Takeover Vulnerability and Pre-Emptive Earnings Management

ABSTRACT

We explore whether firms that are vulnerable to takeovers pre-emptively manage earnings in anticipation of such events. We find a positive relationship between firms' vulnerability to takeovers and their propensity to manage earnings, mainly through the manipulation of real activities. We consider two motivations for firms' pre-emptive earnings management behavior; (1) to deter future takeovers and (2) to optimize M&A outcomes. Concerning the former, we document evidence consistent with entrenched managers using real earnings management to deter or delay future takeovers. Concerning the latter, we find evidence suggesting that, contingent on receiving takeover bids, vulnerable firms that pre-emptively manipulate real activities extract comparatively higher merger premiums. Overall, our findings suggest that managers of vulnerable firms pre-emptively manage earnings to purposefully delay the timing and optimize the outcomes of prospective takeovers.

Keywords:

• Takeovers
• Takeover vulnerability
• Earnings management
• Takeover deterrent
• Merger premiums

Acknowledgments

We thank the Editor (Beatriz Garcia Osma) and two anonymous reviewers for helpful comments and suggestions. We are grateful to participants at the International Finance and Banking Society June 2019 annual conference and seminar participants at the University of Sheffield, University of Nottingham, University of the Witwatersrand and SOAS University of London for helpful discussions. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any organization with which the authors are affiliated.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental Data and Research Materials

Supplemental data for this article can be accessed online at http://dx.doi.org/10.1080/09638180.2022.2116064.

Notes

1 Billett & Xue discuss the case of Sears, Roebuck & Co. reported in The Wall Street Journal (WSJ) (1988). Sears took aggressive actions, including repurchasing shares and selling its corporate headquarters, which was then the World's tallest building (a crown jewel) after it was rumored to be a target of an impending takeover.

2 While any benefits from EM are likely to be transitory (Cohen & Zarowin, 2010; Gunny, 2010; Kothari et al., 2016), REM, in particular, might have long-lasting adverse effects on future cash flows (Graham et al., 2005).

3 These studies assume that, because acquirers initiate M&As, they can purposefully manage earnings before making a bid. However, takeover targets, being the recipient of bids, are often unaware of their timing and hence have limited opportunities to manage earnings before a bid announcement (Erickson & Wang, 1999).

4 See, for example, Rule 21 of the UK Takeover Code on restrictions on frustrating action.

5 Contrary to Anagnostopoulou and Tsekrekos (2013, 2015), who find evidence that ‘seeking-buyer’ firms manage earnings (AEM) downwards before takeovers, we document evidence of upwards EM (REM) in vulnerable firms. Moreover, in contrast to these studies, we provide more generalizable evidence from a large panel dataset.

6 REM could, for example, be achieved by curbing discretionary expenses such as R&D and selling, general and advertising expenditures (Cohen & Zarowin, 2010), overproduction to reduce the cost of goods sold (Roychowdhury, 2006), sale of profitable assets (Herrmann et al., 2003), timing securitization activities (Dechow & Shakespear, 2009) and deferring expenditures on essential maintenance and investments in new projects (Graham et al., 2006), amongst others.

7 Consistent with a signaling motive of REM, Gunny (2010), for example, finds that REM allows firms to attain current-period benefits that, in turn, enable them to achieve improved future performance.

8 As we do not analyze bidders in this study, the bidder can be public or private, as well as be a UK firm or a foreign firm.

9 we multiply REMdisx by negative 1 so that higher values are indicative of higher levels of REM.

10 Roychowdhury (2006) contends that price discounts and overproduction negatively impact abnormal CFO while a reduction of discretionary expenses positively impacts the same.

11 If EM by vulnerable firms deters acquirers from making takeover bids as we predict in H2, then the results of this out-of-sample test will be biased downwards.

12 To the extent that our model is poorly specified and incapable of correctly ascribing takeover likelihood, we bias our findings negatively and are likely to improve rather than impair our results with a more optimal model.

13 In untabulated robustness checks available in our online appendices, we re-estimate the model using a panel regression specification and also control for firm-level governance characteristics (including board size, CEO duality, board gender diversity, board independence, director experience, board tenure and the presence of an audit committee). Our governance data is patchy and largely missing for over 50% of our sample. Our results are robust to these additional controls and alternative model specification.

14 In untabulated analyses (online appendices), we re-estimate our AEM results using alternative measures of AEM. Botsari and Meeks (2008) contend that working capital accruals are relatively more opaque and more open to manipulation when compared to depreciation. Secondly, J.-B. Kim et al. (2003) argues that discretionary accruals computed using Jones-type models (Dechow et al., 1995; Jones, 1991) are potentially biased due to measurement errors. We follow Botsari and Meeks (2008) and DeFond and Park (2001) to estimate alternative measures of AEM which we use to re-estimate our main results. Our conclusions are robust to these alternatives. That is, the relationship between vulnerability and AEM is statistically insignificant. F statistics in Table 4 are significant across all models and variance inflation factors (VIF) are below standard thresholds. The adjusted R${}^2$ values are arguably low but in line with those reported in related large-sample studies (Achleitner et al., 2014; Gunny, 2010). In additional tests (online appendices), the adjusted R${}^2$ values slightly improve after controlling for corporate governance characteristics.

15 In untabulated analyses (online appendices), we run quantile regressions to strengthen our findings even further. If our results merely capture poorly performing firms that are more likely to manage earnings, we should observe that our results are positive and significant at certain levels of EM and insignificant or negative at others. Quantile regressions allow us to test the sensitivity of the coefficient of vulnerability at different levels or quantiles of EM. The results suggest that the documented relationship is consistently positive at all levels or quantiles of EM. Therefore, it is unlikely that the results we observe are an artefact of poor performance. Additionally, in unreported results, we have explored pairwise correlations between measures of performance (ROA, ROCE, Tobin's q and AAR) and REM and find the correlation coefficients (rho) to be close to zero, suggesting that performance is unlikely to explain the vulnerability–EM relationship in our sample.

16 Following Roychowdhury (2006), Gunny (2010) and Zang (2012), but mindful of the UK institutional context, we identify suspect firms as (1) firms with a small net profit to asset ratio (ROA) of between 0 and 0.005, or (2) a small positive net income less than £500,000, or (3) a small growth in net income of between zero and £500,000. All other firms are classified as non-suspect firms.

17 This is our proxy for merger activity or external takeover pressure within an industry.

18 Our results are consistent when we use alternative industry definitions such as 4-digit SIC code industries.

19 Merger activity within an industry incentivises other firms within that industry to engage in mergers in order to retain their competitive positions

20 Notice that our results are consistent when we use the median-vulnerability in other earlier years up to (t−6). Our results are also consistent when we use alternative industry definitions e.g., 4-digit SIC codes.

21 The Inverse Mills Ratio is estimated as, $\frac{\mathrm{\psi Z}}{\mathrm{\varphi Z}}$ where Z is the fitted value of the probit regression function; $\mathrm{\psi Z}$ is the probability density function (PDF) for standard normal distribution; and $\mathrm{\varphi Z}$ is the cumulative density function (CDF) for a standard normal distribution.

Additional information

Funding

We acknowledge financial support from the University of Sheffield (CRAFiC).