Fund managers’ ability to evaluate risk has important implications for their portfolio management and performance. We use a state-of-the-art deep learning model to measure fund managers’ forward-looking risk assessments from their narrative discussions. We validate that managers’ negative (positive) risk assessments lead to subsequent decreases (increases) in their portfolio risk-taking. However, only managers who identify negative risk generate superior risk-adjusted returns and higher Sharpe ratios, and have better intraquarter trading skills, suggesting that cautious, skilled managers are less subject to overconfidence biases. interestingly, only sophisticated investors respond to the narrative-based risk assessment measure, consistent with limited attention by retail investors.

We uncover a signiffcant relationship between the persistence of marketing and investment skills among U.S. mutual fund companies. Using regulatory filings, we calculate the share of marketing-oriented employees to total employment and reveal alarge heterogeneity in its level and persistence. A framework based on costly signaling and learning helps explain the observed marketing decision. The model features a separating equilibrium in which fund companies’ optimal marketing employment share responds to their past performance differently, conditional on the skill level. We confirm the model prediction that the volatility of the marketing employment share negatively predicts the fund companies’ long-term performance.