Does the interplay between the personality traits of CEOs and CFOs influence corporate mergers and acquisitions intensity? An econometric analysis with machine learning-based constructs

作者:

Highlights:

• The interplay between CEO and CFO personality traits and corporate M&A intensity;

• An econometric model with novel machine learning-based constructs;

• CEO's personalities of openness, consciousness, and neuroticism are associated with M&A intensity.

• CEO-CFO personality similarity moderates the relationships above.

• Our findings have implications for corporate decision makers and institutional investors.

摘要

Although the upper echelons theory posits that senior executives' personal characteristics influence firm performance, very few studies have examined the impact of the interplay between CEO and CFO characteristics on corporate activities. To fill this research gap, we propose an econometric analysis model to examine the interplay between the personality traits of CEOs and CFOs and corporate mergers and acquisitions (M&A) intensity. In particular, our econometric analysis model is empowered by novel personality constructs extracted using a state-of-the-art machine learning-based personality detector that automatically mines CEO/CFO personality traits from firms' earnings call transcripts. Based on historical M&A data of S&P 1500 firms, our econometric analysis reveals that the “openness” personality trait of CEOs is positively associated with corporate M&A intensity, while CEOs' “consciousness” and “neuroticism” personality traits are negatively associated with corporate M&A intensity. Moreover, the impacts of CEOs' “openness” and “consciousness” personality traits on corporate M&A intensity are more pronounced when CFOs have similar personality traits to those of CEOs.

论文关键词:Mergers and acquisitions,Econometric analysis,Personality mining,Machine learning,Corporate finance

论文评审过程:Received 5 June 2020, Revised 9 October 2020, Accepted 13 October 2020, Available online 17 October 2020, Version of Record 6 November 2020.

论文官网地址:https://doi.org/10.1016/j.dss.2020.113424