Which Factors Drive Mergers & Acquisitions?: Which Factors Drive Mergers & Acquisitions?

Bredahl, Nicolai Hedegård

4. semester

Economics and Business Administration (Finance), MSc.

2024

2024-06-03

86 pages

Abstract This thesis examines whether micro- and macroeconomic factors explain mergers and acquisitions (M&A) within the U.S. software industry in the period of 1994 to 2019. The software industry is relatively lesser studied than long-established industries within the M&A literature such as industrials and banking. Therefore, it is highly relevant to study whether the software industry is affected by the same firm-specific and macroeconomic factors as long-established industries are. Furthermore, this thesis examines the 5th, 6th and 7th M&A waves by using the theoretical framework of eight theories concerning the micro- and macroeconomic factors. These eight theories build upon behavioral and neoclassic economic theories and encompass respectively (1) managerial hubris, (2) herding, (3) agency, (4) misvaluation, (5) efficiency, (6) market power, (7) industry shock and (8) economic prosperity. This thesis employs deal volume as a proxy for M&A activity based on 92 quarterly observations. The methodology applied to examine M&A activity follows previous literature, and the multiple regression analysis is the statistical method commonly used among researchers. As previous literature suggests, the inclusion of micro- and macroeconomic factors is vital to examine the determinants of M&A holistically. The multiple regression model was applied to analyse the underlying drivers of M&A within the U.S. software industry. This thesis performed two regression models: a model on significant micro- and macroeconomic factors and a model containing microeconomic variables exclusively. A model for macroeconomic factors was also performed, but it was identical to the model containing significant micro- and macroeconomic variables and was therefore removed. The empirical findings derived from the regression models indicate support for the hubris, herding, efficiency, and partly economic prosperity theories. Hence, the findings did not support the agency, misvaluation, industry shock and marker power theories as significant for M&A activity within the U.S. software industry. Comprehensively, behavioral and neoclassic factors proved to be effective prognosticators of M&A activity. Consequently, the empirical findings of this thesis support the existing literature suggestions of including both micro- and macroeconomic factors to accurately explain the underlying mechanisms impacting M&A activity. This empirical investigation of M&A activity discovers that both micro- and macroeconomic factors are significant in explaining M&A activity within the U.S. software industry. Furthermore, evidence derived from the regression models indicate that variables from behavioral and neoclassic economics paradigms were significant to explain the observed M&A behaviour.

Abstract This thesis examines whether micro- and macroeconomic factors explain mergers and acquisitions (M&A) within the U.S. software industry in the period of 1994 to 2019. The software industry is relatively lesser studied than long-established industries within the M&A literature such as industrials and banking. Therefore, it is highly relevant to study whether the software industry is affected by the same firm-specific and macroeconomic factors as long-established industries are. Furthermore, this thesis examines the 5th, 6th and 7th M&A waves by using the theoretical framework of eight theories concerning the micro- and macroeconomic factors. These eight theories build upon behavioral and neoclassic economic theories and encompass respectively (1) managerial hubris, (2) herding, (3) agency, (4) misvaluation, (5) efficiency, (6) market power, (7) industry shock and (8) economic prosperity. This thesis employs deal volume as a proxy for M&A activity based on 92 quarterly observations. The methodology applied to examine M&A activity follows previous literature, and the multiple regression analysis is the statistical method commonly used among researchers. As previous literature suggests, the inclusion of micro- and macroeconomic factors is vital to examine the determinants of M&A holistically. The multiple regression model was applied to analyse the underlying drivers of M&A within the U.S. software industry. This thesis performed two regression models: a model on significant micro- and macroeconomic factors and a model containing microeconomic variables exclusively. A model for macroeconomic factors was also performed, but it was identical to the model containing significant micro- and macroeconomic variables and was therefore removed. The empirical findings derived from the regression models indicate support for the hubris, herding, efficiency, and partly economic prosperity theories. Hence, the findings did not support the agency, misvaluation, industry shock and marker power theories as significant for M&A activity within the U.S. software industry. Comprehensively, behavioral and neoclassic factors proved to be effective prognosticators of M&A activity. Consequently, the empirical findings of this thesis support the existing literature suggestions of including both micro- and macroeconomic factors to accurately explain the underlying mechanisms impacting M&A activity. This empirical investigation of M&A activity discovers that both micro- and macroeconomic factors are significant in explaining M&A activity within the U.S. software industry. Furthermore, evidence derived from the regression models indicate that variables from behavioral and neoclassic economics paradigms were significant to explain the observed M&A behaviour.

Mergers & Acquisitions

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