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Published in: Review of Accounting Studies 4/2008

01-12-2008

Does acquirer cash level predict post-acquisition returns?

Author: Derek K. Oler

Published in: Review of Accounting Studies | Issue 4/2008

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Abstract

This paper investigates whether an acquirer’s pre-announcement cash level can predict post-acquisition returns. Harford (1999, Journal of Finance, 54, 1969–1997) shows that some cash-rich acquirers have lower announcement period returns than other acquirers, suggesting the market partially anticipates poor future performance. This paper shows that the acquirer’s cash level is also strongly and negatively predictive of post-acquisition returns, indicating that the announcement response is incomplete. Post-acquisition return on net operating assets (RNOA) is significantly decreasing in acquirer cash, suggesting that the market responds to subsequent poor operating performance as it is reported. Overall, these results are consistent with the market’s inattention to a less prominent accounting signal (acquirer cash) but attentiveness to a more prominent accounting signal (RNOA), as proposed by Hirshleifer and Teoh (2003, Journal of Accounting Economics, 36, 337–386).

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Appendix
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Footnotes
1
For example, Keynes (1936) articulates several reasons for holding cash, including bridging the gap between incurring costs and receipt of proceeds, and for unforeseen contingencies. Myers and Majluf (1984) reason that cash can mitigate the negative effects of information asymmetry between investors and managers.
 
2
For comparison, a strategy based on only stock consideration and hostility yields annual abnormal returns of 11.1%. Therefore, the addition of information on acquirer cash level increases annual abnormal returns by about 9.4%.
 
3
One factor that suggests the limited attention hypothesis may not hold is the lack of evidence to support “EPS myopia” as an explanation for negative post-acquisition returns. Rau and Vermaelen (1998) investigate whether the acquirer’s fixation on the EPS effect of the acquisition causes them to overpay for targets in accretive (that is, EPS-increasing) acquisitions. However, they do not find evidence to support this explanation. The fact that the market is able to “see through” possible EPS effects suggests that I will not find evidence of a relationship between acquirer cash and post-acquisition returns.
 
4
Healy et al. (1992) also investigate post-acquisition fundamental performance but find that stock acquisitions appear to outperform cash acquisitions. However, their small and non random sample makes extrapolation of their results difficult. Also, paying cash for the target firm’s stock is not the same as the acquirer having a high cash balance at the time the acquisition is announced, because a low-cash acquirer can borrow funds to finance a cash acquisition and a cash-rich acquirer could still choose to issue stock instead of paying cash.
 
5
Harford’s (1999) test shows that firms he classifies as having excess cash underperform on average, but he does not show that performance is decreasing in cash level.
 
6
However, the literature is far from unanimous on this issue; for example, see Villalonga (2004), and Campa and Kedia (2002). In a more recent paper, Bhagat et al. (2005) argue that diversifying acquisitions in themselves are not value-destructive but that they signal bad news about the bidder’s stand-alone prospects.
 
7
However, Rau and Vermaelen (1998) argue that their data fits more closely with their “performance extrapolation hypothesis,” where the market anticipates that the acquirer’s superior pre-acquisition performance will continue into the future when, in fact, it does not. Because an acquirer with overvalued stock because of performance extrapolation is likely to offer that stock as consideration in an acquisition (see Shleifer and Vishney 2003), the performance extrapolation hypothesis is consistent with stock consideration signaling poor post-acquisition returns that both Loughran and Vijh (1997) and Rau and Vermaelen (1998) identify.
 
8
More precisely, net operating assets can be decomposed into cumulative operating accruals and cumulative investment (equation 3 in Hirshleifer et al. 2004).
 
9
Data collection is more convenient with the SDC dataset, but that dataset is less complete for earlier years.
 
10
Specifically, I use the Wall Street Journal Index, Dow Jones Online (later renamed Factiva), and Lexis-Nexis.
 
11
To keep my SDC-drawn sample consistent with my CRSP-drawn sample, I also require that the target firm have a CRSP permno.
 
12
Asquith (1983) compares returns for the announcement and interim periods for both consummated and unconsummated acquisitions.
 
13
GICS codes are generally superior to SIC codes for use in capital markets research (Bhojraj et al. 2003).
 
14
I assume a three-month lag between the fiscal year-end and the time that financial statements are publicly available.
 
15
My approach avoids the estimation error inherent in imposing a model to estimate excess cash. Other benefits of my approach include its simplicity and a reduced loss of observations from the additional data requirements of the estimation model.
 
16
Diversifications (sometimes referred to as conglomerate acquisitions) have been defined a number of different ways in prior research. The strictest definition is employed by Agrawal et al. (1992), who define an acquisition as “conglomerate” if the primary SIC codes of the acquirer and target do not match on all four digits. Harford (1999) uses 19 industry groupings. Moeller et al. (2004) define a conglomerate acquisition as one where the first two digits of the acquirer’s and target’s primary SIC codes differ. I use a definition similar to Moeller et al.’s, modified to accommodate the use of GICS. My results are very similar if I use only SIC codes.
 
17
Results using the direct (that is, statement of cash flows) approach are similar to results using the indirect (that is, balance sheet) approach.
 
18
As I mention in the results section, my conclusions are the same if I select matching firms on the basis of industry and cash only (ignoring size and BTM).
 
19
I also include the delisting return for any delisted peer firm in my returns calculations.
 
20
I use daily returns from day −2 (that is, two days before the announcement) until the first month-end after the target delisting. After that point, to mitigate the effect of bid-ask bounce, I use monthly returns. If the acquirer is delisted before the end of the 24-month period, I include the delisting return in the BHARs, and I invest any remaining funds into the portfolio of peer firms.
 
21
In ordinary OLS, the influence of outliers increases with the square of the error term, but in Huber’s robust regression, the influence of outliers is limited to a fixed amount. My approach is similar to that of Aboody et al. (2004), page 256. My results are similar when OLS is used, but outliers significantly reduce the power of OLS. I also include more conventional Fama-MacBeth regressions in my results section.
 
22
This technique is consistent with the proposed method of Lyon et al. (1999), pages 173–175, except that I do not bootstrap from my initial dataset of acquirer returns. This is because the initial sample is nonrandom (unlike their sample). Results are similar when I bootstrap from the initial sample but are predictably biased because the mean and median long run returns for my acquiring firms are negative.
 
23
I use the same technique to establish the significance of my combined returns in Table 5, except that I bootstrap actual returns rather than skewness-adjusted t-statistics.
 
24
Harford (2005) notes that tests of operating performance are inherently noisy (page 556). To reduce the influence of outliers, I winsorize RNOA at the top and bottom 4% levels. NOA, accruals, and sales growth are winsorized at the 2% level.
 
25
I include year and industry dummies in this regression that are not shown.
 
26
In addition, I consider whether the addition of a dummy variable set to one if the acquirer used the pooling method (and zero otherwise) has any effect on my results. The pooling method could only be used when the acquirer offered only voting stock, creating a confound between stock and pooling. When both a stock and pooling dummy are included, both load with significantly negative coefficients for announcement period returns, but neither is significant for post-acquisition returns. Both estimated coefficients, however, are negative. The use of the pooling method was disallowed by FAS141, effective June 30, 2001.
 
27
Specifically, my results are similar (and generally stronger) after removing the top and bottom 5% of acquirers, ranked by the difference between acquirer values and peer firm values for size, book-to-market, and cash level to remove the worst-fitting matches. Results are also very similar if I select my peer firms on the basis of cash and industry only (ignoring size and book-to-market), if I use only SIC codes to define industry (instead of using GICS and SIC), or if I include only the first acquisition of each acquiring firm and exclude subsequent acquisitions. When OLS is used instead of robust regressions, I find that acquirer cash loads significantly negatively in the post-acquisition period (p = 0.01), but the overall p-value on the regression’s F-value is marginal (p = 0.095).
 
28
For ease of presentation, I multiply the average estimated coefficients by 100. I exclude the interaction variable “Acquirer Cash × Diff. Ind.” here; when it is included, neither acquirer cash nor the interaction loads significantly because of multicollinearity problems. Results are very similar when raw acquirer returns (with acquirer size and book-to-market added as additional controls) instead of peer adjusted returns.
 
29
Consistent with my winsorizing of acquirer RNOA, I winsorize the RNOA of peer firms at the 4% level. Pre- and post-acquisition RNOA for peer firms are defined based on target’s delisting date. To avoid unnecessary loss of observations, I include acquirers and peers where year −2 and/or +2 are missing, although my results are similar when these observations are excluded.
 
30
In untabulated results, I find that my results are robust to the inclusion of a dummy variable signaling extreme prior performance (identified by prior RNOA being more than three standard deviations from the mean for any given year) and the inclusion of an interaction variable of prior RNOA and the extreme RNOA dummy.
 
31
Using cutoffs based on same-year acquisitions would induce a peek-ahead bias because one would have to know statistics on all acquisitions announced in a given year before determining those that are high cash.
 
32
As I discuss in the methodology section, significance in Table 5 is established by bootstrapping from a portfolio of pseudo-acquirers randomly selected from the same industry, size quintile, and book-to-market quintile as each acquiring firm. Ordinary t-statistics suggest higher levels of significance.
 
33
This problem is not solved by bootstrapping.
 
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Metadata
Title
Does acquirer cash level predict post-acquisition returns?
Author
Derek K. Oler
Publication date
01-12-2008
Publisher
Springer US
Published in
Review of Accounting Studies / Issue 4/2008
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-007-9052-1

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