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Earnings Performance Targets in Annual Incentive Plans and Management Earnings Guidance

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Xiumin Martin, Hojun Seo, Jun Yang, Daniel Sungyeon Kim & Jordan Martel — The Accounting Review, Vol. 98, No. 4 (2023), pp. 289–319. doi:10.2308/TAR-2018-0532

Research Question

How do corporate boards set earnings performance targets in CEO annual incentive plans (AIPs), and can managers influence those targets through their own public earnings guidance? This paper investigates the information sources boards use when calibrating EPS-based bonus thresholds and whether the target-setting process creates incentives for managers to strategically bias the earnings forecasts they release to the market.

The study addresses two linked questions. First, do boards rely on analyst forecasts (AF) and management forecasts (MF) as benchmarks when setting AIP targets — and does reliance on each signal vary with its precision? Second, do managers exploit the board's reliance on their guidance by issuing pessimistic forecasts in the window before AIP targets are approved, thereby lowering the performance bar they must clear to earn a bonus?

Data and Methodology

The authors construct a novel dataset by hand-collecting AIP performance targets from proxy statements filed by S&P 500 firms between 2006 and 2015, yielding 2,242 firm-year observations with explicit EPS-based bonus targets from the Incentive Lab database. For the guidance analysis, they assemble 11,485 management forecast instances matched to target-setting windows.

The target-setting analysis regresses the AIP EPS target on the most recent analyst consensus forecast and management earnings guidance available to the board at the time of target approval. To isolate the informational role of each signal, the authors decompose both AF and MF into common and idiosyncratic components — the idiosyncratic portion captures information unique to each source beyond what is shared between them. A revenue-target falsification test verifies that the results are specific to EPS targets (where guidance is most informative) rather than reflecting a mechanical correlation.

The guidance pessimism analysis examines management forecasts issued in an event window around AIP target approval, measuring pessimism as the signed difference between the management forecast and the subsequent actual earnings (scaled). Cross-sectional tests explore whether pessimism varies with the manager's bonus incentives, prior target attainment, and payout function convexity.

Key Results

Target-Setting: Boards Use Both Analyst and Management Signals

Boards rely heavily on both information sources when calibrating EPS targets. The coefficient on analyst forecasts in the target-setting regression is 0.559 (p < 0.01) and on management forecasts is 0.533 (p < 0.01). Critically, the idiosyncratic components of each signal — the portion unique to analysts (Idio AF = 0.538***) and unique to management (Idio MF = 0.414***) — are both independently significant, confirming that boards extract distinct information from each source rather than treating them as substitutes. In the revenue-target falsification test, neither AF nor MF coefficients are significant, confirming that the EPS results reflect the specific informational relevance of earnings guidance to EPS target-setting.

Signal Precision Moderates Reliance

Board reliance on each signal increases with its precision and decreases with its noise. For management forecasts, reliance rises significantly when guidance is more specific (point vs. range estimates: interaction coefficient 0.210*) and when guidance has been more precise historically (MF precision: 0.586***). For analyst forecasts, reliance increases when analyst dispersion is low (0.296*), when more analysts cover the firm (0.285*), and notably when directors have industry expertise (0.421***) — suggesting that expert directors are better able to evaluate and weight analyst information.

Strategic Pessimism in Management Guidance

Managers issue systematically more pessimistic earnings forecasts in the event window surrounding AIP target approval. The pessimism coefficient is 0.141 (p < 0.01), corresponding to incremental pessimism of 7.47 cents per share — economically meaningful given that it represents 16.89% of one standard deviation of management forecast pessimism. This pessimism is specific to the EPS-target event window and does not appear around revenue-target approvals, ruling out general optimism-management explanations. The pessimistic forecasts are costly in terms of accuracy: MF pessimism is positively associated with absolute forecast errors (0.244***), confirming that managers sacrifice forecast quality to influence the target-setting process.

Pessimism Is Strongest When Bonus Incentives Are High

Cross-sectional tests reveal that strategic pessimism intensifies precisely when the manager's bonus incentives are most acute:

  • Prior target miss: Managers who missed the prior year's EPS target issue significantly more pessimistic guidance (interaction coefficient 0.198**), consistent with motivated efforts to avoid consecutive misses.
  • High target bonus: When the dollar value of the target bonus is large, pessimism increases (0.242***), indicating that larger financial stakes amplify the incentive to manipulate guidance.
  • Payout convexity: Greater convexity in the bonus payout function — where small performance improvements yield disproportionate bonus gains — is associated with stronger pessimism (0.207**).

Compensation Consequences

The strategic pessimism translates directly into excess compensation. A one-standard-deviation increase in MF pessimism is associated with $244,000 in excess bonus (coefficient 0.151***) and $291,000 in excess total compensation (coefficient 0.040*). Importantly, boards do not offset this effect by reducing the target bonus amount in response to detected pessimism — the manipulation produces a net wealth transfer from shareholders to management.

Implications for Institutional Investors

This paper has direct relevance for stewardship teams, proxy advisors, and governance analysts evaluating executive compensation structures and board oversight effectiveness.

  • Scrutinize management guidance around AIP approval windows. The finding that managers issue strategically pessimistic forecasts before bonus targets are set provides a concrete red flag for investors. Stewardship teams should cross-reference the timing of management earnings guidance with proxy-disclosed AIP target approval dates. Guidance issued in the weeks before compensation committee meetings warrants particular scrutiny for downward bias.
  • Evaluate AIP target-setting process disclosure. Boards that disclose which information sources they use to calibrate targets — and how they weight analyst vs. management inputs — provide more governable compensation structures. Investors should engage companies to improve target-setting process transparency in compensation discussion and analysis (CD&A) sections.
  • Assess board capacity to filter management signals. The cross-sectional results show that director industry expertise increases reliance on analyst (external) signals, suggesting expert boards are better at triangulating information. Compensation committee composition — particularly the presence of directors with relevant financial or industry expertise — is a meaningful governance quality indicator for AIP oversight.
  • Flag high-convexity payout functions. The finding that payout function convexity amplifies guidance pessimism provides a specific design feature to flag in say-on-pay analysis. AIPs with steep slopes around the target threshold create stronger manipulation incentives. Investors should favor linear or near-linear payout schedules that reduce the marginal benefit of small target reductions.
  • Monitor for consecutive target misses. Firms where the CEO missed the prior year's EPS target face elevated manipulation risk in the current year. This is a quantifiable, observable signal that stewardship teams can incorporate into their compensation review workflows.

Selected References

  1. Martin, X., Seo, H., Yang, J., Kim, D.S., & Martel, J. (2023). Earnings performance targets in annual incentive plans and management earnings guidance. The Accounting Review, 98(4), 289–319.
  2. Murphy, K.J. (1999). Executive compensation. In O. Ashenfelter & D. Card (Eds.), Handbook of Labor Economics (Vol. 3, pp. 2485–2563). Elsevier.
  3. Matějka, M., & Ray, K. (2017). Balancing difficulty of performance targets. Review of Accounting Studies, 22, 1666–1697.
  4. Indjejikian, R.J., Matějka, M., Merchant, K.A., & Van der Stede, W.A. (2014). Earnings targets and annual bonus incentives. The Accounting Review, 89(4), 1227–1258.
  5. Bertrand, M., & Mullainathan, S. (2001). Are CEOs rewarded for luck? Quarterly Journal of Economics, 116(3), 901–932.