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Erschienen in: Review of Quantitative Finance and Accounting 4/2020

17.04.2020 | Original Research

Wealth effects of relative firm value in M&A deals: reallocation of physical versus intangible assets

verfasst von: Debarati Bhattacharya, Wei-Hsien Li

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 4/2020

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Abstract

This paper distinguishes between value creation through redistribution of physical assets and that from intangible assets. We decompose the market-to-book ratio into fundamental value and unexplained components and find that mergers create wealth when high-value firms primarily acquire physical assets from low-value firms. In contrast, deals motivated by transfer of investment opportunities generate wealth when growth-constrained low-value firms acquire substantial intangible assets from high-value targets. By separating the two motives for mergers, we provide empirical evidence of two diametrically opposed effects of relative firm value on wealth gains to shareholders, thus reconciling the conflicting evidence of the ‘high-buys-low’ effect from earlier studies. Concomitantly, our findings also explain the patterns of firm pairings in merger data that run contrary to conventional wisdom. Our empirical framework considers the effects of mispricing, governance, and size of assets reallocated and addresses concerns of selection bias. Additionally, we find evidence of post-merger wealth generation through the acquisition of growth opportunities in the form of intangible asset transfer from a high-value target to a low-value acquirer.

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Fußnoten
1
Other motives for mergers, such as managerial hubris (Roll 1986; Jensen 1986), mispricing (Shleifer and Vishny 2003; Rhodes-Kropf and Viswanathan 2004; Rhodes-Kropf et al. 2005), and asset complementarity (Rhodes-Kropf and Robinson 2008) have also been considered in the merger literature.
 
2
Because the focus of this paper is on M&A, we do not discuss any impact of relative firm value on internal investment.
 
3
“…Innovation has seen a structural shift toward smaller companies since the 1980 s, which is reflected in research and development (R&D) spending in the United States from 1989 to 2009. Large companies now account for just half of R&D spending, down from over 77 percent in the mid-1980 s. Consequently, in addition to internal R&D, large companies have turned to acquiring innovation as a core business practice…”—Lak Ananth, Head, Siemen’s next47 Startup Unit.
 
4
Peters and Taylor (2017) find that Tobin’s Q explains physical and intangible investments equally well, and thus it should capture the net effect of future growth opportunities that can be seized by investing either in physical assets or intangible assets.
 
5
Intangible targets have higher investment rates than acquirers and are less cost effective than acquirers. Even though tangible mergers show a similar pattern, the difference between the investment rates (cost estimates) of the targets and the acquirers is significantly lower than that for intangible targets, which is consistent with Levine (2017).
 
6
Though not reported, when we examine the distribution of target V/B ratios, we find that only 35% of high-intangible targets are in the bottom V/B decile, whereas over 46% of the time the low-intangible targets are in the bottom V/B decile, which is consistent with Levine (2017) and the intangible asset transfer hypothesis.
 
7
At least 33% (52%) of the time acquirers’ M/B (V/B) are below that of the targets. These findings do not support the neoclassical view of mergers, but instead echo the views of asset complementarity (Rhodes-Kropf and Robinson 2008) and product market synergy (Hoberg and Phillips 2010). Furthermore, 67% of the time the M/V ratios of the acquirers are above those of the targets, supporting the evidence that high overvalued acquirers buy fewer high overvalued targets, as documented by RKRV.
 
8
We use Loughran and Ritter’s (2004) definition of a high-tech industry. Although not reported, our findings are qualitatively similar when we sort firms into terciles or use different measures of intangibility and Fama-French (FF) 12 high-tech industries.
 
9
It is standard empirical practice in the literature to include RS as a standalone regressor in acquisition performance models (e.g., Mulherin and Boone 2000; Ishii and Xuan 2014).
 
10
Because BCF indices are only available for firms covered in the RiskMetrics database, we add a dummy BCF missing to account for the unavailability. In addition, we obtain qualitatively similar results with the GIM-index proposed by Gompers et al. (2003). Beginning in 2007, RiskMetrics (which acquired IRRC in 2005) has applied a new methodology to collect governance data that do not include all the information needed to construct the GIM-index. Thus, the observations after 2006 are dropped when the GIM-index is used.
 
11
RKRV further decompose M/V into a firm-specific pricing error and a sector-specific pricing error to differentiate between situations where a firm’s pricing differs from the pricing of its sector in the current period from situations where the pricing of the sector in the current time period differs from the sector’s long-run pricing. We sum the two errors and label them as M/V, because our hypotheses do not differentiate between the two sources of error. We refer to M/V as the unexplained component of market-to-book for expositional simplicity. We also follow Hertzel and Li (2010)’s modifications to address the potential look-ahead bias of the original RKRV procedure.
 
12
Hertzel and Li (2010) document a connection between mispricing and SEO decisions. Van Bekkum et al. (2011), Ben-David et al. (2015), and Chuang (2018) provide further evidence supporting mispricing or market-timing as a motive for mergers.
 
13
A simple model of asset reallocation shows that the combined announcement returns are a function not of the Q-difference alone, but are also the product of the Q-difference and RS of the assets being redeployed. Appendix 3 presents the model.
 
14
The idea that the wealth effects of M&As should be related to the size of the target relative to the acquirer can be traced back to Asquith et al. (1983), who include relative size as a standalone independent variable in their announcement return regressions. Several recent studies interact their primary variables of interest with relative size and find that their wealth effects, both positive and negative, increase with relative size (Fuller et al. 2002; Hoberg and Phillips 2010; Li 2013).
 
15
CARC measures the effect of synergies on the market value of equity for the combined firm, and therefore to remain consistent, we define RS by the target firm’s market capitalization as a fraction of the sum of the two firms’ market capitalizations.
 
16
We begin our sample period in 1989, because Netter et al. (2011) show that the SDC’s coverage of M&As is not consistent with other data sources before the third quarter of 1988.
 
17
In unreported regressions, we exclude the regulated utility industry (four-digit SIC code from 4900 to 4999) and deals with a relative value of < 1. Our results are robust in this alternative deal sample.
 
18
We delete one additional observation that was withdrawn shortly after the announcement following unusual price behavior. The acquirer was investigated by the SEC with questions about the “adequacy and accuracy” of its disclosures regarding its proposed purchase. The trading of the acquirer was also suspended due to the investigation. Our results are qualitatively identical when this observation is included.
 
19
We measure run-ups as the buy-and-hold abnormal returns during the window of (− 20, − 2). This run-up window includes pre-announcement price changes that might reflect information about the impending acquisition. In choosing an event window for the announcement returns, researchers face a trade-off between a longer window with more noise versus a shorter window that might omit the target’s stock price run-up. We focus on the conventional three-day abnormal returns, but control for target run-ups by including it as a control variable in our regressions.
 
20
Although not reported, analyses of the correlation matrix for the acquirer’s and the target’s M/B, V/B, and M/V show that, as expected, the two components are highly positively correlated with M/B. In contrast, the two components are uncorrelated with each other, indicating that the RKRV method successfully breaks down the market-to-book ratio into two nearly orthogonal components.
 
21
Results are similar using M/B-difference.
 
22
1.32 (SD of V/B-difference for low-intangible deals) * 0.46 = 0.61%; and -1.53(SD of V/B-difference for high-intangible deals) * (− 0.625) = 0.96%.
 
23
The wealth effect of mispricing-driven M&As is still under debate. While Savor and Lu (2009) and Golubov et al. (2016) find that stock-financed mergers that are likely to be driven by the acquirer’s overvaluation do not destroyvalue in the long run, Fu et al. (2013) and Ben-David et al. (2015) document the opposite. Our findings here and later vis-à-vis the relation between long-term stock returns and mispricing are both inconclusive.
 
24
1.32(SD of V/B-difference for low-intangible deals) * 0.21(mean RS for low-intangible deals) *3.356 = 0.93%.
 
25
In unreported regressions, clustering standard errors by acquiring firms hasno impact on our findings.
 
26
− 1.51 (SD of V/B-difference for high-intangible deals where BHAR is available) * 0.127 (mean RS for high-intangible deals where BHAR is available) * (− 126.075) = 24.177% increase in 2-year BHAR or about 11.43% annualized BHAR.
 
27
Although not reported, we re-estimate probit models in the 1st step and CARC models in the 2nd step of the Heckman process using Post 1995, Post 1996, Post 1997, and Post 1998 as identifying instruments, finding consistent results across all of those instruments.
 
28
For the sake of brevity, we do not report the results of the 1st-step regression or the exclusivity criterion check, but they are available upon request.
 
29
Net income (NI) is item 172 from Compustat and the leverage ratio is defined as (1—market value of equity/market value of assets). RKRV list two simpler models as well. We use only the third and more sophisticated of their models.
 
30
This addresses the potential look-ahead bias created by RKRVs’ use of the average over the entire sample period for long-run sector multiples.
 
31
We also use 2-digit SIC codes to classify industries and perform RKRV’s methodology using these definitions of industries. Subsequent results are largely the same using 2-digit SIC codes and the 12 Fama-French industries for the M/B decomposition. We report the results using Fama-French to maintain comparability to RKRV and Hertzel and Li (2010).
 
32
It can be shown that using acquirer returns instead of combined returns would cause substantial downward bias. We do not present such a discussion for the sake of brevity.
 
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Metadaten
Titel
Wealth effects of relative firm value in M&A deals: reallocation of physical versus intangible assets
verfasst von
Debarati Bhattacharya
Wei-Hsien Li
Publikationsdatum
17.04.2020
Verlag
Springer US
Erschienen in
Review of Quantitative Finance and Accounting / Ausgabe 4/2020
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI
https://doi.org/10.1007/s11156-020-00882-0

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