1. 1 Survey and Field Research in Finance: Miscalibration and Corporate Actions Campbell R. Harvey Duke University, Durham, NC USA National Bureau of Economic Research, Cambridge, MA USA April 15, 2005 Yale University

2. 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. 3 Survey and Field Research 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 21 st Century” forthcoming in JFE 2005

4. 4 Survey and Field Research 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. 5 Survey and Field Research 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. 6 Survey and Field Research Methodology

7. 7 Survey and Field Research “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. 8 Survey and Field Research “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. 9 Survey and Field Research “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”

10. 10 Survey and Field Research Asset Pricing

11. 11 Survey and Field Research Expectations Key asset pricing theories relate expected returns to “risk” Expected returns are never observed Variances and covariances are never observed

12. 12 Survey and Field Research 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. 13 Survey and Field Research 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. 14 Survey and Field Research 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. 15 Survey and Field Research Expectations: Measurement

16. 16 Survey and Field Research Expectations: Mean

17. 17 Survey and Field Research Expectations: Mean

18. 18 Survey and Field Research Expectations: Mean Determinants – Persistence of Expectations

19. 19 Survey and Field Research Expectations: Mean Determinants – Persistence of Expectations

20. 20 Survey and Field Research Expectations: Mean Determinants – Extrapolation of Past Returns

21. 21 Survey and Field Research Expectations: Mean Determinants – Extrapolation of Past Returns

22. 22 Survey and Field Research Expectations: Mean Determinants – Extrapolation of Past Returns

23. 23 Survey and Field Research Expectations: Mean Determinants – Expectations of Fundamentals

24. 24 Survey and Field Research Expectations: Mean Determinants – Expectations of Fundamentals

25. 25 Survey and Field Research Expectations: Mean Determinants – Expectations of Risk

26. 26 Survey and Field Research Expectations: Volatility We measure two components of volatility Individual volatility Disagreement among individuals

27. 27 Survey and Field Research 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. 28 Survey and Field Research Expectations: Disagreement Volatility

29. 29 Survey and Field Research Expectations: Individual Volatility

30. 30 Survey and Field Research Expectations: Total Volatility

31. 31 Survey and Field Research Expectations: Volatility determinants – Persistence of expectations

32. 32 Survey and Field Research Expectations: Volatility determinants – Persistence of expectations

33. 33 Survey and Field Research Expectations: Volatility determinants – Persistence of expectations

34. 34 Survey and Field Research Expectations: Volatility determinants – Influence of past returns (Individual vol)

35. 35 Survey and Field Research Expectations: Volatility determinants – Influence of past returns (Disagreement vol)

36. 36 Survey and Field Research Expectations: Volatility determinants – Influence of past returns (Disagreement vol)

37. 37 Survey and Field Research Expectations: Volatility determinants – Fundamentals (Individual vol)

38. 38 Survey and Field Research Expectations: Volatility determinants – Fundamentals (Disagreement vol)

39. 39 Survey and Field Research Expectations: Volatility determinants – Fundamentals (Total vol)

40. 40 Survey and Field Research Expectations: Volatility determinants – Risk measures (Individual)

41. 41 Survey and Field Research Expectations: Volatility determinants – Risk measures (Disagreement)

42. 42 Survey and Field Research Expectations: Volatility determinants – Risk measures (Total)

43. 43 Survey and Field Research Expectations: Individual Skewness

44. 44 Survey and Field Research Expectations: Disagreement Skewness

45. 45 Survey and Field Research Expectations: Skewness determinants– Influence of past returns (Disagreement skewness)

46. 46 Survey and Field Research 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. 47 Survey and Field Research Optimism Returns forecasting ability

48. 48 Survey and Field Research Optimism Returns bias (Average=10% per annum)

49. 49 Survey and Field Research Optimism

50. 50 Survey and Field Research Optimism

51. 51 Survey and Field Research Optimism

52. 52 Survey and Field Research Optimism

53. 53 Survey and Field Research Optimism

54. 54 Survey and Field Research Optimism

55. 55 Survey and Field Research Optimism

56. 56 Survey and Field Research Optimism

57. 57 Survey and Field Research Optimism

58. 58 Survey and Field Research Optimism

59. 59 Survey and Field Research Optimism

60. 60 Survey and Field Research Optimism

61. 61 Survey and Field Research Optimism

62. 62 Survey and Field Research Optimism

63. 63 Survey and Field Research Optimism

64. 64 Survey and Field Research Optimism

65. 65 Survey and Field Research Optimism Volatility (individual) forecasting ability

66. 66 Survey and Field Research Optimism Volatility (disagreement) forecasting ability

67. 67 Survey and Field Research Optimism Volatility (total) forecasting ability

68. 68 Survey and Field Research Optimism Volatility (total) bias

69. 69 Survey and Field Research Optimism Skewness (individual) forecasting ability

70. 70 Survey and Field Research Optimism Skewness (disagreement) forecasting ability

71. 71 Survey and Field Research Company Valuation

72. 72 Survey and Field Research Company Valuation

73. 73 Survey and Field Research Company Valuation

74. 74 Survey and Field Research 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. 75 Survey and Field Research Overconfidence: % of time realized returns fall outside 80% confidence range

76. 76 Survey and Field Research Overconfidence: % of time realized returns fall outside 80% confidence range

77. 77 Survey and Field Research Overconfidence: Number of standard deviations realized return from forecasts

78. 78 Survey and Field Research Corporate Finance

79. 79 Survey and Field Research 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. 80 Survey and Field Research Link to Corporate Actions

81. 81 Survey and Field Research 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. 82 Survey and Field Research 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. 83 Survey and Field Research 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. 84 Survey and Field Research 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. 85 Survey and Field Research 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. 86 Survey and Field Research Sample Firm characteristics (self reported) Free cash flow effects –Profitability –Leverage Informational effects –Public/private –Which stock exchange Industry Credit rating

87. 87 Survey and Field Research 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. 88 Corporate Financial Reporting 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 Timing Sec 6.3 Table 13 Fig. 1 Flowchart depicting the outline of the paper

89. 89 Corporate Financial Reporting 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 Timing Sec 6.3 Table 13 Fig. 1 Flowchart depicting the outline of the paper

90. 90 Graham/Harvey/Rajgopal: Corporate Reporting Motivation DeGeorge, Patel, Zeckhauser, JB 1999

91. 91 Corporate Financial Reporting 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 Timing Sec 6.3 Table 13 Fig. 1 Flowchart depicting the outline of the paper

92. 92 Graham/Harvey/Rajgopal: Corporate Reporting Why meet earnings benchmarks? Responses to the statement: “Meeting earnings benchmarks helps …” based on a survey of 401 financial executives.

93. 93 Graham/Harvey/Rajgopal: Corporate Reporting Consequences of missing benchmarks Responses to the statement: “Failing to meet benchmarks…” based on a survey of 401 financial executives.

94. 94 Graham/Harvey/Rajgopal: Corporate Reporting 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. 95 Corporate Financial Reporting 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 Timing Sec 6.3 Table 13 Fig. 1 Flowchart depicting the outline of the paper

96. 96 Graham/Harvey/Rajgopal: Corporate Reporting Actions taken to meet benchmarks “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. 97 Graham/Harvey/Rajgopal: Corporate Reporting 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 $1.80 $1.70 $1.40$1.30

98. 98 Graham/Harvey/Rajgopal: Corporate Reporting Sacrificing long-term value Probability of accepting project

99. 99 Graham/Harvey/Rajgopal: Corporate Reporting Sacrificing long-term value Only 45% would take the project for sure – even if they are projected to meet consensus [Table 7]

100. 100 Graham/Harvey/Rajgopal: Corporate Reporting Sacrificing long-term value Reminiscent of Brav, Graham, Harvey and Michaely Sacrifice positive NPV projects before cutting dividends

101. 101 Graham/Harvey/Rajgopal: Corporate Reporting 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. 102 Graham/Harvey/Rajgopal: Corporate Reporting Other insights on meeting benchmarks 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. 103 Graham/Harvey/Rajgopal: Corporate Reporting Other insights on meeting benchmarks Break out of the game Why not declare that you will not play the earnings management game?

104. 104 Corporate Financial Reporting 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 Timing Sec 6.3 Table 13 Fig. 1 Flowchart depicting the outline of the paper

105. 105 Graham/Harvey/Rajgopal: Corporate Reporting Smoothing 96.9% and 20/20 interviews prefer smooth earnings over more volatile holding cash flows constant

106. 106 Graham/Harvey/Rajgopal: Corporate Reporting Smoothing Responses to the question: “Do the following factors contribute to your company preferring a smooth earnings path?”

107. 107 Graham/Harvey/Rajgopal: Corporate Reporting 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. 108 Graham/Harvey/Rajgopal: Corporate Reporting Sacrificing value for smoothing Responses to the question: “How large a sacrifice in value would your firm make to avoid a bumpy earnings path?”

109. 109 Graham/Harvey/Rajgopal: Corporate Reporting 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. 110 Graham/Harvey/Rajgopal: Corporate Reporting 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. 111 Graham/Harvey/Rajgopal: Corporate Reporting 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. 112 Graham/Harvey/Rajgopal: Corporate Reporting 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