Corporate serial acquisitions: An empirical test of the learning hypothesis

Recent empirical papers report a declining trend in the cumulative abnormal return (CAR) of acquirers during an M&A program. Does this necessarily imply that acquiring CEOs are infected by hubris and are not learning from previous mistakes? We first confirm the existence of this declining trend on average. However, we find a positive CAR trend for CEOs likely to be infected by hubris, which is significantly different from the negative trend found for CEOs who are more likely to be rational. We also explore the time between successive deals and find empirical evidence to suggest that many CEOs learn substantially during acquisition programs.

Learning, hubris and corporate serial acquisitions

Recent empirical research has shown that, from deal to deal, serial acquirers' cumulative abnormal returns (CAR) are declining. This has been most often attributed to CEOs hubris. We question this interpretation. Our theoretical analysis shows that (i) a declining CAR from deal to deal is not sufficient to reveal the presence of hubris, (ii) if CEOs are learning, economically motivated and rational, a declining CAR from deal to deal should be observed, (iii) predictions can be derived about the impact of learning and hubris on the time between successive deals and, finally, (iv) predictions about the CAR and about the time between successive deal trends lead to testable empirical hypotheses.

Serial acquirer bidding: An empirical test of the learning hypothesis

Recent academic studies indicate that acquirers' cumulative abnormal returns (CAR) decline from deal to deal in acquisition programs. Does this pattern suggest hubristic CEO behaviors are significant enough to influence average CAR patterns during acquisition programs? An alternative explanation is CEO learning. This study therefore tests for learning using successive acquisitions of large U.S. public targets undertaken by U.S. acquirers. A dynamic framework reveals that both rational and hubristic CEOs take on average investor reactions to their previous deals into account and adjust their bidding behavior accordingly. These results are consistent with a learning hypothesis

Improved Methods for Detecting Acquirer Skills

Large merger and acquisition (M&A) samples feature the pervasive presence of repetitive acquirers. They offer an attractive empirical context for revealing the presence of acquirer skills (persistent superior performance). But panel data M&A are quite heterogeneous; just a few acquirers undertake many M&As. Does this feature affect statistical inference? To investigate the issue, our study relies on simulations based on real data sets. The results suggest the existence of a bias, such that extant statistical support for the presence of acquirer skills appears compromised. We introduce a new resampling method to detect acquirer skills with attractive statistical properties (size and power) for samples of acquirers that complete at least five acquisitions. The proposed method confirms the presence of acquirer skills but only for a marginal fraction of the acquirer population. This result is robust to endogenous attrition and varying time periods between successive transactions. Claims according to which acquirer skills are a first order factor explaining acquirer cross-­‐sectional cumulated abnormal returns appears overstated

Empirical Evidence of Overbidding in M&A Contests

Surprisingly few papers have attempted to develop a direct empirical test for overbidding in M&A contests. We develop such a test grounded on a necessary condition for profit maximizing bidding behavior. The test is not subject to endogeneity concerns. Our results strongly support the existence of overbidding. We provide evidence that overbidding is related to conflicts of interest, but also some indirect evidence that it arises from failing to fully account for the winner’s curse

Full Stock Payment Marginalization in M&A Transactions

The number of merger and acquisition (M&A) transactions paid fully in stock in the U.S. market declined sharply after 2001, when pooling and goodwill amortization were abolished by the Financial Accounting Standards Board. Did this accounting rule change really have such far-reaching implications? Using a differences-in-differences test and Canada as a counterfactual, this study reveals that it did. We also report several other results confirming the role of pooling abolishment, including (i) that the decrease in full stock payment relates to CEO incentives and (ii) that previously documented determinants of the M&A mode of payment cannot explain the post pooling abolishment pattern. These results are also robust to controls for various factors, such as the Internet bubble, the exclusion of cross-border deals, the presence of Canadian cross-listed firms, the use of a constant sample of acquirers across the pooling and post pooling abolishment periods, the use of Europe as an alternative counterfactual, and controls for the SEC Rule 10b-18 share repurchase safe harbor amendments of 2003

AXAF user interfaces for heterogeneous analysis environments

The AXAF Science Center (ASC) will develop software to support all facets of data center activities and user research for the AXAF X-ray Observatory, scheduled for launch in 1999. The goal is to provide astronomers with the ability to utilize heterogeneous data analysis packages, that is, to allow astronomers to pick the best packages for doing their scientific analysis. For example, ASC software will be based on IRAF, but non-IRAF programs will be incorporated into the data system where appropriate. Additionally, it is desired to allow AXAF users to mix ASC software with their own local software. The need to support heterogeneous analysis environments is not special to the AXAF project, and therefore finding mechanisms for coordinating heterogeneous programs is an important problem for astronomical software today. The approach to solving this problem has been to develop two interfaces that allow the scientific user to run heterogeneous programs together. The first is an IRAF-compatible parameter interface that provides non-IRAF programs with IRAF's parameter handling capabilities. Included in the interface is an application programming interface to manipulate parameters from within programs, and also a set of host programs to manipulate parameters at the command line or from within scripts. The parameter interface has been implemented to support parameter storage formats other than IRAF parameter files, allowing one, for example, to access parameters that are stored in data bases. An X Windows graphical user interface called 'agcl' has been developed, layered on top of the IRAF-compatible parameter interface, that provides a standard graphical mechanism for interacting with IRAF and non-IRAF programs. Users can edit parameters and run programs for both non-IRAF programs and IRAF tasks. The agcl interface allows one to communicate with any command line environment in a transparent manner and without any changes to the original environment. For example, the authors routinely layer the GUI on top of IRAF, ksh, SMongo, and IDL. The agcl, based on the facilities of a system called Answer Garden, also has sophisticated support for examining documentation and help files, asking questions of experts, and developing a knowledge base of frequently required information. Thus, the GUI becomes a total environment for running programs, accessing information, examining documents, and finding human assistance. Because the agcl can communicate with any command-line environment, most projects can make use of it easily. New applications are continually being found for these interfaces. It is the authors' intention to evolve the GUI and its underlying parameter interface in response to these needs - from users as well as developers - throughout the astronomy community. This presentation describes the capabilities and technology of the above user interface mechanisms and tools. It also discusses the design philosophies guiding the work, as well as hopes for the future

CEO Narcissism and the Takeover Process: From Private Initiation to Deal Completion

Chief executive officer (CEO) narcissism affects the takeover process. Acquirer shareholders react less favorably to a takeover announcement when the target CEO is more narcissistic. Narcissistic acquiring CEOs negotiate faster. They are also marginally more likely to initiate deals. Acquirer CEO narcissism and target CEO narcissism are associated with a lower probability of deal completion and reduce the likelihood that the target CEO will be employed by the merged firm. Our findings highlight the importance of both acquirer and target CEO psychological characteristics throughout the takeover process

Spatial region filtering in IRAF/PROS

In order to analyze x ray data, it is nearly always necessary to extract source and background events from a data set. Typically, this is done by defining geometric spatial regions of the data set to describe the source and background. For example, one might wish to extract source events from a circular or elliptical region centered at a particular pixel, and background events from a circular or elliptical annulus whose inner radius matches the source region. At the same time, it might be necessary to exclude one or more nearby sources from the source or background region in question. Thus, it might be necessary to define a pie-shaped region or even an entirely irregularly-shaped region to exclude. A spatial filtering scheme called REGIONS was implemented in IRAF/PROS to support these and other types of spatial region extraction. It allows users to create a spatial mask by specifying one or more ASCII geometric shape descriptors (box, circle, ellipse, pie, point, annulus, and polygon) as regions to be included or excluded in the mask. In addition, two or more shapes can be combined using Boolean algebra to create an infinite variety of sophisticated regions. Each geometric shape has a specific set of parameters that describe that shape. For example, a circle is described by a center and a radius, while a box is described by a center, length, width, and rotation angle. These quantities can be specified in units of pixels or, in cases where the target image contains world coordinate system information, they can be described in units such as RA and Dec. Users can create region mask files by feeding an ASCII region descriptor to the IRAF/PROS plcreate task. Temporary masks can also be created from ASCII region descriptors by individual applications that call the routines in the region creation library. This library implements a yacc-based region parser that compiles the ASCII descriptors into 'software CPU' instructions which are then executed to create the mask. The mask created from these region descriptors is a standard IRAF PLIO mask. It can be combined with other PLIO masks (e.g., exposure masks) to provide complete spatial filtering capabilities. The capabilities of the region filtering scheme are described. It also discusses the design philosophy guiding our work, as well as our plans for the future