Transcript Brown Bag workshop - November 30, 2007: The Determinants

Conference on Contemporary Issues of Firms and Institutions
Chinese University of Hong Kong
December 14, 2007
“What about liars, cheats, and thieves?”
Jonathan Karpoff, Scott Lee, Gerald Martin
University of Washington, Texas A&M University, American University
… A summary of three related papers on the
consequences and causes of financial
misrepresentation

Question #1: What are the consequences at the firm level?
Paper title: “The cost to firms of cooking the books”

Question #2: What are the consequences for the individual
perpetrators?
Paper title: “The consequences to managers for financial
misrepresentation”

Question #3: What motivates the decision to misrepresent
financial statements?
Paper title: “The determinants of managerial decisions to cook the
books”
The invisible hand
“[Each person] generally, indeed, neither intends to promote the
public interest, nor knows how much he is promoting it … by
directing [his] industry in such a manner as its produce may be of
the greatest value, he intends only his own gain, and he is in this,
as in many other cases, led by an invisible hand to promote an
end which was no part of his intention… By pursuing his own
interest he frequently promotes that of the society more effectually
than when he really intends to promote it.”
– Adam Smith
(IV.ii.6-9, page 456 of the 1776 Glasgow Edition of Smith’s works; vol. IV, ch. 2, p. 477 of 1776 U. of
Chicago Edition.)
Not always?
When does the
pursuit of selfinterest not
promote society’s
interest?
What about liars, cheats, and thieves?
John Rigas, Founder and CEO of Adelphia
Communications. He took home the popcorn.
Dennis Kozlowski, CEO of Tyco
International, Ltd.
– “One of theTop 25 Managers of
the Year” (Business Week
magazine in 2001)
– Now Prisoner 05A4820, in jail
Kenneth Lay, former CEO of Enron Corp.
- Convicted of fraud, false statements
(died, July 5, 2006, before sentencing)
Cheating, and its effects, have become important
concerns in financial markets

“In many countries, expropriation of minority shareholders and creditors
by the controlling shareholders is extensive.”


La Porta, Lopez-de-Silanes, Shleifer, and Vishny (JFE 2000, p.4)
“…[T]he nexus between the accounting firms and the corporations, and
the aggressive attitudes of CFOs and CEOs, has created a pattern of
deception; that has created a situation which has eroded public
confidence in the sanctity of the numbers which is the basis of our
markets today.”

... Arthur Levitt, SEC Chairman 1993-2001, quoted in
http://www.pbs.org/wgbh/pages/frontline/shows/regulation/lessons/
What to do?

“A more activist SEC is what’s needed.”
– The Christian Science Monitor


La Porta et al. (JF 2006) - public enforcement does not work

Jackson and Roe (wp 2007) - public enforcement does work
“It’s time to stop coddling white-collar crooks. Send them
to jail ... Enough is enough: They lie, they cheat, they steal
and they’ve been getting away with it for too long.”
– Fortune magazine
What else can we do?
“The first thing we do, let's kill all the lawyers.”
– William Shakespeare, in Henry VI
“…[L]et's kill all the accountants.”
– New York Daily News
The economic problem


Fraudulent, deceptive, and opportunistic behavior creates a
lemons problem

Information and contracts are costly

Contracts are incomplete

Contracts are costly to enforce
Buyers will demand a discount for the expected amount of
cheating by sellers
The economic problem, continued




All sellers know better than their counterparties whether
they are trustworthy
At “average” terms, only sellers with less-than-average
trustworthiness will engage in trade
Buyers know this, and demand even higher “cheating
discounts”
The market breaks down
What keeps it all together?
Why isn’t fraud, self-dealing, and opportunistic cheating the norm in most
transactions?

Agency theory and internal governance

External discipline and the market for control

International law and finance

Business misconduct, markets, and reputation


The focus
of these
slides
Examine the exceptional transactions, where someone acts
opportunistically
What can we learn about most normal transactions – in which no one gets
cheated or ripped off?
Concern about business misconduct drives
business school curriculae

Ethics teaching is big business




AACSB accreditation requires ethics courses
“Beyond Grey Pinstripes” – A World Resources Institute annual award for
teaching ethics (and sustainability)
“Teaching the Moral Leader” – well-publicized HBS course
Finance scholars have little influence in these areas


Have abandoned the topic – both the budgets and the intellectual
leadership – to non-economics based disciplines
But, we have a lot to say as a positive science
Question #1:
What are the consequences at the firm level?

Data: All (788) SEC and DOJ enforcement actions for financial
misrepresentation initiated from 1978 through September 30, 2006 for:



Books and records violations (15 USC § 78m(b)(2)(A))
Internal controls violations (15 USC § 78m(b)(2)(B))
Data sources:






Lexis-Nexis database
SEC website (all public releases since September 19, 1995)
DOJ staff (direct communications)
DOJ’s Corporate Fraud Task Force website
SEC’s EDGAR database
Stanford Law School’s Securities Class Action Clearinghouse
Cumulative abnormal returns and total dollar
losses
Large losses
Panel A: 424 Firms with Available Returns Data
Mean
Median
Cumulative Abnormal Return
-50.86%
-30.56%
Total Dollar Loss ($ millions)
380.50
20.16
Aggregate
161,330.84
Panel B: 384 Firms Listed in CRSP and Compustat
Mean
Median
Cumulative Abnormal Return
-38.06%
-29.61%
Total Dollar Loss ($ millions)
397.24
21.49
Aggregate
152,539.00
Panel C: 194 Firms that Survived through Enforcement Period
Mean
Median
Cumulative Abnormal Return
-34.43%
-24.84%
Total Dollar Loss ($ millions)
591.75
34.21
Aggregate
114,799.40
Firm Losses Partitioned…
All SEC, DOJ, and
state-level fines,
plus class action
and derivative
lawsuit awards
Fine effect
Class action effect
3%
6%
Present value of
higher future
financing costs
and lower net
revenues
Reputation loss
66%
Readjustment effect
25%
Estimate of the
amount by which
firm value was
artificially inflated
by the cooked
books
Takeaways regarding the consequences
to firms


Large losses in share values (~ 40%)
Most of the loss is due to lost reputation, i.e., the value of higher
financing costs and lower net revenues


Consistent with prior law and economics literature on the impact of
cheating on operations
For each dollar that firm value was inflated due to the inflated earnings
and assets…


The firm loses this dollar…
Plus an additional $3.08



$0.36 due to legal costs and settlements
$2.71 due to lost reputation
Evidence of the magnitude of Jensen’s agency cost of overvalued equity
Question #2:
What happens to the culpable individuals?
1.
2.
3.
Job loss
Determinants of being replaced
Other consequences:

Debarment from executive positions
(affects future employment opportunities)




Regulatory fines
Direct wealth losses through shareholdings
Criminal charges
Jail time
0.50
0.75
Execs in general
0.25
Culpable execs
Non-culpable execs
in targeted firms
0.00
Survival Rate
1.00
Job survival rates for culpable executives
0
12
24
36
48
60
Months
Target Firm Respondents
Target Firm Non-Respondents
72
84
96
108
120
Non-Target Executives
Culpable managers tend to be fired (contrary to most
“normal” turnover)
What determines who loses his/her job?


Logistic regressions (dependent var = 1 if
terminated)
Report each coefficient’s odds ratio


> 1 indicates a positive effect
< 1 indicates a negative effect
Turnover is facilitated by better governance
(dependent variable = 1 if culpable manager loses job)
Size of Harm Measure
Provable loss %
Governance
Chara cteristics
Board independence %
CEO Only
2.5081
0.091
6.9359
0.025
Top 3 Executives
3.3177
0.020
8.2199
0.012
All Executives
3.8039
0.005
8.2850
0.002
1.6577
0.479
2.2871
0.087
5.5626
0.078
7.5321
0.032
3.5476
0.088
11.44335
0.083
17.7341
0.062
50.7394
0.011
Non-respondent CHM/CEO duality
Non-respondent
blockholder ownership %
Non-respondent
insider ownership %
Board independence is positively related
to the likelihood of ouster
Turnover is facilitated by better governance
(dependent variable = 1 if culpable manager loses job)
Size of Harm Measure
Provable loss %
Governance
Chara cteristics
Board independence %
CEO Only
2.5081
0.091
6.9359
0.025
Top 3 Executives
3.3177
0.020
8.2199
0.012
All Executives
3.8039
0.005
8.2850
0.002
1.6577
0.479
2.2871
0.087
5.5626
0.078
7.5321
0.032
3.5476
0.088
11.44335
0.083
17.7341
0.062
50.7394
0.011
Non-respondent CHM/CEO duality
Non-respondent
blockholder ownership %
Non-respondent
insider ownership %
Shareholdings by non-culpable blockholders
and other insiders is positively related
to the likelihood of ouster
Takeaways regarding culpable individuals

When misrepresentation is exposed, the perpetrators experience
significant costs:









Job loss (91% of culpable execs)
Losses through shareholdings
SEC fines (average $14.4 million)
Class action/derivative lawsuit awards
Debarment (42% of all culpable execs)
Criminal charges (26% of all culpable execs)
Jail time (average sentence = 5.7 yrs)
Internal governance plays an important role
The sky’s not falling, i.e., “They lie, they cheat, they steal, and they’ve
been getting away with it for too long” is wrong
Question #3: Getting caught is really costly
… So why do they risk it?
Impacts on firms:



40% loss in share value
About 25% of that is
revaluation
Most (66%) of the loss in
value is lost reputation, i.e.,
higher future costs, lower
revenues
Impacts on perpetrators:






91% lose jobs (60% explicitly
are fired)
42% barred from serving as
officers/directors
SEC fine = $14.4 million
Shareholdings decline from
$6m – $39m
26% indicted on criminal
charges
Avg jail time = 5.7 years
Ideas from prior research:
Managers manipulate financials to…
Boost compensation




Beneish (TAR 1999)
Erickson et al. (JAR 2006)
Denis, Hanouna, Sarin (JCF 2006)
Burns, Kedia (JFE 2006)
Meet earnings thresholds




Robb (JFR 1998)
Degorge et al. (JB 2000)
Payne, Robb (JAAF 2000)
Schilit (book, 2002)
Facilitate new financing




Dechow, Sloan, Sweeney (CAR 1996)
Richardson, Tuna, Wu (wp 2002)
Efendi et al. (JFE 2007)
Dechow,Ge,Larson,Sloan (wp 2007)
Forestall distress-related
problems



Maksimovic, Titman (RFS 1990)
DeFond, Jiambalvo (JAE 1994)
Efendi et al. (JFE 2007)
More ideas from prior research:
Managers are constrained by…
Good governance




Beasley (TAR 1996)
Klein (JAE 2002)
Dechow, Sloan, Sweeney (CAR 1996)
Agrawal, Chadha (JLE 2005)
Firm transparency



Gerety, Lehn (MDE 1997)
Dechow,Ge,Larson,Sloan (wp 2007)
Kedia, Philippon (RFS 2007)
Well-structured ownership



Gerety, Lehn (MDE 1997)
Cornett et al. (wp 2006)
Denis et al. (JCF 2006)
Other factors


Povel, Singh, Winton (RFS 2007)
Cohen, Dey, Lys (wp 2005)
However…
Prior results are all over the place
Possible motive:
Dechow, Sloan,
Sweeney (CAR
1996)
Beneish (TAR
1999)
Boost
compensation
No
Yes
Facilitate new
financing
Yes
No
Forestall
distress-related
problems
Yes
No
Another example of mixed prior results
Possible
motive:
Burns and
Kedia (JFE
2006)
Erickson,
Hanlon,
Maydew
(JAR 2006)
Efendi,
Srivastsva
and
Swanson
(JFE 2007)
Johnson,
Ryan and
Tian (wp
2006)
Kedia and
Philippon
(RFS 2007)
Boost
compensa
tion
Yes - option
sensitivity to
stock price;
No-other
measures
No
(various
measures)
Yes
(in-money
option
holdings)
No
(option
holdings)
Yes
(option
exercise)
A second issue:
Prior results are based on small samples

Median sample size = 101 misrepresentation events
Mean = 176

In actual tests, sample sizes are severely reduced due to data availability
problems
A third issue:
Prior tests have omitted variables issues
Eight types of hypotheses
Like the blind men and the elephant(?)
“…[I]t is not feasible to consider all proposed motivations…”
– Dechow, Sloan, and Sweeney (CAR 1996)
Most focus on one or two types of motive
Efendi et al. (JFE 2007)
Burns-Kedia (JFE 2006)
Agrawal-Chadha (JLE 2005)
So we try a megastudy approach

Group various conjectures, theories into eight types
of motives or constraints



Consider many proxies for each hypothesis



Call them “hypotheses”
Avoid – or at least reduce – omitted variable bias
We have more than 100 proxy variables
Preliminary results use a subset of these proxies
Use large, comprehensive sample

Most prior studies have small samples
Ways to construct a sample

Our approach: All 868 SEC/DOJ enforcement actions from
1978-June 30, 2007 for financial misrepresention



Securities class action (10b-5) lawsuits (e.g., Gande-Lewis,
JFQA 2007; Fich-Shivdasani JFE 2007)


46% of our sample events have corresponding 10b-5 lawsuits
Accounting and auditing enforcement release (AAER)




15 U.S.C. §§ 78m(b)(2)(A) - requires accurate books and records
15 U.S.C. §§ 78m(b)(2)(B) - requires internal controls
AAER is a secondary designation assigned when the enforcement
release names an accountant or auditor
Created in 1982
May not have anything to do with financial misrepresentation
GAO (2002, 2003) restatement database

Currently popular, it’s easy
Universe of financial
misrepresentations
Our sample
• Type I error (miss events in which
misrepresentation occurred) may be high
• Type II error (events include innocent firms or
individuals) is essentially zero
AAER samples
• AAERs miss 18% of enforcement actions, and
29% of all Administrative and Litigation
Releases - so Type I error is higher
• AAERS include many instances in which there
is no financial misrepresentation (e.g., Boston
Scientific – Securities Exchange Act Release
34-43183, also assigned AAER-1295) - so Type
II error is non-trivial.
GAO restatement sample
• 1997–June 2002 (total = 919)
• Attempted to screen for fraud, but SEC now says that
over 50% are not misrepresentations or violations
(implying that Type I and Type II error rates are very
large)
• Hand-collected restatement database in PalmroseScholz (e.g., CAR 2004) does not have these problems
Other data issues

What is the event we wish to examine?

Called 13(b)
(of the SEC
Act of 1934)
violations
We focus on SEC/DOJ actions for financial misrepresentation



Others (e.g., Dechow et al. 1996) look at 13(a) “disclosure” violations




15 U.S.C. §§ 78m(b)(2)(A) - requires accurate books and records
15 U.S.C. §§ 78m(b)(2)(B) - requires internal controls
… Failure to report (e.g., 10-Qs and 10-Ks), failure to disclose information that
should be disclosed, inaccurate info other than in financial statements
(inaccuracies in financial statements trigger violations in bullet item above)
Example #1: Changing from LIFO to FIFO and not disclosing the gain
Example #2: Filing a 10-Q late
Is it “fraud?”


Most researchers say they examine “fraud”
But 25% of all enforcement actions do not include fraud charges
Our sample
{
Lexis-Nexis
SEC website (since Sept. 19, 1995)
DOJ staff (direct communications)
DOJ’s Corporate Fraud Task Force website
Panel A: Enf orcement Actions
Total number of enforcement actions
SEC involved
DO J involved
Number with booksand records violations
Number with internal controls violations
Number with cir cumvention violations
Number that include fraud violations
Number that include insider trading violations
Number that include Sarbanes-Oxley violations
Number that received an AAER
Panel D: SEC Enf orcement Releases
Administrative Releases
Securities Act Releases
Exchange Act Releases
Investment Advisers Act Releases
Investment Company Act Releases
P ublic Utility Holding Company Act Releases
Administrative Law Judge Releases
Litigation Releases
Number receiving asecondary designation as an
Accounting andAuditing Enforcement Release (AAER)
N
868
826
288
787
711
358
652
163
50
711
1,601
193
1,355
14
6
2
31
1,716
2,085
(15 USC § 78m(b)(2)(A))
(15 USC § 78m(b)(2)(B))
(15 USC § 78m(b)(5))
Univariate comparisons (Table 3)
… Performance-based pay is
relatively high in violation firm-years
Univariate comparisons (Table 3)
Panel B: Financing hypothesis variables
Ex-ante need for fi nancing flag
Security issuance flag
Acquisition flag
Free cash flow / total assets x 100
Statistic
N
% of total
N
% of total
N
% of total
N
Mean
Median
Violation
Non-Violation
Test
Firm-Years Firm-Years Statistic
1,978
271,162
79.12
78.68
0.48
1,978
271,162
83.37
70.63
15.13
1,978
271,162
12.79
6.55
8.30
1,825
253,323
-2.61
-39.21
0.87
2.39
3.84
-7.25
… Some need-for-financing variables are
relatively high in violation firm-years
P-Value
0.629
<.001
<.001
0.382
<.001
Univariate results - summary

Many things are correlated with the incidence of financial
misrepresentation when viewed in isolation.

But all univariate comparisons suffer from omitted variables
(and possibly problems of simultaneity and endogeneity)

Question: Which hypotheses (stories) are most important?

And which proxies should be used in empirical tests?

Use of multiple proxies causes attenuation bias
Our current approach – 4 types of tests
Kitchen sink approach
Straw-man (a bit unfair)
version of many previous
papers’ approaches
Each
hypothesis
(considered
in isolation)
All
hypotheses
(considered
at once)
Primitives Column (1) of Column (3) of
(basic
Table 5
Table 5
proxies)
Recognize
multiple
proxies and
cherrypicking
problems
Factors
(linear
functions of
proxies)
Column (2) of Column (4) of
Table 5
Table 5
Comprehen
sive (if you
believe the
factors)
Example with compensation hypothesis
variables
Table 4: Factor loadings
Panel A: Compensation (N = 23,674)
Variable
Black-Scholes option value /
total compensation
Sensitivity of the value of
executive options toa 1% change in
stock price (delta) ($000)
Salary / total compensation x 100
P erformance-based bonusflag
Eigenvalue
The factor explains a
substantial amount of
the covariance in these
four proxies
Factor
0.2098
0.8244
-0.8545
0.5080
1.7120
The loadings
make sense
Table 5:
Random effects cross-sectional time-series logistic regressions
Panel A: Compensation hypothesis variables
Black-Scholes option value /
total compensation
Sensitivity of the value of
executive options toa 1% change in
stock price (delta) ($000)
Salary / total compensation x 100
P erformance-based bonus flag
Panel Only
B ase
Factor
-0.0029
(0.749)
0.5676
(0.000)
-0.1200
(0.783)
0.0540
(0.857)
0.6579
(0.000)
Full Model
B ase
Factor
1.0203
(0.478)
2.5219
(0.023)
0.8738
(0.001)
2.8672
(0.447)
4.3802
(0.126)
• Columns 1 & 2 focus on one hypothesis at a time.
• Columns 3 & 4 reflect joint tests of all 8 hypotheses.
• The factor analysis captures the information from multiple proxy variables as a linear
combination of the proxies.
Table 5:
Random effects cross-sectional time-series logistic regressions
Panel B: Financing hypothesis variables
Ex-ante need for fi nancing flag
Security issuance flag
Acquisition flag
Free cash flow / total assets x 100
Panel Only
B ase
Factor
0.5089
(0.000)
1.0736
(0.000)
0.2744
(0.000)
0.4289
(0.000)
-0.0004
(0.1000)
Full Model
B ase
Factor
3.3150
(0.126)
1.7164
(0.588)
0.3998
(0.007)
2.1497
(0.134)
1.0868
(0.964)
• Columns 1 & 2 focus on one hypothesis at a time.
• Columns 3 & 4 reflect joint tests of all 8 hypotheses.
• The factor analysis captures the information from multiple proxy variables as a linear
combination of the proxies.
Table 5:
Random effects cross-sectional time-series logistic regressions
Panel E: Governance hypothesis variables
Corporate G overnance Factors
Bebchuck, Cohen, and Ferrell Index
CHM/CEO duality
% Board independ ence
Average board memberships
Panel Only
B ase
Factor
-0.1748
(0.288)
1.2562
(0.008)
0.0689
(0.667)
-3.9609
(0.000)
1.2052
(0.000)
Full Model
B ase
Factor
0.6639
(0.138)
8.5251
(0.019)
0.2067
(0.362)
0.2980
(0.433)
1.0655
(0.891)
• Columns 1 & 2 focus on one hypothesis at a time.
• Columns 3 & 4 reflect joint tests of all 8 hypotheses.
• The factor analysis captures the information from multiple proxy variables as a linear
combination of the proxies.
Table 5:
Random effects cross-sectional time-series logistic regressions
Summary of preliminary results
Hypotheses about motives
Hypotheses about constraints
• Compensation – Yes
• New financing – Yes (probably)
• Earnings threshold – Possibly
• Governance – No
•
Factor is significant, with
intuitive sign
• Distress – No
•
Prior stock performance seems
important
• (CEO/Chm duality seems important
• Ownership – No
• (Some individual proxies seem important)
• Transparency – No
• (Significant only when other hypotheses
are omitted)
• External factors:
• Macro trends (Povel et al.) – No
• Regulatory regimes – Yes (probably)
Ongoing steps in this project


Data reduction techniques to consider more of the 100+ proxies in
our dataset
Other empirical methods issues








Unconditional vs. conditional factor analysis
Factor rotation
Uneven data limitations (e.g., use all available data for each factor?)
Multiple factors/eigenvalue cut offs
Datamine for “best” factors, then use these in empirical tests?
Alternative empirical methods (e.g., the binomial test in my paper with
Boone, Field, and Raheja)?
Compare violation firm-years to non-violation firm-years for same
firms (or used fixed firm effects)?
Be more explicit about using both factor loadings and factor
coefficients in drawing inferences?