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Acquisition experience and the winner’s curse in corporate acquisitions

Acquisition experience and the winner’s curse in corporate acquisitions APPLIED ECONOMICS https://doi.org/10.1080/00036846.2023.2206108 a,b b,c,d Marta Arroyabe and Katrin Hussinger a b Essex Business School, University of Essex, Southend-On-Sea, UK; Department of Economics and Management, University of Luxembourg, c d Esch-sur-Alzette, Luxembourg; Department of Management, Strategy and Innovation, K.U. Leuven, Leuven, Belgium; Centre for European Economic Research (ZEW), Mannheim, Germany ABSTRACT KEYWORDS Firm acquisitions; winner’s The winner’s curse describes the behavioural phenomenon that the winner of a bidding contest pays curse; bidding contest; a price that is too high. This paper shows that experiential learning cannot prevent a winner’s curse on acquisition experience; the market of corporate control as acquiring firms with acquisition experience still pay a higher price for experiential learning the target in a bidding contest. Acquisition experience, however, is related to a superior post- acquisition performance of the winning firm after acquisitions associated with a bidding contest. JEL CLASSIFICATION G34; G14; D80 I. Introduction A question that remains is whether experiential The winner’s curse describes the behavioural phe- learning can help avoiding a winner’s curse in the nomenon that the winner of a bidding contest pays market for corporate control. It is not obvious that a price that is too high for the object at stake (Thaler learning from past acquisition occurs (Barkema 1988). Following the seminal article on the winner’s and Schijven 2008a). Firm acquisitions are com- curse at the market for corporate control (Varaiya and plex, multi-stage processes that include various Ferris 1987), corporate acquisitions became different tasks from the selection and evaluation a textbook example for a winner’s curse where an of the target firm, to the due diligence process, the acquiring firm overpays for the target firm (Roll negotiation of the deal, and the potential integra- 1986; Thaler 1988; Barberis and Thaler 2003; tion of two firms. The complexity of a firm acquisi- Hietala, Kaplan, and Robinson 2003; Baker, Ruback, tion obscures the causal link between an action and and Wurgler 2007; Malmendier, Moretti, and Peters its outcome so that learning becomes difficult 2018; De Bondt, Cousin, and Roll 2018). In the pre- (Zollo and Winter 2002; Heimeriks, Schijven, and sence of competition for the target firm, acquiring Gates 2012; Castellaneta and Conti 2017). firms tend to fail to adapt their bidding strategy Prior literature focuses largely on the relation- (Roll 1986; Varaiya 1988; Boone and Mulherin 2008; ship between acquisition experience and post- Brander and Egan 2017), the management becomes acquisition performance and finds mixed results overconfident in their own ability to create value from (see Barkema and Schijven 2008a, for a survey) the acquisition (Thaler 1988; Roll 1986; Hietala, with some studies documenting a positive learning Kaplan, and Robinson 2003; Malmendier and Tate effect (e.g. Fowler and Schmidt 1989; Bruton, 2008) and more aggressive bidding occurs because Oviatt, and White 1994; Barkema, Bell, and each firm wants to maintain the chance of winning Pennings 1996; Nadolska and Barkema 2014; (Kagel and Levin 1986; Hong and Shum 2002). The Cuypers, Cuypers, and Martin 2017; Schweizer result is a winning bid, which is higher due to the mere et al. 2022). This evidence suggests that experiential presence of competition and overestimates the value learning may help avoiding a winner’s curse. In this of the target firm (Thaler 1988; Varaiya and Ferris paper, we argue that a winner’s curse is mitigated 1987; Varaiya 1988; Malmendier, Moretti, and Peters by acquisition experience only if, in the presence of 2018; De Bondt, Cousin, and Roll 2018). CONTACT Marta Arroyabe mf17255@essex.ac.uk Essex Business School, University of Essex, Elmer Approach, Southend-On-Sea SS1 1LW, UK; Katrin Hussinger katrin.hussinger@uni.lu Department of Economics and Management, University of Luxembourg, 6 Rue Richard Coudenhove- Kalergi, Esch-sur-Alzette 1359, Luxembourg © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc- nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 M. ARROYABE AND K. HUSSINGER acquisition experience, (1) the acquisition price we find partial evidence for experiential learning to paid for a contested acquisition is lower and (2) mitigate a winner’s curse in the market for corpo- rate control because experienced winners of bid- the post-acquisition performance decline is smal- ding contests at the market for corporate control ler. Both conditions are important because a higher outperform winners without prior acquisition acquisition price alone can be rational when it experience and losers of the competition. reflects higher expected synergy effects This study contributes to the scarce empirical (Adegbesan 2009; Laamanen 2007) and experience literature on the winner’s curse on the market for may help identifying a target that is worth a high corporate control (Roll 1986; Varaiya and Ferris acquisition price (Castellaneta and Conti 2017). In 1987; Varaiya 1988; Schwert 1996; Sirower 1997; a similar vein, post-acquisition performance below Hietala, Kaplan, and Robinson 2003; Boone and expectations may have explanations unrelated to Mulherin 2008; Brander and Egan 2017; a winner’s curse, such as an insufficiently planned Malmendier, Moretti, and Peters 2018; De Bondt, and poorly executed post-acquisition integration Cousin, and Roll 2018). While prior studies on (Chatterjee et al. 1992; Datta 1991; Haspeslagh experiential learning focus mainly on post- and Jemison 1991; Larsson and Finkelstein 1999; acquisition performance (Barkema and Schijven Arroyabe, Hussinger, and Hagedoorn 2020). 2008a; King et al. 2021; King et al. 2004; Datta, To assess whether the winner of a contest pays too Pinches, and Narayanan 1992; Trichterborn, Zu much and whether acquisition experience can lead Knyphausen-Aufseß, and Schweizer 2016; to a lower price, we compare contested firm acquisi- Schweizer et al. 2022), we focus on the effect of tions to those that had only one interested buyer. For experiential learning on the acquisition price and the investigation of the post-acquisition perfor- post-acquisition performance. This approach pro- mance and potential learning effects from prior vides more complete evidence on the likely exis- acquisitions, we employ a novel identification strat- tence of a winner’s curse at the market for egy proposed by Malmendier, Moretti, and Peters corporate control. (2018) where the winners of contested acquisitions We also contribute to the empirical M&A litera- are compared to the losers of those contests. Our ture by employing a novel approach to investigate empirical analysis is based on a large sample includ- the post-acquisition performance, which compares ing all contested U.S. acquisitions of publicly listed the winners of a bidding contest to the losers of the firms in the period 1980–2020 as identified by SDC same acquisition contest (Malmendier, Moretti, Platinum (Refinitiv). and Peters 2018). Lastly, our analysis is based on Our results suggest that corporate acquisitions a large sample of contested firm acquisitions involving competition for the target firm are asso- (Malmendier, Moretti, and Peters 2018). ciated with a higher takeover price (e.g. Hietala, Kaplan, and Robinson 2003; Malmendier, Moretti, and Peters 2018; De Bondt, Cousin, and II. Theory & hypotheses Roll 2018). We further find that no evidence for The winner’s curse experiential learning mitigating the winner’s curse: acquiring firms with acquisition experience still A winner’s curse at the market for corporate con- pay a higher price for the acquisition target than trol is a likely phenomenon in the presence of they would pay for a comparable target that is not competition for a target firm. Acquiring firms associated with a bidding contest. tend to fail to adapt their bidding strategy to the Using different measures for the post- presence of competing bidders (Roll 1986; Varaiya acquisition performance, we do not find robust and Ferris 1987; Varaiya 1988; Boone and evidence for the post-acquisition performance of Mulherin 2008; Brander and Egan 2017; the winners of a bidding contest to be lower than Malmendier, Moretti, and Peters 2018; De Bondt, that of the losers. We, however, find robust evi- Cousin, and Roll 2018), the management becomes dence for a superior post-acquisition performance overconfident in their own ability to create value of firms with acquisition experience. In summary, from the acquisition (Thaler 1988; Roll 1986; APPLIED ECONOMICS 3 Sirower 1997; Hietala, Kaplan, and Robinson 2003; Learning from past acquisitions cannot be taken Malmendier and Tate 2015) and their bidding for granted though. The complexity and multi- behaviour becomes more aggressive so that they staged nature of the acquisition process obscures maintain the chance of winning the bidding contest the causal link between an action and its outcome, (Kagel and Levin 1986; Hong and Shum 2002). The which renders learning difficult (Zollo and Winter result is a winning bid that overestimates the value 2002; Heimeriks, Schijven, and Gates 2012; of the target firm. The value of the winning bid is Castellaneta and Conti 2017; Barkema and expected to increase with the number of bidders Schijven 2008a). (Varaiya and Ferris 1987; Varaiya 1988). Nevertheless, learning from past acquisitions can At the market for corporate control, assessing occur when cumulative acquisition experience is the value of the object at stake is difficult because transferred into routines that help managing subse- a firm is composed of a bundle of resources and quent acquisitions (Chao 2018; Halebian and assets from which value can potentially be created Finkelstein 1999; Kim and Finkelstein 2009). (Bruton, Oviatt, and White 1994; Cording, Routines are standard operating procedures that Christmann, and King 2008; Castellaneta and develop as a result of learning from repetition and Conti 2017). In addition, expected synergies that facilitate the implementation of reoccurring tasks between the assets and capabilities of acquiring (Cyert and March 1963). Routines serve as organiza- and target firms enter the value assessment. tional memory (Nelson and Winter 1982) and estab- Superior expected synergies can, in fact, justify lish the building blocks of organizational capabilities a rationally chosen higher price by the acquiring (Dosi, Nelson, and Winter 2000; Winter 2003) and firm (Adegbesan 2009; Conner 1991; Lippman and dynamic capabilities (Eisenhardt and Martin 2000). Rumelt 2003; Laamanen 2007) because the winning As such, routines are a source of superior organiza- firm may expect to create a higher value from the tional performance. In the context of firm acquisi- acquisition than its competitors. This is why, next tions, cumulative acquisition experience has been to a too high acquisition price, a second condition shown to be an important source of organizational for a winner’s curse is required which states that learning with the potential to support the different the post-acquisition performance of the acquiring stages of an acquisition process (Barkema and firm after a bidding contest is lower. This condition Schijven 2008a; Levitt and March 1988; Chao 2018; makes sure that the higher price is not justified Welch et al. 2020). because of higher synergies to be realized. Prior literature that focuses on experiential learning distinguishes broadly between two stages of the acqui- sition process (Barkema and Schijven 2008b; Acquisition experience Puranam, Powell, and Singh 2006; Castellaneta and In the context of corporate acquisitions, experien- Conti 2017). The first stage is the selection stage, tial learning is described as the ability to employ which includes the various steps from target selection acquisition experience for value creation through up to the value assessment of the target (Puranam, a new firm acquisition (Barkema and Schijven Powell, and Singh 2006; Castellaneta and Conti 2017; 2008a). Firms learn from past firm acquisitions Wu and Reuer 2021). The second stage is the restruc- and become familiar with several parts of the turing stage, where the acquiring firm seeks to gen- multi-process of an acquisition including the selec- erate value from the acquisition (Barkema and tion, evaluation of the target, but also the due Schijven 2008b; Heimeriks, Schijven, and Gates diligence process, the negotiation of the deal and 2012; Castellaneta and Conti 2017). the integration of two combined firms to achieve Regarding the post-acquisition stage, it has potential synergy. Some studies have indicated that been shown that firms can simply ‘learn by experienced acquirers that develop acquisition cap- doing’ (Lubatkin 1987; Bruton, Oviatt, and abilities are more successful in their post- White 1994; Halebian and Finkelstein 1999; acquisition performance (Fowler and Schmidt Hayward 2002). Tacit routines evolve by repeat- 1989; Nadolska and Barkema 2014; Cuypers, ing similar tasks without explicit knowledge Cuypers, and Martin 2017; Schweizer et al. 2022). articulation or codification. Learning from past 4 M. ARROYABE AND K. HUSSINGER experience is further improved when tacit rou- contested U.S. acquisitions of publicly listed firms tines are codified after the causal links for post- in the time period 1980–2020. After having used acquisition integration success are understood several filters, our final dataset includes a total of (Zollo and Singh 2004; Heimeriks, Schijven, and 4,646 acquisitions, 303 contested deals and a total Gates 2012). For a following acquisition, the of 4,343 non-contested deals. We retrieve firm results of such an analysis can provide guidance characteristics for all firms involved in the acquisi- for action through a well-managed organizational tions and acquisition contests from Compustat. memory. While the mechanisms of ‘learning by Two samples are created. The first one is a cross- doing’ and ‘learning through codification of tacit sectional sample consisting of 4,646 observations, routines’ are the same at the selection stage, some which allows to relate the price paid for the target authors argue that the codification of tacit knowl- firm to the target and acquiring firms’ characteris- edge is easier in this first stage because the tasks tics and the presence of a bidding contest. This are less complex and more similar for different sample allows to test H1. The second sample, acquisitions than those of the post-acquisition used to test H2, is a firm-level panel dataset for integration stage and because the time distance the 336 firms (both winners and losers) involved in between action and outcome is shorter contested deals following Malmendier, Moretti, (Castellaneta and Conti 2017). and Peters (2018). This sample contains financial Empirical evidence that distinguishes the selection information of the firms for a maximum of 9 years and integration stage supports experiential learning at before and after the acquisition. The panel is unba- both stages (Puranam, Powell, and Singh 2006; lanced because information is not available for all Barkema and Schijven 2008b; Heimeriks, Schijven, firm-years and consists of 5,149 observations. and Gates 2012; Castellaneta and Conti 2017). These arguments and evidence lead us to argue that experi- Variables ential learning can help mitigating a winner’s curse as tacit and codified routines developed through past Table 1 shows a summary of the dependent and acquisition experience can facilitate the value assess- independent variables used in our analyses. Two ment of the target firm in the selection stage and also different dependent variables are used. To test H1, foster value creation in the post-acquisition phase. the price paid for the acquisition target is used as a dependent variable (Grimpe and Hussinger 2014). The post-acquisition performance of the Hypothesis 1: The price increase due to competi- acquiring firm (H2) is measured as Tobin’s tion for the target firm is smaller when the acquiring Q normalized by year and Standard Industry firm has acquisition experience. Classification (SIC3) industry, i.e. the market value of the acquiror over its book value Hypothesis 2: Following a firm acquisition asso- (Laamanen 2007). We chose Tobin’s Q as the ciated with a bidding contest, the post-acquisition main measure for firm performance because it is performance of an acquiring firm is greater due to a forward-looking measure that incorporates the acquisition experience. expectations about future profits. To show the robustness of our results for the post-acquisition performance analysis, we further employ the sales- to-assets ratio and the return on assets (ROA) as III. Data, variables and descriptive statistics dependent variables. Both variables are normalized by year and SIC3 industry. Data For testing H1, the main independent vari- Our data is retrieved from SDC Platinum ables are a binary variable that captures whether (Refinitiv) and includes all contested and non- the acquisition was associated with a bidding Learning can also be achieved by engagement in alliances prior to the acquisition (Zollo and Winter 2002; Chang and Tsai 2013). Our dataset excludes deals that are not completed or withdrawn. We also exclude firms that are not publicly listed U.S. firms. We also exclude firms that enter as white knights (Malmendier, Moretti, and Peters 2018). APPLIED ECONOMICS 5 Table 1. Description of variables. Variable Variable label Variable definition type Source Dependent Variables Acquisition Price Logarithm of the value of the deal in millions of USD Continuous SDC Platinum Tobin’s Q Acquiring firms’ Tobin’s Q in year t over the SIC-3 industry Tobin’s Q in year t. The Tobin’s Q the Continuous Compustat market value of the acquiror over its book value (in millions of USD). Sales/Assets Acquiring firms’ sales-to-assets ratio in year t over the SIC-3 industry Sales-to-Assets ratio in year t. Continuous Compustat Sales and assets are in millions of USD. ROA Acquiring firms’ return on assets (ROA) in year t over the SIC-3 industry ROA in year t. ROA is the net Continuous Compustat income over book value of total assets. Independent Variables Bidding contest Equal to one if the acquisition is flagged as a contested deal Binary SDC Platinum Number of competing Number of firms (regardless of the public status) involved in a contested deal bid Continuous SDC Platinum bidders Acquisition experience Equal to one if firm has previous experience in M&As and belongs to the contested M&As Binary SDC Platinum (contested M&As) subsample Acquisition experience Equal to one if firm has previous experience in M&As and belongs to the contested non-M&As Binary SDC Platinum (contested M&As) subsample Log(Target Assets) Logarithm of the target’s assets (in millions of USD) Continuous Compustat Target Debt/Assets Target’s debt (in millions of USD) over target’s assets (in millions of USD) Continuous Compustat Target Cash/Assets Target’s cash (in millions of USD) over target’s assets (in millions of USD) Continuous Compustat Target R&D/Assets Target’s R&D expenditures (in millions of USD) over target’s assets (in millions of USD). Note that for Continuous Compustat those observations for which the value was missing, this has been replaced by zero. Target missing R&D Equal to one if target’s R&D expenditure information was missing Binary Compustat Target & acq. conduct Equal to one if both target and acquiring firm have a positive value for the R&D expenditures Binary Compustat R&D Same industry Equal to one if both target and acquiring firm belong to the same SIC-2 industry group Binary Compustat Log(Acq. Assets) Logarithm of the acquiror’s assets (in millions of USD) Continuous Compustat Acq. Debt/Assets Acquiror’s debt (in millions of USD) over acquiror’s assets (in millions of USD) Continuous Compustat Acq. Cash/Assets Acquiror’s cash (in millions of USD) over acquiror’s assets (in millions of USD) Continuous Compustat Acq. R&D/Assets Acquiror’s R&D expenditures (in millions of USD) over acquiror’s assets (in millions of USD). Note Continuous Compustat that for those observations for which the value was missing, this has been replaced by zero. Acq. missing R&D Equal to one if acquiror’s R&D expenditure information was missing Binary Compustat Post Acq Equal to one after the acquisition year Binary SDC Platinum Winner Equal to one if the firm won the bidding contest Binary SDC Platinum Exp. Equal to one if firm has previous M&A experience Binary SDC Platinum Winner*Post Acq*Exp. The interaction term of the variables Post Acq, Winner and Post Acq. Binary SDC Platinum contest, a binary variable that indicates whether assets are used to measure firm size. The natural the acquiror was involved in an acquisition logarithm is employed to account for the skewness prior to the focal acquisition for contested of the variable. Debt and cash are used to measure acquisitions and a binary variable that indicates the financial fitness of both firms (Slusky and Caves whether the acquirer has experience for the 1991). Those variables are divided by total assets to non-contested acquisition subsample. We also avoid a high correlation with firm size. R&D invest- employ the number of competing bidders to ment (divided by total assets) of the target and show robustness for the results of H1 (Varaiya acquiror is employed (Chan, Lakonishok, and and Ferris 1987). Sougiannis 2001). For those firms for which the For testing H2, our main variables of interest are R&D investment is missing, we replace the value a set of binary variables that indicate the post- by zero and create a dummy variable, which we acquisition period, whether the focal firm was the also include in the regression. Access to a target winner of the deal and whether the firm was firm’s innovative assets can be a motivation to involved in an acquisition prior to the focal deal. acquire the firm, and their value is reflected in the To test H2, we include the interaction of the post- deal value (Grimpe and Hussinger 2014). Further, acquisition period, winner and prior experience for testing H1, two binary variables are used to binary variables. capture the market and technological relatedness The control variables used to test the hypotheses between target and acquiring firms (Cassiman related to the price paid for the target (H1) and the et al. 2005). The first one captures whether the acquirer post-acquisition performance (H2) are lar- both firms belong to the same Standard Industry gely the same. For both, target and acquiror, total Classification (SIC2) industry sector. The second 6 M. ARROYABE AND K. HUSSINGER variable captures potential technology synergies by IV. Empirical results capturing whether both firms invest in R&D. Results for H1 Lastly, year and industry dummies are used to con- trol for a possible general time trend and industry Table 4 shows the results for the deal price regres- conditions. sions that test H1. The first specification only includes the binary variables, which indicate that the firm acquisition was associated with a bidding contest. The second specification adds target firm Descriptive statistics characteristics and specification (3) the character- Table 2 shows the descriptive statistics for the deal istics of the acquiring firm. The last specification price sample (H1) for the full sample as well as for adds the binary variables indicating whether the acquisitions with and without a bidding contest acquiring firm has acquisition experience in separately. It appears that, as expected, acquisitions a contested or non-contested deal. associated with a bidding contest show a higher The results show that the price paid for an acquisition price. Target firms involved in bidding acquisition target is higher when there is competi- contests are larger and less involved in R&D than tion for the target firm. The presence of a bidding others. They are more likely to be affiliated with the contest increases the price paid for the target firm same industry sector than their acquirers than by a minimum of 69% (exp(0.523) = 169) (specifi - others. Acquiring firms involved in bidding con- cation (4)). tests have both a higher debt to assets and a higher The results presented in Table 4 do not provide cash-to-asset ratio. These differences may be support for H1, which states that the price paid in related to the acquisition that takes place in the a bidding competition is smaller when the acquiror same year for which the mean values are reported. has acquisition experience. Interestingly, experi- In terms of firm size and R&D, they are ence matters in non-contested deals. Here, the comparable. price paid for the acquisition target is significantly Table 3 shows the descriptive statistics for the lower if the acquiring firm has acquisition firm panel used to investigate H2. When distin- experience. guishing winners and losers of acquisition contests, Table 5 shows the robustness of the results when we see that they are very comparable in terms of the the number of competing bidders is used instead of mean values for the variables presented. Some of the binary variable indicating a bidding contest. the small differences are significant. Results are similar to the main results presented Table 2. Descriptive statistics: acquisition price data set. Total sample Bidding contest No bidding contest Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. t-test Acquisition Price 1550.688 6161.299 3381.141 10188.890 1422.982 5756.737 *** Log(acquisition price) 5.206 2.174 6.293 1.934 5.130 2.169 *** Bidding contest 0.065 0.247 Number of competing bidders 0.109 0.446 Target Assets 2461.103 15872.500 5687.286 45600.010 2236.020 11140.640 *** Log(Target Assets) 5.668 1.988 6.279 2.011 5.626 1.980 *** Target Debt/Assets 0.175 0.214 0.199 0.189 0.173 0.215 * Target Cash/Assets 0.267 4.914 0.088 0.210 0.280 5.082 Target R&D/Assets 0.056 0.145 0.037 0.085 0.057 0.149 ** Target missing R&D 0.532 0.499 0.502 0.501 0.534 0.499 Target & acq. conduct R&D 0.387 0.487 0.439 0.497 0.383 0.486 * Same industry 0.665 0.472 0.736 0.442 0.660 0.474 *** Acq. assets 18782.730 67687.560 23283.740 107669.800 18468.710 63980.460 Log(Acq. Assets) 7.941 2.095 7.815 2.094 7.950 2.095 Acq. Debt/Assets 0.206 0.191 0.266 0.231 0.202 0.187 *** Acq. Cash/Assets 0.094 0.770 0.204 2.043 0.087 0.586 ** Acq. R&D/Assets 0.031 0.081 0.026 0.085 0.031 0.081 Acq. missing R&D 0.526 0.499 0.472 0.500 0.530 0.499 * Acq. experience (contested M&As) 0.327 0.178 Acq. experience (non-contested M&As) 0.495 0.500 APPLIED ECONOMICS 7 Table 3. Descriptive statistics: post-acquisition performance data set. Total sample Winner sample Loser sample Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. t-test Tobin’s Q 0.733 0.528 0.719 0.553 0.771 0.446 *** Sales/Assets 0.913 0.531 0.908 0.551 0.956 0.502 *** ROA 0.301 1.905 0.715 13.364 −0.819 43.144 * Winner*Post Acq 0.388 0.487 0.388 0.487 Winner*Post Acq*Exp. 0.227 0.419 0.227 0.419 Log(Acq. Assets) 7.797 2.231 7.848 2.238 7.654 2.208 *** Acq. Debt/Assets 0.226 0.202 0.236 0.200 0.198 0.204 *** Acq. Cash/Assets 0.133 1.606 0.114 1.010 0.185 2.640 Acq. R&D/Assets 0.027 0.067 0.028 0.071 0.026 0.054 Acq. missing R&D 0.453 0.498 0.424 0.494 0.534 0.499 *** in Table 4. This suggests that the presence of com- a dummy indicating the post-acquisition period peting bids matter, rather than the number of and the variable that takes the value one for the competing bidders. post-acquisition period when the focal firm won a bidding contest. The second specification adds an interaction term between the post- Results for H2 acquisition, winner and experience variables. This interaction term (Winner*Post Table 6 shows the results from fixed effects Acquisition*Experience) takes the value one in regressions that control for firm-specific effects the post-acquisition period when the focal firm for the acquiring firm’s post-acquisition perfor- has experience and is the winner of the con- mance, testing H2. The first specification shows tested deal. Note that the variables Winner and a lean specification, which only includes Table 4. Acquisition price regressions I. (1) (2) (3) (4) Bidding contest 1.163*** 0.588*** 0.615*** 0.523*** (0.128) (0.078) (0.077) (0.107) Acq. experience (contested M&As) −0.142 (0.144) Acq. experience (non-contested M&As) −0.312*** (0.044) Log(Target Assets) 0.860*** 0.754*** 0.751*** (0.012) (0.014) (0.014) Target Debt/Assets −0.517*** −0.407*** −0.416*** (0.101) (0.100) (0.100) Target Cash/Assets 0.018*** 0.016*** 0.016*** (0.004) (0.004) (0.004) Target R&D/Assets 0.144 −0.189 −0.181 (0.157) (0.158) (0.157) Target missing R&D −0.168*** 0.001 0.016 (0.055) (0.075) (0.079) Target & acq. conduct R&D 0.201** 0.281*** (0.080) (0.105) Same industry 0.366*** 0.349*** (0.044) (0.044) Log(Acq. Assets) 0.165*** 0.205*** (0.012) (0.013) Acq. Debt/Assets −0.180 −0.182 (0.121) (0.121) Acq. Cash/Assets −0.010 −0.000 (0.025) (0.025) Acq. R&D/Assets 0.822*** 0.818*** (0.276) (0.275) Constant 5.130*** −0.293 −1.096* −1.487** (0.033) (0.616) (0.604) (0.607) Observations 4646 4646 4646 4646 Log likelihood −10157.914 −7636.188 −7509.357 −7482.738 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year and industry dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. 8 M. ARROYABE AND K. HUSSINGER Post-Acquisition are not included in the fixed We check for the robustness of our results by effects regressions because they are time- employing alternative dependent variables. The invariant. Specifications (3) and (4) add the interaction term Winner*Post Acquisition acquiring firm control variables. *Experience is positive and significant as well when the performance is measured with the ratio The results support H2 by consistently showing of sales to assets normalized by the industry aver- that the post-acquisition performance decline of age (Table 7) and ROA normalized by the industry the acquirer is smaller when the acquiring firm of average (Table 8). a bidding contest has acquisition experience. The post-acquisition performance decline, as measured with the Tobin’s Q, is about 21% lower when the V. Discussion acquiring firm is a winner and has previous acqui- sition experience (specification (4)). This paper shows that experiential learning cannot Our results are graphically displayed in avoid increased prices paid for a target in a bidding Figures 1-3 where we show event study graphs of contest. Acquisition experience is, however, asso- the relative performance of winners and losers. ciated with a superior post-acquisition perfor- Figure 1 shows that winners outperform losers of mance as compared to winners of bidding a bidding contest in the period immediately after contests without acquisition experience and as the acquisition. When distinguishing between win- compared to losers of bidding competitions. ners with and without acquisition experience, it The fact that even experienced firms pay acqui- appears that it is the experienced winners that out- sition prices that are too high in the presence of perform the losers of a bidding contest (Figures 2 competition is in line with lab experiments (Thaler and 3). 1988). Lab experiments show that learning through Table 5. Acquisition price regressions II. (1) (2) (3) (4) Number of competing bidders 0.629*** 0.300*** 0.309*** 0.230*** (0.071) (0.043) (0.042) (0.056) Acq. experience (contested M&As) −0.032 (0.139) Acq. experience (non-contested M&As) −0.322*** (0.044) Log(Target Assets) 0.860*** 0.755*** 0.752*** (0.012) (0.014) (0.014) Target Debt/Assets −0.519*** −0.412*** −0.420*** (0.101) (0.100) (0.100) Target Cash/Assets 0.018*** 0.016*** 0.016*** (0.004) (0.004) (0.004) Target R&D/Assets 0.132 −0.200 −0.189 (0.157) (0.158) (0.158) Target missing R&D −0.168*** 0.003 0.017 (0.055) (0.075) (0.079) Target & acq. conduct R&D 0.205** 0.283*** (0.080) (0.105) Same industry 0.367*** 0.350*** (0.044) (0.044) Log(Acq. Assets) 0.164*** 0.205*** (0.012) (0.013) Acq. Debt/Assets −0.169 −0.172 (0.121) (0.121) Acq. Cash/Assets −0.006 0.002 (0.025) (0.025) Acq. R&D/Assets 0.814*** 0.810*** (0.276) (0.275) Constant 5.137*** −0.301 −1.107* −1.497** (0.033) (0.616) (0.605) (0.608) Observations 4646 4646 4646 4646 Log likelihood −10159.740 −7640.342 −7514.903 −7486.522 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year and industry dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. APPLIED ECONOMICS 9 Table 6. Fixed effects regressions for post-acquisition performance (Tobin’s Q). (1) (2) (3) (4) Post Acq −0.069** −0.067** −0.052* −0.051* (0.029) (0.029) (0.028) (0.028) Winner*Post Acq −0.035 −0.094*** 0.018 −0.018 (0.027) (0.032) (0.026) (0.031) Winner*Post Acq*Exp. 0.095*** 0.058** (0.028) (0.027) Log(Acq. Assets) −0.146*** −0.145*** (0.010) (0.010) Acq. Debt/Assets 0.045 0.050 (0.041) (0.041) Acq. Cash/Assets 0.054*** 0.053*** (0.005) (0.005) Acq. R&D/Assets 1.021*** 1.025*** (0.152) (0.152) Constant 0.841*** 0.840*** 1.429*** 1.422*** (0.148) (0.148) (0.152) (0.152) Observations 5149 5149 5149 5149 Log likelihood −2382.145 −2376.029 −2130.280 −2127.817 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. experience happens rarely and slowly in the market 2006). Such a mechanism may explain that the for corporate control (Thaler 1988). Empirical stu- winner’s of a bidding contest do not adjust their dies argue that the complexity and multi-staged bid when there is competition for the target firm. nature of the acquisition process render learning This study makes several contributions to the difficult because the causal link between an action literature. First, the study contributes to the scarce and its outcome is obscured (Zollo and Winter empirical evidence on a winner’s curse at the mar- 2002; Heimeriks, Schijven, and Gates 2012; ket for corporate control (Varaiya and Ferris 1987; Castellaneta and Conti 2017; Barkema and Roll 1986; Varaiya 1988; Sirower 1997; Hietala, Schijven 2008a). Acquisition experience further Kaplan, and Robinson 2003; Boone and Mulherin has been shown to lead to less sensitivity towards 2008; Brander and Egan 2017; Malmendier, negative information during the due diligence pro- Moretti, and Peters 2018; De Bondt, Cousin, and cess, which may reflect a higher confidence in the Roll 2018). As it is not straightforward to empiri- original valuation (Puranam, Powell, and Singh cally identify a winner’s curse because the true -3 -2 0 1 2 3 4 5 6 7 8 9 Figure 1. Post-acquisition performance (Tobin’s q): winners versus losers. -.1 .1 .2 .3 0 10 M. ARROYABE AND K. HUSSINGER -3 -2 0 1 2 3 4 5 6 7 8 9 Figure 2. Post-acquisition performance (Tobin’s q): winners with experience versus losers. -3 -2 0 1 2 3 4 5 6 7 8 9 Figure 3. Post-acquisition performance (Tobin’s q): winners without experience versus losers. value of the acquisition target is unknown, this acquisition integration (Chatterjee et al. 1992; paper suggests to investigate the likelihood of the Datta 1991; Haspeslagh and Jemison 1991; presence of a winner’s curse along two dimensions: Larsson and Finkelstein 1999; Arroyabe, the acquisition price and the post-acquisition per- Hussinger, and Hagedoorn 2020). formance of the acquiring firm. Both dimensions Second, this study contributes to the litera- should be considered because a higher acquisition ture on experiential learning in the market for price alone can speak for higher expected and corporate control (Barkema and Schijven 2008a; potentially also realized synergy effects between Trichterborn, Zu Knyphausen-Aufseß, and the acquiring and the target firm (Adegbesan Schweizer 2016; Schweizer et al. 2022). While 2009; Laamanen 2007) and because the post- lab experiments mimicking firm’s price deci- acquisition performance may be affected by an sions in auctions for corporate acquisitions insufficiently planned and poorly executed post- show that learning based on experience happens -.2 -.1 -.1 .1 .2 .3 .4 .1 .2 .3 0 APPLIED ECONOMICS 11 Table 7. Fixed effects regressions for post-acquisition performance (sales/assets). (1) (2) (3) (4) Post Acq −0.159*** −0.157*** −0.119*** −0.118*** (0.026) (0.026) (0.025) (0.025) Winner*Post Acq 0.055** −0.023 0.098*** 0.039 (0.024) (0.028) (0.023) (0.027) Winner*Post Acq*Exp. 0.129*** 0.096*** (0.025) (0.024) Log(Acq. Assets) −0.138*** −0.136*** (0.009) (0.009) Acq. Debt/Assets −0.248*** −0.238*** (0.043) (0.043) Acq. Cash/Assets −0.022*** −0.023*** (0.008) (0.008) Acq. R&D/Assets 0.976*** 0.988*** (0.124) (0.124) Constant 1.064*** 1.061*** 1.694*** 1.682*** (0.130) (0.130) (0.133) (0.133) Observations 5059 5059 5059 5059 Log likelihood −1666.740 −1652.021 −1441.129 −1432.439 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. Table 8. Fixed effects regressions for post-acquisition performance (ROA). (1) (2) (3) (4) Post Acq −0.000 0.004 0.003 0.006 (0.134) (0.134) (0.134) (0.134) Winner*Post Acq 0.061 −0.082 0.072 −0.076 (0.121) (0.144) (0.122) (0.145) Winner*Post Acq*Exp. 0.237* 0.240* (0.128) (0.128) Log(Acq. Assets) −0.025 −0.020 (0.046) (0.046) Acq. Debt/Assets 0.058 0.083 (0.233) (0.233) Acq. Cash/Assets −0.022 −0.025 (0.043) (0.043) Acq. R&D/Assets 1.056 1.086 (0.665) (0.665) Constant −0.112 −0.117 −0.147 −0.177 (0.668) (0.668) (0.715) (0.715) Observations 5059 5059 5059 5059 Log likelihood −9942.079 −9940.208 −9939.675 −9937.781 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. rarely and slowly (Thaler 1988), empirical evi- Lubatkin 1982; Zollo and Leshchinskii 2004). dence is somewhat more optimistic about learn- Conflicting empirical results from acquisition ing effects for value creation through corporate experience on different measures of acquisition acquisitions (Barkema and Schijven 2008a). performance are confirmed in meta-analyses Nevertheless, only a few studies report positive (King et al. 2021). experiential learning effects for post-acquisition Lastly, while early studies use small sample of performance (e.g. Fowler and Schmidt 1989; contested acquisitions due to a lack of available Bruton, Oviatt, and White 1994; Barkema and data (e.g. Varaiya 1988; Boone and Mulherin Drogendijk 2007; Nadolska and Barkema 2014; 2008; Hayward 2002), we contribute to recent Cuypers, Cuypers, and Martin 2017; Schweizer empirical evidence that exploits the availability of et al. 2022), while most studies suggest the larger datasets of contested M&As (e.g. Betton, absence of learning through experience (e.g. Eckbo, and Thorburn 2008; Malmendier, Moretti, 12 M. ARROYABE AND K. HUSSINGER and Peters 2018) and exploit a novel identification VII. Conclusion strategy that compares the winners and the losers This paper shows that acquisition experience does of acquisition contests (Malmendier, Moretti, and not help avoiding to overpay for firm acquisitions. Peters 2018). The post-acquisition performance of experienced A caveat of our analysis is that our sample is winners of bidding contests at the market for cor- based on publicly listed firms and porate control is superior though. Taken together, U.S. acquisitions only, while we know that these results provide partial evidence for experien- acquisition premia are higher in more efficient tial learning to help avoiding a winner’s curse at the markets (Tampakoudis, Subeniotis, and market for corporate control. Dalakiouridou 2011). This suggests a need for research investigating whether the observed effects hold for private firms and also for other Acknowledgments markets. For example, Europe has fewer hostile For helpful comments, we would like to thank David King. acquisitions that may invite competitive bids, and researchers have questioned whether U.S. acquisition research findings hold in Disclosure statement Europe (Moschieri and Campa 2009). No potential conflict of interest was reported by the author(s). VI. Implications ORCID Our results suggest that experiential learning Marta Arroyabe http://orcid.org/0000-0003-3223-0268 does not help against a too high acquisition price paid by a winning firm. This raises the question whether experience, rather than creat- References ing an experiential advantage for the acquiring Adegbesan, J. A. 2009. “On the Origins of Competitive firm, may lead to overconfidence when it comes Advantage: Strategic Factor Markets and Heterogeneous to the bidding competition. 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Acquisition experience and the winner’s curse in corporate acquisitions

Arroyabe, Marta; Hussinger, Katrin
Applied Economics , Volume 56 (27): 15 – Jun 8, 2024
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APPLIED ECONOMICS https://doi.org/10.1080/00036846.2023.2206108 a,b b,c,d Marta Arroyabe and Katrin Hussinger a b Essex Business School, University of Essex, Southend-On-Sea, UK; Department of Economics and Management, University of Luxembourg, c d Esch-sur-Alzette, Luxembourg; Department of Management, Strategy and Innovation, K.U. Leuven, Leuven, Belgium; Centre for European Economic Research (ZEW), Mannheim, Germany ABSTRACT KEYWORDS Firm acquisitions; winner’s The winner’s curse describes the behavioural phenomenon that the winner of a bidding contest pays curse; bidding contest; a price that is too high. This paper shows that experiential learning cannot prevent a winner’s curse on acquisition experience; the market of corporate control as acquiring firms with acquisition experience still pay a higher price for experiential learning the target in a bidding contest. Acquisition experience, however, is related to a superior post- acquisition performance of the winning firm after acquisitions associated with a bidding contest. JEL CLASSIFICATION G34; G14; D80 I. Introduction A question that remains is whether experiential The winner’s curse describes the behavioural phe- learning can help avoiding a winner’s curse in the nomenon that the winner of a bidding contest pays market for corporate control. It is not obvious that a price that is too high for the object at stake (Thaler learning from past acquisition occurs (Barkema 1988). Following the seminal article on the winner’s and Schijven 2008a). Firm acquisitions are com- curse at the market for corporate control (Varaiya and plex, multi-stage processes that include various Ferris 1987), corporate acquisitions became different tasks from the selection and evaluation a textbook example for a winner’s curse where an of the target firm, to the due diligence process, the acquiring firm overpays for the target firm (Roll negotiation of the deal, and the potential integra- 1986; Thaler 1988; Barberis and Thaler 2003; tion of two firms. The complexity of a firm acquisi- Hietala, Kaplan, and Robinson 2003; Baker, Ruback, tion obscures the causal link between an action and and Wurgler 2007; Malmendier, Moretti, and Peters its outcome so that learning becomes difficult 2018; De Bondt, Cousin, and Roll 2018). In the pre- (Zollo and Winter 2002; Heimeriks, Schijven, and sence of competition for the target firm, acquiring Gates 2012; Castellaneta and Conti 2017). firms tend to fail to adapt their bidding strategy Prior literature focuses largely on the relation- (Roll 1986; Varaiya 1988; Boone and Mulherin 2008; ship between acquisition experience and post- Brander and Egan 2017), the management becomes acquisition performance and finds mixed results overconfident in their own ability to create value from (see Barkema and Schijven 2008a, for a survey) the acquisition (Thaler 1988; Roll 1986; Hietala, with some studies documenting a positive learning Kaplan, and Robinson 2003; Malmendier and Tate effect (e.g. Fowler and Schmidt 1989; Bruton, 2008) and more aggressive bidding occurs because Oviatt, and White 1994; Barkema, Bell, and each firm wants to maintain the chance of winning Pennings 1996; Nadolska and Barkema 2014; (Kagel and Levin 1986; Hong and Shum 2002). The Cuypers, Cuypers, and Martin 2017; Schweizer result is a winning bid, which is higher due to the mere et al. 2022). This evidence suggests that experiential presence of competition and overestimates the value learning may help avoiding a winner’s curse. In this of the target firm (Thaler 1988; Varaiya and Ferris paper, we argue that a winner’s curse is mitigated 1987; Varaiya 1988; Malmendier, Moretti, and Peters by acquisition experience only if, in the presence of 2018; De Bondt, Cousin, and Roll 2018). CONTACT Marta Arroyabe mf17255@essex.ac.uk Essex Business School, University of Essex, Elmer Approach, Southend-On-Sea SS1 1LW, UK; Katrin Hussinger katrin.hussinger@uni.lu Department of Economics and Management, University of Luxembourg, 6 Rue Richard Coudenhove- Kalergi, Esch-sur-Alzette 1359, Luxembourg © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc- nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 M. ARROYABE AND K. HUSSINGER acquisition experience, (1) the acquisition price we find partial evidence for experiential learning to paid for a contested acquisition is lower and (2) mitigate a winner’s curse in the market for corpo- rate control because experienced winners of bid- the post-acquisition performance decline is smal- ding contests at the market for corporate control ler. Both conditions are important because a higher outperform winners without prior acquisition acquisition price alone can be rational when it experience and losers of the competition. reflects higher expected synergy effects This study contributes to the scarce empirical (Adegbesan 2009; Laamanen 2007) and experience literature on the winner’s curse on the market for may help identifying a target that is worth a high corporate control (Roll 1986; Varaiya and Ferris acquisition price (Castellaneta and Conti 2017). In 1987; Varaiya 1988; Schwert 1996; Sirower 1997; a similar vein, post-acquisition performance below Hietala, Kaplan, and Robinson 2003; Boone and expectations may have explanations unrelated to Mulherin 2008; Brander and Egan 2017; a winner’s curse, such as an insufficiently planned Malmendier, Moretti, and Peters 2018; De Bondt, and poorly executed post-acquisition integration Cousin, and Roll 2018). While prior studies on (Chatterjee et al. 1992; Datta 1991; Haspeslagh experiential learning focus mainly on post- and Jemison 1991; Larsson and Finkelstein 1999; acquisition performance (Barkema and Schijven Arroyabe, Hussinger, and Hagedoorn 2020). 2008a; King et al. 2021; King et al. 2004; Datta, To assess whether the winner of a contest pays too Pinches, and Narayanan 1992; Trichterborn, Zu much and whether acquisition experience can lead Knyphausen-Aufseß, and Schweizer 2016; to a lower price, we compare contested firm acquisi- Schweizer et al. 2022), we focus on the effect of tions to those that had only one interested buyer. For experiential learning on the acquisition price and the investigation of the post-acquisition perfor- post-acquisition performance. This approach pro- mance and potential learning effects from prior vides more complete evidence on the likely exis- acquisitions, we employ a novel identification strat- tence of a winner’s curse at the market for egy proposed by Malmendier, Moretti, and Peters corporate control. (2018) where the winners of contested acquisitions We also contribute to the empirical M&A litera- are compared to the losers of those contests. Our ture by employing a novel approach to investigate empirical analysis is based on a large sample includ- the post-acquisition performance, which compares ing all contested U.S. acquisitions of publicly listed the winners of a bidding contest to the losers of the firms in the period 1980–2020 as identified by SDC same acquisition contest (Malmendier, Moretti, Platinum (Refinitiv). and Peters 2018). Lastly, our analysis is based on Our results suggest that corporate acquisitions a large sample of contested firm acquisitions involving competition for the target firm are asso- (Malmendier, Moretti, and Peters 2018). ciated with a higher takeover price (e.g. Hietala, Kaplan, and Robinson 2003; Malmendier, Moretti, and Peters 2018; De Bondt, Cousin, and II. Theory & hypotheses Roll 2018). We further find that no evidence for The winner’s curse experiential learning mitigating the winner’s curse: acquiring firms with acquisition experience still A winner’s curse at the market for corporate con- pay a higher price for the acquisition target than trol is a likely phenomenon in the presence of they would pay for a comparable target that is not competition for a target firm. Acquiring firms associated with a bidding contest. tend to fail to adapt their bidding strategy to the Using different measures for the post- presence of competing bidders (Roll 1986; Varaiya acquisition performance, we do not find robust and Ferris 1987; Varaiya 1988; Boone and evidence for the post-acquisition performance of Mulherin 2008; Brander and Egan 2017; the winners of a bidding contest to be lower than Malmendier, Moretti, and Peters 2018; De Bondt, that of the losers. We, however, find robust evi- Cousin, and Roll 2018), the management becomes dence for a superior post-acquisition performance overconfident in their own ability to create value of firms with acquisition experience. In summary, from the acquisition (Thaler 1988; Roll 1986; APPLIED ECONOMICS 3 Sirower 1997; Hietala, Kaplan, and Robinson 2003; Learning from past acquisitions cannot be taken Malmendier and Tate 2015) and their bidding for granted though. The complexity and multi- behaviour becomes more aggressive so that they staged nature of the acquisition process obscures maintain the chance of winning the bidding contest the causal link between an action and its outcome, (Kagel and Levin 1986; Hong and Shum 2002). The which renders learning difficult (Zollo and Winter result is a winning bid that overestimates the value 2002; Heimeriks, Schijven, and Gates 2012; of the target firm. The value of the winning bid is Castellaneta and Conti 2017; Barkema and expected to increase with the number of bidders Schijven 2008a). (Varaiya and Ferris 1987; Varaiya 1988). Nevertheless, learning from past acquisitions can At the market for corporate control, assessing occur when cumulative acquisition experience is the value of the object at stake is difficult because transferred into routines that help managing subse- a firm is composed of a bundle of resources and quent acquisitions (Chao 2018; Halebian and assets from which value can potentially be created Finkelstein 1999; Kim and Finkelstein 2009). (Bruton, Oviatt, and White 1994; Cording, Routines are standard operating procedures that Christmann, and King 2008; Castellaneta and develop as a result of learning from repetition and Conti 2017). In addition, expected synergies that facilitate the implementation of reoccurring tasks between the assets and capabilities of acquiring (Cyert and March 1963). Routines serve as organiza- and target firms enter the value assessment. tional memory (Nelson and Winter 1982) and estab- Superior expected synergies can, in fact, justify lish the building blocks of organizational capabilities a rationally chosen higher price by the acquiring (Dosi, Nelson, and Winter 2000; Winter 2003) and firm (Adegbesan 2009; Conner 1991; Lippman and dynamic capabilities (Eisenhardt and Martin 2000). Rumelt 2003; Laamanen 2007) because the winning As such, routines are a source of superior organiza- firm may expect to create a higher value from the tional performance. In the context of firm acquisi- acquisition than its competitors. This is why, next tions, cumulative acquisition experience has been to a too high acquisition price, a second condition shown to be an important source of organizational for a winner’s curse is required which states that learning with the potential to support the different the post-acquisition performance of the acquiring stages of an acquisition process (Barkema and firm after a bidding contest is lower. This condition Schijven 2008a; Levitt and March 1988; Chao 2018; makes sure that the higher price is not justified Welch et al. 2020). because of higher synergies to be realized. Prior literature that focuses on experiential learning distinguishes broadly between two stages of the acqui- sition process (Barkema and Schijven 2008b; Acquisition experience Puranam, Powell, and Singh 2006; Castellaneta and In the context of corporate acquisitions, experien- Conti 2017). The first stage is the selection stage, tial learning is described as the ability to employ which includes the various steps from target selection acquisition experience for value creation through up to the value assessment of the target (Puranam, a new firm acquisition (Barkema and Schijven Powell, and Singh 2006; Castellaneta and Conti 2017; 2008a). Firms learn from past firm acquisitions Wu and Reuer 2021). The second stage is the restruc- and become familiar with several parts of the turing stage, where the acquiring firm seeks to gen- multi-process of an acquisition including the selec- erate value from the acquisition (Barkema and tion, evaluation of the target, but also the due Schijven 2008b; Heimeriks, Schijven, and Gates diligence process, the negotiation of the deal and 2012; Castellaneta and Conti 2017). the integration of two combined firms to achieve Regarding the post-acquisition stage, it has potential synergy. Some studies have indicated that been shown that firms can simply ‘learn by experienced acquirers that develop acquisition cap- doing’ (Lubatkin 1987; Bruton, Oviatt, and abilities are more successful in their post- White 1994; Halebian and Finkelstein 1999; acquisition performance (Fowler and Schmidt Hayward 2002). Tacit routines evolve by repeat- 1989; Nadolska and Barkema 2014; Cuypers, ing similar tasks without explicit knowledge Cuypers, and Martin 2017; Schweizer et al. 2022). articulation or codification. Learning from past 4 M. ARROYABE AND K. HUSSINGER experience is further improved when tacit rou- contested U.S. acquisitions of publicly listed firms tines are codified after the causal links for post- in the time period 1980–2020. After having used acquisition integration success are understood several filters, our final dataset includes a total of (Zollo and Singh 2004; Heimeriks, Schijven, and 4,646 acquisitions, 303 contested deals and a total Gates 2012). For a following acquisition, the of 4,343 non-contested deals. We retrieve firm results of such an analysis can provide guidance characteristics for all firms involved in the acquisi- for action through a well-managed organizational tions and acquisition contests from Compustat. memory. While the mechanisms of ‘learning by Two samples are created. The first one is a cross- doing’ and ‘learning through codification of tacit sectional sample consisting of 4,646 observations, routines’ are the same at the selection stage, some which allows to relate the price paid for the target authors argue that the codification of tacit knowl- firm to the target and acquiring firms’ characteris- edge is easier in this first stage because the tasks tics and the presence of a bidding contest. This are less complex and more similar for different sample allows to test H1. The second sample, acquisitions than those of the post-acquisition used to test H2, is a firm-level panel dataset for integration stage and because the time distance the 336 firms (both winners and losers) involved in between action and outcome is shorter contested deals following Malmendier, Moretti, (Castellaneta and Conti 2017). and Peters (2018). This sample contains financial Empirical evidence that distinguishes the selection information of the firms for a maximum of 9 years and integration stage supports experiential learning at before and after the acquisition. The panel is unba- both stages (Puranam, Powell, and Singh 2006; lanced because information is not available for all Barkema and Schijven 2008b; Heimeriks, Schijven, firm-years and consists of 5,149 observations. and Gates 2012; Castellaneta and Conti 2017). These arguments and evidence lead us to argue that experi- Variables ential learning can help mitigating a winner’s curse as tacit and codified routines developed through past Table 1 shows a summary of the dependent and acquisition experience can facilitate the value assess- independent variables used in our analyses. Two ment of the target firm in the selection stage and also different dependent variables are used. To test H1, foster value creation in the post-acquisition phase. the price paid for the acquisition target is used as a dependent variable (Grimpe and Hussinger 2014). The post-acquisition performance of the Hypothesis 1: The price increase due to competi- acquiring firm (H2) is measured as Tobin’s tion for the target firm is smaller when the acquiring Q normalized by year and Standard Industry firm has acquisition experience. Classification (SIC3) industry, i.e. the market value of the acquiror over its book value Hypothesis 2: Following a firm acquisition asso- (Laamanen 2007). We chose Tobin’s Q as the ciated with a bidding contest, the post-acquisition main measure for firm performance because it is performance of an acquiring firm is greater due to a forward-looking measure that incorporates the acquisition experience. expectations about future profits. To show the robustness of our results for the post-acquisition performance analysis, we further employ the sales- to-assets ratio and the return on assets (ROA) as III. Data, variables and descriptive statistics dependent variables. Both variables are normalized by year and SIC3 industry. Data For testing H1, the main independent vari- Our data is retrieved from SDC Platinum ables are a binary variable that captures whether (Refinitiv) and includes all contested and non- the acquisition was associated with a bidding Learning can also be achieved by engagement in alliances prior to the acquisition (Zollo and Winter 2002; Chang and Tsai 2013). Our dataset excludes deals that are not completed or withdrawn. We also exclude firms that are not publicly listed U.S. firms. We also exclude firms that enter as white knights (Malmendier, Moretti, and Peters 2018). APPLIED ECONOMICS 5 Table 1. Description of variables. Variable Variable label Variable definition type Source Dependent Variables Acquisition Price Logarithm of the value of the deal in millions of USD Continuous SDC Platinum Tobin’s Q Acquiring firms’ Tobin’s Q in year t over the SIC-3 industry Tobin’s Q in year t. The Tobin’s Q the Continuous Compustat market value of the acquiror over its book value (in millions of USD). Sales/Assets Acquiring firms’ sales-to-assets ratio in year t over the SIC-3 industry Sales-to-Assets ratio in year t. Continuous Compustat Sales and assets are in millions of USD. ROA Acquiring firms’ return on assets (ROA) in year t over the SIC-3 industry ROA in year t. ROA is the net Continuous Compustat income over book value of total assets. Independent Variables Bidding contest Equal to one if the acquisition is flagged as a contested deal Binary SDC Platinum Number of competing Number of firms (regardless of the public status) involved in a contested deal bid Continuous SDC Platinum bidders Acquisition experience Equal to one if firm has previous experience in M&As and belongs to the contested M&As Binary SDC Platinum (contested M&As) subsample Acquisition experience Equal to one if firm has previous experience in M&As and belongs to the contested non-M&As Binary SDC Platinum (contested M&As) subsample Log(Target Assets) Logarithm of the target’s assets (in millions of USD) Continuous Compustat Target Debt/Assets Target’s debt (in millions of USD) over target’s assets (in millions of USD) Continuous Compustat Target Cash/Assets Target’s cash (in millions of USD) over target’s assets (in millions of USD) Continuous Compustat Target R&D/Assets Target’s R&D expenditures (in millions of USD) over target’s assets (in millions of USD). Note that for Continuous Compustat those observations for which the value was missing, this has been replaced by zero. Target missing R&D Equal to one if target’s R&D expenditure information was missing Binary Compustat Target & acq. conduct Equal to one if both target and acquiring firm have a positive value for the R&D expenditures Binary Compustat R&D Same industry Equal to one if both target and acquiring firm belong to the same SIC-2 industry group Binary Compustat Log(Acq. Assets) Logarithm of the acquiror’s assets (in millions of USD) Continuous Compustat Acq. Debt/Assets Acquiror’s debt (in millions of USD) over acquiror’s assets (in millions of USD) Continuous Compustat Acq. Cash/Assets Acquiror’s cash (in millions of USD) over acquiror’s assets (in millions of USD) Continuous Compustat Acq. R&D/Assets Acquiror’s R&D expenditures (in millions of USD) over acquiror’s assets (in millions of USD). Note Continuous Compustat that for those observations for which the value was missing, this has been replaced by zero. Acq. missing R&D Equal to one if acquiror’s R&D expenditure information was missing Binary Compustat Post Acq Equal to one after the acquisition year Binary SDC Platinum Winner Equal to one if the firm won the bidding contest Binary SDC Platinum Exp. Equal to one if firm has previous M&A experience Binary SDC Platinum Winner*Post Acq*Exp. The interaction term of the variables Post Acq, Winner and Post Acq. Binary SDC Platinum contest, a binary variable that indicates whether assets are used to measure firm size. The natural the acquiror was involved in an acquisition logarithm is employed to account for the skewness prior to the focal acquisition for contested of the variable. Debt and cash are used to measure acquisitions and a binary variable that indicates the financial fitness of both firms (Slusky and Caves whether the acquirer has experience for the 1991). Those variables are divided by total assets to non-contested acquisition subsample. We also avoid a high correlation with firm size. R&D invest- employ the number of competing bidders to ment (divided by total assets) of the target and show robustness for the results of H1 (Varaiya acquiror is employed (Chan, Lakonishok, and and Ferris 1987). Sougiannis 2001). For those firms for which the For testing H2, our main variables of interest are R&D investment is missing, we replace the value a set of binary variables that indicate the post- by zero and create a dummy variable, which we acquisition period, whether the focal firm was the also include in the regression. Access to a target winner of the deal and whether the firm was firm’s innovative assets can be a motivation to involved in an acquisition prior to the focal deal. acquire the firm, and their value is reflected in the To test H2, we include the interaction of the post- deal value (Grimpe and Hussinger 2014). Further, acquisition period, winner and prior experience for testing H1, two binary variables are used to binary variables. capture the market and technological relatedness The control variables used to test the hypotheses between target and acquiring firms (Cassiman related to the price paid for the target (H1) and the et al. 2005). The first one captures whether the acquirer post-acquisition performance (H2) are lar- both firms belong to the same Standard Industry gely the same. For both, target and acquiror, total Classification (SIC2) industry sector. The second 6 M. ARROYABE AND K. HUSSINGER variable captures potential technology synergies by IV. Empirical results capturing whether both firms invest in R&D. Results for H1 Lastly, year and industry dummies are used to con- trol for a possible general time trend and industry Table 4 shows the results for the deal price regres- conditions. sions that test H1. The first specification only includes the binary variables, which indicate that the firm acquisition was associated with a bidding contest. The second specification adds target firm Descriptive statistics characteristics and specification (3) the character- Table 2 shows the descriptive statistics for the deal istics of the acquiring firm. The last specification price sample (H1) for the full sample as well as for adds the binary variables indicating whether the acquisitions with and without a bidding contest acquiring firm has acquisition experience in separately. It appears that, as expected, acquisitions a contested or non-contested deal. associated with a bidding contest show a higher The results show that the price paid for an acquisition price. Target firms involved in bidding acquisition target is higher when there is competi- contests are larger and less involved in R&D than tion for the target firm. The presence of a bidding others. They are more likely to be affiliated with the contest increases the price paid for the target firm same industry sector than their acquirers than by a minimum of 69% (exp(0.523) = 169) (specifi - others. Acquiring firms involved in bidding con- cation (4)). tests have both a higher debt to assets and a higher The results presented in Table 4 do not provide cash-to-asset ratio. These differences may be support for H1, which states that the price paid in related to the acquisition that takes place in the a bidding competition is smaller when the acquiror same year for which the mean values are reported. has acquisition experience. Interestingly, experi- In terms of firm size and R&D, they are ence matters in non-contested deals. Here, the comparable. price paid for the acquisition target is significantly Table 3 shows the descriptive statistics for the lower if the acquiring firm has acquisition firm panel used to investigate H2. When distin- experience. guishing winners and losers of acquisition contests, Table 5 shows the robustness of the results when we see that they are very comparable in terms of the the number of competing bidders is used instead of mean values for the variables presented. Some of the binary variable indicating a bidding contest. the small differences are significant. Results are similar to the main results presented Table 2. Descriptive statistics: acquisition price data set. Total sample Bidding contest No bidding contest Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. t-test Acquisition Price 1550.688 6161.299 3381.141 10188.890 1422.982 5756.737 *** Log(acquisition price) 5.206 2.174 6.293 1.934 5.130 2.169 *** Bidding contest 0.065 0.247 Number of competing bidders 0.109 0.446 Target Assets 2461.103 15872.500 5687.286 45600.010 2236.020 11140.640 *** Log(Target Assets) 5.668 1.988 6.279 2.011 5.626 1.980 *** Target Debt/Assets 0.175 0.214 0.199 0.189 0.173 0.215 * Target Cash/Assets 0.267 4.914 0.088 0.210 0.280 5.082 Target R&D/Assets 0.056 0.145 0.037 0.085 0.057 0.149 ** Target missing R&D 0.532 0.499 0.502 0.501 0.534 0.499 Target & acq. conduct R&D 0.387 0.487 0.439 0.497 0.383 0.486 * Same industry 0.665 0.472 0.736 0.442 0.660 0.474 *** Acq. assets 18782.730 67687.560 23283.740 107669.800 18468.710 63980.460 Log(Acq. Assets) 7.941 2.095 7.815 2.094 7.950 2.095 Acq. Debt/Assets 0.206 0.191 0.266 0.231 0.202 0.187 *** Acq. Cash/Assets 0.094 0.770 0.204 2.043 0.087 0.586 ** Acq. R&D/Assets 0.031 0.081 0.026 0.085 0.031 0.081 Acq. missing R&D 0.526 0.499 0.472 0.500 0.530 0.499 * Acq. experience (contested M&As) 0.327 0.178 Acq. experience (non-contested M&As) 0.495 0.500 APPLIED ECONOMICS 7 Table 3. Descriptive statistics: post-acquisition performance data set. Total sample Winner sample Loser sample Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. t-test Tobin’s Q 0.733 0.528 0.719 0.553 0.771 0.446 *** Sales/Assets 0.913 0.531 0.908 0.551 0.956 0.502 *** ROA 0.301 1.905 0.715 13.364 −0.819 43.144 * Winner*Post Acq 0.388 0.487 0.388 0.487 Winner*Post Acq*Exp. 0.227 0.419 0.227 0.419 Log(Acq. Assets) 7.797 2.231 7.848 2.238 7.654 2.208 *** Acq. Debt/Assets 0.226 0.202 0.236 0.200 0.198 0.204 *** Acq. Cash/Assets 0.133 1.606 0.114 1.010 0.185 2.640 Acq. R&D/Assets 0.027 0.067 0.028 0.071 0.026 0.054 Acq. missing R&D 0.453 0.498 0.424 0.494 0.534 0.499 *** in Table 4. This suggests that the presence of com- a dummy indicating the post-acquisition period peting bids matter, rather than the number of and the variable that takes the value one for the competing bidders. post-acquisition period when the focal firm won a bidding contest. The second specification adds an interaction term between the post- Results for H2 acquisition, winner and experience variables. This interaction term (Winner*Post Table 6 shows the results from fixed effects Acquisition*Experience) takes the value one in regressions that control for firm-specific effects the post-acquisition period when the focal firm for the acquiring firm’s post-acquisition perfor- has experience and is the winner of the con- mance, testing H2. The first specification shows tested deal. Note that the variables Winner and a lean specification, which only includes Table 4. Acquisition price regressions I. (1) (2) (3) (4) Bidding contest 1.163*** 0.588*** 0.615*** 0.523*** (0.128) (0.078) (0.077) (0.107) Acq. experience (contested M&As) −0.142 (0.144) Acq. experience (non-contested M&As) −0.312*** (0.044) Log(Target Assets) 0.860*** 0.754*** 0.751*** (0.012) (0.014) (0.014) Target Debt/Assets −0.517*** −0.407*** −0.416*** (0.101) (0.100) (0.100) Target Cash/Assets 0.018*** 0.016*** 0.016*** (0.004) (0.004) (0.004) Target R&D/Assets 0.144 −0.189 −0.181 (0.157) (0.158) (0.157) Target missing R&D −0.168*** 0.001 0.016 (0.055) (0.075) (0.079) Target & acq. conduct R&D 0.201** 0.281*** (0.080) (0.105) Same industry 0.366*** 0.349*** (0.044) (0.044) Log(Acq. Assets) 0.165*** 0.205*** (0.012) (0.013) Acq. Debt/Assets −0.180 −0.182 (0.121) (0.121) Acq. Cash/Assets −0.010 −0.000 (0.025) (0.025) Acq. R&D/Assets 0.822*** 0.818*** (0.276) (0.275) Constant 5.130*** −0.293 −1.096* −1.487** (0.033) (0.616) (0.604) (0.607) Observations 4646 4646 4646 4646 Log likelihood −10157.914 −7636.188 −7509.357 −7482.738 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year and industry dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. 8 M. ARROYABE AND K. HUSSINGER Post-Acquisition are not included in the fixed We check for the robustness of our results by effects regressions because they are time- employing alternative dependent variables. The invariant. Specifications (3) and (4) add the interaction term Winner*Post Acquisition acquiring firm control variables. *Experience is positive and significant as well when the performance is measured with the ratio The results support H2 by consistently showing of sales to assets normalized by the industry aver- that the post-acquisition performance decline of age (Table 7) and ROA normalized by the industry the acquirer is smaller when the acquiring firm of average (Table 8). a bidding contest has acquisition experience. The post-acquisition performance decline, as measured with the Tobin’s Q, is about 21% lower when the V. Discussion acquiring firm is a winner and has previous acqui- sition experience (specification (4)). This paper shows that experiential learning cannot Our results are graphically displayed in avoid increased prices paid for a target in a bidding Figures 1-3 where we show event study graphs of contest. Acquisition experience is, however, asso- the relative performance of winners and losers. ciated with a superior post-acquisition perfor- Figure 1 shows that winners outperform losers of mance as compared to winners of bidding a bidding contest in the period immediately after contests without acquisition experience and as the acquisition. When distinguishing between win- compared to losers of bidding competitions. ners with and without acquisition experience, it The fact that even experienced firms pay acqui- appears that it is the experienced winners that out- sition prices that are too high in the presence of perform the losers of a bidding contest (Figures 2 competition is in line with lab experiments (Thaler and 3). 1988). Lab experiments show that learning through Table 5. Acquisition price regressions II. (1) (2) (3) (4) Number of competing bidders 0.629*** 0.300*** 0.309*** 0.230*** (0.071) (0.043) (0.042) (0.056) Acq. experience (contested M&As) −0.032 (0.139) Acq. experience (non-contested M&As) −0.322*** (0.044) Log(Target Assets) 0.860*** 0.755*** 0.752*** (0.012) (0.014) (0.014) Target Debt/Assets −0.519*** −0.412*** −0.420*** (0.101) (0.100) (0.100) Target Cash/Assets 0.018*** 0.016*** 0.016*** (0.004) (0.004) (0.004) Target R&D/Assets 0.132 −0.200 −0.189 (0.157) (0.158) (0.158) Target missing R&D −0.168*** 0.003 0.017 (0.055) (0.075) (0.079) Target & acq. conduct R&D 0.205** 0.283*** (0.080) (0.105) Same industry 0.367*** 0.350*** (0.044) (0.044) Log(Acq. Assets) 0.164*** 0.205*** (0.012) (0.013) Acq. Debt/Assets −0.169 −0.172 (0.121) (0.121) Acq. Cash/Assets −0.006 0.002 (0.025) (0.025) Acq. R&D/Assets 0.814*** 0.810*** (0.276) (0.275) Constant 5.137*** −0.301 −1.107* −1.497** (0.033) (0.616) (0.605) (0.608) Observations 4646 4646 4646 4646 Log likelihood −10159.740 −7640.342 −7514.903 −7486.522 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year and industry dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. APPLIED ECONOMICS 9 Table 6. Fixed effects regressions for post-acquisition performance (Tobin’s Q). (1) (2) (3) (4) Post Acq −0.069** −0.067** −0.052* −0.051* (0.029) (0.029) (0.028) (0.028) Winner*Post Acq −0.035 −0.094*** 0.018 −0.018 (0.027) (0.032) (0.026) (0.031) Winner*Post Acq*Exp. 0.095*** 0.058** (0.028) (0.027) Log(Acq. Assets) −0.146*** −0.145*** (0.010) (0.010) Acq. Debt/Assets 0.045 0.050 (0.041) (0.041) Acq. Cash/Assets 0.054*** 0.053*** (0.005) (0.005) Acq. R&D/Assets 1.021*** 1.025*** (0.152) (0.152) Constant 0.841*** 0.840*** 1.429*** 1.422*** (0.148) (0.148) (0.152) (0.152) Observations 5149 5149 5149 5149 Log likelihood −2382.145 −2376.029 −2130.280 −2127.817 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. experience happens rarely and slowly in the market 2006). Such a mechanism may explain that the for corporate control (Thaler 1988). Empirical stu- winner’s of a bidding contest do not adjust their dies argue that the complexity and multi-staged bid when there is competition for the target firm. nature of the acquisition process render learning This study makes several contributions to the difficult because the causal link between an action literature. First, the study contributes to the scarce and its outcome is obscured (Zollo and Winter empirical evidence on a winner’s curse at the mar- 2002; Heimeriks, Schijven, and Gates 2012; ket for corporate control (Varaiya and Ferris 1987; Castellaneta and Conti 2017; Barkema and Roll 1986; Varaiya 1988; Sirower 1997; Hietala, Schijven 2008a). Acquisition experience further Kaplan, and Robinson 2003; Boone and Mulherin has been shown to lead to less sensitivity towards 2008; Brander and Egan 2017; Malmendier, negative information during the due diligence pro- Moretti, and Peters 2018; De Bondt, Cousin, and cess, which may reflect a higher confidence in the Roll 2018). As it is not straightforward to empiri- original valuation (Puranam, Powell, and Singh cally identify a winner’s curse because the true -3 -2 0 1 2 3 4 5 6 7 8 9 Figure 1. Post-acquisition performance (Tobin’s q): winners versus losers. -.1 .1 .2 .3 0 10 M. ARROYABE AND K. HUSSINGER -3 -2 0 1 2 3 4 5 6 7 8 9 Figure 2. Post-acquisition performance (Tobin’s q): winners with experience versus losers. -3 -2 0 1 2 3 4 5 6 7 8 9 Figure 3. Post-acquisition performance (Tobin’s q): winners without experience versus losers. value of the acquisition target is unknown, this acquisition integration (Chatterjee et al. 1992; paper suggests to investigate the likelihood of the Datta 1991; Haspeslagh and Jemison 1991; presence of a winner’s curse along two dimensions: Larsson and Finkelstein 1999; Arroyabe, the acquisition price and the post-acquisition per- Hussinger, and Hagedoorn 2020). formance of the acquiring firm. Both dimensions Second, this study contributes to the litera- should be considered because a higher acquisition ture on experiential learning in the market for price alone can speak for higher expected and corporate control (Barkema and Schijven 2008a; potentially also realized synergy effects between Trichterborn, Zu Knyphausen-Aufseß, and the acquiring and the target firm (Adegbesan Schweizer 2016; Schweizer et al. 2022). While 2009; Laamanen 2007) and because the post- lab experiments mimicking firm’s price deci- acquisition performance may be affected by an sions in auctions for corporate acquisitions insufficiently planned and poorly executed post- show that learning based on experience happens -.2 -.1 -.1 .1 .2 .3 .4 .1 .2 .3 0 APPLIED ECONOMICS 11 Table 7. Fixed effects regressions for post-acquisition performance (sales/assets). (1) (2) (3) (4) Post Acq −0.159*** −0.157*** −0.119*** −0.118*** (0.026) (0.026) (0.025) (0.025) Winner*Post Acq 0.055** −0.023 0.098*** 0.039 (0.024) (0.028) (0.023) (0.027) Winner*Post Acq*Exp. 0.129*** 0.096*** (0.025) (0.024) Log(Acq. Assets) −0.138*** −0.136*** (0.009) (0.009) Acq. Debt/Assets −0.248*** −0.238*** (0.043) (0.043) Acq. Cash/Assets −0.022*** −0.023*** (0.008) (0.008) Acq. R&D/Assets 0.976*** 0.988*** (0.124) (0.124) Constant 1.064*** 1.061*** 1.694*** 1.682*** (0.130) (0.130) (0.133) (0.133) Observations 5059 5059 5059 5059 Log likelihood −1666.740 −1652.021 −1441.129 −1432.439 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. Table 8. Fixed effects regressions for post-acquisition performance (ROA). (1) (2) (3) (4) Post Acq −0.000 0.004 0.003 0.006 (0.134) (0.134) (0.134) (0.134) Winner*Post Acq 0.061 −0.082 0.072 −0.076 (0.121) (0.144) (0.122) (0.145) Winner*Post Acq*Exp. 0.237* 0.240* (0.128) (0.128) Log(Acq. Assets) −0.025 −0.020 (0.046) (0.046) Acq. Debt/Assets 0.058 0.083 (0.233) (0.233) Acq. Cash/Assets −0.022 −0.025 (0.043) (0.043) Acq. R&D/Assets 1.056 1.086 (0.665) (0.665) Constant −0.112 −0.117 −0.147 −0.177 (0.668) (0.668) (0.715) (0.715) Observations 5059 5059 5059 5059 Log likelihood −9942.079 −9940.208 −9939.675 −9937.781 Prob>chi2 0.000 0.000 0.000 0.000 Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. All regressions contain year dummies. If R&D over assets is included, the regressions also include a dummy variable that equals one if information for R&D was missing. rarely and slowly (Thaler 1988), empirical evi- Lubatkin 1982; Zollo and Leshchinskii 2004). dence is somewhat more optimistic about learn- Conflicting empirical results from acquisition ing effects for value creation through corporate experience on different measures of acquisition acquisitions (Barkema and Schijven 2008a). performance are confirmed in meta-analyses Nevertheless, only a few studies report positive (King et al. 2021). experiential learning effects for post-acquisition Lastly, while early studies use small sample of performance (e.g. Fowler and Schmidt 1989; contested acquisitions due to a lack of available Bruton, Oviatt, and White 1994; Barkema and data (e.g. Varaiya 1988; Boone and Mulherin Drogendijk 2007; Nadolska and Barkema 2014; 2008; Hayward 2002), we contribute to recent Cuypers, Cuypers, and Martin 2017; Schweizer empirical evidence that exploits the availability of et al. 2022), while most studies suggest the larger datasets of contested M&As (e.g. Betton, absence of learning through experience (e.g. Eckbo, and Thorburn 2008; Malmendier, Moretti, 12 M. ARROYABE AND K. HUSSINGER and Peters 2018) and exploit a novel identification VII. Conclusion strategy that compares the winners and the losers This paper shows that acquisition experience does of acquisition contests (Malmendier, Moretti, and not help avoiding to overpay for firm acquisitions. Peters 2018). The post-acquisition performance of experienced A caveat of our analysis is that our sample is winners of bidding contests at the market for cor- based on publicly listed firms and porate control is superior though. Taken together, U.S. acquisitions only, while we know that these results provide partial evidence for experien- acquisition premia are higher in more efficient tial learning to help avoiding a winner’s curse at the markets (Tampakoudis, Subeniotis, and market for corporate control. Dalakiouridou 2011). This suggests a need for research investigating whether the observed effects hold for private firms and also for other Acknowledgments markets. For example, Europe has fewer hostile For helpful comments, we would like to thank David King. acquisitions that may invite competitive bids, and researchers have questioned whether U.S. acquisition research findings hold in Disclosure statement Europe (Moschieri and Campa 2009). No potential conflict of interest was reported by the author(s). VI. Implications ORCID Our results suggest that experiential learning Marta Arroyabe http://orcid.org/0000-0003-3223-0268 does not help against a too high acquisition price paid by a winning firm. This raises the question whether experience, rather than creat- References ing an experiential advantage for the acquiring Adegbesan, J. A. 2009. “On the Origins of Competitive firm, may lead to overconfidence when it comes Advantage: Strategic Factor Markets and Heterogeneous to the bidding competition. 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Journal

Applied Economics – Taylor & Francis

Published: Jun 8, 2024

Keywords: Firm acquisitions; winner’s curse; bidding contest; acquisition experience; experiential learning; G34; G14; D80

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