Why do banks fail? An investigation via text mining

Figures & data

Table 1. Descriptive on bank’s failure report

Year	Number of failed banks
2009	22
2010	47
2011	19
2012	5
2014	1
2015	4
Total	98

Table 2. Number of topics

Dataset	Number of documents	Number of topics
Toy data set	12	3
Authorship data set	834	15 to 25
NIPS dataset	1500	100 to 120
Associated press	2246	140

Figure 1. The essential steps in mining documents.

Figure 2. Top 15 frequent words.

Table 3. Correlation matrix of words X and Y combination

	Word Y	No Word Y	Total
Word X	N11	N10	N1
No Word X	N01	N00	N0
Total	N.1	N.0	N

Table 4. The 30 most frequent words

Word	Frequency	Word	Frequency	Word	Frequency
Loan	14492	Asset	4908	Portfolio	2482
Exam	13847	Review	4390	Growth	2452
Manage	8885	Deposit	4378	Audit	2406
Report	7961	Fund	3885	Perform	2400
Risk	7706	ADC	3767	Increase	2399
Supervision	7135	CRE	3481	Level	2052
Capital	6788	Board	3117	Lend	2050
Concentration	5765	Plan	2992	Market	1978
Loss	5061	Credit	2948	ALLL	1958
Financial	4994	Significant	2804	Portfolio	2482

Figure 3. Word cloud.

Figure 4. Word cloud.

Figure 5. Correlation matrix of words.

Table 5. Correlation coefficient

Interpretation	Correlation
Small	0.10–0.29
Medium	0.30–0.39
Large	0.50–1.00

Figure 6. The number of optimal topics.

Figure 7. The common frequent vocabularies of 2 sub-group.

Figure 8. An example of the classification of 3 topics.

Figure 9. Optimal number of clusters.

Figure 10. The dendrogram for hierarchies clustering.

Table 6. The common frequent vocabularies of 2 sub-group

Loan	Loss
Exam	Deposit
Manage	Review
Report	Concentr
Risk	Fund
Supervision	CRE
Capital	Plan
Financial	Board
Asset	Credit