Skip to main content

Risk-based forecasting and planning and management earnings forecasts


This study examines the association between a firm’s internal information environment and the accuracy of its externally disclosed management earnings forecasts. Internally, firms use forecasts to plan for uncertain futures. The risk management literature argues that integrating risk-related information into forecasts and plans can improve a firm’s ability to forecast financial outcomes. We investigate whether this internal information manifests itself in the accuracy of external earnings guidance. Using detailed survey data and publicly disclosed management earnings forecasts from a sample of publicly traded U.S. companies, we find that more sophisticated risk-based forecasting and planning processes are associated with smaller earnings forecast errors and narrower forecast widths. These associations hold across a variety of different planning horizons (ranging from annual budgeting to long-term strategic planning), providing empirical support for the theoretical link between internal information quality and the quality of external disclosures.

This is a preview of subscription content, access via your institution.


  1. 1.

    The survey used for our study covers the major elements of the Committee of Sponsoring Organizations of the Treadway Commission’s enterprise risk management (COSO 2004) and internal control (COSO 2013) frameworks, which the SEC is advocating as best practice in risk and control assessment, especially over financial reporting (Stout 2014).

  2. 2.

    AFP (2014) find that only 23% of financial planning and analysis groups regularly employ risk analysis, and just 21% of these groups have a high degree of collaboration with risk management. A survey by Marsh and RIMS (2014) adds that only 20% of firms believe that risk management significantly influences the setting of business strategy.

  3. 3.

    Risk appetite represents the amount of risk the firm is willing to accept to achieve its objectives. Risk tolerance is the acceptable variation in outcomes related to each risk. And risk capacity is the maximum level of risk the firm can assume given its financial and nonfinancial resources.

  4. 4.

    These claims are consistent with studies finding that forecasts that are based at least partially on quantitative methods are more accurate than those based purely on qualitative judgment (Lawrence et al. 2006). Similarly, Cassar and Gerakos (2017) find that the accuracy of hedge fund managers’ performance forecasts during the financial crisis were positively associated with their use of value-at-risk modeling and stress testing.

  5. 5.

    Neither author has received any compensation or funding from Aon.

  6. 6.

    The Committee of Sponsoring Organizations of the Treadway Commission (COSO) is an initiative supported by the Institute of Management Accountants, the American Accounting Association, the American Institute of Certified Public Accountants, the Institute of Internal Auditors, and Financial Executives International. COSO’s enterprise risk management model is a widely adopted framework that defines the essential components of an ERM process.

  7. 7.

    Due to the ad hoc nature of the solicitation process, we cannot determine a response rate.

  8. 8.

    We also estimated the models in Table 5 after including Width as an additional control variable to examine whether firms that provided wider earnings forecast ranges had larger or smaller forecast errors. Width was not significant in any of the models, while the signs and significance of the RBFP variables exhibited no substantial change. We also assess forecast accuracy using the upper and lower bounds of the forecasted range. These results are reported in section 4.1.4.

  9. 9.

    We also estimated models with interactions between each pair of the three information gathering and processing variables and the three planning variables. The only other significant relation is a positive interaction between Distribs. and Budgeting. In this model, the coefficient on the Budgeting main effect is −0.139 (p = 0.02, two-tailed) and the coefficient on the interaction term is 0.139 (p = 0.08, two-tailed), indicating that greater incorporation of risk considerations when budgeting is associated with smaller earnings forecast errors unless distributions are used in forecasting, in which case Budgeting has no significant effect. We also find that Distribs. has a significantly negative main effect on forecast errors, regardless of the other RBFP practice included in the model, suggesting that the use of probability distributions or stochastic models when forecasting can improve forecasting ability, independent of the other RBFP practices adopted by the firm.

  10. 10.

    One question is whether our inclusion of Analyst Uncertain. and Analyst Error, which are highly correlated with Mgmt Error, is over-controlling for the influence of RBFP or is driving our results. Estimating the Mgmt Error model using only the other control variables produces an adjusted R-squared of 0.19. When we introduce the RBFP variables to this is base model, the models’ adjusted R-squareds range from 0.20 to 0.21 and the coefficients on all of the RBFP variables are negative and significant, with the exception of Strategy, which is negative but insignificant. This evidence suggests that the inclusion of Analyst Uncertain. and Analyst Error as control variables is not driving our overall results.

  11. 11.

    The results in Panel B of Table 6 are not driven by pre-existing forecasting improvement trends in the 2005–2007 period. When we estimate the models using only these years and code Trend as one in 2005, two in 2006, and three in 2007, the coefficients on Trend, the RBFP components, and their interactions are all statistically insignificant.

  12. 12.

    We also estimated logit models with the dependent variables coded one if actual EPS fell within the upper and lower bounds of management’s earnings forecast and zero otherwise. None of the RBFP variables was statistically significant in these tests.

  13. 13.

    To offer evidence on how large an omitted variable’s association with Width and RBFP w/o Distribs. would have to be to overturn the estimated negative association between these two variables, we again calculate the impact threshold for a confounding variable (ITCV). The ITCV for Width and RBFP w/o Distribs. is −0.119, indicating that an omitted variable would need a partial correlation with both these variables of 0.345 in magnitude (with one of the correlations negative) to overturn this result. Our included controls suggest associations of this magnitude are unlikely, as the impact of all of the control variables are far below this threshold. We also find that the results are robust to estimating a Heckman selection model. The coefficient on the main effect of RBFP w/o Distribs. remains negative and significant, the coefficient on the interaction between this variable and Distribs. remains positive and significant, and the estimated inverse Mills ratio is insignificant.

  14. 14.

    Given the observed association between more sophisticated RBFP practices and higher earnings forecast quality, we examined whether the use of these more sophisticated practices is also associated with greater disclosure of risk management practices. We searched the firms’ 10 K and proxy statements for the number of times the following phrases were mentioned: risk_management, COSO, president_of_risk or risk_management or enterprise_risk_management, chief_risk_officer, risk_committee, strategic_risk_management, consolidated_risk_management, holistic_risk_management, or integrated_risk_management. We find no association between this word count and the firm’s RBFP scores. One explanation for this result (in addition to the crudeness of this disclosure measure) is that risk disclosures are not credible and may simply reflect cheap talk. See Dobler (2008) for a discussion.


  1. Aberdeen. (2012). Financial planning, budgeting, and forecasting: Leveraging risk-adjusted strategies to enable accuracy. Boston: Aberdeen Group.

    Google Scholar 

  2. AFP. (2012). 2012 AFP risk survey. Bethesda: Assocation for Financial Professionals.

    Google Scholar 

  3. AFP. (2014). AFP risk survey. Bethesda: Assocation for Financial Professionals.

    Google Scholar 

  4. Ai, J., Brockett, P., Cooper, W., & Golden, L. (2012). Enterprise risk management through strategic allocation of capital. Journal of Risk and Insurance, 79(1), 29–55.

    Article  Google Scholar 

  5. Ai, J., Brockett, P., & Wang, T. (2013). Optimal enterprise risk management and decision making with shared and dependent risks. Unpublished paper, Available at SSRN:

  6. Ajinkya, B. B., Bhojraj, S., & Sengupta, P. (2005). The association between outside directors, institutional investors and the properties of management earnings forecasts. Journal of Accounting Research, 43, 343–376.

    Article  Google Scholar 

  7. Ali, A., Klasa, S., & Yeung, E. (2014). Industry concentration and corporate disclosure policy. Journal of Accounting and Economics, 58, 240–264.

    Article  Google Scholar 

  8. Alviniussen, A., & Jankensgard, H. (2009). Enterprise risk budgeting - bringing risk management into the financial planning process. Journal of Applied Finance, 18, 178–192.

    Google Scholar 

  9. Aon. (2010). Global enterprise risk management survey. Chicago: Aon Corporation.

    Google Scholar 

  10. Armstrong, J. S. (2001). Priciples of forecasting: A handbook for researchers and practitioners. Boston: Kluwer.

    Book  Google Scholar 

  11. Barron, O. E., Kim, O., Lim, S. C., & Stevens, D. E. (1998). Using analysts forecasts to measure properties of analysts information environment. The Accounting Review, 73, 421–433.

    Google Scholar 

  12. Baxter, R., Bedard, J. C., Hoitash, R., & Yezegel, A. (2013). Enterprise risk management program quality: Determinants, value relevance, and the financial crisis. Contemporary Accounting Research, 30, 1264–1295.

    Article  Google Scholar 

  13. Beasley, M., & Frigo, M. (2010). ERM and its role in strategic planning and strategy execution. In J. Fraser & B. Simkins (Eds.), Enterprise Risk Management (pp. 31–50). Hoboken: Wiley.

    Google Scholar 

  14. Bradfield, R. (2008). Cognitive barriers in the scenario development process. Advances in the Development of Human Resources, 10, 198–215.

    Article  Google Scholar 

  15. Cassar, G., Gerakos, J. (2017). Do risk management practices work?: Evidence from hedge funds. doi:10.1007/s11142-017-9403-5

  16. Cassar, G., & Gibson, B. (2008). Budgets, internal reports and manager forecast accuracy. Contemporary Accounting Research, 25, 707–737.

    Article  Google Scholar 

  17. Cheng, Q., Luo, T., & Yue, H. (2013). Managerial incentives and management forecast precision. The Accounting Review, 88, 1575–1602.

    Article  Google Scholar 

  18. Ciconte III, W., Kirk, M., & Tucker, J. (2014). Does the midpoint of range earnings forecasts represent managers’ expectations? Review of Accounting Studies, 19, 628–660.

    Article  Google Scholar 

  19. COSO. (2004). Enterprise risk management – Integrated framework. New York: Committee of Sponsoring Organizations of the Treadway Commission.

    Google Scholar 

  20. COSO. (2013). Internal control – Integrated framework. New York: Committee of Sponsoring Organizations of the Treadway Commission.

    Google Scholar 

  21. Curtis, P., & Carey, M. (2012). Risk assessment in practice. New York: Committee of Sponsoring Organizations of the Treadway Commission.

    Google Scholar 

  22. Curtis, A., Lundholm, R., & McVay, S. (2014). Forecasting sales: A model and some evidence from the retail industry. Contemporary Accounting Research, 31, 581–608.

    Article  Google Scholar 

  23. Danese, P., & Kalchschmidt, M. (2011). The impact of forecasting on companies’ performance: Analysis in a multivariate setting. International Journal of Production Economics, 133, 459–469.

    Article  Google Scholar 

  24. Daníelsson, J. (2008) Blame the models. Journal of Financial Stability, 4(4), 321–328.

  25. Dechow, P., Sloan, R., & Sweeney, A. (1995). Detecting earnings management. The Accounting Review, 70, 193–225.

    Google Scholar 

  26. Deloitte. (2012). Risk-adjusted forecasting and planning: Navigating the “new normal” of increased volatility. London: Deloitte LLP.

    Google Scholar 

  27. Deloitte. (2013). Risk-adjusted forecasting: More certainty for planning. CFO Journal. Available at

  28. Dichev, I., & Tang, V. (2009). Earnings volatility and earnings predictability. Journal of Accounting and Economics, 47, 160–181.

    Article  Google Scholar 

  29. Dobler, M. (2008). Incentives for risk reporting – A discretionary disclosure and cheap talk approach. International Journal of Accounting, 43, 184–206.

    Article  Google Scholar 

  30. Dorantes, C.-A., Li, C., Peters, G. F., & Richardson, V. J. (2013). The effect of enterprise systems implementation on the firm information environment. Contemporary Accounting Research, 30, 1427–1461.

    Article  Google Scholar 

  31. Durand, R. (2003). Predicting a firm’s forecasting ability: The roles of organizational illusion of control and organizational attention. Strategic Management Journal, 24, 821–838.

    Article  Google Scholar 

  32. Ellul, A., & Yerramilli, V. (2013). Stronger risk controls, lower risk: evidence from U.S. bank holding companies. Journal of Finance, LXVIII, 1757–1803.

    Article  Google Scholar 

  33. Feng, M., Li, C., & McVay, S. (2009). Internal control and management guidance. Journal of Accounting and Economics, 48, 190–209.

    Article  Google Scholar 

  34. Frank, K. A. (2000). Impact of a confounding variable on a regression coefficient. Sociological Methods & Research, 29, 147–194.

    Article  Google Scholar 

  35. Froot, K., & Stein, J. (1998). Risk management, capital budgeting, and capital structure policy for financial institutions: An integrated approach. Journal of Financial Economics, 47, 55–82.

    Article  Google Scholar 

  36. Gallemore, J., & Labro, E. (2015). The importance of the internal information environment for tax avoidance. Journal of Accounting and Economics, 60, 149–167.

    Article  Google Scholar 

  37. Gatzert, N., & Martin, M. (2015). Determinants and value of enterprise risk management: Empirical evidence from the literature. Risk Management and Insurance Review, 18, 29–53.

    Article  Google Scholar 

  38. Goodman, T., Neamtiu, M., Shroff, N., & White, H. (2014). Management forecast quality and capital investment decisions. The Accounting Review, 89, 331–365.

    Article  Google Scholar 

  39. Gordon, L., Loeb, M., & Tseng, C. (2009). Enterprise risk management and firm performance: A contingency perspective. Journal of Accounting and Public Policy, 28, 301–327.

    Article  Google Scholar 

  40. Heitzman, S., & M. Huang (2014). Internal information and investment sensitivities to market value and cash flow. Unpublished paper. University of Southern California and University of Rochestrer. Available at SSRN:

  41. Hemmer, T., & Labro, E. (2008). On the optimal relation between the properties of managerial and financial reporting systems. Journal of Accounting Research, 46, 1209–1240.

    Article  Google Scholar 

  42. Hirst, D. E., Koonce, L., & Venkataraman, S. (2008). Management earnings forecasts: A review and framework. Accounting Horizons, 22, 315–338.

    Article  Google Scholar 

  43. Hogarth, R., & Makridakis, S. (1981). Forecasting and planning: An evaluation. Management Science, 27, 115–138.

    Article  Google Scholar 

  44. Hoyt, R., & Liebenberg, A. (2011). The value of enterprise risk management. Journal of Risk and Insurance, 78, 795–822.

    Article  Google Scholar 

  45. International Standards Organization. (2009). Risk management—Principles and guidelines. ISO 31000:2009. Geneva: International Standards Organization.

    Google Scholar 

  46. Ittner, C., & Keusch, T. (2016). Incorporating risk considerations into planning and control systems: The influence of risk management value creation objectives. In P. Linsley & M. Woods (Eds.), The Routledge Companion to Accounting and Risk. London: Routledge forthcoming.

    Google Scholar 

  47. Jorgensen, M. (2010). Identification of more risks can lead to increased over-optimism of and over-confidence in software development effort estimates. Information and Software Technology, 52, 506–516.

    Article  Google Scholar 

  48. Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science, 39, 17–31.

    Article  Google Scholar 

  49. KPMG. (2007). Forecasting with confidence: Insights from leading finance functions. London: KPMG.

    Google Scholar 

  50. Lang, M., & Lundholm, R. (1993). Cross-sectional determinants of analyst ratings of corporate disclosures. Journal of Accounting Research, 31, 246–271.

    Article  Google Scholar 

  51. Lang, M., & Lundholm, R. (2000). Voluntary disclosure and equity offerings: Reducing information asymmetry or hyping the stock? Contemporary Accounting Research, 17, 623–662.

    Article  Google Scholar 

  52. Larcker, D. F., & Rusticus, T. O. (2010). On the use of instrumental variables in accounting research. Journal of Accounting and Economics, 49, 186–205.

    Article  Google Scholar 

  53. Lawrence, M., Goodwin, P., O'Connor, M., & Onkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22, 493–518.

    Article  Google Scholar 

  54. Makridakis, S., & Taleb, N. (2009). Decision making and planning under low levels of predictability. International Journal of Forecasting, 25, 716–733.

    Article  Google Scholar 

  55. Makridakis, S., Wheelwright, S., & Hyndman, R. (1998). Forecasting: Methods and applications. New York: Wiley.

    Google Scholar 

  56. Marsh & RIMS. (2014). Excellence in risk management survey XI. New York: Marsh and Risk and Insurance Management Society.

    Google Scholar 

  57. McKinsey. (2014). Enterprise-risk-management practices: Where’s the evidence? McKinsey Working Papers on Risk, Number 53.

  58. Mcshane, M. K., Nair, A., & Rustambekov, E. (2011). Does enterprise risk management increase firm value? Journal of Accounting, Auditing & Finance, 26, 641–658.

    Article  Google Scholar 

  59. Meissner, P., & Wulf, T. (2013). Cognitive benefits of scenario planning: Its impact on biases and decision quality. Technological Forecasting and Social Change, 80, 801–814.

    Article  Google Scholar 

  60. Mentzer, J., & Bienstock, C. (1998). Sales forecasting management. London: Sage Publications.

    Google Scholar 

  61. Morlidge, S., Partners, S., & Player, S. (2013). Mastering risk with business forecasting. Somers: IBM Corporation.

    Google Scholar 

  62. Mun, J. (2010). Modeling risk: Applying Monte Carlo simulation, strategic real options, stochastic forecasting, and portfolio optimization. Hoboken: John Wiley & Sons.

    Google Scholar 

  63. Newman, P., & Sansing, R. (1993). Disclosure policies with multiple users. Journal of Accounting Research, 31, 92–112.

    Article  Google Scholar 

  64. Nocco, B. W., & Stulz, R. M. (2006). Enterprise risk management: Theory and practice. Journal of Applied Corporate Finance, 18, 8–20.

    Article  Google Scholar 

  65. Noe, C. (1999). Voluntary disclosure and insider transactions. Journal of Accounting and Economics, 27, 305–326.

    Article  Google Scholar 

  66. Power, M. (2009). The risk management of nothing. Accounting, Organizations, and Society, 34, 849–855.

    Article  Google Scholar 

  67. PriceWaterhouseCoopers. (2011). Financial planning: Realizing the value of budgeting and forecasting. Boston: CFO Publishing LLC.

    Google Scholar 

  68. Rogers, J. L., & Stocken, P. C. (2005). Credibility of management forecasts. The Accounting Review, 80, 1233–1260.

    Article  Google Scholar 

  69. Schoemaker, P. (1993). Multiple scenario development: Its conceptual and behavioral foundation. Strategic Management Journal, 14, 193–213.

    Article  Google Scholar 

  70. Stout K. (2014). Remarks before the 2014 AICPA conference on current SEC and PCAOB developments.

  71. Taleb, N. (2007). The black swan: The impact of the highly improbable. New York: Random House.

    Google Scholar 

  72. Toneguzzo, T. (2010). How to allocate resources based on risk. In J. Fraser & B. Simkins (Eds.), Enterprise Risk Management (pp. 189–217). Hoboken: Wiley.

    Google Scholar 

  73. Yang, H. (2012). Capital market consequences of managers’ voluntary disclosure styles. Journal of Accounting and Economics, 53, 167–184.

    Article  Google Scholar 

  74. Zotteri, G., & Kalchschmidt, M. (2007). Forecasting practices: Empirical evidence and a framework for research. International Journal of Production Economics, 108, 84–99.

    Article  Google Scholar 

Download references


We thank EY (Ittner) and Wharton for research support and Stella Park and Mengxue Shi for research assistance. We also thank Aon for providing access to the survey data used in this study. The comments of Russ Lundholm (editor), Asher Curtis (discussant), Ted Christensen, Isabel Wang, and seminar participants at the 2016 RAST Conference, Harvard Information, Markets, and Organizations Conference, London Business School, Michigan State University, UCLA Spring Accounting Conference, University of North Carolina Chapel Hill, and Yale Summer Accounting Research Conference are greatly appreciated.

Author information



Corresponding author

Correspondence to Christopher D. Ittner.



Survey Questions and Response Frequencies for Risk-Based Forecasting and Planning Constructs

Indicators for Risk Drivers

Risk drivers (i.e., causes of risk) are identified/documented: rarely or never (7%), inconsistently or on an ad hoc basis for selected risks (35%), consistently for key risks (58%).

Risk drivers (i.e., causes of risk) are analyzed to identify common drivers between risks: rarely or never (14%), inconsistently or on an ad hoc basis for selected risks (48%), consistently for key risks (38%).

Risk management activities are analyzed and mapped to specific risk drivers: rarely or never (19%), inconsistently or on an ad hoc basis for selected risks (44%), consistently for key risks (38%).

Risk drivers (i.e., causes of risk) are analyzed in depth and support the identification of emerging risks through understanding of common risk drivers: rarely or never (25%), inconsistently or on an ad hoc basis for selected risks (40%), consistently for key risks (35%).

The organization leverages common risk driver information to identify correlation/relationships between risks: N/A, analysis of correlation is not conducted (39%), informally in management discussions and perceptions of risk (48%), formally, and has documented the need for its consideration in risk assessment processes (13%)

Indicators for Quant. Assess.

Risk assessment scales at the organizational level are: not used in risk management exercises (7%), primarily qualitative criteria (i.e., high, medium, low) (51%), developed with both qualitative and quantitative criteria (42%).

Risk assessment criteria are developed to align with: N/A, risk assessment criteria are not developed (7%), management perceptions of risk tolerance (73%), a quantitative risk appetite and statements of risk tolerance (20%).

Is risk assessment analysis supplemented with additional quantitative evaluations of exposure? no (18%); yes, where perceived necessary (55%); yes, for risks that meet specific criteria/thresholds (27%).

Criteria for evaluation of risk management activity effectiveness for key risks are: not yet developed (19%), primarily qualitative (e.g., “adequate,” “weak”) (58%), quantitative, measuring change in risk exposure (24%).

Documentation of risk management effectiveness for key risks: N/A, risk management effectiveness is not documented (22%); is primarily qualitative (i.e., commentary on results) (52%); incorporates both qualitative commentary and qualitative evidence (i.e., citing metrics or indicators) (26%).

Quantitative thresholds and tolerances have been established: no (32%), inconsistently or on an ad-hoc basis (39%), consistently for key risks (29%).

Any ranges of values or distributions used are developed: N/A, ranges or distributions are not used (11%); informally, based on management judgment (39%); formally, with incorporation of historical data or other quantitative methods (51%)

Indicator for Distribs

The firm uses distributions, stochastic modeling techniques, or both in developing forecasts (29%).

Indicators for Budgeting

The organization’s budget/resource allocation processes explicitly reference results of established risk assessment and analysis plans: rarely or never (40%); yes, inconsistently or on an ad hoc basis (45%); yes, consistently through a defined process (15%).

The organization’s budget/resource allocation process includes evaluation of risk management spend for effectiveness (i.e., cost savings vs. exposure reduction): rarely or never (38%); yes, inconsistently or on an ad hoc basis (44%); yes, consistently through a defined process (19%).

Are different (i.e., higher, lower) risk-based return expectations set for different business units and functions? no (27%); yes, but the information is not explicitly considered in budget decisions (42%); yes, and the different return expectations are incorporated into budget decisions and resource allocation decisions (31%)

Indicators for Capital Invest

Risks are primarily identified and assessed in significant project or investment decisions: N/A, risks are not identified (9%); through SWOT (strengths, weaknesses, opportunity, threat) analysis or similar (44%); through a special and dedicated risk identification and assessment methodology separate from SWOT (47%).

The focus of risk identification activities for projects or investments: N/A, no risk identification process exists (13%); is on existing risks (29%); is on both existing and emerging risks (58%).

Significant project or investment decisions are made with explicit reference to quantified risk appetite and tolerance: rarely or never (34%); yes, inconsistently (36%); yes, consistently (29%).

In making significant capital investment decisions, the project risk profile is evaluated against/compared to the organization’s overall risk profile: rarely or never (13%); yes, inconsistently or informally (54%); yes, consistently as part of a defined process (33%).

Management uses project risk information to adjust the hurdle rates for significant capital investment decisions: rarely or never (22%); informally or based on management judgment or previous experience (44%); formally, using quantitative analysis of project risk (34%)

Indicators for Strategy

The board and executive management highlight the alignment of risk management strategy with overall strategy when communicating strategic direction: no, communications do not highlight alignment (31%); yes, and include informal references to concepts of risk appetite and tolerance (53%); yes, and include formal references to concepts of risk appetite and tolerance (16%).

Executive management applies concepts of risk appetite and/or tolerance to strategy development: rarely or never (29%); yes, on an ad hoc basis (51%); yes, through a formal process (20%).

How does information from the risk management process inform the strategic planning process? N/A, key risk information is not included (12%); key risk information is informally incorporated (56%); key risk information is formally incorporated and integrated (32%).

Risk identification exercises during the strategic planning process are used to develop an emerging risk profile: N/A, risk identification is not conducted during strategic planning (26%); no (36%); yes (38%).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ittner, C.D., Michels, J. Risk-based forecasting and planning and management earnings forecasts. Rev Account Stud 22, 1005–1047 (2017).

Download citation


  • Management earnings guidance
  • Budgeting
  • Planning
  • Forecasting
  • Risk management

JEL classifications

  • G31
  • G32
  • M40
  • M41