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Applying Rough Set Theory into Risk Identification of M & A

In this paper, we make an attempt to apply rough set theory into risk identification of merger and acquisition (abbreviated by M & A) from the angle of the acquirer. Although M & A helps enterprises to optimize productions and appropriately allocates social resources, it can also bring about great risks. To identify possible risks before an M & A being carried out, we first classify all kinds of risks throughout the process, from the pre-merger phase to the post-merger phase. Then, we adopt the rough set theory and establish a decision table with the M & A risks as decision attribute and various impact indexes as condition attributes. Afterwards, minimal entropy discretization method is used to map numerical values to categories and simple genetic algorithm is adopted to remove redundant attributes. We generate rules, based on the discrete and reduced decision data. Finally, we specifically choose a happened case to verify the effect of this application, analyzing the risk of M & A between Shanghai Fosun Pharmaceutical (Group) Co., Ltd. and Kailin Pharmaceutical Co., Ltd. and giving suggestions to both Shanghai Fosun and the whole industry.