University of Texas at Arlington professor Gene Moo Lee has developed a technique that uses big data analytics and text-mining methods to improve market intelligence and explain potential mergers and acquisitions of high-technology startups.
"By analyzing unstructured, publicly available descriptions of any startup's business, we can quantify any two firms' business, geographic, investor, and social proximity and from there identify potential targets for mergers and acquisitions," Lee says.
In its initial analysis, Lee's team used publicly available information from the CrunchBase database on 24,382 companies, accounting for each business' headquarters location, industry sector, co-founders, board members, key employees, investments, and the business description. They then utilized topic modeling, which examines the language used in the business descriptions concerning shared products, technologies, and markets. The companies' business proximity was then measured according to the similarity of these topic descriptions.
The probability of a possible merger was calculated based on business proximity, geographic vicinity, social links between individuals within the two firms, and common investor ownership.
The researchers have demonstrated the method's utility by developing a cloud-based information system.
From UT Arlington News Center
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