturing Early Warning Signals in Non-Numeric Communication Channels Using Computational Intelligence
AU - Kumar, Rahul
AU - Deb, Soumya Guha
AU - Mukherjee, Shubhadeep
PY - 2023/3/13
Y1 - 2023/3/13
N2 - 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.
AB - 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.
KW - Corporate communications
KW - corporate failure
KW - early warning signals
KW - loser firms
KW - machine learning
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=85150207696&partnerID=8YFLogxK
UR - https://books.emeraldinsight.com/page/detail/advances-in-accounting-behavioral-research/?k=9781804557990
U2 - 10.1108/S1475-148820230000026005103
DO - 10.1108/S1475-148820230000026005103
M3 - Chapter
AN - SCOPUS:85150207696
SN - 9781804557990
VL - 26
T3 - Advances in Accounting Behavioral Research
SP - 103
EP - 137
BT - Advances in Accounting Behavioral Research
A2 - Karim, Khondkar E.
PB - Emerald Publishing
ER -