Examining the relationship between the textual content of merger and acquisition filings with the SEC and the outcomes of such transactions, the author’s goal is to provide an understanding of managerial attitudes and beliefs toward merger negotiations and outcomes from the perspective of both the bidder and the target. In particular, he finds that negative tone used by bidding managers is an indicator of a better post-merger performance.
The author uses a textual algorithm to examine the content of merger and acquisition (M&A) filings with the SEC. His goal is to better understand the role of managerial beliefs and attitudes in merger negotiations and outcomes. With regard to the bidders, the author inspects the degree to which their filings ignore risks and negative results of M&As, which should mimic managerial overconfidence. As for the targets, he measures how positive or negative their management is toward the M&A, which may indicate the resistance toward the planned transaction. Moreover, he attempts to verify the relationships between these attitudes and an M&A premium and financial constraints faced by the target because these measures may also contribute to the overall tone of the target filings.
The results indicate that the more pessimistic or cautious the bidding manager is, the more probable positive outcomes of the transaction are. Cautious bidders may spot some risks or potential problems that more optimistic managers may overlook. The more negative the tone of the target is, the more complex and tougher the M&A negotiations may become. A negative stance may be a reflection of the unwillingness of shareholders to accept an M&A offer and premium or result from financial constraints of the target.
How Is This Research Useful to Practitioners?
Corporate takeovers are among the largest and most complex transactions and are characterized by substantial risks and trading. It is no surprise that company stakeholders try to understand them as much as possible.
The author contributes to the literature on manager overconfidence in merger decisions. Regardless of the fact that bidding managers attempt to maximize the wealth of their shareholders, some of them may take a less realistic stance toward the outcomes of the transaction. Target managers tend to form their tone on the back of market reaction to the deal announcement.
The research presented builds on the recent textual analysis technique that has become more accessible because of the development of textual databases, computation techniques, and a growth of related literature. This analysis enables researchers to capture and measure the qualitative information in text filings.
This research is of primary interest to investors and shareholders looking to understand the motivation behind M&A transactions, from both the bidder and target perspectives, as well as to interpret the behavior of each side. Because of its contribution to a relatively new field in financial literature, the research may also be of interest to academics.
How Did the Author Conduct This Research?
The author uses a simple negative tone measure developed by Tetlock (Journal of Finance 2011) and enhanced by Loughran and Mcdonald (Journal of Finance 2011). It was constructed using the “bag of words” term-weighted technique. The word selection is based on a financial words dictionary developed by Loughran and Mcdonald. The idea of term weighting is to extract rare words that may have more weighting.
To identify takeover events, the author uses the Thomson Reuters SDC Platinum dataset. The sample starts on 1 January 1994 and ends on 31 December 2010. Both target and bidder are listed and the only transactions accepted are M&As. The resulting sample includes 6,126 events. Next, the author extracts firm filings from the SEC EDGAR website. Excluding the amendment files, the author downloads 58,327 files in total. Both databases are merged by using the historical company and security indexes. For firms with multiple filings, firms closest to the takeover are selected.
The research may be distorted by survivorship bias. This bias is bypassed by the author by referring to the CRSP delisting dataset. If the firm is delisted, the author checks the delisting return and date or last reported monthly return.
This article is very informative and sheds some new light on the most complex transaction that firms normally are involved in. The methodology used is also relatively new and not yet fully covered in the literature. Hence, my overall assessment of the article is very positive. Nevertheless, it would be interesting to see similar research applied in an international context.