Linguistic cues of chief executive officer personality and its effect on performance

Chief Executive Officer (CEO) personality belongs to the most important constructs for business practice and research, but its measurement remains a major obstacle. Therefore, I propose a language-based approach of freely available, large-scale corporate documents to study linguistic hints regarding CEOs' personality dimensions by applying a validated, closed-based approach developed to a sample of Fortune 500 companies. I compare the results to validated, machine learning-trained open-based results obtained from conference calls of 3000 CEOs of S&P 1500 companies. In order to provide a basis for comparison, I supplement these linguistic personality profiles with a sample of convicted criminal CEOs that provide an archetype of extreme linguistic cases. Finally, after controlling for commonly used content analytical constructs (e.g., entrepreneurial orientation, market orientation) to capture CEOs' cognitive state, I link these cross-sectional linguistic profiles to longitudinal firm-level performance data. The analysis indicates that the linguistic manifestations of CEO personality explain a great share of firm performance in conjunction with observable individual, industry, and firm-level data. While there appear to be complementarities between open-based and closed-based language approaches, the analysis also indicates that the open-based linguistic trait of neuroticism is negatively related to various measures of firm performance. In particular, these measures appear to be more robust against genre effects and language that are unique among CEOs (self-selection effects). Therefore, the approach sheds light on the important role of validation procedures in the realm of computer-aided content analysis (CATA) and the conducive and detrimental effects of CEO personality cues on firm performance. The paper discusses practical implications for stakeholders and shareholders wishing to infer personality cues of key decision-makers from large-scale corporate documents and theoretical implications for the advancement of the upper echelon research.     

Closed-loop Nash competition for liquidity

We study a multiplayer stochastic differential game, where agents interact through their joint price impact on an asset that they trade to exploit a common trading signal. In this context, we prove that a closed-loop Nash equilibrium exists if the price impact parameter is small enough. Compared to the corresponding open-loop Nash equilibrium, both the agents' optimal trading rates and their performance move towards the central-planner solution, in that excessive trading due to lack of coordination is reduced. However, the size of this effect is modest for plausible parameter values.