Post-Acquisition Performance: A Propensity Score Matching Approach
Milena Petrova 1 and Michael T. Shafer This Draft: January 2010
Abstract We examine the long term post acquisition performance of 1,385 firms over 1980-2006. Prior merger long run return studies show that acquiring firms experience a negative stock price drift after performing an acquisition. One of the methods used in this area of research involves matching a similar non-acquiring firm to the event firm to create a benchmark for expected returns. However, the matching methods used are often forced to employ only a small number of firm characteristics to perform the matching procedure. In this study, we use propensity score matching to solve this dimensionality problem. We find that acquiring firms do not underperform matching firms in the one year following an acquisition, while companies that complete two or more deals within one year from the first acquisition outperform their peers. Our analysis of the two-year acquirers’ return performance suggests a significant underperformance of the acquirers vs. their peers, with firms conducting multiple acquisitions experiencing only slightly higher returns than their matching firms. In addition, our findings suggest that hostile takeovers and acquisitions by firms with higher leverage experience higher long term returns.
1
Corresponding author: Assistant Professor of Finance, Syracuse University; address: Martin J. Whitman School of Management, Department of Finance, 721 University Ave., Syracuse; e-mail:
[email protected]; phone: (315) 4439631. Michael T. Shafer is a PhD student in the Department of Finance, Syracuse University, e-mail:
[email protected]; phone: (315) 443-7182;
Post-Acquisition Performance: A Propensity Score Matching Approach
A large body of theoretical and empirical research examines corporate mergers and acquisitions, mostly focusing on shareholder wealth effects. While it appears that economic value is created during the announcement period window, with most of the gains accruing to the shareholders of the target company, long-run studies show that this value may deteriorate over time (Agrawal, Jaffe and Mandelker (1992), Gregory (1997), Loughran and Vijh (1997)). However, another stream of literature presents evidence that mergers do not destroy value in the long run (Mitchell and Stafford (2000), Rau and Vermaelen (1998)). If the market adjusted quickly and accurately to an informative event, then we would not expect any investor to be able to obtain returns beyond what could have been obtained before this event. Therefore, long-run post-merger performance studies can be used to provide evidence about market efficiency. However, long run studies have faced criticism due to the methodology they use. One of the methods used in this area of research involves matching a similar nonacquiring firm to the event firm to create a benchmark for expected returns. However, standard matching methods use only a small number of firm characteristics (typically industry group and size) to perform the matching procedure. In this paper we use propensity score matching to examine the one- and two-year post merger treatment effects on acquiring firms. By using propensity score matching we seek to overcome the dimensionality problem that plagues many of the long-run studies in this area of research. We employ nearest neighbor one-to-one matching, which seems to be the most intuitive method of propensity score matching available. This involves matching an acquiring 2
firm with the control firm that has the closest probability of acquiring to the actual acquiring firm. We use firm size, profitability, and market-to-book ratio to estimate the propensity of firms to merge. To control for macroeconomic factors on the probability to merge, we estimate propensity scores for each quarter in the dataset separately. We also force the control firm to be in the same industry as the acquiring, or treated, firm. This controls for the clustering of M&A activities within industries. Once firms are matched, we calculate one- and two-year abnormal returns for each firm beginning the month following the acquisition. To do this, we use the Jensen-alpha approach as described in Kothari and Warner (2007), but we estimate the abnormal return for each firm separately instead of using a portfolio approach. We then compute the one- and two-year treatment effects of the merger as the difference between the abnormal returns of the treated and control firms. Using the propensity score matching approach, we find that there is no significant one year treatment effect on acquiring firms. Additionally, we find that the treatment effects of the acquisition cannot be explained by the probability of the merger occurring or by other firm specific factors, with exception of leverage. The contributions of this paper are as follows. First, we use propensity score matching to identify the characteristics of public US firms associated with an increased probability of being an acquirer in a merger or acquisition. Second, we document the magnitude of the long term wealth effects that accrue to the shareholders of acquirer over one and two-year periods after the first acquisition. We distinguish between acquirers that do not conduct subsequent acquisitions and firms that perform one, or two or more acquisitions and examine long term performance for these groups of acquirers. Finally, we investigate the determinants of long run post-merger abnormal returns. Our results can be summarized as follows. We find that acquiring firms on average do not underperform their peers in the one year following the acquisition. However, when we distinguish by 3
firms that do not complete other acquisitions and serial acquirers we find that the latter outperform significantly their matching firms, while single acquisition firms underperform their peers. Our analysis of the two-year acquirers’ return performance suggests a significant underperformance of the treatment firms vs. their matching firms, with firms conducting multiple acquisitions experiencing only slightly higher returns than their matching firms. In addition, our findings suggest that hostile takeovers and acquisitions by firms with higher leverage experience higher long term returns. The remainder of the paper is organized as follows. In section 2, we discuss the relevant literature. Section 3 contains a description and analysis of the data. Our methodology and empirical results are presented in section 4. Section 5 concludes.
II. Literature Review 1.1 Long-Run Event S tudies Most long-term studies of abnormal returns tend to follow either the Jensen-alpha approach, based on multifactor asset pricing models, or the buy-and-hold abnormal return (BHAR) approach, which is based on matching firms that undergo some sort of informational event to a similar non-event firm. Neither technique is uniformly accepted as better than the other. Each model has different strengths and weaknesses, which earns the praise and criticism of many researchers. For a thorough discussion on each of these methods, see Kothari and Warner (2007). One of the key issues with the BHAR approach is the accuracy with which an event firm can be matched with a non-event firm. The main assumption followed by the BHAR approach is that the non-event firm is essentially the same as the event firm with the exception of the event itself. Traditional matching methods match event firms to non-event firms (or portfolios of nonevent firms) by sorting potential matches into portfolios based on various firm characteristics. For instance, a researcher may choose a match by sorting firms into portfolios based on firm size 4
and book-to-market ratio and choosing the portfolio that most closely resembles the event firm. However, this prevents the use of a number of dimensions that may provide for a more adequate match. One way that this dimensionality problem can be solved is through using propensity score matching. To implement propensity score matching, one must first estimate the probability for each firm to undertake an event. Then, these probabilities are used to match event firms to a non-event firm. This is essentially matching a treated firm to a control firm or control group. Using a probit or logit model, we can estimate the probability of undergoing treatment based on a number of observable firm characteristics. The importance of propensity scores is discussed by Rosenbaum and Rubin (1983), who introduce the propensity score theorem. Angrist and Pischke (2009) compare the propensity score theorem to the omitted variable bias formula for regression in stating that “the propensity score theorem says that you only need to control for the covariates that affect the probability of treatment.” They also point out another key interpretation of the propensity score theorem: “The only covariate you need to control for is the probability of treatment itself.” The main point is that we can use non-experimental data to match treated firms to control firms, and if the matching is done properly we can obtain reliable estimates of the treatment effect of some corporate event on firms’ stock returns. Despite the potential gains that propensity score matching offers, it seems to have achieved little use in the corporate finance literature. Villalonga (2004) implements propensity score matching and finds that diversification does not destroy firm value. Li and Zhao (2006) find that traditional matching methods yield significant long-run underperformance for SEO issuers, but when propensity score matching is used with the same sample they find that
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underperformance is statistically and economically insignificant. We intend to use propensity score matching to study the long-term treatment effect of mergers and acquisitions. 1.2 Mergers and Acquisitions Loughran and Vijh (1997) summarize the conclusions that most M&A researchers make based on announcement period returns: target shareholders gain in any type of acquisition, acquiring shareholders earn little or no return from a tender offer, and acquiring shareholders earn negative abnormal returns from mergers. Kaplan (2006) draws heavily from Andrade, Mitchell and Stafford (2001) and concludes that target shareholders gain while evidence is mixed for acquiring firm shareholders. Overall, much of the announcement period literature seems to show that economic gains do occur, but that much (if not all) of the gains are captured by target firm shareholders. While it appears that economic value is created during the announcement period window, some long-run evidence shows that this value may deteriorate over time. Agrawal, Jaffe and Mandelker (1992) examine long-run bidder returns and find that the shareholders of acquiring firms that engage in mergers lose 10% over a five year post-merger period, while tender offers yield insignificant long-run abnormal returns. Gregory (1997) uses a large dataset from the UK to study post-acquisition returns to bidding firms and finds that takeovers were, on average, wealth reducing events for acquiring firms’ shareholders for a two year post-acquisition period. Loughran and Vijh (1997) find a negative post acquisition return for stock financed mergers and a positive post acquisition return for cash tender offers. Many researchers claim that the results showing significant long-run abnormal returns provide evidence against market efficiency. However, not all research shows evidence of a negative post-acquisition drift. Mitchell and Stafford (2000) show insignificant abnormal returns 6
for each subset of acquisitions they study except for stock financed acquisitions when using equally weighted returns of acquirers. Rau and Vermaelen (1998) use the Fama-French three factor model as well as a bootstrapping procedure and find that in a three year post-acquisition period negative abnormal returns occur only for acquisitions by “glamour” firms with low book to-market ratios. Some of the biggest criticisms about long-run underperformance come from criticisms about long-run return methodology itself, which includes the work of Fama (1998), Kothari and Warner (1997), and many others. 3. Data From the SDC database we obtained a sample of 4,869 acquisitions taking place between 1980 and 2006, which excludes utility and financial companies and firms that were involved in M&A within two years prior to completing an acquisition. These firms will be considered to be “treated” firms. We matched the companies’ 6 digit CUSIP to the CRSP and Compustat databases to obtain 8 digit CUSIPs, which left 3,017 observations. To calculate abnormal returns, 24 months of return data was required for each firm in order to estimate regression coefficients for each Fama-French factor as well as an intercept term. This left 1,358 observations. For “control” firms, the entire Compustat universe of quarterly accounting data was obtained for the years 1980-2008. A firm was excluded from the control group for the quarters in which the firm was in the treatments group within 8 quarters prior to or 8 quarters after a given quarter. This was done to be sure that no acquiring firms would be used as a control firm during the event period. This left 812,147 observations for the control group from the period of 1980-2008.
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Our data selection procedure is described in Table 1. Table 2 exhibits the distribution of our sample by industry. We note that approximately 50 percent of our sample in concentrated in the high tech (SIC codes 35, 36, 38 and 48) and service industries (SIC codes 70-89). 4. Methodology and Results 4.1 Jensen-Alpha Approach Before matching treated firms to control firms, we estimated abnormal returns for each individual treated firm using the Jensen-alpha approach. We implement the Jensen-alpha approach as described in Kothari and Warner (2007), but rather than using a portfolio approach we estimate the abnormal return for each firm separately. To do so, we obtained monthly FamaFrench factors from WRDS. We then estimated the following model for each firm starting the month following the acquisition announcement:
(1)
In the model,
represents that abnormal return for firm i,
is the risk free rate for month m,
is the return on the market portfolio in month m,
is the return on a portfolio long
in small cap stocks and short in big cap stocks in month m, and
is the return on a
portfolio long in high book-to-market firms and short in low book-to-market firms in month m. This allowed me to obtain cross-sectional abnormal returns for each treated firm. Abnormal returns were calculated for one and two years following the acquisition. 4.2 Propensity Score Matching
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Traditional methods to match a treated firm to a control firm (or a control portfolio) involve sorting potential controls into portfolios based on various firm characteristics. For instance, a researcher may choose a control group by sorting potential controls into portfolios based on firm size and book-to-market ratio. However, this prevents the use of a number of dimensions that may provide for a more adequate match to the control firm. This can result in selection bias because important firm characteristics that are involved in making certain decisions may be omitted from the matching approach. Additionally, the control portfolio might include some firms that may not be very similar to the treated firm. Instead of sorting control firms into portfolios to choose a match, we employ a propensity score matching approach. Propensity scores provide the opportunity to match on more dimensions than can be used in the traditional matching approaches employed by much of the finance literature. This will help to overcome the selection bias that can occur by choosing a control firm that is supposed to be observationally equivalent (or as close to equivalent as possible) to the treated firm to the outside observer. There are many different matching approaches used in the propensity score literature. we decided to use one-to-one nearest neighbor matching, which seems to be the most intuitive matching approach available in the p-score literature. This involves matching a treated firm to the single control firm with the closest propensity score. Hence, the absolute value of the difference between the treated and control’s propensity to diversify is minimized. This employs local minimization of the difference in propensity scores and is thus considered a “greedy” approach because it does not employ global minimization of the difference in propensity scores.
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To implement propensity score matching, a model for the probability of a firm to acquire is needed. Villalonga (2004) estimates a single probit model for all treatment and control firms in a sample of diversifying and non-diversifying firms. This encompasses the years of 19781997. To control for economic conditions and firm industry, macroeconomic factors and industry characteristics of the firm are included in the model for propensity to diversify. Because evidence shows that M&A activity tends to occur in waves, we choose to estimate a separate probit model for each quarter of each year. This will cause probability estimates to control for the decision to acquire given whatever the economic conditions are at the time of the acquisition announcement is made. Hence, macroeconomic conditions that influence M&A activity do not need to be identified. Additionally, evidence shows that M&A clusters within industries. Therefore, we force the potential control firms for each acquiring firm to have the same two-digit SIC code as the acquiring firm. Therefore, industry characteristics that may induce industry clustering do not have to be identified. This measure, combined with estimating separate propensity scores for each quarter in the sample, helps to further reduce the selection bias that may occur when choosing a control firm. Other than forcing the control firm to be within the same industry as the treated firm, we match based on the following three variables: Firm size, as measured by the natural log of total assets. Profitability, measured by the firm’s operating income after depreciation divided by total assets.
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Market to book ratio, measured by market value of common shares outstanding at the close of a quarter, total liabilities, and total preferred stock divided by total assets. If the treated firm’s acquisition announcement occurs in quarter q, we match the firm to a control firm based on the following propensity score model: (2) As previously stated, the probability of undergoing treatment, i.e. performing an acquisition, is estimated through a probit model for each quarter in which an acquisition occurs. This encompasses a total of 104 quarters from 1980-2006. Once the propensity scores were estimated, treated firms were matched to the control firm with the closest propensity score. Once treated and control firms were matched, we obtained returns for the control firms from CRSP and estimated abnormal returns using the Jensen-alpha approach discussed in the previous section. Not all of the matched control firms had enough returns data available to estimate abnormal returns. If this was the case, this observation was dropped from the sample because the best control firm available could not be used. After the matching procedure was done, there were 1,358 observations of acquiring and control firms with abnormal returns could be calculated for each firm. To estimate a true abnormal return for the treated firm, we can compute the difference between the alpha coefficients attained for the treated and control firms from the Jensen-alpha approach. Therefore, we can define the treatment effect for acquiring firm i as follows: ,
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where and
is the alpha computed in the firm-by-firm Jensen-alpha regression for acquiring firm t is the alpha computed in the firm-by-firm Jensen-alpha regression for control firm c.
Because the treated and control firms are observationally similar and are within the same industry, this new abnormal return measure controls for firm specific and industry specific factors. Additionally, any macroeconomic factors that may impact the probability of acquiring another firm are controlled for because the propensity to acquire is estimated separately for each quarter. Therefore, this abnormal return measure is robust to industry and macroeconomic factors and also helps to reduce the selection bias that can occur with traditional matching methods. Summary statistics for the abnormal returns experienced by acquiring and control firms can be found in Table 3A. The mean acquirers’ one and two-year abnormal returns are 0.78% and 0.36%, respectively. The average control firms’ abnormal returns are 0.79% in the one-year period and 0.69% in the two-year period. While the average abnormal returns are significantly positive at conventional significance levels, we can see that the means are still close to zero. Considering the magnitude of the abnormal returns, they hardly seem economically significant. The evidence seems to indicate that there are no long-term economically significant abnormal returns that can be attained by stockholders of an acquiring firm, at least on average. However, we do observe that two year abnormal return for the treated group is significantly lower than the corresponding return for the control group. In addition, our approach of estimating abnormal returns for each firm does not account for industry conditions. Andrade, Mitchell and Stafford (2001) show that mergers appear to cluster over time and that within each of these merger waves there is clustering within industries. The industries that are most prevalent are different in each merger wave. Additionally, Bruner (2005) reviews M&A evidence and finds that acquisitions 12
tend to cluster by industry. This shows that the restructuring of industries may be an important driver for acquisitions. In light of this evidence, it seems that controlling for a firm’s industry is important. To control for industry and to obtain more robust results, we implement a method to match acquiring firms with a non-acquiring control firm in the same industry. Summary statistics by industry group are presented in Table 3B. We note that one and two-year abnormal returns in the high tech, services and chemicals industries are roughly about 1% and 0.5% respectively. In addition, while typically abnormal returns of treated firms are insignificantly lower than the corresponding returns for the control firms, we observe that one-year abnormal return in the services industries is higher for the treatment group than for the control group, although not significantly so. 4.3 Observable Characteristics of Successful Acquiring Firms While it seems that, on average, an acquisition does not offer any opportunity for an investor to obtain economically significant long run abnormal returns beyond those that can be attained by a control firm, we try to identify firm characteristics that may have an impact on this treatment effect. Theory suggests many different reasons that may impact the decision of a firm to perform an acquisition. Additionally, empirical studies have identified some general characteristics of acquisitions that may impact the success acquisitions. To explain the variation in post-acquisition returns, we draw on some theoretical motivations for acquisitions as well as empirical observations about what may create a successful merger. Myers and Majluf (1984) introduce a theory of financial decision making based on information asymmetry in which financial slack is valuable to the firm. One of the implications of the model is that a slack rich firm may merge with a slack poor firm, inducing gains due to the
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extra access to cash to finance projects. Bruner (1988) empirically examines financial slack as a motive for mergers. He finds that successful bidders tend to have more financial slack and successful targets have less slack than in unsuccessful attempts at takeovers. A similar theory is presented by Jensen (1986), who links free cash flow with agency costs and takeovers. He posits that managers with too much free cash flow and not enough positive NPV projects will misuse cash. For this reason, an acquisition may create benefits for the bidding firm even if the acquisition results in some operating inefficiencies. Therefore, we include liquidity as measured by cash and short term assets divided by total assets to attempt to explain treatment effects experienced by acquirers. If the market adjusts the stock price accordingly upon the announcement of the acquisition, the liquidity measure should have no impact on the treatment effect of the merger. Another potential implication of the financial slack argument is that if the target is highly levered, merging with the acquiring firm may provide easier and cheaper access to debt financing for the target. Indeed, Bruner’s study finds that targets appear to be highly levered. If this is the case, the lower the leverage that the acquiring firm has, the greater the return should be due to the acquisition. If the market does not incorporate this impact properly, we would expect it to have an impact on long-run treatment effect of the acquisition. Rau and Vermaelen (1998) find that the poor performance in mergers can largely be explained by breaking acquirers into glamour (high market to book) and value (low market to book) stocks. In a three year post-acquisition period they find that glamour bidders underperform glamour bidders. They propose an extrapolation hypothesis in which the market extrapolates a firm’s previous performance to judge the potential success of an acquisition. This causes the market to overstate the value of an acquisition performed by a glamour firm and 14
understate the value of an acquisition performed by a value firm. Thus, we include the market to book ratio as an explanatory variable in the model to explain the treatment effects on acquiring firms. If the extrapolation hypothesis is true, the market to book ratio would have a negative impact on the treatment effect of acquiring firms. Another important variable to include in explaining the treatment effect to the acquiring firm is the firm’s propensity score itself. Before an acquisition, the market can incorporate the probability of performing an acquisition into its valuation of the firm. Once the acquisition is announced, the public can adjust the perceived valuation of the firm accordingly based on publicly available information. However, the managers of the acquiring firm may have private information about the potential success of the acquisition. So, if the propensity score has a negative impact on the post-merger treatment effect, this could mean that the public overestimates the value of management’s private information. It could also mean that managers themselves overestimate the effect that the merger would have. This could lend some support to the hubris hypothesis presented by Roll (1986). This hypothesis is based on the idea that the observed samples of bidders are not random. Bids are abandoned when the acquirer’s valuation is below the market price, and so we only see the bids that are above the market price. An average manager of a bidding company may only have a few chances throughout his career to make a bid on a target, and so he convinces himself that his valuation is correct. Thus, failing to adjust for the winner’s curse and attempting to enhance shareholder value, the observed bidding manager actually overpays to acquire the target company. If the propensity score has a positive impact on the treatment effect of the acquisition, shareholders and/or managers underestimate the impact of the acquisition. Regardless of the result, the propensity score should help to control
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for the endogeneity of management’s decision to acquire because it incorporates a measure of the impact of management’s private information. Additionally, the natural log of total assets of the acquiring firm is included as a control variable in case firms of different sizes experience different impacts from the acquisition. One might hypothesize that smaller firms do not get as much attention from the market as larger firms, in which case we would expect smaller firms to be more likely to exhibit larger treatment effects than smaller firms. Summary statistics of the variables used in the regression analysis are provided in Table 4. We note that the average deal size is $307 million and the acquirers in the sample have a leverage of 44%, liquidity of 23%, Market to Book ratio of 3.03. In addition, 38% of the deals are financed by cash only, while 16% of the acquisitions use stock only; 99% of the mergers are friendly and only 4% of the deals are tender offers. We should note that many of the explanatory variables that are included to explain the long-run treatment effects are also included as estimators of the firms’ propensity to acquire. Therefore, multicollinearity may be a concern when these variables are included with the propensity scores to attempt to explain the long-run treatment effects. However, because probit models are non-linear, this should not impact these regressions. In order to control for the potential issue, each model that includes the variables used in the estimate of the propensity score is estimated with and without the propensity score. We find that propensity score is not significant in any of the estimated models and therefore we exclude propensity score in our final reported models .
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In Tables 5 and 6 several specifications containing the variables previously discussed are examined to attempt to explain the one and two year treatments effects on acquiring firms. From the various specifications in models 1 though 3, we can see that when using year and industry fixed effects the only variables that carry an explanatory power are the acquirers’ leverage one quarter prior to acquisition and the dummy variable indicating two or more subsequent acquisitions. We conclude that the higher the firm’s leverage the higher the one-year abnormal return is and that serial acquirers enjoy higher returns. However, these effect are weakened in the two-years following the acquisition. Additionally, the R2 for each model is extremely low, and it would be difficult to make any generalizations about predictors of long-term treatment effects from models with such a poor overall fit. Models 4 through 6 include firm fixed effects. The R2 for each model is improved significantly to approximately 94%. However, the only control variable that retains significance in all three firm fixed effects models in both periods examined is the dummy variable, indicating a hostile takeover. A merger is generally considered to be a friendly transaction where negotiation takes place between the target company’s management and the bidding firm’s management. A hostile takeover and a tender offer does not involve negotiation between the management of the firms in the final outcome. Thus, we conclude that firms that use hostile takeover to acquire targets experience significantly higher long term returns. This is somewhat against our intuition, since hostile takeovers may imply increase acquisition cost. However, such acquisitions may signal that the benefits extracted from the acquisition are much higher than the costs. Finally, these results are similar to previously established research, which shows that tender offers are followed by more positive long run returns than mergers.
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4. Conclusion We examine the long term post acquisition performance of 1385 firms over 1980-2006. Prior merger long run performance studies show that acquiring firms experience a negative stock price drift after performing an acquisition. The evidence provided shows that an acquisition has an insignificant impact on the acquiring firm’s stock returns in the year following the merger. Additionally, the average stock price effect of the acquisition is slightly positive. These results are contrary to much of the previous evidence provided in the M&A literature which shows significant negative abnormal returns following the merger announcement. Perhaps some of these results can be explained by choosing improper benchmarks for acquiring firms. Our analysis of the two-year acquirers’ return performance suggests a significant underperformance of the treatment firms vs. their matching firms, with firms conducting multiple acquisitions experiencing only slightly higher returns than their matching firms. We also attempt to explain the one- and two-year treatment effects on the acquirer using a number of factors. Our findings suggest that hostile takeovers and acquisitions by firms with higher leverage experience higher long term returns. Overall, our results provide some support in favor of the efficient markets hypothesis.
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Table 1: Sample Formation of 1,385 Mergers and Acquisitions during 1980-2006
Data Selection Original sample - firms with no data for total assets, market to book ratio, leverage, profitability, liquidity, or the deal size - firms that could not be matched to a control firm with the same 2 digit SIC code - events where the matched firm does not have 2 years of returns Final Sample
19
Dropped Remaining 4,869
1,852
3,017
6
3,011
1,653
1,358 1,358
Table 2: Sample Distribution by Major Two-Digit Industry Codes of 1,385 Mergers and Acquisitions during 1980-2006
2 Digit SIC 35,36,38,48 70-89 28 13 49 37 20 26 50 59 Other Total
# of Events 404 290 138 49 48 36 33 27 25 23 285 1358
20
% of Sample 29.75 21.35 10.16 3.61 3.53 2.65 2.43 1.99 1.84 1.69 20.99 100
Table 3, Panel A: Summary Statistics for Abnormal Returns for Acquiring and Control Firms
AR for Treated AR for Control Difference AR for Treated AR for Control Difference AR for Treated AR for Control Difference AR for Treated AR for Control Difference
Full Sample Mean Median t-stat Mean 0.78% * 0.45% 5.29 0.36% * 0.79% * 0.35% 4.64 0.69% * -0.01% -0.02% -0.05 -0.33% * No Subsequent Acquisitions in the Two Years Following Announcements (N=632) 0.42% * 0.07% 2.1 0.16% 0.85% * 0.24% 3.48 0.72% * -0.42% -0.14% -1.37 -0.56% * 1 Acquisition in the Two Years Following Announcements (N=380) 0.70% * 0.42% 2.56 0.42% * 0.94% * 0.42% 2.94 0.76% * -0.24% 0.01% -0.58 -0.34% 2 or More Acquisitions in the Two Years Following Announcements (N=346) 1.53% * 1.06% 4.64 0.67% * 0.53% 0.41% 1.47 0.55% * 1.00% * 0.14% 2.32 0.12%
Median 0.18% 0.37% -0.08%
t-stat 3.89 7.03 -2.44
-0.04% 0.39% -0.20%
1.08 5.28 -2.76
0.43% 0.48% -0.24%
2.63 4.29 -1.4
0.34% 0.19% 0.16%
3.76 2.51 0.48
These summary statistics were generated based on a sample of 1,358 firms that performed an acquisition between the years of 1980 and 2006. Each acquiring firm was matched to a treated firm using the estimated propensity to acquire. Each control firm was chosen such that the industry was the same as the acquiring firm based on the two-digit SIC code. Abnormal returns were calculated for each group using the Jensen-alpha approach on each firm.
Table 3, Panel B: Summary Statistics for Abnormal Returns for Acquiring and Control Firms by Industry Group 1 Ye a r A R M ean
2 Ye a r A R
M e d ia n
t-s ta t
M ean
1 Ye a r A R
M e d ia n t-s ta t
M ean
M e d ia n
-0.27%
-0.06%
2 D ig it SIC: 35,36,38,48 A R fo r T re a te d
0.97%
*
A R fo r Co n tro l
1.61%
*
D iffe re n c e
-0.64%
0.38%
3.5
0.55%
*
t-s ta t
M ean
M e d ia n
t-s ta t
2 D ig it SIC: 20 0.01%
3.1
-0.4
0.46%
0.31%
1.5
0.64%
5.0
1.12%
*
0.62%
5.9
0.38%
0.44%
0.6
0.22%
0.30%
0.5
-0.27%
-1.6
-0.58%
*
-0.40%
-2.2
-0.65%
-0.12%
-0.9
0.24%
0.42%
0.5
2 D ig it SIC: 70-89 *
2 Ye a r A R
2 D ig it SIC: 26
A R fo r T re a te d
0.93%
0.09%
2.1
0.51%
*
-0.10%
1.9
0.02%
0.49%
0.1
0.12%
0.39%
0.5
A R fo r Co n tro l
0.25%
0.21%
0.5
0.61%
*
0.06%
2.3
-0.32%
-0.11%
-0.6
-0.13%
0.21%
-0.3
D iffe re n c e
0.68%
-0.14%
1.1
-0.10%
-0.01%
-0.3
0.34%
0.15%
0.6
0.25%
0.33%
0.6
2 D ig it SIC: 28 A R fo r T re a te d
1.04%
*
0.61%
2.2
A R fo r Co n tro l
1.51%
*
1.27%
2.4
0.66%
D iffe re n c e
-0.47%
-0.30%
-0.6
-0.26%
A R fo r T re a te d
0.09%
-0.26%
0.2
A R fo r Co n tro l
-0.54%
-0.33%
D iffe re n c e
0.63%
-0.12%
2 D ig it SIC: 50
0.40%
0.40%
1.5
1.19%
0.52%
2.5
0.30%
-0.7
0.23%
0.69%
-1.0
0.32%
0.8
-0.10%
*
*
0.82%
2.0
0.17%
0.09%
0.4
0.61%
0.48%
0.5
-0.16%
-0.21%
-0.3
0.58%
0.45%
0.5
0.33%
0.59%
0.4
0.7
1.08%
1.35%
1.2
0.88%
-0.16%
1.4
0.43%
0.9
2.36%
0.36%
1.8
1.26%
0.44%
1.3
-0.03%
-0.2
-1.28%
-0.8
-0.39%
0.74%
-0.3
2 D ig it SIC: 13
2 D ig it SIC: 59
*
-0.35%
2 D ig it SIC: 49
Re ma in in g A c q u ire rs
A R fo r T re a te d
0.04%
0.51%
0.1
0.19%
0.27%
0.8
0.66%
0.66%
2.7
0.03%
0.11%
0.2
A R fo r Co n tro l
0.00%
0.13%
0.0
0.23%
0.29%
1.3
0.34%
-0.20%
1.2
0.51%
*
0.35%
2.9
D iffe re n c e
0.03%
-0.19%
0.1
-0.04%
-0.06%
-0.1
0.32%
0.40%
0.9
-0.48%
*
-0.31%
-2.0
A R fo r T re a te d
0.47%
0.79%
1.1
-0.16%
0.34%
-0.4
A R fo r Co n tro l
0.00%
0.31%
0.0
0.23%
-0.02%
0.5
D iffe re n c e
0.47%
0.57%
0.6
-0.39%
-0.04%
-0.6
2 D ig it SIC: 37
22
*
Table 4: Summary Statistics for Acquiring Firms and Deal Characteristics in 1,358 Mergers and Acquisitions during 1980-2006 0 E ve nts in 2 Y e a rs Follow ing Full Sa m ple V a ria ble
A nnounc e m e nt
Mean
M e dia n
Mean
M e dia n
P rofita bility
0.02
0.03
0.02
0.02
L N of T ota l A sse ts
6.15
6.05
6.07
5.96
D e a l size
307.34
40.18
354.56
46.37
L e ve ra ge
0.44
0.45
0.45
0.45
L iquidity
0.23
0.12
0.22
0.11
M a rke t to B ook
3.03
1.83
2.55
1.69
C a sh
0.38
0.00
0.37
0.00
Stoc k
0.16
0.00
0.15
0.00
T e nde r O ffe r
0.04
0.00
0.05
0.00
H ostile T a ke ove r
0.00
0.00
0.01
0.00
Frie ndly M e rge r
0.99
1.00
0.98
1.00
P c t. O w ne d a fte r the A c q.
98.96
100.00
98.90
100.00
P rofita bility L N of T ota l A sse ts
1 E ve nt in 2 Y e a rs
2+ E ve nts in 2 Y e a rs Follow ing
Follow ing A nnounc e m e nt
A nnounc e m e nt
0.03
0.03
0.03
0.03
6.14
6.00
6.32
6.19
D e a l size
346.47
31.45
178.12
43.94
L e ve ra ge
0.43
0.42
0.45
0.46
L iquidity
0.23
0.12
0.24
0.12
M a rke t to B ook
2.99
1.89
3.96
2.02
C a sh
0.38
0.00
0.40
0.00
Stoc k
0.12
0.00
0.21
0.00
T e nde r O ffe r
0.04
0.00
0.03
0.00
H ostile T a ke ove r
0.00
0.00
0.00
0.00
Frie ndly M e rge r
0.99
1.00
1.00
1.00
P c t. O w ne d a fte r the A c q.
99.36
100.00
98.63
100.00
Table 5: Determinants of One Year Abnormal Returns of Acquirers in 1,385 Mergers and Acquisitions during 1980-2006 This table presents regression statistics of the determinants of one year abnormal returns following an acquisition. The treatment effects were calculated as the difference between the alpha of the acquiring firm and the alpha of the control firm as estimated by the Jensen-alpha approach. The sample consists of 1,358 acquisitions between 1980 and 2006. Standard errors are adjusted for heteroskedasticity following White (1980) and are shown in the parentheses below the coefficient estimates. . D ependent variable is the one-y ear abnorm al return Liquidity Leverage
M odel 1
M odel 2
M odel 3
M odel 4
M odel 5
0.0087
0.0109
0.0078
-0.0384
-0.0415
-0.0416
(0.0161)
(0.0161)
(0.0163)
(0.0742)
(0.0761)
(0.0757)
0.0321***
0.0297**
0.0293**
0.0097
0.0076
0.0064
(0.0110)
(0.0116)
(0.0116)
(0.0293)
(0.0324)
(0.0299)
4.39e-06
3.05e-06
0.0001
0.0001
(0.0004)
(0.0004)
(0.0003)
(0.0003)
Mark et to B ook LN of T otal A s s ets
0.0015
0.0017
-0.0067
-0.0067
(0.0016)
(0.0016)
(0.0106)
(0.0105)
P rofitab ility C as h S toc k O ne s ub s equent ac quis ition
M odel 6
-0.0683
0.0279
(0.0707)
(0.1430)
-0.0024
-0.0028
-0.0023
-0.0098
-0.0092
-0.0094
(0.0049)
(0.0049)
(0.0049)
(0.0081)
(0.0081)
(0.0081)
-0.0038
-0.0038
-0.0039
-0.0027
-0.0022
-0.0022
(0.0081)
(0.0081)
(0.0081)
(0.0138)
(0.0140)
(0.0140)
0.0038
0.0033
0.0037
0.0120
0.0120
0.0119
(0.0056)
(0.0056)
(0.0056)
(0.0119)
(0.0117)
(0.0118)
0.0171***
0.0164***
0.0167***
0.0340**
0.0334**
0.0332**
(0.0060)
(0.0060)
(0.0060)
(0.0157)
(0.0161)
(0.0160)
-1.76E -08
-1.38E -07
-1.10E -07
-1.34E -06
-1.13E -06
-1.07E -06
(3.60E -07)
(3.57E -07)
(3.65E -07)
(1.43E -06)
(1.39E -06)
(1.42E -06)
-6.84E -05
-5.87E -05
-0.0000327
-0.0017***
-0.0017***
-0.0017***
(0.0004)
(0.0004)
(0.0004)
(0.0006)
(0.0006)
(0.0006)
0.0023
0.0015
0.0014
0.0125
0.0107
0.0108
(0.0098)
(0.0097)
(0.0097)
(0.0150)
(0.0161)
(0.0162)
T wo or m ore s ub s equent ac quis itions D eal S iz e P erc ent O wned T ender O ffer H os tile T ak eover F riendly Merger C ons tant
0.0424
0.0451
0.0478
0.0802***
0.0745**
0.0720**
(0.0420)
(0.0425)
(0.0437)
(0.0286)
(0.0303)
(0.0310)
0.0145
0.0142
0.0144
0.0128
0.0124
0.0125
(0.0169)
(0.0172)
(0.0172)
(0.0186)
(0.0190)
(0.0188)
-0.0110
-0.0223
-0.0263
0.187***
0.221**
0.220**
(0.0407)
(0.0412)
(0.0410)
(0.0664)
(0.0898)
(0.0909)
Y ear F ixed E ffec ts
YES
YES
YES
YES
YES
YES
Indus try F ixed E ffec ts
YES
YES
YES YES
YES
YES
O b se rva tio n s
1358
1358
1358
1358
1358
1358
R -sq u a re d
0.053
0.054
0.055
0.938
0.938
0.938
F irm F ixed E ffec ts
R obus t s tandard errors are in parenthes es . *** denotes p< 0.01, ** denotes p< 0.05, and * denotes p< 0.1
24
Table 6: Determinants of Two Year Abnormal Returns of Acquirers in 1,385 Mergers and Acquisitions during 1980-2006 This table presents regression statistics of the determinants of two year abnormal returns following an acquisition. The treatment effects were calculated as the difference between the alpha of the acquiring firm and the alpha of the control firm as estimated by the Jensen-alpha approach. The sample consists of 1,358 acquisitions between 1980 and 2006. Standard errors are adjusted for heteroskedasticity following White (1980) and are shown in the parentheses below the coefficient estimates.
D ependent variable is the tw o-y ear abnorm al return
M odel 1
M odel 2
M odel 3
M odel 4
M odel 5
Liquidity
-0.0018
-0.0007
0.0019
-0.0497
-0.0501
-0.0497
(0.0090)
(0.0092)
(0.0091)
(0.0387)
(0.0392)
(0.0392)
Leverage
0.0106*
0.0091
0.0094
-0.0031
-0.0106
-0.0066
(0.0062)
(0.0066)
(0.0066)
(0.0155)
(0.0155)
(0.0160)
Mark et to B ook
0.0001
0.0001
-0.0002
-0.0002
(0.0003)
(0.0003)
(0.0002)
(0.0002)
0.0009
0.0008
-0.0142**
-0.0142**
(0.0010)
(0.0010)
(0.0058)
(0.0059)
LN of T otal A s s ets P rofitab ility C as h S toc k O ne s ub s equent ac quis ition T wo or m ore s ub s equent ac quis itions D eal S iz e P erc ent O wned T ender O ffer H os tile T ak eover F riendly Merger C ons tant
M odel 6
0.0579
-0.0950
(0.0443)
(0.0796)
0.0005
0.0003
-0.0001
-0.0016
-0.0003
0.0002
(0.0029)
(0.0029)
(0.0029)
(0.0057)
(0.0055)
(0.0055)
0.0070
0.0068
0.0068
0.0124
0.0106
0.0107
(0.0051)
(0.0052)
(0.0052)
(0.0090)
(0.0091)
(0.0090)
0.0029
0.0026
0.0023
0.0036
0.0033
0.0036
(0.0035)
(0.0035)
(0.0035)
(0.0074)
(0.0071)
(0.0071)
0.00681*
0.00632*
0.00610*
-0.0006
-0.0018
-0.0011
(0.0037)
(0.0036)
(0.0036)
(0.0083)
(0.0084)
(0.0084)
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
(0.0000)
(0.0000)
(0.0000)
(0.0000)
(0.0000)
(0.0000)
-0.0001
-0.0001
-0.0001
-0.0002
-0.0003
-0.0003
(0.0002)
(0.0002)
(0.0002)
(0.0004)
(0.0003)
(0.0003)
-0.0012
-0.0017
-0.0017
0.0063
0.0021
0.0017
(0.0064)
(0.0064)
(0.0065)
(0.0098)
(0.0098)
(0.0099)
0.0232
0.0249
0.0226
0.0523**
0.0382*
0.0465**
(0.0281)
(0.0283)
(0.0276)
(0.0225)
(0.0217)
(0.0219)
0.0003
0.0002
0.0001
0.0112
0.0092
0.0088
(0.0107)
(0.0109)
(0.0108)
(0.0144)
(0.0131)
(0.0131)
0.0239
0.0168
0.0203
0.0167
0.0919**
0.0942**
(0.0215)
(0.0221)
(0.0219)
(0.0378)
(0.0462)
(0.0466)
Y ear F ixed E ffec ts
YES
YES
YES
YES
YES
YES
Indus try F ixed E ffec ts
YES
YES
YES YES
YES
YES
O b se rva tio n s
1358
1358
1358
1358
1358
1358
R -sq u a re d
0.056
0.056
0.058
0.939
0.942
0.942
F irm F ixed E ffec ts
R obus t s tandard errors are in parenthes es . *** denotes p< 0.01, ** denotes p< 0.05, and * denotes p< 0.1
25
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