TY - GEN
T1 - Investigating bank failures using text mining
AU - Gupta, Aparna
AU - Simaan, Majeed
AU - Zaki, Mohammed J.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/9
Y1 - 2017/2/9
N2 - We extend beyond healthiness assessment of banks using quantitative financial data by applying textual sentiment analysis. Looking at public annual reports for a large sample of U.S. banks in the 2000-2014 period, we identify 52 public bank holding companies that were associated with bank failures during the global financial crisis. Utilizing sentiment dictionaries designed for financial context, we find that negative and positive sentiments discriminate between failed and non-failed banks 88% and 79%, respectively, of the time. However, we find that positive sentiment contains stronger predictive power than negative sentiment; out of ten failed banks, on average positive sentiment can identify six true events, while negative sentiment identifies five failed banks at most. While one would link financial soundness with more positive sentiment, it appears that failed banks exhausted more positive sentiment than their non-failed peers, whether ex-ante in anticipation of good news or ex-post to conceal financial distress.
AB - We extend beyond healthiness assessment of banks using quantitative financial data by applying textual sentiment analysis. Looking at public annual reports for a large sample of U.S. banks in the 2000-2014 period, we identify 52 public bank holding companies that were associated with bank failures during the global financial crisis. Utilizing sentiment dictionaries designed for financial context, we find that negative and positive sentiments discriminate between failed and non-failed banks 88% and 79%, respectively, of the time. However, we find that positive sentiment contains stronger predictive power than negative sentiment; out of ten failed banks, on average positive sentiment can identify six true events, while negative sentiment identifies five failed banks at most. While one would link financial soundness with more positive sentiment, it appears that failed banks exhausted more positive sentiment than their non-failed peers, whether ex-ante in anticipation of good news or ex-post to conceal financial distress.
UR - http://www.scopus.com/inward/record.url?scp=85016053036&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016053036&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2016.7850006
DO - 10.1109/SSCI.2016.7850006
M3 - Conference contribution
AN - SCOPUS:85016053036
T3 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Y2 - 6 December 2016 through 9 December 2016
ER -