Recommending untapped M&A opportunities: A combined approach using principal component analysis and collaborative filtering

This study proposes an analysis framework for recommending untapped mergers and acquisitions (M&A) opportunities by combining principal component analysis (PCA) and collaborative filtering. The framework is particularly suitable for suggesting M&A targets that may possess the complementary knowledge resources for improving performance gains. In this study, PCA was firstly applied to reduce the dimensionality of original data and to identify key knowledge categories of firms. Based on these results, the potential knowledge requirements and priority areas of firm's knowledge base can be located. Collaborative filtering technique was then adopted to a firm's knowledge portfolio, which is represented as a set of SIC (standard industrial classification) codes, for making recommendations. The applicability of the framework was demonstrated using a case study approach based on the analysis of the biotechnology M&A market. The proposed framework would be a good complement to existing practice of identifying novel M&A opportunities and can create a basis for constructing a data-driven business intelligence system. From an expert and intelligent systems point of view, the study provides decision makers with a general and flexible modeling approach for assisting them in the formulation of M&A strategy. It enlarges the possibility of decomposing the problem into manageable units by automating the emulation of human decision making. (C) 2019 Elsevier Ltd. All rights reserved.