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Text-based analysis of mergers and acquisitions

Oct 01, 2010

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Research by Gerard Hoberg and Gordon Phillips

Some mergers work better than others, and the question of why has been a subject of intense interest to researchers. Are firms that are similar better merger candidates? Gerard Hoberg, assistant professor of finance, and Gordon Phillips, Bank of America Professor of Finance, recently developed much richer data to understand firm relatedness and more accurately predict merger success or failure.

Financial research has historically focused on the numbers in company filings—the accounting statements. But the words which compose the vast bulk of those documents have been virtually ignored. Hoberg and Phillips used Web-crawling techniques to examine the vocabulary in the filings of all publicly traded companies over a ten-year period—over 50,000 filings—through the Securities and Exchange Commission’s Electronic Data Gathering, Analysis, and Retrieval system (EDGAR).

Hoberg and Phillips represent this information in a 3-d format that looks like a globe. Every product developed by every firm that filed 10Ks through EDGAR is represented at a point on that globe—with congested clusters at points where many products exist, and empty places where no products exist. This geometric representation of the product space allows researchers to measure the degree to which firms are related in a much more precise manner. “What we have that other metrics for relatedness don’t, is that we know where every firm is in a well defined space, and so we know the degree of relatedness as well as the direction.,” says Hoberg.

With this data, Hoberg and Phillips analyzed how similarity and competition affect mergers. Existing studies of merger pairs measure similarity by asking if both firms reside within the standard industrial classification (SIC) codes used in company filings to the SEC. Hoberg and Phillips find that most merger pairs are far more similar than other methods of classification would suggest. Even merger pairs with different SIC codes generally had a high degree of similarity with each other.

One of the benefits of the model is its ability to show the similarity between firms not in the same industry. The authors cite a merger between Antheon, a firm that produces military intelligence technologies, and General Dynamics, which is a traditional military firm that makes ships and tanks. Past ways of measuring similarity would have labeled this merger pair as dissimilar because their SIC codes are different. But the similarity that drove this successful merger was the fact that both firms work with the military.

It is new products that generate the profits in such mergers. What is the new product here? “Probably classified,” says Hoberg. “But I can guess that if you had access to real-time intelligence and you could link it into the technology aboard military equipment, the military could have a much better, quicker response to crises. Now General Dynamics can sell the military a more integrated product that would have more value.”

Hoberg and Phillips found that firms were more likely to merge with partners whose language describing their assets was similar. These mergers were more likely to result in increased stock returns, longer-term gains in cash flow and higher growth in their product descriptions—evidence of new products being offered.

Mergers were most successful, and resulted in improved profit margins, when a firm merged with a target that was similar to itself, but different enough from its rivals to differentiate its product offerings. Firms with a higher potential for unique products, as captured by patents, copyrights and trademarks, are more likely to be involved in acquisitions.

Gordon Phillips sums up the research, saying, "Managers frequently cite synergies for a motivation for mergers. However existing research has not been able to find existence or benefits of synergies. Our research shows that many related mergers do produce increased cash flows and new products that will benefit firms and their shareholders."

Hoberg and Phillips re-draw their Variable Industry Classification sphere every year. That enables them to see the product space evolving as companies merge and new products are created.

“Product Market Synergies and Competition in Mergers and Acquisitions: A Text Based Analysis” is forthcoming from the Review of Financial Studies. For more information about this research, contact ghoberg@rhsmith.umd.edu.

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