How much does a CEO’s personality impact the performanceof their company? Management theory posits a great influence, but it is difficult to show empirically—there is a lack of publicly available self-reported personality data of top managers. Instead, we propose a text-based personality regressor based on crowd-sourced Myers–Briggs Type Indicator (MBTI) assessments. The ratings have a high internal and external validity and can be predicted with moderate to strong correlations for three out of four dimensions. Providing evidence for the upper echelons theory, we demonstrate that the predicted CEO personalities have explanatory power of financial risk.

Financial risk, defined as the chance to deviate from return expectations, is most commonly measured with volatility. Due to its value for investment decision making, volatility prediction is probably among the most important tasks in finance and risk management. Although evidence exists that enriching purely financial models with natural language information can improve predictions of volatility, this task is still comparably underexplored. We introduce PRoFET, the first neural model for volatility prediction jointly exploiting both semantic language representations and a comprehensive set of financial features. As language data, we use transcripts from quarterly recurring events, so-called "earnings calls"; in these calls, the performance of publicly traded companies is summarized and prognosticated by their management. We show that our proposed architecture, which models verbal context with an attention mechanism, significantly outperforms the previous state-of-the-art and other strong baselines. Finally, we visualize this attention mechanism on the token-level, thus aiding interpretability and providing a use case of PRoFET as a tool for investment decision support.