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.

On the use of self-organizing maps to accelerate vector quantization

Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantified (or classified) either on the same location or on neighbor ones on a predefined grid. SOM are also widely used for their more classical vector quantization property. We show in this paper that using SOM instead of the more classical Simple Competitive Learning (SCL) algorithm drastically increases the speed of convergence of the vector quantization process. This fact is demonstrated through extensive simulations on artificial and real examples, with specific SOM (fixed and decreasing neighborhoods) and SCL algorithms.Comment: A la suite de la conference ESANN 199

Legal insider trading and stock market reaction: evidence from the Netherlands

This paper provides an analysis of legal insider trading on the Euronext Amsterdam stock exchange by using data published in the register held by the AFM, the dutch financial markets authority. The sample includes 822 transactions executed by corporate insiders between the beginning of January 1999 and the end of September 2005. Our analysis shows that the financial markets' response is not significant for purchases, and that the abnormal returns associated with the sales do not have the expected sign. However, over a longer time horizon, the average cumulated abnormal returns are positive for the stocks purchased, and negative for stocks sold by insiders. This result suggests either that insiders use long-term information for their trading activities or that they are able to time the market.

Some known facts about financial data

Numéro Spécial Neural Networks ACSEG 2002 BoulogneMany researchers are interesting in applying the neural networks methods to financial data. In fact these data are very complex, and classical methods do not always give satisfactory results. They need strong hypotheses which can be false, they have a strongly non-linear structures, and so on. But neural models must also be cautiously used. The black box aspect can be very dangerous. In this very simple paper, we try to indicate some specificity of financial data, to prevent some bad use of neural models

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

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

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

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