关于并购估值波动问题的研究--Paper代写范文

Paper代写范文:“关于并购估值波动问题的研究”，这篇论文主要描述的是想要开展企业的并购活动，我们就需要先对并购的企业展开估值，估算该企业在市场上的价值，企业合并如果估值存在被高估的现象，那么将驱动人们以错误的价格开展购买行为。

To test recent theories suggesting that valuation errors affect merger activity, we develop a decomposition that breaks the market-to-book ratio (M=B) into three components: the firmspecific pricing deviation from short-run industrypricing; sector-wide, short-run deviations from firms’ long-run pricing; and long-run pricing to book. We find strong support for recent theories byRhodes-Kropf and Viswanathan [2004. Market valuation and merger waves.

Journal of Finance, forthcoming] and Shleifer and Vishny[2003. Stock market driven acquisitions. Journal of Financial Economics 70, 295, which predict that misvaluation drives mergers. So much of the behavior of M=B is driven byfirm-specific deviations from short-run industrypricing, that long-run components of M=B run counter to the conventional wisdom: Low long-run value to book firms buy high long-run value-to-book firms.

We are also grateful for the comments of an anonymous referee.

Keywords: Mergers and acquisitions; Merger waves; Valuation

1. Introduction

The goal of this paper is to test the effect of misvaluation on merger activity. The last 125 years of business history indicate that periods of high M=B ratios coincide with periods of intense merger activity, especially for stock-financed deals.1 This fact is open to two interpretations. Under the neoclassical view, this fact is evidence that assets are being redeployed toward more productive uses.2 In contrast, if financial markets value firms incorrectlyor managers have information not held by the market, this result can be interpreted as evidence that acquisition frenzies are driven byoverval uation. Indeed, the fact that each of the last five great merger waves on record ended with a precipitous decline in equityprices has led manyto believe that misvaluation drives merger activity.

While this idea is compelling, it seems inconsistent with a broader equilibrium that endogenizes the target’s response to the offer. To put it simply, why is the target fooled? Whywoul d a value-maximizing target knowinglyaccept overvalued currencyin a takeover offer? Two recent theories offer answers to this question and, thus, to the role that valuation waves playin merger activity.

In this paper we test the empirical predictions of RKV and SV and find strong support for the idea that misvaluation shapes merger activity. We show that misvaluation affects the level of merger activity, the decision to be an acquirer or target, and the transaction medium. To guard against the possible alternative interpretations for our findings, we run a batteryof empirical horseraces.

To explore misvaluation empirically, we decompose M=B into two parts:Market to book
Market to value  Value to book. (1)If we had an accurate measure of value, we could assign labels to each of the two pieces on the right-hand side of Eq. (1). The first piece would measure the discrepancybe tween price and true value, and would therefore measure misvaluation.

2. Theoretical background and testable implications

If firms use stock as an acquisition currencywhen their stock is overvalued, and this is widelyknown, then whyare targets fooled? In this section, we review the main features of SV and RKV, which offer answers to this question. Then we explore their empirical implications. In RKV, private information on both sides rationallyleads to a correlation between stock merger activity an  market valuation. In their theory misvaluation has a market- or sector-wide component as well as a firm-specific component. The target’s and bidding firm’s private information tells them whether theyare over- or undervalued, but theycann ot separatelyidenti fythe sources of the misvaluation.

Thus, when the market is overvalued, the target is more likelyto overestimate the synergies because it underestimates the component of misvaluation that it shares with the bidders. In contrast, SV posit inefficient capital markets and differences in managerial time-horizons as the key drivers of merger activity. They hypothesize that short-run

managers sell their firm for stock in a long-run manager’s firm when both firms are overvalued, even though the transaction price gives the short-run manager less than

he knows his firm will be worth in the long run. The short run manager then sells his stock.

2.1. Relative value predictions

In both models, overvaluation leads to mergers. Therefore, the central prediction of either theoryis Empirical Prediction 1. Overvalued firms use stock to buy relatively undervalued firms when both firms are overvalued. In SV this occurs because the overvalued short-run managers wish to sell out while their stock is overvalued. The acquirer is also overvalued because onlylong-r un managers whose companies are more overvalued have room in their stock price to over pay for a target that is also overvalued and still make money in the long run. Empirical Prediction 3. Cash targets are more undervalued than stock targets. Cashacquirers are less overvalued than stock acquirers.

2.2. Merger intensity predictions

The first three predictions relate to levels of relative misvaluation across types of transactions conditional on a merger. The SV and RKV theories also demonstrate how misvaluation can cause merger waves. Thus, the predictions from theoryshould also be stated in terms of how increases in misvaluation cause increases in merger activity. For the theories to have empirical relevance, merger activity should be more likelycond itional on high valuation errors. Therefore, theoryleads to Empirical Prediction 4. Increasing misvaluation increases the probability that a firm is in a merger, is the acquirer, and uses stock as the method of payment.

In both theories, the greater a firm’s overvaluation, the more likelyit is to win the bidding for a target. RKV also predict that even the probabilityof being a target should increase with sector overvaluation.  Empirical Prediction 5. Increasing sector misvaluation increases merger activity, and the use of stock as method of payment, in that sector. These predictions allow us to examine the importance of valuation, and the components of valuation, in merger activity. However, a number of other prominent explanations exist for merger waves. For example, Holmstrom and Kaplan (2001) argues that corporate governance issues led to the merger waves of the 1980s and 1990s.

3. Data and trends in merger activity

Our sample includes all merger activitybe tween publiclytraded bidders and targets listed on the Securities Data Corporation (SDC) Merger and Acquisition Database between 1978 and 2001. Because our sample includes onlypubliclytraded firms, this excludes transactions such as leveraged buyouts (LBOs) and management buyouts (MBOs). We then match these data with Compustat fiscal year-end accounting data and stock price data from the Center for Research in Securities Prices (CRSP) to obtain a final sample.

We use the following conventions to merge data from the three sources. First, to calculate M=B, we match fiscal year-end data from Compustat with CRSP market values occurring three months afterward. Because firms have different fiscal year end dates, this involves compensating for Compustat’s year-of-record scheme, so that the year of the data corresponds to the year in which the accounting information was filed. Then, we associate this CRSP and Compustat observation with an SDC merger announcement if the announcement occurs at least one month after the date of the CRSP market value.

Market value is CRSP market equityplus Compustat book assets (item 6) minus deferred taxes (item 74) minus book equity(item 60). In addition, we obtain the following size-related measures: Total Plant, Property, Equipment (item 8), Total Cash (item 1), Long-term Debt (item 9), capital expenditures (CAPEX) (item 128),and Net Income (item 172).

4. Decomposing market to book

This section and the next discuss the two methodological innovations that we use to studyhow valuation waves affect merger waves. The theories of SV and RKV Characteristics of merger and nonmerger firms Summarystatistics for size, performance and leverage taken from Compustat between 1977 and 2000 to match the availabilityof the SDC data. Merger observations are firms appearing on the SDC as either a bidder or target in the period 1977–2001. Observations are required to have book-to-market ratios below 100 and market equitylarger than $10 million. Market value of assets is market value of equity eCRSP price  shares outstandingTtbook assets ed6T book equity ed60T deferred taxes ed74T.

570 M. Rhodes–Kropf et al. / Journal of Financial Economics 77 (2005) 561–603 both suggest that a merger is more likelywhen a firm’s market value, M, is greater than its true value, V. Therefore, both theories implicitlysuggest that a firm’s market-to-book ratio should be broken into two components: market value-to-true value, M=V, and true value-to-book, V=B.

The keydiff erence in the veyit;  T expressions is that time-t multiples are represented as ajt while long-run multiples are represented by aj .  To avoid these and other shortcomings, we take a different approach to obtain a measure of value. Our strategyis to impose identifying restrictions on Eq. (6). This approach does not relyon analysts’ forecasts that could include expectations of future merger activity, it does not bias our sample toward large transactions, and it allows us to recover the market’s estimates of growth and discount rates. Depending on the identifying assumptions imposed, Eq. (6) yields to a variety of econometric specifications.

5.2. Model 2: market value, book value, and net income

Recent scholarship in accounting has pointed to the importance of net income for explaining cross-sectional variation in market values. Examining the value-relevance of various accounting measures via equations similar in spirit to Eq. (8) has a long tradition in the accounting literature. That literature is far too large to discuss fully here, but Holthausen and Watts (2001), Kothari and Zimmerman (1995), Kothari (2001), and Barth et al. (2001) contain excellent surveys of this literature and debates about the conclusions that can be drawn from it. 

A number of authors (for example Amir and Lev, 1996; Lev, 1997) have argued that the value relevance of accounting has declined, in part because of the rise in importance of intangible assets that are not captured in book equity. Collins et al. (1997) counter that accounting information continues to be important in the face of intangibles, pointing instead to the increasing importance of net income for explaining cross-sectional variation in  market value.

To develop a valuation model that includes net income as well as book value, we can impose slightlyless restrictive assumptions on Eq. (6). For example, if we assume that

book value and net income are growing at constant rates, we can rewrite Eq. (6) as Fama and French 12 industryclassificatio ns are reported across the top. Output from valuation regressions are reported in each row. The second panel of Table 4 reports time-series average values of the fajg for each industry. The cross-industry comparisons matchModel 1, except that the addition of net income to the model uniformlyincrea ses average R2 values.

5.3. Model 3: market value, book value, net income and leverage

Models 1 and 2 implicitlyimpos e the restriction that firms be priced against the average multiples for firms in that industry-year. To account for the fact that withinindustrydiff erences in leverage could potentiallyinfluence this, we estimate a third model in which leverage also appears. Accounting for leverage allows for the fact that firms with higher or lower than industry-average leverage have a different cost of capital, forcing them to differ from industryaverage multiples.

M. Rhodes– Kropf et al. / Journal of Financial Economics 77 (2005) 561–603 577 (Fama-Macbeth standard errors are reported below point estimates). Moreover, the value of intangibles rises when we account for cross-sectional differences in leverage. Finally, the average R2 values range between 80% and 94%, indicating that accounting information and leverage alone explain the vast majorityof crosssectional variation in market values within a given industryat a given time.

Looking across the three models reported in Table 4, it is generallyeasy to reject the null hypothesis that the average a0 ?0. There is less time-series volatilityin the loadings on accounting variables for each industrythan on the a0 terms, however, which suggests that while discount rates and growth rates varya great deal across industries, theyare less variable within industries over time.

5.4. Discussion

Table 5 summarizes our decomposition methodologybyidenti fying each component of our M=B decomposition and describing how it is calculated.The term mit  veyit; ^ajtT is the regression error obtained from annual, industrylevel, cross-sectional regressions. We label this piece firm-specific error. Because the multiples obtained from annual, cross-sectional regressions contain time-varying market expectations of industryaverage growth rates and discounts rates, firmspecific error can be interpreted either as one component of misvaluation or as firmspecific deviations from contemporaneous, industry-average growth and discount rates. Because average regression error is zero byconstr uction, our valuation measure prices firms correctlyon average relative to their industryvaluat ion.

Naturally, these interpretations rest on a correct measure of v. Because we are estimating v, we face the standard joint hypothesis problem: It is impossible to distinguish empiricallybetween a purelybehavior al explanation for misvaluation and one based on rational behavior in the presence of asymmetric information.

6. Tests and findings

We now use our methodologyto test the predictions discussed in Section 2. Because the SV and RKV theories explicitlylink misvaluation levels to merger waves, we proceed in two steps. First, we examine the valuation characteristics of the sample of firms that participated in mergers. In Section 6.1 we examine the relative value predictions. Second, we also studywhether times of high aggregate valuation errors are times of high merger activity. These merger intensity predictions are tested in Section 6.2.

6.1. Testing relative value predictions

The first row of Table 6 reports differences in mit  bit ratios bytarge t, acquirer, and method of payment. From this we see that it is not the case that high M=B buys low M=B, but that high M=B targets are bought byeven higher M=B acquirers. This finding is driven bythe characteristics of targets in stock transactions. In this group, both acquirers and targets have significantlyhigher M=B ratios than in other method-of-payment categories. When we examine cash-only or mixed payment transactions, we find no difference in M=B between target firms and nonmerger firms.

The last prediction that can be tested with Table 6 is Empirical Prediction 3. This also holds for all models. First, cash targets are more undervalued than stock targets.

For example, in Model 3, the firm-specific error for stock targets (0.05) is larger than that of cash targets (0.08). The same is true of time-series sector error for stock and cash targets (0.12 for stock targets is greater than 0.06 for cash targets).

In addition, firm-specific error is higher for stock acquirers than cash acquirers. From Model 3, the stock acquirer firm-specific error is 0.44, while for cash acquirers it is only0.29. Time-series sector error of 0.17 for stock acquirers exceeds the 0.14 for cash acquirers. Although the theorydoes not discuss mixed payment acquisitions, by extension it would seem that all stock acquirers should be more overvalued than mixed payment acquirers, which is supported by the data [0.44 (stock) versus 0.29 (mixed) for firm-specific, and 0.17 (stock) versus 0.12 (mixed) for sector-specific].

6.1.1. Do low growth firms acquire high growth?

Table 6 also contains a new finding that is not predicted byeithe r efficient markets or the possibilityof misvaluation. Although high M=B firms buylow M=B, this difference between bidders and targets is driven byfirm-specific deviations from short-run average value, not from fundamental differences between targets’ and ’ long-run pricing. To see this in Table 6, compare the top row of the table,which reports lneM=BT with the bottom row of each model, which reports long-run value-to-book. For example, the average logeM=BT of acquirers is 0.83 and the average logeM=BT of targets is 0.69, while, in model 3, the average long-run value to book of acquirers is 0.39; targets, 0.58. In all of our models, we find that low long-run value to book firms acquire high long-run value-to-book targets, both in stockfinanced and cash-financed transactions.

Thus, while it is true that high M=B acquirers buylow er M=B targets, so much of this is driven byshort-ru n valuation dynamics that the long-run value to book measures work in the opposite direction.

pricing. Long-run value is not lower due to the merger. Long-run value is a function of pre-merger accounting variables and longrun industry-wide valuation multiples. Thus, a low long-run value arises from an industrywith low long-run pricing.

6.1.2. Robustness checks on relative value predictions

Table 6 contains striking evidence in support of the idea that temporaryfirmspecific and industry-specific fluctuations in value drive acquisition activity. However, a number of potential alternative explanations could be clouding the results in Table 6. Tables 7 and 8 provide robustness checks and further extensions to our primaryrelative value predictions.

One concern with the preceding analysis is that the results are being driven by the late 1990s, when valuations were high and our long-run value calculations are the

most backward-looking. To see whylate 1990s mergers might be a problem for our analysis, consider a typical merger occurring in 1999. During this period, valuations were at all-time highs. Thus, mit  bit is likelyto be large, and ajt values are likelyto be above their long-term values, which toward the end of the sample are mostly backward-looking (an a contains onlytwo years of forward-looking data in 1999).

Moreover, because this period was a time of intense merger activity, such mergers could make up a disproportionate fraction of our sample. To control for this possibility, Table 7 includes a column that repeats Table 6 except that onlymerge rs occurring prior to 1996 are included.  Table 7 includes a number of additional robustness checks. The fact that we get the same results when we split mergers according to whether theywere within or across industryshows that our results cannot be attributed to explanations based on industrye xpansion or contraction.6 Another potential concern is that firms at risk for LBO were systematicallymis valued byour valuation technique because theyhad low growth prospects.

6.2. Overvaluation and takeover intensity

Now we turn the analysis from the previous section around and ask whether increases in valuation levels cause increases in merger activity. We approach this in two steps. First, at the firm level, we explore how valuation error affects the probabilityof being involved in a merger. Second, at the sector level, we relate aggregate merger activityto overall levels of valuation error. This allows us to test Empirical Predictions 4 and 5 directly. We address the question of whether valuation error affects the probabilityof an individual firm being involved in a merger. This is presented in Section 6.2.1. In Section 6.2.2, we examine aggregate merger intensity.

6.2.1. Firm-level intensity regressions

Panel A of Table 9 presents tests of the probabilitythat a firm is involved in a merger as a function of its valuation characteristics. Columns 3–8 repeat the analysis of Columns 1 and 2 but replace mit  bit with the components of our decomposition. No matter which model we use, firm-specific error and time-series sector error have a positive and statisticallysignifican t effect on the probabilitythat a firm is involved in a merger, while long-run value to book has a negative, significant effect. Introducing year fixed effects eliminates the significance of the sector valuation error, but neither the firm-specific error nor the long-run value to book is affected. These findings hold across each of the three models.

This table reports probit regressions of merger activityon valuation characteristics. The dependent variable in Panel A is a dummyfor whether the firm in question is involved a merger (this includes acquirers and targets). Panel A uses the entire intersection of Compustat and SDC. Panels B and C focus only on the sample of merger observations.

Panel C of Table 9 relates our decomposition to method of payment. It reports probit regressions in which the dependent variable is one if the acquisition was 100% stock-financed, zero otherwise. It shows that high lneM=BT firms are more likelyto use stock. Each element of the decomposition has a positive, significant affect on this probability. This supports the findings of Martin (1996), which relates q to method of payment. It also supports the predictions of RKV and SV. These findings show that positive firm-level deviations from industrypricing increase the probabilitythat a firm is involved in a merger, that a firm is an acquirer, and that the acquisition is financed with stock. Thus, this table offers strong support for Empirical Prediction 4.

6.2.2. Sector-level intensity regressions

To test Empirical Prediction 5 we regress merger activityin sector , year t, on a varietyof aggregate valuation measures. These are reported in Table 10. In Panel A, the dependent variable is a count of the total merger activityin sector  during year t. The first five columns regress this measure of merger activityon the average lneM=BT in that sector.The independent variables are obtained byaveragin g the firm-level data (this includes Compustat nonmerger firms as well as firms engaged in merger activity) down to the sector level each year. In particular, because firmspecific error is zero at the sector-year level. The results from Column 1 indicate that merger activityloads positivelyand significantlyon lneM=BT. Because this regression includes sector fixed effects, the interpretation is that sectors experience more merger activityas their valuation levels

increase.

In Table 9 which shows that firm-level lneM=BT does not predict increased probabilityof merger once we control for year effects. In Columns 6 and 7, we repeat these regressions but replace ˉ mt  ˉbt with average time-series sector error. From these regressions, we see that the inclusion of sector and year fixed effects does not destroythe significance of our decomposition.

In both cases, we see that increases in average sector valuation error lead to increases in merger activity. Because we control for sector and year fixed effects, the interpretation is that sectors with larger increases in valuation (relative to other sectors) experience greater increases in merger activity.

Because fixed effects seem to be important for understanding how valuation (and valuation error) affects merger activity, the remaining columns of Panel A explore possible explanations for the economic forces that year and sector fixed effects are capturing. In Columns 3 and 8 we replace the year fixed effect with a count of the total number of mergers across all sectors in year.Finally, the inclusion of a variable that measures overall, economy-wide merger activity in a given year helps us to guard against a potential objection to our analysis,which is that there have been onlya verysmal l number of merger waves in our sample period of 1977–2001. Instead, our results indicate that industries experience valuation-specific merger waves that differ from the overall, economy-wide trends in merger activity, corroborating evidence in Mitchell and Mulherin (1996) and Harford (2005), which shows that mergers cluster in time at the industrylevel .

7. A horse race between competing theories of merger activity

The neoclassical explanation for merger activityis that mergers are an efficient response to reorganization opportunities that arise as a result of some underlying economic event (see, for example, Gort, 1969). Explanations along these lines could account for some of our findings if mergers cluster when opportunities for reorganization are rich, which in turn are periods of high valuation because markets bid up prices in anticipation of the restructuring.

To guard against the possibilitythat neoclassical explanations are responsible for our findings, we use two approaches. The first approach is based on arguments made by Jovanovic and Rousseau (2002) and others, who saythat dispersion in Tobin’s Q reflects opportunities for organizational change. This Q theoryof mergers suggests that some exogenous economic shock occurs in an industry. Some firms are well positioned to take advantage of this shock, while others are not, thus creating fruitful opportunities for reorganization.

The first approach probablygives too little weight to the neoclassical story by looking onlyat one potential neoclassical explanation for merger activity. The second approach could give too much weight to the neoclassical story, because it attributes neoclassical explanations, ex post, to all mergers that occurred during times of extreme merger activity.

7.1. Comparing failed and successful mergers

Our first horse race comes from comparing successful acquisitions with failed acquisitions. Because assets are being efficientlyreorganized , a Q-based explanation

would give a higher chance of completion to a merger between two firms with a larger disparityin M=B. Thus, if Tobin’s Q explains merger activity, then we would expect the bidder/target Q differential to be higher among successful deals than among failed deals.

Table 11 reports the same breakdown as in Table 6 but splits the sample according to whether or not the deal was successful. The Q difference between bidder and target is higher in failed deals, not in successful deals. First, efficient asset redeployment is unlikely to be responsible for our findings, because Q dispersion is higher in failed deals than in successful ones. Instead, misvaluation seems to be at work, because overall valuation levels are higher in successful than in failed bids, and more of the level is attributable to misvaluation in successful deals as well.

596 M. Rhodes–Kropf et al. / Journal of Financial Economics 77 (2005) 561–603 seems unlikelythat our analysis is simplycapturi ng ex ante valuation differences that vanish between the announcement and consummation of the merger, because the overall misvaluation level is higher in deals that go through than in ones that are withdrawn.

7.2. Can Q dispersion explain merger intensity?

Next, we conduct a horse race based on the merger intensitypredictions by introducing dispersion in M=B as a proxyfor reorganization opportunities. The measure of Q dispersion we use is the within-industrystandar d deviation in mit  bit in a given year. Table 12 A horse race between competing theories of merger Q dispersion is the standard deviation in lnembT within an industryin a given year. All other variables are defined in Table 5. The right half of Table 12 reports these results. Comparing Columns 4 and 6, we see that Q dispersion predicts merger activityonlyin the low valuation subsample.

During high misvaluation periods, Q dispersion is not statisticallysignifi cant. To ensure that this is not being driven bythe fact that Q dispersion was low in the high value period, we checked the mean and standard deviation of the Q dispersion variable in each subsample. Theyare roughlythe same (0.897 in the low sample, 0.839 in the high sample, with standard deviations of 0.26 and 0.298, respectively), indicating that this is not being driven by a problem of limited variance in one subsample. This suggests that while Q dispersion could reflect some underlying economic force that drives merger activity, many mergers occur during periods of high misvaluation that are unrelated to these forces. The large and statisticallysignific ant loadings on sector misvaluation suggest that misvaluation drives merger activity. The fact that Q dispersion works in times of low misvaluation, but not high misvaluation, indicates that misvaluation is not simplycap turing liquidity.

7.3. Economic shocks as an alternative explanation for merger intensity

One problem with the previous analysis is that many potential neoclassical explanations exist for merger activitythat do not involve dispersion in Tobin’s Q. Thus, we could be giving too little weight to neoclassical explanations for merger activity. To guard against this potential objection, this section conducts a horse race using a measure designed to capture a broad range of potential neoclassical ex ante motivations for merger. To do this, we use the classification of merger waves conducted by Harford (2005).  Examining R2 values across the three regressions illustrates an important point.

At the same time, this evidence leaves open the question of who buys whom during merger waves. For this, we turn to Panels B and C of Table 13. In Panel B, we provide several statistics of merger activitybroke n down according to the quantile of firm-specific misvaluation that the acquirer came from when the acquisition was announced. Panel B shows that the quintile of the most overvalued firms is responsible for 42% of merger transactions and an even larger fraction (47%) of stock-financed transactions. This quintile is responsible for nearly60% of the dollar volume of merger transactions.

Taken together, these results allow us to compare the neoclassical explanation for merger activitywith misvaluation. The results show that while sector misvaluation is an important determinant of merger waves, manyother factors are also important. Misvaluation is byno means the whole story at the sector level. Yet at the firm level, misvaluation is critical for understanding who participates in these merger waves.

8. Summaryand conclusions

This paper uses regression techniques to decompose the M=B ratio into components that track misvaluation at the firm and sector levels and a component that tracks long-run growth opportunities. This decomposition allows us to test recent theories arguing misvaluation drives merger activity. To summarize our main findings, our breakdown of M=B finds the following:  Acquirers with high firm-specific error use stock to buytarge ts with relatively lower firm-specific error at times when both firms benefit from positive time-series sector error.  Cash targets are undervalued relative to stock targets. Cash acquirers are less overvalued than stock acquirers.

Merger intensityis highly positively correlated with short-run deviations in valuation from long-run trends, especiallywhen stock is used as the method of payment. This holds for individual firms, as well as at the aggregate level. After controlling for firm-specific and time-series sector error, we find that low long-run value-to-book firms actuallybuy high long-run value-to-book targets.

Pitting our predictions against neoclassical, Q oriented explanations for merger activityreveal s that a significant fraction of merger activityis explained by misvaluation.  theorysuggests that successful transactions have large market-tobook differences between bidder and target. However, we find that failed transactions have larger differences than completed transactions, while successful deals displayhigh er levels of misvaluation. Even in industries that appear to have experienced an economic shock, most acquirers come from the highest misvaluation quintile. Therefore, our findings support misvaluation theories based either on behavioral explanations or on asymmetric information between otherwise rational managers and markets.

Economic shocks could well be the fundamental drivers of merger activity, but misvaluation affects how these shocks are propagated through the economy. Misvaluation affects who buys whom, as well as the method of payment they use to conduct the transaction.

51due留学教育原创版权郑重声明：原创留学生作业代写范文源自编辑创作，未经官方许可，网站谢绝转载。对于侵权行为，未经同意的情况下，51Due有权追究法律责任。

51due为留学生提供最好的加拿大math辅导服务，亲们可以进入主页了解和获取更多paper代写范文以及美国作业代写，详情可以咨询我们的客服QQ:800020041哟。-xz