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CEO Stock Options and Analysts’ Forecast Accuracy and Bias

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Handbook of Financial Econometrics and Statistics

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Abstract

In this study, we investigate the relations between CEO stock options and analysts’ earnings forecast accuracy and bias. We argue that a higher level of stock options may induce managers to undertake riskier projects, to change and/or reallocate their effort, and to possibly engage in gaming (such as opportunistic earnings and disclosure management) and hypothesize that these managerial behaviors will result in an increase in the complexity of forecasting and, hence, in less accurate analysts’ forecasts. We also posit that analysts’ optimistic forecast bias will increase as the level of stock options pay increases. We reason that as forecast complexity increases with stock options pay, analysts, needing greater access to management’s information to produce accurate forecasts, have incentives to increase the optimistic bias in their forecasts. Alternatively, a higher level of stock options pay may lead to improved disclosure because it better aligns managers’ and shareholders’ interests. The improved disclosure, in turn, may result in more accurate and less biased analysts’ forecasts.

Using ordinary least squares estimation, we test these hypotheses relating the level of CEO stock options pay to analysts’ forecast accuracy and bias on a sample of firms from the Standard & Poor’s ExecuComp database over the period 1993–2003. Our OLS models relate forecast accuracy and forecast bias (the dependent variables) to CEO stock options (the independent variable) and controls for earnings characteristics, firm characteristics, and forecast characteristics. We measure forecast accuracy as negative one times the absolute value of the difference between forecasted and actual earnings scaled by beginning of period stock price and forecast bias as forecasted minus actual earnings scaled by beginning of period stock price. We control for differences in earnings characteristics by including earnings volatility, whether the firm has a loss, and earnings surprise; for differences in firm characteristics by including firm size, growth (measured as book-to-market ratio, percentage change in total assets, and percentage change in annual sales), and corporate governance quality (measured as percentage of shares outstanding owned by the CEO, whether the CEO is also chairman of the board of directors, number of annual board meetings, and whether directors are awarded stock options); and for differences in forecast characteristics by including analyst following and analyst forecast dispersion. In addition, the models include controls for industry and year. We use four measures of options: new options, existing exercisable options, existing unexercisable options, and total options (sum of the previous three), all scaled by total number of shares outstanding, and estimate two models for each dependent variable, one including total options and the other including new options, existing exercisable options, and existing unexercisable options. We also use both contemporaneous as well as lagged values of options in our main tests.

Our results indicate that analysts’ earnings forecast accuracy decreases and forecast optimism increases as the level of stock options (particularly new options and existing exercisable options) in CEO pay increases. These findings suggest that the incentive alignment effects of stock options are more than offset by the investment, effort allocation, and gaming incentives induced by stock options grants to CEOs. Given that analysts’ forecasts are an important source of information to capital markets, our finding of a decline in the quality of the information provided by analysts has implications for the level and variability of stock prices. It also has implications for information asymmetry and cost of capital, as well as for valuation models that rely on analysts’ earnings forecasts.

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Notes

  1. 1.

    Other reasons for granting stock options are to attract and retain executives, to conserve cash, to reduce reported accounting expense, and to defer taxes.

  2. 2.

    Although there is evidence relating earnings management to stock options compensation, little is known about whether this earnings management actually results in higher payouts or about its effect on other goals of the firm.

  3. 3.

    Ke and Yu (2006) and Chen and Matsumoto (2006) are examples of recent research on analysts’ incentives for access to management, i.e., the management relations hypothesis.

  4. 4.

    Furthermore, Feltham and Xie (1994) show that, if there are multiple tasks and multiple public signals that are influenced by the manager’s action, it is unlikely that the market price provides an efficient single performance measure. Therefore, overly relying on stock-based compensation may lead to incongruent behavior by CEOs, further increasing the difficulty of the forecasting task.

  5. 5.

    However, in related research, Hribar and Nichols (2007) provide evidence that not controlling for operating volatility increases the risk of over-rejecting the null hypothesis of no earnings management.

  6. 6.

    Although Aboody and Kaznik (2000) study only fixed schedule awards, we argue that the incentive to maximize the stock options pay by manipulating the stock price at the grant date is present for all stock options awards, and that the incentive is especially strong for firms that make multiple grants in a given year. We note that a large number of our sample firms made multiple grants in the same fiscal year thus increasing this incentive. It is also interesting to note that the stock options award dates are generally not publicly known until the issue of proxy statements which are available only 2–3 months after the fiscal year-end (Yermack 1997).

  7. 7.

    To be consistent with the prior literature on executive pay (Core et al. 1999; Hanlon et al. 2003), we omit financial institutions and agricultural companies. However, for completeness, we also conducted the analysis with these companies included in the sample. Our main conclusions are unaffected by this inclusion.

  8. 8.

    We repeat all our analyses using 3-month-ahead forecasts to examine the robustness of our results to the forecast horizon. Most results for the 3-month forecasts mirror those presented for the 9-month forecasts.

  9. 9.

    The sample firms represent a variety of industries, with the largest representation being retail (8 %), electronic equipment (6.7 %), business services (5.3 %), and telecommunications (6 %).

  10. 10.

    As a sensitivity check, when using lagged OPTIONS, we delete observations that have a new CEO in the current year. Our main results are robust to deletion of these observations.

  11. 11.

    Gu (2003) argues that inclusion of earnings level as a control variable will induce spurious relationships between the variable capturing forecast efficiency and other control variables. Our main results are stronger when we exclude earnings level as a control variable.

  12. 12.

    Prior research on forecast accuracy and bias (e.g., Duru and Reeb 2002) does not control for differences in growth. Our results are robust to the exclusion of GROWTH as a control variable in the regressions.

  13. 13.

    The variance inflation factors for variables in our main regressions are all below three, indicating that there are no severe multicollinearity problems.

  14. 14.

    We also estimate the regression without the absolute value of earnings surprise (ABSESUP) that might be mechanically related to accuracy. The main results are not affected by the exclusion of that variable. We note that inclusion of ABSESUP can only weaken the hypothesized relationship between ACCURACY and OPTIONS because ABSESUP is closely related to ACCURACY.

  15. 15.

    We also estimate the model without earnings surprise (ESUP), negative earnings surprise (NEGESUP), and level of earnings (LEVEARN) that might be mechanically related to bias. The relationship between level of options and bias is not affected by the omission of those variables.

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Acknowledgments

We thank Zhaoyang Gu, Emad Mohd, Mohamed Shehata, K. (Shiva) Sivaramakrishnan, Scott Whisenant, Yoonseok Zang, Jian Zhou, the anonymous reviewers, and workshop participants at the University of Houston, Drexel University, Wilfrid Laurier University, the 2005 American Accounting Association Meeting, the 2005 Canadian Academic Accounting Association Meeting, and the 2005 European Accounting Association Meeting for their helpful comments and Alper Ozesen and Li Wang for their research assistance. We gratefully acknowledge the contribution of Thompson Financial for providing forecast data, available through the Institutional Brokers Estimate System (I/B/E/S). These data have been provided as part of a broad academic program to encourage earnings expectations research. We also thank the Social Sciences and Humanities Research Council of Canada (SSHRC) for their financial support.

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Authors and Affiliations

  1. Schulich School of Business, York University, M3J 1P3, Toronto, ON, Canada

    Kiridaran Kanagaretnam

  2. C.T. Bauer College of Business, University of Houston, Houston, TX, 77204, USA

    Gerald J. Lobo

  3. School of Business and Economics, Wilfrid Laurier University, Waterloo, ON, L8S 4M4, Canada

    Robert Mathieu

Authors
  1. Kiridaran Kanagaretnam
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  2. Gerald J. Lobo
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  3. Robert Mathieu
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Correspondence to Kiridaran Kanagaretnam .

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Editors and Affiliations

  1. Department of Finance and Economics, Rutgers Business School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA

    Cheng-Few Lee

  2. Center for PBBEF Research, North Brunswick, New Jersey, USA

    John C. Lee

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Kanagaretnam, K., Lobo, G.J., Mathieu, R. (2015). CEO Stock Options and Analysts’ Forecast Accuracy and Bias. In: Lee, CF., Lee, J. (eds) Handbook of Financial Econometrics and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7750-1_97

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  • DOI: https://doi.org/10.1007/978-1-4614-7750-1_97

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