Effect of Business Relatedness on Mergers and Acquisitions: Using 10-k Annual Reports

Business relatedness or similarity is a widely used measure in merger and acquisition studies. The effectiveness of the relatedness measure is central for managerial judgement and performance analysis of diversification decisions. Traditionally the Standard Industrial Classification (SIC Code) is used to represent business similarity, however the search for a more effective metric have led to the creation of alternative industry classifications and business relatedness measures. This paper uses text mining and topic modelling techniques to create new text based similarity metrics. The model is created from texts retrieved from 10-k annual reports submitted to the Securities and Exchange Commission. From these reports two sections of relevance are selected and used as sources for possible business capabilities that form our similarity measure. The new metrics are based on the topic distributions obtained from our Topic Model and on a dictionary of terms that had an impact on the topic model creation. These measures are then compared against the traditionally used SIC Code and a simple cosine similarity comparison of the 10-k text of merging firms.