1 Survey and Field Research in Finance: Miscalibration and Corporate Actions Campbell R. Harvey Duke...

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Survey and Field Research in Finance:Survey and Field Research in Finance:Miscalibration and Corporate ActionsMiscalibration and Corporate Actions

Campbell R. HarveyDuke University, Durham, NC USA

National Bureau of Economic Research, Cambridge, MA USA

April 15, 2005Yale University

2

Survey and Field Research

Background

• In 1995, Duke and Financial Executives International make a deal to conduct a quarterly CFO survey

• The deal allows for some special ‘academic’ surveys outside of the quarterly survey that would use the FEI e-mail and fax list

3


Background

1. Graham and Harvey conduct a survey on capital structure and project evaluation– “Theory and Practice of Corporate Finance:

Evidence from the Field” appears in JFE 2001

2. Brav, Graham, Harvey & Michaely survey on dividend and repurchase policy– “Payout Policy in the 21st Century” forthcoming in

JFE 2005

4


Background

3. Graham, Harvey and Rajgopal, survey on corporate financial reporting and disclosure.– “The Economic Implications of Corporate

Financial Reporting”

4. Graham and Harvey, quarterly survey on risk premium – “Expectations, Optimism, and Overconfidence”

5


Plan

1. “Methodology” in the true sense of the term

2. Asset pricing • Measuring expectations (mean, variance, skew),

optimism, overconfidence.

3. Corporate Finance• Understanding corporate financial reporting

6


Methodology

7


“Methodology”

General goals our research program:• To learn what people say they believe• To examine assumptions• To provide a complement to the usual research

methods: archival empirical work and theory

8


“Methodology”

Approach sharply contrasts with Friedman’s (1953) “The Methodology of Positive Economics”

• Goals of positive science are predictive• Don’t reject theory based on “unrealistic

assumptions”• Also, rejects notion that all the predictions of a theory

matter to its validity – goal is “narrow predictive success”

9


“Methodology”

Alternative view, Daniel Hausman (1992)

• “No good way to know what to try when a prediction fails or whether to employ a theory in a new application without judging its assumptions”

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Asset Pricing

11


Expectations

Key asset pricing theories relate expected returns to “risk”

• Expected returns are never observed

• Variances and covariances are never observed

12


Expectations

Many asset pricing theories also postulate the existence of the representative agent, i.e. there is no disagreement

• Recent research has made some progress both theoretically (heterogeneous expectations) and empirically (modeling disagreement)– Disagreement proxy of choice is the I/B/E/S standard

deviation of analysts’ forecasts

13


Expectations

Many asset pricing tests rely on a rational expectations argument

• Empirical models of expectations– Average returns (unconditional expectations)

– Linear projection (conditional expectations)

– ARCH/GARCH weighted average of past squared return surprises (which embeds an expectation of the return)

– Skewness extremely difficult to measure

14


Expectations: Measurement • Survey CFOs every quarter

• Q2 2000 through Q1 2005 (20 quarters)• 200+ responses per quarter (4,346 total observations)• We have other data back to Q3 1996

• Why CFOs? – We have access to CFOs– We know from previous surveys and interviews that part of their job is

to try to understand both the market and their stock’s performance relative to the market

– Should not be biased the way that analyst forecasts might be

15


Expectations: Measurement

16

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Mea

n pr

emiu

m

A. One-year risk premium

Jun., Sept., Dec., Mar., Jun., Sept., Dec., Mar., Jun., Sept., Dec., Mar., Jun., Sept., Dec., Mar., Jun., Sept. Dec., Feb., 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03 04 04 04 04 05


Expectations: Mean

17


Expectations: Mean

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Mea

n pr

emiu

m


B. Ten-year risk premium

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Survey and Field Research Expectations: Mean Determinants –

Persistence of Expectations

y = 0.6263x + 1.6052

R2 = 0.4188

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8 9

Past quarter premium

One

yea

r pre

miu

m %

19


Persistence of Expectations

y = 0.0949x + 4.1509

R2 = 0.0124

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8 9

One-year lagged premium

One

yea

r pre

miu

m %

20


Extrapolation of Past Returns

y = 0.1902x + 4.0305

R2 = 0.2917

0

1

2

3

4

5

6

7

8

-15 -10 -5 0 5 10

Past 1-month S&P 500 return

One

yea

r pre

miu

m %

21


Expectations: Mean Determinants –Extrapolation of Past Returns

y = 0.1383x + 4.2567

R2 = 0.3786

0

1

2

3

4

5

6

7

8

-20 -15 -10 -5 0 5 10 15 20

Past 1-quarter S&P 500 return

One

yea

r pre

miu

m %

22


Expectations: Mean Determinants –Extrapolation of Past Returns y = 0.0624x + 4.2108

R2 = 0.311

0

1

2

3

4

5

6

7

8

-30 -20 -10 0 10 20 30

Past 1-year S&P 500 return

One

yea

r pre

miu

m %

23


Expectations: Mean Determinants –Expectations of Fundamentals

y = 0.1889x - 5.851

R2 = 0.4163

0

1

2

3

4

5

6

7

8

40 45 50 55 60 65

ISM Manufacturing

One

yea

r pre

miu

m %

24


Expectations: Mean Determinants –Expectations of Fundamentals

y = 0.9145x + 2.1983

R2 = 0.4223

0

1

2

3

4

5

6

7

8

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

10yr-3mo yield spread %

One

yea

r pre

miu

m %

25


Expectations: Mean Determinants –Expectations of Risk

y = -0.0847x + 6.1826

R2 = 0.1909

0

1

2

3

4

5

6

7

8

0 5 10 15 20 25 30 35 40 45 50

Current VIX

One

yea

r pre

miu

m %

26


Expectations: Volatility

• We measure two components of volatility• Individual volatility• Disagreement among individuals

27


Expectations: Volatility

• Market volatility

Var[r]= E[Var(r|Z)] + Var(E[r|Z])

average vol. + disagreement vol.

• Individual volatilities (Davidson and Cooper)

Variance = {[r(0.90) - r(0.10)]/2.65}2

28


Expectations: Disagreement Volatility

0

1

2

3

4

5

6

7

8

9

10

Vol

dis

agre

e


29


Expectations: Individual Volatility

0

1

2

3

4

5

6

7

8

9

10

Vol

ind

ivid

ual


30


Expectations: Total Volatility

0

1

2

3

4

5

6

7

8

9

10

Vol

tot

al


31


Expectations: Volatility determinants –Persistence of expectations

y = 0.2704x + 4.5935

R2 = 0.0899

4

5

6

7

8

9

10

4 5 6 7 8 9 10

One-year lagged individual vol

Indi

vidu

al v

ol

32



y = -0.3048x + 4.9013

R2 = 0.1191

2

3

4

5

6

2 3 4 5 6

One-year lagged disagreement vol

Dis

agre

emen

t vol

33



y = 0.193x + 5.9427

R2 = 0.0396

5

6

7

8

9

10

11

5 6 7 8 9 10 11

One-year lagged total vol

Tot

al v

ol

34


Expectations: Volatility determinants –Influence of past returns (Individual vol)

y = -0.0638x + 6.9193

R2 = 0.0725

4

5

6

7

8

9

10

-15 -10 -5 0 5 10

One-month lagged return

Indi

vidu

al v

ol

35


Expectations: Volatility determinants –Influence of past returns (Disagreement vol)

y = -0.0666x + 3.7914

R2 = 0.1948

2

3

4

5

6

7

8

-15 -10 -5 0 5 10


Dis

agre

emen

t vol

36


Expectations: Volatility determinants –Influence of past returns (Disagreement vol)

y = -0.0666x + 3.7914

R2 = 0.1948

y = 0.0194x2 + 0.0138x + 3.2792

R2 = 0.5828

2

3

4

5

6

7

8

-15 -10 -5 0 5 10


Dis

agre

emen

t vol

37


Expectations: Volatility determinants –Fundamentals (Individual vol)

y = -0.141x + 14.314

R2 = 0.5118

4

5

6

7

8

9

10

40 45 50 55 60 65

ISM Manufacturing

Indi

vidu

al v

ol

38


Expectations: Volatility determinants –Fundamentals (Disagreement vol)

y = -0.0689x + 7.396

R2 = 0.3016

2

3

4

5

6

7

8

40 45 50 55 60 65

ISM Manufacturing

Dis

agre

emen

t vol

39


Expectations: Volatility determinants –Fundamentals (Total vol)

y = -0.1591x + 16.248

R2 = 0.5564

4

5

6

7

8

9

10

11

40 45 50 55 60 65

ISM Manufacturing

Tot

al v

ol

40


Expectations: Volatility determinants –Risk measures (Individual)

y = 0.0597x + 5.4198

R2 = 0.2092

4

5

6

7

8

9

10

11

0 5 10 15 20 25 30 35 40 45 50

VIX

Indi

vidu

al v

ol

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Expectations: Volatility determinants –Risk measures (Disagreement)

y = 0.0416x + 2.7417

R2 = 0.2504

2

3

4

5

6

7

8

9

10

11

0 5 10 15 20 25 30 35 40 45 50

VIX

Dis

agre

emen

t vol

42


Expectations: Volatility determinants –Risk measures (Total)

y = 0.0729x + 6.0779

R2 = 0.2665

4

5

6

7

8

9

10

11

0 5 10 15 20 25 30 35 40 45 50

VIX

Tot

al v

ol

43


Expectations: Individual Skewness

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

Ske

w i

ndiv

idua

l


44


Expectations: Disagreement Skewness

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

Ske

w i

ndiv

idua

l


45


Expectations: Skewness determinants–Influence of past returns (Disagreement skewness)

y = 0.0502x + 0.2289

R2 = 0.3917

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

-15 -10 -5 0 5 10

One-month lagged S&P

Dis

agre

emen

t ske

w

46


Optimism

• Will be measured as the mean difference between the expected returns and the realized returns– Notice that we have no way to calibrate the quality

of the expected returns – given the “true” expected return is unobservable

– We can only make inference about forecasting ability

47


Optimism Returns forecasting ability

y = -2.1815x + 11.855

R2 = 0.0368

-40

-30

-20

-10

0

10

20

30

4.0 5.0 6.0 7.0 8.0 9.0 10.0

Expected 1-yr S&P return

Rea

lize

d 1-

yr S

&P

retu

rn %

48


Optimism Returns bias (Average=10% per annum)

-30

-20

-10

0

10

20

30

40

Opt

imis

m


49


Optimism

Lower Tail 00q3

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

50


Optimism

Lower Tail 00q4

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

51


Optimism

Lower Tail 01q1

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cen

tag

e

52


Optimism

Lower Tail 01q2

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cen

tag

e

53


Optimism

Lower Tail 01q3

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cen

tag

e

54


Optimism

Lower Tail 01q4

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cen

tag

e

55


Optimism

Lower Tail 02q1

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cen

tag

e

56


Optimism

Lower Tail 02q2

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

57


Optimism

Lower Tail 02q3

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

58


Optimism

Upper Tail 02q4

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

59


Optimism

Upper Tail 03q1

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

60


Optimism

Upper Tail 03q2

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

61


Optimism

Upper Tail 03q3

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

62


Optimism

Upper Tail 03q4

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

63


Optimism

Upper Tail 04q1

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

64


Optimism

Upper Tail 04q2

0

10

20

30

40

50

60

-30

-25

-20

-15

-10 -5 0 5 10 15 20 25 30

Mor

e

Per

cent

age

65


Optimism Volatility (individual) forecasting ability

y = 1.5837x + 7.7953

R2 = 0.1085

0

5

10

15

20

25

30

4 5 6 7 8 9 10 11

Individual vol

Rea

lize

d vo

l

66


Optimism Volatility (disagreement) forecasting ability

y = 1.2082x + 14.484

R2 = 0.0297

0

5

10

15

20

25

30

2 3 4 5 6 7 8 9 10 11

Disagreement vol

Rea

lize

d vo

l

67


Optimism Volatility (total) forecasting ability

y = 1.4665x + 7.1491

R2 = 0.1068

0

5

10

15

20

25

30

4 5 6 7 8 9 10 11

Total vol

Rea

lize

d vo

l

68


Optimism Volatility (total) bias

-25

-20

-15

-10

-5

0

Tot

al v

ol m

inus

rea

lize

d


69


Optimism Skewness (individual) forecasting ability

y = 0.1686x + 0.2217

R2 = 0.0943

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

-1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4

Individual skew

Rea

lize

d sk

ew

70


Optimism Skewness (disagreement) forecasting ability

y = -0.0563x + 0.1672

R2 = 0.0124

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

Disagreement skew

Rea

lize

d sk

ew

71


Company Valuation

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Q396

Q496

Q197

Q297

Q397

Q497

Q198

Q298

Q398

Q498

Q199

Q299

Q399

Q499

Q100

Q200

. . . Q202

How does the market value your company's stock?

Undervalued Correctly valued Overvalued

72


Company Valuation

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1996 1997 1998 1999 2000 2001 2002

How does the market value your company's stock?


73


Company Valuation

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Q396

Q496

Q197

Q297

Q397

Q497

Q198

Q298

Q398

Q498

Q199

Q299

Q399

Q499

Q100

Q200

How does the market value your company's stock? Tech industry


74


Overconfidence

• In the psychology literature, overconfidence can mean either believing that the distribution of your knowledge is tighter than it actually is or believing that your mean skill is higher than it actually is – We will focus on the subjective probability being

tighter than true probability following other finance papers such as Odean (1998), Gervais and Odean (2001)

75


Overconfidence:% of time realized returns fall outside 80% confidence range

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% o

ut o

f ra

nge


76


Overconfidence:% of time realized returns fall outside 80% confidence range

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% o

ut o

f ra

nge


77


Overconfidence:Number of standard deviations realized return from forecasts

0

2

4

6

8

10

12

Ave

rage

# s

td.

devi

atio

ns o

ut


78


Corporate Finance

79


Link to Corporate Actions

• Corporate decisions– IPO/SEO– Capital structure – Payout policy– Investment decisions– Mergers/acquisitions– Corporate financial reporting

• Our data is aggregate so we can only study economy-wide variation

80


Link to Corporate Actions

0

1

2

3

4

5

6

7

8

00q3

00q4

01q1

01q2

01q3

01q4

02q1

02q2

02q3

02q4

03q1

03q2

03q3

03q4

04q1

04q2

04q3

04q4

05q1

05q2

Survey for quarter

One

yea

r pre

miu

m %

0

20

40

60

80

100

120

140

160

Average Premium Number of IPOs in survey quarter

81


Corporate Financial Reporting

Insight on following issues:• Importance of reported earnings and earnings

benchmarks• Are earnings managed? How? Why?

– Real versus accounting earnings management– Does missing consensus indicate deeper problems?

• Consequences of missing earnings targets• Importance of earnings paths• Why make voluntary disclosures?

82


Strengths and limitations Strengths:• Surveys enable us to ask decision-makers specific qualitative

questions about motivations• Less of a variable specification problem• Complements large sample analyses • A unique angle to confront theories with data

Limitations: • Questions may be misunderstood• Truthful responses?• Non-response bias • Friedman (1953)

83


Method

Survey and Interview Design• Draft survey instrument “refereed” by both finance

and accounting researchers as well as experts in survey design

• Interviewed structured to adhere to best scientific practices of interviews, e.g. Sudman and Bradburn (1983)

• IRB certification for human subject research

84


Sample

• 401 usable survey responses– response rate of 10.4%

• 25% response rate at a practitioner conference• 8% response rate to Internet survey

• Interview 20 CFOs– 40-90 minutes in length– More give and take than in the survey– Interviewed firms are much larger, more levered and more

profitable than the average Compustat firm.• Relative to Compustat firms

– Surveyed firms are larger, more levered, greater dividend-yield, fewer firms report negative earnings

– Similar B/M and positive P/E

85


Sample

Firm characteristics (self reported)• Agency

– CEO age, tenure, education– Inside ownership

• Size– Revenues– Number of employees

• Growth opportunities– P/E– Growth in earnings

86


Sample

Firm characteristics (self reported)• Free cash flow effects

– Profitability– Leverage

• Informational effects– Public/private– Which stock exchange

• Industry• Credit rating

87


Sample

Firm characteristics (self reported)• Financial reporting practices

– Number of analysts– Do they give “guidance”?

• Ticker symbol!

Demographic correlations in Table 1– Note positive relation between whether you give

guidance and number of analysts (Lang and Lundholm TAR 1996)

88


Performance measurements (earnings, cash flows): Sec 3.1,Table 2

Voluntary disclosure

Earnings benchmarks

Sec 3.2, Table 3

Earnings trends:

Why meet benchmarks?Sec 3.3, Table 4

What if miss benchmarks? Sec 3.4, Table 5

How to meet benchmarks: Sec 4.1, Table 6

Value sacrifice to meet benchmarks:Sec 4.2, Table 7

Why smooth earnings?Sec 5.1, Table 8

Value sacrifice for smooth earnings Sec 5.2, Table 9

Why disclose?Sec 6.1,Table 11

Why not disclose?Sec 6.2, Table 12

TimingSec 6.3

Table 13

Fig. 1 Flowchart depicting the outline of the paper

89




Earnings benchmarks

Sec 3.2, Table 3

Earnings trends:









TimingSec 6.3

Table 13


90

Graham/Harvey/Rajgopal: Corporate Reporting

Motivation

DeGeorge, Patel, Zeckhauser, JB 1999

91




Earnings benchmarks

Sec 3.2, Table 3

Earnings trends:









TimingSec 6.3

Table 13


92

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

avoid violating debt-covenants

achieve desired credit rating

employees achieve bonuses

assures stakeholders business is stable

reduce stock price volatility convey future growth prospects to investors

external reputation of management

maintain or increase our stock price

build credibility with capital market

Percent agree or strongly agree


Why meet earnings benchmarks?

Responses to the statement: “Meeting earnings benchmarks helps …” based on a survey of 401 financial executives.

93

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

increases the possibility of lawsuits

outsiders might think firm lacks flexibility

increases scrutiny of all aspects of earnings releases

have to spend time explaining why we missed

outsiders think there are previously unknown problems

creates uncertainty about our future prospects


Consequences of missing benchmarks

Responses to the statement: “Failing to meet benchmarks…” based on a survey of 401 financial executives.

94


Consequences of missing benchmarks

Cockroach problem• “You have to start with the premise that everyone

manages earnings”• If you can’t come up with a few cents, there must be

some previously unknown serious problems at the firm

• “If you see one cockroach, you immediately assume there are hundreds behind the walls, even though you have no proof that this is the case”

95




Earnings benchmarks

Sec 3.2, Table 3

Earnings trends:









TimingSec 6.3

Table 13


96


Actions taken to meet benchmarks0% 20% 40% 60% 80% 100%

Decrease discretionary spending (e.g. R&D,advertising, maintenance, etc.)

Delay starting a new project even if this entails asmall sacrifice in value

Book revenues now rather than next quarter (ifjustified in either quarter)

Provide incentives for customers to buy moreproduct this quarter

Draw down on reserves previously set aside

Postpone taking an accounting charge

Sell investments or assets to recognize gains thisquarter

Repurchase common shares

Alter accounting assumptions (e.g. allowances,pensions etc.)

“Near the end of the quarter, it looks like your company might come in below the desired earnings target. Within what is permitted by GAAP, which of the following choices might your company make?”

97


Sacrificing long-term value

Hypothetical scenario: Your company’s cost of capital is 12%. Near the end of the quarter, a new opportunity arises that offers a 16% internal rate of return and the same risk as the firm. The analyst consensus EPS estimate is $1.90. What is the probability that your company will pursue this project in each of the following scenarios?

Actual EPS if you do not pursue the project

Actual EPS if you pursue the project

The probability that the project will be pursued in this scenario is …

(check one box per row)

0% 20% 40% 60% 80% 100%

$2.00 $1.90

$1.90 $1.80

$1.80 $1.70

$1.40 $1.30

98


Sacrificing long-term value 0% 20% 40% 60% 80% 100%

If you take project, youwill exactly hit consensus

earnings

If you take project, youwill miss consensusearnings by $0.10



Probability of accepting project

99



Only 45% would take the project for sure – even if they are projected to meet consensus

EPS if you do not pursue

EPS if you

pursue

Average probability of

pursuing 0% 20% 40% 60% 80% 100%

$2.00 $1.90 4% 4% 5% 10% 32% 45%$1.90 $1.80 10% 14% 10% 20% 28% 18%$1.80 $1.70 14% 12% 13% 21% 22% 17%$1.40 $1.30 20% 13% 12% 15% 20% 19%

Probability that the project will be pursued: (Percent of respondents indicating)

[Table 7]

100



Reminiscent of Brav, Graham, Harvey and Michaely• Sacrifice positive NPV projects before cutting

dividends

101


Other insights on meeting benchmarks

Interviews• 18/20 interview mentioned trade off of short-run

earnings and long-term optimal decisions• Investment banks offer products that create

accounting income with negative cash flow consequences

102



Guidance• Goal of guidance is to meet or exceed consensus

every quarter• Analysts complicit in game of always meeting or

exceeding• Large positive surprises lead to “ratchet-up effect”• Asymmetric

103



Break out of the game• Why not declare that you will not play the earnings

management game?

104




Earnings benchmarks

Sec 3.2, Table 3

Earnings trends:









TimingSec 6.3

Table 13


105


Smoothing

96.9% and 20/20 interviews prefer smooth earnings over more volatile holding cash flows constant

106


Smoothing 0% 20% 40% 60% 80% 100%

Is perceived as less risky by investors

Makes it easier for analysts/investors to predictfuture earnings

Assures customers/suppliers that business is stable

Reduces the return that investors demand (i.e.smaller risk premium)

Promotes a reputation for transparent and accuratereporting

Conveys higher future growth prospects

Achieves or preserves a desired credit rating

Clarifies true economic performance

Increases bonus payments

Responses to the question: “Do the following factors contribute to your company preferring a smooth earnings path?”

107


Smoothing

Reasons• Lowers “risk”; increased predictability; lower “risk”

premium• Clear from survey and interviews that CFOs believe

that this risk is priced• Possible link to literature on: estimation error,

disagreement in asset pricing, information risk premium, and behavioral literature on risk versus uncertainty

108


Sacrificing value for smoothing 0% 20% 40% 60% 80% 100%

None

Small sacrifice

Moderate sacrifice

Large sacrifice

Responses to the question: “How large a sacrifice in value would your firm make to avoid a bumpy earnings path?”

109


Other insights on smoothing

Interviews• Volatile earnings will create trading incentives for

speculators, hedge funds and legal vultures• Volatile earnings mean that you will have a number

of misses – which CFOs want to avoid

Smoothing example

110


Conclusions

• Consensus earnings factors into decisions• Cash secondary to accounting earnings• Strong desire to meet benchmarks – cockroach

problem• It is routine to sacrifice long-term value to meet these

benchmarks• Meeting benchmarks is important both for the firm’s

stock price and managers reputation and mobility• Agents optimizing over short-term horizon

111


Conclusions

• Having predictable smooth earnings is thought to both reduce the cost of capital and enhance manager reputation

• Voluntary disclosure is an important tool in manager’s arsenal

• Disclosure can potentially reduce information risk and enhance a manager’s reputation

112


Future research

Last survey instrument!

• We are thinking of administering the identical survey before it is published to non-management members of Boards of Directors.

Also…• “Detection of Financial Earnings Management”• “Detection of Real Earnings Management”We have the tickers for 107 firms many of which admit to both

financial and real earnings management

1 Survey and Field Research in Finance: Miscalibration and Corporate Actions Campbell R. Harvey Duke...

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