Exploring qualitative data as predictors for M&A: Empirical analysis of target firms’ letters to shareholders

Abstract

M&A prediction is a growing interest in the field of business studies. However, prevailing M&A prediction techniques are still widely based from the analyses of financial variables through quantitative approaches. This has led M&A scholars’ attention to call for the contribution of non-financial studies coupled with the opportunity for qualitative approaches to supplement new methodological insights. Accordingly, this study delved to explore the potential of qualitative data to describe M&A target firms and develop an M&A completion prediction model that is based on categorical patterns found among American and European owned firms’ letters to shareholders ranked in fortune/global/fast 500. This study postulates that analyzing M&A targets’ letters to shareholders could provide relevant categories to describe the attractiveness of firms as M&A targets that are also indicative to the prediction of the completion of the offered deals. This study employed a mixed methodology using content analysis and decision tree analysis. The results of the study had led to provide four interesting category observations that described M&A target firms’ letters to shareholders, which showed less sensitivity to ownership and border-related offers. Further, the study developed an M&A completion prediction model with 67% predictive accuracy. This study provides implications for firms’ management on the posturing contents featured in firm’s letters to shareholders could expose to signal their firms as M&A targets and to readers as such as stock traders and corporate investors to look into the posturing contents of firms’ letters to shareholders, so as to identify potential M&A targets.