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Review of Quantitative Finance and Accounting

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01.10.2014 | Original Research

Leverage and acquisition performance

verfasst von: Jeffrey S. Harrison, Matthew Hart, Derek K. Oler

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 3/2014

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Abstract

From an agency perspective, leverage may have a positive effect on firm performance by limiting managers’ ability to allocate resources to unproductive uses, as well as increasing pressure on them to perform well. Consequently, we might expect leverage to have a positive impact on acquisition performance. However, the increased risks associated with higher leverage, combined with the other risks inherent in an acquisition, could also cause managers to take actions to reduce risk even if doing so is contrary to value maximization. High debt levels might also limit managerial discretion over how resources are allocated during the acquisition process, which can have a negative impact on performance. We investigate the effect of leverage on post-acquisition stock performance and find that post-acquisition performance is decreasing in leverage brought by the target firm and in additional leverage taken on to execute the acquisition. This negative performance is clustered among acquirers who are already financially constrained. Our results are robust to various returns measurement methodologies and to the inclusion of several controls known to predict future returns. Our results also represent viable investment strategies, and suggest that the market underestimates difficulties that arise from acquisition-related increases in leverage.

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Loughran and Vijh (1997) report the results of an untabulated robustness check on whether post-acquisition returns for cash acquirers are explained by changes in acquirer debt to equity. They find that debt to equity does not appear to explain their results.

One exception is that our estimated coefficient on target leverage is only marginally significant when we used time- and firm-clustered standard errors.

Specifically, agency theory suggests that debt disciplines management by limiting their discretion on the use of cash flows. As we discuss in the next section, risk aversion is another aspect of agency theory that suggests high debt may not always be desirable.

One anecdotal example from our data is COMFORCE Corporation (CFS), who acquired a highly leveraged target, Uniforce Temporary Personnel Inc., on February 14, 1998. CFS later warned investors that its high leverage may affect its ability to realize its financial objectives. In the same press release, CFS announced plans to continue to repurchase its debt in an effort to reduce its interest expense and improve its capital structure (PR Newswire, March 15, 2001). This suggests that CFS was diverting cash flows to debt reduction that would otherwise have been used to fund new investments (or returned to shareholders).

In untabulated results we also compare reported cash levels from pre- to post-acquisition years. As expected, we find that cash significantly decreases from the pre- to post-acquisition period as debt obligations (with their required interest and principal payments) increase.

These changes in leverage from year −1 to post-acquisition years are all statistically significant, except for the increase in leverage for stock acquisitions from year −1 to year +1. We note that the number of observations change the further we get from the acquisition year because of data availability problems. Our conclusions are the same if we examine only firms with data available as of year +5. Another concern is that our increases in leverage may be caused by decreases in total assets (i.e., a “denominator effect”). However, we find that total assets increase from years −1 to +5, ruling out this possibility.

The significance of this difference is easily illustrated. Suppose firms A and B have $1,000 of debt requiring interest at 10 % and $4,000 of total assets measured using book value. However, Firm A has a market value of assets of $8,000 while firm B has a market value of assets of $5,000. Book leverage is 0.25 for both firms, but market leverage is lower for A (0.125) than for B (0.20) even though managers of both firms are obligated to pay $100 per year for interest. Further, if A’s market value decreases and B’s market value increases than A’s market leverage will appear to have increased and B’s will appear to have decreased even though there is no underlying change in either firm’s debt and interest costs. Firms with a high book-value leverage but low market-value leverage may be able to refinance their debt, depending on the prevailing condition of credit markets, but would incur transaction costs in doing so.

However, in spite of the differences between our samples, when we use firm market value as our denominator instead of firm assets we also find evidence that leverage is beginning to decrease by year +4 after the acquisition, which is roughly consistent with Harford et al.’s results.

This is not to say that stock consideration or holding of a high cash balance causes poor acquisition performance. For example, Shleifer and Vishny (2003) argue that managers may use stock as consideration when they recognize their stock price is overvalued (and, therefore, the firm would have low future returns even without an acquisition). We attempt to control for this by modeling manager’s endogenous choice in our analysis. However, even if stock consideration is only a signal and not the cause of poor future performance, the fact that future returns are predicable based on that prior signal suggests a market under-reaction.

Petersen (2009) shows that although both Fama–MacBeth and Newey–West standard errors are effective against year effects, they are significantly biased in the presence of firm effects.

As a robustness check, we add additional variables (such as Altman’s Z) from our returns regressions to our probit regression. Even with this “kitchen sink” approach, we find our conclusions are unchanged.

SDC Platinum’s coverage is less complete for years before 1995.

If any of our control firms are delisted before the end of the 2-year period, we include the delisting return of the control firm and replace that firm with a new control firm also selected as of the announcement date. If the acquiring firm is delisted before the end of the 2-year period, we include the delisting return and invest the remaining funds in our portfolio of peer firms (so that delisted firms do not disappear from our dataset).

GICS are determined by Standard and Poor's, and are available from Compustat’s Price, Dividend, and Earnings (PDE) files. GICS codes are 8-digits long, and matching on the first 4 digits approximates the common practice of matching on the first 2-digits of a firm’s SIC code. Bhojraj et al. (1999) show that GICS codes are generally superior to SIC codes for financial research.

One could argue that our use of matching firms also controls for management’s self-selection discussed earlier, and therefore the Heckman’s 2-step approach is unnecessary. However, matching assumes that manager’s private information is useless (Li and Prabhala 2007, page 53), and this seems unlikely in our setting.

Added leverage requires information from the first quarter’s financial statements immediately following the consummation of the acquisition. To ensure that the market is aware of added leverage before we open our “post-acquisition” period, we wait until 45 days after the first acquirer quarter following target delisting before we open this period.

This calculation has the potential to understate added leverage because post-acquisition total assets will include goodwill, which could be inflated by overpayment. This bias serves to work against our finding significant results. Also, to control for the possibility of overpayment, we include the target premium in our regressions, as we discuss in the next paragraph.

We calculate abnormal target returns in the same manner as abnormal acquirer returns, using a reference portfolio of 4 firms based on industry, size, and book-to-market (and then matching on the closest fit to target leverage).

The target premium here is smaller than that reported by Betton et al. (2009); however, their premium is based on raw target returns while ours is based on abnormal target returns.

Although Altman’s Z is venerable, it remains a reasonably accurate predictor of financial distress (see Altman and Hotchkiss 2006) that is still commonly used in academic research (for example, see Chang et al. 2006; Kalay et al. 2007; Eisdorfer 2008; Guner et al. 2008).

One difference is that Harford (1999) finds that his leverage measure loads significantly negatively, using the market value of leverage instead of the book value of leverage. This is a curious result that emphasizes the difference between using book value leverage and market value leverage, and could be a fruitful avenue of further investigation. To show that our subsequent results are not driven by our choice of leverage measure we substitute the market value of leverage in our probit regression and find (1) that it loads significantly negatively, just as Harford found, and (2) when using our revised Inverse Mills ratio from the “market-leverage” probit in our later regressions, our results are substantially the same. Finally, our results are similar if we include year dummies in our probit regression.

To avoid confusion as to whether we are referring to p values based on corrected OLS standard errors following Li and Prabhala (2007) or clustered standard errors following Petersen (2009), all our references will be to clustered standard error p values, which are generally more conservative.

We also note that acquirer returns are higher for small acquirers (+0.013), consistent with the findings of Moeller et al. (2004), but that this result hold only when we use OLS standard errors (p = 0.06). The significance disappears once clustered standard errors are used (p = 0.19).

There are several speculative reasons as to why this might be. For example, Morck et al. (1990) argue that value-destructive acquisitions occur because managers place personal benefits from the acquisition above the interests of shareholders. This information is revealed to the market when the acquisition is announced, and the market’s response appears to be complete by the end of the interim period before the first quarterly financial statements are issued in the post-acquisition period.

One drawback to our definition of post-acquisition returns beginning after the first quarter’s post-acquisition financial statements become available is that there may be a market response to information released after the target delisting but before the first quarter’s financial statements are made available. This works against our finding of significant results. Our selection of a post-acquisition period beginning only after the first quarter’s financial statements are released ensures that we are not peeking ahead when we include added leverage in our analysis.

We also note that explanatory variables in the probit regression differ from those in our OLS (2nd stage) regression. This is econometrically permissible (for example, see Breen 1996, page 35. For another example of Heckman’s technique used in a similar manner to ours, see Louis 2005).

We also investigate whether high-added-leverage acquirers, our worst-performers in Panel C of Table 5, show other indications of poor performance; specifically, whether they show less ability to take advantage of post-acquisition profitable opportunities. We conducted additional analyses (not included in a table in this paper, but available from the authors upon request) and found that high-added-leverage acquirers show a significant decrease in cash and a significant decrease in capital expenditures in the post-acquisition period, suggesting that these acquirers have less ability to make profitable investments in the post-acquisition period.

For example, see http://accounting-information.net/accounting_information/financial_ratios/Interest_Coverage_Ratio-times_interest_earned_ratio.shtml. More sophisticated models to identify financially constrained firms exist (e.g., Kaplan and Zingales 1997; Whited and Wu 2006); however, the data requirements for these models increase our loss of observations and also introduce an endogeneity problem because they include variables that we already control for (e.g., acquirer size). In contrast, times-interest-earned is a measure that is independent of our other controls, requires little additional data, and is also likely to be familiar to most investors as it is covered in most financial statement analysis textbooks.

Because we establish cutoffs based on the prior year, we lose 10 acquisitions from 1972 in this analysis. Our results are similar if we set an arbitrary cutoff at the 75 % percentile of target leverage from Table 2.

Note that both the returns to the short-position and long-position strategies are each hedge returns. For example, the returns of 11 % for the short position result from shorting 430 acquisitions where target leverage is above our cutoff level and going long on the related portfolio of four matching firms per acquirer. Returns to the long position strategy are similarly determined. Our results suggest that only the short position strategy is successful at generating positive returns.

We do not tabulate our split of Panel E combined returns into constrained and non-constrained acquirers because only 8 of the 50 acquirers were financially constrained. However, of those 8, abnormal returns were 76 % (as compared to the 31 % from the 50 observations in Panel E).

We do not tabulate our results to breaking our observations into financially constrained versus non-constrained acquirers in our calendar time analysis because our results become more erratic with the reduction in observations.

Similarly, more recent research by Cao and Lerner (2009) also suggests that the future returns of reverse leveraged buyout firms are not significantly decreasing in total leverage.

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Titel: Leverage and acquisition performance
verfasst von: Jeffrey S. Harrison
Matthew Hart
Derek K. Oler
Publikationsdatum: 01.10.2014
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 3/2014
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-013-0385-5

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