1. An Analysis of Database Challenges in Financial Misconduct Research
Jon Karpoff
University of Washington
Allison Koester
Georgetown University
Scott Lee
University of Nevada, Las Vegas
Jerry Martin
American University

2. Databases – misreporting studies
Restatement announcements
GAO – Government Accountability Office
AA – Audit Analytics
Securities class action lawsuits
SCAC – Stanford Securities Class Action Clearinghouse
Administrative proceedings & Litigation releases that censure accountants
AAER – Accounting and Auditing Enforcement Releases secondary designation assigned by the SEC

3. Databases are simply tools
Like any tool, databases can be well-suited for one purpose…
…and poorly suited for other purposes.

4. 5 Features of the Databases
Initial revelation of misconduct
- Do databases identify the initial revelation
Scope limitations
- How many “other-type” events does each database omit?
Omissions
- How many “same-type” events does each database miss?
4. Multiple events per case
- Event-level observations often are NOT independent
Extraneous observations
- Does the database capture financial misrepresentation? (Hennes et al. 2008)
- Does the database capture fraud?

5. Documenting each database’s features
Create a comprehensive database with:
All 1,099 cases (includes 10,415 events) for which the SEC brings an enforcement action for §13(b) violations
Books & records and internal control violations (FCPA)
2. Assemble data related to these cases from:
Wolters Kluwer Law & Business Securities electronic library
PACER (court documents)
Lexis-Nexis’ All News and Dow Jones’ Factiva (press releases and articles)
3. Merge in GAO, AA, SCAC, and AAER database events
Which §13(b) violations are included/missed by each database?
What ancillary information is included/missed by each database?

6. Brocade Communications
Consider a firm picked up by all 4 databases:
Brocade Communications
A total of 23 unique event days with specific incremental information about Brocade’s financial misrepresentation

7. How can so many dates be relevant?
Jan 6, 2005: Brocade press release (issued after trading hours) announces its financial statements will be restated due to improper accounting for stock options
Mar 10, 2005: SEC begins an informal inquiry
June 10, 2005: SEC begins a formal investigation
Brocade Communications was chosen specifically because all 4 databases flag it.
This is NOT common.

8. Brocade Communications
Brocade makes four restatement announcements…

9. Brocade Communications
… a class action lawsuit is filed, and settled 3 years later…

10. Brocade Communications
… and the SEC issues 15 different Administrative Proceedings
and/or Litigation Releases spanning 5 years

11. GAO hits 4 of 23 events AA hits 2 of 23 events Audit Analytics
Identifies Restatement #1 & 3
misses # 2 & 4
Neither GAO & AA inform you that the information was released after the market closed on Thursday, Jan 6, 2005  Friday, Jan 7, market response

12. SCAC hits 2 of 23 events

13. AAER hit rate = 2 of 23 events
Both AAERs relate to the SEC’s censure of two former Brocade executives who are CPAs.

14. 5 Database Features and Brocade
Initial revelation dates
GAO and AA identify the initial misconduct date
Brocade’s announcement occurred after the U.S. markets close
First event in SCAC is 4 months after initial revelation
AAERs are 4 YEARS later
2. Scope limitations
Of 23 events, GAO captures 4, AA, SCAC, and AAER capture 2 each
3. Omissions
Only AA misses “same-type” events (unusual by selection)
4. Multiple events per case
All databases have this
5. Extraneous events
Can’t illustrate with a case chosen because it includes a §13(b)

15. Focusing on one aspect of a complex event
Parable of the blind men and the elephant

16. Number of Events Integrated into Cases for each Database
Table 2, Panel A
GAO AA
SCAC
AAER
FSR
Events
Cases
In each
database
2,707
11,001
3,421
3,568
10,415
1,099
Associated with a §13(b) violation

17. Number of Cases after Integrating Related Events for each Database
Table 2, Panel A
GAO AA
SCAC
AAER
FSR
Events
Cases
In each
database
2,707
2,321
11,001
8,358
3,421
3,116
3,568
1,356
10,415
1,099
Associated with a §13(b) violation

18. Number of Events and Integrated Cases associated with a §13(b) violation
Table 2, Panel A
GAO AA
SCAC
AAER
FSR
Events
Cases
In each
database
2,707
2,321
11,001
8,358
3,421
3,116
3,568
1,356
10,415
1,099
Associated with a §13(b) violation
427
290
239
188
389
346
2,865
939

19. We are concerned with how these databases perform in describing §13(b) violations
Table 2, Panel A
GAO AA
SCAC
AAER
FSR
Events
Cases
In each
database
2,707
2,321
11,001
8,358
3,421
3,116
3,568
1,356
10,415
1,099
Associated with a §13(b) violation
427
290
239
188
389
346
2,865
939
Important: All comparisons refer ONLY to the subset of events and cases in each database associated with a §13(b) violation

20. Feature #5: Extraneous cases
Table 6, Panel A
NOT suggesting that extraneous events/cases should be omitted from each database
Documenting the culling process facing researchers who use these databases to study financial misrepresentation

21. Feature #3b: Omitted Cases
(during coverage period)
Table 4, Panel B
Omitted cases with at least one same-type event AND a 13(b) violation
during the database time period
GAO AA
SCAC
AAER
Cases identified by the database
290
188
346
939
Cases missed by the database
127
220 36
160
Number of cases that should have been identified
417
408
382
1,099
% of cases missed
30.5%
53.9%
9.4%
14.6%

22. Feature #2: Scope Limitations
Table 3, Panel B
NOT suggesting GAO should include all 4,336 events
But, remember the hazard of considering only the “elephant’s tusk”

23. % market reaction is understated using
Economic Magnitude of Features #2 and #3
(Scope Limitation and Omissions)
Table 7, Panel B: Mean market-adj. CAR over all event dates identified in each case
GAO AA
SCAC
AAER
FSR
Using all event dates per case identified by the database
-7.82%
-4.64%
-5.61%
-7.49%
-39.93%
Using all event dates per case identified by the FSR database
-50.36%
-38.38%
-51.41%
-44.38%
% market reaction is understated using
non-FSR database
84%
88%
90%
83% 0%

24. Feature #1: Initial revelation dates
Figure 3: Initial Revelation Date, by Event Type

25. Feature #1: Initial revelation dates
Table 3, Panel A

26. Lessons Financial misconduct cases are complex
Scope limitations can affect economic significance inferences
Researchers should look beyond individual databases
High event/case omission rates  contaminated control samples
Ad hoc culling tends to select extreme incidents which yields unrepresentative and biased inferences
Financial misconduct is NOT necessarily fraud
25% of 13(b) violations do NOT involve fraud charges
50% for AAERs
90% for securities class action lawsuits and restatement announcements

27. Thank you